Diagnostic Model Research Articles

Risk factors of non-arteritic anterior ischaemic optic neuropathy and central retinal artery occlusion

AIM: To investigate the difference in risk factors between non-arteritic anterior ischaemic optic neuropathy (NAION) and central retinal artery occlusion (CRAO) and develop a predictive diagnostic nomogram. METHODS: The study included 37 patients with monocular NAION, 20 with monocular CRAO, and 24 with hypertension. Gender, age, and systemic diseases were recorded. Blood routine, lipids, hemorheology, carotid and brachial artery doppler ultrasound, and echocardiography were collected. The optic disc area, cup area, and cup-to-disc ratio (C/D) of the unaffected eye in the NAION and CRAO group and the right eye in the hypertension group were measured. RESULTS: The carotid artery intimal medial thickness (C-IMT) of the affected side of the CRAO group was thicker (P=0.039) and its flow-mediated dilation (FMD) was lower (P=0.049) than the NAION group. Compared with hypertension patients, NAION patients had higher whole blood reduced viscosity low-shear (WBRV-L) and erythrocyte aggregation index (EAI; P=0.045, 0.037), and CRAO patients had higher index of rigidity of erythrocyte (IR) and erythrocyte deformation index (EDI; P=0.004, 0.001). The optic cup and the C/D of the NAION group were smaller than the other two groups (P<0.0001). The diagnostic prediction model showed high diagnostic specificity (83.7%) and sensitivity (85.6%), which was highly related to hypertension, the C-IMT of the affected side, FMD, platelet (PLT), EAI, and C/D. CONCLUSION: CRAO patients show thicker C-IMT and worse endothelial function than NAION. NAION and CRAO may be related to abnormal hemorheology. A small cup and small C/D may be involved in NAION. The diagnostic nomogram can be used to preliminarily identify NAION and CRAO.

A ferroptosis-related signature predicts the clinical diagnosis and prognosis, and associates with the immune microenvironment of lung cancer

Targeting ferroptosis-related pathway is a potential strategy for treatment of lung cancer (LC). Consequently, exploration of ferroptosis-related markers is important for treating LC. We collected LC clinical data and mRNA expression profiles from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained through FerrDB database. Expression analysis was performed to obtain differentially expressed FRGs. Diagnostic and prognostic models were constructed based on FRGs by LASSO regression, univariate, and multivariate Cox regression analysis, respectively. External verification cohorts GSE72094 and GSE157011 were used for validation. The interrelationship between prognostic risk scores based on FRGs and the tumor immune microenvironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were used for diagnostic models, 8 FRGs were used for prognostic models. The diagnostic model distinguished well between LC and normal samples in training and validation cohorts of TCGA. The prognostic models for TCGA, GSE72094, and GSE157011 cohorts significantly confirmed lower overall survival (OS) in high-risk group, which demonstrated excellent predictive properties of the survival model. Multivariate Cox regression analysis further confirmed risk score was an independent risk factor related with OS. Immunoassays revealed that in high-risk group, a significantly higher proportion of Macrophages_M0, Neutrophils, resting Natural killer cells and activated Mast cells and the level of B7H3, CD112, CD155, B7H5, and ICOSL were increased. In conclusion, diagnostic and prognostic models provided superior diagnostic and predictive power for LC and revealed a potential link between ferroptosis and TIME.

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease

The purpose of this study was to explore the diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease. In this study, we first collected 161 samples from the GEO database (including 87 in the AD group and 74 in the normal group). Subsequently, through differential expression analysis and the iUUCD 2.0 database, we obtained 3450 Differentially Expressed Genes (DEGs) and 806 Ubiquitin-related genes (UbRGs). After taking the intersection, we obtained 128 UbR-DEGs. Secondly, by conducting GO and KEGG enrichment analysis on these 128 UbR-DEGs, we identified the main molecular functions and biological pathways related to AD. Furthermore, through the utilization of GSEA analysis, we have gained insight into the enrichment of functions and pathways within both the AD and normal groups. Further, using lasso regression analysis and cross-validation techniques, we identified 22 characteristic genes associated with AD. Subsequently, we constructed a logistic regression model and optimized it, resulting in the identification of 6 RUbR-DEGs: KLHL21, WDR82, DTX3L, UBTD2, CISH, and ATXN3L. In addition, the ROC result showed that the diagnostic model we built has excellent accuracy and reliability in identifying AD patients. Finally, we constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, ceRNA) regulatory network for AD based on six RUbR-DEGs, further elucidating the interaction between UbRGs and lncRNA, miRNA. In conclusion, our findings will contribute to further understanding of the molecular pathogenesis of AD and provide a new perspective for AD risk prediction, early diagnosis and targeted therapy in the population.

