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Background/Objectives: Intrahepatic cholestasis of pregnancy (ICP) is characterised by unexplained intense pruritus during pregnancy. While serum bile acid (BA) is the standard diagnostic marker for ICP, we explored the potential of serum calprotectin as an alternative diagnostic marker for ICP. Methods: Leftover serum specimens with known serum BA levels, collected from non-pregnant women and pregnant women with an ICP, were used to measure serum calprotectin levels using the Human calprotectin L1/S100-A8/A9 ELISA kit. Results: Serum calprotectin levels were measured in 79 pregnant women with ICP (median [interquartile range] 28 year; serum BA 20 [13.7–35.7] μMol/L; calprotectin159 pg/mL [122.2–212.3]); 43 pregnant women without ICP (age 28 years; serum BA 3.6 [2.1–5.8] μMol/L; calprotectin 146.5 pg/mL [75.8–194.8]), and 59 non-pregnant women (age 28 years; serum BA 3.5 [1.6–5.1 μMol/L; calprotectin 82.4 pg/mL [48.8–137.2]). Compared to non-pregnant women, calprotectin levels were significantly elevated among pregnant women with (p < 0.001) or without ICP (p = 0.01). Calprotectin levels were comparable between pregnant women with and without ICP (p = 0.15). The areas under the ROC curve, to differentiate the presence and absence of ICP, were 0.940 (0.903–0.977; p < 0.001) and 0.681 (0.604–0.759; p < 0.001) for BA and calprotectin, respectively. Conclusions: Serum calprotectin is raised in pregnant women regardless of the presence or absence of ICP and had an inferior diagnostic performance for ICP compared to BA. This information is crucial for understanding the challenges in ICP diagnosis and the limitations of serum calprotectin as an alternative marker. [ABSTRACT FROM AUTHOR]

Background: Coronary artery disease (CAD) is the leading cause of death worldwide. High-Density lipoprotein (HDL) is a well-established marker associated with CAD. The current research goes beyond the conventional HDL-C measurement in previous studies and dives into the functional intricacies of HDL. By understanding how HDL works, rather than just how much of it exists, we can better tailor diagnostic and therapeutic strategies for CAD and related conditions. Hence, the current study quantifies the serum levels of two novel HDL-associated markers, Paraoxonase-1 (PON-1) and Scavenger Receptor Class B Type 1 (SRB-1), in CAD cases vs. controls. Methods: A total of 92 subjects, including 69 CAD and 23 healthy controls, were included, based on the prevalence of the disease. Further, based on the severity of the disease, CAD cases were subcategorized as CAD-I, -II, and -III. Serum PON-1 and SRB-1 levels were measured and compared between patient and control groups. Results: The levels of PON-1 and SRB-1 (32.6 ng/mL and 12.49 ng/mL) were significantly lower in CAD patients vs. the healthy control, at 60.36 ng/mL and 15.85 ng/mL, respectively (p < 0.000). A further intergroup comparison showed a statistically significant difference between the CAT-I and -III for PON-1 (p < 0.025), the CAT-I and -III, and CAT-II and -III for SRB-1 (p < 0.000). The receiver operating characteristics (ROC) curve showed cutoff values of 48.20 ng/mL and 14.90 ng/mL for PON-1 and SRB-1. Conclusions: The current study found that serum levels of HDL-associated PON-1 and SRB-1 are significantly lower in CAD cases, and were also inversely related to the increasing severity of coronary artery disease. This inference implies that serum PON-1 and SRB-1 could be used as non-invasive tools for the identification of coronary atherosclerosis and risk assessment in CAD cases. [ABSTRACT FROM AUTHOR]

Background/Objectives: Pancreatic stone protein (PSP) is an emerging biomarker of sepsis that is secreted from pancreas sensing remote organ damages. We explored the diagnostic and prognostic utilities of PSP in patients with suspected sepsis. Methods: In a total of 285 patients (suspected sepsis, n = 148; sepsis, n = 137), we compared PSP with procalcitonin (PCT) and sequential organ failure assessment (SOFA) score. Sepsis diagnoses were explored using receiver operating characteristic curve analyses with area under the curves (AUCs). Clinical outcomes (in-hospital mortality, 30-day mortality, and kidney replacement therapy [KRT]) were explored using the Kaplan–Meier method and a multivariate analysis with hazard ratio (HR). Results: PCT and PSP were comparable for sepsis diagnosis (AUC = 0.71–0.72, p < 0.001). The sepsis proportion was significantly higher when both biomarkers increased than when either one or both biomarkers did not increase (89.0% vs. 21.3–47.7%, p < 0.001). Each biomarker quartile (Q1–Q4) differed significantly according to their SOFA score (all p < 0.001). Compared with Q1, the Q2–Q4 groups showed worse clinical outcomes (p = 0.002–0.041). Both biomarkers added to the SOFA score showed higher HRs than the SOFA score alone (3.3–9.6 vs. 2.8–4.2, p < 0.001–0.011), with nearly 2.5-fold higher HR (9.6 vs. 4.2) for predicting KRT. Conclusions: Although PCT and PSP did not independently predict clinical outcomes in the multivariate analysis, PSP demonstrated diagnostic and prognostic utilities in patients with suspected sepsis, especially for predicting kidney dysfunction. PSP, alone or in combination with PCT, would be a valuable tool that can be added to clinical assessments. [ABSTRACT FROM AUTHOR]

Introduction: Early identification of high-risk traumatic brain injury (TBI) patients is crucial for optimizing treatment strategies and improving outcomes. The C-reactive protein-to-lymphocyte ratio (CLR) reflects systemic immunology and inflammation function and serves as a new biomarker for patient stratification. This study aimed to assess the predictive value of the CLR for mortality in patients with isolated moderate to severe TBI. Methods: A retrospective analysis of trauma registry data from 2009 to 2022 was conducted, including 1641 adult patients with isolated moderate to severe TBI. Patient demographics, the CLR, injury characteristics, and outcomes were compared between deceased and surviving patients. Univariate and multivariate analyses were performed to identify mortality risk factors. The optimal CLR cut-off value for predicting mortality was determined using receiver operating characteristic (ROC) curve analysis. Results: The CLR was significantly higher in deceased patients compared to survivors (60.1 vs. 33.9, p < 0.001). The optimal CLR cut-off value for predicting mortality was 54.5, with a sensitivity of 0.328 and a specificity of 0.812. The area under the ROC curve was 0.566, indicating poor discriminative ability. In the multivariate analysis, the CLR was not a significant independent predictor of mortality (OR 1.03, p = 0.051). After propensity score matching to attenuate the difference in baseline characteristics, including sex, age, comorbidities, conscious level, and injury severity, the high-CLR group (CLR ≥ 54.5) did not have significantly higher mortality compared to the low-CLR group (CLR < 54.5). Conclusion: While the CLR was associated with mortality in TBI patients, it demonstrated poor discriminative ability as a standalone predictor. The association between a high CLR and worse outcomes may be primarily due to other baseline patient and injury characteristics, rather than the CLR itself. [ABSTRACT FROM AUTHOR]

Landslide susceptibility measures the probability of landslides occurring under certain geo-environmental conditions and is essential in landslide hazard assessment. Landslide susceptibility mapping (LSM) using data-driven methods applies statistical models and geospatial data to show the relative propensity of slope failure in a given area. However, due to the rarity of multi-temporal landslide inventory, conventional data-driven LSMs are primarily generated by spatial causative factors, while the temporal factors remain limited. In this study, a spatiotemporal LSM is carried out using machine learning (ML) techniques to assess rainfall-induced landslide susceptibility. To achieve this, two landslide inventories are collected for southwestern Pennsylvania: a spatial inventory and a multi-temporal inventory, with 4543 and 223 historical landslide samples, respectively. The spatial inventory lacks the information to describe landslide temporal distribution; there are insufficient samples in the temporal inventory to represent landslide spatial distribution. A novel paradigm of data augmentation through non-landslide sampling based on domain knowledge is applied to leverage both spatial and temporal information for ML modeling. The results show that the spatiotemporal ML model using the proposed data augmentation predicts well rainfall-induced landslides in space and time across the study area, with a value of 0.86 of the area under the receiver operating characteristic curve (AUC), which makes it an effective tool in rainfall-induced landslide hazard mitigation and forecasting. [ABSTRACT FROM AUTHOR]

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. Traditional methods for predicting patient outcomes rely heavily on the physical characteristics of tumors. Our research aims to develop a new, simple risk score that uses three specific molecular markers found in tumor tissues. By examining the presence and levels of these markers, we hope to better predict which patients have a higher risk of poor outcomes. This can help doctors make more informed decisions about treatment options. Our findings may lead to improved survival rates by identifying high-risk patients who need more aggressive treatment and sparing low-risk patients from unnecessary procedures. Introduction: Several new molecular markers in colorectal carcinomas have been discovered; however, classical histopathological predictors are still being used to predict survival in patients. We present a novel risk score, which uses molecular markers, to predict outcomes in patients with colorectal carcinoma. Methods: The immunohistochemistry of tissue micro arrays was used to detect and quantify H2BUB1, RBM3 and Ki-67. Different intensities of staining were categorized for these markers and a score was established. A multivariate analysis was performed and survival curves were established. Results: 1791 patients were evaluated, and multivariate analysis revealed that our risk score, the 3-biomarker classifier, is an independent marker to predict survival. We found a high risk-score to be associated with dismal median survival for the patients. Conclusions: A more personalized score might be able to better discriminate low- and high-risk patients and suggest adjuvant treatment compared to classical pathological staging. Our score can serve as a tool to predict outcomes in patients suffering from colorectal carcinoma. [ABSTRACT FROM AUTHOR]

