Kidney-Yang deficiency syndrome is a common geriatric disease that underlies chronic conditions such as diabetic nephropathy, chronic kidney disease, and osteoporosis. As age progresses, the kidney-Yang deficiency syndrome showcases increasingly pronounced manifestations, emerging as a key factor in the comorbidities experienced by elderly patients and affecting their quality of life and overall health status. Traditional Chinese medicine(TCM) has been extensively utilized in the treatment of kidney-Yang deficiency syndrome, with Epimedii Folium, Cinnamomi Cortex, and Lycii Fructus widely used in clinical settings. Despite the complexity of the molecular mechanisms involved in treating kidney-Yang deficiency syndrome, the potential therapeutic value of TCM remains compelling. Delving into the mechanisms of TCM treatment of kidney-Yang deficiency syndrome by regulating the neuro-endocrine-immune system can provide a scientific basis for targeted treatments of this syndrome and lay a foundation for the modernization of TCM. The pathophysiology of kidney-Yang deficiency syndrome involves multiple systems, including the interaction of the neuro-endocrine-immune system, the decline in renal function, the intensification of oxidative stress responses, and energy metabolism disorders. Understanding these mechanisms and their interrelationships can help untangle the etiology of kidney-Yang deficiency syndrome, aiding clinicians in making more precise diagnoses and treatments. Furthermore, the research on the specific applications of TCM in research on these pathological mechanisms can enhance the international recognition and status of TCM, enabling it to exert a greater global influence.
Humans ; Yang Deficiency/physiopathology* ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Kidney Diseases/physiopathology* ; Neurosecretory Systems/physiopathology* ; Animals ; Kidney/physiopathology* ; Endocrine System/physiopathology* ; Immune System/physiopathology*

Traditional Chinese medicine (TCM) exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets. However, the syndrome-based system of diagnosis and treatment in TCM can easily lead to bias because of varying medication preferences among physicians, which has been a major challenge in the global acceptance and application of TCM. Therefore, a standardized TCM prescription system needs to be explored to promote its clinical application. In this study, we first developed a gradient weighted disease-target-herbal ingredient-herb network to aid TCM formulation. We tested its efficacy against intracerebral hemorrhage (ICH). First, the top 100 ICH targets in the GeneCards database were screened according to their relevance scores. Then, SymMap and Traditional Chinese Medicine Systems Pharmacology (TCMSP) databases were applied to find out the target-related ingredients and ingredient-containing herbs, respectively. The relevance of the resulting ingredients and herbs to ICH was determined by adding the relevance scores of the corresponding targets. The top five ICH therapeutic herbs were combined to form a tailored TCM prescriptions. The absorbed components in the serum were detected. In a mouse model of ICH, the new prescription exerted multifaceted effects, including improved neurological function, as well as attenuated neuronal damage, cell apoptosis, vascular leakage, and neuroinflammation. These effects matched well with the core pathological changes in ICH. The multi-targets-directed gradient-weighting strategy presents a promising avenue for tailoring precise, multipronged, unbiased, and standardized TCM prescriptions for complex diseases. This study provides a paradigm for advanced achievements-driven modern innovation in TCM concepts.

