Comparison of Machine Learning Models to a Novel Score in the Identification of Patients at Low Risk for Diabetic Retinopathy.

Purpose: To develop an easily applicable predictor of patients at low risk for diabetic retinopathy (DR). Design: An experimental study on the development and validation of machine learning models (MLMs) and a novel retinopathy risk score (RRS) to detect patients at low risk for DR. Subjects: All individuals aged ≥18 years of age who participated in the telemedicine retinal screening initiative through Temple University Health Systems from October 1, 2016 through December 31, 2020. The subjects must have documented evidence of their diabetes mellitus (DM) diagnosis as well as a documented glycosylated hemoglobin (HbA1c) recorded in their chart within 6 months of the retinal screening photograph. Methods: The charts of 1930 subjects (1590 evaluable) undergoing telemedicine screening for DR were reviewed, and 30 demographic and clinical parameters were collected. Diabetic retinopathy is a dichotomous variable where low risk is defined as no or mild retinopathy using the International Clinical Diabetic Retinopathy severity score. Five MLMs were trained to predict patients at low risk for DR using 1050 subjects and further underwent 10-fold cross validation to maximize its performance indicated by the area under the receiver operator characteristic curve (AUC). Additionally, a novel RRS is defined as the product of HbA1c closest to screening and years with DM. Retinopathy risk score was also applied to generate a predictive model. Main Outcome Measures: The performance of the trained MLMs and the RRS model was compared using DeLong's test. The models were further validated using a separate unseen test set of 540 subjects. The performance of the validation models were compared using DeLong's test and chi-square tests. Results: Using the test set, the AUC for the RRS was not statistically different from 4 out of 5 MLM. The error rate for predicting low-risk patients using the RRS was significantly lower than the naive rate (0.097 vs. 0.19; P < 0.0001), and it was comparable to the error rates of the MLMs. Conclusions: This novel RRS is a potentially useful and easily deployable predictor of patients at low risk for DR. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: Utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information.

BACKGROUND: The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy. Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners. Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion. Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model. Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images.

Efficient Creation of Jettability Diagrams Using Active Machine Learning.

The ability to jet a wide variety of materials consistently from print heads remains a key technical challenge for inkjet-based additive manufacturing processes. Drop watching is the most direct method for testing new inks and print head designs but such experiments are also resource consuming. In this work, a data-efficient machine learning technique called active learning is used to construct detailed jettability diagrams that identify complex regions corresponding to "no jetting," "jetting," and "desired jetting," rather than only individually sampled points. Crucially, our active learning method has resolved challenges with model selection that previously limited the accuracy of active learning in practical settings with very small experimental budgets. In addition, the key "desired jetting" zone may be quite small which is a challenge for initializing active learning. We leverage the physical intuition that the "desired jetting" zone tends to exist between the "jetting" and "no jetting" zone, to improve the performance of this highly imbalanced classification problem by performing two binary classifications in sequence. The first binary classification aims to map out the "jetting" zone versus the "no jetting" zone, while the second binary classification targets identifying the "desired jetting" zone with primary drops only. Our experiments use a stroboscopic drop watcher to visualize the jetting behavior of two fluids from a piezoelectric print head with different jetting waveforms. The results obtained from active learning were compared to a grid search method, which involves running more than 200 experiments for each fluid. The active learning method significantly reduces the number of experiments by 80% while achieving a test accuracy of more than 95% in the "jetting" zone prediction for the test fluids. The ability to construct these jettability diagrams will further accelerate new ink and print head developments.

Development and validation of a predictive model for assessing the risk of follicular carcinoma in thyroid nodules identified as suspicious by intraoperative frozen section.