Simple Summary: Cutaneous melanoma (CM) is an aggressive skin cancer that develops from melanocytic cells. The most important prognostic factor is still the vertical tumour thickness according to Breslow. Liver function is associated with better overall patient outcomes and is currently under increasing investigation in cancer patients. Current findings show that liver function is an important factor in different tumour entities and represents a potential biomarker. Therefore, we aimed to evaluate liver metabolism by using liver scores in CM. High tumour thickness (≥1.66 mm) and aspartate transaminase-to-platelet ratio index (APRI ≥ 0.2241) at the initial diagnosis were associated with a worse prognosis in stage I and II melanoma. Background: Neoadjuvant and adjuvant therapies are currently getting increasingly important in cutaneous melanoma (CM) management. However, there is still a lack of prognostic tools to identify which patients have a poor prognosis. There is increasing evidence that the liver score may be a potential prognostic parameter in different tumour types. The aim was to investigate whether established liver scores can establish the prognosis of CM. Methods: According to established methods, the APRI, the MELD score, the MELD-Na score and the De Ritis ratio were calculated from the laboratory values at the time of the initial diagnosis. Survival was compared with the Kaplan–Meier curve and tested with log-rank tests. Risk factors associated with cutaneous melanoma-specific survival (CMSS) and progression-free survival (PFS) were assessed by using the Cox proportional hazards regression model. To determine the diagnostic accuracy, we performed a time-dependent ROC analysis. Results: A total of 423 patients were included, including 141 patients in AJCC stage (2017) I (33.3%), 82 in stage II (19.4%), 128 in stage III (30.3%) and 72 in stage IV (17%). Median time until melanoma-specific death was 99 months (IQR: 37–126). In addition, 37.6% of patients relapsed with a median time to relapse of 88 months (IQR: 17.5–126). In all stages, tumour thickness and ulceration were independent markers for predicting CMSS and PFS (p < 0.05). The multivariable analysis with all stages showed no significant association with CM outcome for liver scores (p > 0.05). The subgroup analysis revealed that the APRI (≥0.2241) was associated with CMSS and PFS in melanoma stages I and II, independently of tumour thickness, age and ulceration (HR 2.57, 95% CI 1.14–5.75; HR 2.94, 95% CI 1.42–6.09, respectively). Conclusions: The 20-year prognosis of AJCC stage I and II CM was dependent on tumour thickness and the APRI. High tumour thickness and an APRI ≥ 0.2241 at the initial diagnosis were associated with a worse prognosis. Future studies should investigate the independent prognostic value of the APRI in low-stage CM. Furthermore, the APRI score could be a potential biomarker for nomograms. [ABSTRACT FROM AUTHOR]

Simple Summary: This study assessed the preoperative diagnostic efficacy of the apparent diffusion coefficient on diffusion-weighted magnetic resonance imaging for identifying lymph node metastasis in biliary tract cancer, comparing it with the diagnostic efficacy of short-axis and long-axis diameters of lymph nodes measured by computed tomography. The results demonstrated that the minimum ADC value provided the highest diagnostic accuracy. Objective: The diagnostic efficacy of the apparent diffusion coefficient (ADC) in diffusion-weighted magnetic resonance imaging (DW-MRI) for lymph node metastasis in biliary tract cancer was investigated in the present study. Methods: In total, 112 surgically resected lymph nodes from 35 biliary tract cancer patients were examined in this study. The mean and minimum ADC values of the lymph nodes as well as the long-axis and short-axis diameters of the lymph nodes were assessed by computed tomography (CT). The relationship between these parameters and the presence of histological lymph node metastasis was evaluated. Results: Histological lymph node metastasis was detected in 31 (27.7%) out of 112 lymph nodes. Metastatic lymph nodes had a significantly larger short-axis diameter compared with non-metastatic lymph nodes (p = 0.002), but the long-axis diameter was not significantly different between metastatic and non-metastatic lymph nodes. The mean and minimum ADC values for metastatic lymph nodes were significantly reduced compared with those for non-metastatic lymph nodes (p < 0.001 for both). However, the minimum ADC value showed the highest accuracy for the diagnosis of histological lymph node metastasis, with an area under the curve of 0.877, sensitivity of 87.1%, specificity of 82.7%, and accuracy of 83.9%. Conclusions: The minimum ADC value in DW-MRI is highly effective for the diagnosis of lymph node metastasis in biliary tract cancer. Accurate preoperative diagnosis of lymph node metastasis in biliary tract cancer should enable the establishment of more appropriate treatment strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigates the influence of various pharmacokinetic parameters on 177Lu-PSMA therapies via physical, biological, and statistical measures. Employing a clinically validated physiologically based pharmacokinetic (PBPK) model, realistic time–activity curves (TACs) in tumors, salivary glands, and kidneys are generated, allowing for the calculation of metrics such as AUC, dose, BED, and fBED. The results demonstrate the significant impact of multiple parameters on the measured outcomes. In addition to the great impact of administered ligand amount, tumor volume, and receptor density, other pharmacokinetic parameters such as rates of radiopharmaceutical association, internalization, and release were identified as key influencers. Notably, alterations in the parameters induced distinct modifications in TAC features, affecting radiobiological, pharmacokinetic, and statistical aspects. These insights contribute to advancing personalized treatment regimens by understanding the key contributions of various parameters towards improving therapeutic efficacy while minimizing radiation-related toxicities. Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time–activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes. [ABSTRACT FROM AUTHOR]

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network's decisions and guide measures to improve its performance. The results demonstrate that the model's classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is a global infectious threat, and the emergence of multidrug-resistant TB has become a major challenge in eradicating the disease that requires the discovery of new treatment strategies. This study aimed to elucidate the immune infiltration and molecular regulatory network of T cell-interacting activating receptors on myeloid cell 1 (TARM1)-related genes based on a bioinformatics analysis. The GSE114911 dataset was obtained from the Gene Expression Omnibus (GEO) and screened to identify 17 TARM1-related differentially expressed genes (TRDEGs). Genes interacting with the TRDEGs were analyzed using a Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A gene set enrichment analysis (GSEA) was used to identify the biological pathways significantly associated with a Mycobacterium tuberculosis (Mtb) infection. The key genes were obtained based on Cytoscape's cytoHubba plug-in. Furthermore, protein–protein interaction (PPI) networks were analyzed through STRING, while mRNA–RNA-binding protein (RBP) and mRNA–transcription factor (TF) interaction networks were developed utilizing the StarBase v3.0 and ChIPBase databases. In addition, the diagnostic significance of key genes was evaluated via receiver operating characteristic (ROC) curves, and the immune infiltration was analyzed using an ssGSEA and MCPCounter. The key genes identified in the GSE114911 dataset were confirmed in an independent GSE139825 dataset. A total of seventeen TRDEGs and eight key genes were obtained in a differential expression analysis using the cytoHubba plug-in. Through the GO and KEGG analysis, it was found that these were involved in the NF-κB, PI3K/Akt, MAPK, and other pathways related to inflammation and energy metabolism. Furthermore, the ssGSEA and MCPCounter analysis revealed a significant rise in activated T cells and T helper cells within the Mtb infection group, which were markedly associated with these key genes. This implies their potential significance in the anti-Mtb response. In summary, our results show that TRDEGs are linked to inflammation, energy metabolism, and immune cells, offering fresh insights into the mechanisms underlying TB pathogenesis and supporting further investigation into the possible molecular roles of TARM1 in TB, as well as assisting in the identification of prospective diagnostic biomarkers. [ABSTRACT FROM AUTHOR]

The aim of this study was to optimize a basophil activation test in the detection of allergy to the house dust mite Dermatophagoides pteronyssinus in children with allergic respiratory diseases. This study involved 32 cases, 13 girls and 19 boys aged 4–17 years, with perennial asthma or allergic rhinitis caused by D. pteronyssinus. The control group consisted of 13 girls and 19 boys aged 4–17 years with seasonal allergic asthma or rhinitis provoked by Timothy or birch pollen. House dust mite (HDM) allergy was excluded in the controls based on their medical history, skin prick test (SPT) results and sIgE determination. In all patients, a basophil activation test (BAT) was performed with five dilutions of D. pteronyssinus allergen (the dilution series ranged from 22.5 to 0.00225 ng/mL). The results were analyzed by using the receiver operating characteristics (ROC) to determine the optimal allergen concentrations, outcome measures and cut-off points that would differentiate most accurately between HDM-allergic and non-allergic patients. As a "gold standard", criteria for allergen-specific immunotherapy with D. pteronyssinus or respective pollens were applied by an experienced pediatric allergist following the guidelines of the European Academy of Allergy and Clinical Immunology. The highest diagnostic efficiency was yielded by the protocol assuming a cut-off value of 9.76% activated basophils after activation with a single allergen concentration of 2.25 ng/mL (sensitivity 90.6%, specificity 100%). This protocol yielded 3 (4.7%) misclassifications, all false negative, when compared with the "gold standard". There was a strong correlation with the BAT results at 22.5, 2.25 and 0.225 ng/mL (respectively r = 0.90 and r = 0.78, p < 0.001), as well as between the BAT at 2.25 ng/mL and SPT (r = 0.82, p < 0.001) and between the SPT and sIgE levels (r = 0.78, p < 0.001). High cross-reactivity between D. pteronyssinus and D. farinae was confirmed based on the BAT at 22.5 ng/mL (r = 0.82, p < 0.001). In conclusion, the BAT showed very good concordance with the result of a meticulous process of decision-making that combined validated allergy tests (SPT, sIgE) with expert guidelines, specialist knowledge and experience. Facing the risk of the incorrect qualification of patients for costly, long-lasting and potentially risky allergen-specific immunotherapy, the inclusion of a basophil activation test into diagnostic process seems fully justified. [ABSTRACT FROM AUTHOR]

