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Uterine fibroids represent one of the most prevalent gynecological tumors; however, their ultrasound images frequently exhibit indistinct boundaries and complex morphologies, thereby complicating accurate segmentation. An enhanced Swin-Unet-based framework, designated the Swin-Unet Edge-Sensitive Segmentation (SES) network, is proposed herein to advance boundary delineation and segmentation accuracy. The SES network incorporates the Residual Channel Attention Network (RCAN) to recalibrate feature responses via channel attention weighting, thereby reinforcing the representation of lesion regions, and the Richer Convolutional Features (RCF) module to preserve multi-scale spatial information through hierarchical feature integration, effectively addressing pixel-level classification in regions with blurred boundaries. The model was evaluated on annotated ultrasound images provided by Shanxi Provincial Children’s Hospital. Experimental findings demonstrate that SES consistently outperforms established architectures, including U-Net, U-Net++, Attention U-Net, and TransUNet, achieving superior performance across multiple indices (Dice coefficient: 0.9452; IoU: 0.8721; accuracy: 0.9358). Ablation analyses further substantiate the pivotal contributions of the RCAN and RCF modules to the overall segmentation performance. The proposed SES framework integrates global modeling capacity, multi-scale attention mechanisms, and edge-sensitive feature extraction to deliver a more accurate and robust solution for the ultrasound image segmentation of uterine fibroids, highlighting its substantial potential for clinical application.

Velamentous cord insertion (VCI) complicates 0.9-1.9% of singleton pregnancies and is associated with adverse perinatal outcomes due to unprotected fetal vessels traversing the membranes. Color Doppler ultrasound enables prenatal diagnosis, yet systematic evaluation of diagnostic performance and risk factor association models remains limited. We hypothesized that systematic second-trimester screening achieves superior diagnostic accuracy before 28 weeks, and that identification of clinical risk factors associated with VCI would enhance understanding of its etiology and inform screening strategies. This retrospective case-control study included 240 singleton pregnancies (120 confirmed VCI cases and 120 age-matched controls) between 2022-2025. All underwent systematic second-trimester color Doppler ultrasound evaluating cord insertion site, membranous vessel length, and vasa previa. Multivariate logistic regression was used to identify independent risk factors associated with VCI and to develop a risk factor association model; model discrimination was assessed using C-index with 1,000-bootstrap internal validation. Color Doppler ultrasound achieved 94.2% sensitivity and 100% specificity for VCI detection, with superior accuracy before 28 weeks (97.3% vs. 85.7%,  = 0.008). Three independent risk factors were identified: Three independent clinical risk factors were identified: ART (aOR = 2.87), maternal anemia (aOR = 2.15), and short cervical length (aOR = 2.34). Placenta previa demonstrated a strong association with VCI (OR = 3.92), likely reflecting a shared spectrum of abnormal trophoblast implantation. The multivariate model demonstrated moderate discrimination (C-index = 0.713, 95% CI: 0.648-0.778) for distinguishing VCI from controls. The VCI cohort was associated with significantly higher rates of adverse outcomes: preterm delivery 22.5% vs. 8.3% (  = 0.002), FGR 18.3% vs. 6.7% (  = 0.004), and perinatal mortality 3.3% vs. 0% (  = 0.042). In tertiary referral settings with experienced operators, targeted color Doppler ultrasound demonstrates high diagnostic accuracy for VCI detection, particularly during second-trimester evaluation. The developed risk factor model demonstrated moderate discrimination in this cohort; external validation in independent populations is required before clinical implementation.

Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6 %. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.

Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6%. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.

Tyrosine kinases are implicated in tumorigenesis and progression, and have emerged as major targets for drug discovery. Tyrosine kinase inhibitors (TKIs) inhibit corresponding kinases from phosphorylating tyrosine residues of their substrates and then block the activation of downstream signaling pathways. Over the past 20 years, multiple robust and well-tolerated TKIs with single or multiple targets including EGFR, ALK, ROS1, HER2, NTRK, VEGFR, RET, MET, MEK, FGFR, PDGFR, and KIT have been developed, contributing to the realization of precision cancer medicine based on individual patient’s genetic alteration features. TKIs have dramatically improved patients’ survival and quality of life, and shifted treatment paradigm of various solid tumors. In this article, we summarized the developing history of TKIs for treatment of solid tumors, aiming to provide up-to-date evidence for clinical decision-making and insight for future studies.

Cancer immunotherapy originated from the use of Coley’s toxins at the end of the nineteenth century. However, immunotherapy had not made great strides in cancer treatment until the discovery of immune checkpoints among which cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death ligand 1 (PD-L1) are the most representative ones. Since the first CTLA-4 inhibitor ipilimumab started the first clinical trial in 2000, immune checkpoint inhibitor (ICI) therapy has gradually emerged as the most successful and widely applied strategy in the field of cancer immunotherapy, revolutionizing treatment paradigms across a broad spectrum of malignancies. Recently, the approvals of monoclonal antibodies targeting lymphocyte activation gene-3 (LAG-3) and novel bispecific antibodies targeting immune checkpoints may indicate the next wave of ICI agents development in cancer immunotherapy. In this review, we aimed to provide a comprehensive overview and in-depth discussion covering the current ICI treatment landscape in the past 26 years (2000-2025), indications and limitations of efficacy-predicting biomarkers, immune-related adverse events, resistance mechanisms and overcoming strategies, as well as future directions of ICI therapy.

