Diagnostic Model Research Articles

Bringing team science to the ambulatory diagnostic process: how do patients and clinicians develop shared mental models?

The ambulatory diagnostic process is potentially complex, resulting in faulty communication, lost information, and a lack of team coordination. Patients and families have a unique position in the ambulatory diagnostic team, holding privileged information about their clinical conditions and serving as the connecting thread across multiple healthcare encounters. While experts advocate for engaging patients as diagnostic team members, operationalizing patient engagement has been challenging. The team science literature links improved team performance with shared mental models, a concept reflecting the team's commonly held knowledge about the tasks to be done and the expertise of each team member. Despite their proven potential to improve team performance and outcomes in other settings, shared mental models remain underexplored in healthcare. In this manuscript, we review the literature on shared mental models, applying that knowledge to the ambulatory diagnostic process. We consider the role of patients in the diagnostic team and adapt the five-factor model of shared mental models to develop a framework for patient-clinician diagnostic shared mental models. We conclude with research priorities. Development, maintenance, and use of shared mental models of the diagnostic process amongst patients, families, and clinicians may increase patient/family engagement, improve diagnostic team performance, and promote diagnostic safety.

ScCaT: An explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing.

Sepsis is a life-threatening condition characterized by an exaggerated immune response to pathogens, leading to organ damage and high mortality rates in the intensive care unit. Although deep learning has achieved impressive performance on prediction and classification tasks in medicine, it requires large amounts of data and lacks explainability, which hinder its application to sepsis diagnosis. We introduce a deep learning framework, called scCaT, which blends the capsulating architecture with Transformer to develop a sepsis diagnostic model using single-cell RNA sequencing data and transfers it to bulk RNA data. The capsulating architecture effectively groups genes into capsules based on biological functions, which provides explainability in encoding gene expressions. The Transformer serves as a decoder to classify sepsis patients and controls. Our model achieves high accuracy with an AUROC of 0.93 on the single-cell test set and an average AUROC of 0.98 on seven bulk RNA cohorts. Additionally, the capsules can recognize different cell types and distinguish sepsis from control samples based on their biological pathways. This study presents a novel approach for learning gene modules and transferring the model to other data types, offering potential benefits in diagnosing rare diseases with limited subjects.

Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine learning.

Metabolic dysfunction-associated steatohepatitis (MASH) is a highly prevalent liver disease globally, with a significant risk of progressing to cirrhosis and even liver cancer. Efferocytosis, a process implicated in a broad spectrum of chronic inflammatory disorders, has been reported to be associated with the pathogenesis of MASH; however, its precise role remains obscure. Thus, we aimed to identify and validate efferocytosis linked signatures for detection of MASH. We retrieved gene expression patterns of MASH from the GEO database and then focused on assessing the differential expression of efferocytosis-related genes (EFRGs) between MASH and control groups. This analysis was followed by a series of in-depth investigations, including protein-protein interaction (PPI), correlation analysis, and functional enrichment analysis, to uncover the molecular interactions and pathways at play. To screen for biomarkers for diagnosis, we applied machine learning algorithm to identify hub genes and constructed a clinical predictive model. Additionally, we conducted immune infiltration and single-cell transcriptome analyses in both MASH and control samples, providing insights into the immune cell landscape and cellular heterogeneity in these conditions. This research pinpointed 39 genes exhibiting a robust correlation with efferocytosis in MASH. Among these, five potential diagnostic biomarkers-TREM2, TIMD4, STAB1, C1QC, and DYNLT1-were screened using two distinct machine learning models. Subsequent external validation and animal experimentation validated the upregulation of TREM2 and downregulation of TIMD4 in MASH samples. Notably, both TREM2 and TIMD4 demonstrated area under the curve (AUC) values exceeding 0.9, underscoring their significant potential in facilitating the diagnosis of MASH. Our study comprehensively elucidated the relationship between MASH and efferocytosis, constructing a favorable diagnostic model. Furthermore, we identified potential therapeutic targets for MASH treatment and offered novel insights into unraveling the underlying mechanisms of this disease.