The association between genetics and lifestyle factors is crucial when determining breast cancer susceptibility, a leading cause of deaths globally. This research aimed to compare the body mass index, smoking behavior, hormonal influences, and BRCA gene mutations between affected patients and healthy individuals, all with a family history of cancer. All these factors were then utilized as features to train a machine learning (ML) model to predict the risk of breast cancer development. Between 2020 and 2023, a total of 1389 women provided detailed lifestyle and risk factor data during visits to a familial cancer center in Italy. Descriptive and inferential statistics were assessed to explore the differences between the groups. Among the various classifiers used, the ensemble of decision trees was the best performer, with a 10-fold cross-validation scheme for training after normalizing the features. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), alongside the accuracy, sensitivity, specificity, precision, and F1 score. Analysis revealed that individuals in the tumor group exhibited a higher risk profile when compared to their healthy counterparts, particularly in terms of the lifestyle and genetic markers. The ML model demonstrated predictive power, with an AUC of 81%, 88% sensitivity, 57% specificity, 78% accuracy, 80% precision, and an F1 score of 0.84. These metrics significantly outperformed traditional statistical prediction models, including the BOADICEA and BCRAT, which showed an AUC below 0.65. This study demonstrated the efficacy of an ML approach in identifying women at higher risk of breast cancer, leveraging lifestyle and genetic factors, with an improved predictive performance over traditional methods. [ABSTRACT FROM AUTHOR]

Detecting abnormal ECG patterns is a crucial area of study aimed at enhancing diagnostic accuracy and enabling early identification of Chronic Kidney Disease (CKD)-related abnormalities. This study compares a unique strategy for abnormal ECG patterns using the LADTree model to standard machine learning (ML) models. The study design includes data collection from the MIT-BIH Arrhythmia dataset, preprocessing to address missing values, and feature selection using the CfsSubsetEval method using Best First Search, Harmony Search, and Particle Swarm Optimization Search approaches. The performance assessment consists of two scenarios: percentage splitting and K-fold cross-validation, with several evaluation measures such as Kappa statistic (KS), Best First Search, recall, precision-recall curve (PRC) area, receiver operating characteristic (ROC) area, and accuracy. In scenario 1, LADTree outperforms other ML models in terms of mean absolute error (MAE), KS, recall, ROC area, and PRC. Notably, the Naïve Bayes (NB) model has the lowest MAE, but the Support Vector Machine (SVM) performs badly. In scenario 2, NB has the lowest MAE but the highest KS, recall, ROC area, and PRC area, closely followed by LADTree. Overall, the findings indicate that the LADTree model, when optimized for ECG signal data, delivers promising results in detecting abnormal ECG patterns potentially related with CKD. This study advances predictive modeling tools for identifying abnormal ECG patterns, which could enhance early detection and management of CKD, potentially leading to improved patient outcomes and healthcare practices. [ABSTRACT FROM AUTHOR]

Full-thickness macular hole (FTMH) leads to central vision loss. It is essential to identify patients with FTMH at high risk of postoperative failure accurately to achieve anatomical closure. This study aimed to construct a predictive model for the anatomical outcome of FTMH after surgery. A retrospective study was performed, analyzing 200 eyes from 197 patients diagnosed with FTMH. Radiomics features were extracted from optical coherence tomography (OCT) images. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained and evaluated. Decision curve analysis and survival analysis were performed to assess the clinical implications. Sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC) were calculated to assess the model effectiveness. In the training set, the AUC values were 0.998, 0.988, and 0.995, respectively. In the test set, the AUC values were 0.941, 0.943, and 0.968, respectively. The OCT-omics scores were significantly higher in the "Open" group than in the "Closed" group and were positively correlated with the minimum diameter (MIN) and base diameter (BASE) of FTMH. Therefore, in this study, we developed a model with robust discriminative ability to predict the postoperative anatomical outcome of FTMH. [ABSTRACT FROM AUTHOR]

(1) Background: This study seeks to employ a machine learning (ML) algorithm to forecast the risk of distant metastasis (DM) in patients with T1 and T2 gallbladder cancer (GBC); (2) Methods: Data of patients diagnosed with T1 and T2 GBC was obtained from SEER, encompassing the period from 2004 to 2015, were utilized to apply seven ML algorithms. These algorithms were appraised by the area under the receiver operating characteristic curve (AUC) and other metrics; (3) Results: This study involved 4371 patients in total. Out of these patients, 764 (17.4%) cases progressed to develop DM. Utilizing a logistic regression (LR) model to identify independent risk factors for DM of gallbladder cancer (GBC). A nomogram has been developed to forecast DM in early T-stage gallbladder cancer patients. Through the evaluation of different models using relevant indicators, it was discovered that Random Forest (RF) exhibited the most outstanding predictive performance; (4) Conclusions: RF has demonstrated high accuracy in predicting DM in gallbladder cancer patients, assisting clinical physicians in enhancing the accuracy of diagnosis. This can be particularly valuable for improving patient outcomes and optimizing treatment strategies. We employ the RF algorithm to construct the corresponding web calculator. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden on radiologists. Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists' annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). Experiments showed that the proposed method reduced the average report generation time to 3.8 min compared to manual processes, even when using relatively low-spec hardware. Generated reports perfectly matched ground truth reports for suspicious masses without a single failure on our evaluation datasets. Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. These improvements not only streamline the report generation process but also allow radiologists to dedicate more time and focus on patient care, ultimately enhancing clinical outcomes and patient satisfaction. [ABSTRACT FROM AUTHOR]

Anterior cruciate ligament (ACL) plays an important role in stabilising the knee joint, prevents excessive anterior translation of the tibia, and provides rotational stability. ACL injuries commonly occur as a result of rapid deceleration, sudden change in direction, or direct impact to the knee during sports activities. Although several deep learning techniques have recently been applied in the detection of ACL tears, challenges such as effective slice filtering and the nuanced relationship between varying tear grades still remain underexplored. This study used an advanced deep learning model that integrated a T-distribution-based slice attention filtering mechanism with a penalty weight loss function to improve the performance for detection of ACL tears. A T-distribution slice attention module was effectively utilised to develop a robust slice filtering system of the deep learning model. By incorporating class relationships and substituting the conventional cross-entropy loss with a penalty weight loss function, the classification accuracy of our model is markedly increased. The combination of slice filtering and penalty weight loss shows significant improvements in diagnostic performance across six different backbone networks. In particular, the VGG-Slice-Weight model provided an area score of 0.9590 under the receiver operating characteristic curve (AUC). The deep learning framework used in this study offers an effective diagnostic tool that supports better ACL injury detection in clinical diagnosis practice. [ABSTRACT FROM AUTHOR]

Obesity is linked to the increasing prevalence of metabolic syndrome (MetS), even among the pediatric population. Some inflammatory and cardioembolic indexes derived from routine laboratory tests have captivated the attention of the medical community. Objectives: The aim of our study was to evaluate whether these markers are effective in distinguishing varying degrees of obesity and MetS in children and adolescents. Methods: We conducted a retrospective study. A total of 71 children and adolescents, aged between 6 and 16, were included in the study. Among them, 5 were overweight, 35 had obesity, and 31 had severe obesity. According to the NCEP ATP III criteria, 32 individuals had Metabolic Syndrome (MetS), while 39 did not have MetS. Results: The MetS positive group had higher values of TG/HDL-C (p < 0.001), TC/HDL-C (p < 0.001), MHR (p = 0.015), LHR (p = 0.001), NHR (p = 0.001), atherogenic index of plasma (p < 0.001), and PHR (p < 0.001). ESR, NLR, PLR, and SII did not progressively increase with the number of MetS criteria. The ROC curve analysis demonstrated that markers such as TG/HDL-C, the atherogenic index of plasma, TC/HDL-C, LHR, NHR, and PHR were effective in identifying MetS in children and adolescents with obesity. Conclusions: In conclusion, we determined that some novel inflammatory and cardioembolic indexes are useful in assessing MetS and obesity in children and adolescents. [ABSTRACT FROM AUTHOR]

