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Purpose: This study aimed to determine whether the initial levels of aerobic fitness and hematological variables in young female road cyclists are related to their athletic performance development during their careers. Methods: Results of graded exercise tests on a cycle ergometer and total hemoglobin mass (tHb-mass) measurements were analyzed in 34 female road cyclists (age 18.6 [1.9] y). Among them, 2 groups were distinguished based on their competitive performance (Union Cycliste Internationale world ranking) over the following 8 years. Areas under the curve in receiver-operating-characteristic curves were calculated as indicators of elite-performance prediction. Results: Initial graded exercise test variables (peak power, peak oxygen uptake, and power at 4 mmol/L blood lactate) were not significantly different in elite (n = 13) versus nonelite (n = 21) riders. In contrast, elite riders had higher tHb-mass expressed either in absolute measures (664 [75] vs 596 [59] g, P =.006) or normalized to body mass (11.2 [0.8] vs 10.3 [0.7] g/kg, P =.001) and fat-free mass (14.4 [0.9] vs 13.1 [0.9] g/kg, P <.001). Absolute and relative erythrocyte volumes were significantly higher in elite subjects (P ranged from <.001 to.006). Of all the variables analyzed, the relative tHb-mass had the highest predictive ability to reach the elite level (area under the curve ranged from.82 to.85). Conclusion: Measurement of tHb-mass can be a helpful tool in talent detection to identify young female road cyclists with the potential to reach the elite level in the future. [ABSTRACT FROM AUTHOR]

Resistance training is used to combat skeletal muscle function decline in older adults. Few studies have been designed specific for females, resulting in very limited treatment options for skeletal muscle atrophy in aging women. Here, we analyzed the gene expression profiles of skeletal muscle samples from sedentary young women, sedentary older women, and resistance-trained older women, using microarray data from public database. A total of 45 genes that were differentially expressed during female muscle aging and reversed by resistance training were identified. Functional and pathway enrichment analysis, protein–protein interaction network analysis, and receiver operating characteristic analysis were performed to reveal the key genes and pathways involved in the effects of resistance training on female muscle aging. The collagen genes COL1A1, COL3A1, and COL4A1 were identified important regulators of female muscle aging and resistance training, by modulating multiple signaling pathways, such as PI3 kinase-Akt signaling, focal adhesions, extracellular matrix-receptor interactions, and relaxin signaling. Interestingly, the expression of CDKN1A and TP63 were increased during aging, and further upregulated by resistance training in older women, suggesting they may negatively affect resistance training outcomes. Our findings provide novel insights into the molecular mechanisms of resistance training on female muscle aging and identify potential biomarkers and targets for clinical intervention. [ABSTRACT FROM AUTHOR]

Context: Decreased trunk neuromuscular control is a risk factor for both upper- and lower-extremity injuries, yet there are few reliable and valid clinical tests to identify deficits. Objective: The purpose of this study was to determine the reliability and known-groups validity of a novel clinical test, the seated trunk control test (STCT). Design: Cross-sectional reliability and known-groups validity study. Setting: Laboratory. Participants: 89 unique participants: 34 were 3 months postoperative anterior cruciate ligament reconstruction (ACLR) and 55 healthy controls. Methods: For the STCT, participants sat on a balance board with their eyes closed for three 30-second trials while investigators counted balance errors. Intraclass correlations (ICCs) were used to assess interrater reliability (N = 20) and test–retest reliability (N = 40). To assess known-groups validity, independent t tests were used to compare STCT errors at 3 months post-ACLR with healthy matched controls (N = 34/group). Area under a receiver operating characteristic curve identified an optimal cutoff for distinguishing between groups. Results: The STCT had perfect interrater reliability (ICC2,3 = 1.00) and good test–retest reliability (ICC3,3 =.79; 95% confidence interval =.61–.89). The ACLR group made significantly more errors on the STCT (mean [SD] = 15.5 [5.4]) than controls (mean [SD] = 8.2 [4.1]; P <.001, Cohen d = 1.52). The STCT's ability to distinguish between groups was excellent (area under a ROC curve = 0.86). A cutoff of 12 errors maximized sensitivity (76%) and specificity (85%). Conclusions: The STCT is reliable between raters and across days. It also has excellent ability to distinguish between individuals with a recent ACLR and healthy matched controls, which provides initial evidence to suggest that the STCT may be clinically useful for identifying deficits in trunk neuromuscular control. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the validity of the Physical Activity Questionnaire for Older Children (PAQ-C) to assess the moderate- to vigorous-intensity physical activity (MVPA) level of children and adolescents diagnosed with HIV and propose cut-points, with accelerometer measures as the reference method. Method: Children and adolescents, aged 8–14 years (mean age = 12.21 y, SD = 2.09), diagnosed with HIV by vertical transmission, participated in the study. MVPA was investigated through the PAQ-C and triaxial accelerometer (ActiGraph GT3X+). Receiver operating characteristic curve and sensitivity and specificity values were used to identify a cut-point for PAQ-C to distinguish participants meeting MVPA guidelines. Results: Fifty-six children and adolescents participated in the study. Among those, 16 met MVPA guidelines. The PAQ-C score was significantly related to accelerometry-derived MVPA (ρ =.506, P <.001). The PAQ-C score cut-point of 2.151 (sensitivity = 0.625, specificity = 0.875) was able to discriminate between those who met MVPA guidelines and those that did not (area under the curve = 0.751, 95% confidence interval, 0.616–0.886). Conclusion: The PAQ-C was useful to investigate MVPA among children and adolescents diagnosed with HIV and to identify those who meet MVPA guidelines. [ABSTRACT FROM AUTHOR]

This prospective study aimed to determine which specific mobility tests were the most accurate for predicting falls in physically active older adults living in the community. Seventy-nine physically active older adults who met the American College of Sports Medicine physical activity guidelines volunteered. Participants were assessed and followed up for 12 months. Mobility assessments included the 30-s sit-to-stand test, five times sit-to-stand test, single-task timed-up-and-go test (TUG), motor dual-task TUG (Mot-TUG), and cognitive dual-task TUG (Cog-TUG). Mot-TUG and Cog-TUG performances were moderately correlated with number of falls (r =.359, p <.01 and r =.372, p <.01, respectively). When Mot-TUG, Cog-TUG, or Age were included as fall predictors, discrimination scores represented by the area under the receiver operating characteristic curve (AUC) were AUC (Mot-TUG) = 0.843 (p <.01), AUC (Cog-TUG) = 0.856 (p <.01), and AUC (Age) = 0.734 (p <.05). The cutoff point for Cog-TUG was 10.98 s, with test sensitivity of 1.00 and specificity of 0.66. Fall predictors for different populations may be based on different test methods. Here, the dual-task TUG test more accurately predicted falls in older adults who met American College of Sports Medicine's physical activity guidelines. [ABSTRACT FROM AUTHOR]

This study evaluated the relationship between the muscle mass of the gluteus medius (GM) and skeletal muscle mass index (SMI) measured in patients with hip fractures. In this study, 141 patients with hip fractures were divided into those with high or low SMI. The GM index (GMI) was calculated by dividing the GM by the square of the height in meters. The correlation between GMI and SMI was subsequently analyzed, and cutoff values for determining the loss of skeletal muscle mass were calculated using the receiver operating characteristic curve. GMI and SMI showed a positive correlation for both sexes (male: r =.890, female: r =.626, p <.001). The GMI cutoff values were 19.460 cm2/m2 for males and 17.850 cm2/m2 for females. Skeletal muscle mass evaluation of the GM could contribute to hip fracture recovery by improving mobility and facilitating the early diagnosis of loss of SMM. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the association between the orofacial implant position distance (ID) and facial bone crest thickness (FBCT) in single-tooth implant crowns (STICs) placed in the esthetic zone and to predict the presence of facial bone above the implant shoulder, as well as its impact on Pink and White Esthetic Scores (PES and WES) and patient-related outcome measures (PROMs). Materials and Methods: In this cross-sectional study, all patients treated with STICs in the esthetic zone between 2006 and 2013 with at least 12 months postloading were invited to enroll. CBCTs were used to assess ID and FBCT. Esthetic outcomes included the PES and WES. PROMs included a participant satisfaction questionnaire with 13 questions formulated for this study. Descriptive statistics and receiver operating characteristic curve analysis were used. The associations between ID, FBCT, esthetic outcomes, and PROMs were calculated. Results: Data were available for 60 implants. Mean ID was 2.02 mm (SD = 0.77). Mean FBCT at the implant shoulder was 0.99 mm (SD = 0.98). FBCT was significantly associated with higher PES scores (P = .040), but not with WES scores (P = 0.149). FBCT was significantly associated with ID (P = .009), and implants positioned = 1.6 mm palatal in relation to the adjacent teeth were likely to have higher FBCT. Conclusions: FBCT influences PES and is more predictable if implants are positioned = 1.6 mm palatal in relation to the adjacent teeth. [ABSTRACT FROM AUTHOR]

