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Background: Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistical models have been proposed for TPE diagnosis, these methods are typically limited by complexities in data processing and difficulties in feature integration. Therefore, this study aims to develop a diagnostic model for TPE using ChatGPT-4, a large language model (LLM), and compare its performance with traditional logistic regression and machine learning models. By highlighting the advantages of LLMs in handling complex clinical data, identifying interrelationships between features, and improving diagnostic accuracy, this study seeks to provide a more efficient and precise solution for the early diagnosis of TPE. Methods: We conducted a cross-sectional study, collecting clinical data from 109 TPE and 54 non-TPE patients for analysis, selecting 73 features from over 600 initial variables. The performance of the LLM was compared with logistic regression and machine learning models (k-Nearest Neighbors, Random Forest, Support Vector Machines) using metrics like area under the curve (AUC), F1 score, sensitivity, and specificity. Results: The LLM showed comparable performance to machine learning models, outperforming logistic regression in sensitivity, specificity, and overall diagnostic accuracy. Key features such as adenosine deaminase (ADA) levels and monocyte percentage were effectively integrated into the model. We also developed a Python package (https://pypi.org/project/tpeai/) for rapid TPE diagnosis based on clinical data. Conclusions: The LLM-based model offers a non-surgical, accurate, and cost-effective method for early TPE diagnosis. The Python package provides a user-friendly tool for clinicians, with potential for broader use. Further validation in larger datasets is needed to optimize the model for clinical application. [ABSTRACT FROM AUTHOR]

Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. We obtained T1-weighted and resting-state functional MRI data from 50 patients with episodic migraine and 50 age- and sex-matched healthy controls, with follow-up data collected after one year. Morphological features, including cortical thickness, curvature, and sulcal depth, and functional connectivity features, such as low-dimensional representation of functional connectivity (gradient), degree centrality, and betweenness centrality, were utilized. We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. By testing the models with varying feature combinations, penalty terms, and spatial granularities within a strict cross-validation framework, we found that the combination of curvature, sulcal depth, cortical thickness, and functional gradient achieved a robust classification performance. The model performance was assessed using the test dataset and achieved 87% accuracy and 0.94 area under the curve (AUC) at distinguishing migraine patients from healthy controls, with 85%, 0.97 and 84%, 0.93 during the interictal and ictal/peri-ictal phases, respectively. When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians. [ABSTRACT FROM AUTHOR]

Background: Ischemic stroke (IS) is a significant global health issue, causing high rates of morbidity, mortality, and disability. Since conventional Diagnosis methods for IS have several shortcomings. It is critical to create new Diagnosis models in order to enhance existing Diagnosis approaches. Methods: We utilized gene expression data from the Gene Expression Omnibus (GEO) databases GSE16561 and GSE22255 to identify differentially expressed genes (DEGs) associated with IS. DEGs analysis using the Limma package, as well as GO and KEGG enrichment analyses, were performed. Furthermore, PPI networks were constructed using DEGs from the String database, and Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification. Use the GSE58294 dataset to evaluate the effectiveness of the scoring model on healthy controls and ischemic stroke samples. The effectiveness of the scoring model was evaluated through AUC analysis, and CIBERSORT analysis was conducted to estimate the immune landscape and explore the correlation between gene expression and immune cell infiltration. Results: A total of 26 significant DEGs associated with IS were identified. Metascape analysis revealed enriched biological processes and pathways related to IS. 10 key DEGs (ARG1, DUSP1, F13A1, NFIL3, CCR7, ADM, PTGS2, ID3, FAIM3, HLA-DQB1) were selected using Random Forest and artificial neural network models. The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy. Additionally, the analysis of the immune landscape demonstrated elevated immune-related networks in IS patients compared to healthy controls. Conclusion: The study uncovers the involvement of specific genes and immune cells in the pathogenesis of IS, suggesting their importance in understanding and potentially targeting the disease. [ABSTRACT FROM AUTHOR]

Summary The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole‐slide images (deep learning [DL] model) and clinical parameters (clinical model). Thousand and fifty‐one MPN and non‐MPN patients were divided into the training, internal testing and one internal and two external validation cohorts (the combined validation cohort). In the combined validation cohort, fusion model achieved higher areas under curve (AUCs) than clinical or DL model or both for MPNs and subtype identification. Compared with haematopathologists with different experience, clinical model achieved AUC which was comparable to seniors and higher than juniors (p = 0.0208) for polycythaemia vera. The AUCs of fusion model were comparable to seniors and higher than juniors for essential thrombocytosis (p = 0.0141), prefibrotic primary myelofibrosis (p = 0.0085) and overt primary myelofibrosis (p = 0.0330) identification. In conclusion, the performances of our proposed models are equivalent to senior haematopathologists and better than juniors, providing a new perspective on the utilization of DL algorithms in MPN morphological assessment. [ABSTRACT FROM AUTHOR]

Background: Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder. Purpose: To develop a cascaded deep semantic‐radiomic‐clinical (DRC) model for diagnosing PAS and its subtypes based on T2‐weighted MRI. Study Type: Retrospective. Population: 361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122). Field Strength/Sequence: Coronal T2‐weighted sequence at 1.5 T and 3.0 T. Assessment: Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes). Statistical Tests: AUC, ACC, Student's t‐test, the Mann–Whitney U test, chi‐squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer–Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P < 0.05 indicated a significant difference. Results: In PAS diagnosis, the DRC‐1 outperformed than other models (AUC = 0.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC‐2 model performed similarly with radiologists (P = 0.773 and 0.579 in the internal testing cohort and P = 0.429 and 0.874 in the external testing cohort, respectively). Data Conclusion: The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Abstract Background Acute myeloid leukemia (AML) is a rapidly progressing blood cancer. The prognosis of AML can be challenging, emphasizing the need for ongoing research and innovative approaches to improve outcomes in individuals affected by this formidable hematologic malignancy. Methods In this study, we used single-cell RNA sequencing (scRNA-seq) from AML patients to investigate the impact of L-glutamine metabolism-related genes on disease progression. Results Our analysis revealed increased glutamine-related activity in CD34 + pre-B cells, suggesting a potential regulatory role in tumorigenesis and AML progression. Furthermore, intercellular communication analysis revealed a significant signaling pathway involving macrophage migration inhibitory factor signaling through CD74 + CD44 within CD34 + pre-B cells, which transmit signals to pre-dendritic cells and monocytes. Ligands for this pathway were predominantly expressed in stromal cells, naïve T cells, and CD34 + pre-B cells. CD74, the pertinent receptor, was predominantly detected in a variety of cellular components, including stromal cells, pre-dendritic cells, plasmacytoid dendritic cells, and hematopoietic progenitors. The study’s results provide insights into the possible interplay among these cell types and their collective contribution to the pathogenesis of AML. Moreover, we identified 10 genes associated with AML prognosis, including CCL5, CD52, CFD, FABP5, LGALS1, NUCB2, PSAP, S100A4, SPINK2, and VCAN. Among these, CCL5 and CD52 have been implicated in AML progression and are potential therapeutic targets. Conclusions This thorough examination of AML biology significantly deepens our grasp of the disease and presents pivotal information that could guide the creation of innovative treatment strategies for AML patients.