Synopsis: Corneal epithelial wavefront error and epithelial thickness variance qualify as highly sensitive and specific biomarkers for epithelial basement membrane dystrophy (EBMD). The biomarkers show a normalization after treatment of EBMD with phototherapeutic keratectomy. Purpose: To gauge the diagnostic value of epithelial basement membrane dystrophy (EBMD), a novel spectral-domain optical coherence tomography (SD-OCT)-based imaging modality for simultaneous morphological (thickness profile) and refractive (optical wavefront) assessment of the corneal epithelial layer in one of the most common but often underdiagnosed corneal dystrophies. Methods: In this prospective observational study, a total of 32 eyes of 32 patients diagnosed with EBMD and 32 eyes of 32 healthy control subjects were examined with high-resolution anterior segment SD-OCT (MS-39; CSO, Florence, Italy). Various epithelial thickness and epithelial wavefront-derived terms were compared between groups and receiver operating characteristic (ROC) curves were computed to analyze the diagnostic capacity of the respective parameters. A total of 17 of 32 EBMD patients underwent treatment with phototherapeutic keratectomy (PTK) and were followed up for 3 months. Results: Epithelial thickness variance (60.4 ± 56.7 µm versus 7.6 ± 6.1 µm) and interquartile range (11.0 ± 6.9 versus 3.3 ± 1.9 µm) were markedly elevated in EBMD patients as compared with healthy controls (both with p < 0.001). Epithelial wavefront analysis showed a highly statistically significant excess in all examined aberration terms in EBMD patients (all with p < 0.001). Significantly greater areas under the curve (AUCs) were yielded by the epithelial wavefront-derived parameters (e.g., total epithelial wavefront error: AUC = 0.966; 95% confidence interval (CI) 0.932–1) than by the epithelial thickness-derived parameters (e.g., variance: AUC = 0.919; 95% CI 0.848–0.990). Conclusions: Corneal epithelial wavefront aberrometry proved valuable as an objective biomarker for EBMD, with high sensitivity and specificity. PTK resulted in a reduction of morphological and refractive epithelial irregularities in EBMD. [ABSTRACT FROM AUTHOR]

(1) Background: Cervical screening and additional diagnostic procedures often lead to depression. This research aimed to develop a prediction model for depression in women who received an abnormal Papanicolaou screening test, prior to and following the diagnostic procedures. (2) Methods: The study included women who had a positive Papanicolaou screening test (N = 172) and attended the Clinical Center of Kragujevac in Serbia for additional diagnostic procedures (colposcopy/biopsy/endocervical curettage). Women filled out a sociodemographic survey and the Center for Epidemiologic Studies Depression questionnaire (CES-D scale) before and after diagnostic procedures. A prediction model was built with multilayer perceptron neural networks. (3) Results: A correlation-based filter method of feature selection indicated four variables that correlated with depression both prior to and following the diagnostic procedures—anxiety, depression, worry, and concern about health consequences. In addition, the use of sedatives and a history of both induced and spontaneous abortion correlated with pre-diagnostic depression. Important attributes for predicting post-diagnostic depression were scores for the domains 'Tension/discomfort' and 'Embarrassment' and depression in personal medical history. The accuracy of the pre-diagnostic procedures model was 70.6%, and the area under the receiver operating characteristic curve (AUROC) was 0.668. The model for post-diagnostic depression prediction showed an accuracy of 70.6%, and an AUROC = 0.836. (4) Conclusions: This study helps provide means to predict the occurrence of depression in women with an abnormal Papanicolaou screening result prior to and following diagnostic procedures, which can aid healthcare professionals in successfully providing timely psychological support to those women who are referred to further diagnostics. [ABSTRACT FROM AUTHOR]

This study evaluated the relationship of non-invasive arterial stiffness parameters with an individual 10-year risk of fatal and non-fatal atherosclerotic cardiovascular disease (ASCVD) events in the cohort post-coronavirus disease 2019 (COVID-19). The study group included 203 convalescents aged 60.0 (55.0–63.0) and 115 (56.7%) women. The ASCVD risk was assessed as low to moderate to very high based on medical history (for 62 participants with pre-existing ASCVD/diabetes/chronic kidney disease in the entire cohort) or calculated in percentages using the Systemic Coronary Risk Evaluation 2 (SCORE2) algorithm based on age, sex, smoking status, systolic blood pressure (BP), and non-high-density lipoprotein cholesterol (for 141 healthy participants). The stiffness index (SI) and reflection index (RI) measured by photoplethysmography, as well as pulse pressure (PP), calculated as the difference between systolic and diastolic BP, were markers of arterial stiffness. Stiffness parameters increased significantly with the increase in ASCVD risk in the entire cohort. In 30 (14.8%) patients in the low- to moderate-risk group, the median SI was 8.07 m/s (7.10–8.73), RI 51.40% (39.40–65.60), and PP 45.50 mmHg (40.00–57.00); in 111 (54.7%) patients in the high-risk group, the median SI was 8.70 m/s (7.40–10.03), RI 57.20% (43.65–68.40), and PP 54.00 mmHg (46.00–60.75); and in 62 (30.5%) patients in the very-high-risk group, the median was SI 9.27 m/s (7.57–10.44), RI 59.00% (50.40–72.40), and PP 60.00 mmHg (51.00–67.00). In healthy participants, the SI ≤ 9.0 m/s (sensitivity of 92.31%, area under the curve [AUC] 0.686, p < 0.001) based on the receiver operating characteristics was the most sensitive variable for discriminating low to moderate risk, and PP > 56.0 mmHg (sensitivity of 74.36%, AUC 0.736, p < 0.001) was used for discriminating very high risk. In multivariate logistic regression, younger age, female sex, PP ≤ 50 mmHg, SI ≤ 9.0 m/s, and triglycerides < 150 mg/dL had the best relationship with low to moderate SCORE2 risk. In turn, older age, currently smoking, PP > 56.0 mmHg, RI > 68.6%, and diastolic BP ≥ 90 mmHg were related to very high SCORE2 risk. In conclusion, arterial stiffness is significantly related to ASCVD risk in post-COVID-19 patients and can be helpful as a single risk marker in everyday practice. Cut-off points for arterial stiffness parameters determined based on SCORE2 may help make individual decisions about implementing lifestyle changes or pharmacological treatment of ASCVD risk factors [ABSTRACT FROM AUTHOR]

Background/Objectives: Diabetic retinopathy (DR) is a common diabetes complication that can lead to blindness through vision-threatening complications like clinically significant macular edema and proliferative retinopathy. Identifying eyes at risk of progression using non-invasive methods could help develop targeted therapies to halt diabetic retinal disease progression. Methods: A set of 82 imaging and systemic features was used to characterize the progression of nonproliferative diabetic retinopathy (NPDR). These features include baseline measurements (static features) and those capturing the temporal dynamic behavior of these static features within one year (dynamic features). Interpretable models were trained to distinguish between eyes with Early Treatment Diabetic Retinopathy Study (ETDRS) level 35 and eyes with ETDRS levels 43–47. The data used in this research were collected from 109 diabetic type 2 patients (67.26 ± 2.70 years; diabetes duration 19.6 ± 7.26 years) and acquired over 2 years. Results: The characterization of the data indicates that NPDR progresses from an initial stage of hypoperfusion to a hyperperfusion response. The performance of the classification model using static features achieved an area under the curve (AUC) of the receiver operating characteristics equal to 0.84 ± 0.07, while the model using both static and dynamic features achieved an AUC of 0.91 ± 0.05. Conclusion: NPDR progresses through an initial hypoperfusion stage followed by a hyperperfusion response. Characterizing and automatically identifying this disease progression stage is valuable and necessary. The results indicate that achieving this goal is feasible, paving the way for the improved evaluation of progression risk and the development of better-targeted therapies to prevent vision-threatening complications. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic underscores the need for accurate prognostic tools to predict patient outcomes. This study evaluates the effectiveness of four prominent COVID-19 prediction scores—PAINT, ISARIC4C, CHIS, and COVID-GRAM—at two critical time points: at admission and seven days post-symptom onset, to assess their utility in predicting mortality among hospitalized patients. Conducted at the Clinical Emergency Hospital Pius Brînzeu in Timișoara, this retrospective analysis included adult patients hospitalized with confirmed SARS-CoV-2 infection. Eligible patients had complete data for the scores at both time points. Statistical analysis involved ROC curves and logistic regression to assess the scores' predictive accuracy for mortality. The study included 215 patients, split into 139 survivors and 76 non-survivors. At admission, the PAINT, ISARIC4C, CHIS, and COVID-GRAM scores significantly differentiated between the survival outcomes (p < 0.0001). The best cutoff values at admission were 6.26 for PAINT, 7.95 for ISARIC4C, 5.58 for CHIS, and 0.63 for COVID-GRAM, corresponding to sensitivities of 85.47%, 80.56%, 88.89%, and 83.33% and specificities of 77.34%, 82.12%, 75.01%, and 78.45%, respectively. By day seven, the cutoff values increased, indicating deteriorating conditions in patients who eventually succumbed to the virus. The hazard ratios at admission for exceeding these cutoffs were significant: PAINT (HR = 3.45), ISARIC4C (HR = 2.89), CHIS (HR = 4.02), and COVID-GRAM (HR = 3.15), highlighting the scores' abilities to predict severe outcomes. One week post symptom onset, these scores' predictive values and corresponding hazard ratios increased, further validating their prognostic significance over time. The evaluated COVID-19 prediction scores robustly predict mortality at admission and become more predictive by the seventh day of symptom onset. These findings support the use of these scores in clinical settings to facilitate early identification and intervention for high-risk patients, potentially improving patient outcomes during the ongoing global health crisis. [ABSTRACT FROM AUTHOR]