First morning urine (FMU) assessment would be a practical and convenient solution for clinically acceptable detection of underhydration prior to competition/training, and for the general public. Thus, we thus sought to determine the diagnostic accuracy of FMU as a valid indicator of recent (previous 24 hr, 5 days average) hydration practices. For 5 consecutive days and one final morning, 67 healthy women (n = 38) and men (n = 29; age: 20 [1] years, body mass index: 25.9 [5.5]) completed 24-hr diet logs for total water intake (from beverages and foods, absolute and relative to body mass), 24-hr urine and FMU collection (last morning only) for osmolality (Osm), specific gravity (SG), and color (Col), and morning blood sampling for plasma osmolality and copeptin. Correlations determined significance and relationship strength among FMU and all other variables. Area under the receiver operating characteristic curves, sensitivity, specificity, and positive likelihood ratios were employed using previously reported values to indicate underhydration (total water intake < 30 ml/kg, osmolality > 500, and >800 mOsm/kg, specific gravity > 1.017, and copeptin > 6.93 pmol/L). FMU_Osm and FMU_SG were significantly correlated (p <.05) to all variables except the previous 5-day plasma osmolality. FMU_Col was only significantly correlated with other color time intervals and total water intake per gram. FMU_Osm held greatest utility (area under the receiver operating characteristic curve, sensitivity, and specificity >80%) overall, with the best outcome being FMU_Osm indicating a previous 24-hr osmolality threshold of 500 mOsm/kg (FMU_Osm criterion >710 mOsm/kg and positive likelihood ratio = 5.9). With less effort and cost restriction, FMU is a viable metric to assess underhydration. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to develop an artificial intelligence (Al) model that can determine automatic tooth numbering, frenulum attachments, gingival overgrowth areas, and gingival inflammation signs on intraoral photographs and to evaluate the performance of this model. Method and materials: A total of 654 intraoral photographs were used in the study (n = 654). All photographs were reviewed by three periodontists, and all teeth, frenulum attachment, gingival overgrowth areas, and gingival inflammation signs on photographs were labeled using the segmentation method in a web-based labeling software. In addition, tooth numbering was carried out according to the FDI system. An Al model was developed with the help of YOLOv5x architecture with labels of 16,795 teeth, 2,493 frenulum attachments, 1,211 gingival overgrowth areas, and 2,956 gingival inflammation signs. The confusion matrix system and ROC (receiver operator characteristic) analysis were used to statistically evaluate the success of the developed model. Results: The sensitivity, precision, F1 score, and AUC (area under the curve) for tooth numbering were 0.990, 0.784, 0.875, and 0.989; for frenulum attachment these were 0.894, 0.775,0.830, and 0.827; for gingival overgrowth area these were 0.757, 0.675, 0.714, and 0.774; and for gingival inflammation sign 0.737, 0.823, 0.777, and 0.802, respectively. Conclusion: The results of the present study show that Al systems can be successfully used to interpret intraoral photographs. These systems have the potential to accelerate the digital transformation in the clinical and academic functioning of dentistry with the automatic determination of anatomical structures and dental conditions from intraoral photographs. [ABSTRACT FROM AUTHOR]

• Good social behavior in infancy was correlated to low autistic traits in toddlerhood. • Social behavior in infancy was positively correlated to IJA in toddlerhood. • Fine motor in infancy is predictive of autistic traits in general population. • Social behavior in infancy is predictive of autistic traits in general population. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder. Early diagnosis in the critical period is important for ASD children. Recent studies of neurodevelopmental behavioral features and joint attention in high-risk infants showed there are some special cues which can distinguish ASD from typical development infant. But the findings of high-risk population may not be applicable to the general population. It is necessary to "analogically" study the potential warning traits of ASD in infancy in the general population. We did a nested case-control study from June 2019 to November 2022 in Tianjin, China, including 76 general infants whom completed the neurodevelopmental evaluation, the Checklist for Autism in Toddlers-23 (CHAT-23) screening, and eye tracking task. Social behavior quotient in infancy was negatively correlated to CHAT-23 total scores in toddlerhood. Social behavior quotient in infancy was positively correlated to initiating joint attention in toddlerhood. Regression model showed that high fine motor scale and social behaviour scale quotient in infancy were associated with an decreased risk of the total score of CHAT-23 ≥ 2 in toddlerhood. The Receiver operating characteristic curve showed the social behaviour in infancy alone and the combination of fine motor and social behaviour in infancy contributed to auxiliary diagnosis of higher level of autistic traits in toddlerhood. These findings suggest that Impaired development of fine motor and social behavior in infancy are potential warning features of high autistic traits in general population. [ABSTRACT FROM AUTHOR]

[Display omitted] • Functional neuroimaging alterations in AOID patients with anxiety. • Correlations analysis support neural basis of anxiety symptoms in AOID. • Candidate imaging biomarkers to distinguish AOID patients with/without anxiety. Psychiatric disturbances are commonly associated with adult-onset isolated dystonia (AOID); however, the mechanisms underlying psychiatric abnormalities in AOID remain unknown. We aimed to investigate the structural and functional brain changes in AOID patients with anxiety, and identify imaging biomarkers for diagnosing anxiety. Structural and functional magnetic resonance was performed on 69 AOID patients and 35 healthy controls (HCs). The Hamilton Anxiety Scale (HAMA) was used to assess anxiety symptoms in AOID patients and assign patients to AOID with and without anxiety groups. Group differences in grey matter volume, amplitude of low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity (ReHo) were evaluated. Area under the receiver operating characteristic curve (ROC AUC) was used as a metric to identify imaging biomarkers for diagnosing anxiety. AOID patients with anxiety exhibited an increased ALFF and ReHo in the left angular gyrus (ANG.L) compared with those without and HCs (voxel P <0.001 and cluster P <0.05, corrected using GRF). A significant positive correlation was observed between ALFF (r = 0.627, P <0.001) and ReHo (r = 0.515, P <0.001) in the ANG.L and HAMA scores in AOID patients. ALFF and ReHo in the ANG.L exhibited an ROC AUC of 0.904 and 0.851, respectively, in distinguishing AOID patients with anxiety from those without and an ROC AUC of 0.887 and 0.853, respectively, in distinguishing AOID patients with anxiety from HCs. These findings provide new insights into the pathophysiology of psychiatric disturbances and highlight potential candidate biomarkers for identifying anxiety in AOID patients. [ABSTRACT FROM AUTHOR]

Exosomes are critical tumor biomarkers, which can be detected by label‐free sensing of plasmonic metasurfaces with expensive antibody functionalization. Due to their low cost and small size, aptamers should be promising to replace antibodies in plasmonic metasensing of exosomes. However, research on aptamer‐based plasmonic metasensing of exosomes is still unexplored. Here, the optical near‐field sensing mechanism of aptamer‐based plasmonic metasurface (APM) is investigated. Based on the study of the evanescent field of APM, using thiol‐modified aptamer boosts the utilization rate of resonance wavelength shift from 27.5% to 83.3% compared to biotinylated aptamers, attributed to a significant reduction in the near‐field occupation of the biofunction layer. Moreover, APM biosensors effectively detect clinical serum exosomes for breast cancer (BC) diagnosis and classification. The receiver operating characteristic analysis shows an area under the curve of 99.3% for APM diagnosis, surpassing the 79.1% for hematological tests of the conventional BC biomarker CA153. The results also indicate that APM biosensors can be a noninvasive tool for molecular classification of BC with great distinguishment of P < 0.0001. The research demonstrates that APM biosensors are enabling a reliable and convenient platform to facilitate clinical hematological tests of exosomes for auxiliary diagnosis of cancer. [ABSTRACT FROM AUTHOR]

Objective This study aimed to compare the predictive values of the American College of Obstetricians and Gynecologists (ACOG), the National Institute for Health and Care Excellence (NICE), and the Society of Obstetricians and Gynecologists of Canada (SOGC) factor-based models for preeclampsia (PE) screening. Study Design We conducted a secondary analysis of maternal and birth data from 32 hospitals. For each delivery, we calculated the risk of PE according to the ACOG, the NICE, and the SOGC models. Our primary outcomes were PE and preterm PE (PE combined with preterm birth) using the ACOG criteria. We calculated the detection rate (DR or sensitivity), the false positive rate (FPR or 1 − specificity), the positive (PPV) and negative (NPV) predictive values of each model for PE and for preterm PE using receiver operator characteristic (ROC) curves. Results We used 130,939 deliveries including 4,635 (3.5%) cases of PE and 823 (0.6%) cases of preterm PE. The ACOG model had a DR of 43.6% for PE and 50.3% for preterm PE with FPR of 15.6%; the NICE model had a DR of 36.2% for PE and 41.3% for preterm PE with FPR of 12.8%; and the SOGC model had a DR of 49.1% for PE and 51.6% for preterm PE with FPR of 22.2%. The PPV for PE of the ACOG (9.3%) and NICE (9.4%) models were both superior than the SOGC model (7.6%; p < 0.001), with a similar trend for the PPV for preterm PE (1.9 vs. 1.9 vs. 1.4%, respectively; p < 0.01). The area under the ROC curves suggested that the ACOG model is superior to the NICE for the prediction of PE and preterm PE and superior to the SOGC models for the prediction of preterm PE (all with p < 0.001). Conclusion The current ACOG factor-based model for the prediction of PE and preterm PE, without considering race, is superior to the NICE and SOGC models. Key Points Clinical factor-based model can predict PE in approximately 44% of the cases for a 16% false positive. The ACOG model is superior to the NICE and SOGC models to predict PE. Clinical factor-based models are better to predict PE in parous than in nulliparous. [ABSTRACT FROM AUTHOR]