Rainfall Enhancement Downwind of Hills Due to Stationary Waves on the Melting Level and the Extreme Rainfall of December 2015 in the Lake District of Northwest England

This paper investigates how stationary gravity waves generated by flow over orography enhance rainfall, with particular attention to the role of induced waves in the melting level. The findings reveal a new mechanism by which gravity wave flow focuses precipitation, amplifying rainfall intensity downwind of hills. This mechanism, which depends on the differential velocities of rain and snow, offers fresh insights into how orographic effects can intensify rainfall. A two-dimensional diagnostic model based on linear gravity wave theory is used to investigate the record-breaking rainfall of December 2015 in the Lake District of northwest England. The pattern of ascent is shown to have a qualitatively good fit to that of the Met Office’s operational high-resolution UKV model averaged over 24 h, suggesting that orographically excited stationary waves were the principal cause of the rain. Precipitation trajectories imply that a persistent downstream elevated wave caused by the Isle of Man supported a spray of seeding ice particles directed towards the Lake District, and that these grew whilst suspended in strong upslope flow before being focused by the undulating melting-level into intense shafts of rain.

Exploring diagnostic biomarkers of type 2 cardio-renal syndrome based on secreted proteins and bioinformatics analysis

Chronic heart failure (CHF) can induce chronic kidney disease (CKD), called type 2 cardio-renal syndrome (CRS2). The mechanism is not completely clear, and there is a lack of early warning biomarkers of CKD in the context of CHF. Two CKD, one CHF-PBMC and four CHF-cardiac tissue expression profile datasets were obtained from GEO database. Differential expression analysis and WGCNA were used to detect CKD key genes and CHF-related secreted proteins. Protein–protein interaction (PPI), functional enrichment, and cMAP analysis reveal potential mechanisms and drugs of CHF-related CKD. Five machine learning algorithms were used to screen candidate biomarkers, construct a diagnostic nomogram for CKD and validate it in two external cohorts. Clinical serum samples were collected in our hospital to evaluate the correlation and diagnostic value of biomarkers and CKD. 225 CKD key genes and 316 CHF-related secreted proteins were identified. Four key subgroups, including 204 genes, were identified as CRS2-related pathogenic genes by PPI analysis. Enrichment analysis revealed that the identified subgroups exhibited significant enrichment in cytokine action, immune responses, and inflammatory processes. The cMAP analysis highlighted metiradone as a drug with greater potential for therapeutic intervention for CRS2. Utilizing five machine learning algorithms, three hub genes (CD48, COL3A1, LOXL1) were pinpointed as potential biomarkers for CKD, and a nomogram model was constructed. Receiver operating characteristic analysis demonstrated that the nomogram’s area under the curve (AUC) exceeded 0.80 in both the CKD combined dataset and two external cohorts. In addition, the three biomarkers were significantly correlated with the glomerular filtration rate, and the AUC of the model predicting disease progression was 0.944. Furthermore, analysis of immune cell infiltration indicated a correlation between the three biomarkers and the infiltration fraction of macrophages, neutrophils, and other immune cells in CKD. Our clinical cohort validated the expression patterns of three biomarkers in serum, and the diagnostic model achieved an AUC of 0.876. CHF has the potential to facilitate the progression of CKD via the release of cardiac and PBMC secreted proteins. Furthermore, CD48, COL3A1, and LOXL1 have been identified as potential biomarkers for the detection of CKD.

Machine Learning Diagnostic Model for Early Stage NSTEMI: Using hs-cTnI 1/2h Changes and Multiple Cardiovascular Biomarkers.

