Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.

Background: Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.

Methods: All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers.

Computed tomography enterography-based radiomics nomograms to predict inflammatory activity for ileocolonic Crohn's disease: a preliminary single-center retrospective study.

Objectives: This study aims to develop and validate nomograms that utilize morphological and radiomics features derived from computed tomography enterography (CTE) to evaluate inflammatory activity in patients with ileocolonic Crohn's disease (CD).

Methods: A total of 54 CD patients (237 bowel segments) with clinically confirmed CD were retrospectively analyzed. The Simple Endoscopic Score for Crohn's Disease (SES-CD) was used as a reference standard to quantify the degree of mucosal inflammation and assess disease severity.

Integration of Mendelian Randomization to explore the genetic influences of pediatric sepsis: a focus on RGL4, ATP9A, MAP3K7CL, and DDX11L2.

Objective: This study aims to explore the genetic characteristics of pediatric sepsis through a combined analysis of multiple methods, including Mendelian Randomization (MR), differential gene expression analysis, and immune cell infiltration assessment. It explores their potential as biomarkers for sepsis risk and their involvement in immune-related pathways.

Methods: Differential expression analysis was performed using public datasets to identify genes with significant expression changes between pediatric sepsis patients and healthy controls.

Identification and validation of prognostic biomarkers in ccRCC: immune-stromal score and survival prediction.

Background: The tumor microenvironment (TME) is integral to tumor progression. However, its prognostic implications and underlying mechanisms in clear cell renal cell carcinoma (ccRCC) are not yet fully elucidated. This study aims to examine the prognostic significance of genes associated with immune-stromal scores and to explore their underlying mechanisms in ccRCC.

Evaluation of placental growth factor, Vitamin D, and systemic inflammatory index as predictive biomarkers for preeclampsia severity: a retrospective cohort study.

Aim: Our study aimed to evaluate the predictive value of Placental growth factor (PlGF), Vitamin D, and systemic inflammatory index for assessing preeclampsia risk and severity.

Materials And Methods: This retrospective cohort study included 457 pregnant individuals who delivered at our hospital between March 2023 and October 2024. Participants were divided into three groups: control (n = 217), mild preeclampsia (n = 101), and severe preeclampsia (n = 67).

Correlation between oxygen reserve index monitoring and blood gas oxygen values during anesthesia in robotic total prostatectomy surgery.

Introduction-objective: Hyperoxia is associated with acute lung injury and atelectasis. Arterial blood gas measurement is an invasive method. The Oxygen Reserve Index (ORI) was developed to monitor the oxygen values of patients.

Diagnostic and prognostic values of tsRNAs in lung cancer: a meta-analysis.

Background: Lung cancer (LC) is the leading cause of cancer-related death in humans. tRNA-derived small RNA (tsRNA) is a novel biomarker that plays a crucial role in the genesis and development of LC. In this study, we aimed to investigate the value of differentially expressed tsRNAs in LC through meta-analysis.

Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis.

Objective: Neonatal sepsis, a severe infectious disease associated with high mortality rates, is characterized by metabolic disturbances that play a crucial role in its progression. The aim of this study is to develop a metabolism-related model for assessing 30-day mortality in neonatal sepsis.

Methods: The clinical data of neonatal sepsis at Ganzhou Women and Children's Health Care Hospital from January 2019 to December 2022 were retrospectively analyzed.

Validating the Accuracy of Parkinson's Disease Clinical Diagnosis: A UK Brain Bank Case-Control Study.

Objective: Despite diagnostic criteria refinements, Parkinson's disease (PD) clinical diagnosis still suffers from a not satisfying accuracy, with the post-mortem examination as the gold standard for diagnosis. Seminal clinicopathological series highlighted that a relevant number of patients alive-diagnosed with idiopathic PD have an alternative post-mortem diagnosis. We evaluated the diagnostic accuracy of PD comparing the in-vivo clinical diagnosis with the post-mortem diagnosis performed through the pathological examination in 2 groups.

Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K-means clustering.

Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

Dissecting Tumor Size Underestimation in Pancreatic Cancer: A Comparative Analysis of Preoperative Treatments.

Background: Tumor size (TS) in pancreatic ductal adenocarcinoma (PDAC) is one of the most important prognostic factors. However, discrepancies between TS on preoperative images (TSi) and pathological specimens (TSp) have been reported. This study aims to evaluate the factors associated with the differences between TSi and TSp.

Identification of ubiquitination-related key biomarkers and immune infiltration in Crohn's disease by bioinformatics analysis and machine learning.

Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD.

Clinical characteristics and prognosis of patients treated as invasive pulmonary aspergillosis outside of severe immunosuppression.