Background: The soluble CD163 (sCD163) was elevated in systemic lupus erythematosus (SLE) patients. Purpose: To study whether serum sCD163 could be used to predict the occurrence and prognosis of lupus nephritis (LN). Research Design: The recruited patients were classified into different groups according to standard identification criteria. Study Sample: The patients with LN. Data Collection and Analysis: 11 indices were analyzed and compared in SLE and LN patients. Furthermore, the level of serum sCD163 was detected using an enzyme-linked immunosorbent assay. Meanwhile, the receiver operating characteristic analysis was performed to evaluate the prediction effect of sCD163. Additionally, spearman correlation analysis of serum sCD163 with indices was conducted. Results: There were six positive indices and one negative risk factor correlated to LN. sCD163 was elevated in LN patients and could be used to diagnose LN. Importantly, sCD163 was increased in LN patients with a heavy SLE disease activity index. Finally, it was revealed that the level of sCD163 was higher in the LN patients with no response than that with complete or partial response, which also could predict the prognosis of LN. Conclusions: Serum sCD163 was elevated in LN patients than in SLE patients, which could be used to predict the occurrence and prognosis of LN. [ABSTRACT FROM AUTHOR]

Background: Systemic lupus erythematosus (SLE) is a chronic inflammatory multisystemic disease. Monitoring disease activity thoughtout the disease course is important for effective management and assessment of disease outcome. Objective: To assess whether the pan-immune inflammation value (PIIV) at diagnosis could predict organ involvement and disease activity in childhood SLE (cSLE) patients after 12 months of disease onst. Methods: This is an observational retrospective multicenter study that comprised cSLE patients seen and followed at the participating centers between January 2010 and December 2022. All patients met the EULAR/ACR-19 criteria, were immunosuppressive drug-naïve at the time of SLE diagnosis and had a minimal follow-up period of 12 months. The data included clinical and laboratory findings and disease activity using the SLEDAI-2K. Receiver operating characteristic (ROC) curves were employed to determine the optimal cut-off value of PIIV and assess its predictive potential for disease activity, and organ involvement. Results: A total of 125 patients (104 female) with a median age of 16.0 (IQR 5.6) years, a median age at disease onset of 10.9 (IQR 3.0) years, and a median disease duration of 4.8 (IQR 5.3) years were included. The most frequent involved organs at diagnosis were hematological (89.6%), musculoskeletal (68.8%), mucocutaneous (63.2%), and renal (58.4%). However, at a 12-month follow-up visit, the most frequent involved organs were renal (40.0%), hematological (39.2%), musculoskeletal (15.2%), and mucocutaneous (10.4%). The median PIIV at diagnosis was 139 (IQR 229.6), while the median SLEDAI was 12 (IQR 6.5) and 3.5 (IQR 7.0) at diagnosis and 12 months, respectively. An optimal PIIV cut-off of 250 was found to be a predicative for disease activity, with a sensitivity of 45% and a specificity of 86%. The study revealed that the PIIV successfully predicted four systems in our cohort of patients. Conclusion: Our work suggests the PIIV might be a reasonable predictor for organ involvement and disease activity in newly diagnosed cSLE, though further research, particularly larger studies, is required to validate these findings, especially regarding organ involvement. [ABSTRACT FROM AUTHOR]

Using the Mood and Feelings Questionnaire (MFQ) to differentiate between depression severity levels remains unexplored. We explored the discriminative validity of the MFQ to identify an optimal cut-off MFQ score to distinguish between subthreshold-to-mild and moderate-to-severe depression severity levels. An observational cross-sectional design was used in a sample (N = 67) of help-seeking youth (ages 13 to 18, inclusive) experiencing depressive symptoms. The MFQ was administered verbatim to youth by a research analyst over the phone. Youth were then grouped into subthreshold-to-mild or moderate-to-severe depression severity categories based on scores received on the Kiddie Schedule for Affective Disorders and Schizophrenia-Depression Rating Scale. Receiver Operating Characteristic curve analyses were conducted, with area under the curve (AUC) and Youden Index (J) as primary indices. We hypothesized that the lower limit of the 95 % confidence interval for the area under the curve would be ≥0.70. The primary analysis yielded an AUC of 0.85 (95 % CI: 0.763–0.947) and an optimal cut-off of ≥43 (J = 0.60, positive predictive value = 91.4 %, negative predictive value = 62.5 %, sensitivity = 72.7 %, specificity = 87.0 %). Our study collected a small sample, and as such cannot identify how subgroup classification (e.g., based on race or gender) may moderate outcomes. Further, unknown measurement error of the predictor and reference variable measures can bias the estimates. Our preliminary findings highlight the potential for the MFQ to support clinical decision-making relevant to adolescents experiencing varying severities of depressive symptoms in secondary care settings. • The Mood and Feelings Questionnaire can assist in discriminating severity of depressive symptoms. • A score of ≥43 on the Mood and Feelings Questionnaire signals moderate-to-severe depression. • This cut-off score corresponds to a positive predictive value of 91.4 %. [ABSTRACT FROM AUTHOR]

Background: Lipid droplets (LD) in renal clear cell carcinoma (ccRCC)play a crucial role in lipid metabolism and immune response modulation. The purpose of this study was to create a LD-related signature to predict prognosis and guide the immunotherapy and targeted therapy in ccRCC patients. Methods: We conducted a comprehensive analysis using transcriptional profiles and clinical data obtained from The Cancer Genome Atlas (TCGA). LD-related genes were identified from existing literature and the GeneCards database, and differentially expressed genes were determined. Sequentially, we conducted Cox regression analysis and Lasso regression analysis, to establish a prognostic risk model. The performance of the risk model was evaluated using Kaplan–Meier (KM) analysis and time-dependent receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and immunophenoscore (IPS) algorithm were used to assess the tumor microenvironment (TME) and treatment response. Results: We constructed a risk signature with four LD-related genes in the TCGA dataset, which could be an independent prognostic factor in ccRCC patients. Then, patients were classified into two risk groups and exhibited notable differences in overall survival (OS), progression-free survival (PFS), and TME characteristics. Furthermore, we developed a comprehensive nomogram based on clinical features, which demonstrated good prognostic predictive value. According to the results of GSEA analysis, immune-related pathways were found to be significantly enriched in the high-risk group. Additionally, the high-risk group displayed high levels of immune cell infiltration, TMB and IPS scores, indicating better efficacy of immune checkpoint inhibitors (ICIs). Finally, high-risk demonstrated reduced IC50 values compared to the low-risk counterpart for specific targeted and chemotherapeutic drugs, suggesting that the patients receiving these targeted drugs in high-risk group had better treatment outcomes. Conclusions: Our findings suggested that the LD-related gene signature could potentially predict the prognosis of ccRCC patients. Additionally, it showed promise for predicting responses to immunotherapy and targeted therapy in ccRCC patients. These insights might potentially have guided the clinical management of these patients, but further validation and broader data analysis are needed to confirm these preliminary observations. [ABSTRACT FROM AUTHOR]

Objectives: This study examined the potential association between nucleated red blood cell (NRBC) levels and mortality in critically ill patients with acute pancreatitis (AP) in the intensive care unit, due to limited existing research on this correlation. Methods: This retrospective cohort study utilized data from the MIMIC-IV v2.0 and MIMIC-III v1.4 databases to investigate the potential relationship between NRBC levels and patient outcomes. The study employed restricted cubic splines (RCS) regression analysis to explore non-linear associations. The impact of NRBC on prognosis was assessed using a generalized linear model (GLM) with a logit link, adjusted for potential confounders. Furthermore, four machine learning models, including Gradient Boosting Classifier (GBC), Random Forest, Gaussian Naive Bayes, and Decision Tree Classifier model, were constructed using NRBC data to generate risk scores and evaluate the potential of NRBC in predicting patient prognosis. Results: A total of 354 patients were enrolled in the study, with 162 (45.8%) individuals aged 60 years or older and 204 (57.6%) males. RCS regression analysis demonstrated a non-linear relationship between NRBC levels and 90-day mortality. Receiver Operating Characteristic (ROC) analysis identified a 1.7% NRBC cutoff to distinguish survivor from non-survivor patients for 90-day mortality, yielding an Area Under the Curve (AUC) of 0.599, with a sensitivity of 0.475 and specificity of 0.711. Elevated NRBC levels were associated with increased risks of 90-day mortality in both unadjusted and adjusted models (all Odds Ratios > 1, P < 0.05). Assessment of various machine learning models with nine variables, including NRBC, Sex, Age, Simplified Acute Physiology Score II, Acute Physiology Score III, Congestive Heart Failure, Vasopressin, Norepinephrine, and Mean Arterial Pressure, indicated that the GBC model displayed the highest predictive accuracy for 90-day mortality, with an AUC of 0.982 (95% CI 0.970–0.994). Post hoc power analysis showed a statistical power of 0.880 in the study. Conclusions: Elevated levels of NRBC are linked to an increased mortality risk in critically ill patients with AP, suggesting its potential for predicting mortality. [ABSTRACT FROM AUTHOR]

Background: Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study whether Deep Learning (DL) can predict HRD status solely based on routine hematoxylin & eosin (H&E) histology images across nine different cancer types. Methods: We developed a deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. As part of our approach, we calculated a genomic scar HRD score by combining loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST) from whole genome sequencing (WGS) data of n = 5209 patients across two independent cohorts. The model's effectiveness was evaluated using the area under the receiver operating characteristic curve (AUROC), focusing on its accuracy in predicting genomic HRD against a clinically recognized cutoff value. Results: Our study demonstrated the predictability of genomic HRD status in endometrial, pancreatic, and lung cancers reaching cross-validated AUROCs of 0.79, 0.58, and 0.66, respectively. These predictions generalized well to an external cohort, with AUROCs of 0.93, 0.81, and 0.73. Moreover, a breast cancer-trained image-based HRD classifier yielded an AUROC of 0.78 in the internal validation cohort and was able to predict HRD in endometrial, prostate, and pancreatic cancer with AUROCs of 0.87, 0.84, and 0.67, indicating that a shared HRD-like phenotype occurs across these tumor entities. Conclusions: This study establishes that HRD can be directly predicted from H&E slides using attMIL, demonstrating its applicability across nine different tumor types. [ABSTRACT FROM AUTHOR]

