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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 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]

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]

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]

Higher serum cortisol levels appear to be associated with stress that can overlap or manifest anxiety, fatigue, depression, and sleep dysfunction. These are common and intrusive non-motor symptoms of Parkinson's disease (PD). Thus, stress has been proposed to mediate Parkinson's disease development, and cortisol has been suggested as a biomarker for the generation of stress-related symptoms in Parkinson's disease. This study describes sensitive and robust disposable pipette extraction (DPX) and ultra-efficient liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method to determine cortisol and cortisone (as potential endocrine biomarkers for Parkinson's disease) in 24-h urine and saliva samples obtained from Parkinson's disease patients. Important parameters on DPX extraction were optimized to achieve the best extraction recovery and cleanup efficiency. The proposed method was linear from 0.5 (lower limit of quantification) to 500 ng mL−1 for cortisol and from 3.0 (lower limit of quantification) to 500 ng mL−1 for cortisone. To determine whether urinary cortisol and urinary cortisone are adequate as biomarkers to evaluate the level of anxiety in patients suffering from Parkinson's disease, twenty-nine Parkinson's disease patients (18 with anxiety and 11 without anxiety) were selected for urine analysis. Based on the obtained results, 24-h urine samples obtained from Parkinson's disease patients with anxiety had higher cortisone levels than samples obtained from healthy controls. Receiving operating curves (ROC) analysis, which presented the area under the ROC curve (AUC = 0.733), showed that urinary cortisone levels (µg/24-h urine) were sensitive (56.3%) and specific (93.3%) for distinguishing Parkinson's disease patients with anxiety from healthy controls. In terms of salivary results, PD patients' samples taken 30 min after waking up had higher cortisol and cortisone levels than healthy controls, while their samples taken at night had lower cortisol and cortisone levels. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether body mass index (BMI) affects the accuracy of vertebral bone quality (VBQ) in determining bone mineral density (BMD) in patients undergoing lumbar fusion surgery. Methods: In this retrospective study, patients with preoperative noncontrast T1-weighted MRI were included. Restricted Cubic Spline (RCS) was employed to explore the nonlinear relationship between BMI and VBQ. Then patients were stratified according to the threshold of BMI. Pearson correlation analysis and linear regression were used to analyze the correlation between VBQ and the BMD in different groups. Receiver operating characteristic (ROC) analysis to calculate the area under the curve (AUC) was used to assess diagnostic efficacy according to BMI. Results: A total of 328 patients (201 female and 127 male patients) with a mean age of 68.3 ± 3.3 years were included in the study. Significant nonlinear relationship was observed given the results of RCS. In patients with BMI < 23.8 kg/m2, the correlation coefficient between VBQ and the lowest BMD was − 0.32 and significant distribution difference of VBQ score was observed between osteoporosis and normal as well as osteopenia subgroups. However, in patients with BMI ≥ 23.8 kg/m2, the correlation coefficient between VBQ and the lowest BMD was − 0.39 and significant distribution difference of VBQ score was observed in all three subgroups. In addition, the ROC analysis revealed that the predictive performance in determining low BMD was superior in patients with BMI ≥ 23.8 kg/m2 (AUC 0.80 vs. AUC 0.66, p = 0.034). Conclusions: In this study, significant nonlinear relationship between BMI and VBQ was observed. Compared with patients with BMI < 23.8 kg/m2, VBQ has better discrimination between higher BMI (≥ 23.8 kg/m2) patients with low BMD and those with normal bone density. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the data on the psychometric properties of the Neck Disability Index (NDI). Materials and methods: Medline, Embase, PsychINFO, Web of Science, and Scopus were searched in April 2023. The random effects meta-analysis was conducted when possible. Results: Of 492 identified records, 79 were included. 70 studies were considered to be of low risk of systematic bias. Alpha was >0.81. Pooled test-retest intraclass correlation coefficient was 0.91 (95% CI 0.90–0.93). The NDI correlations with pain rating scales varied from 0.38 to 0.89. 13 studies found the NDI to be unidimensional and 15 – two- or three-dimensional. The minimal detectable change varied from 3% to 27% and minimal clinically important difference from 5% to 33%. Pooled area under the curve was 0.74 (95% CI 0.68–0.80). Most studies have not detected floor or ceiling effect. Sex-related differential item functioning has been present in one study. Conclusions: The NDI demonstrated good internal consistency and test-retest reliability without floor or ceiling effect. In most situations, the NDI could be considered a unidimensional scale. The NDI well correlated with the common scales of pain and disability. The minimal clinically important difference and minimal detectable change were around 15% (7.5/50 points). IMPLICATIONS FOR REHABILITATION: Based on the 79 observational studies of mostly low risk of systematic bias, the Neck Disability Index demonstrated overall good psychometric properties The Neck Disability Index is a reliable scale to assess the severity of disability caused by neck pain of different musculoskeletal aetiology [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Four‐dimensional computed tomography (4DCT) is an es sential tool in radiation therapy. However, the 4D acquisition process may cause motion artifacts which can obscure anatomy and distort functional measurements from CT scans.We describe a deep learning algorithm to identify the location of artifacts within 4DCT images. Our method is flexible enough to handle different types of artifacts, including duplication, misalignment, truncation, and interpolation.We trained and validated a U‐net convolutional neural network artifact detection model on more than 23 000 coronal slices extracted from 98 4DCT scans. The receiver operating characteristic (ROC) curve and precision‐recall curve were used to evaluate the model's performance at identifying artifacts compared to a manually identified ground truth. The model was adjusted so that the sensitivity in identifying artifacts was equivalent to that of a human observer, as measured by computing the average ratio of artifact volume to lung volume in a given scan.The model achieved a sensitivity, specificity, and precision of 0.78, 0.99, and 0.58, respectively. The ROC area‐under‐the‐curve (AUC) was 0.99 and the precision‐recall AUC was 0.73. Our model sensitivity is 8% higher than previously reported state‐of‐the‐art artifact detection methods.The model developed in this study is versatile, designed to handle duplication, misalignment, truncation, and interpolation artifacts within a single image, unlike earlier models that were designed for a single artifact type. [ABSTRACT FROM AUTHOR]

Study Design. Multicenter, prospective registry study. Objective. To clarify minimal clinically important differences (MCIDs) for surgical interventions for spinal metastases, thereby enhancing patient care by integrating quality of life assessments with clinical outcomes. Background. Despite its proven usefulness in degenerative spinal diseases and deformities, the MCID remains unexplored regarding surgery for spinal metastases. Patients and Methods. This study included 171 (out of 413) patients from the multicenter "Prospective Registration Study on Surgery for Metastatic Spinal Tumors" by the Japan Association of Spine Surgeons. These were evaluated preoperatively and at 6 months postoperatively using the Face Scale, EuroQol-5 Dimensions-5 Levels (EQ-5D-5L), including the Visual Analog Scale, and performance status. The MCIDs were calculated using an anchor-based method, classifying participants into the improved, unchanged, and deteriorated groups based on the Face Scale scores. Focusing on the improved and unchanged groups, the change in the EQ-5D-5L values from before to after treatment was analyzed, and the cutof f value with the highest sensitivity and specificity was determined as the MCID through receiver operating characteristic curve analysis. The validity of the MCIDs was evaluated using a distribution-based calculation method for patient-reported outcomes. Results. The improved, unchanged, and deteriorated groups comprised 121, 28, and 22 participants, respectively. The anchor-based MCIDs for the EQ-5D-5L index, EQ-Visual Analog Scale, and domains of mobility, self-care, usual activities, pain/discomfort, and anxiety/depression were 0.21, 15.50, 1.50, 0.50, 0.50, 0.50, and 0.50, respectively; the corresponding distribution-based MCIDs were 0.17, 15, 99, 0.77, 0.80, 0.78, 0.60, and 0.70, respectively. Conclusion. We identified MCIDs for surgical treatment of spinal metastases, providing benchmarks for future clinical research. By retrospectively examining whether the MCIDs are achieved, factors favoring their achievement and risks affecting them can be explored. This could aid in decisions on surgical candidacy and patient counseling. Level of Evidence. II. [ABSTRACT FROM AUTHOR]