Bioinformatics analysis for constructing a cellular senescence-related age-related macular degeneration diagnostic model and identifying relevant disease subtypes to guide treatment.

Age-related macular degeneration (AMD) is a condition causing progressive central vision loss. Growing evidence suggests a link between cellular senescence and AMD. However, the exact mechanism by which cellular senescence leads to AMD remains unclear. Employing machine learning, we established an AMD diagnostic model. Through unsupervised clustering, two distinct AMD subtypes were identified. GO, KEGG, and GSVA analyses explored the diverse biological functions associated with the two subtypes. By WGCNA, we constructed a coexpression network of differential genes between the subtypes, revealing the regulatory role of hub genes at the level of transcription factors and miRNAs. We identified 5 genes associated with inflammation for the construction of the AMD diagnostic model. Additionally, we observed that the level of cellular senescence and pathways related to programmed cell death (PCD), such as ferroptosis, necroptosis, and pyroptosis, exhibited higher expression levels in subtype B than A. Immune microenvironments also differed between the subtypes, indicating potentially distinct pathogenic mechanisms and therapeutic targets. In summary, by leveraging cellular senescence-associated gene expression, we developed an AMD diagnostic model. Furthermore, we identified two subtypes with varying expression patterns of senescence genes, revealing their differential roles in programmed cell death, disease progression, and immune microenvironments within AMD.

Identification of DNA methylation characteristics associated with metastasis and prognosis in colorectal cancer

Colorectal cancer (CRC) is prone to metastasis and recurrence after surgery, which is one of the main causes for its poor treatment and prognosis. Therefore, it is essential to identify biomarkers associated with metastasis and recurrence in CRC. DNA methylation has a regulatory role in cancer metastasis, tumor immune microenvironment (TME), and prognosis and may be one of the most valuable biomarkers for predicting CRC metastasis and prognosis. We constructed a diagnostic model and nomogram that can effectively predict CRC metastasis based on the differential methylation CpG sites (DMCs) between metastatic and non-metastatic CRC patients. Then, we identified 17 DMCs associated with progression free survival (PFS) of CRC and constructed a prognostic model. The prognosis model based on 17 DMCs can predict the PFS of CRC with medium to high accuracy. The results of immunohistochemical analysis indicated that the protein expression levels of the genes involved in prognostic DMCs were different between normal and colorectal cancer tissues. According to the results of immune-related analysis, we found that the low-risk patients had better immunotherapy response. In addition, high risk scores were negatively correlated with high tumor mutation burden (TMB) levels, and patients with low TMB levels in the high-risk group had the worst PFS. Our work shows the clinical value of DNA methylation in predicting CRC metastasis and PFS, as well as their correlation with TME, immunotherapy, and TMB, which helps understand the changes of DNA methylation in CRC metastasis and improving the treatment and prognosis of CRC.

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans

BackgroundRecent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans. MethodOur proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset. ResultThrough a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3. ConclusionThis study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP).Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed.Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937–0.990) and 0.862 (95% CI 0.742–0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways.Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning

ObjectiveThe macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics.MethodsWe retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation.ResultsAmong all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5–25%), normal (25–75%), high (75–95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE.ConclusionMachine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.