Background: The issue of patients returning to work is increasingly garnering attention from countries worldwide. This study aims to investigate the risk factors associated with patients returning to work after undergoing permanent enterostomies. Additionally, it seeks to establish and validate a nomogram prediction model, thereby providing a more effective reference for patients aiming to return to work. Methods: This study was a cross-sectional investigation conducted between September 2022 and September 2023. We conveniently selected 293 postoperative patients with permanent colorectal stomas due to colorectal cancer from three tertiary hospitals in Liaoning Province. Participants were categorized into Returned and Non-Returned groups based on their return to work status. Data were collected using a general information questionnaire, a Stoma Acceptance Questionnaire, and the Ostomy Adjustment Inventory. Binary logistic regression analysis was performed using SPSS 25.0 software to identify independent influencing factors. A predictive model was constructed using R Studio 4.3.0 software. Internal validation was conducted through 1,000 rounds of Bootstrap resampling, and model performance was assessed using Receiver Operating Characteristic (ROC) curves, the Hosmer-Lemeshow (H-L) test, and calibration curves. Results: After surgery, the return-to-work rate for patients with permanent colorectal stomas was 29.69%. Age, education level, postoperative time, stoma complication, adjuvant therapy, stoma acceptance score, and ostomy adjustment inventory score were identified as independent factors influencing the return-to-work status of these patients (P < 0.05). These factors were incorporated into a logistic regression model generated by R software, resulting in a ROC curve with an area under the curve (AUC) of 0.916 (95% CI: 0.884–0.947). The Youden index was 0.731, and the cutoff value was 0.228. Sensitivity and specificity were 0.920 and 0.811, respectively. The H-L test demonstrated good model fit (χ2 = 12.858, P = 0.117, P > 0.05). Calibration curves indicated a close alignment between predicted and actual probabilities. Conclusions: The postoperative return-to-work rate is low in patients with permanent enterostomies. The prediction model developed in this study demonstrates strong performance and offers predictive value, providing a scientific foundation for assessing patients' return to work. Caregivers should prioritize the early identification of various patient types for proactive intervention to enhance the rate of postoperative return to work. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions We aimed to assess the amniotic fluid glucose concentration cut‐off as an indicator of fetal chromosomal abnormalities, such as trisomy 13, 18, and 21.This prospective observational study included pregnant females who underwent amniocentesis. Participants were divided into two groups on the border of 22 weeks of gestational age (<22 and ≥22‐week groups).In total, 224 pregnant females were included in the analysis. In the <22 week group, 15 females had trisomies 13/18/21 and 174 females had no trisomies. In the ≥22 week group, 18 females had trisomies 13/18/21 and 17 had no trisomies. In each group, there was a difference in amniotic fluid glucose concentration between fetuses with trisomies 13, 18, and 21 and other fetuses with normal karyotype or minor chromosomal abnormalities. In both groups, the amniotic glucose concentration was noticeably lower in trisomies 13/18/21 (p = 0.002 in the <22 week group; p = 0.039 in the ≥22 week group). According to receiver operating characteristic curves, the optimal cut‐off point of glucose concentration was 46 mg/dL in the <22 week group (odds ratio 6.55; 95% confidence interval 1.78–24.1) and 24 mg/dL in the ≥22 week group (odds ratio 8.40; 95% confidence interval 1.83–38.6).Our study suggested that glucose concentration in amniotic fluid is an indicator of trisomy 13, 18, and 21. Amniotic fluid glucose concentration itself does not diagnose fetal trisomy, but this may be helpful in selecting treatment facilities. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the association between the questionnaires SARC-F and SARC-CalF with risk of mortality in patients undergoing hemodialysis (HD). A cohort study, with patients on HD age ≥ 18 years, both sex, between June 2019 and April 2023. Body composition (anthropometry and bioelectrical impedance), muscle functional (handgrip strength and gait speed), screening of sarcopenia using the SARC-F and SARC-CalF, nutritional status and laboratory data were assessed. Follow-up for mortality up to 47 months. The sample consisted of 243 participants and the prevalence of risk of sarcopenia using SARC-F and SARC-CalF were 30% and 45%, respectively; 65 died for all reasons and three patients were censored due to transplantation. Multivariate analysis identified SARC-CalF as predictor of mortality in HD patients [hazard ratio 1.96; 95% CI (1.01–3.79); p = 0.04]. The survival analysis showed that there was a significant difference in the survival curves among the groups stratified by SARC-F and SARC-CalF for log-rank test. A higher specificity was found for SARC-CalF than SARC-F (80% vs. 77%) in receiver operating characteristic (ROC) curve. Both questionnaires were associated with anthropometric, parameters of body composition, physical measurements, and SARC-CalF was predictor of risk for mortality in HD patients. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion The use of medications by emergency medical services (EMS) is increasing. Conventional scores are time‐consuming and therefore difficult to use in an emergency setting. For early decision‐making, an easy‐to‐use score based on the medications administered by the EMS may have prognostic value. The primary objective of this study was to develop the prehospital drug‐derived score (PDDS) for 2‐day mortality.A prospective, multicenter, ambulance‐based cohort study was conducted in adults with undifferentiated acute diseases treated by EMS and transferred to the emergency department. Demographic data, prehospital diagnosis data, prehospital medication and variables for the calculation of the National Early Warning Score 2 (NEWS2), Rapid Emergency Medicine Score (REMS), and Rapid Acute Physiology Score (RAPS) were collected. The PDDS was developed and validated, establishing three levels of risk of 2‐day mortality. The predictive capability of each score was determined by the area under the curve of the receiver operating characteristic curve (AUROC) and compared using the Delong's test (p‐value).A total of 6401 patients were included. The PDDS included age and the use of norepinephrine, analgesics, neuromuscular blocking agents, diuretics, antihypertensive agents, tranexamic acid, and bicarbonate. The AUROC of PDDS was .86 (95% CI: .816–.903) versus NEWS2 .866 (95% CI: .822–.911), p = .828; versus REMS .885 (95% CI: .845–.924), p = .311; versus RAPS .886 (95% CI: .846–.926), p = .335, respectively.The newly developed easy‐to‐use prehospital drug‐derived PDDS score has an excellent predictive value of early mortality. The PDDS score was comparable to the conventional risk scores and therefore might serve as an alternative score in the prehospital emergency setting. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Design Methods Results Conclusion Implications for Patient Care Impact Reporting Method Patient or Public Contribution To refine and validate an electronic version of the Aggressive Behaviour Risk Assessment Tool (ABRAT) and determine the sensitivity and specificity for identifying potentially violent patients in non‐psychiatric inpatient units.A prospective cohort study design was used.All patients admitted or transferred to three inpatient units of an acute care hospital in Nebraska, USA, from 7 February to 9 April 2023, were included. The 10‐item ABRAT assessments were performed daily for the first 3 days of admission. The violent events were collected until discharge in three categories: Physical aggression towards others, physical aggression towards property and verbal intimidation/threat towards others. Kendall's tau tests and a multivariate logistic regression procedure were performed to select a parsimonious set of items that best predict violent events.Of 1179 patients, 69 had ≥1 violent event (5.9%). The revised six‐item tool with item weighting was named ABRAT for Hospitalised Patients (ABRAT‐H). The area under the curve from the Receiver Operating Characteristics analysis was 0.82. The sensitivity and specificity at a cutoff score of two were 68.1% and 85.2%, respectively. As ABRAT‐H scores increased, the percentage of violent patients also increased and for patients with scores ≥5, 55.2% became violent.ABRAT‐H appears to be useful for identifying potentially violent patients in non‐psychiatric inpatient units with satisfactory sensitivity and specificity.The availability of ABRAT‐H may help provide focused preventive measures that target patients at high risk for violence and reduce violent events.A majority of the nursing workforce is employed in acute care hospital setting, and the availability of ABRAT‐H can further enhance the culture of a safe work environment and have positive impacts not only on the nurses' physical and mental health but also on the quality of patient care.We have adhered to relevant STROBE guidelines for reporting observational studies.No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer’s disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers. These enhancements improve the model’s effectiveness and precision in addressing the specified medical issue. The proposed model is rigorously validated and evaluated using publicly available Kaggle MRI Alzheimer’s data consisting of 1280 testing images and 5120 patient training images. For comprehensive performance evaluation, precision, recall, F1-score, and accuracy metrics are utilized. The findings indicate that the Xception method is the most promising of those considered. Without employing five K-fold techniques, this model obtains a 99% accuracy and 0.135 loss score. In addition, integrating five K-fold methods enhances the accuracy to 99.68% while decreasing the loss score to 0.120. The research further included the evaluation of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) for various classes and models. As a result, our model may detect and diagnose Alzheimer’s disease quickly and accurately. [ABSTRACT FROM AUTHOR]

