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The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and well-differentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advanced-stage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds. We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the Integrated Traditional Chinese and Western Medicine Hospital, Jilin, 741 images from 154 patients; and the Weihai Municipal Hospital, Shandong, 11 039 images from 1420 patients). All individuals with suspected thyroid cancer after clinical examination in the validation sets had pathological examination. We also compared the specificity and sensitivity of the DCNN model with the performance of six skilled thyroid ultrasound radiologists on the three validation sets. Between Jan 1, 2012, and March 28, 2018, ultrasound images for the four study cohorts were obtained. The model achieved high performance in identifying thyroid cancer patients in the validation sets tested, with area under the curve values of 0·947 (95% CI 0·935–0·959) for the Tianjin internal validation set, 0·912 (95% CI 0·865–0·958) for the Jilin external validation set, and 0·908 (95% CI 0·891–0·925) for the Weihai external validation set. The DCNN model also showed improved performance in identifying thyroid cancer patients versus skilled radiologists. For the Tianjin internal validation set, sensitivity was 93·4% (95% CI 89·6–96·1) versus 96·9% (93·9–98·6; p=0·003) and specificity was 86·1% (81·1–90·2) versus 59·4% (53·0–65·6; p<0·0001). For the Jilin external validation set, sensitivity was 84·3% (95% CI 73·6–91·9) versus 92·9% (84·1–97·6; p=0·048) and specificity was 86·9% (95% CI 77·8–93·3) versus 57·1% (45·9–67·9; p<0·0001). For the Weihai external validation set, sensitivity was 84·7% (95% CI 77·0–90·7) versus 89·0% (81·9–94·0; p=0·25) and specificity was 87·8% (95% CI 81·6–92·5) versus 68·6% (60·7–75·8; p<0·0001). The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists. The improved technical performance of the DCNN model warrants further investigation as part of randomised clinical trials. The Program for Changjiang Scholars and Innovative Research Team in University in China, and National Natural Science Foundation of China.

Rett syndrome (RTT) and MECP2 duplication syndrome, a subtype of autism spectrum disorder (ASD), are neurodevelopmental disorders caused by MeCP2 loss and gain of function, respectively. While MeCP2 is known to regulate transcription through its interaction with methylated DNA and chromatin-associated factors such as topoisomerase IIβ (TOP2β), the downstream transcriptional consequences of MeCP2 dosage imbalance remain partially characterized. Here, we present a transcriptome-centered analysis of mouse primary cortical neurons subjected to MeCP2 knockdown (KD) or overexpression (OE), which model RTT and ASD-like conditions in parallel. Using a robust computational pipeline integrating generalized linear models with quasi-likelihood F-tests and Magnitude–Altitude Scoring (GLMQL-MAS), we identified differentially expressed genes (DEGs) in KD and OE relative to wild-type (WT) neurons. This study represents a computational analysis of secondary transcriptomic data aimed at nominating candidate genes for future experimental validation. Gene Ontology enrichment revealed both shared and condition-specific biological processes, with KD uniquely affecting neurodevelopmental and stress-response pathways, and OE perturbing extracellular matrix, calcium signaling, and neuroinflammatory processes. To prioritize robust and disease-relevant targets, we applied Cross-MAS and further filtered DEGs by correlation with MeCP2 expression and regulation directional consistency. This yielded 16 high-confidence dosage-sensitive genes that were capable of classifying WT, KD, and OE samples with 100% accuracy using PCA and logistic regression. Among these, RTT-associated candidates such as Plcb1, Gpr161, Mknk2, Rgcc, and Abhd6 were linked to disrupted synaptic signaling and neurogenesis, while ASD-associated genes, including Aim2, Mcm6, Pcdhb9, and Cbs, implicated neuroinflammation and metabolic stress. These findings establish a compact and mechanistically informative set of MeCP2-responsive genes, which enhance our understanding of transcriptional dysregulation in RTT and ASD and nominate molecular markers for future functional validation and therapeutic exploration.

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.

This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15 , OAS1 , IFI44 , IFI27 , IFIT2 , IFIT3 , IFI44L , MX1 , MX2 , OAS2 , RSAD2 , and OASL ) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5 , USP18 , and DDX60 , which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures.