In order to explore the possible role and molecular mechanism of the combined action of leech and bear bile in liver and gallbladder diseases, this study first used network pharmacology methods to screen the components and targets of leech and bear bile, as well as the related target genes of liver and gallbladder diseases. The selected key genes were subjected to interaction network and GO/KEGG enrichment analysis. Then, using sodium oleate induced HepG2 cell lipid deposition model and DL-ethionine induced mouse fatty liver model, the activity of leech and bear bile alone and in combination in reducing liver fat was evaluated in vitro and in vivo, and the expression of key pathway related proteins suggested by network pharmacology was detected by Western blot. The results of network pharmacology analysis showed that the active ingredients of leech and bear bile have 295 intersecting targets with liver and gallbladder related diseases, involving more than 200 signaling pathways, including the PI3K/Akt signaling pathway and phospholipase D signaling pathway closely related to glucose and lipid metabolism. The results of in vitro validation experiments showed that both leech and bear bile, alone and in combination, can significantly inhibit the lipid deposition induced by sodium oleate in human liver cells, reduce the triacylglycerol level in cell culture supernatant, and inhibit the lipid content in liver cells. The observation results of Nile red staining confocal microscopy showed that the combination of leech and bear bile had better activity in reducing lipid deposition in liver cells compared to using them alone. In a mouse fatty liver model, the combination of leech and bear bile can better reduce elevated organ indices, blood lipids, and liver lipid levels, as well as lower the levels of serum liver injury biomarkers. The animals used in this experiment and related disposal meet the requirements of animal welfare. Before the experiment, it was reviewed and approved by IACUC, Institute of Materia Medica, Chinese Academy of Medical Sciences. The Western blot experiment results showed that the combination of leech and bear bile can significantly upregulate the expression levels of p-PI3K and p-Akt proteins, and increase the p-PI3K/PI3K and p-Akt/Akt ratios, which is consistent with the predicted results of network pharmacology. The combination of leech and bear bile has great potential for treating fatty liver disease, and activating the PI3K/Akt pathway may be one of the important mechanisms for reducing lipid deposition in liver cells.

ObjectiveTo investigate the feasibility and safety of endoscopic retrograde cholangiopancreatography （ERCP） combined with oral cholangiopancreatography in the treatment of major duodenal papilla gallbladder polyps. MethodsA retrospective analysis was performed for the clinical data of eight patients with choledocholithiasis and gallbladder polyps who underwent ERCP and combined with oral cholangiopancreatography for major duodenal papilla cholecystectomy in Center of Digestive Endoscopy， Jilin People’s Hospital， from May 2022 to June 2024， and related data were collected， including the success rate of surgery， the technical success rate of gallbladder polyp removal， the superselective method of cystic duct， the time of operation， the time of gallbladder polyp removal， and surgical complications. ResultsBoth the success rate of surgery and the technical success rate of gallbladder polyp removal reached 100%， and of all eight patients， three patients used guide wire to enter the gallbladder under direct view， while five patients received oral cholangiopancreatography to directly enter the gallbladder. The time of operation was 51.88±12.34 minutes， and the time of gallbladder polyp removal was 23.13±10.94 minutes. The diameter of gallbladder polyp was 2‍ ‍—‍ ‍8 mm， and pathological examination showed inflammatory polyps in three patients， adenomatous polyps in one patient， and cholesterol polyps in four patients. There were no complications during or after surgery. The patients were followed up for 2‍ ‍—‍ ‍27 months after surgery， and no recurrence of gallbladder polyp was observed. ConclusionOral cholangiopancreatography is technically safe and feasible in endoscopic major duodenal papilla cholecystectomy.

Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Myelodysplastic syndrome (MDS), a myeloid tumor derived from the malignant clones of hematopoietic stem cells, has an annually increasing incidence. The contemporary research direction has shifted to analyzing the synergistic effect of immune surveillance collapse and abnormal bone marrow microenvironment in the pathological process of MDS. Against this backdrop, the immune checkpoint molecule TIM3 has emerged as a key target because of its persistently high expression on the surface of important immune cells such as T and NK cells. The abnormal activation of the TIM3 pathway is the mechanism by which solid tumors and hematological malignancies achieve immune escape and is a key hub in the formation of immune exhaustion phenotypes. This work integrates the original discoveries of our team with the latest international progress, systematically demonstrating the bidirectional regulatory network of TIM3 between the malignant clone proliferation of MDS and the immunosuppressive microenvironment. Integrating the evidence from emerging clinical trials allows us to consider the clinical significance of TIM3-targeted blocking for MDS, providing a transformative path to overcome the resistance of traditional treatments and marking a new chapter in the active immune reconstitution of MDS treatment.