The aim was to evaluate predictors of clinical outcomes in infliximab (IFX)-treated Crohn's disease (CD) patients in western China and provide evidence for future treatment optimization. Our retrospective study included CD patients at Chongqing General Hospital from July 2022 to July 2023. Clinical data of CD patients at baseline and the endpoint (the seventh IFX treatment, 38 weeks) were collected. Baseline variables of IFX-treated patients with regard to clinical remission [Crohn's Disease Activity Index (CDAI) < 150] at endpoint were assessed, and the correlation of serum vitamin D (Vit-D) levels before initiating IFX therapy and CDAI at week 38 was analyzed. Sixty patients with IFX-treated CD were included. The Vit-D-deficient rate was 51.7% at baseline, 81.7% of patients achieved clinical remission, and 66.7% achieved endoscopic remission at week 38 of IFX treatment. Vit-D level at baseline was an independent predictors of clinical remission after IFX treatment (P < 0.05). Receiver operating characteristic curve analysis showed that when Vit-D concentration was 15.81 ng/ml, the area under the curve was 0.711 (95% CI 0.523–0.899, P = 0.03). The sensitivity and specificity were 81.6% and 63.6%, respectively. Vit-D level in the normal BMI, non-smoking, immunosuppressant-treated subgroup had independent predictive value for CDAI at endpoint (P < 0.05). Baseline Vit-D level predicted clinical remission in CD patients after IFX treatment, especially in those with normal body mass index, who do not smoke, and who take IFX in combination with immunosuppressants. [ABSTRACT FROM AUTHOR]

Objective: The epidemiology of COVID-19 has substantially changed since its emergence given the availability of effective vaccines, circulation of different viral variants, and re-infections. We aimed to develop models to predict 30-day COVID-19 hospitalization and death in the Omicron era for contemporary clinical and research applications. Methods: We used comprehensive electronic health records from a national cohort of patients in the Veterans Health Administration (VHA) who tested positive for SARS-CoV-2 between March 1, 2022, and March 31, 2023. Full models incorporated 84 predictors, including demographics, comorbidities, and receipt of COVID-19 vaccinations and anti-SARS-CoV-2 treatments. Parsimonious models included 19 predictors. We created models for 30-day hospitalization or death, 30-day hospitalization, and 30-day all-cause mortality. We used the Super Learner ensemble machine learning algorithm to fit prediction models. Model performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration intercepts and slopes in a 20% holdout dataset. Results: Models were trained and tested on 198,174 patients, of whom 8% were hospitalized or died within 30 days of testing positive. AUCs for the full models ranged from 0.80 (hospitalization) to 0.91 (death). Brier scores were close to 0, with the lowest error in the mortality model (Brier score: 0.01). All three models were well calibrated with calibration intercepts <0.23 and slopes <1.05. Parsimonious models performed comparably to full models. Conclusions: We developed prediction models that accurately estimate COVID-19 hospitalization and mortality risk following emergence of the Omicron variant and in the setting of COVID-19 vaccinations and antiviral treatments. These models may be used for risk stratification to inform COVID-19 treatment and to identify high-risk patients for inclusion in clinical trials. [ABSTRACT FROM AUTHOR]

Backgroundː: Stroke, primarily resulting from ischemic conditions, is the foremost cause of mortality and long-term impairment and is frequently associated with narrowing of the carotid arteries. Although carotid endarterectomy (CEA) is the treatment of choice, it carries the risk of cerebral ischemia and reduced blood flow. Transcranial Doppler (TCD) ultrasound offers a nonintrusive method to assess cerebral blood circulation during CEA, potentially enhancing surgical outcomes. The objective of this study was to assess the clinical utility and safety of TCD monitoring during CEA and to identify factors influencing postoperative complications. Methods: This retrospective analysis included 158 CEA patients (from January 2021–August 2023) who underwent TCD monitoring and whose data were compared to historical standard care data. The primary outcomes were operation duration and artery occlusion time. Secondary outcomes included carotid shunt usage, seven-day postoperative complications, and six-month carotid artery patency. Logistic regression identified factors linked to adverse reactions, and a predictive model was evaluated with a receiver operating characteristic (ROC) curve. Resultsː: Comparative analysis indicated significant reductions in both the duration of surgery (113.26 ± 7.29 min) and artery occlusion time (21.85 ± 2.92 min) for patients monitored with TCD (P < 0.001) and an increase in carotid shunt implementation (25% as opposed to traditional care). The observed postoperative complications were minor, with a nonsignificant trend that favored the use of TCD-monitored procedures (1% vs. historical rates). Factors such as patient age and plaque echogenicity were found to be predictive of postoperative issues, with plaque echogenicity emerging as a significant predictive factor (OR = 10.70, 95% CI: 2.14–202, P = 0.02) upon multivariate analysis. The predictive model exhibited high precision (AUC = 0.93). Conclusion: This retrospective evaluation suggested that TCD monitoring in the CEA may reduce procedural time and potentially decrease postoperative complications, supporting its use for personalized surgical planning. [ABSTRACT FROM AUTHOR]

Background: Motile Sperm Domain-Containing Protein 1 (MOSPD1) has been implicated in breast cancer (BC) pathophysiology, but its exact role remains unclear. This study aimed to assess MOSPD1 expression levels in BC versus normal tissues and investigate its diagnostic potential. Methods: MOSPD1 expression was analyzed in BC and normal tissues, with Receiver Operating Characteristic analysis for diagnostic evaluation. Validation was performed using immunohistochemistry. Functional studies included tumor growth assays, MOSPD1 suppression and overexpression experiments, and testing BC cell responses to anti-PD-L1 therapy. Results: MOSPD1 expression was significantly higher in BC samples than normal tissues, correlating with poor clinical outcomes in BC patients. MOSPD1 suppression inhibited tumor growth, while overexpression accelerated it. Silencing MOSPD1 enhanced BC cell sensitivity to anti-PD-L1 therapy and decreased Th2 cell activity. In vivo experiments supported these findings, showing the impact of MOSPD1 on tumor growth and response to therapy. Conclusions: Elevated MOSPD1 levels in BC suggest its potential as a biomarker for adverse outcomes. Targeting MOSPD1, particularly with anti-PD-L1 therapy, may effectively inhibit BC tumor growth and modulate immune responses. This study emphasizes the significance of MOSPD1 in BC pathophysiology and highlights its promise as a therapeutic target. [ABSTRACT FROM AUTHOR]

Keratoconus (KC) is an irreversible blinding eye disease; therefore, early screening of KC suspects (KCS) is crucial for protecting patients' quality of life. Scheimpflug imaging is a commonly used screening device in clinical practice. We aimed to evaluate the diagnostic ability of a Scheimpflug imaging device (Scansys) for KC and KCS and compared it with other Scheimpflug-based devices (Pentacam and Corvis ST). This prospective case-control study included 107 normal eyes, 72 KCS, and 57 KC. Scansys screening index Keratoconus probability (KCP) showed excellent performance in diagnosing KC at a cutoff value of 16.4 (area under the receiver operating characteristic [AUROC] = 1.000), with 100% sensitivity and 98.11% specificity. KCP had a better KCS diagnostic ability at a cutoff value of 8.9 (AUROC = 0.813) than Corvis biomechanical index (CBI, AUROC = 0.764), reaching 67.61% sensitivity and 85.85% specificity. Pentacam screening index Belin/Ambrósio enhanced ectasia display deviation (BAD-D) showed the best performance with 92.96% sensitivity and 89.62% specificity at a cutoff value of 1.525 (AUROC = 0.970) in diagnosing KCS. Scansys provides accurate KCP parameters in diagnosing KC; however, the efficiency of diagnosing KCS should be further optimized. [ABSTRACT FROM AUTHOR]

The discovery of Lactylation may pave the way for novel approaches to investigating sepsis. This study focused on the prognostic and diagnostic significance of lactylated genes in sepsis. RNA sequencing was performed on blood samples from 20 sepsis patients and 10 healthy individuals at Southwest Medical University in Luzhou, Sichuan, China. Genes associated with sepsis were identified through analysis of RNA sequencing data. Afterward, the genes that were expressed differently were compared with the lactylation genes, resulting in the identification of 55 lactylation genes linked to sepsis. The overlapping genes underwent analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-Protein Network Interactions were used to screen for the core genes. The datasets GSE65682, GSE69528, GSE54514, GSE63042, and GSE95233 were obtained from the GEO database to validate core genes. Survival analysis evaluated the predictive significance of central genes in sepsis, while Receiver Operating Characteristic (ROC) Curve analysis was employed to establish the diagnostic value of genes. Additionally, a meta-analysis was conducted to confirm the precision of RNA-seq data. We obtained five peripheral blood samples, including two from healthy individuals, one from a patient with systemic inflammatory response syndrome (SIRS), and two from patients with sepsis. These samples were used to identify the specific location of core genes using 10×single-cell sequencing. High-throughput sequencing and bioinformatics techniques identified two lactylation-related genes (S100A11 and CCNA2) associated with sepsis. Survival analysis indicated that septic patients with reduced levels of S100A11 had a decreased 28-day survival rate compared to those with elevated levels. Conversely, individuals exhibiting decreased CCNA2 levels demonstrated a greater likelihood of surviving for 28 days than those in the high expression category, indicating a favorable association with survival rates among sepsis patients (P < 0.05). Both genes showed high sensitivity and specificity based on the ROC curve, with AUC values of 0.961 for S100A11 and 0.890 for CCNA2. The meta-analysis revealed that S100A11 exhibited high levels of expression in the sepsis survivors, whereas it displayed low levels of expression in the non-survivors; on the other hand, CCNA2 demonstrated low expression in the sepsis survivors and high expression in the non-survivors (P < 0.05). Single-cell RNA sequencing ultimately showed that monocyte macrophages, T cells, and B cells exhibited high expression levels of the crucial genes associated with sepsis-induced lactylation. In conclusion, the lactylation genes S100A11 and CCNA2 are strongly linked to sepsis and could be valuable markers for diagnosing, predicting outcomes, and providing guidance for sepsis. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein–protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine–cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD. [ABSTRACT FROM AUTHOR]