Abstract Background Mild autonomous cortisol secretion (MACS) accounts for a significant proportion of adrenal incidentaloma. Current endocrinological screening tests for MACS are complex, particularly in areas with limited medical resources. This study aimed to develop a diagnostic tool based on leukocyte-related parameters to differentiate between MACS and non-functioning adrenal adenoma (NFA). Methods Inthis retrospective case-control study, propensity score-matching was used to select 567 patients from a cohort of 1108 patients (201 MACS, 907 NFA). External validation cohort included 52MACS and 48 NFA from two hospitals, which did not overlap with the modeling cohort patients. Leukocyte-related parameters were evaluated, and the diagnostic efficacy of each parameter was assessed by calculating Youden’s J index (J) and the area under the curve (AUC). The study population was divided into training and testing samples using a 10-fold cross-validation method. Machine learning (ML) and classification and regression tree (CART) model were established. Results After propensity score matching, 567 patients were enrolled, including 197 MACS and 370 NFA. With the exception of basophil percentage, all other parameters differed significantly between the two groups. Lymphocyte count, lymphocyte percentage, eosinophils count, eosinophils percentage, and basophil percentage were lower in the MACS group compared to the NFA group. Eosinophils percentage demonstrated the highest AUC (0.650), with a sensitivity of 51.3% and specificity of 73.2%. The ML model, based on multiple parameters,exhibited better performance in diagnosing MACS (sensitivity 76%, specificity 77.4%, and AUC 0.818). A clinically usable CART model achieved an AUC of 0.872, with a sensitivity of 95% and a specificity of 75.7%. In the validation cohort, the prediction accuracy of the ML model and the CART model were 0.784 and 0.798, respectively. Conclusion TheCART diagnostic model, constructed based on leukocyte-related parameters, could assist clinicians in distinguishing between MACS and NFA.

Lingling Ding,1,&ast; Guowan Zheng,1– 3,&ast; Aoni Zhou,4 Fahuan Song,1– 3 Lei Zhu,5 Yefeng Cai,6 Yehao Guo,1 Tebo Hua,7 Yunye Liu,1 Wenli Ma,1 Yiqun Hu,1– 3 Yawen Guo,1– 3 Chuanming Zheng1– 3 1Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, 310000, People’s Republic of China; 2Zhejiang Provincial Clinical Research Center for Malignant Tumor, Hangzhou, Zhejiang, 310000, People’s Republic of China; 3Zhejiang Key Laboratory of Precision Medicine Research on Head & Neck Cancer, Hangzhou, Zhejiang, 310000, People’s Republic of China; 4Hangzhou Normal University, Hangzhou, Zhejiang, 311121, People’s Republic of China; 5Department of Thyroid Surgery, The Fifth Hospital Affiliated to Wenzhou Medical University, Lishui Central Hospital, Lishui, Zhejiang, 323000, People’s Republic of China; 6Department of Thyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China; 7Department of Thyroid Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, 315000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yawen Guo; Chuanming Zheng, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, No. 158, Shangtang Road, Hangzhou, Zhejiang, People’s Republic of China, Email mingdoc@163.com; gyw20072644@126.comBackground: The incidence of papillary thyroid cancer (PTC) has been increasing annually; however, early diagnosis can improve patient outcomes. Pyroptosis is a programmed cell death modality that has received considerable attention recently. However, no studies have reported using pyroptosis-related genes in PTC diagnosis.Methods: Analyzed 33 pyroptosis-related genes in PTC transcriptome data from the Gene Expression Omnibus database. Subsequently, used the Least Absolute Shrinkage and Selection Operator (LASSO) model to construct a PTC molecular diagnostic model. Furthermore, confirmed differences in the expression of five genes between PTC and non-tumor tissues using immunohistochemistry. Collected 338 PTC and control samples to construct a five-gene PTC diagnostic model, which was then validated using a training set and underwent correlation analysis with immune cell infiltration. Additionally, validated the biological functions of the core gene NOD1 in vitro.Results: The five-gene PTC diagnostic model demonstrated good diagnostic value for PTC. Moreover, identified three reliable subtypes of pyroptosis and found that NOD1 is involved in tumor-suppressive microenvironment formation. Notably, patients with high NOD1 expression had lower Progression-Free Survival (PFS). Additionally, NOD1 expression was positively correlated with immune markers such as CD47, CD68, CD3, and CD8. Lastly, inhibiting NOD1 showed significant anti-PTC activity in vitro.Conclusion: Our results suggest that pyroptosis-related genes can be used for PTC diagnosis, and NOD1 could be a promising therapeutic target.Keywords: pyroptosis-related genes, papillary thyroid cancer, PTC, immune cell infiltration, bioinformatic, diagnosis model

Background: Acute myeloid leukemia (AML) is a rapidly progressing blood cancer. The prognosis of AML can be challenging, emphasizing the need for ongoing research and innovative approaches to improve outcomes in individuals affected by this formidable hematologic malignancy. Methods: In this study, we used single-cell RNA sequencing (scRNA-seq) from AML patients to investigate the impact of L-glutamine metabolism-related genes on disease progression. Results: Our analysis revealed increased glutamine-related activity in CD34 + pre-B cells, suggesting a potential regulatory role in tumorigenesis and AML progression. Furthermore, intercellular communication analysis revealed a significant signaling pathway involving macrophage migration inhibitory factor signaling through CD74 + CD44 within CD34 + pre-B cells, which transmit signals to pre-dendritic cells and monocytes. Ligands for this pathway were predominantly expressed in stromal cells, naïve T cells, and CD34 + pre-B cells. CD74, the pertinent receptor, was predominantly detected in a variety of cellular components, including stromal cells, pre-dendritic cells, plasmacytoid dendritic cells, and hematopoietic progenitors. The study's results provide insights into the possible interplay among these cell types and their collective contribution to the pathogenesis of AML. Moreover, we identified 10 genes associated with AML prognosis, including CCL5, CD52, CFD, FABP5, LGALS1, NUCB2, PSAP, S100A4, SPINK2, and VCAN. Among these, CCL5 and CD52 have been implicated in AML progression and are potential therapeutic targets. Conclusions: This thorough examination of AML biology significantly deepens our grasp of the disease and presents pivotal information that could guide the creation of innovative treatment strategies for AML patients. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is the third leading cause of cancer death worldwide. In the field of medicine, machine learning is widely used in genetic data mining and the construction of diagnostic models. This study proposed an intelligent model DERFS-XGBoost for rapid and accurate diagnosis of GC based on gene expression data. Firstly, the data of GC were collected and preprocessed. Secondly, ANOVA, t-test and fold chang (FC) were used to select genes that had significant differentially expressed genes (DEGs), and random forest (RF) was used to calculate their importance, and then sequential forward selection (SFS) was used to obtain the optimal feature subset. Finally, XGBoost was used for classification after synthetic minority oversampling technique (SMOTE) balanced between tumor and normal samples. In order to objectively evaluate the results, the 10-fold cross-validation and 10 repeated experiments were used in the experiment, and the average value of the evaluation indexes was used to evaluate the classification effect. Based on the experiment, DERFS-XGBoost model accuracy rate was 97.6%, precision was 100%, the recall rate was 97.3%, F1 was 99%, and the area under the ROC receiver operating characteristic curve AUC was 98.7%. The DERFS-XGBoost model has new characteristics which are different from existing diagnostic models, and has achieved a high classification effect with a small number of genes in comparison tests, which provides a new method and basis for the diagnosis of GC. [ABSTRACT FROM AUTHOR]

