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.

Factor V Serves as an Early Biomarker for Graft Loss After Liver Transplant: A Prospective Evaluation.

Background: Low post-operative day (POD) 1 Factor V has been retrospectively associated with graft loss after liver transplantation when stratified by a cutoff of 0.36 U/mL. We aimed to validate this prospectively.

The development and evaluation of nine obesity-based indices for gallstones in U.S. adults.

Objective: Gallstones have gradually become a highly prevalent digestive disease worldwide. This study aimed to investigate the association of nine different obesity-related indicators (BRI, RFM, BMI, WC, LAP, CMI, VAI, AIP, TyG) with gallstones and to compare their predictive properties for screening gallstones.

Methods: Data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) for the 2017-2020 cycle, and weighted logistic regression analyses with multi-model adjustment were conducted to explore the association of the nine indicators with gallstones.

Multimodal assessment predicts cognitive impairment after aneurysmal subarachnoid hemorrhage: a prospective cohort study.

Background: Risk factors and mechanisms of cognitive impairment (CI) after aneurysmal subarachnoid hemorrhage (aSAH) are unclear. This study used a neuropsychological battery, MRI, ERP and CSF and plasma biomarkers to predict long-term cognitive impairment after aSAH.

Materials And Methods: 214 patients hospitalized with aSAH (n = 125) or unruptured intracranial aneurysms (UIA) (n = 89) were included in this prospective cohort study.

Comparative Analysis of Machine Learning Algorithms Used for Translating Aptamer-Antigen Binding Kinetic Profiles to Diagnostic Decisions.

Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.

Prediction of surgical necessity in children with ureteropelvic junction obstruction using machine learning.

Background: Hydronephrosis developing at the ureteropelvic junction due to obstruction poses clinical challenges as it has the potential to cause renal damage.

Aims: This study aims to evaluate how well machine learning models such, as XGBClassifier and Logistic Regression can be used to predict the need for treatment in patients, with hydronephrosis resulting from ureteropelvic junction obstruction.

Methods: Hydronephrosis was diagnosed in the medical records of patients from January 2015 to December 2020.

Distinct Ocular Surface Microbiome in Keratoconus Patients Correlate With Local Immune Dysregulation.

Purpose: Keratoconus (KC) is characterized by irregular astigmatism along with corneal stromal weakness and is associated with altered immune status. Tissue resident microbiomes are known to influence the immune status in other organs, but such a nexus has not been described in ocular conditions. Therefore, we examined the ocular surface microbiome of patients with KC and correlated it to the immune cell and tear molecular factor profiles.

Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.

Purpose: Descemet membrane endothelial keratoplasty (DMEK) has emerged as a novel approach in corneal transplantation over the past two decades. This study aims to identify predisposing risk factors for post-DMEK ocular hypertension (OHT) and develop a preoperative predictive model for post-DMEK OHT.

Methods: Patients who underwent DMEK at Gangnam Severance Hospital between 2017 and 2024 were included in the study.

Identification and Validation of a m6A-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer.

Accumulating research indicates that N6-methyladenosine (m6A) modification plays a pivotal role in colorectal cancer (CRC). Hence, investigating the m6A-related long noncoding RNAs (lncRNAs) significantly improves therapeutic strategies and prognostic assessments. This study aimed to develop and validate a prognostic model based on m6A-related lncRNAs to improve the prediction of clinical outcomes and identify potential immunological mechanisms in CRC.

Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge-based dose prediction.

Purpose: Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on-treatment process is resource-intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high-priority patients.

Methods: For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (Initial) and a reduced margin initial plan (Initial) for adapting with the Ethos treatment planning system.

Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.

Background: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.

Purpose: To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.

Anatomical Predictors of Access-Related Vascular Complications Following Transfemoral Transcatheter Aortic Valve Replacement.

Background: Access-related vascular complications (VCs) after percutaneous transfemoral transcatheter aortic valve replacement (TAVR) are associated with poor clinical outcomes and remain a significant challenge despite technological advances. The aim of this study was to identify anatomic predictors of access-related VCs after TAVR on preprocedural contrast-enhanced multidetector computed tomography (MDCT).