Study Design. Retrospective diagnostic study. Objectives. To evaluate the utility of quantitative assessment of bone density using proximal femoral morphological parameters based on full-spine x-rays. Summary of Background Data. CT and MRI are commonly utilized methods for opportunistic assessment of bone density. However, there is currently a lack of means to quantitatively assess bone density in adult spinal deformity (ASD) patients through radiographs. Methods. Data collection involved medical records of ASD patients treated at our hospital. Patients were categorized into osteoporotic and nonosteoporotic groups based on dual-energy xray absorptiometry T-scores. Demographic information, radiographic parameters (canal bone ratio, canal bone ratio (CBR); cortical bone thickness, cortical bone thickness (CBT)), Hounsfield units, and vertebral body quality (VBQ) scores were compared. Pearson correlation analysis was conducted to assess the correlation between CBR, CBT, and T-scores. Multiple linear regression analysis identified independent predictors of bone density T-scores. Receiver operating characteristic curves and area under the curve calculations were performed to investigate the predictive performance for osteoporosis. Results. A total of 102 patients were included, with the osteoporotic group showing larger CBR and smaller CBT compared with the nonosteoporotic group. Proximal femoral morphological parameters exhibited the strongest correlation with total hip T-scores. Advanced age (β = -0.028, 95% CI = -0.054 to -0.002, P = 0.032), low BMI (β = 0.07, 95% CI = 0.014-0.126, P = 0.015), and high CBR (β = -7.772, 95% CI = -10.519 to -5.025, P < 0.001) were identified as independent predictors of low bone density. Receiver operating characteristic analysis demonstrated that CBR had a similar osteoporosis screening capability as Hounsfield units, followed by CBT and VBQ scores. Conclusions. The utilization of CBR from full-spine x-rays is a simple and effective osteoporosis screening indicator for ASD patients, facilitating bone density assessments by spine surgeons for all attending patients. [ABSTRACT FROM AUTHOR]

Study Design. A retrospective analysis. Objective. This research sought to develop a predictive model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using deep learning and machine learning (ML) techniques. Summary of Background Data. Determining surgical outcomes assists surgeons in communicating prognosis to patients and setting their expectations. Deep learning and ML are computational models that identify patterns from large data sets and make predictions. Methods. Of the 482 patients, 288 patients were included in the analysis. A minimal clinically important difference (MCID) was defined as gain in Japanese Orthopaedic Association (JOA) score of 2.5 points or more. The predictive model for MCID achievement at 1 year postsurgery was constructed using patient background, clinical symptoms, and preoperative imaging features (x-ray, CT, MRI) analyzed through LightGBM and deep learning with RadImagenet. Results. The median preoperative JOA score was 11.0 (IQR: 9.0-12.0), which significantly improved to 14.0 (IQR: 12.0-15.0) at 1 year after surgery (P < 0.001, Wilcoxon signed-rank test). The average improvement rate of the JOA score was 44.7%, and 60.1% of patients achieved the MCID. Our model exhibited an area under the receiver operating characteristic curve of 0.81 and the accuracy of 71.9% in predicting MCID at 1 year. Preoperative JOA score and certain preoperative imaging features were identified as the most significant factors in the predictive models. Conclusion. A predictive ML and deep learning model for surgical outcomes in OPLL patients is feasible, suggesting promising applications in spinal surgery. Level of evidence. 4 [ABSTRACT FROM AUTHOR]

Background: Patients hospitalized with COVID-19 can clinically deteriorate after a period of initial stability, making optimal timing of discharge a clinical and operational challenge. Objective: To determine risks for post-discharge readmission and death among patients hospitalized with COVID-19. Design: Multicenter retrospective observational cohort study, 2020–2021, with 30-day follow-up. Participants: Adults admitted for care of COVID-19 respiratory disease between March 2, 2020, and February 11, 2021, to one of 180 US hospitals affiliated with the HCA Healthcare system. Main Measures: Readmission to or death at an HCA hospital within 30 days of discharge was assessed. The area under the receiver operating characteristic curve (AUC) was calculated using an internal validation set (33% of the HCA cohort), and external validation was performed using similar data from six academic centers associated with a hospital medicine research network (HOMERuN). Key Results: The final HCA cohort included 62,195 patients (mean age 61.9 years, 51.9% male), of whom 4704 (7.6%) were readmitted or died within 30 days of discharge. Independent risk factors for death or readmission included fever within 72 h of discharge; tachypnea, tachycardia, or lack of improvement in oxygen requirement in the last 24 h; lymphopenia or thrombocytopenia at the time of discharge; being ≤ 7 days since first positive test for SARS-CoV-2; HOSPITAL readmission risk score ≥ 5; and several comorbidities. Inpatient treatment with remdesivir or anticoagulation were associated with lower odds. The model's AUC for the internal validation set was 0.73 (95% CI 0.71–0.74) and 0.66 (95% CI 0.64 to 0.67) for the external validation set. Conclusions: This large retrospective study identified several factors associated with post-discharge readmission or death in models which performed with good discrimination. Patients 7 or fewer days since test positivity and who demonstrate potentially reversible risk factors may benefit from delaying discharge until those risk factors resolve. [ABSTRACT FROM AUTHOR]

Background: Blood metabolites have emerged as promising candidates in the search for biomarkers for Alzheimer's disease (AD), as evidence shows that various metabolic derangements contribute to neurodegeneration in AD. Objective: We aim to identify metabolic biomarkers for AD diagnosis. Methods: We conducted an in-depth analysis of the serum metabolome of AD patients and age, sex-matched cognitively unimpaired older adults using ultra-high-performance liquid chromatography-high resolution mass spectrometry. The biomarkers associated with AD were identified using machine learning algorithms. Results: Using the discovery dataset and support vector machine (SVM) algorithm, we identified a panel of 14 metabolites predicting AD with a 1.00 area under the curve (AUC) of receiver operating characteristic (ROC). The SVM model was tested against the verification dataset using an independent cohort and retained high predictive accuracy with a 0.97 AUC. Using the random forest (RF) algorithm, we identified a panel of 13 metabolites that predicted AD with a 0.96 AUC when tested against the verification dataset. Conclusions: These findings pave the way for an efficient, blood-based diagnostic test for AD, holding promise for clinical screenings and diagnostic procedures. [ABSTRACT FROM AUTHOR]

Summary: Background: To prevent complications of paediatric ulcerative colitis (UC), it is critical to understand their predictors. The Paediatric Inflammatory Bowel Disease Ahead (PIBD Ahead) program identified the relevant outcomes and their potential predictors. However, external validation of these results in larger cohorts is required. Aims: The aim of this study is to investigate these outcomes and their predictors. Methods: We included 743 patients aged under 18 years with UC from the multicentre German‐Austrian CEDATA‐GPGE registry. We performed Cox regressions, Kaplan–Meier estimator, and receiver operating characteristics curve analyses to analyse predictors of poor outcomes. Results: Older age at diagnosis was associated with relapse, hospitalisation, the use of immunomodulators, use of biologics, and therapy escalation. Higher disease activity, as in acute severe colitis in the first 3 months, was significantly associated with further acute severe colitis and the need for biologics. Upper gastrointestinal tract involvement was a risk factor for the need of intravenous corticosteroids and biologics. A faecal calprotectin of >685 μg/g was associated with a higher risk of subsequent acute severe colitis with a sensitivity of 79.0% and a specificity of 59.1%. A lower haematocrit at diagnosis was predictive of the use of biologics. Colectomy was rare. Conclusions: This study validates predictors of poor outcomes in paediatric patients with UC. Our results might help physicians to anticipate poor outcomes and initiate appropriate treatment strategies at an early stage. [ABSTRACT FROM AUTHOR]

