OBJECTIVE To explore the improvine mechanism of Yifei xuanfei jiangzhuo formula （YFXF） on vascular dementia （VAD） model rats based on the growth factor receptor-bound protein 2 （GRB2）/extracellular signal-regulated kinase （ERK）/cardiolipin synthase 1 （CRLS1） pathway. METHODS VAD rat model was established by permanent bilateral common carotid artery ligation. Forty-eight successfully modeled rats were randomly divided into the model group （normal saline）， donepezil hydrochloride group （positive control group， 0.2 g/kg）， and YFXF low- and high-dose groups （12.18 and 24.36 g/kg， calculated based on the total amount of crude drug）， respectively. In addition， a sham operation group （normal saline） was set up. There were 12 rats in each group. Daily intragastric administration of drug or normal saline was performed for 30 consecutive days. After the last administration， the spatial cognitive ability of the rats was evaluated， the pathological morphology of the hippocampus was observed， the contents of tumor necrosis factor-α （TNF-α） and interleukin-4 （IL-4） in serum were detected， the expression levels of GRB2/ERK/CRLS1 pathway-related proteins and the mRNA levels of GRB2， CRLS1， NADH dehydrogenase subunit 1（ND1）， Tafazzin （TAZ）， phospholipid scramblase 3（PLSCR3） and the ATP content in hippocampal tissue were measured. RESULTS Compared with the sham operation group， the escape latency of rats in the model group was significantly prolonged （ P ＜0.05）， and the number of crossing platform was significantly reduced （ P ＜0.05）， while the number of pyramidal cells and Nissl bodies in the hippocampus decreased sharply； the content of TNF-α in serum was significantly increased （ P ＜0.05）， and the content of IL-4 was significantly decreased （ P ＜0.05）； the expression levels of GRB2 and CRLS1 proteins， the phosphorylation level of ERK protein， the relative expression levels of GRB2， CRLS1，ND1， TAZ， and PLSCR3 mRNA， and the content of ATP in hippocampal tissue were significantly decreased （ P ＜0.05）. Compared with the model group， the above pathological changes in the hippocampal tissue of each administration group were alleviated， and the quantitative indicators were significantly restored （ P ＜0.05）. CONCLUSIONS YFXF may improve hippocampal neuron injury in VAD rats by activating the GRB2/ERK/CRLS1 pathway, maintaining cardiolipin homeostasis， and improving mitochondrial energy metabolism.

OBJECTIVE:Patients with osteoporotic vertebral compression fractures still have residual back pain after vertebral augmentation.The current research is characterized by limited sample size,complex confounding factors,and inconsistent research results.To gain a deeper understanding of this phenomenon,the aim of this study was to identify and evaluate the risk factors for residual back pain after surgery through a systematic review and meta-analysis.METHODS:A comprehensive search was conducted in CNKI,VIP,WanFang,CBMdisc,PubMed,The Cochrane Library,Embase,and Web of Science for case-control studies on residual back pain after vertebral body augmentation for osteoporotic vertebral compression fractures from database inception to July 2024.The search terms were a combination of subject terms and free terms.The basic information,patient characteristics,surgical-related indicators,and risk factors for surgical back pain of the included studies were extracted.After evaluating the bias risk of all included studies,a meta-analysis was conducted using Stata 14.0 software on the relevant indicators.RESULTS:(1)21 case-control studies with a total of 8 043 patients were included.Among them,965 patients developed back pain.The quality score of all 21 studies was ≥7.(2)The meta-analysis results showed that age(WMD=0.98,95％CI:0.40-1.56,P=0.010),bone mineral density(WMD=-0.28,95％CI:-0.34 to-0.21,P=0.000),the number of vertebral fractures(OR=3.50,95％CI:2.65-4.62,P=0.000),thoracolumbar fracture index(OR=3.65,95％CI:2.61-5.11,P=0.000),cement volume(OR=6.89,95％CI:2.62-18.17,P=0.000),and cement distribution(OR=2.38,95％CI:1.93-2.93,P=0.000)were risk factors for the development of back pain after vertebral body augmentation in patients with osteoporotic vertebral compression fractures.CONCLUSION:Current evidence indicates that age,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,bone cement injection volume,and the distribution of bone cement are risk factors for low back pain.Specifically,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,and non-uniform distribution of bone cement are identified as independent risk factors for low back pain.Patients exhibiting these high-risk factors require vigilant monitoring and prompt intervention to mitigate the occurrence of clinical low back pain,thereby enhancing patient outcomes and quality of life.

