Explore the vast range of titles available.

Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information processing, elevate medical service quality, and aid clinical decision-making. Nonetheless, current methods exhibit limitations in contextual awareness and insufficient consideration of contextual relevance and interactions between entities. In this study, we initially encode medical text inputs using the Chinese pre-trained RoBERTa-wwm-ext model to extract comprehensive contextual features and semantic information. Subsequently, we employ recurrent neural networks in conjunction with the multi-head attention mechanism as the primary gating structure for parallel processing and capturing inter-entity dependencies. Finally, we leverage conditional random fields in combination with the cross-entropy loss function to enhance entity recognition accuracy and ensure label sequence consistency. Extensive experiments conducted on datasets including MCSCSet and CMeEE demonstrate that the proposed model attains F1 scores of 91.90% and 64.36% on the respective datasets, outperforming other related models. These findings confirm the efficacy of our method for recognizing named entities in Chinese medical texts.

Skin diseases are common medical conditions, and early detection significantly contributes to improved cure rates. To address the challenges posed by complex lesion morphology, indistinct boundaries, and image artifacts, this paper proposes a skin lesion segmentation method based on multi-scale attention and bidirectional long short-term memory (Bi-LSTM). Built upon the U-Net architecture, the proposed model enhances the encoder with dense convolutions and an adaptive feature fusion module to strengthen feature extraction and multi-scale information integration. Furthermore, it incorporates both channel and spatial attention mechanisms along with temporal modeling to improve boundary delineation and segmentation accuracy. A generative adversarial network (GAN) is also introduced to refine the segmentation output and boost generalization performance. Experimental results on the ISIC2017 dataset demonstrate that the method achieves an accuracy of 0.950, a Dice coefficient of 0.902, and a mean Intersection over Union (mIoU) of 0.865. These results indicate that the proposed approach effectively improves lesion segmentation performance and offers valuable support for computer-aided diagnosis of skin diseases.

The gut microbiome plays a significant role in dialysis. As disease progresses, the choice of dialysis method and dietary habits change, and the diversity and richness of the gut microbiome in patients on dialysis change as well. The uremic toxins produced exacerbate inflammatory responses and oxidative stress, leading to markedly different incidence rates of complications such as cardiovascular disease and dialysis-associated peritonitis among patients on dialysis. The intake of probiotics, prebiotics, synbiotics, and natural medicines during daily life can regulate the gut microbiome, reduce the production of uremic toxins in patients on dialysis. This review found that the occurrence of complications in dialysis patients is related to changes in the gut microbiome and the accumulation of uremic toxins. The use of probiotics, prebiotics, synbiotics, and natural medicines can improve these conditions and reduce the incidence of dialysis-related complications.

Diabetic kidney disease (DKD), one of the most prevalent microvascular complications of diabetes mellitus, is characterized by a complex pathogenesis in which lipid metabolism dysregulation plays a central role. Increasing evidence indicates impaired lipophagy, a selective autophagic process responsible for degrading lipid droplets, contributes substantially to renal lipid accumulation and subsequent kidney injury in DKD. Natural medicines, leveraging their multi-target and multi-pathway regulatory properties, exert considerable therapeutic potential through modulation of lipophagy and restoration of lipid homeostasis. This review synthesizes current studies on the efficacy of natural medicines in enhancing renal lipophagy and attenuating lipid-mediated kidney injury in DKD. We systematically analyze major classes of natural medicines, including flavonoids, polyphenols, terpenoids, alkaloids, and polysaccharides, and discuss their mechanisms of action through key signaling pathways such as AMPK/mTOR, PPARα/γ, and SIRT1/FoxO1. These natural medicines effectively reduce renal lipid accumulation, mitigate oxidative stress and inflammation, and alleviate pathological damage in various DKD models. Their pleiotropic effects suggest promising therapeutic avenues for DKD through the restoration of lipophagic flux and lipid homeostasis. Nonetheless, significant challenges remain, including incomplete elucidation of precise molecular mechanisms and a scarcity of robust clinical validation. Future research must prioritize the rigorous identification of natural medicines, detailed mechanistic exploration, and well-designed clinical trials to translate the potential of natural medicine-mediated lipophagy regulation into effective therapeutic strategies for DKD.