Face recognition is used in many applications such as access control, automobile security, criminal identification, immigration, healthcare, cyber security, and so on. Each person has his/her own unique face, so the face can help distinguish people from each other. Feature extraction process plays a fundamental role in accuracy of face recognition, and many algorithms have been presented to extract more informative features from the face image. In this paper, an efficient texture descriptor is proposed based on local information of the face image. In the proposed method, at first, face image is split into several sub-images in such a way that each sub-image includes one of the facial parts such as eyes, nose, and lips. Second, texture features are extracted from each sub-image using a new local pattern descriptor, and then features of sub-images are concatenated to construct feature vector. Finally, the face image is compared to images in a dataset based on a similarity measure. In addition, particle swarm optimization algorithm is used to assign weight to the features of different parts of the face image. To evaluate the proposed algorithm, four face datasets, Yale, ORL, GT and KDEF, are used. Implementation results show that the proposed method outperforms recent methods in terms of accuracy, receiver operating characteristic (ROC) curve, and area under ROC curve. [ABSTRACT FROM AUTHOR]

This study aimed to develop and validate a predictive nomogram model applicable to depression risk in stroke patients. Participants from the NHANES database (n = 1097) were enrolled from 2005 to 2018; 767 subjects were randomly assigned to the training cohort, and the remaining subjects composed the testing cohort. A nomogram containing the optimal predictors identified by the least absolute shrinkage and selection operator (LASSO) and logistic regression methods was constructed to estimate the probability of depression in stroke patients. To evaluate the performance of the nomogram, the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis (DCA) and internal validation were utilized. Age, family income, trouble sleeping, coronary heart disease, and total cholesterol were included in the nomogram after filtering predictive variables. The AUCs of the nomogram for the training and testing cohorts were 0.782 (95 % CI = 0.742–0.821) and 0.755 (95 % CI = 0.675–0.834), respectively. The calibration plot revealed that the predicted probability was extremely close to the actual probability of depression occurrence in both the training and testing cohorts. DCA revealed that the nomogram model in the training and testing cohorts had a net benefit when the risk thresholds were 0–0.59 and 0–0.375, respectively. This study was limited by the absence of clinical external validation, which hindered the estimation of the nomogram's external applicability. In addition, this study has a cross-sectional design. A novel nomogram was successfully constructed and proven to be beneficial for identifying individuals at high risk for depression among stroke patients. • Age, family income, trouble sleeping, CHD, and TC are key risk factors for developing depression in stroke patients. • The nomogram, based on above five factors, accurately and effectively predicts depression risk in patients with stroke. • The nomogram holds the potential to discern high-risk individuals for depression among stroke patients and enhance prognosis. [ABSTRACT FROM AUTHOR]

Background: Pediatric cow milk allergies (CMA) can occur in immunoglobulin (Ig) E and non-IgE-mediated forms. Unlike IgE-mediated allergies, the mechanisms of disease pathogenesis in non-IgE-mediated food allergy and an association with microbiome has not been well established. Previous studies have identified the presence of altered humoral responses to gut bacteria in IgE mediated allergies. Here, we analyzed IgA, IgE and IgG responses to gut bacteria in subjects with either IgE or non-IgE mediated CMA to identify relative proportions of Ig-coated bacteria and characterize unique disease specific microbial signatures. Methods: Multi-parametric flow cytometry analysis was used to identify IgA, IgE and IgG responses to gut bacteria in CMA patients. Cell sorting of Ig coated gut bacteria was subsequently performed followed by high throughput 16S rRNA gene sequencing and specific patterns of humoral responses to gut bacteria assessed in each study group. Results: We identified significant alterations in IgA and IgG gut bacterial coating patterns in CMA subjects. Proportions of IgA-coated bacteria were decreased in IgE mediated CMA subjects without atopic dermatitis (ALL) and non-IgE mediated CMA subjects (ENP), compared to healthy controls (CON). In comparison, IgG-coated bacteria was significantly elevated in CMA subjects with atopic dermatitis (AD). Alpha and beta diversities displayed significant differences in IgA-, IgE-, and IgG-coated bacteria in AD and ENP groups. Significant differences in bacteria coated by IgA, IgE and IgG were detected at Phyla, Genus and Species levels and associated bacterial dysbiosis in IgE and non-IgE mediated allergies were identified. Linear discriminant analysis (LDA) effect size (LEFse) revealed unique disease associated bacterial signatures, including several pathogenic bacteria namely Bacteroides fragilis, Ruminococcus gnavus, Eubacterium dolichum, Fusobacterium, Clostridium neonatale and Robinsoniella peoriensis. Receiver operating characteristic curve analysis confirmed the efficiency of using the bacterial signatures identified as biomarkers for disease. Conclusions: Altered IgA and IgG responses to gut bacteria were identified in CMA subjects. The disease-specific responses were associated with alterations in bacterial diversity and concomitant dysbiosis of Ig-coated bacteria in IgE-mediated and non-IgE-mediated CMA pediatric subjects. The identification of pathogenic bacteria uniquely associated with different classes of allergic disease indicates a role of these bacteria in driving disease-specific pathological phenotypes. [ABSTRACT FROM AUTHOR]

Objective: To investigate the prevalence and risk factors of psychological symptoms and quality of life (QoL) in early patients with inflammatory bowel disease (IBD). Methods: From September 2021 to May 2022, a unified questionnaire was developed to collect clinical data from early patients with IBD from 42 tertiary care hospitals. The influencing factors of psychological symptoms and poor QoL are screened by logistic regression analysis for constructing model in predicting poor QoL. The consistency index, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the model. Results: A total of 939 early patients with IBD were surveyed, Among them, 20.3% exhibited anxiety, 21.7% had depression, 57.3% experienced sleep disturbance, and 41.9% reported poor QoL. The factors influencing psychological symptoms varied between ulcerative colitis (UC) and Crohn's disease (CD) patients. The QoL was primarily affected by disease activity, income level and depression. The AUC value of the model in the training group was 0.781 (95% CI: 0.748–0.814). The calibration diagram of the model closely matched the ideal curve. Compared to other prediction models, our model showed superior predictive capability, with NRI and IDI values of 0.324 (95%CI:0.196–0.4513) and 0.026 (95%CI:0.014–0.038), respectively. DCA indicated that the nomogram model could provide clinical benefits. Conclusion: Early patients with IBD exhibit a high prevalence of psychological symptoms and poor QoL. The nomogram prediction model we constructed demonstrates high accuracy and performance in predicting QoL in early patients with IBD. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate risk factors associated with severe postoperative complications following hip fracture surgery in elderly patients and to develop a nomogram-based risk prediction model for these complications. Methods: A total of 627 elderly patients with hip fractures treated at Yongchuan Hospital of Chongqing Medical University from January 2015 to April 2024 were collected. 439 patients were assigned to the training cohort for model development, and 188 to the validation cohort for model assessment. The training cohort was stratified based on the presence or absence of severe complications. We employed LASSO regression, as well as univariate and multivariate logistic regression analyses, to identify significant factors. A nomogram was constructed based on the outcomes of the multivariate regression. The model's discriminative ability was assessed using the area under the receiver operating characteristic curve (AUC), while calibration plots and decision curve analysis (DCA) evaluated its calibration and stability. Internal validation was performed using the validation cohort. Results: Out of the 627 patients, 118 (18.82%) experienced severe postoperative complications. Both LASSO regression and multivariate logistic analysis identified the modified 5-item frailty index (mFI-5) and the preoperative C-reactive protein to albumin ratio (CAR) as significant predictors of severe complications. The nomogram model, derived from the multivariate analysis, exhibited strong discriminative ability, with an AUC of 0.963 (95% CI: 0.946–0.980) for the training cohort and 0.963 (95% CI: 0.938–0.988) for the validation cohort. Calibration plots demonstrated excellent agreement between the nomogram's predictions and actual outcomes. Decision curve analysis (DCA) indicated that the model provided clinical utility across all patient scenarios. These findings were consistent in the validation cohort. Conclusions: Both the mFI-5 and CAR are predictive factors for severe postoperative complications in elderly patients undergoing hip fracture surgery. [ABSTRACT FROM AUTHOR]