BackgroundLiver ischemia-reperfusion injury (LIRI) is a critical condition after liver transplantation. Understanding the role of immunogenic cell death (ICD) may provide insights into its diagnosis and potential therapeutic targets.MethodsDifferentially expressed genes (DEGs) between LIRI and normal samples were identified, and pathway enrichment analyses were performed, followed by immune infiltration assessment through the CIBERSORT method. The consensus clustering analysis was conducted to separate LIRI clusters and single-sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the distinct immune states between clusters. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify hub genes associated with ICD. To establish diagnostic models, four machine learning techniques, including Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Generalized Linear Models (GLM), were applied to filter gene sets. The receiver operating characteristic (ROC) curves were utilized to assess the performance of the models.ResultsPathway enrichment results revealed significant involvement of cytokines and chemokines among DEGs of LIRI. Immune infiltration analysis indicated higher levels of specific immune functions in Cluster 2 compared to Cluster 1. WGCNA identified significant modules linked to LIRI with strong correlations between module membership and gene significance. The RF and SVM machine learning algorithms were finally chosen to construct the models. Both demonstrated high predictive accuracy for diagnosing LIRI not only in training cohort GSE151648 but also in validation cohorts GSE23649 and GSE15480.ConclusionsThe study highlights the pivotal roles of ICD-related genes in LIRI, providing diagnosis models with potential clinical applications for early detection and intervention strategies against LIRI.

BackgroundIschemic stroke (IS) is a significant global health issue, causing high rates of morbidity, mortality, and disability. Since conventional Diagnosis methods for IS have several shortcomings. It is critical to create new Diagnosis models in order to enhance existing Diagnosis approaches.MethodsWe utilized gene expression data from the Gene Expression Omnibus (GEO) databases GSE16561 and GSE22255 to identify differentially expressed genes (DEGs) associated with IS. DEGs analysis using the Limma package, as well as GO and KEGG enrichment analyses, were performed. Furthermore, PPI networks were constructed using DEGs from the String database, and Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification. Use the GSE58294 dataset to evaluate the effectiveness of the scoring model on healthy controls and ischemic stroke samples. The effectiveness of the scoring model was evaluated through AUC analysis, and CIBERSORT analysis was conducted to estimate the immune landscape and explore the correlation between gene expression and immune cell infiltration.ResultsA total of 26 significant DEGs associated with IS were identified. Metascape analysis revealed enriched biological processes and pathways related to IS. 10 key DEGs (ARG1, DUSP1, F13A1, NFIL3, CCR7, ADM, PTGS2, ID3, FAIM3, HLA-DQB1) were selected using Random Forest and artificial neural network models. The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy. Additionally, the analysis of the immune landscape demonstrated elevated immune-related networks in IS patients compared to healthy controls.ConclusionThe study uncovers the involvement of specific genes and immune cells in the pathogenesis of IS, suggesting their importance in understanding and potentially targeting the disease.

Abstract Background Endometriosis (EMs) is an enigmatic disease of yet-unknown pathogenesis. Disulfidptosis, a novel identified form of programmed cell death resulting from disulfide stress, stands a chance of treating diverse ailments. However, the potential roles of disulfidptosis-related genes (DRGs) in EMs remain elusive. This study aims to thoroughly explore the key disulfidptosis genes involved in EMs, and probe novel diagnostic markers and candidate therapeutic compounds from the aspect of disulfidptosis based on bioinformatics analysis, machine learning, and animal experiments. Results Enrichment analysis on key module genes and differentially expressed genes (DEGs) of eutopic and ectopic endometrial tissues in EMs suggested that EMs was closely related to disulfidptosis. And then, we obtained 20 and 16 disulfidptosis-related DEGs in eutopic and ectopic endometrial tissue, respectively. The protein-protein interaction (PPI) network revealed complex interactions between genes, and screened nine and ten hub genes in eutopic and ectopic endometrial tissue, respectively. Furthermore, immune infiltration analysis uncovered distinct differences in the immunocyte, human leukocyte antigen (HLA) gene set, and immune checkpoints in the eutopic and ectopic endometrial tissues when compared with health control. Besides, the hub genes mentioned above showed a close correlation with the immune microenvironment of EMs. Furthermore, four machine learning algorithms were applied to screen signature genes in eutopic and ectopic endometrial tissue, including the binary logistic regression (BLR), the least absolute shrinkage and selection operator (LASSO), the support vector machine-recursive feature elimination (SVM-RFE), and the extreme gradient boosting (XGBoost). Model training and hyperparameter tuning were implemented on 80% of the data using a ten-fold cross-validation method, and tested in the testing sets which determined the excellent diagnostic performance of these models by six indicators (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Accuracy, and Area Under Curve). And seven eutopic signature genes (ACTB, GYS1, IQGAP1, MYH10, NUBPL, SLC7A11, TLN1) and five ectopic signature genes (CAPZB, CD2AP, MYH10, OXSM, PDLIM1) were finally identified based on machine learning. The independent validation dataset also showed high accuracy of the signature genes (IQGAP1, SLC7A11, CD2AP, MYH10, PDLIM1) in predicting EMs. Moreover, we screened 12 specific compounds for EMs based on ectopic signature genes and the pharmacological impact of tretinoin on signature genes was further verified in the ectopic lesion in the EMs murine model. Conclusion This study verified a close association between disulfidptosis and EMs based on bioinformatics analysis, machine learning, and animal experiments. Further investigation on the biological mechanism of disulfidptosis in EMs is anticipated to yield novel advancements for searching for potential diagnostic biomarkers and revolutionary therapeutic approaches in EMs.

Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela‐like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross‐validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age‐ and sex‐matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real‐time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring. [ABSTRACT FROM AUTHOR]