Prognosis of type A acute aortic syndrome in elderly patients

Abstract Funding Acknowledgements None. Introduction Type A acute aortic syndrome (AAS-A) is associated with a high mortality. Emergent surgery is the treatment of choice for patients with AAS-A. However, this surgery is a highly complex procedure associated with a significant morbidity. There are uncertainties about the best treatment strategy for elderly patients, which pose a challenge in clinical practice. Purpose The aim of this study is to evaluate the prognosis of elderly patients with AAS-A and to analyse the impact and outcome of surgery in this patient population. Methods Patients with AAS-A were prospectively and consecutively collected from April-2019 to March-2023 in a four-hospital network with a tertiary care referral centre for cardiac surgery. Patients were classified according to age into two groups: ≥75 years and under 75 years. Clinical, diagnostic, treatment and prognostic variables were analysed. Results A total of 113 patients with AAS were included, of whom 82 were AAS-A patients. Of these, 27 were aged 75 years or older. Older patients had more comorbidities: COPD, atrial fibrillation, renal failure and stroke (table). Elderly patients were diagnosed later (symptom-to-diagnosis time: 10 hours (6.2-62) vs. 4 hours (2.2-23), p=0.004). At diagnosis, older patients had more complications: cardiac tamponade (33% vs 23%, p= 0.352), hemopericardium (69% vs 38%, p= 0.01), shock (45% vs 30%, p=0.180) and renal failure (40% vs 20%, p= 0.132). The surgical risk of older patients was higher (table) and they underwent surgery in a lower proportion (59.3% vs. 87.3%, p= 0.004). Once diagnosed, time to surgery was similar between groups (3.2h (1.6-10) vs. 2.7h (2.1-5.7), p= 0.728). Despite the increased risk, surgery was associated with a mortality reduction in both patient groups (figure). In patients who underwent surgery, the incidence of post-surgical atrial fibrillation was higher in the group of older patients, with no significant differences in the remaining complications (table). Conclusion Elderly patients with AAS-A have more comorbidities, are diagnosed later and have a higher mortality than those with younger age. Despite this, surgery is associated with a mortality reduction even in the older population. Age alone should not be a contraindication for surgical treatment of AAS-A.Mortality in SAA by age and treatment

Homeless people: a review of personality disorders

Personality disorders in homeless people pose a challenge to the medical community and society, requiring specialized approaches for these super-difficult patients. The prevalence of personality disorders is higher in homeless populations than in the general population. However, there is a knowledge gap regarding personality disorders among people experiencing homelessness, and the implications of this lack of recognition are substantial. This paper provides a brief narrative review of personality disorders among homeless individuals. The primary importance and specificity of these disorders in this population remain unexplored. We searched PubMed and Web of Science databases in February and November 2023 using the keywords ‘homeless’ and ‘personality disorder’, and selected fifty-eight studies to be included in this literature review. The main themes of the results were personality disorders in homeless individuals and comorbid psychiatric disorders; risk factors and other psychological and behavioral data; clinical and intervention outcomes; and challenges linked to assessment, treatment, and intervention. The homeless population experiences significant diagnostic variability and the diagnosis of personality disorders is still evolving, contributing to difficulties in diagnosis, assessment, and treatment. A future challenge is to raise clinical awareness and optimize research knowledge, assessment, and intervention in personality disorders among homeless individuals with comorbid psychiatric disorders.

An association study of m6A methylation with major depressive disorder

ObjectiveTo find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD).MethodsDifferential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated.ResultsDifferential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed.ConclusionAmong all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.

Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires

Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients with ischemic heart disease (IHD), stroke, or both, using specific health examination data. Lifestyle habits affecting CCVD development were analyzed using five causal inference methods. This study included 473,734 patients aged ≥ 40 years who underwent specific health examinations in Kanazawa, Japan between 2009 and 2018 to collect data on basic physical information, lifestyle habits, and laboratory parameters such as diabetes, lipid metabolism, renal function, and liver function. Four machine learning algorithms were used: Random Forest, Logistic regression, Light Gradient Boosting Machine, and eXtreme-Gradient-Boosting (XGBoost). The XGBoost model exhibited superior area under the curve (AUC), with mean values of 0.770 (± 0.003), 0.758 (± 0.003), and 0.845 (± 0.005) for stroke, IHD, and CCVD, respectively. The results of the five causal inference analyses were summarized, and lifestyle behavior changes were observed after the onset of CCVD. A causal relationship from ‘reduced mastication’ to ‘weight gain’ was found for all causal species theory methods. This prediction algorithm can screen for asymptomatic myocardial ischemia and stroke. By selecting high-risk patients suspected of having CCVD, resources can be used more efficiently for secondary testing.

