Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, and if it is accurately predicted, severe fibrosis and cirrhosis can be prevented. While liver biopsies, the gold standard for NAFLD diagnosis, is intrusive, expensive, and prone to sample errors, noninvasive studies are extremely promising but are still in their infancy due to a dearth of comprehensive study data and sophisticated multimodal data methodologies. This paper proposes a novel approach for diagnosing NAFLD by integrating a comprehensive clinical dataset with a multimodal learning-based prediction method. The dataset comprises physical examinations, laboratory and imaging studies, detailed questionnaires, and facial photographs of a substantial number of participants, totaling more than 6000. This comprehensive collection of data holds significant value for clinical studies. The dataset is subjected to quantitative analysis to identify which clinical metadata, such as metadata and facial images, has the greatest impact on the prediction of NAFLD. Furthermore, a multimodal learning-based prediction method (DeepFLD) is proposed that incorporates several modalities and demonstrates superior performance compared to the methodology that relies only on metadata. Additionally, satisfactory performance is assessed through verification of the results using other unseen data. Inspiringly, the proposed DeepFLD prediction method can achieve competitive results by solely utilizing facial images as input rather than relying on metadata, paving the way for a more robust and simpler noninvasive NAFLD diagnosis.

Efficiently capturing multi-scale local information and building long-range dependencies among pixels are essential for medical image segmentation because of the various sizes and shapes of the lesion regions or organs. In this paper, researchers propose the multi-scale cross-axis attention (MCA) mechanism to address these challenges through enhanced axial attention. To address the issues of insufficient learning of positional bias and limited long-distance interaction in axial attention caused by the small dataset, researchers propose using a dual cross-attention mechanism instead of axial attention to enhance global information capture. Meanwhile, to compensate for the lack of explicit attention to local information in axial attention, researchers use multiple convolutions of strip-shaped kernels with different kernel sizes in each axial attention path, which improves the efficiency of MCA in local information encoding. By integrating MCA into the multi-scale cross-axis attention network (MSCAN) backbone, researchers develop their network architecture, termed MCANet. With merely 4 M+ parameters, MCANet outperforms previous heavyweight approaches (e.g., swin transformer-based methods) across four challenging tasks: skin lesion segmentation, nuclei segmentation, abdominal multi-organ segmentation, and polyp segmentation. The code is available at https://github.com/haoshao-nku/medical_seg. 

The study reveals that nicotinic acid (vitamin B3) significantly protects the liver during ischemia-reperfusion injury by suppressing ferroptosis through improved mitochondrial health. In mouse and cell models, pretreatment with nicotinic acid reduced oxidative stress, enhanced mitophagy and mitochondrial biogenesis, and lowered liver enzyme levels, offering a promising, low-cost therapeutic strategy for liver surgery and transplantation.

This study reveals how lipid metabolism dysregulation promotes colorectal cancer liver metastasis (CRLM) through a novel YTHDF3-mediated mechanism involving m⁶A RNA modification and liquid-liquid phase separation.

Though it has long been recognized that the liver exhibits remarkable immune tolerance, the underlying mechanisms driving this phenomenon remain poorly understood. Here, we explore the liver’s unique immune tolerance—a critical feature that enables it to process gut- and diet-derived inflammogens without eliciting excessive inflammation. We propose that entero-pancreatic peptide hormones (e.g., GLP-1, GIP, CCK, glucagon, VIP, amylin) and postprandially reabsorbed bile acids (BAs), delivered at high concentrations via the portal vein, activate G protein-coupled receptors and trigger cAMP signalling pathways that ultimately promote anti-inflammatory responses through mechanisms such as PKA–CREB and Epac activation. These pathways have been implicated in suppressing inflammation, and fostering a tolerogenic phenotype in resident immune cells.

Lymphatic malformations (LMs) are congenital vascular anomalies characterized by abnormal lymphatic development, leading to disfigurement and severe clinical complications. However, the immunopathological mechanisms underlying LMs remain poorly defined. Here, we provide a comprehensive single-cell immune landscape of LMs by integrating single-cell RNA, T-cell receptor, and B-cell receptor sequencing (scRNA-seq, scTCR-seq, and scBCR-seq) on peripheral blood and pleural effusion samples, uncovering profound immune dysregulation and chronic inflammation.

The intestinal epithelium undergoes rapid renewal every 3–5 days, a process driven by intestinal stem cells (ISCs) located at the base of crypts. While ISCs play an essential role in epithelial regeneration following injury, such as that induced by chemotherapy or radiation, the underlying regulatory mechanisms remain incompletely understood.