The current worldwide pandemic has significantly increased the need for online learning platforms, hence presenting difficulty in choosing appropriate course materials from the vast online educational resources due to user knowledge frameworks variations. This paper presents a novel course recommendation system called the Deep Learning-based Course Recommendation System (DLCRS). The DLCRS combines a hybrid Sequential GRU+adam optimizer with collaborative filtering techniques to offer accurate and learner-centric course suggestions. The proposed approach integrates modules for learner feature extraction and course feature extraction that is performed using (Embeddings from Language Models) ELMO word embedding technique in order to gain a thorough understanding of learner and course profiles and feedback. In order to evaluate the efficacy of the proposed DLCRS, several extensive experiments were carried out utilizing authentic datasets sourced from a reputable public organization. The results indicate a notable area under the receiver operating characteristic curve (AUC) score of 89.62%, which exceeds the performance of similar advanced course recommendation systems. The experimental findings support the viability of the DLCRS, as seen by a significant hit ratio of 0.88, indicating high accuracy in its suggestions. [ABSTRACT FROM AUTHOR]

Background: The risk factors and association of venous thromboembolism (VTE) following emergent ventral hernia repair (EVHR) remains uncertain. This aim of the study aims was to establish the predictors of VTE after EVHR and its influence on the long‐term outcomes. Methods: A total of 2093 patients from the MIMIC‐IV database who underwent EVHR were recruited. Multivariate logistic regression and nomogram models were developed to predict in‐hospital VTE and mortality. Calibration and receiver operating characteristic (ROC) curves were utilized to assess the model's effectiveness and reliability. Decision curve analysis (DCA) was performed to evaluate the net clinical benefits of the model. Results: The rate of in‐hospital VTE was 1.6% (33/2093) after EVHR. Four independent potential factors were established after multivariate analysis, and the abovementioned risk factors fit into the nomogram. The prediction model presented good performance metrics (C‐index: 0.857), the calibration and ROC curves demonstrated the accurate prediction power, and DCA indicated the superior net benefit of the established model. In‐hospital and 1‐year mortality rates were 0.8% (17/2093) and 4.1% (86/2076) after EVHR. The potential factors were included in the mortality prediction nomogram. The prediction model presented good performance metrics (C‐index of 0.957 and 0.828, respectively), the calibration and ROC curves were consistent with the actual results, and DCA indicated the superior net benefit of the established model. Conclusion: The nomogram, derived from the logistic regression model, demonstrated excellent predictive performance for VTE occurrence and prognosis in patients following EVHR. This model could serve as a valuable reference for clinical decision‐making regarding VTE prevention and for enhancing post‐EVHR prognosis. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs). Subsequently, receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways—tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis—were enriched, providing insight into the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as key components. In summary, this study highlights metabolic biomarkers that could potentially be used for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data offers a validated and in-depth understanding of TNBC metabolism. [ABSTRACT FROM AUTHOR]

Background: Post-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning (ML) methods for predicting PSD. Methods: 9 acceptable features were screened out by the Spearman correlation analysis and Boruta algorithm. We developed and evaluated 8 ML models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, random forest, and multilayer perceptron. Results: A total of 539 stroke patients were included in this study. Among the 8 models used to predict PSD, extreme gradient boosting and random forest showed the highest area under the curve (AUC) of the receiver operating characteristic curve (ROC), with values of 0.7287 and 0.7285, respectively. The most important features for predicting PSD included age, high sensitivity C-reactive protein, stroke side and location, and the occurrence of cerebral hemorrhage. Conclusion: Our findings suggest that ML models, especially extreme gradient boosting, can best predict the risk of PSD. [ABSTRACT FROM AUTHOR]

Background: While previous reports have shown that hepatitis B virus (HBV) infection affects lipid metabolism and vice versa, the impact of dyslipidemia on the functional cure of HBV infection following peginterferon alfa (PegIFNα) therapy remains unknown. Hence, this study aimed to investigate the effect of dyslipidemia on hepatitis B surface antigen (HBsAg) clearance and develop a nomogram model for predicting patients for whom PegIFNα therapy is indicated. Methods: A total of 160 nucleos(t)ide analogues (NAs)- experienced chronic hepatitis B (CHB) patients treated with PegIFNα (180 µg/week) were enrolled in this study. The relationship between serum lipid and HBsAg clearance was analysed. Univariate and multivariate COX analyses were used to construct and plot the nomogram model. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to evaluate the discrimination and calibration of the model, respectively. Results: After 48 weeks of PegIFNα therapy, a total of 33 patients in the cohort achieved HBsAg clearance. Univariate and multivariate COX analyses indicated that dyslipidemia was significantly associated with HBsAg clearance and was an independent predictor of HBsAg clearance (HR = 0.243, P = 0.001). Kaplan-Meier survival analyses show that cumulative HBsAg clearance was significantly higher in the normolipidemic group than in the dyslipidemia group (log-rank test, P = 0.007). During the treatment, triglyceride showed an increasing trend, while the levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, apolipoprotein A1 and apolipoprotein B decreased. Dyslipidemia and other indicators independently associated with HBsAg clearance were used to construct the nomogram model. The AUC of the model at 36-week and 48-week were 0.879 and 0.856, and the model demonstrated good discrimination and calibration. Conclusion: Dyslipidemia can affect the antiviral efficacy of PegIFNα in NAs-experienced CHB patients. Our findings suggest that the nomogram model constructed using serum lipid has good predictive power and may help physicians to identify the superior patients for PegIFNα therapy. [ABSTRACT FROM AUTHOR]

Summary: Background: Alterations in haematological, biochemical parameters and cytokine levels, were reported in patients with COVID-19, however, there is an underrepresentation of the African population, which could provide evidence for understanding SARS-CoV-2 pathogenesis and useful tools for clinical management of cases. In this study, we aimed to determine the haematological, biochemical and cytokine profile in Mozambican individuals with SARS-CoV-2. Methods: A cohort of 85 Mozambican individuals with RT-PCR SARS-CoV-2 results, was stratified into negative, asymptomatic, mild, moderate, and severe categories. Haematological, biochemical and cytokines measurement were performed on samples from the study participants. Principal component analysis (PCA) was performed to identify similar patterns among the study cases. Comparisons between groups were performed using the Kruskal-Wallis test. Receiver operating characteristic (ROC) and area under the curve (AUC) analysis were conducted to evaluate the ability of these parameters to distinguish severe from non-severe cases of SARS-CoV-2 infection. Results: SARS-CoV-2 infection was associated with a significant (p < 0.05) decrease in peripheral blood absolute counts of total lymphocytes and eosinophils, below the reference values along with no abnormal change (p > 0.05) in red blood cell count, haemoglobin, platelets and other red series parameters. At the serum level, SARS-CoV-2 infection was associated with an increase in serum levels of C-reactive protein (C-RP) and glucose above the reference values and to a significant reduction a significant (p < 0.05) reduction in levels of interferon-gamma (INF-γ), Tumour Necrosis Factor alfa (TNF-α) and the interleukin 1 beta (IL-1β) and IL-6 in severe cases, when compared to negative cases. Haematological, biochemical and cytokine profiles segregate severe from non-severe cases of COVID-19 with an excellent performance of C-RP (AUC = 0.95; p < 0.001) and good performance of lymphocytes (AUC = 0.88; p < 0.001) and IL-15 (AUC = 0.86; p < 0.001). Conclusion: The lack of variation in red and platelet series, coupled with a decrease in the levels of classical pro-inflammatory in severe cases, deviates from what has been reported in other contexts suggesting, that there may be peculiarities in COVID-19 manifestation within the context of this study population. Furthermore, these results identify parameters with potential for clinical management of COVID-19 and therefore good resource allocation, particularly for severe cases. [ABSTRACT FROM AUTHOR]