Introduction: Follicular thyroid carcinoma (FTC) is the second most common thyroid malignancy and is characterized by a higher risk of distant metastasis compared to papillary thyroid cancer. Intraoperative frozen section (IOFS) diagnosis of FTC is challenging due to its limited sensitivity and accuracy, leading to uncertainty in intraoperative surgical decision-making. In response, we developed a predictive model to assess the risk of follicular carcinoma in thyroid nodules identified as suspicious for follicular neoplasm by IOFS. Methods: This model was derived from preoperative clinical and ultrasound data of 493 patients who underwent thyroid surgery at Ningbo Medical Center Lihuili Hospital. It identified five significant predictors of follicular carcinoma: nodule size, thyroglobulin (Tg) level, hypoechogenicity, lobulated or irregular margins, and thick halo. Results: The model demonstrated robust discrimination and calibration, with an area under the curve (AUC) of 0.83 (95% CI: 0.77-0.90) in the training set and 0.78 (95% CI: 0.68-0.88) in the validation set. In addition, it achieved a sensitivity of 81.63% (95% CI: 69.39-91.84) and 68.00% (95% CI: 48.00--4.00), a specificity of 77.42% (95% CI: 72.18-82.66) and 72.51% (95% CI: 65.50-78.96), an accuracy of 78.1% (95% CI: 73.4-82.4) and 71.9% (95% CI: 65.3-78.6), a positive predictive value (PPV) of 41. 67% (95% CI: 35.65-48.84) and 26.79% (95% CI: 19.40-34.33), respectively, and a negative predictive value (NPV) of 95.61% (95% CI: 92.86-97.99) and 94.07% (95% CI: 90.44-97.08) in the training and validation sets, respectively. Conclusion: The model can accurately rule out FTC in low-risk nodules, thereby providing surgeons with a practical tool to determine the necessary extent of surgical intervention for nodules flagged as suspicious by IOFS.

Do Temperature Abuses Along the Frozen Açaí Pulp Value Chain Increase Microbial Hazards?

Does temperature abuse during storage, distribution, marketing, and consumption of unpasteurized frozen açaí pulp increase microbial hazards? This study investigated the behavior of potentially pathogenic (Escherichia coli, Listeria monocytogenes and Salmonella spp.) and spoilage (mesophilic bacteria, yeasts and molds) microorganisms in two simulated thawing conditions: under refrigeration and at room temperature. The effect of repeated cold chain abuse was observed by thawing and refreezing (-20°C) açaí pulp four times over a period of 90 days. Freezing resulted in inhibition of all microorganisms except for mesophilic aerobic bacteria in one single sample. After thawing at 5°C, the kinetic parameters obtained by the Weibull model indicated that mesophilic aerobic bacteria, yeasts and molds and L. monocytogenes showed a longer inactivation time with δ values reaching 35, 126, and 46 days, respectively. The shortest inactivation time for a reduction of 4 log CFU.g-1 was for E. coli. The concentration of Salmonella spp. and L. monocytogenes in control samples was higher (p < 0.01) than in samples exposed to abusive conditions after 90 days of storage. The results indicate that the abusive thawing conditions studied do not increase the potential hazards of pathogens.

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

Identifying key predictive features for live birth rate in advanced maternal age patients undergoing single vitrified-warmed blastocyst transfer.

BACKGROUND: Infertility affects one in six couples worldwide, with advanced maternal age (AMA) posing unique challenges due to diminished ovarian reserve and reduced oocyte quality. Single vitrified-warmed blastocyst transfer (SVBT) has shown promise in assisted reproductive technology (ART), but success rates in AMA patients remain suboptimal. This study aimed to identify and refine predictive factors for live birth following SVBT in AMA patients, with the goal of enhancing clinical decision-making and enabling personalized treatment strategies. METHODS: This retrospective cohort study analyzed 1,168 SVBT cycles conducted between June 2016 and December 2022 at the First Affiliated Hospital of Guangxi Medical University and Nanning Maternity and Child Health Hospital. Nineteen machine-learning models were applied to identify key predictive factors for live birth. Feature selection and 10-fold cross-validation were employed to validate the models. RESULTS: The most significant predictors of live birth included inner cell mass quality, trophectoderm quality, number of oocytes retrieved, endometrial thickness, and the presence of 8-cell blastomeres on day 3. The stacking model demonstrated the best predictive performance (AUC: 0.791), followed by Extra Trees (AUC: 0.784) and Random Forest (AUC: 0.768). These models outperformed traditional methods, achieving superior accuracy, sensitivity, and specificity. CONCLUSION: Leveraging advanced machine-learning models and identifying critical predictive factors can improve the accuracy of live birth outcome predictions for AMA patients undergoing SVBT. These findings offer valuable insights for enhancing clinical decision-making and managing patient expectations. Further research is needed to validate these results in larger, multi-center cohorts and to explore additional factors, including fresh embryo transfers, to broaden the applicability of these models in clinical practice.