Background: Discrimination may further impede access to medical care for individuals in socially disadvantaged positions. Sociodemographic information and perceived discrimination intersect and define multiple contexts or strata that condition the risk of refraining from seeking physician's care. By applying analysis of individual heterogeneity and discriminatory accuracy (AIHDA) we aimed to improve the mapping of risk by considering both strata average risk differences and the accuracy of such strata risks for distinguishing between individuals who did or did not refrain from seeking physician's care. Methods: We analysed nine annual National Public Health Surveys (2004, 2007–2014) in Sweden including 73,815 participants. We investigated the risk of refraining from seeking physician's care across 64 intersectional strata defined by sex, education, age, country of birth, and perceived discrimination. We calculated strata-specific prevalences and prevalence ratios (PR) with 95% confidence intervals (CI), and the area under the receiver operating characteristic curve (AUC) to evaluate the discriminatory accuracy (DA). Results: Discriminated foreign-born women aged 35–49 with a low educational level show a six times higher risk (PR = 6.07, 95% CI 5.05–7.30) than non-discriminated native men with a high educational level aged 35–49. However, the DA of the intersectional strata was small (AUC = 0.64). Overall, discrimination increased the absolute risk of refraining from seeking physician's care, over and above age, sex, and educational level. Conclusions: AIHDA disclosed complex intersectional inequalities in the average risk of refraining from seeking physician's care. This risk was rather high in some strata, which is relevant from an individual perspective. However, from a population perspective, the low DA of the intersectional strata suggests that potential interventions to reduce such inequalities should be universal but tailored to the specific contextual characteristics of the strata. Discrimination impairs access to healthcare. [ABSTRACT FROM AUTHOR]

Background: Advances in medical imaging have made it possible to classify ankle fractures using Artificial Intelligence (AI). Recent studies have demonstrated good internal validity for machine learning algorithms using the AO/OTA 2018 classification. This study aimed to externally validate one such model for ankle fracture classification and ways to improve external validity. Methods: In this retrospective observation study, we trained a deep-learning neural network (7,500 ankle studies) to classify traumatic malleolar fractures according to the AO/OTA classification. Our internal validation dataset (IVD) contained 409 studies collected from Danderyd Hospital in Stockholm, Sweden, between 2002 and 2016. The external validation dataset (EVD) contained 399 studies collected from Flinders Medical Centre, Adelaide, Australia, between 2016 and 2020. Our primary outcome measures were the area under the receiver operating characteristic (AUC) and the area under the precision-recall curve (AUPR) for fracture classification of AO/OTA malleolar (44) fractures. Secondary outcomes were performance on other fractures visible on ankle radiographs and inter-observer reliability of reviewers. Results: Compared to the weighted mean AUC (wAUC) 0.86 (95%CI 0.82–0.89) for fracture detection in the EVD, the network attained wAUC 0.95 (95%CI 0.94–0.97) for the IVD. The area under the precision-recall curve (AUPR) was 0.93 vs. 0.96. The wAUC for individual outcomes (type 44A-C, group 44A1-C3, and subgroup 44A1.1-C3.3) was 0.82 for the EVD and 0.93 for the IVD. The weighted mean AUPR (wAUPR) was 0.59 vs 0.63. Throughout, the performance was superior to that of a random classifier for the EVD. Conclusion: Although the two datasets had considerable differences, the model transferred well to the EVD and the alternative clinical scenario it represents. The direct clinical implications of this study are that algorithms developed elsewhere need local validation and that discrepancies can be rectified using targeted training. In a wider sense, we believe this opens up possibilities for building advanced treatment recommendations based on exact fracture types that are more objective than current clinical decisions, often influenced by who is present during rounds. [ABSTRACT FROM AUTHOR]

Background: In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers. Objective: We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF. Methods: This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort. Results: Binary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668–0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652–0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups. Conclusions: The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy. [ABSTRACT FROM AUTHOR]

Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type. The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification. FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification. None of the hybrid BOR models outperformed the hybrid FSA models. Lasso-based models, excluding the LR type, were comparable to the best models for six of the 10 datasets for classification. Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately, providing the field with a useful tool for investors. [ABSTRACT FROM AUTHOR]

Background: Invasive pulmonary aspergillosis (IPA) is a serious fungal infection. However, current diagnostic methods have limitations. The purpose of this study was to use artificial intelligence to achieve a more accurate diagnosis of IPA. Methods: Totally 263 patients (148 cases of IPA, 115 cases of non-IPA) were retrospectively enrolled from a single institution and randomly divided into training and test sets at a ratio of 7:3. Clinic-radiological independent risk factors for IPA were screened using univariate analysis and multivariate logistic regression analysis, after which a clinic-radiological model was constructed. The optimal radiomics features were extracted and screened based on CT images to construct the radiomics label score (Rad-score) and radiomics model. The optimal DL features were extracted and screened using four pre-trained convolutional neural networks, respectively, followed by the construction of the DL label score (DL-score) and DL model. Then, the radiomics-DL model was constructed. Finally, the combined model was constructed based on clinic-radiological independent risk factors, the Rad-score, and the DL-score. LR was adopted as the classifier. Receiver operating characteristic (ROC) curves were drawn, and the areas under the curve (AUC) were calculated to evaluate the efficacy of each model in predicting IPA. Additionally, based on the best-performing model on the LR classifier, four other machine learning (ML) classifiers were constructed to evaluate the predictive value for IPA. Results: The AUC of the clinic-radiological model for predicting IPA in the training and test sets was 0.845 and 0.765, respectively. The AUC of the radiomics-DL and combined models in the training set was 0.871 and 0.932, while in the test set was 0.851 and 0.881, respectively. The combined model showed better predictive performance than all other models. DCA showed that taking 0.00–1.00 as the threshold, the clinical benefit of the combined model was higher than that of all other models. Then, the combined model was trained on four other machine learning classifiers, all of which achieved AUC values above 0.80 in the test set, showing good performance in predicting IPA. Conclusion: Clinic, CT radiomics, and DL combined model could be used to predict IPA effectively. [ABSTRACT FROM AUTHOR]

Objective: To evaluate value of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection (ISMAD). Methods: Symptomatic ISMAD patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, relevant risk factors for conservative treatment failure in ISMAD patients were analyzed, and a Nomogram prediction model for treatment outcome of ISMAD was constructed with risk factors. The predictive value of the model was evaluated. Results: Low true lumen residual ratio (TLRR), long dissection length, and large arterial angle (superior mesenteric artery [SMA]/abdominal aorta [AA]) were identified as independent high-risk factors for conservative treatment failure (P < 0.05). The receiver operating characteristic curve (ROC) results showed that the area under curve (AUC) of Nomogram prediction model was 0.826 (95% CI: 0.740–0.912), indicating good discrimination. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between the predicted curve and the ideal curve of the Nomogram prediction model. The decision curve analysis (DCA) analysis results showed that when probability threshold for the occurrence of conservative treatment failure predicted was 0.05–0.98, patients could obtain more net benefits. Similar results were obtained for the predictive value in the validation set. Conclusion: Low TLRR, long dissection length, and large arterial angle (SMA/AA) are independent high-risk factors for conservative treatment failure in ISMAD. The Nomogram model constructed with independent high-risk factors has good clinical effectiveness in predicting the failure. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is associated with airflow limitation resulting from a combination of small airway disease (SAD) and parenchymal destruction. Although various diagnostic methods for SAD exist, access to these tools can be limited. Objectives: This study aimed to explore the correlation between handgrip strength (HGS) and SAD in COPD patients. Design: Cross-sectional prospective study. Methods: HGS was measured using a hand dynamometer. SAD was evaluated using impulse oscillometry, with results reported as the difference between respiratory resistance at 5 and 20 Hz (R5–R20). SAD was defined as R5–R20 ⩾0.07 kPa/L/s. The receiver operator characteristic (ROC) curves, sensitivity, and specificity values were calculated to determine the optimal cutoff value of HGS for predicting SAD. Results: Sixty-four patients (90.6% male) were included. The average age was 72.1 ± 8.3 years, and body mass index was 23.4 ± 4.2 kg/m2. FEV1 was 71.6 ± 21.3%, and HGS was 30.2 ± 8.1 kg. R5–R20 was 0.11 ± 0.08 kPa/L/s. SAD was found in 64.1% of patients. A negative correlation between HGS and R5–R20 was observed (r = −0.332, p = 0.007). The best cutoff value for HGS in detecting SAD was determined to be 28.25 kg, with a sensitivity of 73.9%, specificity of 65.9%, and an area under ROC curve of 0.685 (95% CI 0.550–0.819, p = 0.015). Conclusion: SAD is common in COPD patients, and HGS is significantly negatively correlated with SAD. This tool might serve as an alternative or adjunctive assessment for small airway dysfunction in COPD patients. Registration: This study was registered with ClinicalTrials.gov with number NCT06223139. Plain language summary: Association of handgrip strength with small airway disease in COPD In chronic obstructive pulmonary disease (COPD), the condition of small airways (those less than 2 mm wide) is closely linked to disease severity. Small airway disease (SAD) leads to reduced lung function, increased retention of excess gas in the lung, and poorer health in COPD patients. While various diagnostic methods exist for SAD, they may not always be accessible. This study investigated handgrip strength as a potential alternative way to assess SAD. Our findings show a significant negative correlation between handgrip strength and SAD in COPD patients. This suggests that handgrip strength could be used alongside existing methods to evaluate small airway dysfunction in these patients. [ABSTRACT FROM AUTHOR]