Background: Currently, the incidence rate of Medullary Thyroid Microcarcinoma (micro-MTC) has an increasing trend, but the incidence of LNM and prognosis were still ambiguous. We analyzed the status of neck LNM of micro-MTC patients and created a prognostic nomogram to predict the probability of lateral lymph node metastasis (LLNM) for clinical practice. Methods: This is a retrospective study included patients with micro-MTC from SEER database for the period from 2004 to 2017 and patients from our medical center for the period from 2011 to 2019. A nomogram was constructed and the accuracy and clinical practicability were separately tested by Harrell's C-indexes, calibration plots, Receiver operating characteristic curve (ROC) and decision curve analyses (DCA). Results: A total of 413 patients with micro-MTC from SEER database and 64 patients with micro-MTC from our department enrolled in the study. There were 16.0% and 9.4% cases in SEER database and 39.1% and 25.0% cases in our department appeared LNM and LLNM, respectively. Besides, a nomogram was constructed to assess the incidence of LLNM with good C-index, which was 0.850 in training cohort and 0.856 in validation cohort. The results of the area under the curve (AUC) were 0.830 in training cohort, 0.801 in validation cohort and 0.832 in external testing cohort, respectively. Conclusion: A relatively high rate of LLNM than expected was found, which should be emphasized. The prediction model could facilitate clinicians to assess the probability of LLNM and make a personalized treatment strategy. [ABSTRACT FROM AUTHOR]

ABSTRACT To complement the conventional area under the ROC curve (AUC) which cannot fully describe the diagnostic accuracy of some non‐standard biomarkers, we introduce a transformed ROC curve and its associated transformed AUC (TAUC) in this article, and show that TAUC can relate the original improper biomarker to a proper biomarker after a non‐monotone transformation. We then provide nonparametric estimation of the non‐monotone transformation and TAUC, and establish their consistency and asymptotic normality. We conduct extensive simulation studies to assess the performance of the proposed TAUC method and compare with the traditional methods. Case studies on real biomedical data are provided to illustrate the proposed TAUC method. We are able to identify more important biomarkers that tend to escape the traditional screening method. [ABSTRACT FROM AUTHOR]

Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe ImmugenX, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. ImmugenX comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, ImmugenX's encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. ImmugenX outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve = 0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. ImmugenX shows further improved performance on immunogenicity prediction with the integration of TCR context information. ImmugenX performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets. Author summary: Accurate prediction of neoantigen immunogenicity has the potential to greatly improve the effectiveness of targeted therapies for cancer. While there are a number of associated tasks, such as peptide-HLA elution prediction, which can be now predicted with high accuracy by a number of published models, direct prediction of which epitopes will produce an immune response has proven more difficult. In this paper we demonstrate a modular protein language model approach which is trained iteratively to include data from related sub-tasks and can be extended to include information such as candidate TCRs of interest when available. There is a relatively small amount of immunogenicity data available, with even less data available with paired TCRs. This makes directly training a model to predict immunogenicity challenging. Our approach has the advantage of utilising data from sub-tasks and masked language modelling to allow for training a highly performant model with a small dataset. Using a cancer-specific benchmarking dataset we show this approach improves on existing state-of-the-art models and can be improved further with the addition of TCR context. This provides a framework that can serve as the basis for utilising additional information sources and datasets as they become available. [ABSTRACT FROM AUTHOR]

Purpose Methods Results Conclusions To discuss the characteristics of anterior corneal elevation asymmetry in myopic eyes and clarify which kind of asymmetry most influenced lens position.In this retrospective study of 199 consecutive myopic participants, corneal topography was used to analyse asymmetry in anterior corneal elevation. Amongst them, 65 participants (65 eyes) who underwent orthokeratology (31 and 34 with spherical and toric lenses, respectively) were re‐evaluated. Stepwise multiple linear regression analysis was used to identify the contributing factors that influenced lens decentration. Receiver operating characteristic curve (ROC) analysis was employed to assess how the corneal asymmetry vector could predict decentration.There were no significant differences in treatment zone decentration (TZDec) between participants wearing toric and spherical lenses (p = 0.60 and 0.64 for 1 week and 1 month of wear, respectively). Amongst the underlying factors, the magnitude of TZDec was only correlated with the amount of corneal asymmetry vector (standardised β = 0.44, 0.48, p < 0.001 for all) after 1 week and 1 month of wear, and the direction of TZDec after 1 month of lens wear was associated with the angle of the asymmetry vector (r = 0.25, p = 0.04). ROC analysis showed that the magnitude of corneal asymmetry vector produced accurate discrimination between non‐severe and severe decentration for 1 week and 1 month of wear (area under the curve was 0.93 ± 0.04 and 0.89 ± 0.05, respectively, p < 0.001). Amongst participants whose corneal asymmetry vector exceeded 41.06 μm and was oriented inferiorly, 35.29% showed severe decentration after 1 month of lens wear.In myopic participants, corneal asymmetry existed in the 8.0 mm chord diameter. If the asymmetry vector >41.06 μm and the direction was oriented inferiorly, then practitioners must be vigilant about severe decentration which would not be alleviated by a toric design. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Methods Results Conclusion Within Aotearoa | New Zealand, rates of largely preventable severe caries and dental hospitalisations among children are increasing and inequalities exist. However, little population‐based empirical evidence exists describing this oral health burden among children with neurodevelopmental disabilities (NDDs). This study aimed to estimate and compare the rates of dental hospital admissions in a near‐national population of children aged ≤ 14 years with attention‐deficit hyperactivity disorder, autism, intellectual disability or any NDD after accounting for key confounding variables. Caries status for these children was derived from an oral health screening at 4 years and also examined.The cohort were children who had their B4 School Check (B4SC) national health screening assessment undertaken between 1 January 2011 and 31 December 2018 and followed until 1 January 2020 (the study end date). Linked administrative databases, which include NDD indication and dental hospital records, were utilised. Dental hospital admissions were assessed using unadjusted and adjusted Cox proportional hazard regression models treating NDD as a discrete time‐varying covariate. Caries status at 4 years of age was investigated cross‐sectionally and the area under the receiver operating characteristic curve used to assess predictive accuracy.The eligible sample included 433 569 children (48.6% female) with a mean age of 9.3 years at the study end date. Overall, 16 359 (3.8%) children had at least one NDD indication and 38 574 (8.9%) had at least one dental hospitalisation. In adjusted analyses, the hazard ratio of dental hospitalisation admissions was 3.40 (95% CI: 3.22–3.60) for children indicated with any NDD compared to their non‐NDD counterparts. At 4 years of age B4SC screening, 465 (17.6%) children out of 2640 indicated with any NDD had visible caries compared to 61 026 (14.3%) from 427 254 children without NDD indication (prevalence ratio = 1.24 [95% CI: 1.14–1.35]). However, the area under the receiver operating characteristic curve for this association was 0.52 (95% CI: 0.51–0.52), suggesting negligible predictive capacity.Children with NDDs in Aotearoa, New Zealand suffer from substantial oral health inequities. Targeted preventive strategies and adaptation to primary oral health services are needed to meet the needs of neurodiverse children and redress this substantial inequity. However, targeting children with NDDs at the B4SC is unlikely to mitigate these oral health inequities. [ABSTRACT FROM AUTHOR]