Hedgehog (Hh) signaling patterns embryonic tissues and contributes to homeostasis in adults. In Drosophila , Hh transport and signaling are thought to occur along a specialized class of actin-rich filopodia, termed cytonemes. Here, we report that Interference hedgehog (Ihog) not only forms a Hh receptor complex with Patched to mediate intracellular signaling, but Ihog also engages in trans -homophilic binding leading to cytoneme stabilization in a manner independent of its role as the Hh receptor. Both functions of Ihog ( trans -homophilic binding for cytoneme stabilization and Hh binding for ligand sensing) involve a heparin-binding site on the first fibronectin repeat of the extracellular domain. Thus, the Ihog-Ihog interaction and the Hh-Ihog interaction cannot occur simultaneously for a single Ihog molecule. By combining experimental data and mathematical modeling, we determined that Hh-Ihog heterophilic interaction dominates and Hh can disrupt and displace Ihog molecules involved in trans -homophilic binding. Consequently, we proposed that the weaker Ihog-Ihog trans interaction promotes and stabilizes direct membrane contacts along cytonemes and that, as the cytoneme encounters secreted Hh ligands, the ligands trigger release of Ihog from trans Ihog-Ihog complex enabling transport or internalization of the Hh ligand-Ihog-Patched -receptor complex. Thus, the seemingly incompatible functions of Ihog in homophilic adhesion and ligand binding cooperate to assist Hh transport and reception along the cytonemes.

Background Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM), with patients typically remaining asymptomatic until reaching an advanced stage. We aimed to develop and validate a predictive model for DKD in patients with an initial diagnosis of type 2 diabetes mellitus (T2DM) using real-world data. Methods We retrospectively examined data from 3,291 patients (1740 men, 1551 women) newly diagnosed with T2DM at Ningbo Municipal Hospital of Traditional Chinese Medicine (2011–2023). The dataset was randomly divided into training and validation cohorts. Forty-six readily available medical characteristics at initial diagnosis of T2DM from the electronic medical records were used to develop prediction models based on linear, non-linear, and SuperLearner approaches. Model performance was evaluated using the area under the curve (AUC). SHapley Additive exPlanation (SHAP) was used to interpret the best-performing models. Results Among 3291 participants, 563 (17.1%) were diagnosed with DKD during median follow-up of 2.53 years. The SuperLearner model exhibited the highest AUC (0.7138, 95% confidence interval: [0.673, 0.7546]) for the holdout internal validation set in predicting any DKD stage. Top-ranked features were WBC_Cnt*, Neut_Cnt, Hct, and Hb. High WBC_Cnt, low Neut_Cnt, high Hct, and low Hb levels were associated with an increased risk of DKD. Conclusions We developed and validated a DKD risk prediction model for patients with newly diagnosed T2DM. Using routinely available clinical measurements, the SuperLearner model could predict DKD during hospital visits. Prediction accuracy and SHAP-based model interpretability may help improve early detection, targeted interventions, and prognosis of patients with DM.

Cases of severe pneumonia complicated by empyema due to normal anaerobic flora from the oral cavity are infrequent. Diagnosing anaerobic infections through conventional microbiological test (CMT) is often challenging. This study describes the case of a 67-year-old man, bedridden long-term, who developed severe pneumonia with empyema caused by multiple anaerobic bacterial infections. The patient was hospitalized with a 5-day history of cough, sputum and fever, accompanied by a 2-day history of dyspnea. Despite CMT, the specific etiology remained elusive. However, metagenomic next-generation sequencing (mNGS) identified various anaerobic bacteria in bronchoalveolar lavage fluid (BALF), blood and pleural effusion. The patient was diagnosed with a polymicrobial infection involving multiple anaerobic bacteria. Following treatment with metronidazole and moxifloxacin, the patient's pulmonary symptoms improved. mNGS serves as a valuable adjunctive tool for diagnosting and managing patients whose etiology remains unidentified following CMT.

Nanoparticle (NP)–hydrogel hybrid materials have emerged as promising platforms for controlled drug delivery, combining the tunable chemistry of NPs (e.g., liposomes, polymeric, and inorganic NPs) with the porous, biocompatible networks of hydrogels (e.g., alginate or poly(ethylene glycol)‐based systems). These composites can encapsulate a wide range of bioactive agents—small molecules, peptides, proteins, and nucleic acids—within hydrogel matrices, guided by molecular interactions such as electrostatic forces, hydrogen bonding, and hydrophobic/hydrophilic balance. Such interactions influence both the physicochemical stability and drug release profiles of the system. This review highlights recent advances in NP–hydrogel composites, emphasizing how molecular‐level interactions shape the nanostructure, drug encapsulation, and release behavior. The enhanced mechanical strength, stimuli responsiveness, pharmacokinetics, and biological performance of these materials are also discussed. Particular focus is placed on how improved mechanistic understanding can guide the design of next‐generation hybrid systems with tunable, predictable release for biomedical applications. This review provides a comprehensive overview of NP–hydrogel hybrid materials as versatile drug delivery systems and outlines future research directions for their use in personalized therapy, targeted treatment, and broader clinical translation. This review highlights recent advances in nanoparticle–hydrogel hybrid materials for controlled drug delivery. It explores diverse nanocarrier–hydrogel combinations, drug loading strategies, release mechanisms, and stimuli‐responsive behaviors. Emphasis is placed on the molecular interactions driving hybrid material performance, offering insights into rational design for applications in cancer therapy, wound healing, antimicrobial delivery, and gene‐based therapeutics

