Human gaze-based dual teacher guidance learning for semi-supervised medical image segmentation.

In the field of medical image segmentation, the scarcity of labeled data poses a major challenge for existing models to accurately perceive target regions. Compared with manual annotation, gaze data is easier and cheaper to obtain. As a classical semi-supervised learning framework, mean-teacher can effectively use a large number of unlabeled medical images for stable training through self-teaching and collaborative optimization.

An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules.

Purpose: Accurate classification of pulmonary pure ground-glass nodules (pGGNs) is essential for distinguishing invasive adenocarcinoma (IVA) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), which significantly influences treatment decisions. This study aims to develop a high-precision integrated strategy by combining radiomics-based feature extraction, Quantum Machine Learning (QML) models, and SHapley Additive exPlanations (SHAP) analysis to improve diagnostic accuracy and interpretability in pGGN classification.

Methods: A total of 322 pGGNs from 275 patients were retrospectively analyzed.

The impact of Inter-observation variation on radiomic features of pulmonary nodules.

Objective: In this study, we aimed to comprehensively and systematically analyze the radiomic features of pulmonary nodules and explore the influence of inter-observation variation (IOV) in segmentation regions of interest (ROI) on radiomic features, providing reference information for pulmonary nodule radiomics research.

Method: Six clinicians with varying experience and expertise manually outlined ROIs for 232 pulmonary nodules, while an artificial intelligence (AI) algorithm was trained for automated segmentation. The segmentation by the most experienced cardiothoracic diagnostician (Doctor A) served as the reference standard.

CT-Based radiomics nomogram of lung and mediastinal features to identify cardiovascular disease in chronic obstructive pulmonary disease: a multicenter study.

Rationale And Objectives: To investigate the performance of two diagnostic models based on CT-derived lung and mediastinum radiomics nomograms for identifying cardiovascular disease (CVD) in Chronic Obstructive Pulmonary Disease (COPD) patients.

Materials And Methods: Hospitalized participants with COPD were retrospectively recruited between September 2015 and April 2023. Clinical data and visual coronary artery calcium score (CACS) were collected.

Plant-nanoparticles enhance anti-PD-L1 efficacy by shaping human commensal microbiota metabolites.

Diet has emerged as a key impact factor for gut microbiota function. However, the complexity of dietary components makes it difficult to predict specific outcomes. Here we investigate the impact of plant-derived nanoparticles (PNP) on gut microbiota and metabolites in context of cancer immunotherapy with the humanized gnotobiotic mouse model.

The prevalence of -carrying in the vaginal micro-ecology of pregnant women in late pregnancy.

The colonization of in the female vagina leads to neonatal and pediatric enterococcal septicemia. Linezolid (LZD) is a kind of mainstream drug for treating multidrug-resistant Gram-positive infections. is the main LZD-resistance gene at in human isolates.

Organic Solvent Nanofiltration Membrane with In Situ Constructed Covalent Organic Frameworks as Separation Layer.

Organic solvent nanofiltration (OSN) technology is advantageous for separating mixtures of organic solutions owing to its low energy consumption and environmental friendliness. Covalent organic frameworks (COFs) are good candidates for enhancing the efficiency of solvent transport and ensuring precise molecular sieving of OSN membranes. In this study, p-phenylenediamine (Pa) and 1,3,5-trimethoxybenzene (Tp) are used to construct, in situ, a TpPa COF skin layer via interfacial polymerization (IP) on a polyimide substrate surface.

A novel computed tomography enterography radiomics combining intestinal and creeping fat features could predict surgery risk in patients with Crohn's disease.

Objective: The objective of this study is to segment creeping fat and intestinal wall on computed tomography enterography (CTE) and develop a radiomic model to predict 1-year surgery risk in patients with Crohn's disease.

Methods: This retrospective study included 135 Crohn's disease patients who underwent CTE between January and December 2021 (training cohort) and 69 patients between January and June 2022 (test cohort). A total of 1874 radiomic features were extracted from the intestinal wall and creeping fat respectively on the venous phase CTE images, and radiomic models were constructed based on the selected features using the Boruta and extreme gradient boosting algorithms.

Tryptophan As a New Member of RNA-Induced Silencing Complexes Prevents Colon Cancer Liver Metastasis.

Essential amino acids (EAA) and microRNAs (miRs) control biological activity of a cell. Whether EAA regulates the activity of miR has never been demonstrated. Here, as proof-of-concept, a tryptophan (Trp, an EAA) complex containing Argonaute 2 (Ago2) and miRs including miR-193a (Trp/Ago2/miR-193a) is identified.