Background: Endometriosis (EMs) is an enigmatic disease of yet-unknown pathogenesis. Disulfidptosis, a novel identified form of programmed cell death resulting from disulfide stress, stands a chance of treating diverse ailments. However, the potential roles of disulfidptosis-related genes (DRGs) in EMs remain elusive. This study aims to thoroughly explore the key disulfidptosis genes involved in EMs, and probe novel diagnostic markers and candidate therapeutic compounds from the aspect of disulfidptosis based on bioinformatics analysis, machine learning, and animal experiments. Results: Enrichment analysis on key module genes and differentially expressed genes (DEGs) of eutopic and ectopic endometrial tissues in EMs suggested that EMs was closely related to disulfidptosis. And then, we obtained 20 and 16 disulfidptosis-related DEGs in eutopic and ectopic endometrial tissue, respectively. The protein-protein interaction (PPI) network revealed complex interactions between genes, and screened nine and ten hub genes in eutopic and ectopic endometrial tissue, respectively. Furthermore, immune infiltration analysis uncovered distinct differences in the immunocyte, human leukocyte antigen (HLA) gene set, and immune checkpoints in the eutopic and ectopic endometrial tissues when compared with health control. Besides, the hub genes mentioned above showed a close correlation with the immune microenvironment of EMs. Furthermore, four machine learning algorithms were applied to screen signature genes in eutopic and ectopic endometrial tissue, including the binary logistic regression (BLR), the least absolute shrinkage and selection operator (LASSO), the support vector machine-recursive feature elimination (SVM-RFE), and the extreme gradient boosting (XGBoost). Model training and hyperparameter tuning were implemented on 80% of the data using a ten-fold cross-validation method, and tested in the testing sets which determined the excellent diagnostic performance of these models by six indicators (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Accuracy, and Area Under Curve). And seven eutopic signature genes (ACTB, GYS1, IQGAP1, MYH10, NUBPL, SLC7A11, TLN1) and five ectopic signature genes (CAPZB, CD2AP, MYH10, OXSM, PDLIM1) were finally identified based on machine learning. The independent validation dataset also showed high accuracy of the signature genes (IQGAP1, SLC7A11, CD2AP, MYH10, PDLIM1) in predicting EMs. Moreover, we screened 12 specific compounds for EMs based on ectopic signature genes and the pharmacological impact of tretinoin on signature genes was further verified in the ectopic lesion in the EMs murine model. Conclusion: This study verified a close association between disulfidptosis and EMs based on bioinformatics analysis, machine learning, and animal experiments. Further investigation on the biological mechanism of disulfidptosis in EMs is anticipated to yield novel advancements for searching for potential diagnostic biomarkers and revolutionary therapeutic approaches in EMs. [ABSTRACT FROM AUTHOR]

Abstract Objective To develop and validate a diagnosis model to inform risk stratified decisions for idiopathic pulmonary fibrosis patients experiencing acute exacerbations (AE-IPF). Methods In this retrospective cohort study performed from 1 January 2016 to 31 December 2022, we used data from the West China Hospital of Sichuan University for model development and validation. Blood test results and the underlying diseases of patients were collected through the HIS system and LIS system. An algorithm for filtering candidate variables based on least absolute shrinkage and selection operator (LASSO) regression. Logistic regression was performed to develop the risk model. Multiple imputation handled missing predictor data. Model performance was assessed through calibration and diagnostic odds ratio. Results 311 and 133 participants were included in the development and validation cohorts, respectively. 3 candidate predictors (29 parameters) were included. A logistic regression analysis revealed that dyspnea, percentage of CD4+ T-lymphocytes, and percentage of monocytes are independent risk factors for AE-IPF. Nomographic model was constructed using these independent risk factors, and the C-index was 0.69. For internal validation, the C-index was 0.69, and that indicated good accuracy. Diagnostic odds ratio was 5.40. Meanwhile, in mild, moderate, and severe subgroups, AE positivity rates were 0.37, 0.47, and 0.81, respectively. The diagnostic model can classify patients with AE-IPF into different risk classes based on dyspnea, percentage of CD4+ T-lymphocytes, and percentage of monocytes. Conclusion A diagnosis model was developed and validated that used information collected from HIS system and LIS system and may be used to risk stratify idiopathic pulmonary fibrosis patients experiencing acute exacerbations.

Background. Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells. Methods. We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves. Results. We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta Tcells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set. Conclusion. Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a diagnosis model to inform risk stratified decisions for idiopathic pulmonary fibrosis patients experiencing acute exacerbations (AE-IPF). Methods: In this retrospective cohort study performed from 1 January 2016 to 31 December 2022, we used data from the West China Hospital of Sichuan University for model development and validation. Blood test results and the underlying diseases of patients were collected through the HIS system and LIS system. An algorithm for filtering candidate variables based on least absolute shrinkage and selection operator (LASSO) regression. Logistic regression was performed to develop the risk model. Multiple imputation handled missing predictor data. Model performance was assessed through calibration and diagnostic odds ratio. Results: 311 and 133 participants were included in the development and validation cohorts, respectively. 3 candidate predictors (29 parameters) were included. A logistic regression analysis revealed that dyspnea, percentage of CD4+ T-lymphocytes, and percentage of monocytes are independent risk factors for AE-IPF. Nomographic model was constructed using these independent risk factors, and the C-index was 0.69. For internal validation, the C-index was 0.69, and that indicated good accuracy. Diagnostic odds ratio was 5.40. Meanwhile, in mild, moderate, and severe subgroups, AE positivity rates were 0.37, 0.47, and 0.81, respectively. The diagnostic model can classify patients with AE-IPF into different risk classes based on dyspnea, percentage of CD4+ T-lymphocytes, and percentage of monocytes. Conclusion: A diagnosis model was developed and validated that used information collected from HIS system and LIS system and may be used to risk stratify idiopathic pulmonary fibrosis patients experiencing acute exacerbations. [ABSTRACT FROM AUTHOR]

Background To date, no study has systematically explored the potential role of serum metabolites and lipids in the diagnosis of small cell lung cancer (SCLC). Therefore, we aimed to conduct a case-cohort study that included 191 cases of SCLC, 91 patients with lung adenocarcinoma, 82 patients with squamous cell carcinoma, and 97 healthy controls. Methods Metabolomics and lipidomics were applied to analyze different metabolites and lipids in the serum of these patients. The SCLC diagnosis model (d-model) was constructed using an integrated machine learning technology and a training cohort (n = 323) and was validated in a testing cohort (n =138). Results Eight metabolites, including 1-mristoyl-sn-glycero-3-phosphocholine, 16b-hydroxyestradiol, 3-phosphoserine, cholesteryl sulfate, D-lyxose, dioctyl phthalate, DL-lactate and Leu-Phe, were successfully selected to distinguish SCLC from controls. The d-model was constructed based on these 8 metabolites and showed improved diagnostic performance for SCLC, with the area under curve (AUC) of 0.933 in the training cohort and 0.922 in the testing cohort. Importantly, the d-model still had an excellent diagnostic performance after adjusting the stage and related clinical variables and, combined with the progastrin-releasing peptide (ProGRP), showed the best diagnostic performance with 0.975 of AUC for limited-stage patients. Conclusion This study is the first to analyze the difference between metabolomics and lipidomics and to construct a d-model to detect SCLC using integrated machine learning. This study may be of great significance for the screening and early diagnosis of SCLC patients. [ABSTRACT FROM AUTHOR]

Background: The prognosis of hepatocellular carcinoma (HCC) is universally poor. Early diagnosis plays a pivotal role in determining the outcome of HCC. Methods: We employed a comparative proteomics approach to identify potential biomarkers and validated the application of retinol-binding protein 4 (RBP4) as a biomarker for HCC. RBP4 protein expression was examined in liver tissues from 80 HCC patients through immunohistochemical analysis. Serum RBP4 concentrations were measured by ELISA in a cohort comprising 290 HCC patients, matched 202 chronic hepatitis B patients and 269 healthy controls. Survival data were collected from HCC patients. The diagnostic and prognostic values of RBP4 were evaluated using receiver operating curve (ROC) analysis. Results: The validation results demonstrated a significant reduction in RBP4 levels in both liver tissues and serum samples from HCC patients. ROC analysis of the diagnostic value of RBP4 revealed an AUC of 0.879 (95 % CI: 0.854∼0.903) for HCC. When combined with AFP, the AUC increased to 0.919, with a sensitivity of 87.9 % and specificity of 80 %. Survival analysis revealed significantly reduced overall survival time in individuals with low-expression of RBP4 compared to those with high-expression. The joint prognostic model exhibited an AUC of 0.926 (95 % CI: 0.888∼0.964), which was significantly higher than that of AFP alone (AUC=0.809; P

Abstract Background The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods. Methods Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set. Results SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set. Conclusion SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model.