This study demonstrates differences in the distribution of multiple cardiovascular biomarkers between non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) patients. Diagnostic machine learning predictive models measured at the time of admission and 1/2 h post-admission, achieving competitive diagnostic predictive results. This study aims to explore the diagnostic value of changes in high-sensitivity cardiac troponin I (hs-cTnI) levels in patients with suspected NSTEMI. A total of 267 patients presented with chest pain, requiring confirmation of acute coronary syndrome (ACS) subtypes (NSTEMI vs. UA). Hs-cTnI and other cardiac markers, such as creatine kinase-MB (CK-MB) and Myoglobin (Myo), were analyzed. Machine learning techniques were employed to assess the application of hs-cTnI level changes in the clinical diagnosis of NSTEMI. Levels of CK-MB, Myo, hs-cTnI measured at admission, hs-cTnI measured 1-2 h after admission, and NT-proBNP in NSTEMI patients were significantly higher than those in UA patients (p < 0.001). There was a positive correlation between hs-cTnI and CK-MB, as well as Myo (R = 0.72, R = 0.51, R = 0.60). The optimal diagnostic model, Hybiome_1/2h, demonstrated an F1-Score of 0.74, an AUROC of 0.96, and an AP of 0.89. This study confirms the significant value of hs-cTnI as a sensitive marker of myocardial injury in the diagnosis of NSTEMI. Continuous monitoring of hs-cTnI levels enhances the accuracy of distinguishing NSTEMI from UA. The models indicate that the Hybiome hs-cTnI assays perform comparably well to the Beckman assays in predicting NSTEMI. Moreover, incorporating hs-cTnI measurements taken 1-2 h post-admission significantly enhances the model's effectiveness.

Establishing a Risk Model for Diabetic Nephropathy and Addressing the Therapeutic Effect of Combined Epalrestat- Dapagliflozin Regimen.

To explore the construction of a diagnostic prediction model of diabetic nephropathy (DN) in type 2 diabetic patients for prognostic risk prediction and observe the therapeutic effect of Epalrestat combined with Dapagliflozin on DN. The study consisted of two phases, phase I: A retrospective analysis was conducted on the case information and clinical treatment related data of a total of 460 patients who underwent kidney biopsy from June 2018 to June 2021. They were randomly divided into validation queue and training queue. The predictive factors of the diagnostic prediction model were obtained through multivariate logistic regression. An interventional study of 94 patients with DN admitted between January 2022 and August 2023 was conducted, and they were randomized into a control group (n = 47) receiving Dapagliflozin and a research group (n = 47) receiving Epalrestat combined with Dapagliflozin. The glucose metabolism, renal function, and treatment safety of the two groups before and after treatment were compared. In addition, the adverse reactions during the treatment of the two groups were counted. In the phase I of the study, the DN risk model established showed a good performance in the diagnosis and risk assessment of patients with DN and could provide certain reference opinions for future clinical practice. In the phase II of the study, the research group showed better glucose metabolism and renal function than the control group after treatment (P < .05), but no statistical difference was identified between groups in the incidence of adverse reactions (P > .05). Conclusion. Epalrestat combined with Dapagliflozin is significantly effective in the treatment of DN, which can effectively improve glucose metabolism and renal function in DN patients.

Exploring the predictive values of SERP4 and FRZB in dilated cardiomyopathy based on an integrated analysis

Background and objectiveThe aim of this study was to investigate potential hub genes for dilated cardiomyopathy (DCM).MethodsFive DCM-related microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were used for identification. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, disease ontology, gene ontology annotation and protein-protein interaction (PPI) network analysis were then performed, while a random forest was constructed to explore central genes. Artificial neural networks were used to compare with known genes and to develop new diagnostic models. 240 population blood samples were collected and expression of hub genes was verified in these samples using RT-PCR and demonstrated by Nomogram.ResultsAfter differential analysis, 33 genes were statistically significant (adjusted P < 0.05). Functional enrichment of these differential genes resulted in 85 Gene Ontology (GO) functions identified and 6 pathways enriched for the KEGG pathway. PPI networks and molecular complex assays identified 10 hub genes (adjusted P < 0.05). Random forest identified SMOC2 and SFRP4 as the most important, followed by FCER1G and FRZB. NeuraHF models (SMOC2, SFRP4, FCER1G and FRZB) were selected by artificial neural network model and had better diagnostic efficacy for the onset of DCM, compared with the traditional KG-DCM models (MYH7, ACTC1, TTN and LMNA). Finally, SFRP4 and FRZB were expressed higher in DCM verified by RT-PCR and as a factor for DCM identified by Nomogram.ConclusionsWe performed an integrated analysis and identified SFRP4 and FRZB as a new factor for DCM. But the exact mechanism still needs further experimental verification.

