Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study.

Background: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.

Materials And Methods: We retrospectively analysed the patients' clinical baseline data, serological indicators, and ultrasound imaging data.

CT features with histopathological correlation in inflammatory versus benign & malignant neoplastic appendiceal mucoceles: a retrospective cross-sectional study.

Background & Objectives: Differentiation of histologic subtypes of appendiceal mucoceles may prove to be difficult on computed tomography (CT). The main objective of this study was to identify the CT features of mucocele of the appendix and correlate the imaging findings with histopathology in inflammatory, benign, and malignant neoplastic lesions, and whether these entities can be accurately differentiated on CT imaging.

Materials And Methods: CT scans of 31 patients with diagnosis of appendiceal mucocele were retrospectively reviewed and compared with histopathology.

Correcting scale distortion in RNA sequencing data.

RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used for population-based studies designed to identify genetic determinants of various diseases. Naturally, the accuracy of these tests should be verified and improved if possible.

Validation of mortality risk scores after esophagectomy.

Purpose: Oncological esophagectomy is the mainstay in esophageal cancer treatment, but perioperative mortality remains a significant concern. Various scoring systems exist to identify patients at high risk for postoperative complications and death. In the following, we aim to evaluate and compare these different scoring systems.

Bile-Derived cfDNA of Syncytin-1 and SLC7A11 as a Potential Molecular Marker for Early Diagnosis of Cholangiocarcinoma.

Purpose: Liquid biopsy technology has received widespread attention in the early diagnosis of cholangiocarcinoma (CCA).

Methods: We collected bile samples from 48 patients with CCA and 48 patients with gallstones at Shandong Provincial third Hospital. We quantified bile circulating free DNA (cfDNA) of syncytin-1 and SLC7A11, calculated the correlation between syncytin-1 and SLC7A11 expression and clinical parameters by Spearman rank correlation, plotted Receiver Operating Characteristic (ROC) curves, and compared the Area Under Curve (AUC) values to explored early diagnostic utility in patients.

Value of multi-parameter I-MIBG scintigraphy in the differential diagnosis of Parkinson's disease.

Background: I-MIBG scintigraphy plays a significant role in diagnosing Parkinson's disease (PD), with most studies primarily targeting cardiac uptake and relying on traditional ratio-based parameters for assessment. However, due to variations in scanning conditions and image processing methodologies, the clinical utility of different parameters remains a subject of debate. This study aims to evaluate the diagnostic accuracy of multi-parameter I-3-Iodobenzylguanidine (MIBG) scintigraphy and to identify the most reliable metrics for distinguishing PD from Parkinson-plus syndromes.

Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.

The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD.

Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images.

Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV.

The link between sleep bruxism and oxidative stress based on a polysomnographic study.

Oxidative stress is proven to increase cardiovascular risk and to diminish healthy life expectancy. Sleep bruxism (SB) is a prevalent masticatory muscle activity during sleep characterized by heterogeneous etiology and inadequately recognized pathophysiology. Recent theories have proposed a potential association between SB and oxidative stress.

Prediction of subsequent vertebral fracture after percutaneous vertebral augmentation using MRI-based vertebral bone quality and CT-based Hounsfield units: a retrospective cross-sectional study.

Subsequent vertebral fracture (SVF) is a common and refractory complication after percutaneous vertebral augmentation (PVA) for osteoporotic vertebral compression fracture (OVCF). Computed tomography (CT)-based Hounsfeld units (HU) and magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score can evaluate osteoporosis quantitatively, hyperlipidemia(HLP) might affect measurement result of VBQ score. The primary objective of this study is to compare the predictive capabilities of HU and VBQ for SVF, and to clarify the impact of hyperlipidemia on the predictive abilities.

Renal and Peripheral Blood Transcriptome Signatures That Predict Treatment Response in Proliferative Lupus Nephritis-A Prospective Study.

Mechanisms contributing to non-response to treatment in lupus nephritis (LN) are unclear. We characterised the transcriptome of paired peripheral blood mononuclear cells (PBMCs) and renal tissues in LN before and after cyclophosphamide (CYC) treatment and identified markers that predicted treatment response. Total RNA isolated from paired PBMCs (n = 32) and renal tissues (n = 25) of 16 proliferative LN before CYC treatment, 6 months post-treatment, and during renal flare, was sequenced on Illumina Novaseq-6000 platform.

Stroke Triage Alpha Rescue Score (STARS): Prehospital phone-based tool to identify cerebral large vessel occlusion.

Introduction: Prehospital identification of stroke patients with large vessel occlusion (LVO) is crucial to optimize transport to an endovascular thrombectomy (EVT)-capable center. Existing scores require medical or paramedical expertise and specific teachings. We aimed to validate a simple prehospital phone-based score for LVO identification.

