High-content imaging and deep learning-driven detection of infectious bacteria in wounds.

Fast and accurate detection of infectious bacteria in wounds is crucial for effective clinical treatment. However, traditional methods take over 24 h to yield results, which is inadequate for urgent clinical needs. Here, we introduce a deep learning-driven framework that detects and classifies four bacteria commonly found in wounds: Acinetobacter baumannii (AB), Escherichia coli (EC), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA).

Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma.

The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC). One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: = 88 and validation cohort: = 38) were retrospectively recruited. Basic information was obtained from medical records.

Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram.

Purpose: This study aimed to explore the predictive performance of diffusion-weighted imaging with apparent diffusion coefficient map in predicting the proliferation rate of hepatocellular carcinoma and to develop a radiomics-based nomogram.

Methods: This was a single-center retrospective study. A total of 110 patients were enrolled.

Radiomic model to predict the expression of PD-1 and overall survival of patients with ovarian cancer.

Background: Programmed cell death 1 (PD-1), encoded by programmed cell death protein 1 (PDCD1), is widely investigated in clinical trials. We aimed to develop a radiomic model to discriminate its expression levels patients with ovarian cancer (OC) and explore its prognostic value.

Methods: Computed tomography (CT) images with the corresponding sequencing data and clinicopathological features were used.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma.

Background: The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning.

Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis.

Objectives: To develop and validate a combined model based on Gd-BOPTA-enhanced MRI to identify advanced liver fibrosis.

Methods: A total of 102 patients with chronic HBV infection were divided into a training cohort (n = 80) and a time-independent testing cohort 1 (n = 22). In the training cohort, radiomics signatures were extracted from the hepatobiliary phase.

Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma.

Objectives: Microvascular invasion (MVI) affects the postoperative prognosis in hepatocellular carcinoma (HCC) patients; however, there remains a lack of reliable and effective tools for preoperative prediction of MVI. Radiomics has shown great potential in providing valuable information for tumor pathophysiology. We constructed and validated radiomics models with and without clinico-radiological factors to predict MVI.

Continual Decline in Azole Susceptibility Rates in Over a 9-Year Period in China.

Background: There have been reports of increasing azole resistance in , especially in the Asia-Pacific region. Here we report on the epidemiology and antifungal susceptibility of causing invasive candidiasis in China, from a 9-year surveillance study.

Methods: From August 2009 to July 2018, isolates ( = 3702) were collected from 87 hospitals across China.

A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation.

Background: Patients with small hepatocellular carcinoma (HCC) (3 cm) still have a poor prognosis. The purpose of this study was to develop a radiomics nomogram to preoperatively predict early recurrence (ER) (2 years) of small HCC.

Methods: The study population included 111 patients with small HCC who underwent surgical resection (SR) or radiofrequency ablation (RFA) between September 2015 and September 2018 and were followed for at least 2 years.

A radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma.

Background: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive form of hepatocellular carcinoma and is associated with poor survival outcomes.

Aims: This study aimed to develop a radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of MTM-HCC.

Methods: This study enrolled 88 patients with histologically confirmed HCC, including 32 MTM-HCCs and 56 Non-MTM-HCCs.