A noise-enhanced machine fault diagnosis method with multi-channel information fusion

ABSTRACT Condition health monitoring and fault diagnosis is able to safeguard against potential threats to machines. Nevertheless, the acquired signals by using a plenty of sensors are often accompanied by noise and meanwhile the useful information contained in the signals is not both complete and reliable, thereby together reducing the accuracy and stability of the fault diagnosis. Therefore, this paper proposes a noise-enhanced intelligent fault diagnosis method with multi-channel information fusion. Here, it would inject the varying levels of Gaussian noise into multi-channel input data as well as intelligent diagnostic models, with a view to elucidating the benefits of noise. Furthermore, the proposed method is validated through three experiments. The experimental results demonstrate that the diagnostic accuracy of the hydraulic motor and the two different bearings reaches 99.85%, 99.89%, and 99.85% by using the proposed method, respectively, which are increased by 2.50%, 4.09%, and 2.50%, respectively, compared with one without the injection of noise. In addition, by comparing the diagnostic accuracy of ones with single-channel and multi-channel information fusion under the optimal noise level in both cases, it is found that one with multi-channel information fusion shows an average of 3.20% increase than the single-channel one.

Revealing Fibrosis Genes as Biomarkers of Ulcerative Colitis: A Bioinformatics Study Based on ScRNA and Bulk RNA Datasets.

This study aimed to uncover biomarkers associated with fibroblasts to diagnose ulcerative colitis (UC) and predict sensitivity to TNFα inhibitors. We identified fibrosis-related genes by analyzing eight bulk RNA and one single-cell RNA sequencing dataset from UC patients. Three machine learning algorithms were employed to identify common significant genes. We utilized five machine learning models, namely Random Forest (RF), Support Vector Machine (SVM), Xgboost, Multilayer Perceptron (MLP), and Logistic Regression, to develop diagnostic models for UC. Following hyperparameter tweaking using grid search, we evaluated Matthew's Correlation Coefficient (MCC) of each model on the validation set. Finally, we identified five hub genes in UC patients and evaluated their response to infliximab or golimumab. We identified 23 genes associated with fibroblasts. Further analysis using three ML models revealed BIRC3, IFITM2, ANXA1, ISG20, and MSN as critical fibroblast genes. Following hyperparameter adjustment, the SVM model exhibited the most favorable characteristics in the validation set, achieving an MCC of 0.7. ANXA1 contributed the most to the model that predicts UC. The optimal model was implemented on the website. Among UC patients receiving TNFα inhibitor treatment, the ineffective group showed considerably increased expression of the five critical genes than the responsive group. BIRC3, IFITM2, ANXA1, ISG20, and MSN may serve as potential diagnostic biomarkers in UC. Through the interaction between characteristic biomarkers and immune infiltrating cells, the immune response mediated by these characteristic biomarkers plays a crucial role in the occurrence and development of UC.

Inversion Method for Material Parameters of Concrete Dams Using Intelligent Algorithm-Based Displacement Separation

Integrating long-term observational data analysis with numerical simulations of dam operations provides an effective approach to dam safety evaluation. However, analytical results are often subject to errors due to challenges in accurately surveying and modeling the foundation, as well as temporal changes in foundation properties. This paper proposes a concrete dam displacement separation model that distinguishes between deformation caused by foundation restraint and that induced by external loads. By combining this model with intelligent optimization techniques and long-term observational data, we can identify the actual mechanical parameters of the dam and conduct structural health assessments. The proposed model accommodates multiple degrees of freedom and is applicable to both two- and three-dimensional dam modeling. Consequently, it is well-suited for parameter identification and health diagnosis of concrete gravity and arch dams with extensive observational data. The efficacy of this diagnostic model has been validated through computational case studies and practical engineering applications.

The value of nomogram based on MRI functional imaging in differentiating cerebral alveolar echinococcosis from brain metastases

ObjectiveThis study aims to evaluate the effectiveness of a nomogram model constructed using Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging techniques in distinguishing between cerebral alveolar echinococcosis (CAE) and brain metastases (BM).MethodsProspectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. There were 155 lesions in total in the two groups of patients. We used SPSS to randomly select 70% as the training set (108 lesions) and the remaining 30% as the test set (47 lesions) and performed a difference analysis between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed as a nomogram.ResultUnivariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility.ConclusionThe nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions.

