Social jetlag and obstructive sleep apnea severity: A retrospective study based on polysomnography.

Background: Social jetlag, representing the misalignment between endogenous circadian rhythms and socially imposed sleep schedules, has been associated with various adverse health outcomes. However, its potential relationship with obstructive sleep apnea (OSA) severity, as quantified by the apnea-hypopnea index (AHI), remains unclear.

Methods: This retrospective study analyzed data from participants with OSA (AHI ≥5 events/hour) who completed sleep questionnaires and underwent polysomnography at our sleep center.

The effects of a solution-focused model discharge readiness coaching intervention on the parents of Chinese pediatric liver transplantation patients: a single-center observational study.

Background: Liver transplantation (LT) is the only effective treatment for pediatric diseases such as cholestatic diseases, genetic metabolic diseases, acute liver failure, and liver malignancies. With the continuous improvement of LT technology, more and more children with end-stage liver disease are receiving this life-saving treatment. Parents of pediatric LT patients often face significant psychological challenges, including anxiety, depression, and a lack of confidence in their ability to provide postoperative care.

Accelerating prostate rs-EPI DWI with deep learning: Halving scan time, enhancing image quality, and validating in vivo.

Objectives: This study aims to evaluate the feasibility and effectiveness of deep learning-based super-resolution techniques to reduce scan time while preserving image quality in high-resolution prostate diffusion-weighted imaging (DWI) with readout-segmented echo-planar imaging (rs-EPI).

Methods: We retrospectively and prospectively analyzed prostate rs-EPI DWI data, employing deep learning super-resolution models, particularly the Multi-Scale Self-Similarity Network (MSSNet), to reconstruct low-resolution images into high-resolution images. Performance metrics such as structural similarity index (SSIM), Peak signal-to-noise ratio (PSNR), and normalized root mean squared error (NRMSE) were used to compare reconstructed images against the high-resolution ground truth (HR).

Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.

Background: Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization.

A high arousal threshold is associated with nocturnal gastroesophageal reflux in obstructive sleep apnea.

Objective: Nocturnal gastroesophageal reflux (nGER) was reported to be associated with obstructive sleep apnea (OSA). However, pathophysiological traits in OSA patients with nGER were poorly understood, and arousal threshold (ArTH) might play an important role in this relationship. This study aimed to identify the clinical characteristics of patients with OSA comorbid with nGER, and investigate the association between ArTH and nGER in patients with OSA.

Assessment of viable tumours by [Ga]Ga-FAPI-04 PET/CT after local regional treatment in patients with hepatocellular carcinoma.

Purpose: To investigate the efficacy of [Ga]Ga-FAPI-04 PET/CT for assessing viable tumours (VTs) after local regional treatment (LRT) in hepatocellular carcinoma (HCC) patients. The related imaging features of HCC after LRT are preliminarily discussed.

Methods: A cohort of 37 LRT patients with HCC (encompassing 51 lesions) was retrospectively included from a prospective parent study (ChiCTR2000039099), and sequential PET/CT using [F]FDG and [Ga]Ga-FAPI-04 was performed.

Identification and validation of a novel anoikis-related prognostic model for prostate cancer.

Background: Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis-related prognostic model for prostate cancer (PCa).

Methods: We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset.

Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics.

Objective: Accurate identification of testicular tumors through better lesion characterization can optimize the radical surgical procedures. Here, we compared the performance of different machine learning approaches for discriminating benign testicular lesions from malignant ones, using a radiomics score derived from magnetic resonance imaging (MRI).

Methods: One hundred fifteen lesions from 108 patients who underwent MRI between February 2014 and July 2022 were enrolled in this study.

A molecular classification system for estimating radiotherapy response and anticancer immunity for individual breast cancer patients.

Objective: Radiotherapy is a cornerstone of breast cancer therapy, but radiotherapy resistance is a major clinical challenge. Herein, we show a molecular classification approach for estimating individual responses to radiotherapy.

Methods: Consensus clustering was adopted to classify radiotherapy-sensitive and -resistant clusters in the TCGA-BRCA cohort based upon prognostic differentially expressed radiotherapy response-related genes (DERRGs).

A glycosylation signature for predicting the progression and immunotherapeutic response of prostate cancer.

Background: Glycosylation has been proposed as a new cancer hallmark. However, focusing on specific glycans or glycoproteins may lose much data relevant to glycosylation alterations. The present study aimed to first comprehensively investigate the expression and mutation profiles of glycosylation-related genes (GRgenes) in prostate cancer (PCa) and then develop a glycosylation signature and explore its role in predicting the progression and immunotherapeutic response of PCa.