Breast cancer, a widespread and life-threatening disease, necessitates precise diagnostic tools for improved patient outcomes. Tumor-Infiltrating Lymphocytes (TILs), reflective of the immune response against cancer cells, are pivotal in understanding breast cancer behavior. However, inter-observer variability in TILs scoring methods poses challenges to reliable assessments. This study introduces a novel and interpretable proof-of-principle framework comprising two innovative inter-observer agreement measures. The first method, Boundary-Weighted Fleiss’ Kappa (BWFK), addresses tissue segmentation predictions, focusing on mitigating disagreements along tissue boundaries. BWFK enhances the accuracy of stromal segmentation, providing a nuanced assessment of inter-observer agreement. The second proposed method, the Distance Based Cell Agreement Algorithm (DBCAA), eliminates the need for ground truth annotations in cell detection predictions. This innovative approach offers versatility across histopathological analyses, overcoming data availability challenges. Both methods were applied to assess inter-observer agreement using a clinical image dataset consisting of 25 images of invasive ductal breast carcinoma tissue, each annotated by four pathologists, serving as a proof-of-principle. Experimental investigations demonstrated that the BWFK method yielded gains of up to 32% compared to the standard Fleiss’ Kappa model. Furthermore, a procedure for conducting clinical validations of artificial intelligence (AI) based cell detection methods was elucidated. Thoroughly validated on a clinical dataset, the framework contributes to standardized, reliable, and interpretable inter-observer agreement assessments. This study is the first examination of inter-observer agreements in stromal segmentation and lymphocyte detection for the TILs scoring problem. The study emphasizes the potential impact of these measures in advancing histopathological image analysis, fostering consensus in TILs scoring, and ultimately improving breast cancer diagnostics and treatment planning. The source code and implementation guide for this study are accessible on our GitHub page, and the full clinical dataset is available for academic and research purposes on Kaggle.

In this study, we used a three-dimensional airway “organ tissue equivalent” (OTE) model at an air-liquid interface (ALI) to mimic human airways. We investigated the effects of three viruses (Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3) on this model, incorporating various control conditions for data integrity. Our primary objective was to assess gene expression using the NanoString platform in OTE models infected with these viruses at 24- and 72-hour intervals, focusing on 773 specific genes. To enhance the comprehensiveness of our analysis, we introduced a novel algorithm, namely MAS (Magnitude-Altitude Score). This innovative approach uniquely combines biological significance, as indicated by fold changes in gene expression, with statistical rigor, as represented by adjusted p-values. By incorporating both dimensions, MAS ensures that the genes identified as differentially expressed are not mere statistical artifacts but hold genuine biological relevance, providing a more holistic understanding of the airway tissue response to viral infections. Our results unveiled distinct patterns of gene expression in response to viral infections. At 24 hours post-IAV infection, a robust interferon-stimulated gene (ISG) response was evident, marked by the upregulation of key genes including IFIT2, RSAD2, IFIT3, IFNL1, IFIT1, IFNB1, ISG15, OAS2, OASL, and MX1, collectively highlighting a formidable antiviral defense. MPV infection at the same time point displayed a dual innate and adaptive immune response, with highly expressed ISGs, immune cell recruitment signaled by CXCL10, and early adaptive immune engagement indicated by TXK and CD79A. In contrast, PIV3 infection at 24 hours triggered a transcriptional response dominated by ISGs, active immune cell recruitment through CXCL10, and inflammation modulation through OSM. The picture evolved at 72 hours post-infection. For IAV, ISGs and immune responses persisted, suggesting a sustained impact. MPV infection at this time point showed a shift towards IL17A and genes related to cellular signaling and immune responses, indicating adaptation to the viral challenge over time. In the case of PIV3, the transcriptional response remained interferon-centric, indicating a mature antiviral state. Our analysis underscored the pivotal role of ISGs across all infections and time points, emphasizing their universal significance in antiviral defense. Temporal shifts in gene expression indicative of adaptation and fine-tuning of the immune response. Additionally, the identification of shared and unique genes unveiled host-specific responses to specific pathogens. IAV exerted a sustained impact on genes from the initial 24 hours, while PIV3 displayed a delayed yet substantial genomic response, suggestive of a gradual and nuanced strategy.