Data-Driven and Machine Learning to Screen Metal-Organic Frameworks for the Efficient Separation of Methane.

With the rapid growth of the economy, people are increasingly reliant on energy sources. However, in recent years, the energy crisis has gradually intensified. As a clean energy source, methane has garnered widespread attention for its development and utilization.

Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy.

Objective: To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy.

Methods: A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort.

Deep learning and big data mining for Metal-Organic frameworks with high performance for simultaneous desulfurization and carbon capture.

Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the "one-step" adsorption technology with high-performance metal-organic frameworks (MOFs) was proposed to simultaneously capture the SO and CO. Four machine learning algorithms were used to predict the performance indicators (N, S, and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R = 0.

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD.

Background: Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients.

Methods: This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group.

Outer Membrane Vesicles Released from Garlic Exosome-like Nanoparticles (GaELNs) Train Gut Bacteria that Reverses Type 2 Diabetes via the Gut-Brain Axis.

Gut microbiota function has numerous effects on humans and the diet humans consume has emerged as a pivotal determinant of gut microbiota function. Here, a new concept that gut microbiota can be trained by diet-derived exosome-like nanoparticles (ELNs) to release healthy outer membrane vesicles (OMVs) is introduced. Specifically, OMVs released from garlic ELN (GaELNs) trained human gut Akkermansia muciniphila (A.

CT-based whole lung radiomics nomogram: a tool for identifying the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease.

Objectives: To evaluate the value of CT-based whole lung radiomics nomogram for identifying the risk of cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD).

Materials And Methods: A total of 974 patients with COPD were divided into a training cohort (n = 402), an internal validation cohort (n = 172), and an external validation cohort (n = 400) from three hospitals. Clinical data and CT findings were analyzed.

Genomic Features and Comparative Genomic Analysis of Streptococcus sp. v1. nov., Isolated from an Endophthalmitis Patient.

Endophthalmitis is an acute inflammatory intraocular condition that can cause permanent vision loss. The treatment strategy and visual outcome partly depend on the identification of the agents of pathogens. In this study, metagenomic sequencing was conducted to investigate the microbial and antibiotic resistance genes (ARGs) composition in the vitreous (intraocular body fluid) of an endophthalmitis patient, who progressed rapidly and accompanied by severe pain.

An extracellular vesicular mutant KRAS-associated protein complex promotes lung inflammation and tumor growth.

Extracellular vesicles (EVs) contain more than 100 proteins. Whether there are EVs proteins that act as an 'organiser' of protein networks to generate a new or different biological effect from that identified in EV-producing cells has never been demonstrated. Here, as a proof-of-concept, we demonstrate that EV-G12D-mutant KRAS serves as a leader that forms a protein complex and promotes lung inflammation and tumour growth via the Fn1/IL-17A/FGF21 axis.

Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy.

Objective: To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy.

Methods: Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2-3 immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment.

The value of CT-based radiomics nomogram in differential diagnosis of different histological types of gastric cancer.

To establish and verify a nomogram based on computed tomography (CT) radiomics analysis to predict the histological types of gastric cancer preoperatively for patients with surgical indications. A sum of 171 patients with gastric cancer were included into this retrospective study. The least absolute shrinkage and selection operator (LASSO) was used for feature selection while the multivariate Logistic regression method was used for radiomics model and nomogram building.

Gut bacterial isoamylamine promotes age-related cognitive dysfunction by promoting microglial cell death.

The intestinal microbiome releases a plethora of small molecules. Here, we show that the Ruminococcaceae metabolite isoamylamine (IAA) is enriched in aged mice and elderly people, whereas Ruminococcaceae phages, belonging to the Myoviridae family, are reduced. Young mice orally administered IAA show cognitive decline, whereas Myoviridae phage administration reduces IAA levels.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

Objectives: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination.

Methods: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison.

Garlic exosome-like nanoparticles reverse high-fat diet induced obesity via the gut/brain axis.

Obesity is becoming a global epidemic and reversing the pathological processes underlying obesity and metabolic co-morbidities is challenging. Obesity induced chronic inflammation including brain inflammation is a hallmark of obesity via the gut-brain axis. The objective of this study was to develop garlic exosome-like nanoparticles (GaELNs) that inhibit systemic as well as brain inflammatory activity and reverse a HFD induced obesity in mice.