This study aimed to identify clinical characteristics and develop a prognostic model for non-neutropenic patients with invasive pulmonary aspergillosis (IPA). A retrospective analysis of 151 IPA patients was conducted, with patients categorized into survival (n = 117) and death (n = 34) groups. Clinical data, including demographics, laboratory tests, and imaging, were collected.

Multi scale multi attention network for blood vessel segmentation in fundus images.

Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).

Development of a predictive nomogram for postoperative acute respiratory distress syndrome in Stanford type A aortic dissection patients: a retrospective study.

Introduction And Objectives: This retrospective study aimed to develop a nomogram to predict the risk of postoperative acute respiratory distress syndrome (ARDS) in patients with Stanford type A acute aortic dissection.

Methods: The study included patients who underwent surgical repair for Stanford type A acute aortic dissection between January 2020 and December 2023. Demographic data, surgical details, intraoperative information, and postoperative outcomes were collected.

Diagnostic value of three urine miRNAs composite score in BK virus nephropathy.

Noninvasive detection of BK virus, for early detection of BK polyomavirus-associated nephropathy post-renal transplantation, is currently an active subject of investigation. In this study, we developed and validated a novel risk score diagnostic assay (PymiR Score) based on measurements of three urine miRNAs, including BKV-related miRNA (bkv-miR-B1-5p), polyomavirus-related miRNA (bkv-miR-B1-3p) and renal tubular injury-related miRNA (miR-21-5p), by quantitative polymerase chain reaction. The limit of detection of the three miRNAs was 2 × 10 copies/mL, while the intra- and inter-assay coefficients of variation were in the ranges of 2.

Clinical significance analysis of microRNA-199a-3p in gingival crevicular fluid for patients with chronic periodontitis.

Objective: The aim was to investigate the clinical performance of microRNA-199a-3p (miR-199a-3p) in patients with chronic periodontitis.

Methods: 91 patients with chronic periodontitis and 78 healthy individuals were enrolled for the research subjects. MiR-199a-3p expression was detected using real-time quantitative PCR (RT-qPCR) assay.

External validation of the "simplified hospital" scale as a predictor of 30-day readmission after hospitalisation in osi araba medical services.

Background: A readmission is defined as a new admission to hospital for at least one night in the 30 days following a discharge. Some are caused by events unrelated to the reason for the initial admission (unavoidable) and others caused by the same pathology (related). Different predictive models allow the identification of patients at higher risk of readmission.

Hypomethylation of the low-density lipoprotein receptor class A domain containing 4 gene in rheumatoid arthritis.

Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease linked to epigenetic changes, particularly DNA methylation. While LDLRAD4 has been implicated in RA through GWAS, its role in RA via methylation remains unclear.

Objectives: To investigate LDLRAD4 methylation patterns in RA and evaluate its potential as a diagnostic and inflammatory biomarker.

Development and validation of a risk stratification model for stroke recurrence after acute ischemic stroke in young adults: A nomogram-based, multicenter retrospective study.

Objective: The prevalence of ischemic stroke in young adults has increased dramatically. However, factors associated with prognosis in this cohort have not been well studied. This study primary aimed to construct and validate a nomogram for predicting stroke recurrence and to achieve risk stratification of young adults after acute ischemic stroke (AIS).

Enhancing Osteoblast Activity and Accelerating Fracture Healing via miR-656-3p Downregulation: A Novel Targeting Strategy Focused on BMP-2 Expression.

Delayed fracture healing (DFH), a common complication of post-fracture surgery, exhibits an incompletely understood pathogenesis. The present study endeavors to investigate the roles and underlying mechanisms of miR-656-3p and Bone Morphogenetic Protein-2 (BMP-2) in DFH. It was recruited 94 patients with normal fracture healing (NFH) and 88 patients with DFH of the femoral neck.

The link between the atherogenic index of plasma and the risk of hypertension: Analysis from NHANES 2017-2020.

Background: The atherogenic index of plasma (AIP) is a newly identified metabolic marker for atherosclerosis. However, there are inconsistent conclusions regarding the relationship between AIP and hypertension.

Methods: The study subjects were sourced from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2020.

Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning.

Introduction: Lupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a new form of programmed cell death, has been implicated in a variety of renal diseases, but the relationship between autophagy and LN remains unelucidated.

Methods: We analyzed differentially expressed genes (DEGs) in kidney tissues from 14 LN patients and 7 normal controls using the GSE112943 dataset.

Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.

In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning.

Distinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists.

Introduction: Benign and malignant myxoid soft tissue tumors have shared clinical, imaging, and histologic features that can make diagnosis challenging. The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists.

Methods: Manual segmentation of 90 myxoid soft tissue tumors (45 myxomas and 45 myxofibrosarcomas) was performed on axial T1, and T2FS or STIR magnetic resonance imaging sequences.