Radiomic signatures derived from baseline 18F FDG PET/CT imaging can predict tumor-infiltrating lymphocyte values in patients with primary breast cancer.

To determine the value of radiomics data extraction from baseline 18F FDG PET/CT in the prediction of tumor-infiltrating lymphocytes (TILs) among patients with primary breast cancer (BC).We retrospectively evaluated 74 patients who underwent baseline 18F FDG PET/CT scans for BC evaluation between October 2020 and April 2022. Radiomics data extraction resulted in a total of 131 radiomic features from primary tumors.

Prognostic significance of cytokine dysregulation in critically ill COVID-19 patients.

Background: Understanding the immunopathogenesis of COVID-19 has yielded valuable insights into predicting adverse outcomes-particularly mortality. However, significant gaps persist in our comprehension of the complex interplay among the proposed pathophysiological mechanisms. Here, we aim to investigate the immunological factors associated with mortality in critically ill, unvaccinated COVID-19 patients admitted to the intensive care unit (ICU).

Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis.

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three lung cancer patients undergoing chemotherapy treatments. Serum samples were taken before and after the treatment.

Research on predicting radiographic exposure time in imaging based on neural network prediction models.

Objective: To explore the anatomical and clinical factors that affect the radiographic exposure time in radial artery cerebral angiography and to establish a model.

Method: A total of 210 patients who underwent radial artery cerebral angiography at this center from September 2021 to May 2022 were selected, and their anatomical and clinical factors were analyzed to evaluate the correlation between these factors and the duration of radiographic exposure. A related neural network prediction model was established.

Population-level impact of COVID-19 on thyroid function tests: Results from a repeated cross-sectional study.

Purpose: Reports have highlighted thyroid abnormalities, including subacute thyroiditis and thyrotoxicosis, in COVID-19 patients, with a potential link between thyroid dysfunction and disease severity. However, population-level studies on COVID-19's impact on thyroid hormone levels are limited. We aimed to assess the impact of the COVID-19 pandemic on thyroid function tests at the population level.

Risk Prediction Model of Peristomal Skin Complications Among Patients with Colorectal Cancer and an Ostomy: A Cross-sectional Study in Shanghai, China.

Objective: To analyze the risk factors for peristomal skin complications (PSCs) in patients with colorectal cancer and an ostomy, construct a prediction model, and verify its effectiveness.

Methods: In this cross-sectional study, researchers recruited 265 patients with an ostomy at the stoma clinic of a tertiary hospital, from May 2022 to August 2023. Patients were divided into two groups: complications group (n = 81) and no complications group (n = 184).

Prognostic factors in severe dengue patients: A multi-center retrospective cohort study.

Background/purpose: Early detection of severe dengue (SD) and appropriate management are crucial in reducing the case fatality rate. The objective of this study was to investigate the clinical characteristics of SD and identify independent risk factors associated with mortality among SD patients.

Methods: A retrospective study was conducted at two medical center hospitals between 2002 and 2019, involving patients aged ≧18 years with laboratory-confirmed SD.

A clinical study exploring the prediction of microvascular invasion in hepatocellular carcinoma through the use of combined enhanced CT and MRI radiomics.

Objective: To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI).

Methods: A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue.

The Added Effect of Artificial Intelligence in CT Assessment of Abdominal Lymphadenopathy.

Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).

Biomarker Panels Associated with Diagnosis and Overall Survival in Hepatocellular Carcinoma Revealed from Protein-Protein and mRNA-miRNA Interaction Networks.

Background: Hepatocellular carcinoma (HCC), the most common form of liver cancer, has a significant mortality rate, largely due to late diagnosis. Recent advances in medical research have demonstrated the potential of biomarkers for early detection. Moreover, the discovery and use of prognostic biomarkers offer a ray of hope in the fight against liver cancer.

Preoperative predictive indicators for resolution of hypertension in patients with unilateral primary aldosteronism: development of a nomogram model.

Background: Primary aldosteronism (PA) is the leading surgically treatable cause of hypertension, with adrenalectomy as the definitive treatment for unilateral PA (UPA). However, some patients have persistent hypertension after surgery. This study aims to identify preoperative factors affecting surgical outcomes and develop a predictive model for postoperative hypertension resolution.

Construction and validation of a nomogram prediction model for the occurrence of complications in patients following robotic radical surgery for gastric cancer.

Background: In the last two decades, robotic-assisted gastrectomy has become a widely adopted surgical option for gastric cancer (GC) treatment. Despite its popularity, postoperative complications can significantly deteriorate patient quality of life and prognosis. Therefore, identifying risk factors for these complications is crucial for early detection and intervention.