This study investigated whether the textural features of peritumoral adipose tissue (AT) on [18F]fluoro-2-deoxy-2-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) can predict the pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. We retrospectively enrolled 147 female breast cancer patients who underwent staging FDG PET/CT and completed NAC and underwent curative surgery. We extracted 10 first-order features, 6 gray-level co-occurrence matrix (GLCM) features, and 3 neighborhood gray-level difference matrix (NGLDM) features of peritumoral AT and evaluated the predictive value of those imaging features for pathological complete response (pCR) and PFS. The results of our study demonstrated that GLCM homogeneity showed the highest predictability for pCR among the peritumoral AT imaging features in the receiver operating characteristic curve analysis. In multivariate logistic regression analysis, the mean standardized uptake value (SUV), 50th percentile SUV, 75th percentile SUV, SUV histogram entropy, GLCM entropy, and GLCM homogeneity of the peritumoral AT were independent predictors for pCR. In multivariate survival analysis, SUV histogram entropy and GLCM correlation of peritumoral AT were independent predictors of PFS. Textural features of peritumoral AT on FDG PET/CT could be potential imaging biomarkers for predicting the response to NAC and disease progression in breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Neoadjuvant chemotherapy (NAC) is currently used for treating breast cancer in selected cases. Our study aims to evaluate the role of automated breast ultrasound (ABUS) in the assessment of response to NAC and compare the ABUS results with MRI. Methods: A total of 52 consecutive patients were included in this study. ABUS and MRI sensitivity (SE), specificity (SP), diagnostic accuracy (DA), positive predictive value (PPV), and negative predictive value (NPV) were calculated and represented using Area Under ROC Curve (ROC) analysis, searching for any significant difference (p < 0.05). The McNemar test was used searching for any significant difference in terms of sensitivity by comparing the ABUS and MRI results. The inter-observer agreement between the readers in evaluating the response to NAC for both MRI and ABUS was calculated using Cohen's kappa k coefficient. Results: A total of 35 cases of complete response and 17 cases of persistent disease were found. MRI showed SE, SP, DA, PPV, and NPV values of 100%, 88%, 92%, 81%, and 100%, respectively, with an AUC value of 0.943 (p < 0.0001). ABUS showed SE, SP, DA, PPV, and NPV values of 88%, 94%, 92%, 89%, and 94%, respectively, with an AUC of 0.913 (p < 0.0001). The McNemar test revealed no significant difference (p = 0.1250). The inter-observer agreement between the two readers in evaluating the response to NAC for MRI and ABUS was, respectively, 0.88 and 0.89. Conclusions: Automatic breast ultrasound represents a new accurate, tri-dimensional and operator-independent tool for evaluating patients referred to NAC. [ABSTRACT FROM AUTHOR]

Background/Objectives: The objective of this study was to assess the connection between the systemic inflammation response index (SIRI) values and failure patterns of patients with IDH wild-type glioblastoma (GB) who underwent radiotherapy (RT) with FLAIR-based gross tumor volume (GTV) delineation. Methods: Seventy-one patients who received RT at a dose of 60 Gy to the GTV and 50 Gy to the clinical target volume (CTV) and had documented recurrence were retrospectively analyzed. Each patient's maximum distance of recurrence (MDR) from the GTV was documented in whichever plane it extended the farthest. The failure patterns were described as intra-GTV, in-CTV/out-GTV, distant, and intra-GTV and distant. For analytical purposes, the failure pattern was categorized into two groups, namely Group 1, intra-GTV or in-CTV/out-GTV, and Group 2, distant or intra-GTV and distant. The SIRI was calculated before surgery and corticosteroid administration. A receiver operating characteristic (ROC) curve analysis was used to determine the optimal SIRI cut-off that distinguishes between the different failure patterns. Results: Failure occurred as follows: intra-GTV in 40 (56.3%), in-CTV/out-GTV in 4 (5.6%), distant in 18 (25.4%), and intra-GTV + distant in 9 (12.7%) patients. The mean MDR was 13.5 mm, and recurrent lesions extended beyond 15 mm in only seven patients. Patients with an SIRI score ≥ 3 demonstrated a significantly higher incidence of Group 1 failure patterns than their counterparts with an SIRI score < 3 (74.3% vs. 50.0%; p = 0.035). Conclusions: The present results show that using the SIRI with a cut-off value of ≥3 significantly predicts failure patterns. Additionally, the margin for the GTV can be safely reduced to 15 mm when using FLAIR-based target delineation in patients with GB. [ABSTRACT FROM AUTHOR]

It is challenging to differentiate between central nervous system (CNS) virus infections and neurological autoimmune diseases in the emergency department. Considering their different pathogenesis, we assume they differ in neuropsychiatric symptoms and laboratory results. A total of 80 patients were included in this study, 50 with CNS virus infections and 30 with CNS autoimmune diseases, confirmed by a polymerase chain reaction (PCR) of cerebrospinal fluid (CSF). A binary logistic regression model and receiver operating characteristic (ROC) curve were employed to examine the discrimination between the two types of diseases based on neuropsychiatric symptoms and laboratory results. Compared to patients with neurological autoimmune diseases, patients with CNS virus infections had a higher incidence of abnormal behavior (p = 0.026) and abnormal sensation/thought (p = 0.029); higher total (p = 0.005), direct (p = 0.004), and indirect bilirubin (p = 0.004); and increased CSF cell (p = 0.01) and CSF white cell counts (p = 0.01). Patients with disturbance of consciousness and abnormal sensation/thought were 7.79-fold and 5.07-fold more likely to be diagnosed with CNS virus infections (OR = 7.79, p = 0.008; OR = 5.07, p = 0.032). Each unit increase in blood indirect bilirubin concentration and CSF white cell counts increased the risk of developing CNS virus infections by 1.25-fold and 1.01-fold (OR = 1.25, p = 0.016; OR = 1.01, p = 0.011). ROC analysis showed that the area under the curve was 88.0% (p < 0.001). Our study found that patients with CNS viral infections tend to have higher blood indirect bilirubin concentration, CSF leukocyte count, frequency of disorders of consciousness, and abnormal sensation and thought, which may help differentiate them from those with neurological autoimmune diseases. [ABSTRACT FROM AUTHOR]

Purpose: Investigate the oxylipin profiles in the aqueous humor of primary open-angle glaucoma (POAG) patients. Methods: Aqueous humor samples were collected from 17 POAG patients and 15 cataract subjects and subjected to a liquid chromatography/mass spectrometry (LC-MS) analysis to detect the oxylipins. The prediction potential of the differential abundant oxylipins was assessed by the receiver operating characteristic (ROC) curves. Pathway and correlation analyses on the oxylipins and clinical and biochemical parameters were also conducted. Results: The LC-MS analysis detected a total of 76 oxylipins, of which 29 oxylipins reached the detection limit. The multivariate analysis identified five differential abundant oxylipins, 15-keto-prostaglandin F2 alpha (15-kPGF2α), Leukotriene B4 (LTB4), 12,13-Epoxyoctadecenoic acid (12,13-Epome), 15-Hydroxyeicosatetraenoic acid (15-HETE) and 11-Hydroxyeicosatetraenoic acid (11-HETE). The five oxylipins are enriched in the arachidonic acid metabolism and linoleic acid metabolism pathways. Pearson correlation analysis showed that 11-HETE was positively correlated with intraocular pressure and central corneal thickness and negatively with cup/disk area ratio in the POAG patients. In addition, 15-kPGF2α was moderately and positively correlated with the mean deviation (MD) of visual field defect, and LTB4 was moderately and negatively correlated with macular thickness. Conclusions: This study revealed the oxylipin profile in the aqueous humor of POAG patients. Oxylipins involved in the arachidonic acid metabolism pathway could play a role in POAG, and anti-inflammatory therapies could be potential treatment strategies for POAG. [ABSTRACT FROM AUTHOR]

The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditional methods, often compromised by suboptimal feature selection and limited generalizability, are outpaced by the enhanced classification model, MissenseNet (Missense Classification Network). This model, advancing beyond standard predictive features, incorporates structural insights from AlphaFold2 protein predictions, thus optimizing structural data utilization. MissenseNet, built on the ShuffleNet architecture, incorporates an encoder-decoder framework and a Squeeze-and-Excitation (SE) module designed to adaptively adjust channel weights and enhance feature fusion and interaction. The model's efficacy in classifying pathogenicity has been validated through superior accuracy compared to conventional methods and by achieving the highest areas under the Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves (Area Under the Curve and Area Under the Precision-Recall Curve) in an independent test set, thus underscoring its superiority. [ABSTRACT FROM AUTHOR]

Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the multivariate normality, which is often not true in practice, as most biomarkers follow distributions that deviate from normality. While the likelihood ratio combination is recognized to be the optimal approach, it is complicated to calculate. To achieve a more accurate and effective combination of biomarkers, especially when these biomarkers deviate from normality, we propose using a receiver operating characteristic (ROC) curve methodology based on the optimal combination of elliptically distributed biomarkers. In this paper, we derive the ROC curve function for the elliptical likelihood ratio combination. Further, proceeding from the derived best combinations of biomarkers, we propose an efficient technique via nonparametric maximum likelihood estimate (NPMLE) to build empirical estimation. Simulation results show that the proposed elliptical combination method consistently provided better performance, demonstrating its robustness in handling various distribution types of biomarkers. We apply the proposed method to two real datasets: Autism/autism spectrum disorder (ASD) and neural tube defects (NTD). In both applications, the elliptical likelihood ratio combination improves the AUC value compared to the multivariate normal likelihood ratio combination and the best linear combination. [ABSTRACT FROM AUTHOR]

Avalanche susceptibility assessment is a core aspect of regional avalanche early warning and risk analysis and is of great significance for disaster prevention and mitigation on proposed highways. Using sky–ground integration investigation, 83 avalanche points within the G219 Wen Quan to Horgos transportation corridor were identified, and the avalanche hazard susceptibility of the transportation corridor was partitioned using the certainty factor (CF) model and the coupled coefficient of the certainty factor–Geodetector (CF-GD) model. The CF model analysis presented nine elements of natural conditions which influence avalanche development; then, by applying the Geodetector for each of the factors, a weighting coefficient was given depending on its importance for avalanche occurrence. The results demonstrate the following: (1) According to the receiver operating characteristic (ROC) curve used to verify the accuracy, the area under the ROC curve (AUC) value for the CF-GD coupled model is 0.889, which is better than the value of 0.836 of the CF model's evaluation accuracy, and the coupled model improves the accuracy by about 6.34% compared with the single model, indicating that the coupled model is more accurate. The results provide avalanche prevention and control recommendations for the G219 Wen Quan to Horgos transportation corridor. (2) The slope orientation, slope gradient, and mean winter temperature gradient are the main factors for avalanche development in the study area. (3) The results were validated based on the AUC values. The AUCs of the CF-GD coupled model and the CF model were 0.889 and 0.836, respectively. The accuracy of the coupled model was improved by about 6.34% compared to the single model, and the coupled CF-GD model was more accurate. The results provide avalanche control recommendations for the G219 Wen Quan to Horgos transportation corridor. [ABSTRACT FROM AUTHOR]

Doxorubicin (DOX), a commonly used anticancer agent, causes cardiotoxicity that begins with the first dose and may progress to heart failure years after treatment. An inflammatory response associated with neutrophil recruitment has been recognized as a mechanism of DOX-induced cardiotoxicity. This study aimed to validate mRNA expression of the previously identified biomarkers of DOX-induced cardiotoxicity, PGLYRP1, CAMP, MMP9, and CEACAM8, and to assay their protein expression in the peripheral blood of breast cancer patients. Blood samples from 40 breast cancer patients treated with DOX-based chemotherapy were collected before and after the first chemotherapy cycle and > 2 years after treatment. The protein and gene expression of PGLYRP1/Tag7, CAMP/LL37, MMP9/gelatinase B, and CEACAM8/CD66b were determined using ELISA and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of each candidate biomarker. Patients with cardiotoxicity (n = 20) had significantly elevated levels of PGLYRP1, CAMP, MMP9, and CEACAM8 at baseline, after the first dose of DOX-based chemotherapy, and at > 2 years after treatment relative to patients without cardiotoxicity (n = 20). The first dose of DOX induced significantly higher levels of all examined biomarkers in both groups of patients. At > 2 years post treatment, the levels of all but MMP9 dropped below the baseline. There was a good correlation between the expression of mRNA and the target proteins. We demonstrate that circulating levels of PGLYRP1, CAMP, MMP9, and CEACAM8 can predict the cardiotoxicity of DOX. This novel finding may be of value in the early identification of patients at risk for cardiotoxicity. [ABSTRACT FROM AUTHOR]

The early detection of tumors is one of the key factors in increasing overall survival in cancer patients. A wide range of cancers still do not have a system of early diagnosis; therefore, the development of new non-invasive tools in this line is essential. Accordingly, the objective of our work was to develop a non-invasive screening method for the early detection of various carcinomas in plasma using a panel that combines two markers using RT-qPCR. A retrospective case-control study was conducted to develop a cancer screening test based on the detection of stromal and epithelial biomarkers (COL1A2 and KRT19) in plasma. The expression of biomarkers was evaluated using multiplex quantitative PCR applied to 47 cases with non-metastatic tumors and 13 control participants. For both biomarkers, a cut-off value was stablished using Youden's J index through ROC curve analysis and areas under the curve (AUC) were calculated. The plasma mRNA expression level of both biomarkers was significantly higher in diseased versus healthy patients. Moreover, ROC curve analysis showed an AUC value of 0.897 for the combined model. This model also resulted in a cutoff value of 0.664, as well as a sensitivity of 83% and a specificity of 84.6%. These results suggest that the plasma expression levels of COL1A2 and KRT19 could a have potential role in detecting various types of cancer at the early stages. The combined analysis of both stromal and epithelial biomarkers would provide a non-invasive screening method that would allow us to differentiate patients with an active neoplastic process. [ABSTRACT FROM AUTHOR]

Chronic spontaneous urticaria (CSU) is associated with skin mast cell activation, and its triggering mechanisms are not completely elucidated. Evidence suggests an autoimmune component of CSU. Our aim was to assess the usefulness of an autoimmune mast cell activation test (aiMAT) for diagnosing and differentiating CSU into different subtypes. We enrolled 43 patients with active, uncontrolled CSU before starting treatment with omalizumab and 15 controls. Patients were evaluated based on omalizumab response. aiMATs were performed using non-IgE-sensitized (NS) or myeloma IgE-sensitized (S) LAD2 cells, which were then stimulated with CSU/control sera (25 µL and 10 µL). The expression of CD63 was assessed with flow cytometry. CD63 response on NS-LAD2 was significantly increased in CSU patients compared to controls after the stimulation with 25 µL CSU/control sera (p = 0.0007) and with 10 µL CSU/control sera (p = 0.0001). The ROC curve analysis demonstrated an area under the curve (AUC) of 0.82. The cutoff for autoimmune-non-IgE-sensitized-MAT was 40.3% CD63+ LAD2, which resulted in 73.3% sensitivity and 81.4% specificity. CD63 response on S-LAD2 was significantly increased in CSU patients compared to controls after the stimulation with 25 µL CSU/control sera (p = 0.03). The ROC curve analysis demonstrated an AUC of 0.66. The cutoff for the autoimmune-myeloma IgE-sensitized-MAT was 58.4% CD63+ cells, which resulted in 62.8% sensitivity and 66.7% specificity. Overall, 36 out of 43 (84%) patients responded to omalizumab, and 7 (16%) were nonresponders. We found no differences between LAD2 CD63 response and response to omalizumab. In conclusion, aiMAT could represent a new diagnostic tool in CSU. Additional studies are needed to evaluate the potential benefits during omalizumab therapy. [ABSTRACT FROM AUTHOR]

Proliferative diabetic retinopathy (PDR) is a vision-threatening complication of diabetes mellitus (DM). Anterior chamber (AC) flare and intraocular cytokines are potent biomarkers reflecting the intraocular immune status in PDR. This study aimed to elucidate the complex interrelationship between AC flare and intraocular cytokines in PDR eyes. A retrospective observational study was conducted on 19 PDR eyes of 19 patients with type 2 DM, and on 19 eyes of 19 patients with idiopathic macular hole or epiretinal membrane as controls. AC flare was measured before pars plana vitrectomy (PPV). Aqueous humor (AH) and vitreous fluid (VF) samples were collected at the time of PPV, and the quantities of 27 cytokines in both intraocular fluids were analyzed. In the PDR and control groups, Spearman's rank correlation analysis revealed a positive correlation between AC flare and IL-8 level in both AH and VF. Additionally, IL-8 levels in AH correlated positively with IL-8 levels in VF. In the PDR group, receiver operating characteristic curve analysis identified IL-8 level in AH as a significant predictor for both diabetic macular edema (DME) and vitreous hemorrhage (VH) complications. The cut-off values of IL-8 were established at ≥26.6 pg/mL for DME and ≥7.96 pg/mL for VH. Given the positive correlation between AC flare and AH IL-8 level, the present findings suggest that AC flare value may potentially be a non-invasive biomarker for predicting DME. [ABSTRACT FROM AUTHOR]