Intestinal flora and inflammatory bowel disease: Causal relationships and predictive models.

Background: Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is significantly influenced by intestinal flora. Understanding the genetic and microbiotic interplay is crucial for IBD prediction and treatment. Methods: We used Mendelian randomization (MR), transcriptomic analysis, and machine learning techniques, integrating data from the MiBioGen Consortium and various GWAS datasets. SNPs associated with intestinal flora were mapped to genes, with LASSO regression refining gene selection. Differentially expressed genes (DEGs) and immune infiltration patterns were identified through transcriptomic analysis. Six machine learning models were used for predictive modeling. Findings: MR analysis identified 25 gut microbiota classifications causally related to IBD. SNP mapping and gene expression analysis highlighted 24 significant genes. Drug target MR and colocalization validated these genes' causal relationships with IBD. Key pathways identified included the PI3K-Akt signaling pathway and epithelial-mesenchymal transition. Immune infiltration analysis revealed distinct patterns between high and low LASSO score groups. Machine learning models demonstrated high predictive value, with soft voting enhancing reliability. Interpretation: By integrating MR, transcriptomic analysis, and sophisticated machine learning approaches, this study elucidates the causal relationships between intestinal flora and IBD. The application of machine learning not only enhanced predictive modeling but also offered new insights into IBD pathogenesis, highlighted potential therapeutic targets, and established a robust framework for predicting IBD onset.

First impressions count: Therapists' impression on patients' motivation and helping alliance predicts psychotherapy dropout.

OBJECTIVE: With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed. METHOD: We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (N = 1,691 and N = 12,473) using baseline and initial process variables reported by patients and therapists. RESULTS: Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients' motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout. CONCLUSIONS: Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists' first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients' motivation and the therapeutic alliance.

Predicting the Recurrence of Ovarian Cancer Based on Machine Learning.

Background: Recurrence is the main factor for poor prognosis in ovarian cancer, but few prognostic biomarkers were reported. In this study, we used machine learning methods based on multiple biomarkers to develop a specific prediction model for the recurrence of ovarian cancer. Methods: A total of 277 ovarian cancer patients were enrolled in this study and randomly classified into training and testing cohorts. The prediction information was obtained through 47 clinical parameters using six supervised clustering machine learning algorithms, including K-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Results: In predicting the recurrence of ovarian cancer, machine learning algorithm was superior to conventional logistic regression analysis. In this study, XGBoost showed the best performance in predicting the recurrence of ovarian cancer, with an accuracy of 0.95. In addition, neoadjuvant chemotherapy, Monocyte ratio (MONO%), Hematocrit (HCT), Prealbumin (PAB), Aspartate aminotransferase (AST), and carbohydrate antigen 125 (CA125) are the most important biomarkers to predict the recurrence of ovarian cancer. Conclusion: The machine learning techniques can achieve a more accurate assessment of the recurrence of ovarian cancer, which can help clinicians make decisions, and develop personalized treatment strategies.

Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises.

BACKGROUND: The transition from explanative modeling of fitted data to the predictive modeling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis-Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters. RESULTS: We quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find the CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis-Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis-Menten, and linear-logarithmic kinetics, with good resilience to noise. Nonetheless, we could not identify the parameters of convenience kinetics using any algorithm. CONCLUSIONS: Altogether, we identify a protocol for predicting reaction parameters under marked measurement noise, as a step towards predictive modeling for systems biology endeavors.

AI illuminates paths in oral cancer: transformative insights, diagnostic precision, and personalized strategies.

Oral cancer retains one of the lowest survival rates worldwide, despite recent therapeutic advancements signifying a tenacious challenge in healthcare. Artificial intelligence exhibits noteworthy potential in escalating diagnostic and treatment procedures, offering promising advancements in healthcare. This review entails the traditional imaging techniques for the oral cancer treatment. The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. The review also encompasses the utilization of artificial intelligence such as automated image analysis, computer-aided detection and diagnosis integration of machine learning algorithms for oral cancer diagnosis and treatment. The customizing treatment approaches for oral cancer through artificial intelligence based personalized medicine is also part of this review. See also the graphical abstract(Fig. 1).

Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis.

