Clinical predictive model for the 1-year remission probability of IgA vasculitis nephritis

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning

BACKGROUND

T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis.

METHODS

By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the "DrugBank" database to serve as potential therapeutic medications for future TEX-AMI basic research.

RESULTS

We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered.

CONCLUSION

Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.

Semiquantitative classification (SQC) and Oxford classifications predict poor renal outcome better than The International Study of Kidney Disease in Children (ISKDC) and Haas in patients with IgAV nephritis: a multicenter study

INTRODUCTION

Several histologic classifications are used in the evaluation of IgA vasculitis nephritis (IgAVN), however, to date, no studies have determined which one has the strongest association with the severity of IgAVN and, as a consequence, its outcomes.

MATERIALS AND METHODS

Patients included in the study were diagnosed with IgAV and IgAVN in seven tertiary university medical centers in Croatia, Italy and Israel. The International Study of Kidney Disease in Children (ISKDC), Haas, Oxford, and Semiquantitative classification (SQC) classifications were used in the analysis and description of renal biopsy. Time from biopsy to outcome evaluation was a statistically significant factor in outcome prediction that was used to define the base model, and was a covariate in all the tested models.

RESULTS

Sixty-seven patients were included in this study. The SQC classification proved to be the best one in outcome prediction, followed by the Oxford classification. The ISKDC and Haas classifications could not predict renal outcome. The Oxford parameters for mesangial hypercellularity and tubular atrophy, as well as the SQC parameters for cellular crescents showed an independent statistically significant contribution to outcome prediction. High level of twenty-four hour protein excretion was associated with a higher grade in the Oxford, SQC and ISKDC classifications. Endocapillary proliferation was positively associated with the Pediatric Vasculitis Activity Score (PVAS) at diagnosis, while tubular atrophy was negatively associated.

CONCLUSION

The SQC, followed by the Oxford classification were found to provide the best classifications of renal biopsy analysis in patients to predict the outcome in patients with IgAVN. Cellular crescents, mesangial hypercellularity and tubular atrophy showed significant contributions, indicating that active and chronic variables should be included in the estimation.

Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS

The aim of the present study was to develop a radiomics nomogram to assess whether thyroid nodules (TNs) < 1 cm are benign or malignant. From March 2021 to March 2022, 156 patients were admitted to the Affiliated Hospital of Nantong University, and from September 2017 to March 2022, 116 patients were retrospectively collected from the Jiangsu Provincial Hospital of Integrated Traditional Chinese and Western Medicine. These patients were divided into a training group and an external test group. A radiomics nomogram was established using multivariate logistics regression analysis using the radiomics score and clinical data, including the ultrasound feature scoring terms from the thyroid imaging reporting and data system (TI-RADS). The radiomics nomogram incorporated the correlated predictors, and compared with the clinical model (training set AUC: 0.795; test set AUC: 0.783) and radiomics model (training set AUC: 0.774; test set AUC: 0.740), had better discrimination performance and correction effects in both the training set (AUC: 0.866) and the test set (AUC: 0.866). Both the decision curve analysis and clinical impact curve showed that the nomogram had a high clinical application value. The nomogram constructed based on TI-RADS and radiomics features had good results in predicting and distinguishing benign and malignant TNs < 1 cm.