Elucidating the Role of Autophagy-related Genes in Polycystic Ovary Syndrome: Implications for Diagnostic Models and Immune Response Regulation.

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder that negatively affects female reproductive capacity. Although the association between autophagy and PCOS is known, there are few detailed studies on the association between autophagy-related genes and PCOS. Publicly available gene expression datasets (GSE102293, GSE138518, GSE34526, GSE114419, GSE137684, GSE155489) were used in a comprehensive analysis to identify a role for autophagy in PCOS. Batch effects were mitigated using the sva package, followed by WGCNA (weighted gene correlation network analysis) and ss- GSEA (single sample gene set enrichment analysis) to identify autophagy-related genes. Recursive feature elimination (RFE) and LASSO COX methods were used to identify important hub genes, and their correlation with immune cell activity was assessed using ssGSEA and Pearson correlation analysis. High autophagy scores were observed in PCOS samples, and the dark green gene module with the highest autophagy correlation was identified. The differential analysis identified a total of 169 up-regulated genes versus 2 down-regulated genes in the PCOS samples, which were intersected by taking the intersection with the deep green module genes and resulted in 121 key genes. Subsequently, 6 hub genes (MMP25, CSF3R, SLPI, MMP9, CLEC4E, and SIGLEC10) were further identified based on RFE and LASSO algorithms. Diagnostic efficacy based on ROC curves showed six autophagy- associated hub genes with AUC values as high as 0.959 and 0.896 in the training and validation sets, respectively. Finally, we observed that these hub genes are strongly associated with immune function, especially chronic inflammation and aberrant immune activation pathways. In this study, we identified autophagy genes closely related to PCOS and constructed a gene model with high diagnostic accuracy. These findings not only provided potential new biomarkers for the diagnosis of PCOS but also revealed the key role of autophagy in the pathogenesis of PCOS.

Integrative biomarker discovery and immune profiling for ulcerative colitis: a multi-methodological approach

Background We aimed to pinpoint biomarkers, create a diagnostic model for ulcerative colitis (UC), and delve into its immune features to better understand this autoimmune condition. Methods The sequencing data for both the UC and the control groups were obtained from GEO, including both bulk and single-cell data. Using GSE87466 as training group, we applied differential analysis, WGCNA, PPI, LASSO, RF, and SVM-RFE for biomarker selection. A neural network shaped our diagnostic model, corroborated by GSE92415 as the validation cohort with ROC assessment. Immune cell profiling was conducted using CIBERSORT. Results 53 disease-associated genes were screened. Enrichment analysis highlighted roles in complement cascades and cell adhesion. Eight biomarkers were finally identified through multiple machine learning and PPI: B4GALNT2, PDZK1IP1, FAM195A, REG4, MTMR11, FLJ35024, CD55, and CD44. The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group). UC tissues revealed heightened plasma cells, CD8 T cells, and other immune cells. Two unique UC immune patterns emerged, with certain T and NK cells central to immune modulation. Conclusion We identified eight biomarkers of UC by various methods, constructed a diagnostic model through neural networks, and explored the immune complexity of the disease, which contributes to the diagnosis and treatment of UC.

Osteocalcin: may be a useful biomarker for early identification of rapidly progressive central precocious puberty in girls.