ObjectiveTo investigate the alleviating effect of calcium cyanamide （CaCN₂） soil fumigation on replanting problems of Rehmannia glutinosa. MethodsNewly soil （NP） was used as the control group， while three treatment groups were established： replanted soil （RP）， newly soil treated with CaCN₂ （120 g·m²， tillage depth 25 cm） （NPCC）， and replanted soil treated with CaCN₂ （RPCC）. R. glutinosa was cultivated in all groups. At harvest， the tuber agronomic traits （number of enlarged roots， maximum root diameter， fresh weight， dry weight） were measured. The content of catalpol and rehmannioside D was quantified by ultra-high-performance liquid chromatography （UPLC） to evaluate medicinal quality. Rhizosphere soil available nutrients and enzyme activities were analyzed by assay kits. The community structure and composition of fungi and bacteria in rhizosphere soil were assessed via internal transcribed spacer 2 （ITS2） sequencing and 16S rDNA sequencing， respectively. ResultsCompared with NP， the RP group showed obviously reduced in tuber agronomic traits and quality indicators （P0.05）. However， the RPCC group showed significant improvement in agronomic traits and a notable increase in rehmannioside D content compared to RP （P0.05）. The contents of available phosphorus and potassium in RPCC and NP groups were obviously lower than those in RP （P0.05）. The polyphenol oxidase soil （S-PPO） activity in RP was obviously lower than in NP （P0.05）， while sucrose soil （S-SC）， acid phosphatase soil （S-ACP）， and S-PPO activities in RPCC were obviously higher than in RP （P0.05）. Microbial richness and diversity in RP were obviously higher than in NP （P0.05）， whereas no significant differences were observed between the RPCC and NP. The relative abundances of fungal genera Nectria， Myrothecium， Tomentella， and bacterial genus Skermanella were obviousl lower in RPCC and NP than in RP （P0.05）. Correlation analysis that S-ACP activity was positively correlated with the content of rehmannioside D （P0.05）. Fungal genera Engyodontium and Alternaria， and bacterial genera Pir4 lineage， Pirellula， Methyloversatilis， Brevundimonas， Ralstonia， and Acidibacter were obviously positively correlated with tuber dry weight （P0.05）. Conversely， fungal genera Pseudaleuria， Nectria， Haematonectria， Ceratobasidium， and bacterial genera Streptomyces， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated with dry weight （P0.05）. The fungal genus Alternaria and bacterial genera Brevundimonas， Ralstonia， Acidibacter， and Dongia showed positive correlations with medicinal quality of R.glutinosa tuber， while fungal genera Pseudaleuria， Nectria， Stachybotrys， Fusarium， Gibberella， Ceratobasidium， and bacterial genera Sphingomonas， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated （P0.05）. ConclusionCaCN₂ soil fumigation can significantly improve enzyme activities in replanted Rehmannia rhizosphere soil， enhance the utilization of available nutrients， reshape microbial community structure of replanted R.glutinosa at the family and genus level， and notably improve tuber agronomic traits and medicinal quality. This study provides a novel approach to alleviating replanting problems and offers insights for the integrated development of standardized cultivation techniques， including soil disinfection， nutrient-targeted regulation， and microbial inoculant application.

Nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) is a cytosolic pattern recognition receptor that recognizes multiple pathogen-associated molecular patterns and damage-associated molecular patterns. It is a cytoplasmic immune factor that responds to cellular stress signals, and it is usually activated after infection or inflammation, forming an NLRP3 inflammasome to protect the body. Aberrant NLRP3 inflammasome activation is reportedly associated with some inflammatory diseases and metabolic diseases. Recently, there have been mounting indications that NLRP3 inflammasomes play an important role in liver injuries caused by a variety of diseases, specifically hepatic ischemia/reperfusion injury, hepatitis, and liver failure. Herein, we summarize new research pertaining to NLRP3 inflammasomes in hepatic injury, hepatitis, and liver failure. The review addresses the potential mechanisms of action of the NLRP3 inflammasome, and its regulation in these liver diseases.
Humans ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/physiology* ; Animals ; Liver Diseases/metabolism* ; Liver/metabolism* ; Reperfusion Injury/metabolism*

BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*