GALAD score as a prognostic model for recurrence of hepatocellular carcinoma after local ablation

BackgroundCurrently, the high recurrence rate still forms severe challenges in hepatocellular carcinoma (HCC) treatment. The GALAD score, including age, gender, alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), and des-gamma-carboxyprothrombin (DCP) was developed as a diagnostic model. However, evidence is still lacking to confirm the capability of the GALAD score to predict the recurrence of HCC.MethodsThis study included 390 HCC patients after local ablation at Beijing You'an Hospital from January 1, 2018, to December 31, 2022. Firstly, the area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the predictive capability of the GALAD score. Then, the Kaplan–Meier (KM) curve and log-rank test were used to compare the prognosis between two groups classified by GALAD score. Finally, a nomogram for high-risk patients was established by Lasso-Cox regression. It was assessed by ROC curves, calibration curves, and decision curve analysis (DCA).ResultsThe ROC curve (AUC: 0.749) and KM curve showed the GALAD score had good predictive ability and could clearly stratify patients into two groups through the risk of recurrence. Prognostic factors selected by Lasso-Cox regression contained tumor number, tumor size, and globulin. The nomogram for high-risk patients showed reliable discrimination, calibration, and clinical utility.ConclusionThis research displayed that the GALAD score is an effective model for predicting the recurrence of HCC. Meanwhile, we found the poor prognosis of the high-risk group and created a nomogram for these patients.

Designing a diagnostic method to predict the optimal artificial insemination timing in cows using artificial intelligence

Dairy farmers and beef cattle breeders aim for one calf per year to optimize breeding efficiency, relying on artificial insemination of both dairy and beef cows. Accurate estrus detection and timely insemination are vital for improving conception rates. However, recent challenges such as operational expansion, increased livestock numbers, and heightened milk production have complicated these processes. We developed an artificial intelligence (AI)-based pregnancy probability diagnostic tool to predict the optimal timing for artificial insemination. This tool analyzes external uterine opening image data through AI analysis, enabling high conception rates when inexperienced individuals conduct the procedure. In the initial experimental phase, images depicting the external uterine opening during artificial insemination were acquired for AI training. Static images were extracted from videos to create a pregnancy probability diagnostic model (PPDM). In the subsequent phase, an augmented set of images was introduced to enhance the precision of the PPDM. Additionally, a web application was developed for real-time assessment of optimal insemination timing, and its effectiveness in practical field settings was evaluated. The results indicated that when PPDM predicted a pregnancy probability of 70% or higher, it demonstrated a high level of reliability with accuracy, precision, and recall rates of 76.2%, 76.2%, and 100%, respectively, and an F-score of 0.86. This underscored the applicability and reliability of AI-based tools in predicting optimal insemination timing, potentially offering substantial benefits to breeding operations.

A fault diagnosis framework based on heterogeneous ensemble learning for air conditioning chiller with unbalanced samples

Abstract The studies on air conditioning fault diagnosis based on big data have developed rapidly in recent years. However, the data imbalance phenomenon due to the small amount of fault data and the large amount of normal data shared in engineering will trigger problems such as poor model prediction and low accuracy. In order to solve the problem of imbalance fault diagnosis of air-conditioning chillers, this paper proposes an ensemble learning-based diagnostic model, which achieves diagnosis by combining multiple base models and through a majority voting method. The method uses four classification models, random forest model, decision tree model, k-nearest neighbor model, and isomorphic ensemble model, as base classifiers and synthesizes the four base classifiers into a heterogeneous ensemble algorithm through ensemble learning, and performs diagnostic detection of seven types of typical faults of chiller units using the majority voting method of integrated learning. The effectiveness of the proposed algorithm is verified on the RP-1043 dataset, and the experimental results show that the accuracy of the heterogeneous ensemble model can reach 96.87%, which is a significant improvement compared with the accuracy of the other four base classifier models (81.04% to 96.25%). Therefore, it can be considered that ensemble learning model is a promising way to solve the problem of unbalanced data fault diagnosis in engineering.