A majority (>80%) of maternal deaths in the United States are preventable. Using machine learning (ML) models that are generated from electronic medical records (EMRs) may be a promising approach to predict the risk of adverse maternal outcomes and enable proactive intervention to prevent maternal mortality. Current evidence syntheses of such ML approaches either focus only on specific maternal outcomes, aspects other than risk prediction, or do not consider the full pipeline of studies from the development to implementation in clinical practice. The goal of this scoping review is to document evidence for the use of ML models for predicting the risk of maternal morbidity and mortality outcomes (research objective [RO1]), the translation of such models into applications for clinical use by providers (RO2), and factors associated with the implementation of clinical applications in practice (RO3). The review was limited to studies in health care settings, using data from EMRs. A detailed search string was developed in collaboration with a health sciences librarian and implemented on February 20, 2023, on PubMed, CINAHL Plus, Scopus, Embase, and IEEE Xplore. Two reviewers independently reviewed titles and abstracts for inclusion, and a third reviewer resolved conflicts. Only full-length journal articles published in English were included. Studies using non-EMR data exclusively were excluded. Two reviewers independently reviewed full texts for inclusion, and a third reviewer resolved conflicts. A structured template was used for data extraction, and findings were summarized descriptively. From 480 deduplicated studies identified from the search, 142 studies were included for full-text review, and 39 studies were included in the review. More than half of the included studies were conducted in 2022, and 34 studies were from just 3 countries (United States, China, and Israel). More studies focused on identifying the risk of pregnancy and delivery outcomes compared with postpartum outcomes. The top 3 most common outcomes for risk prediction were cardiovascular risks and hypertensive disorders of pregnancy (9 studies), gestational diabetes (7 studies), and postpartum hemorrhage (6 studies). Data were labeled with computable phenotypes in 30 studies, and the most often used method in ML models was boosting methods (18 studies). The most common metric used to assess model performance was area under the precision-recall curve (AUPRC; 33 studies). No studies described clinical applications of ML models for providers (RO2) or associated implementation factors (RO3). Key recommendations for future research and practice include expanding efforts to study maternal morbidity and mortality outcomes in the postpartum period, increasing transparency and reproducibility of studies through use of reporting checklists, and expanding efforts to implement ML models in clinical practice.

Background: Postpartum depression is a common mental health condition that can occur up to one year after childbirth. Recent studies have increasingly used machine learning techniques to predict its occurrence; however, few have comprehensively explored the use of electronic health record data, particularly in tertiary care settings where such data can be fragmented. Methods: We analyzed electronic health record data from 12,284 women who delivered at The Birth Center at Atrium Health Wake Forest Baptist Medical Center, excluding those with missing data or no prenatal or postpartum visits. To define the target variable, we examined different combinations of depression screening tools (Edinburgh Postnatal Depression Scale and Patient Health Questionnaire-9), along with diagnosis codes specific to postpartum depression. We then trained a random forest classification model to predict postpartum depression. Results: The model achieved an area under the receiver operating characteristic curve of 0.733 ± 0.008, which is comparable to previous studies. Adding socioeconomic features from census tract data did not improve predictive performance, underscoring the importance of individual-level data. Incorporating national survey data, such as the Pregnancy Risk Assessment Monitoring System, also did not improve performance due to limited overlap in data features. Interestingly, model performance was slightly lower among Hispanic patients (area under the curve = 0.713 ± 0.040), although this difference was not statistically significant (p = 0.17), likely due to the small sample size. A similar, but statistically significant trend was observed in the larger national survey dataset (area under the curve = 0.699 ± 0.019 for Hispanic patients versus 0.735 ± 0.010 for White patients, p < 0.01). Conclusions: While our model demonstrates moderate predictive capability, further validation and prospective testing are needed before clinical implementation. This work also identified an optimal approach for digital phenotyping postpartum depression in electronic health record data and highlighted key gaps in data quality and completeness. These findings emphasize the importance of robust data when developing predictive models for real-world clinical use.

In a prospective study, we examined the recovery trajectory of patients with lower extremity fractures to better understand the healing process in the absence of complications. Using a chest-mounted inertial measurement unit (IMU) device for gait analysis and collecting patient-reported outcome measures, we focused on 12 key gait variables, including Mean Leg Lift Acceleration, Stance Time, and Body Orientation. We employed a linear mixed model (LMM) to analyze these variables over time, incorporating both fixed and random effects to account for individual differences and the time since injury. This model also adjusted for varying intervals between assessments. Our study provided insights into gait recovery across different fracture types using data from 318 patients who experienced no complications or readmissions during their recovery. Through LMM analysis, we found that Tibia-Distal fractures demonstrated the fastest recovery, particularly in terms of mobility and strength. Tibia-Proximal fractures showed balanced improvements in both mobility and stability, suggesting that rehabilitation should target both strength and balance. Femur fractures exhibited varied recovery, with Diaphyseal fractures showing clear improvements in stability, while Distal fractures reflected gains in limb strength but with some variability in stability. To examine patients with readmissions, we conducted a Chi-squared test of independence to determine whether there was a relationship between fracture type and readmission rates, revealing a significant association (p < 0.001). Pelvis fractures had the highest readmission rates, while Tibia-Diaphyseal and Tibia-Distal fractures were more prone to infections, highlighting the need for enhanced infection control strategies. Femur fractures showed moderate readmission and infection rates, indicating a mixed risk profile. In conclusion, our findings emphasize the importance of fracture-specific rehabilitation strategies, focusing on infection prevention and individualized treatment plans to optimize recovery outcomes.