BACKGROUND/OBJECTIVES: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. METHODS: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifiers from four distinct categories were utilized for comparison. RESULTS: The results demonstrated that hybrid sampling methods, particularly SMOTEENN, achieved the highest mean performance at 98.19%, followed by IHT (97.20%) and RENN (96.48%). In terms of classifiers, Random Forest showed the best performance with a mean value of 94.69%, with Balanced Random Forest and XGBoost following closely. The baseline method (no resampling) yielded a significantly lower performance of 91.33%, highlighting the effectiveness of resampling techniques in improving model outcomes. CONCLUSIONS: This research underscores the importance of resampling methods in enhancing classification performance on imbalanced datasets, providing valuable insights for researchers and healthcare professionals. The findings serve as a foundation for future studies aimed at integrating machine learning techniques in cancer diagnosis and prognosis, with recommendations for further research on hybrid models and clinical applications.

Impact of lipid oxidation products on the digestibility and structural integrity of Myofibrillar proteins during thermal processing.

This study explores the effects of lipid oxidation products (LOPs), specifically CHP, t,t-DDE, and MDA, on the digestibility and structural integrity of myofibrillar proteins (MP) during processing. LOPs were first assessed by heating at 180 °C for 15 min, showing a significant reduction in digestibility in MDA-treated samples (65.40 %), followed by t,t-DDE (45.10 %) and CHP (13.07 %). MALDI-TOF MS analysis revealed decreased peptide abundance and lower average molecular weight in t,t-DDE- and MDA-treated samples. Notably, substantial decreases in α-helix content and increases in random coil structures were detected, particularly in MDA-treated samples. Assessments of surface hydrophobicity and thiol content underscored the detrimental impact of secondary LOPs on MP structure. Higher MDA concentrations led to a substantial reduction in intrinsic fluorescence intensity, along with an increase in Schiff base content. A PLS regression model demonstrated strong predictive capabilities for MP digestibility, highlighting the importance of optimizing meat processing parameters to minimize nutritional degradation.

Histological Analysis of Oral Tissue Grafting: A Focus on Donor Site Selection.

The science of dental tissue grafting is evolving, with an increased understanding of factors influencing graft behavior. Despite the widespread clinical use of soft tissue grafts, the histological characteristics of different gingival harvesting sites are still underexplored. This study aimed to fill this gap by analyzing 50 tissue samples harvested from 25 patients across three sites: the hard palate, maxillary tuberosity, and palatal rugae. Each sample underwent thorough histological and histomorphometric analysis. Conventional statistical analysis was performed using SPSS, while predictive modeling was conducted with RapidMiner Studio. The study identified significant histological differences among the graft sites, with notable variations in total graft height, epithelial height, and interdigitation perimeter. These findings underscore the importance of donor site selection in influencing graft success. Pair plots and principal component analysis (PCA) further highlighted the distinct histological features of each tissue type. The random forest classifier identified total graft height, epithelial height, and perimeter as the most influential factors in predicting graft site behavior. This study offers valuable insights into the histological characteristics of soft tissue grafts, potentially leading to more predictable clinical outcomes.

Enhancing Predictive Accuracy for Recurrence-Free Survival in Head and Neck Tumor: A Comparative Study of Weighted Fusion Radiomic Analysis.

Despite advancements in oncology, predicting recurrence-free survival (RFS) in head and neck (H&N) cancer remains challenging due to the heterogeneity of tumor biology and treatment responses. This study aims to address the research gap in the prognostic efficacy of traditional clinical predictors versus advanced radiomics features and to explore the potential of weighted fusion techniques for enhancing RFS prediction. We utilized clinical data, radiomic features from CT and PET scans, and various weighted fusion algorithms to stratify patients into low- and high-risk groups for RFS. The predictive performance of each model was evaluated using Kaplan-Meier survival analysis, and the significance of differences in RFS rates was assessed using confidence interval (CI) tests. The weighted fusion model with a 90% emphasis on PET features significantly outperformed individual modalities, yielding the highest C-index. Additionally, the incorporation of contextual information by varying peritumoral radii did not substantially improve prediction accuracy. While the clinical model and the radiomics model, individually, did not achieve statistical significance in survival differentiation, the combined feature set showed improved performance. The integration of radiomic features with clinical data through weighted fusion algorithms enhances the predictive accuracy of RFS outcomes in head and neck cancer. Our findings suggest that the utilization of multi-modal data helps in developing more reliable predictive models and underscore the potential of PET imaging in refining prognostic assessments. This study propels the discussion forward, indicating a pivotal step toward the adoption of precision medicine in cancer care.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Modeling Behavior of Salmonella spp. and Listeria monocytogenes in Raw and Processed Vegetables.