Evaluating lumbar disc degeneration by MRI quantitative metabolic indicators: the perspective of factor analysis

PurposeThis study aimed to investigate an early diagnostic method for lumbar disc degeneration (LDD) and improve its diagnostic accuracy.MethodsQuantitative biomarkers of the lumbar body (LB) and lumbar discs (LDs) were obtained using nuclear magnetic resonance (NMR) detection technology. The diagnostic weights of each biological metabolism indicator were screened using the factor analysis method.ResultsThrough factor analysis, common factors such as the LB fat fraction, fat content, and T2* value of LDs were identified as covariates for the diagnostic model for the evaluation of LDD. This model can optimize the accuracy and reliability of LDD diagnosis.ConclusionThe application of biomarker quantification methods based on NMR detection technology combined with factor analysis provides an effective means for the early diagnosis of LDD, thereby improving diagnostic accuracy and reliability.

Intraoperative quantitative crystalline lens nuclear opacities analysis based on crystalline lenSx platform

PurposeThe main objective is to quantify the lens nuclear opacity using spectral-domain optical coherence tomography (SD-OCT) and to evaluate its association with Lens Opacities Classification System III (LOCS-III) system, lens thickness (LT), and surgical parameters. The secondary objective is to assess the diagnostic model performance for hard nuclear cataract.MethodsThis study included 70 eyes of 57 adults with cataract, with 49 (70%) and 21 (30%) in training and validation cohort, respectively. Correlations of the average nuclear density (AND) /maximum nuclear density (MND) with LOCS-III scores, LT, and surgical parameters were analyzed. Univariate and multivariate logistic regression analysis, receiver operating characteristic curves and calibration curves were performed for the diagnostic of hard nuclear cataract.ResultsThe pre-operative uncorrected distance visual acuity (UDVA), intraocular pressure (IOP), mean axial length (AL), and LT were 1.20 ± 0.47 log MAR, 15.50 ± 2.87 mmHg, 27.34 ± 3.77 mm and 4.32 ± 0.45 mm, respectively. The average nuclear opalescence (NO) and nuclear colour (NC) scores were 3.61 ± 0.94 and 3.50 ± 0.91 (ranging from 1.00 to 6.90), respectively. The average AND and MND were 137.94 ± 17.01 and 230.01 ± 8.91, respectively. NC and NO scores both significantly correlated with the AND (rNC = 0.733, p = 0.000; rNO = 0.755, p = 0.000) and MND (rNC = 0.643, p = 0.000; rNO = 0.634, p = 0.000). In the training cohort, the area under the curve (AUC) of the model was 0.769 (P < 0.001, 95%CI 0.620–0.919), which had a good degree of differentiation (Fig. 2a). The calibration curve showed good agreement between predicted and actual probability.ConclusionThe nuclear density measurement on SD-OCT images can serve as an objective and reliable indicator for quantifying nuclear density.

Comprehensive bioinformatics analytics and in vivo validation reveal SLC31A1 as an emerging diagnostic biomarker for acute myocardial infarction.

Globally, Acute Myocardial Infarction (AMI) is a common cause of heart failure (HF), which has been a leading cause of mortality resulting from non-communicable diseases. On the other hand, increasing evidence suggests that the role of energy production within the mitochondria strongly links to the development and progression of heart diseases, while Cuproptosis, a newly identified cell death mechanism, has not yet been comprehensively analyzed from the aspect of cardiovascular medicine. 8 transcriptome profiles curated from the GEO database were integrated, from which a diagnostic model based on the Stacking algorithm was established. The efficacy of the model was evaluated in a multifaced manner (i.e., by Precision-Recall curve, Receiver Operative Characteristic curve, etc.). We also sequenced our animal models at the bulk RNA level and conducted qPCR and immunohistochemical staining, with which we further validated the expression of the key contributor gene to the model. Finally, we explored the immune implications of the key contributor gene. A merged machine learning model containing 4 Cuproptosis-related genes (i.e., PDHB, CDKN2A, GLS, and SLC31A1) for robust AMI diagnosis was developed, in which SLC31A1 served as the key contributor. Through in vivo modeling, we validated the aberrant overexpression of SLC31A1 in AMI. Besides, further transcriptome analysis revealed that its high expression was correlated with significant potential immunological implications in the infiltration of many immune cell types, especially monocyte. We constructed an AMI diagnostic model based on Cuproptosis-related genes and validated the key contributor gene in animal modeling. We also analyzed the effects on the immune system for its overexpression in AMI.