Aims: The aim of this study was to identify anatomical predictors of access-related VCs after TAVR on preprocedural contrast-enhanced MDCT.

[A new way for the differential diagnosis of hypogonadotropic hypogonadism and constitutional delay of puberty in adolescent men aged 13.5-17 years].

Background: Differential diagnosis of hypogonadotropic hypogonadism (HH) and constitutional delay of puberty (CDP) is extremely important since with the latter puberty begins and completes without any medical intervention and in the case of HH puberty does not occur or is incomplete. Failure to start treatment on time leads to medical and psychosocial maladjustment of the patient.

Aim: Development of a method for differential diagnosis of hypogonadotropic hypogonadism and constitutional delay of puberty in boys 13.

A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study.

Purpose: To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.

Material And Methods: In this multicenter diagnostic study, we retrospectively collected basic information and CT images about the patients concerned from three hospitals. An interactive deep learning-based bladder cancer image segmentation framework was constructed using the Swin UNETR algorithm for further features extraction.

The Association Between the CALLY Index and All-Cause Mortality in Patients with COPD: Results from the Cohort Study of NHANES 2007-2010.

Purpose: The C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index is a newly developed biomarker that combines measurements of CRP, serum albumin, and lymphocyte count. This index provides a thorough assessment of a patient's inflammation level, nutritional condition, and immunological function. The objective of this study is to examine the correlation between the CALLY index and all-cause mortality in COPD patients.

The Diagnostic Value of Plasma NETs Levels and iCEB in Silent Myocardial Ischemia in Maintenance Hemodialysis Patients.

Objective: This study evaluated the diagnostic value of plasma Neutrophil extracellular traps (NETs) levels and the index of cardiac electrophysiological balance (iCEB) in identifying silent myocardial ischemia (SMI) in maintenance hemodialysis (MHD) patients.

Methods: This cross-sectional observational study involved patients receiving MHD treatment. Data were collected on coronary angiography performed in our hospital from February 2023 to February 2024.

Comprehensive Sepsis Risk Prediction in Leukemia Using a Random Forest Model and Restricted Cubic Spline Analysis.

Background: Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.

Diagnostic accuracy of Bellavere's score in cardiac autonomic neuropathy among chronic kidney disease patients: a study of prevalence and dialysis impact.

Background: Cardiac autonomic neuropathy (CAN) is a significant complication in chronic kidney disease (CKD), leading to increased morbidity and mortality. Early detection is essential for managing CKD patients effectively, especially those on hemodialysis. This study evaluated the prevalence CAN in CKD and diagnostic accuracy of Bellavere's Score in predicting CAN in CKD patients, including those undergoing hemodialysis.

The Relationship of Fatty Liver with Body Composition Indices in Individuals with Metabolic Disorders - A Cross-Sectional Study.

Objective: In this study, it was aimed to screen fatty liver in individuals with metabolic disorders, and to investigate the use of some anthropometric calculations and body composition indices in demonstrating fatty liver disease.

Methods: The cross-sectional study included 224 participants with metabolic diseases. Anthropometric measurements of the participants were measured.

Relationship between pregnant women's combined exposure to heavy metals and their offspring's congenital heart defects in Lanzhou, China.

Background: Previous research has demonstrated that exposure to individual heavy metals elevates the incidence rate of congenital heart defects (CHDs). However, there is a paucity of data concerning the relationship between combined exposure to multiple heavy metals and the occurrence of CHDs. This study seeks to investigate the association between combined heavy metal exposure in pregnant women and the incidence of CHDs in their offspring in Lanzhou, China.

Qualitative and Quantitative Evaluation of the Image Quality of MDCT Multiphasic Liver Scans in HCC Patients.

The quality of CT images obtained from hepatocellular carcinoma (HCC) patients is complex, affecting diagnostic accuracy, precision, and radiation dose assessment due to increased exposure risks. The study evaluated image quality qualitatively and quantitatively by comparing quality levels with an effective radiation dose to ensure acceptable quality accuracy. This study retrospectively reviewed 100 known HCC patients (Li-RADS-5) who underwent multidetector computed tomography (MDCT) multiphasic scans for follow-up of their health condition between January and October 2023.