InterNet: Detection of Active Abdominal Arterial Bleeding Using Emergency Digital Subtraction Angiography Imaging With Two-Stage Deep Learning.

Objective: Active abdominal arterial bleeding is an emergency medical condition. Herein, we present our use of this two-stage InterNet model for detection of active abdominal arterial bleeding using emergency DSA imaging.

Methods: Firstly, 450 patients who underwent abdominal DSA procedures were randomly selected for development of the region localization stage (RLS).

Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram.

Background: Implementation of deep learning systems (DLSs) for analysis of barium esophagram, a cost-effective diagnostic test for esophageal cancer detection, is expected to reduce the burden to radiologists while ensuring the accuracy of diagnosis.

Objective: To develop an automated DLS to detect esophageal cancer on barium esophagram.

Methods: This was a retrospective study using deep learning for esophageal cancer detection.

Comparing the effects of dexmedetomidine versus propofol on the treatment of emergence agitation in adult patients after general anesthesia: study protocol for a randomized, superiority, controlled trial (DP-TEA Trial).

Background: Emergence agitation (EA) after general anesthesia is a common complication in the post-anesthesia care unit (PACU). Once EA occurs, there are still no guidelines established for the treatment in adults. Propofol is excessively used in managing agitated patients in the PACU, but it lacks analgesia and can result in apnea.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset.

Background: The aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge.

Methods: A total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected.

Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions.

Purpose: To compare the performance of histogram analysis and intra-perinodular textural transition (Ipris) for distinguishing between benign and malignant testicular lesions.

Patients And Methods: This retrospective study included 76 patients with 80 pathologically confirmed testicular lesions (55 malignant, 25 benign). All patients underwent preoperative T2-weighted imaging (T2WI) on a 3.

Assessment of the Severity of Coronavirus Disease: Quantitative Computed Tomography Parameters versus Semiquantitative Visual Score.

Objective: To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19).

Materials And Methods: We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical).

Noninvasive Differentiation of Obstructive Azoospermia and Nonobstructive Azoospermia Using Multimodel Diffusion Weighted Imaging.

Rationale And Objectives: To evaluate the diagnostic performance of parameters derived from multimodel diffusion weighted imaging (monoexponential, stretched-exponential diffusion weighted imaging and diffusion kurtosis imaging [DKI]) from noninvasive magnetic resonance imaging in distinguishing obstructive azoospermia (OA) from nonobstructive azoospermia (NOA).

Materials And Methods: Forty-six patients with azoospermia were prospectively enrolled and classified into two groups (21 OA patients and 25 NOA patients). The multimodel parameters of diffusion-weighted imaging (DWI; apparent diffusion coefficient [ADC], distributed diffusion coefficient [DDC], diffusion heterogeneity [α], diffusion kurtosis diffusivity [D], and diffusion kurtosis coefficient [K]) were derived.

Discrimination between benign and malignant testicular lesions using volumetric apparent diffusion coefficient histogram analysis.

Purpose: This study aimed to evaluate the role of volumetric apparent diffusion coefficient (ADC) histogram analysis in discriminating between benign and malignant testicular masses.

Methods: In this retrospective study, fifty-nine patients with 61 pathologically confirmed testicular masses were consecutively enrolled, including 18 benign lesions and 43 malignant lesions. All patients conducted preoperative magnetic resonance imaging (MRI) with diffusion-weighted imaging.

T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma.

To evaluate the performance of a T2-weighted image (T2WI)-based radiomics signature for differentiating between seminomas and nonseminomas. In this retrospective study, 39 patients with testicular germ-cell tumors (TGCTs) confirmed by radical orchiectomy were enrolled, including 19 cases of seminomas and 20 cases of nonseminomas. All patients underwent 3T magnetic resonance imaging (MRI) before radical orchiectomy.

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

Purpose: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).

Materials And Methods: Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient.

Abbreviated Biparametric Versus Standard Multiparametric MRI for Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis.

Objective: The objective of our study was to evaluate the diagnostic accuracy of abbreviated biparametric MRI (bpMRI) versus standard multiparametric MRI (mpMRI) for prostate cancer (PCa) using guided biopsy or prostatectomy histopathology results as the reference standard.

Materials And Methods: A comprehensive literature search of PubMed, Web of Science, and Cochrane Library databases was performed by two researchers independently and the relevant references were assessed. Original research studies comparing bpMRI with mpMRI in diagnosing PCa were included.