We developed a modified cardiovascular (CV) Sequential Organ Failure Assessment (SOFA) score using an emergency department-based cohort data, incorporating norepinephrine equivalent dose and lactate to represent current clinical practice patterns for vasopressor utilization and the diagnostic significance of lactate, respectively. In this study, we sought to validate this modified CV-SOFA score in intensive care unit patients with suspected infection using the Marketplace for Medical Information in Intensive Care (MIMIC)-IV database. This was a retrospective study that utilized data from the MIMIC-IV database. Modified CV/total SOFA score and original CV/total SOFA score were compared for predicting in-hospital mortality. Area under the receiver operating characteristic curve (AUROC) and the calibration curve were employed to evaluate discrimination and calibration, respectively. A total of 29,618 ICU patients with suspected infections was analyzed. The in-hospital mortality rate was 12.4% (n = 3,675). Modified CV-SOFA score (AUROC 0.667; 95% confidence interval [CI] 0.657–0.677 vs. 0.663; 95% CI 0.654–0.673; p = 0.283) and modified total SOFA score (0.784 [95% CI 0.776–0.793] vs. 0.785 [95% CI 0.777–0.793], p = 0.490) did not differ significantly from the original CV-SOFA score and original total SOFA score, respectively. The calibration curve of the original CV-SOFA score was inferior to that of the modified CV-SOFA score. The modified CV- and total SOFA scores were better calibrated than the original CV- and total SOFA scores, but their discriminative performance was not significantly different. Further studies of the modified CV-SOFA score in different settings and populations are required to assess the generalizability of this score. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Many thyroid nodules are detected incidentally with the widespread use of sensitive imaging techniques; however, only a fraction of these nodules are malignant, resulting in unnecessary medical expenditures and anxiety. The major challenge is to differentiate benign thyroid nodules from malignant ones. The application of dual‐energy computed tomography (DECT) and radiomics provides a new diagnostic approach. Studies applying radiomics from primary tumours on iodine maps to differentiate malignant from benign thyroid nodules are still lacking.To determine the ability of an iodine map‐based radiomic nomogram in the venous phase for differentiating malignant thyroid nodules from benign nodules.A total of 141 patients with thyroid nodules who underwent DECT were enrolled and randomly assigned to the training and test cohorts between January 2018 and January 2019. The radiomic score (Rad‐score) was derived from nine quantitative features of the iodine maps. Stepwise logistic regression analysis was used to develop radiomic, clinical and combined models. Age, normalized iodine concentration (NIC), and cyst changes were used to construct the clinical model. Receiver operating characteristic (ROC) curve analysis, sensitivity and specificity were performed to analyse the ability of the models to predict malignant thyroid nodules. Calibration analysis was used to test the fitness of the models. Decision curve analysis (DCA) and nomogram construction were also performed.According to the clinical model, age (0.989 [0.984, 0.995]; p < 0.001), NIC (0.778 [0.640, 0.995]; p = 0.01), and cyst changes (0.617 [0.507, 0.751]; p < 0.001) were independently associated with malignant thyroid nodules. According to the combined model, age (0.994 [0.989, 0.999]; p = 0.01), NIC (0.797 [0.674, 0.941]; p = 0.008), cyst changes (0.786 [0.653, 0.947]; p = 0.01), and the rad‐score (1.106 [1.070, 1.143]; p < 0.001) were independently associated with malignant thyroid nodules. The combined model achieved satisfactory discrimination in predicting malignant thyroid nodules and had greater predictive value in the training (AUC [areas under the curve], 0.96 vs. 0.87; p = 0.01) and test (AUC, 0.90 vs. 0.79; p = 0.04) cohorts than did the clinical model.The radiomics nomogram based on iodine maps is useful to distinguish malignant thyroid nodules from benign thyroid nodules. [ABSTRACT FROM AUTHOR]

Background and purpose Methods Results Conclusions ‘Neurovascular coupling’ (NVC) alterations, assessing the interplay between local cerebral perfusion and neural activity within a given brain region or network, may reflect neurovascular unit impairment in amyotrophic lateral sclerosis (ALS). The aim was to explore NVC as a correlation between the functional connectivity and cerebral blood flow within the large‐scale resting‐state functional magnetic resonance imaging brain networks in a sample of ALS patients compared to healthy controls (HCs).Forty‐eight ALS patients (30 males; mean age 60.64 ± 9.62 years) and 32 HC subjects (14 males; mean age 55.06 ± 16 years) were enrolled and underwent 3 T magnetic resonance imaging. ALS patients were screened by clinical and neuropsychological scales and were retrospectively classified as very fast progressors (VFPs), fast progressors and slow progressors (SPs).Neurovascular coupling reduction within the default mode network (DMN) (p = 0.005) was revealed in ALS patients compared to HCs, observing, for this network, significant NVC differences between VFP and SP groups. Receiver operating characteristic curve analysis showed that impaired NVC in the DMN at baseline best discriminated VFPs and SPs (area under the curve 75%). Significant correlations were found between NVC and the executive (r = 0.40, p = 0.01), memory (r = 0.32, p = 0.04), visuospatial ability (r = 0.40, p = 0.01) and non‐ALS‐specific (r = 0.40, p = 0.01) subscores of the Edinburgh Cognitive and Behavioural ALS Screen.The reduction of brain NVC in the DMN may reflect largely distributed abnormalities of the neurovascular unit. NVC alterations in the DMN could play a role in anticipating a faster clinical progression in ALS patients, aiding patient selection and monitoring during clinical trials. [ABSTRACT FROM AUTHOR]

Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for diagnosing tricuspid regurgitation (TR). The diagnostic procedures associated with conventional methods are intricate and labor-intensive, with human errors leading to measurement variability, with outcomes critically dependent on the operators' diagnostic expertise. In this study, we present an innovative assessment methodology for evaluating TR severity utilizing an end-to-end deep learning system. This deep learning system comprises a segmentation model of single cardiac cycle TR continuous wave (CW) Doppler spectra and a classification model of the spectra, trained on the TR CW Doppler spectra from a cohort of 11,654 patients. The efficacy of this intelligent assessment methodology was validated on 1500 internal cases and 573 external cases. The receiver operating characteristic (ROC) curves of the internal validation results indicate that the deep learning system achieved the areas under curve (AUCs) of 0.88, 0.84, and 0.89 for mild, moderate, and severe TR, respectively. The ROC curves of the external validation results demonstrate that the system attained the AUCs of 0.86, 0.79, and 0.87 for mild, moderate, and severe TR, respectively. Our study results confirm the feasibility and efficacy of this novel intelligent assessment method for TR severity. [ABSTRACT FROM AUTHOR]

The objective of the study was to use anthropometric measurements (age, BMI, and subcutaneous fat) in conjunction with biochemical parameters (sex hormone-binding globulin (SHBG), homeostasis model assessment-insulin resistance (HOMA-IR), fasting glucose, serum insulin, and total cholesterol) to predict the probability of gestational diabetes mellitus (GDM) in the first trimester.The study enrolled 48 pregnant women with GDM and 64 high-risk pregnant women without GDM. During the first-trimester examination, maternal blood samples were collected to measure SHBG, fasting blood glucose, serum insulin, and total cholesterol levels. Regression model analysis was used to examine the variables that showed statistically significant differences between the groups and were independent predictors of GDM. Receiver operating characteristic (ROC) curve analysis was employed to determine the risk of developing GDM based on cut-off values.The levels of SHBG, HOMA-IR, serum insulin, fasting glucose, and total cholesterol were identified as significant independent markers for predicting GDM. Meanwhile, age, body mass index, and subcutaneous fat values were found to be non-independent predictors of GDM. The areas under the ROC curve were calculated to determine the predictive accuracy of total cholesterol, HOMA-IR, SHBG, and subcutaneous fat for developing into GDM, and were 0.869, 0.977, 0.868, and 0.822 respectively. The sensitivities for a false positive rate of 5 % for predicting GDM were 68.7 , 91.67, 91.7, and 97.9 % for total cholesterol, HOMA-IR, SHBG, and subcutaneous fat, respectively.The independent predictors for the subsequent development of GDM in high-risk pregnancies are HOMA-IR, SHBG, Total cholesterol, and subcutaneous fat (SC) levels. These parameters can be used to create a regression model to predict the occurrence of GDM. [ABSTRACT FROM AUTHOR]