To assess serum osteocalcin (OC) as a potential biomarker for the early detection of rapidly progressive central precocious puberty (RP-CPP) in girls. Serum OC levels were quantified using enzyme-linked immunosorbent assays (ELISAs). In the retrospective analysis, receiver operating characteristic (ROC) curve analysis was employed to evaluate the ability of OC to identify RP-CPP. A prospective study and screening tests were utilized to assess the potential of OC for use in the early prediction of RP-CPP. Variable selection in the multivariate analysis was conducted using the Bayesian Information Criterion (BIC) and binary logistic regression was employed to construct the diagnostic prediction model. Girls with RP-CPP had significantly higher serum OC levels compared to girls with non-rapidly progressive central precocious puberty (NRP-CPP) (149.04±40.50 vs. 89.10±31.83 ng/mL, P < 0.001). The optimal OC cut-off point for differentiating RP-CPP from NRP-CPP was 107.05 ng/mL, the area under the ROC curve (AUC) was 0.90 (95%CI: 0.851-0.949; P < 0.001), with a sensitivity of 91.1% and specificity of 70.7%. The results of the prospective study indicated that changes in OC precede alterations in estradiol (E2) and bone age (BA). A diagnostic prediction model that includes duration of breast development, BA, OC, high-density lipoprotein cholesterol (HDL-C), and uterine length achieved an AUC of 0.961, with a sensitivity of 94.1% and specificity of 91.5% for the detection of RP-CPP. If OC is excluded from the model, the AUC decreases to 0.894, with sensitivity and specificity declining to 80.5% and 83.1%, respectively. Serum OC levels may serve as a promising biomarker for the early differentiation between RP-CPP and NRP-CPP in girls. The diagnostic prediction model that incorporates duration of breast development, BA, OC, HDL-C, and uterine length effectively identifies girls with RP-CPP.

Diagnostic models for differentiating fatty liver disease of alcohol and non-alcoholic genesis

Introduction. Fatty liver disease is the largest contributor to the burden of chronic liver disease worldwide. Current approaches do not allow sufficient differentiation between alcoholic and non-alcoholic etiology of the process.Aim. Create diagnostic panels including electrical and viscoelastic parameters of erythrocytes to differentiate fatty liver disease of alcoholic and non-alcoholic genesis.Materials and methods. The study included 38 men (47.5 ± 2.9 years) with NAFLD; 31 men with alcoholic fatty liver disease (AFLD) (45.1 ± 3.1 years) according to ultrasound of the abdominal organs, the degree of fibrosis did not exceed F1 (FibroScan® 502). Electrical and viscoelastic parameters of erythrocytes were studied by dielectrophoresis using an electro-optical cell detection system. To determine the parameters of erythrocytes – biomarkers for distinguishing between AFLD and NAFLD, a system of machine learning methods – Random Forest was used.Results. Electrical, viscoelastic parameters of erythrocytes, which are biomarkers for distinguishing between AFLD and NAFLD, were established: cell membrane capacity (p = 1.21E-11), the degree of change in the deformation amplitude at a frequency of 5 x 105 Hz (p = 2.38E-08), cell polarizability at a frequency of 106 Hz (p = 9.38E-08), the speed of erythrocyte movement to the electrodes (p = 4.32E-06), the magnitude of the dipole moment (p = 1.66E-05), relative polarizability (p = 2.35E-05), the index of erythrocyte destruction at a frequency of 5 x 105 Hz (p = 0.016), the position of the crossover frequency (p = 2.13E- 06). The diagnostic model, including five parameters – the position of the crossover frequency, cell polarizability at a frequency of 106 Hz, cell electrical conductivity, membrane capacity, the degree of change in the deformation amplitude at a frequency of 5 x 105 Hz, provided the highest diagnostic accuracy with an AUC of 0.975, a sensitivity of 96.3%, and a specificity of 91.8% in differentiating between AFLD and NAFLD.Conclusion. Thus, systematic exposure to alcohol modifies the structure of erythrocyte membranes, leading to a decrease in the surface charge, the barrier function of membranes, reducing the resistance of cells, their ability to deform, which determines the key role of the identified electrical, viscoelastic parameters of erythrocytes in differentiating between AFLD and NAFLD.

Development of a locating method for optical windows in shock tube

A method was proposed in the present study for locating single and/or multiple optical windows in shock tubes. It involved establishing a diagnostic time calculation model by transforming challenging conventional conditions into equivalent special scenarios. The method strategically balanced experimental pressure and optical window locations. Pressure-time history experiments were conducted in a shock tube to verify the reliability of the proposed locating method. An example showcased the versatility of the locating method for determining the positions of single or multiple optical windows. Lastly, the proposed locating method provided evaluation for optimizing several existing shock tube setups.