The recent discovery of large magnetoresistance in tungsten ditelluride provides a unique playground to find new phenomena and significant perspective for potential applications. The large magnetoresistance effect originates from a perfect balance of hole and electron carriers, which is sensitive to external pressure. Here we report the suppression of the large magnetoresistance and emergence of superconductivity in pressurized tungsten ditelluride via high-pressure synchrotron X-ray diffraction, electrical resistance, magnetoresistance and alternating current magnetic susceptibility measurements. Upon increasing pressure, the positive large magnetoresistance effect is gradually suppressed and turned off at a critical pressure of 10.5 GPa, where superconductivity accordingly emerges. No structural phase transition is observed under the pressure investigated. In situ high-pressure Hall coefficient measurements at low temperatures demonstrate that elevating pressure decreases the population of hole carriers but increases that of the electron ones. Significantly, at the critical pressure, a sign change of the Hall coefficient is observed. Tungsten ditelluride has been recently discovered to possess very large and unsaturated magnetoresistance, which opens interesting perspectives for potential applications. Here the authors show suppression of the magnetoresistance and emergence of superconductivity at high pressure.

Pulmonary fibrosis (PF) is a chronic, progressive interstitial lung disease characterized by excessive deposition of extracellular matrix (ECM) and abnormal fibroblast proliferation, which is mainly caused by air pollution, smoking, aging, occupational exposure, environmental pollutants exposure, and microbial infections. Although antifibrotic agents such as pirfenidone and nintedanib, approved by the United States (US) Food and Drug Administration (FDA), can slow the decline in lung function and disease progression, their side effects and delivery inefficiency limit the overall prognosis of PF. Therefore, there is an urgent need to develop effective therapeutic targets and delivery approaches for PF in clinical settings. This review provides an overview of the pathogenic mechanisms, therapeutic drug targeting signaling pathways, and promising drug delivery strategies for treating PF.

H5N1 avian influenza viruses bearing the clade 2.3.4.4b hemagglutinin gene have been widely circulating in wild birds and are responsible for the loss of over 70 million domestic poultry in Europe, Africa, Asia, and North America since October 2020. During our routine surveillance, 13 H5N1 viruses were isolated from 26,767 wild bird and poultry samples that were collected between September 2021 and March 2022 in China. To investigate the origin of these Chinese isolates and understand their genetic relationship with the globally circulating H5N1 viruses, we performed a detailed phylogenic analysis of 233 representative H5N1 strains that were isolated from 28 countries. We found that, after they emerged in the Netherlands, the H5N1 viruses encountered complicated gene exchange with different viruses circulating in wild birds and formed 16 genotypes. Genotype one (G1) was predominant, being detected in 22 countries, whereas all other genotypes were only detected in one or two continents. H5N1 viruses of four genotypes (G1, G7, G9, and G10) were detected in China; three of these genotypes have been previously reported in other countries. The H5N1 viruses detected in China replicated in mice, with pathogenicity varying among strains; the G1 virus was highly lethal in mice. Moreover, we found that these viruses were antigenically similar to and well matched with the H5-Re14 vaccine strain currently used in China. Our study reveals the overall picture of H5N1 virus evolution and provides insights for the control of these viruses.

Background Triglyceride-glucose (TyG) index as a reliable surrogate of insulin resistance (IR) has been shown to be related to adverse clinical outcomes in patients with acute coronary syndrome, heart failure, ischemic stroke and so on. However, the relationship between TyG index and all-cause mortality in intensive care unit (ICU) patients remains unknown. The purpose of this study was to investigate the correlation between TyG index and all-cause mortality to evaluate the impact of IR on the prognosis of this population. Methods This was a retrospective observational study that included 3026 patients who had an initial triglyceride and glucose data on the first day of ICU admission, and all data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. These patients were grouped into quartiles (Q1–Q4) according to TyG index. The Kaplan–Meier analysis was used to compare all-cause mortality among the above four groups. Cox proportional hazards analyses were performed to examine the association between TyG index and all-cause mortality. Results During 10.46 years of follow-up, 1148 (37.9%) patients died, of which 350 (11.6%) occurred during the hospital stay and 258 (8.5%) occurred during the ICU stay. Kaplan–Meier analysis showed that the risk of all-cause mortality was significantly higher in patients with higher TyG index (log-rank P  = 0.021). Multivariable Cox proportional hazards analyses showed that the TyG index was an independent risk predictor of ICU death (HR: 1.72, 95% CI 1.18–2.52, P  = 0.005) and hospital death (HR: 2.19, 95% CI 1.59–3.03, P  < 0.001), and each 1-unit increased in the TyG index, a 1.19-fold increase in the risk of death during the hospital stay. Conclusions TyG index is strongly related to the all-cause mortality increasing in critically ill patients. This finding indicates that the TyG index might be useful in identifying people at high risk of ICU death and hospital death.