Given the persistent occurrence of foodborne illnesses linked to both raw and processed vegetables, understanding microbial behavior in these foods under distribution conditions is crucial. This study aimed to develop predictive growth models for Salmonella spp. and Listeria monocytogenes in raw (mung bean sprouts, onion, and cabbage) and processed vegetables (shredded cabbage salad, cabbage and onion juices) at various temperatures, ranging from 4 to 36 °C. Growth models were constructed and validated using isolated strains of Salmonella spp. (S. Bareilly, S. Enteritidis, S. Typhimurium) and L. monocytogenes (serotypes 1/2a and 1/2b) from diverse food sources. The minimum growth temperatures for Salmonella varied among different vegetable matrices: 8 °C for mung bean sprouts, 9 °C for both onion and cabbage, and 10 °C for ready-to-eat (RTE) shredded cabbage salad. Both pathogens grew in cabbage juice at temperatures above 17 °C, while neither demonstrated growth in onion juice, even at 36 °C. Notably, Salmonella spp. exhibited faster growth than L. monocytogenes in all tested samples. At 8 °C, the lag time (LT) and specific growth rate (SGR) for Salmonella spp. in mung bean sprouts were approximately tenfold longer and threefold slower, respectively, compared to those at 10 °C. A decrease in refrigerator storage temperature by 1 or 2 degrees significantly prevented the growth of Salmonella in raw vegetables. These findings offer valuable insights into assessing the risk of foodborne illness associated with the consumption of raw and processed vegetables and inform management strategies in mitigating these risks.

Development and Validation of Machine Learning Models to Predict Postoperative Delirium Using Clinical Features and Polysomnography Variables.

Background: Delirium affects up to 50% of patients following high-risk surgeries and is associated with poor long-term prognosis. This study employed machine learning to predict delirium using polysomnography (PSG) and sleep-disorder questionnaire data, and aimed to identify key sleep-related factors for improved interventions and patient outcomes. Methods: We studied 912 adults who underwent surgery under general anesthesia at a tertiary hospital (2013-2024) and had PSG within 5 years of surgery. Delirium was assessed via clinical diagnoses, antipsychotic prescriptions, and psychiatric consultations within 14 days postoperatively. Sleep-related data were collected using PSG and questionnaires. Machine learning predictions were performed to identify postoperative delirium, focusing on model accuracy and feature importance. Results: This study divided the 912 patients into an internal training set (700) and an external test set (212). Univariate analysis identified significant delirium risk factors: midazolam use, prolonged surgery duration, and hypoalbuminemia. Sleep-related variables such as fewer rapid eye movement (REM) episodes and higher daytime sleepiness were also linked to delirium. An extreme gradient-boosting-based classification task achieved an AUC of 0.81 with clinical variables, 0.60 with PSG data alone, and 0.84 with both, demonstrating the added value of PSG data. Analysis of Shapley additive explanations values highlighted important predictors: surgery duration, age, midazolam use, PSG-derived oxygen saturation nadir, periodic limb movement index, and REM episodes, demonstrating the relationship between sleep patterns and the risk of delirium. Conclusions: The artificial intelligence model integrates clinical and sleep variables and reliably identifies postoperative delirium, demonstrating that sleep-related factors contribute to its identification. Predicting patients at high risk of developing postoperative delirium and closely monitoring them could reduce the costs and complications associated with delirium.

Socioeconomic resources in youth are linked to divergent patterns of network integration/segregation across the brain's transmodal axis.

Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully relate to intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neuroscientific meaning of these widespread neural differences remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage graph theory to precisely characterize multivariate and univariate associations between SER across household and neighborhood contexts and the intrinsic functional architecture of brain regions in 5,821 youth (9-10 years) from the Adolescent Brain Cognitive Development Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are associated with greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that topological associations with SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread associations between SER and brain organization. This study highlights both higher-order and somatomotor networks that are differentially implicated in environmental stress, disadvantage, and opportunity in youth.