Background and Aims Methods Results Conclusions This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction‐associated steatotic liver disease (MASLD).We included individuals from NHANES 2017–2018. The validity and reliability of MASLD diagnosis by GPT‐3.5 and GPT‐4 were quantitatively examined and compared with those of the Fatty Liver Index (FLI) and United States FLI (USFLI). A receiver operating characteristic curve was conducted to assess the accuracy of MASLD diagnosis via different scoring systems. Additionally, GPT‐4V's potential in clinical diagnosis using ultrasound images from MASLD patients was evaluated to provide assessments of LLM capabilities in both textual and visual data interpretation.GPT‐4 demonstrated comparable performance in MASLD diagnosis to FLI and USFLI with the AUROC values of .831 (95% CI .796–.867), .817 (95% CI .797–.837) and .827 (95% CI .807–.848), respectively. GPT‐4 exhibited a trend of enhanced accuracy, clinical relevance and efficiency compared to GPT‐3.5 based on clinician evaluation. Additionally, Pearson's r values between GPT‐4 and FLI, as well as USFLI, were .718 and .695, respectively, indicating robust and moderate correlations. Moreover, GPT‐4V showed potential in understanding characteristics from hepatic ultrasound imaging but exhibited limited interpretive accuracy in diagnosing MASLD compared to skilled radiologists.GPT‐4 achieved performance comparable to traditional risk scores in diagnosing MASLD and exhibited improved convenience, versatility and the capacity to offer user‐friendly outputs. The integration of GPT‐4V highlights the capacities of LLMs in handling both textual and visual medical data, reinforcing their expansive utility in healthcare practice. [ABSTRACT FROM AUTHOR]

Objectives: This study was performed to investigate the relationship between hemoglobin, albumin, lymphocyte and platelet (HALP) score and Oxford classification severe tubular atrophy/interstitial fibrosis (T2) in IgA nephropathy (IgAN). Methods: The clinical data and pathological findings of patients with IgA nephropathy diagnosed through renal biopsy at Hangzhou Hospital of Traditional Chinese Medicine between June 1, 2019 and May 31, 2022 were retrospectively collected and analyzed. The HALP score was calculated as hemoglobin (g/L) × albumin (g/L) × lymphocytes (/L)/ platelets (/L). According to the quartile of HALP scores in the study population, the subjects were divided into four groups: Quartile 1 (< 30.72), Quartile 2 (30.72–39.97), Quartile 3 (39.97–53.25) and Quartile 4(> 53.25). According to the extent of tubular atrophy/interstitial fibrosis, patients were categorized into mild to moderate injury (T0 + T1, ≤ 50%) and severe injury (T2, > 50%). The relationship between HALP score and severe tubular atrophy/interstitial fibrosis was investigated using Spearman's rank correlation coefficient, logistic regression analysis, restricted cubic splines (RCS), and receiver operating characteristic (ROC) curve analysis. Results: A total of 895 patients diagnosed with IgAN were included in this study, with an average age of 40.97 ± 12.261 years. Among them, 384 (42.9%) were male and 61 (6.8%) exhibited severe tubular atrophy/interstitial fibrosis. Multifactorial logistic regression analysis revealed that HALP independently influenced T2 (OR = 0.952, 95% CI 0.923–0.982, P = 0.002). Compared to Quartile 1, patients in Quartile 4 exhibited a significantly reduced risk of T2 (OR = 0.205, 95% CI 0.058–0.722, P = 0.014). Restricted cubic splines analysis revealed a linear inverse association between HALP and T2 risk (nonlinear P = 0.896). Furthermore, the receiver operating characteristic curve demonstrated that HALP possessed predictive value for T2 (AUC = 0.693, Jorden index = 0.324), and the cutoff value of HALP score is 36.54. Conclusions: The risk of severe renal tubular atrophy/interstitial fibrosis is higher in IgAN patients with low HALP. HALP greater than 36.54 May reduce the risk of severe tubular atrophy/interstitial fibrosis. [ABSTRACT FROM AUTHOR]

Background and objectives: Maintenance hemodialysis (MHD) patients are at a higher risk of cardiovascular disease (CVD), a common complication and leading cause of death. Persistent micro-inflammation is a unique feature of MHD. Given the established role of inflammation in the pathogenesis of atherosclerosis, this study aims to explore whether novel inflammatory markers (inflammation index) can serve as independent risk factors for CVD in MHD patients. Methods: A cross-sectional survey was conducted on patients from three dialysis centers, categorized into a CVD and non-CVD group based on medical history, laboratory tests, and physical examination. Fasting blood samples were collected from all participants for indicator testing. Results: The analysis of 209 patients revealed that 104 had concurrent CVD. Patients in the CVD group were significantly older and exhibited higher anxiety and depression scores. Forward stepwise multivariate logistic regression results identified the inflammation index neutrophil-to-lymphocyte ratio (NLR) (OR = 1.27, 95% CI 1.082–1.491, P < 0.05) and systemic immune-inflammation index (SII) (OR = 1.001, 95% CI 1.0001–1.002, P < 0.05) as independent risk factors for CVD in MHD patients. Receiver operating characteristic (ROC) curve analysis demonstrated that SII, platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and NLR all possess predictive diagnostic values for CVD events in this patient population. Conclusions: Hemodialysis centers can utilize simple and cost-effective inflammatory markers to proactively identify patients at risk of CVD. Future research into how inflammation contributes to CVD in MHD is required. [ABSTRACT FROM AUTHOR]

Background: This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI). Methods: A total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group) were collected and randomly divided into a training set (246 benign and 245 malignant lesions) and a testing set (28 benign and 28 malignant lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models—VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2—were evaluated. Model performance was assessed using accuracy (Ac) in the training and testing sets, and precision (Pr), recall (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set. Results: The accuracy of VGG19 on the test set (0.96) is higher than that of VGG16 (0.91), DenseNet201 (0.91), ResNet50 (0.67), and MobileNetV2 (0.88). For the validation set, VGG19 achieved higher performance metrics (Pr 0.75, Rc 0.76, F1 0.73, AUC 0.76) compared to the other models, specifically VGG16 (Pr 0.73, Rc 0.75, F1 0.70, AUC 0.73), DenseNet201 (Pr 0.71, Rc 0.74, F1 0.69, AUC 0.71), ResNet50 (Pr 0.65, Rc 0.68, F1 0.60, AUC 0.65), and MobileNetV2 (Pr 0.73, Rc 0.75, F1 0.71, AUC 0.73). S4 model achieved higher performance metrics (Pr 0.89, Rc 0.88, F1 0.87, AUC 0.89) compared to the other four fine-tuned models, specifically S1 (Pr 0.75, Rc 0.76, F1 0.74, AUC 0.75), S2 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77), S3 (Pr 0.76, Rc 0.76, F1 0.73, AUC 0.75), and S5 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77). Additionally, S4 model showed the lowest loss value in the testing set. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5. Conclusions: The S4 model we propose has demonstrated superior performance in predicting the likelihood of malignancy in DCE-BMRI, making it a promising candidate for clinical application in patients with breast diseases. However, further validation is essential, highlighting the need for additional data to confirm its efficacy. [ABSTRACT FROM AUTHOR]