Background Anemia is one of the main complications of chronic kidney disease especially kidney failure, which includes treatment with erythropoiesis-stimulating agents and iron supplementation, including intravenous and oral iron. However, intravenous iron may pose limitations, such as potential infusion reactions. Oral iron is mainly composed of divalent iron, which can excessively stimulate the gastrointestinal tract. Iron polysaccharide complex capsules are a novel oral iron trivalent supplement with higher iron content and lower gastrointestinal irritation. However, since high-quality evidence-based medicinal support is lacking, it is necessary to conduct clinical studies to further evaluate the effectiveness and safety of oral iron polysaccharide complex in chronic kidney disease patients. Methods This randomized controlled trial uses an open-label, parallel group design, where the efficacy and safety of maintenance hemodialysis (MHD) participants is evaluated. The experimental group is assigned erythropoietins and iron polysaccharide complex (two capsules each time, bid), and the control group is assigned erythropoietin and sucrose iron (100mg, 2w) injection. Participants (aged 18–75 years) undergoing maintenance hemodialysis were considered for screening. Inclusion criteria included hemoglobin (Hb) ≥110g/L and < 130g/L, transferrin saturation (TSAT) > 20% and < 50%, and serum ferritin (SF) > 200μg/L and < 500μg/L. Exclusion criteria included acute or chronic bleeding, serum albumin < 35g/L, hypersensitive C-reactive protein (HsCRP) > 10 mg/L, and severe secondary hyperparathyroidism (iPTH ≥ 800 pg/mL). Full inclusion and exclusion criteria are described in the “Methods” section. The primary endpoint is TSAT of the participants at week 12. Secondary endpoints include Hb, SF, hematocrit (Hct), HsCRP, pharmacoeconomic evaluation, drug costs, quality of life, and indicators of oxidative stress. The treatment will last for 24 weeks with a follow-up visit at baseline (within 7 days prior to initial treatment) and weeks 4, 8, 12, 16, 20, and 24 after initial treatment. This clinical research includes 9 hemodialysis centers in mainland China and plans to enroll 186 participants. Discussion It is expected that it will provide strong evidence to reveal the clinical efficacy and safety of oral iron in the treatment of chronic CKD-related anemia in MHD patients through this clinical trial. Trial registration Chinese Clinical Trial Registry ChiCTR2000031166 . Registered on March 23, 2020

Antimicrobial resistance (AMR) poses a global health crisis demanding innovative solutions. Traditional antibiotics, though pivotal over the past century in combating bacterial infections, face diminished efficacy against evolving bacterial defense mechanisms, especially in Gram‐negative strains. This study explores self‐assembled ionizable lipid nanoparticles (LNPs) with the incorporation of two ionizable lipid components (one cationic, one anionic) in nanocarriers for advanced antimicrobial drug delivery of the broad‐spectrum antibiotic Piperacillin (Pip). Incorporating cationic ionizable lipid ALC‐0315, recognized as a functional lipid in the Pfizer‐BioNTech mRNA‐based SARS‐CoV‐2 vaccine, into LNPs allowed mesophase transition, pH responsiveness, and ionization behavior in acidic environments found in sites of bacterial infections, to be studied using synchrotron small angle X‐ray scattering, dynamic light scattering, and a 2‐(p‐toluidino)‐6‐naphthalene sulfonic acid assay. Incorporating another anionic ionizable lipid, oleic acid not only modulates the LNPs’ physicochemical properties, such as size, internal phase nanostructure, and surface charge but also synergistically enhances the antimicrobial potency together with ALC‐0315 with a benefit enhancing permeability and fusion with bacterial membranes. This study introduces a strategy for tailoring ionizable lipid compositions in LNPs, providing a new approach to antimicrobial treatment contributing to the fight against AMR. Lipid cubosomes comprising monoolein, ionizable cationic lipid ALC‐0315, and ionizable anionic oleic acid are developed as an antimicrobial drug delivery system. The synergistic effect of the internal inverse cubic structure and the ionic lipid molecular interactions significantly enhances the membrane permeability of piperacillin‐loaded particles, leading to increased Gram‐negative bacteria growth inhibition.