Background: Investigation into the immune heterogeneity linked with atherosclerosis remains understudied. This knowledge gap hinders the creation of a robust theoretical framework essential for devising personalized immunotherapies aimed at combating this disease. Methods: Single-cell RNA sequencing (scRNA-seq) analysis was employed to delineate the immune cell-type landscape within atherosclerotic plaques, followed by assessments of cell-cell interactions and phenotype characteristics using scRNA-seq datasets. Subsequently, pseudotime trajectory analysis was utilized to elucidate the heterogeneity in cell fate and differentiation among macrophages. Through integrated approaches, including single-cell sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, we identified hallmark genes. A risk score model and a corresponding nomogram were developed and validated using these genes, confirmed through Receiver Operating Characteristic (ROC) curve analysis. Additionally, enrichment and immune characteristic analyses were conducted based on the risk score model. The model's applicability was further corroborated by in vitro and in vivo validation of specific genes implicated in atherosclerosis. Result: This comprehensive scRNA-seq analysis has shed new light on the intricate immune landscape and the role of macrophages in atherosclerotic plaques. The presence of diverse immune cell populations, with a particularly enriched macrophage population, was highlighted by the results. Macrophage heterogeneity was intricately characterized, revealing four distinct subtypes with varying functional attributes that underscore their complex roles in atherosclerotic pathology. Intercellular communication analysis revealed robust macrophage interactions with multiple cell types and detailed pathways differing between proximal adjacent and atherosclerotic core groups. Furthermore, pseudotime trajectories charted the developmental course of macrophage subpopulations, offering insights into their differentiation fates within the plaque microenvironment. The use of machine learning identified potential diagnostic markers, culminating in the identification of RNASE1 and CD14. The risk score model based on these biomarkers exhibited high accuracy in diagnosing atherosclerosis. Immune characteristic analysis validated the risk score model's efficacy in defining patient profiles, distinguishing high-risk individuals with pronounced immune cell activities. Finally, experimental validation affirmed RNASE1's involvement in atherosclerotic progression, suggesting its potential as a therapeutic target. Conclusion: Our findings have advanced our understanding of atherosclerosis immunopathology and paved the way for novel diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis (RA) patients suffering from chronic renal insufficiency tend to exhibit subtle manifestations at the beginning. Urine albumin to creatinine ratio (ACR) is a sensitive indicator for early assessment of renal function. However, it is unclear whether it serves as an independent risk factor influencing the prognosis of RA patients. Methods: National Health and Nutrition Examination Survey (NHANES) data from 2009-2018 were included. Kaplan-Meier (K-M) curves were plotted to compare the cumulative survival probability of RA patients with different urinary albumin excretion. The association of ACR with mortality among RA patients was investigated with Cox regression model, restricted cubic spline (RCS) and stratified analyses. The prognostic efficacy of ACR and estimated glomerular filtration rate (eGFR) was evaluated by receiver operating characteristic (ROC) curves. Results: The Cox regression model adjusted with covariates showed a 53% (HR 1.53, 95% CI 1.06-2.21) increase in all-cause mortality and a statistically nonsignificant increase in cardiovascular disease (CVD) mortality in RA patients with microalbuminuria (30mg/g ≤ACR<300mg/g). ACR≥300mg/g was associated with an increase in all-cause mortality (HR 2.62, 95% CI 1.55-4.45) and CVD mortality (HR 5.67, 95% CI 1.96-16.39). RCS demonstrated a nonlinear correlation between ACR and all-cause mortality in RA patients with microalbuminuria. Subgroup analysis showed that CVD mortality was higher in RA patients with microalbuminuria characterized by the following features: female, other ethnicity, eGFR≥60 ml/min/1.73 m2, hypertension or hyperlipidemia. Compared with eGFR, ACR provided better prognostic efficacy than eGFR with higher values of the area under the curve (AUC) for all-cause mortality (AUC=0.683, 95% CI 0.613-0.754) and CVD mortality (AUC=0.681, 95% CI 0.541-0.820). Conclusion: ACR is an independent risk factor affecting the prognosis of RA patients. The all-cause mortality was increased in RA patients with albuminuria. There was an upward trend in the CVD mortality of those with macroalbuminuria when ACR increased. [ABSTRACT FROM AUTHOR]

Groundwater resource management and development should be done sustainably, which calls for accurate assessment based on new methods and scientific principles. There is an increasing demand for potable water supplies, to meet the woefully inadequate domestic water needs in the Batcham municipality (west of Cameroun). The dearth of knowledge on sustainable management of water resources is to blame for this predicament. As integrated methodologies are not used in groundwater exploration programs, the current problem has gotten worse. Innovative scientific principles of groundwater resource exploration are required to effectively counteract the non-rationalization of water and the problem of unacceptable failure rates in drilled water borehole development. This will improve the appropriate and sustainable use of these resources. Therefore, the goal of this research is to map groundwater potential zones by utilizing readily available datasets and remote sensing (RS), GIS, Frequency Ratios (FR), and multi-criteria analysis (AHP) techniques. The database used is made up of geological data, hydroclimatic data, well data, and satellite images. Each of the methods used is based on the study of the relationships between the factors controlling the groundwater potential. Among these factors, ten (10) were retained based on previous work; these are slope, elevation, land use, land cover, lineament density, drainage density, topographic wetness index, curvature, geology, rainfall, and the static water level. The AHP allowed, according to the user's expertise, to assign weights to each factor controlling the water potential. The FR made it possible to study the relationships linking each variable with high flow rates. The thematic layers obtained were integrated into a GIS environment to generate the groundwater potential map. Four classes of groundwater potential have been identified: bad, moderate, good, and excellent. The bad classes represent 8.32% and 11.21%; moderate 36.57% and 28.70%; good 43.55% and 38.93%; and excellent 11.56% and 21.16%, for the AHP and FR models, respectively. The overlying of wells with high flow rates on areas of excellent groundwater potential shows perfect concordance, which validated the final map. Likewise, the resulting map was validated by sensitivity curve analysis, and the receiver operating characteristic (ROC) curve from which result accuracy was obtained is 70.63% and 80.28% for the AHP method and FR method, respectively. Finally, the FR model outperformed the AHP model in terms of performance and it is possible to use these dependable models to describe groundwater potential zones in the other regions of Cameroon. [ABSTRACT FROM AUTHOR]

Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from communityacquired pneumonia (CAP). Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value. Conclusions: This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB. [ABSTRACT FROM AUTHOR]

Background: Oral lichen planus (OLP) is a chronic inflammatory condition that can impact patients' quality of life. While its exact etiology remains unclear, it is associated with an increased risk of malignant transformation. Currently, the diagnosis of OLP relies on clinical examination and histopathological analysis, which can be invasive. Therefore, there is an urgent need for non-invasive and accurate diagnostic biomarkers. This systematic review and meta-analysis aims to investigate the potential of salivary microRNAs as promising candidates for OLP diagnosis. This meta-analysis seeks to identify specific microRNAs that are differentially expressed and could serve as reliable biomarkers for OLP diagnosis. Methods: Our strategy involved searching for pertinent keywords in multiple academic databases including Cochrane Library, Embase, LIVIVO, MEDLINE, Ovid, ProQuest, Scopus, Web of Science, Espacenet, and Google Scholar search engine. Upon identification, articles were screened and data extracted from the eligible studies. Split component synthesis method was utilized to assess specificity, sensitivity, likelihood and diagnostic odds ratios. The random-effects meta-analysis approach was used to combine study findings and develop pooled diagnostic performance metrics. Hierarchical summary receiver operating characteristic (ROC) plots were generated to determine area under the curve. Subgroup analyses concerning the type of saliva and control groups were also performed. Results: Among the fourteen studies included in our systematic review, five were eligible for meta-analysis. Salivary microRNAs showed the pooled sensitivity of 0.80 (95% Confidence Interval (95% CI): 0.68–0.88), specificity of 0.89 (95% CI: 0.82–0.94), diagnostic odds ratio of 28.45 (95% CI: 10.40–77.80), and area under the curve (AUC) of 0.93 for OLP diagnosis. Unstimulated saliva had higher sensitivity and specificity than oral swirl samples as the biomarker medium for OLP diagnosis. Meta-analysis uncovered that miR-27a, miR-137, miR-1290, miR-27b, miR-4484, miR-142, and miR-1246 had the highest diagnostic odds ratio for OLP. Conclusions: Our systematic review and meta-analysis demonstrate that salivary microRNAs can serve as valuable biomarkers for the diagnosis of OLP. The findings highlight the exceptional accuracy of salivary microRNAs in differentiating OLP patients from healthy controls and assessing the risk of malignant transformation. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the accuracy of detecting vertical root fractures in Biodentine™-filled teeth using the Promax 3Dmax cone-beam computed tomography (CBCT) unit compared to periapical radiographs. It tested hypotheses regarding CBCT's diagnostic superiority in non-root-filled and Biodentine™-root-filled maxillary central incisors and assessed the impact of smaller field of view and lower intensity settings on detection accuracy. Materials and methods: Extracted maxillary incisors were divided into groups based on fracture status and root filling material, then placed in a Thiel-embalmed skull to simulate clinical conditions. The teeth were imaged using periapical radiographs and the CBCT unit under different settings. Fracture thickness was measured with microcomputed tomography for accuracy benchmarking. Multiple observers assessed the images, and statistical analyses were conducted to evaluate diagnostic performance. Results: Intra-rater reliabilities of consensus scores ranged from good to very good. Specificities were generally higher than sensitivities across all imaging modalities, but sensitivities remained constantly low. None of the Area Under the Curve scores exceeded 0.6, indicating poor overall accuracy for all imaging modalities. Paired comparisons of the area differences under Receiver Operator Characteristic curves revealed no significant differences between the CBCT and periapical radiograph techniques for detecting vertical root fractures in either Biodentine™-filled or non-root-filled teeth. Conclusions: There was no significant accuracy improvement of the current CBCT device (Promax 3Dmax, Planmeca, Finland) over periapical radiographs in detecting small vertical root fractures in both non-root-filled and Biodentine™-root-filled maxillary central incisors. A smaller field of view with lower intensity did not enhance detection accuracy. These results highlight the challenges in accurately detecting small VRFs, emphasizing the need for further research and technological advancements in this domain. [ABSTRACT FROM AUTHOR]