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 "kidney transplant"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms--LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs--were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO. [ABSTRACT FROM AUTHOR]

Dysbiosis in the gut microbiota has been intimately implicated in shrimp (Litopenaeus vannamei, Penaeidae) diseases. However, considering the variety of shrimp diseases and the variability in experimental conditions, studies addressing common features of the gut microbiota−shrimp disease relationship are limited. Through an unbiased subject‐level meta‐analysis framework, 463 shrimp gut bacterial communities from 27 studies were re‐analysed, including six lifestages and eight diseases of shrimp, with the causal agents of viral, bacterial, eukaryotic, and unknown pathogens. Shrimp lifestages and diseases were the predominant factors governing the gut microbiota. After ruling out the top lifestage‐ and disease‐specific discriminatory amplicon sequence variants (ASVs) from the gut microbiota, the top 27 disease common‐discriminatory ASVs were identified, contributing to an overall accuracy of 95.9% in diagnosing shrimp health status. By using these optimisation procedures, the accuracy of our diagnosis model was unbiased by shrimp lifestage, specific disease, sampling size, hypervariable region and sequencing platform. The shrimp eight diseases consistently and significantly increased the relative importance of stochasticity, the relative abundance of pathogenic potentials and diversified core ASVs, whereas decreased the diversity and stability of gut microbiota. Collectively, these findings illustrate the effectiveness of meta‐analysis in determining the robust and common features of the shrimp gut microbiota in response to diverse diseases. In particular, disease common‐discriminatory ASVs could accurately diagnose shrimp health status, although the data are divergent in biotic and technical variances. [ABSTRACT FROM AUTHOR]

Background: The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods. Methods: Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set. Results: SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set. Conclusion: SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model. [ABSTRACT FROM AUTHOR]

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein-protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug-gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674-0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma. [ABSTRACT FROM AUTHOR]

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting time-domain, time-frequency-domain and multi-scale features at different scales. These features are imported into an adaptive online convolutional network-based classification inference module for arrhythmia diagnosis. Experimental results show that the AOCT-based deep learning neural network diagnostic module has excellent parallel computing and classification inference capabilities, and the overall performance of the model improves with increasing scales. In particular, when multi-scale features are used as inputs, the model is able to learn both time-frequency domain information and other rich information, thus significantly improving the performance of the end-to-end diagnostic model. The final results show that the AOCT-based deep learning neural network model has an average accuracy of 99.72%, a recall of 99.62%, and an F1 score of 99.3% in diagnosing four common heart diseases. [ABSTRACT FROM AUTHOR]

Abstract Background Neonatal sepsis (NS), a life-threatening condition, is characterized by organ dysfunction and is the most common cause of neonatal death. However, the pathogenesis of NS is unclear and the clinical inflammatory markers currently used are not ideal for diagnosis of NS. Thus, exploring the link between immune responses in NS pathogenesis, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets is of great significance in clinical practice. Herein, our study aimed to explore immune-related genes in NS and identify potential diagnostic biomarkers. Datasets for patients with NS and healthy controls were downloaded from the GEO database; GSE69686 and GSE25504 were used as the analysis and validation datasets, respectively. Differentially expressed genes (DEGs) were identified and Gene Set Enrichment Analysis (GSEA) was performed to determine their biological functions. Composition of immune cells was determined and immune-related genes (IRGs) between the two clusters were identified and their metabolic pathways were determined. Key genes with correlation coefficient > 0.5 and p

Early screening and detection of non-small cell lung cancer (NSCLC) is crucial due to the significantly low survival rate in advanced stages. Blood-based liquid biopsy is non-invasive test to assistant disease diagnosis, while cell-free RNA is one of the promising biomarkers in blood. However, the disease related signatures have not been explored completely for most cell-free RNA transcriptome sequencing (cfRNA-Seq) datasets. To address this gap, we developed a comprehensive cfRNA-Seq pipeline for data analysis and constructed a machine learning model to facilitate noninvasive early diagnosis of NSCLC. The results of our study have demonstrated the identification of differential mRNA, lncRNAs and miRNAs from cfRNA-Seq, which have exhibited significant association with development and progression of lung cancer. The classifier based on gene expression signatures achieved an impressive area under the curve (AUC) of up to 0.9, indicating high specificity and sensitivity in both cross-validation and independent test. Furthermore, the analysis of T cell and B cell immune repertoire extracted from cfRNA-Seq have provided insights into the immune status of cancer patients, while the microbiome analysis has revealed distinct bacterial and viral profiles between NSCLC and normal samples. In our future work, we aim to validate the existence of cancer associated T cell receptors (TCR)/B cell receptors (BCR) and microorganisms, and subsequently integrate all identified signatures into diagnostic model to improve the prediction accuracy. This study not only provided a comprehensive analysis pipeline for cfRNA-Seq dataset but also highlights the potential of cfRNAs as promising biomarkers and models for early NSCLC diagnosis, emphasizing their importance in clinical settings.

Abstract Background Colorectal cancer (CRC), a commonly diagnosed cancer often develops slowly from benign polyps called adenoma to carcinoma. Altered gut microbiota is implicated in colorectal carcinogenesis. It is warranted to find non-invasive progressive microbiota biomarkers that can reflect the dynamic changes of the disease. This study aimed to identify and evaluate potential progressive fecal microbiota gene markers for diagnosing advanced adenoma (AA) and CRC. Results Metagenome-wide association was performed on fecal samples from different cohorts of 871 subjects (247 CRC, 234 AA, and 390 controls). We characterized the gut microbiome, identified microbiota markers, and further constructed a colorectal neoplasms classifier in 99 CRC, 94 AA, and 62 controls, and validated the results in 185 CRC, 140 AA, and 291 controls from 3 independent cohorts. 21 species and 277 gene markers were identified whose abundance was significantly increased or decreased from normal to AA and CRC. The progressive gene markers were distributed in metabolic pathways including amino acid and sulfur metabolism. A diagnosis model consisting of four effect indexes was constructed based on the markers, the sensitivities of the Adenoma Effect Index 1 for AA, Adenoma Effect Index 2 for high-grade dysplasia (HGD) adenoma were 71.3% and 76.5%, the specificities were 90.5% and 90.3%, respectively. CRC Effect Index 1 for all stages of CRC and CRC Effect Index 2 for stage III–IV CRC to predict CRC yielded an area under the curve (AUC) of 0.839 (95% CI 0.804–0.873) and 0.857 (95% CI 0.793–0.921), respectively. Combining with fecal immunochemical test (FIT) significantly improved the sensitivity of CRC Effect Index 1 and CRC Effect Index 2 to 96.7% and 100%. Conclusions This study reports the successful diagnosis model establishment and cross-region validation for colorectal advanced adenoma and carcinoma based on the progressive gut microbiota gene markers. The results suggested that the novel diagnosis model can significantly improve the diagnostic performance for advanced adenoma.