Objective: This study explored whether assessing flexibility levels in clinical settings might predict the odds of oxytocin administration and caesarean section to stimulate labour. Methods: Pregnant women from the GESTAFIT Project (n = 157), participated in this longitudinal study. Maternal upper-body flexibility was assessed at 16 gestational weeks (g.w.) through the Back-scratch test. Clinical data, including oxytocin administration and type of birth, were registered from obstetric medical records. Results: Pregnant women who required oxytocin administration or had caesarean sections showed lower flexibility scores (p < 0.05 and p < 0.01, respectively). The receiver operating characteristic curve analysis showed that the Back-scratch test was able to detect the need for oxytocin administration ((area under the curve [AUC] = 0.672 (95% confidence interval [CI]: 0.682 (95% CI: 0.59–0.78, p = 0.001)). The AUC to establish the ability of flexibility to discriminate between vaginal and caesarean section births was 0.672 (95% CI: 0.60–0.77, p = 0.002). A Back-scratch test worse than 4 centimetres was associated with a ~5 times greater increased odds ratio of requiring exogenous oxytocin administration (95% CI: 2.0–11.6, p = 0.001) and a ~4 times greater increased odds ratio of having a caesarean section (95% CI: 1.7–10.2, p = 0.002). Conclusions: These findings suggest that lower flexibility levels at the 16th g.w. discriminates between pregnant women who will require oxytocin and those who will not, and those with a greater risk of a caesarean section than those with a vaginal birth. Pregnant women below the proposed Back-scratch test cut-offs at 16th g.w. might specifically benefit from physical therapies that include flexibility training. [ABSTRACT FROM AUTHOR]

Simple Summary: Preoperative risk prediction prior to oncologic esophagectomy is crucial for assisting surgeons in accurate patient selection and patients in their informed decision making. A new risk stratification tool, the IESG prediction model, was recently introduced, categorizing patients into different risk levelsMachine learning is a subfield of artificial intelligence and may allow for a more accurate identification of patients at risk. Therefore, we evaluated the IESG risk model and compared its performance with ML models. We found that the IESG risk model provided an overall adequate risk estimation. However, ML showed better results in the accurate risk stratification of patients, demonstrating its potential as a novel and powerful approach for future patient assessment. Risk prediction prior to oncologic esophagectomy is crucial for assisting surgeons and patients in their joint informed decision making. Recently, a new risk prediction model for 90-day mortality after esophagectomy using the International Esodata Study Group (IESG) database was proposed, allowing for the preoperative assignment of patients into different risk categories. However, given the non-linear dependencies between patient- and tumor-related risk factors contributing to cumulative surgical risk, machine learning (ML) may evolve as a novel and more integrated approach for mortality prediction. We evaluated the IESG risk model and compared its performance to ML models. Multiple classifiers were trained and validated on 552 patients from two independent centers undergoing oncologic esophagectomies. The discrimination performance of each model was assessed utilizing the area under the receiver operating characteristics curve (AUROC), the area under the precision–recall curve (AUPRC), and the Matthews correlation coefficient (MCC). The 90-day mortality rate was 5.8%. We found that IESG categorization allowed for adequate group-based risk prediction. However, ML models provided better discrimination performance, reaching superior AUROCs (0.64 [0.63–0.65] vs. 0.44 [0.32–0.56]), AUPRCs (0.25 [0.24–0.27] vs. 0.11 [0.05–0.21]), and MCCs (0.27 ([0.25–0.28] vs. 0.15 [0.03–0.27]). Conclusively, ML shows promising potential to identify patients at risk prior to surgery, surpassing conventional statistics. Still, larger datasets are needed to achieve higher discrimination performances for large-scale clinical implementation in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Studies analyzing specific treatment-related prognostic factors can provide important information about disease biology and improve the use of biomarkers to optimize treatment decisions. The aim of our retrospective study was to determine the prognostic performance of clinicopathological factors and blood biomarkers in patients with unresectable metastatic melanoma treated with BRAF and MEK inhibitors. A total of 199 patients were included in the multivariate analysis of the risk of progression and death. We found that primary tumor localization on the limbs, Clark invasion level V, M1c stage or M1d stage at the start of therapy, and elevated baseline serum S100B level were independently and significantly associated with poor outcomes. The present study suggests that clinicopathological factors, including primary tumor characteristics and stage of metastatic disease, as well as serum markers may provide information on the probability of survival in patients with advanced melanoma treated with BRAF and MEK inhibitors. Prognostic studies can provide important information about disease biology and improve the use of biomarkers to optimize treatment decisions. Methods: A total of 199 patients with advanced melanoma treated with BRAF + MEK inhibitors were included in our single-center retrospective study. We analyzed the risk of progression and death using multivariate Cox proportional hazard models. The predictive effect of prognostic factors on progression-free survival (PFS) was evaluated in ROC analysis. Results: We found that primary tumor localization, Clark level, pT category, baseline M stage and baseline serum S100B are independent and significant prognostic factors for PFS. The discriminative power of the combination of these factors was excellent for predicting 18 month PFS (AUC 0.822 [95% CI 0.727; 0.916], p < 0.001). Primary tumor localization on the extremities, Clark level V, baseline M1c stage or M1d stage, and elevated baseline serum S100B and LDH levels were independently and significantly associated with unfavorable overall survival (OS). Conclusion: Baseline M stage and serum S100B appear to be independent prognostic factors for both PFS and OS in melanoma patients treated with BRAF + MEK inhibitors. We newly identified significant and independent prognostic effects of primary tumor localization and Clark level on survival that warrant further investigation. [ABSTRACT FROM AUTHOR]

Simple Summary: Total thyroidectomy and dissection of cervical lymph node compartments, depending on serum calcitonin levels and ultrasound findings, is standard treatment for patients with medullary thyroid cancer. The aim of this study was to evaluate whether pre-operative calcitonin and CEA levels can be useful as biomarkers of the extent of lymph node metastases at diagnosis. Results indicate that pre-operative serum calcitonin and CEA levels can predict presence, number, and site of lymph node metastases and, more specifically, values of 90 pg/mL for calcitonin and 17 ng/mL for CEA accurately indicate the N1b status. Since surgery is the only curative treatment for medullary thyroid cancer and there is not a strong indication regarding the extent of lymphadenectomy, these findings may help in the choice of the extent of neck dissection. Background: In medullary thyroid cancer (MTC), lymph node metastases are often present at diagnosis and the extent of surgery is usually based upon pre-operative calcitonin and CEA levels as well as ultrasound findings. The aim of this study was to evaluate the role of pre-operative calcitonin and CEA levels as predictive markers of the burden of lymph node metastases at diagnosis. Methods: we conducted a retrospective study analyzing 87 MTC patients. Results: The median levels of calcitonin and CEA were 88.4 pg/mL and 7.0 ng/mL, respectively, in patients with no lymph nodes metastases; 108.0 pg/mL and 9.6 ng/mL, respectively, in patients with metastases to 1–5 lymph nodes; 520.5 pg/mL and 43.2 ng/mL, respectively, in patients with metastases to >5 lymph nodes. There were no significant differences in pre-operative calcitonin and CEA values between N0 and N1a patients, whereas they were significantly higher in N1b patients. Pre-operative cut-off levels distinguishing N0/N1a from N1b patients were 90 pg/mL for calcitonin (sensitivity 100%, specificity 59.3%, AUC = 0.82) and 17 ng/mL for CEA (sensitivity 100%, specificity 75%, AUC = 0.89). Conclusions: in patients with MTC, pre-operative serum calcitonin and CEA levels may drive the decision-making process to better define the extent of surgery. [ABSTRACT FROM AUTHOR]

Background: The prognostic value of baseline inflammatory markers in sepsis remains controversial, with conflicting evidence regarding their association with mortality. The dynamic changes in these markers over time might offer additional insights into disease progression and patient outcomes. Methods: This retrospective observational study included 138 patients with severe infections. The inflammatory biomarkers procalcitonin (PCT), C-reactive protein (CRP), and lactate (LAC) were measured at three time points: upon hospital admission (baseline), approximately 24–48 h after admission (second measurement; M2), and 48–72 h after admission (third measurement; M3). The primary outcome was 30-day mortality. A Mann–Whitney U test was used to compare the biomarker levels between the survivors and non-survivors. A Spearman's correlation was used to assess the relationships between the baseline parameters. A logistic regression and a receiver operating characteristic (ROC) curve analysis were employed to evaluate the prognostic value of the baseline markers and their dynamic changes. Results: The baseline LAC and SOFA score were significantly associated with 30-day mortality. The percentage decrease in PCT, CRP, and LAC from the baseline to M3 emerged as strong predictors of survival, with the ROC curve analysis demonstrating superior discriminatory ability compared to the baseline values. CRP_Delta exhibited the highest AUC (0.903), followed by PCT_Delta (0.843) and LAC_Delta (0.703). Conclusions: The dynamic changes in these inflammatory biomarkers, particularly PCT, CRP, and LAC, offer valuable prognostic information beyond their baseline levels in predicting 30-day mortality in severe infections. These findings highlight the importance of monitoring biomarker trends for early risk stratification and potential treatment guidance. [ABSTRACT FROM AUTHOR]