Background: Recurrent gliomas rapidly progress and have high mortality and poor prognosis in the central nervous system. Therefore, further investigation of prognostic and therapeutic markers is needed. Methods: The mRNA expression, clinical data, and coagulation-related genes (CRGs) associated with recurrent glioma were obtained and calculated from the Chinese Glioma Genome Atlas and Kyoto Encyclopedia of Genes and Genomes databases. The significant CRGs were calculated by Weighted gene co-expression network analysis and PPI network. A prediction model was constructed using the least absolute shrinkage and selection operator regression analysis. Recurrent gliomas were stratified into high and low-risk groups based on the median risk score (RS). The Kaplan–Meier curve was used to analyze the difference in overall survival (OS) between these groups, while the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the gene model at 1-, 3-, and 5-years. Clinical factors, including age, gender, MGMT methylation status, radiotherapy, chemotherapy, and RS, were included in the univariate and multivariate regression analysis. A prognostic nomogram and calibration curve were established based on these factors. Results: Overall, seven CRGs associated with the prognosis were identified, including BTK, ITGB1, GNAI3, CFH, LYN, CFI, and F3. OS and survival rates were lower in the high-risk group compared with the low-risk group. The ROC curve demonstrated the area under the curve values >0.65 at 1-, 3-, and 5-years, confirming the reliability of the prognostic model. The univariate regression analysis indicated that tumor grade (grades 2, 3, and 4), histopathology (GBM or not), chemotherapy, IDH mutation, and 1p19q co-deletion status were independent prognostic indicators. Univariate and multivariate regression analyses indicated that RS was an independent prognostic factor for patients with recurrent glioma. Immune analysis revealed low infiltration of resting dendritic cells and high expression of programmed death receptor 1 in the high-risk group. Conclusion: This study comprehensively investigated the correlation between CRGs and recurrent glioma prognosis, offering crucial insights for further research into glioma recurrence mechanisms and treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Fibroepithelial lesions (FELs) of the breast encompass a broad spectrum of lesions, ranging from commonly encountered fibroadenomas (FAs) to rare phyllodes tumors (PTs). Accurately diagnosing and grading these lesions is crucial for making management decisions, but it can be challenging due to their overlapping features and the subjective nature of histological assessment. Here, we evaluated the role of digital nuclear morphometric analysis in FEL diagnosis and prognosis. Methods: A digital nuclear morphometric analysis was conducted on 241 PTs and 59 FAs. Immunohistochemical staining for cytokeratin and Leukocyte common antigen (LCA) was used to exclude non-stromal components, and nuclear area, perimeters, calipers, circularity, and eccentricity in the stromal cells were quantified with QuPath software. The correlations of these features with FEL diagnosis and prognosis was assessed. Results: All nuclear features, including area, perimeter, circularity, maximum caliper, minimum caliper and eccentricity, showed significant differences between FAs and benign PTs (p ≤ 0.002). Only nuclear area, perimeter, minimum caliper and eccentricity correlated significantly with PT grading (p ≤ 0.022). For differentiation of FAs from benign PTs, the model integrating all differential nuclear features demonstrated a specificity of 90% and sensitivity of 70%. For PT grading, the nuclear morphometric score showed a specificity of 78% and sensitivity of 96% for distinguishing benign/borderline from malignant PTs. In addition, a relationship of nuclear circularity was found with PT recurrence. The Kaplan-meier analysis, using the best cutoff determined by ROC curve, showed shorter event free survival in benign PTs with high circularity (chi-square = 4.650, p = 0.031). Conclusions: Our data suggested the digital nuclear morphometric analysis could have potentials to objectively differentiate different FELs and predict PT outcome. These findings could provide the evidence-based data to support the development of deep-learning based algorithm on nuclear morphometrics in FEL diagnosis. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is typically assessed through hand grip strength, walking speed, and chair stand tests. However, it has been inadequately examined in terms of other physical fitness (PF) components in community-dwelling older adults. Thus, in this study, we explored factors influencing the risk of sarcopenia in community-dwelling older adults. In addition, we analyzed the clinicodemographic characteristics of older adults with or without sarcopenia and investigated the effect of sex on their PF. Methods: This cross-sectional study included 745 older adults from a community health promotion program in Taiwan. Their clinicodemographic characteristics were recorded. PF was assessed through various tests, such as hand grip strength evaluation, 8-foot up-and-go test (8-UGT), 2-min step test, and 6-m walk test. PF and factors influencing sarcopenia risk were compared between older adults with sarcopenia (sarcopenia group) and those without it (nonsarcopenia group). A logistic regression model was performed to identify key factors associated with sarcopenia. Its predictive performance was evaluated by calculating the area under the receiver operating characteristic curve (ROC) curve. Results: Regardless of sex, the sarcopenia group performed worse in almost all components of PF—for example, upper and lower limb muscular strength and endurance, cardiopulmonary fitness, and balance—than did the nonsarcopenia group. However, for men, no significant between-group difference was observed in flexibility. The logistic regression model indicated age (odds ratio [OR]: 1.107), sex (OR: 2.881), Mini Nutritional Assessment—Short Form scores (OR: 0.690), and performance in 8-UGT (OR: 1.346) as factors influencing the risk of sarcopenia. The model exhibited excellent discriminative ability in predicting sarcopenia, as indicated by an area under the curve value of 0.867 (95% confidence interval: 0.827–0.906; p < 0.05). Conclusion: Older adults without sarcopenia tend to outperform those with sarcopenia in almost all PF measures, regardless of sex. Older age, male sex, low Mini Nutritional Assessment—Short Form scores, and poor performance in 8-UGT are associated with a high risk of sarcopenia. [ABSTRACT FROM AUTHOR]

The article in the Blood Cancer Journal presents a simplified, two-factor clinical prognostic scoring system for patients with newly diagnosed Hodgkin Lymphoma. The study analyzed patient data to create a prognostic model based on age and albumin levels, resulting in a Simplified Prognostic Score (SPS) that categorizes patients into low, intermediate, and high-risk groups. The SPS showed higher concordance index compared to established prognostic models and offers a simple and globally applicable tool for predicting outcomes in patients with Hodgkin Lymphoma. The study acknowledges limitations and the need for further validation in diverse populations before implementing treatment strategies based on the SPS. [Extracted from the article]

Objectives: We focus on utilizing the Labyrinth system for the detection of circulating tumor cells (CTCs) in patients with lung nodules. Our aim is to evaluate CTCs isolated through the Labyrinth system as a biomarker for early-stage lung cancer (LC) detection. Methods: 167 patients with low dose computed tomography (LDCT) diagnostic results for lung nodules and 31 healthy volunteers (HV) were enrolled. Blood samples were processed for CTC detection. LDCT positive (LDCT+) patients underwent surgery and were categorized into those with LC and those with benign lung diseases (BLD) based on their biopsy results. BLD Patients, LDCT negative (LDCT-) patients and HV served as controls. The correlation of CTC counts with LC, BLD, LDCT- and HV was investigated. Receiver operating characteristic (ROC) curves were used to assess the Labyrinth system's diagnostic potential for early-stage LC. Results: Median CTC counts for LC, BLD, LDCT- and HV were 2.7 CTC/mL, 0.6 CTC/mL, 0.4 CTC/mL, 0 CTC/mL, respectively. Statistical analysis indicated CTC counts could distinguish LC from BLD, LDCT- and HV (p-Values < 0.001). Using a cut-off of 1 CTC/mL, the study showed 84.4% sensitivity and 82.4% specificity for LDCT+ patients. Specificity increased to 85.9% for patients with lung nodules and 88.2% for all participants. In conclusion, CTCs detected by the Labyrinth system can serve as a biomarker for early-stage LC detection for patients with lung nodules. Conclusions: CTCs identified by the Labyrinth system are a promising biomarker for early-stage LC detection in clinical practice. [ABSTRACT FROM AUTHOR]