Objective: This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients. Methods: Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMSpeak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve. Results: The results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter. Conclusion: These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future. [ABSTRACT FROM AUTHOR]

The aim of the work described here was to develop a diagnostic model based on contrast-enhanced ultrasound (CEUS) features to improve performance in predicting the probability of malignancy for breast lesions with an enlarged enhancement extent on CEUS. In total, 299 consecutive patients who underwent CEUS examination and had confirmed pathological results were retrospectively enrolled. Among the 299 patients, an enlarged enhancement extent on CEUS was found in 142 patients. In this special cohort, we analyzed the association of malignant pathologic results with perfusion patterns emphatically by reclassifying the patterns. A diagnostic model was developed and presented as a nomogram, assessed with discrimination and calibration. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of the conventional perfusion and modified perfusion patterns were 0.58 and 0.76 (p < 0.001), respectively. A diagnostic model was built and exhibited good discrimination with a C -index of 0.95 (95% confidence interval: 0.91–0.98), which was confirmed to be 0.93 via internal bootstrapping validation. The nomogram based on CEUS features provides radiologists with a quantitative tool to predict the probability of malignancy in this special cohort of breast lesions. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a contrast-enhanced CT-based radiomics nomogram for the diagnosis of neuroendocrine carcinoma of the digestive system. Methods: The clinical data and contrast-enhanced CT images of 60 patients with pathologically confirmed neuroendocrine carcinoma of the digestive system and 60 patients with non-neuroendocrine carcinoma of the digestive system were retrospectively collected from August 2015 to December 2021 at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and randomly divided into a training cohort (n=84) and a validation cohort (n=36). Clinical characteristics were analyzed by logistic regression and a clinical diagnosis model was developed. Radiomics signature were established by extracting radiomic features from contrast-enhanced CT images. Based on the radiomic signature and clinical characteristics, radiomic nomogram was developed. ROC curves and Delong's test were used to evaluate the diagnostic efficacy of the three models, calibration curves and application decision curves were used to analyze the accuracy and clinical application value of nomogram. Results: Logistic regression results showed that TNM stage (stage IV) (OR 6.8, 95% CI 1.320-43.164, p=0. 028) was an independent factor affecting the diagnosis for NECs of the digestive system, and a clinical model was constructed based on TNM stage (stage IV). The AUCs of the clinical model, radiomics signature, and radiomics nomogram for the diagnosis of NECs of the digestive system in the training, validation cohorts and pooled patients were 0.643, 0.893, 0.913; 0.722, 0.867, 0.932 and 0.667, 0.887, 0.917 respectively. The AUCs of radiomics signature and radiomics nomogram were higher than clinical model, with statistically significant difference (Z=4.46, 6.85, both p < 0.001); the AUC difference between radiomics signature and radiomics nomogram was not statistically significant (Z=1.63, p = 0.104). The results of the calibration curve showed favorable agreement between the predicted values of the nomogram and the pathological results, and the decision curve analysis indicated that the nomogram had favorable application in clinical practice. Conclusions: The nomogram constructed based on contrast-enhanced CT radiomics and clinical characteristics was able to effectively diagnose neuroendocrine carcinoma of the digestive system. [ABSTRACT FROM AUTHOR]

Increasing evidence has supported dysbiosis in the faecal microbiome along control-adenoma-carcinoma sequence. In contrast, the data is lacking for in situ tumor bacterial community over colorectal cancer (CRC) progression, resulting in the uncertainties of identifying CRC-associated taxa and diagnosing the sequential CRC stages. Through comprehensive collection of benign polyps (BP, N = 45) and the tumors (N = 50) over the four CRC stages, we explored the dynamics of bacterial communities over CRC progression using amplicons sequencing. Canceration was the primarily factor governing the bacterial community, followed by the CRC stages. Besides confirming known CRCassociated taxa using differential abundance, we identified new CRC driver species based on their keystone features in NetShift, including Porphyromonas endodontalis, Ruminococcus torques and Odoribacter splanchnicus. Tumor environments were less selective for stable core community, resulting in heterogeneity in bacterial communities over CRC progression, as supported by higher average variation degree, lower occupancy and specificity compared with BP. Intriguingly, tumors could recruit beneficial taxa antagonizing CRCassociated pathogens at CRC initiation, a pattern known as "cry-for-help". By distinguishing age- from CRC stage-associated taxa, the top 15 CRC stagediscriminatory taxa contributed an overall 87.4% accuracy in diagnosing BP and each CRC stage, in which no CRC patients were falsely diagnosed as BP. The accuracy of diagnosis model was unbiased by human age and gender. Collectively, our findings provide new CRC-associated taxa and updated interpretations for CRC carcinogenesis from an ecological perspective. Moving beyond stratifying case-control, the CRC-stage discriminatory taxa could add the diagnosis of BP and the four CRC stages, especially the patients with poor pathological feature and un-reproducibility between two observers. [ABSTRACT FROM AUTHOR]

Objective To build a diagnostic model of gastric cancer based on deep learning and evaluate the performance of the model. Methods The pathological sections of patients diagnosed with normal gastric mucosa, chronic gastritis, high-grade intraepithelial neoplasia or gastric adenocarcinoma by endoscopic examination in Zhejiang Provincial People's Hospital from January 2015 to January 2020 were retrospectively selected. The pathology slides were scanned at ×20 magnification to generate whole slide images (WSIs). These WSIs were randomly divided into patch classification data set, slide classification training set and slide classification test set at a ratio of 2:2:1. After the lesion regions of the patch classification data set were annotated and the patches were selected, they were randomly divided into training set, test set and validation set at a ratio of 20:1:1. The deep learning model Efficientnet and ResNet were used to train and the convolutional neural network (CNN) model for cancer and non-cancer classification was constructed. Based on the patch classification test set and validation set, the performance of the model was evaluated. The results were evaluated by the patch classification accuracy and the area under the curve (AUC). This model was used for image stitching to generate the cancerous heat map of WSIs and extract the slide-level cancer and non-cancer classification features of the heat map. LightGBM slide-level classification algorithm were trained and evaluated, and the gastric cancer of WSIs were diagnosed and recognized. The results were evaluated by AUC, accuracy, sensitivity and specificity. Results A total of 500 pathological sections of benign gastric diseases (normal gastric mucosa, chronic gastritis) and 500 pathological sections of gastric cancer (high-grade intraepithelial neoplasia and gastric adenocarcinoma) that met the inclusion and exclusion criteria were selected. The patch classification data set, slide classification training set and slide classification test set were 400, 400 and 200, respectively. The patch classification training set, test set, validation set were 402 000, 20 000, 20 000, respectively. CNN model based on Efficientnet-b1 network structure for patch classification in test set and validation set achieved the highest accuracy[test set: 91.3% (95% CI: 88.2%-95.4%); validation set: 92.5%(95% CI: 89.0%-95.3%)]and the highest AUC[test set: 0.95(95% CI: 0.93-0.98); validation set: 0.96(95% CI: 0.92-0.98)]. The AUC of the model based on LightGBM algorithm was 0.98(95% CI: 0.89-0.98), with accuracy of 88.0%(95% CI: 81.6%-94.3%), sensitivity 100%(95% CI: 88.0%-100%), and specificity 67.0%(95% CI: 57.0%-85.0%). Conclusion The CNN diagnostic model based on the pathology slides of gastric biopsy can locate the cancerous tissues, classify patch-level and slide-level lesion natures accurately, identify gastric cancer accurately, which has the potential to improve the diagnosis efficiency.