For patients on ventilation without acute respiratory distress syndrome (ARDS), there are, as yet, limited data on ventilation strategies. We hypothesized that driving pressure (DP) and mechanical power (MP) may play key roles for the late development of ARDS in patients without initial ARDS. A post hoc analysis of a database from our previous cohort was performed. The mean DP/MP was computed from the data before ARDS development or until ventilator support was discontinued within 28 days. The association between DP/MP and late development of ARDS within 28 days was determined. One hundred and twelve patients were enrolled, among whom seven developed ARDS. Univariate Cox regression showed that congestive heart failure (CHF) history and higher levels of mean MP and DP were associated with ARDS development. Multivariate models revealed that the mean MP and mean DP were still factors independently associated with ARDS development at hazard ratios of 1.177 and 1.226 after adjusting for the CHF effect. Areas under the receiver operating characteristic curves for mean DP/MP in predicting ARDS development were 0.813 and 0.759, respectively. In conclusion, high mean DP and MP values may be key factors associated with late ARDS development. The mean DP had a better predicted value for the development of ARDS than the mean MP. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the precision of different Pentacam indices in diagnosing keratoconus (KC) in pediatric patients with and without Down Syndrome (DS) and determine suitable cutoff values. This prospective multicenter cross-sectional study evaluated 216 eyes of 131 patients aged 6–18 years (mean age 12.5 ± 3.2 years) using Pentacam. Patients were categorized into four groups: KC, forme fruste keratoconus (FK), DS, and control, excluding DS patients with topographic KC. Receiver operating characteristic curves were generated to determine the optimal cutoff points and compare the accuracy in identifying KC and FK in patients with and without DS. In DS patients, corneal morphology resembled KC features. The most effective indices for distinguishing KC in DS patients were the average pachymetric progression index (AUC = 0.961), higher-order aberration of the anterior cornea (AUC = 0.953), anterior elevation (AUC = 0.946), posterior elevation (AUC = 0.947), index of vertical asymmetry (AUC = 0.943), and Belin/Ambrosio enhanced ectasia total derivation value (AUC = 0.941). None of the indices showed good accuracy for distinguishing FK in DS patients. The thresholds of these indices differed significantly from non-DS patients. The results highlighted the need for DS-specific cutoff values to avoid false-positive or false-negative diagnoses in this population. [ABSTRACT FROM AUTHOR]

Background: Cirrhosis is a major global cause of mortality, and upper gastrointestinal (GI) bleeding significantly increases the mortality risk in these patients. Although scoring systems such as the Child–Pugh score and the Model for End-stage Liver Disease evaluate the severity of cirrhosis, none of these systems specifically target the risk of mortality in patients with upper GI bleeding. In this study, we constructed machine learning (ML) models for predicting mortality in patients with cirrhosis and upper GI bleeding, particularly in emergency settings, to achieve early intervention and improve outcomes. Methods: In this retrospective study, we analyzed the electronic health records of adult patients with cirrhosis who presented at an emergency department (ED) with GI bleeding between 2001 and 2019. Data were divided into training and testing sets at a ratio of 90:10. The ability of three ML models—a linear regression model, an XGBoost (XGB) model, and a three-layer neural network model—to predict mortality in the patients was evaluated. Results: A total of 16,025 patients with cirrhosis and 32,826 ED visits for upper GI bleeding were included in the study. The in-hospital and ED mortality rates were 11.2% and 2.2%, respectively. The XGB model exhibited the highest performance in predicting both in-hospital and ED mortality (area under the receiver operating characteristic curve: 0.866 and 0.861, respectively). International normalized ratio, renal function, red blood cell distribution width, age, and white blood cell count were the strongest predictors in all the ML models. The median ED length of stay for the ED mortality group was 17.54 h (7.16–40.01 h). Conclusions: ML models can be used to predict mortality in patients with cirrhosis and upper GI bleeding. Of the three models, the XGB model exhibits the highest performance. Further research is required to determine the actual efficacy of our ML models in clinical settings. [ABSTRACT FROM AUTHOR]

Knee effusion, a common and important indicator of joint diseases such as osteoarthritis, is typically more discernible on magnetic resonance imaging (MRI) scans compared to radiographs. However, the use of radiographs for the early detection of knee effusion remains promising due to their cost-effectiveness and accessibility. This multi-center prospective study collected a total of 1413 radiographs from four hospitals between February 2022 to March 2023, of which 1281 were analyzed after exclusions. To automatically detect knee effusion on radiographs, we utilized a state-of-the-art (SOTA) deep learning-based classification model with a novel preprocessing technique to optimize images for diagnosing knee effusion. The diagnostic performance of the proposed method was significantly higher than that of the baseline model, achieving an area under the receiver operating characteristic curve (AUC) of 0.892, accuracy of 0.803, sensitivity of 0.820, and specificity of 0.785. Moreover, the proposed method significantly outperformed two non-orthopedic physicians. Coupled with an explainable artificial intelligence method for visualization, this approach not only improved diagnostic performance but also interpretability, highlighting areas of effusion. These results demonstrate that the proposed method enables the early and accurate classification of knee effusions on radiographs, thereby reducing healthcare costs and improving patient outcomes through timely interventions. [ABSTRACT FROM AUTHOR]

This study aims to enhance breast cancer detection accuracy through an AI-driven ultrasound tool, Vis-BUS, developed by Barreleye Inc., Seoul, South Korea. Vis-BUS incorporates Lesion Detection AI (LD-AI) and Lesion Analysis AI (LA-AI), along with a Cancer Probability Score (CPS), to differentiate between benign and malignant breast lesions. A retrospective analysis was conducted on 258 breast ultrasound examinations to evaluate Vis-BUS's performance. The primary methods included the application of LD-AI and LA-AI to b-mode ultrasound images and the generation of CPS for each lesion. Diagnostic accuracy was assessed using metrics such as the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision-Recall curve (AUPRC). The study found that Vis-BUS achieved high diagnostic accuracy, with an AUROC of 0.964 and an AUPRC of 0.967, indicating its effectiveness in distinguishing between benign and malignant lesions. Logistic regression analysis identified that 'Fatty' lesion density had an extremely high odds ratio (OR) of 27.7781, suggesting potential convergence issues. The 'Unknown' density category had an OR of 0.3185, indicating a lower likelihood of correct classification. Medium and large lesion sizes were associated with lower likelihoods of correct classification, with ORs of 0.7891 and 0.8014, respectively. The presence of microcalcifications showed an OR of 1.360. Among Breast Imaging-Reporting and Data System categories, category C5 had a significantly higher OR of 10.173, reflecting a higher likelihood of correct classification. Vis-BUS significantly improves diagnostic precision and supports clinical decision-making in breast cancer screening. However, further refinement is needed in areas like lesion density characterization and calcification detection to optimize its performance. [ABSTRACT FROM AUTHOR]

Lane changing behavior (LCB) prediction is a crucial functionality of advanced driver-assistance systems and autonomous vehicles. Predicting whether or not the driver of a considered ego vehicle is likely to change lanes in the near future plays an important role in improving road safety and traffic efficiency. Understanding the underlying intentions behind the driver's behavior is an important factor for the effectiveness of assistance and monitoring systems. Machine learning (ML) algorithms have been broadly used to predict this behavior by analyzing datasets of traffic and driving data related to the considered ego vehicle. However, this technology has not yet been widely adopted in commercial products. Further improvements in these algorithms are necessary to enhance their robustness and reliability. In some domains, receiver operating characteristic and precision-recall curves are commonly used to evaluate ML algorithms, not considering the effects of process parameters in the evaluation, while it might be necessary to access the performance of these algorithms with respect to such parameters. This paper proposes the use of deep autoencoders to extract multi-level features from datasets, which can then be used to train an ensemble of classifiers. This allows for taking advantage of high feature-extraction capabilities of deep learning models and improving the final result using ensemble learning techniques. The concept of probability of detection is used in combination with the networks employed here to evaluate which classifiers can detect the correct LCB better in a statistical sense. Applications on data acquired from a driving simulator show that the proposed method can be adopted to improve the reliability of the classifiers, and ensemble ANNs perform best in predicting the upcoming human behavior in this dynamical context earlier than 3 s before the event itself. [ABSTRACT FROM AUTHOR]

Introduction: Vault particles are large cytoplasmic ribonucleoprotein particles that participate in inflammation. The aim of this study was to assess the diagnostic and prognostic value of major vault protein (MVP) in patients with inflammation, in order to determine whether MVP could be used as a biomarker for infection or inflammation. We also aimed to compare the diagnostic impact of MVP compared to other conventional measurements, such as CRP or white blood cell (WBC) counts. Methods: CRP and MVP levels were measured in 111 sera samples from 85 patients with inflammation admitted to a tertiary-care hospital and 26 healthy individuals during an 18-month period (2019–2020), using nephelometry and a custom MVP sandwich ELISA assay, respectively. In addition, WBC counts were measured using a commercial assay. Results: MVP levels were found to be elevated in patients with inflammation compared to healthy individuals (p < 0.0001). Moreover, MVP levels were higher in patients with inflammation due to an infectious etiology compared to those with non-infectious etiology (p = 0.0006). MVP levels significantly decreased during the first four days of infection in response to antibiotic treatment, while CRP levels showed a less-sensitive decline. An ROC curve analysis demonstrated that MVP and CRP have similarly high diagnostic accuracy, with AUCs of 0.955 and 0.995, respectively, followed by WBCs with an AUC of 0.805. Conclusions: The ROC curves demonstrated that MVP has the potential to serve as a diagnostic biomarker for inflammation and infection. Additionally, MVP levels may reflect the efficacy of antibiotic treatment. [ABSTRACT FROM AUTHOR]

Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs. [ABSTRACT FROM AUTHOR]