Prognosis biomarkers for endometrial cancer (EC) are in need. Recent evidence demonstrated the critical role of disulfidptosis, a novel cell death modality, in cancer. However, limited studies have developed a disulfidptosis-related gene model for EC. Disulfidptosis prognosis score of EC (disulfidptosis-PSEC) were constructed using disulfidptosis-related differently expression genes with the RNA data of 544 EC patients from The Cancer Genome Atlas (TCGA) dataset. Model was evaluated using time-dependent receiver operating characteristic curve analysis for overall survival (OS) and disease-free survival (DFS), along with the hazard ratio (HR) between risk groups. Then, the cellular and molecular profile for different risk groups were performed, along with drug target inference. Disulfidptosis-PSEC demonstrated outstanding prognostic value for OS and DFS, with 5-year area under curve of 0.71 (95% CI, 0.58–0.75) and 0.69 (95% CI, 0.62–0.76), respectively. Low risk group demonstrated better survival with an HR of 0.38 (95% CI, 0.24–0.59) and 0.46 (95% CI, 0.32–0.66) for OS and DFS, respectively. The model was independent of TCGA subtype. Low risk group were featured with more immune cell infiltration and less gene mutation. Serval drug targets, and the therapeutic value of serotonin receptor among copy number (CN)-low subpopulation, were identified. Disulfidptosis-PSEC was a potential prognosis biomarker for EC with targetable biological process. [ABSTRACT FROM AUTHOR]

Objective: Due to low prevalence and few studies, the morphologic risk factors for hip osteoarthritis (HOA) in Chinese population remain unknown. The purpose of this study was to investigate the relationship between 10 radiographic parameters measured via anteroposterior pelvic X‐ray radiography and HOA in Chinese population. Methods: Thirty‐three patients who required total hip arthroplasty for unilateral HOA (2017–2022) and 132 healthy individuals were selected for this case–control study. We measured 10 radiological parameters via anteroposterior pelvic X‐ray radiography, which were sharp angle, center edge angle, sourcil angle, neck shaft angle, α angle, pelvic height, pelvic width, femoral head diameter, femoral neck width, and ratio of the femoral head diameter to the femoral neck width. After measurements were obtained, logistic regression analysis was utilized to calculate the adjusted odds ratios (ORs) for confounding variables such as age, sex, and body mass index (BMI). Receiver operating characteristic (ROC) curves were utilized to determine the proportional risk contribution (PRC) of each radiographic factor. Results: After adjustment for confounding factors, individuals with a larger sourcil angle (SA) (OR = 4.89, 95% CI 1.66–14.42, p = 0.004), larger α angle (OR = 4.14, 95% CI 1.53–11.23, p = 0.005), and wider femoral neck (OR = 5.27, 95% CI 1.50–18.51, p = 0.01) were found to have a greater risk of developing HOA. Among all radiographic parameters, the SA demonstrated the greatest risk contribution (PRC = 13.695%). Conclusions: Radiographic parameters correlate with the incidence of HOA. The SA is probably the most powerful of all the parameters related to HOA. [ABSTRACT FROM AUTHOR]

Summary: Pedicle screw loosening after posterior lumbar fusion is associated with poor bone quality, which often determines screw pull‐out strength, insertion torque, and vertebral body loading characteristics. Magnetic resonance imaging (MRI)‐based vertebral bone quality (VBQ) score were associated with poor bone quality. Current evidence suggests that pedicle bone quality (PBQ) has a greater impact on screw stability. However, the correlation between MRI‐based PBQ score and screw loosening has not been reported. Purpose: To introduce and evaluate an MRI‐based PBQ score to determine its effectiveness in predicting pedicle screw loosening following lumbar fusion surgery. Methods: The retrospective study analyzed 244 patients who underwent posterior lumbar interbody fusion (PLIF) with pedicle screws between December 2017 and December 2021, with CT and MRI imaging before surgery. Data collected included patient demographics and preoperative radiological data. Radiographic screw loosening was measured at 12 months postoperatively. Clinical assessments included pain visual analog scale (VAS) and Oswestry Disability Index (ODI) scores. The PBQ score was measured using MRI scans. We use univariate analysis for preliminary screening of the risk factors of screw loosening. Subsequent analysis involved multivariate logistic regression to identify independent predictive factors for screw loosening. We constructed the receiver operating characteristic (ROC) curve to ascertain the discriminative capacity of the PBQ score. The area under the curve (AUC) quantified its predictive accuracy. Additionally, we evaluated the association between PBQ score and screw loosening using Spearman's correlation analysis. Results: Overall, 244 patients who underwent PLIF with pedicle screw fixation participated in this study, including 35 in the loosening group and 209 in the non‐loosening group. PBQ score in the loosening group was significantly higher than that in the non‐loosening group. On multivariate logistic regression, the higher PBQ score (OR = 8.481, 95% CI: 3.158–22.774; p < 0.001) and the lower mean Hounsfield unit (HU) value of L1‐4 (OR = 0.967, 95% CI 0.951–0.984; p < 0.001) were the variables that significantly predicted screw loosening. The AUC for the PBQ score and HU value were 0.751 (95% CI: 0.673–0.828) and 0.702 (95% CI: 0.612–0.791). The PBQ score optimal cutoff to differentiate patients with loosening and with non‐loosening was calculated as 3.045 with a sensitivity of 85.7% and specificity of 76.9%, while the optimal cutoff of the HU value was 151.5 with a sensitivity of 64.6% and specificity of 89.5%. Conclusions: The association between the PBQ score and the propensity for lumbar pedicle screw loosening was found to be substantial. As a predictive measure, the PBQ score outperformed the HU value in forecasting the likelihood of screw loosening post‐posterior lumbar fusion. [ABSTRACT FROM AUTHOR]

Introduction: Dysphagia after anti-reflux surgery (ARS) is one of the most common indications for re-operative anti-reflux surgery and a leading cause of patient dissatisfaction. Unfortunately, the factors affecting its development are poorly understood. We investigated the correlation between pre-operative manometric and the intra-operative impedance planimetry (EndoFLIP™) measurements and development of post-operative dysphagia. Methods: A review of patients who underwent index robotic ARS in our institution. Patients who underwent pre-operative manometry and intra-operative EndoFLIP™ were included in our study. Dysphagia was assessed pre-operatively and at 3-month after surgery. Results: Fifty-five patients (26.9%) reported post-operative dysphagia, and 34 (16.6%) reported new or worsening dysphagia. On pre-operative manometry, patients with post-operative dysphagia had a lower distal contractile integral [868.7 (IQR 402.2–1447) mmHg s cm vs 1207 (IQR 612.1–2111) mmHg s cm, p = 0.006) and lower esophageal sphincter (LES) pressure [14.7 IQR (8.9–23.6) mmHg vs 20.7 IQR (10.2–32.6) mmHg, p = 0.01] compared to those without post-operative dysphagia. They were also found to have higher pre-operative cross-sectional surface area (CSA) [83 IQR (44.5–112) mm2 vs 66 IQR (42–93) mm2, p = 0.02], and distensibility index (DI) [4.2 IQR (2.2–5.5) mm2/mmHg vs 2.9 IQR (1.6–4.6) mm2/mmHg, p = 0.003] compared to patients without post-operative dysphagia. Additionally, the decrease in CSA [− 34 (− 18.5, − 74.5) mm2 vs − 26.5 (− 10.5, − 53.7) mm2, p = 0.03] and DI [− 2.3 (− 1.2, − 3.7) mm2/mmHg vs − 1.6 (− 0.7, − 3.3) mm2/mmHg, p = 0.03] measurements were greater in patients with post-operative dysphagia. Conclusion: Patients who developed dysphagia post-operatively had poorer pre-operative motility and a greater change in LES characteristics intra-operatively. This finding suggests the utility of pre-operative manometry and intra-operative EndoFLIP in identifying patients at risk of developing dysphagia post-operatively. [ABSTRACT FROM AUTHOR]