For a long time, power supply enterprises have been plagued by the problem of electricity theft and default. Although power supply enterprises have strengthened anti-theft measures from various aspects, the huge benefits brought by electricity theft and default have driven the development trend of electricity theft methods to be more complicated and concealed, which has brought great challenges to the anti-theft work. In order to quickly and accurately locate the suspected users of ”defaulting on electricity consumption and stealing electricity”, based on the massive data of marketing business application system and electricity consumption information collection system, this paper analyzes and studies the existing common means of stealing electricity, and establishes an anti-stealing diagnostic analysis model by combining the correlation analysis algorithm, extracts the abnormal data related to stealing electricity and its prevention and investigation, and accurately locks the suspected users. The aim is to improve the efficiency and accuracy of preventing and investigating electricity theft, so as to effectively curb the occurrence of electricity theft, reduce the line loss rate, ensure the safety of the power grid and improve the efficiency of power supply enterprises.

• Studies using brain effective connectivity to identify mental illness were reviewed. • Thirty-five papers were included through systematic literature search. • Methods applied to build diagnosis models and the performance were summarized. • Limitations, challenges and future directions were discussed. Brain effective connectivity (EC) is a functional measurement that reflects the causal effects and topological relationships of neural activities. Recent research has increasingly focused on the classification for mental illnesses and healthy controls using brain EC; however, no comprehensive reviews have synthesized these studies. Therefore, the aim of this review is to thoroughly examine the existing literature on constructing diagnosis model for mental illnesses using brain EC. We first conducted a systematical literature search and thirty-five papers met the inclusion criteria. Subsequently, we summarized the approaches for estimating EC, the classification and validation methods used, the accuracies of models, and the main findings. Finally, we discussed the limitations of current research and the challenges in future research. These summaries and discussion provide references for future research on mental illnesses identification based on brain EC. [ABSTRACT FROM AUTHOR]

BackgroundAtherosclerosis (AS) is one of the leading causes of the cardio-cerebral vascular incident. The constantly emerging evidence indicates a close association between nonalcoholic fatty liver disease (NAFLD) and AS. However, the exact molecular mechanisms underlying the correlation between these two diseases remain unclear. This study proposed exploring the common signature genes, pathways, and immune cells among AS and NAFLD.MethodsThe common differentially expressed genes (co-DEGs) with a consistent trend were identified via bioinformatic analyses of the Gene Expression Omnibus (GEO) datasets GSE28829 and GSE49541, respectively. Further, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. We utilized machine learning algorithms of lasso and random forest (RF) to identify the common signature genes. Then the diagnostic nomogram models and receiver operator characteristic curve (ROC) analyses were constructed and validated with external verification datasets. The gene interaction network was established via the GeneMANIA database. Additionally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to explore the co-regulated pathways and immune cells.ResultsA total of 11 co-DEGs were identified. GO and KEGG analyses revealed that co-DEGs were mainly enriched in lipid catabolic process, calcium ion transport, and regulation of cytokine. Moreover, three common signature genes (PLCXD3, CCL19, and PKD2) were defined. Based on these genes, we constructed the efficiently predictable diagnostic models for advanced AS and NAFLD with the nomograms, evaluated with the ROC curves (AUC = 0.995 for advanced AS, 95% CI 0.971–1.0; AUC = 0.973 for advanced NAFLD, 95% CI 0.938–0.998). In addition, the AUC of the verification datasets had a similar trend. The NOD-like receptors (NLRs) signaling pathway might be the most crucial co-regulated pathway, and activated CD4 T cells and central memory CD4 T cells were significantly excessive infiltration in advanced NAFLD and AS.ConclusionWe identified three common signature genes (PLCXD3, CCL19, and PKD2), co-regulated pathways, and shared immune features of NAFLD and AS, which might provide novel insights into the molecular mechanism of NAFLD complicated with AS.

Background: Neonatal sepsis (NS), a life-threatening condition, is characterized by organ dysfunction and is the most common cause of neonatal death. However, the pathogenesis of NS is unclear and the clinical inflammatory markers currently used are not ideal for diagnosis of NS. Thus, exploring the link between immune responses in NS pathogenesis, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets is of great significance in clinical practice. Herein, our study aimed to explore immune-related genes in NS and identify potential diagnostic biomarkers. Datasets for patients with NS and healthy controls were downloaded from the GEO database; GSE69686 and GSE25504 were used as the analysis and validation datasets, respectively. Differentially expressed genes (DEGs) were identified and Gene Set Enrichment Analysis (GSEA) was performed to determine their biological functions. Composition of immune cells was determined and immune-related genes (IRGs) between the two clusters were identified and their metabolic pathways were determined. Key genes with correlation coefficient > 0.5 and p < 0.05 were selected as screening biomarkers. Logistic regression models were constructed based on the selected biomarkers, and the diagnostic models were validated. Results: Fifty-two DEGs were identified, and GSEA indicated involvement in acute inflammatory response, bacterial detection, and regulation of macrophage activation. Most infiltrating immune cells, including activated CD8 + T cells, were significantly different in patients with NS compared to the healthy controls. Fifty-four IRGs were identified, and GSEA indicated involvement in immune response and macrophage activation and regulation of T cell activation. Diagnostic models of DEGs containing five genes (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and IRG with one gene (NSUN7) constructed using LASSO algorithm were validated using the GPL6947 and GPL13667 subset datasets, respectively. The IRG model outperformed the DEG model. Additionally, statistical analysis suggested that risk scores may be related to gestational age and birth weight, regardless of sex. Conclusions: We identified six IRGs as potential diagnostic biomarkers for NS and developed diagnostic models for NS. Our findings provide a new perspective for future research on NS pathogenesis. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the relationship between systemic family dynamics and adolescent depression. Methods: An offline survey was distributed to 4,109 students in grades 6-12, with the final analysis including 3,014 students (1,524 boys and 1,490 girls) aged 10-18 years. The questionnaire included the Self-Rating Scale of Systemic Family Dynamics (SSFD), the Self-Rating Depression Scale (SDS), and demographic characteristics. Results: Family dynamics were negatively correlated with depressive symptoms, with better family dynamics (high scores) associated with lower levels of depression based on the SDS score. After adjusting for sociodemographic characteristics, an ordinal multiclass logistic regression analysis identified family atmosphere (OR = 0.952, 95% CI: 0.948-0.956, p < 0.001) as the most important protective family dynamic against depression, followed by individuality (OR = 0.964, 95% CI: 0.960-0.968, p < 0.001). Latent class analysis (LCA) created the low family dynamic and high family dynamic groups. There were significant differences in the mean SDS scores between the two groups (45.52 ± 10.57 vs. 53.78 ± 11.88; p < 0.001) that persisted after propensity matching. Family atmosphere and individuation had a favorable diagnostic value for depression, with AUCs of 0.778 (95% CI: 0.760-0.796) and 0.710 (95% CI: 0.690-0.730), respectively. The diagnostic models for depression performed well. Conclusion: Poor family dynamics may be responsible for adolescent depression. A variety of early intervention strategies focused on the family may potentially avoid adolescent depression. [ABSTRACT FROM AUTHOR]