Construction and evaluation of a combined diagnostic model for chronic periprosthetic joint infection based on serological tests

BackgroundEarly diagnosis of chronic periprosthetic joint infection (CPJI) is crucial for ensuring effective treatment and improving patient outcomes. However, many auxiliary diagnostic tests are challenging to implement on a large scale due to economic and technical constraints, making CPJI diagnosis difficult. This study aims to design and validate a combined diagnostic model based on commonly used serological tests to evaluate its diagnostic value for CPJI and develop a diagnostic nomogram.MethodsA retrospective study from January 2019 to February 2024 involving 170 patients undergoing knee and hip arthroplasty revision for CPJI and aseptic loosening (AL) was conducted across two medical centers. These patients were divided into the training set and validation set. Patients were categorized into CPJI and AL groups based on infection status. Serological tests conducted upon admission were collected, and single-factor and multi-factor logistic regression analyses were used to identify independent diagnostic factors for early infection. These factors were integrated to construct a nomogram model. The model's performance was evaluated using the receiver operating characteristic area under the curve (AUC), Hosmer–Lemeshow test, decision curve analysis (DCA), and calibration curve, with external validation conducted on the validation set.ResultsMultivariate logistic regression analysis showed that C-reactive protein (CRP), procalcitonin (PCT), and Platelet count/mean platelet volume ratio (PVR) were independent diagnostic factors for CPJI (p < 0.05). The AUCs for diagnosing CPJI using these individual factors were 0.806, 0.616, and 0.700 (p < 0.05), respectively, while their combined detection achieved an AUC of 0.861 (p < 0.05). The DCA clinical impact curve shows the combined model has good clinical utility when the threshold probability of infection presence is between 0.16 and 0.95. Similar results were obtained in the external validation cohort, with the combined detection having an AUC of 0.893.ConclusionThe combined diagnostic model of CRP, PCT, and PVR significantly improves the The combined diagnostic model of CRP, PCT, and PVR significantly improves the diagnostic performance for CPJI compared to individual serum biomarkers. It exhibits good sensitivity, specificity, and clinical applicability, providing valuable references for CPJI diagnosis.

Enhancing multi-type fault diagnosis in lithium-ion battery systems: Vision transformer-based transfer learning approach

Ensuring the accurate diagnosis of internal short circuit (ISC) faults and consistency anomalies is crucial for maintaining the high safety and longevity of battery systems. The challenge lies in the high similarity between the features of ISC faults and consistency anomalies, which can complicate accurate fault diagnosis. This study introduces a multi-fault diagnostic model that leverages the Vision Transformer (ViT) and employs simulated data to accurately pinpoint ISC faults and intricate consistency anomalies, such as capacity and state of charge anomalies, achieving an impressive accuracy rate of up to 100 %. The process begins with an in-depth analysis of fault mechanisms to extract and differentiate multi-fault features using a mean difference model. Subsequently, the ViT's exceptional ability to capture temporal information, which aids in the accurate identification of fault features. The integration of transfer learning with experimental data further refines the real-world diagnosis accuracy. The results confirm the robustness of the proposed method, highlighting its potential to substantially mitigate safety hazards in battery systems and enhance operational reliability.

Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model

Bladder lesion commonly occurs in patients with benign prostatic hyperplasia (BPH), and the routine screening of bladder lesion is vital for its timely detection and treatment, in which the risk of bladder lesion progression can be effectively alleviated. However, current clinical methods are inconvenient for routine screening. In this study, we proposed a convenient routine screening method to diagnose bladder lesions based on several clinical risk factors, which can be obtained through non-invasive, easy-to-operate, and low-cost examinations. The contribution of each clinical risk factor was further quantitatively analyzed to understand their impact on diagnostic decision-making. Based on a cohort study of 253 BPH patients with or without bladder lesions, the proposed diagnostic model achieved high accuracy using these clinical risk factors. Bladder compliance, maximum flow rate (Qmax), prostate specific antigen (PSA), and postvoid residual (PVR) were identified as the four most important clinical risk factors. To the best of our knowledge, this is the innovative research to predict bladder lesions based on the risk factors and quantitatively reveal their contributions to diagnostic decision-making. The proposed model has the potential to serve as an effective routine screening tool for bladder lesions in BPH patients, enabling early intervention to prevent lesion progression and improve the quality of life.