The morbidity rate of ulcerative colitis (UC) in the world is increasing year by year, recurrent episodes of diarrhea, mucopurulent and bloody stools, and abdominal pain are the main symptoms, reducing the quality of life of the patient and affecting the productivity of the society. In this study, we sought to develop robust diagnostic biomarkers for UC, to uncover potential targets for anti-TNF-ɑ drugs, and to investigate their associated pathway mechanisms. We collected single-cell expression profile data from 9 UC or healthy samples and performed cell annotation and cell communication analysis. Revealing the possible pathogenesis of ulcerative colitis by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis. Based on the disease-related modules obtained from weighted correlation network analysis (WGCNA) analysis, we used Lasso regression analysis and random forest algorithm to identify the genes with the greatest impact on disease (EPB41L3, HSD17B3, NDRG1, PDIA5, TRPV3) and further validated the diagnostic value of the model genes by various means. To further explore the relationship and mechanism between model genes and drug sensitivity, we collected gene expression profiles of 185 UC patients before receiving anti-tumor necrosis factor drugs, and we performed functional analysis based on the results of differential analysis between NR tissues and R tissues, and used single-sample GSEA (ssGSEA) and CIBERSORT algorithms to explore the important role of immune microenvironment on drug sensitivity. The results suggest that our model is not only helpful in aiding diagnosis, but also has implications for predicting drug efficacy; in addition, model genes may influence drug sensitivity by affecting immune cells. We suggest that this study has developed a diagnostic model with higher specificity and sensitivity, and also provides suggestions for clinical administration and drug efficacy prediction. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC), a commonly diagnosed cancer often develops slowly from benign polyps called adenoma to carcinoma. Altered gut microbiota is implicated in colorectal carcinogenesis. It is warranted to find non-invasive progressive microbiota biomarkers that can reflect the dynamic changes of the disease. This study aimed to identify and evaluate potential progressive fecal microbiota gene markers for diagnosing advanced adenoma (AA) and CRC. Results: Metagenome-wide association was performed on fecal samples from different cohorts of 871 subjects (247 CRC, 234 AA, and 390 controls). We characterized the gut microbiome, identified microbiota markers, and further constructed a colorectal neoplasms classifier in 99 CRC, 94 AA, and 62 controls, and validated the results in 185 CRC, 140 AA, and 291 controls from 3 independent cohorts. 21 species and 277 gene markers were identified whose abundance was significantly increased or decreased from normal to AA and CRC. The progressive gene markers were distributed in metabolic pathways including amino acid and sulfur metabolism. A diagnosis model consisting of four effect indexes was constructed based on the markers, the sensitivities of the Adenoma Effect Index 1 for AA, Adenoma Effect Index 2 for high-grade dysplasia (HGD) adenoma were 71.3% and 76.5%, the specificities were 90.5% and 90.3%, respectively. CRC Effect Index 1 for all stages of CRC and CRC Effect Index 2 for stage III–IV CRC to predict CRC yielded an area under the curve (AUC) of 0.839 (95% CI 0.804–0.873) and 0.857 (95% CI 0.793–0.921), respectively. Combining with fecal immunochemical test (FIT) significantly improved the sensitivity of CRC Effect Index 1 and CRC Effect Index 2 to 96.7% and 100%. Conclusions: This study reports the successful diagnosis model establishment and cross-region validation for colorectal advanced adenoma and carcinoma based on the progressive gut microbiota gene markers. The results suggested that the novel diagnosis model can significantly improve the diagnostic performance for advanced adenoma. [ABSTRACT FROM AUTHOR]

As the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aims at the diagnosis method of questionnaire survey, hoping to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating (CDR) is used as the data source, and various methods are used for modeling and feature selection, so as to combine similar attributes in the data set, reduce the categories, and finally use the confusion matrix to judge the effect. The experimental results show that the model established by the bagging method has the best effect, and the accuracy rate can reach 80% of the true diagnosis rate; in terms of feature selection, the principal component analysis (PCA) has the best effect compared with other methods. [ABSTRACT FROM AUTHOR]

Abstract Objective: Breast cancer (BC) is becoming the leading cause of cancer-related death in women all over the word. Identification of diagnostic biomarkers for early detection of BC is one of the most effective ways to reduce the mortality.Methods: Plasma samples from BC patients (n = 120) and normal controls (n = 50) were collected to determine the differentially expressed circulating miRNAs in BC patients. Binary logistic regression was applied to develop miRNA diagnostic models. Receiver operating characteristic (ROC) curves were applied to calculate the area under the curve (AUC). MMTV-PYMT mammary tumor mice were used to validate the expression change of those circulating miRNAs. Plasma samples from patients with other tumor types were collected to determine the specificity of the model in diagnosis of BC.Results: In the screening phase, 5 circulating miRNAs (miR-16, miR-17, miR-19b, miR-27a, and miR-106a) were identified as the most significantly upregulated miRNAs in plasma of BC patients. In consistence, the 5 miRNAs showed upregulation in the circulation of additional 80 BC patients in a tumor stage-dependent manner. Application of a tumor-burden mice model further confirmed upregulation of the 5 miRNAs in circulation. Based on these data, five models with diagnostic potential of BC were developed. Among the 5 miRNAs, miR-19b ranked at the top position with the highest specificity and the biggest contribution. In combination with miR-16 and miR-106a, a miR-19b-based 3-circulating miRNA model was selected as the best for further validation. Taken the samples together, the model showed 92% of sensitivity and 90% of specificity in diagnosis of BC. In addition, three other tumor types including prostate cancer, thyroid cancer and colorectal cancer further verified the specificity of the BC diagnostic model. Conclusion: The current study developed a miR-19b-based 3-miRNA model holding potential for diagnosis of BC using blood samples.

Background: The disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD. Methods: We retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017–April 2019). Results: In total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively. Conclusion: We successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods. Clinical Trial Registration: ClinicalTrial.gov, NCT03865914. [ABSTRACT FROM AUTHOR]

BackgroundThe disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD.MethodsWe retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017–April 2019).ResultsIn total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively.ConclusionWe successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods.Clinical Trial RegistrationClinicalTrial.gov, NCT03865914.