OBJECTIVE:Patients with osteoporotic vertebral compression fractures still have residual back pain after vertebral augmentation.The current research is characterized by limited sample size,complex confounding factors,and inconsistent research results.To gain a deeper understanding of this phenomenon,the aim of this study was to identify and evaluate the risk factors for residual back pain after surgery through a systematic review and meta-analysis.METHODS:A comprehensive search was conducted in CNKI,VIP,WanFang,CBMdisc,PubMed,The Cochrane Library,Embase,and Web of Science for case-control studies on residual back pain after vertebral body augmentation for osteoporotic vertebral compression fractures from database inception to July 2024.The search terms were a combination of subject terms and free terms.The basic information,patient characteristics,surgical-related indicators,and risk factors for surgical back pain of the included studies were extracted.After evaluating the bias risk of all included studies,a meta-analysis was conducted using Stata 14.0 software on the relevant indicators.RESULTS:(1)21 case-control studies with a total of 8 043 patients were included.Among them,965 patients developed back pain.The quality score of all 21 studies was ≥7.(2)The meta-analysis results showed that age(WMD=0.98,95％CI:0.40-1.56,P=0.010),bone mineral density(WMD=-0.28,95％CI:-0.34 to-0.21,P=0.000),the number of vertebral fractures(OR=3.50,95％CI:2.65-4.62,P=0.000),thoracolumbar fracture index(OR=3.65,95％CI:2.61-5.11,P=0.000),cement volume(OR=6.89,95％CI:2.62-18.17,P=0.000),and cement distribution(OR=2.38,95％CI:1.93-2.93,P=0.000)were risk factors for the development of back pain after vertebral body augmentation in patients with osteoporotic vertebral compression fractures.CONCLUSION:Current evidence indicates that age,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,bone cement injection volume,and the distribution of bone cement are risk factors for low back pain.Specifically,bone mineral density,the number of vertebral fractures,thoracolumbar fracture index,and non-uniform distribution of bone cement are identified as independent risk factors for low back pain.Patients exhibiting these high-risk factors require vigilant monitoring and prompt intervention to mitigate the occurrence of clinical low back pain,thereby enhancing patient outcomes and quality of life.

Background Platelet parameters are important indicators of cardiovascular risk, and environmental pollutants such as polychlorinated biphenyls (PCBs) may impair platelet function through oxidative stress. Objective To investigate the differential effects of single and mixed exposure to PCBs on platelet parameters among individuals with normal glucose tolerance (NGT), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM), and to evaluate the potential modifying role of a healthy lifestyle. Methods This study included 2249 participants (including 707 with NGT, 759 with IFG, and 783 with T2DM). Plasma PCB concentrations were measured using triple quadrupole gaschromatography-tandem mass spectrometry. Generalized linear regression was used to assess the associations between individual PCB congeners and platelet parameters. Quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR) models were used to evaluate the overall effects of PCBs mixture exposure on platelet parameters across different glycemic states, as well as its interaction with healthy lifestyle score (HLS). Results Generalized linear regression analyses showed significant differences in the effects of PCBs on platelet parameters across different glycemic states (P<0.05). After adjusting for confounders, PCBs mixture exposure was significantly associated with lower platelet counts (PLT) in individuals with NGT (b=−10.60, 95%CI: −16.48, −4.71) and IFG (b=−12.91, 95%CI: −18.90, −6.92), whereas no significant association was observed in individuals with T2DM (P=0.051). Mean platelet volume (MPV) and platelet-large cell ratio (P-LCR) increased significantly with higher PCBs exposure levels across all three groups (P<0.05). BKMR analysis showed a positive association between PCBs mixture exposure and P-LCR, with the strongest association observed in the NGT group. Furthermore, a significant interaction was observed between HLS and PCBs mixture exposure, and a higher HLS attenuated the effects of PCBs on P-LCR. Conclusion Glycemic glycemic states may modify the effects of PCBs on platelets. Individuals with NGT appear more sensitive to PCBs exposure, whereas the T2DM state may attenuate this effect. Moreover, healthy lifestyles, including not smoking, moderate alcohol consumption, maintaining moderate-to-high physical activity, a healthy diet, and an appropriate body mass index (BMI), may mitigate the adverse effects of most PCBs on platelet parameters.

Background Platelet parameters are important indicators of cardiovascular risk, and environmental pollutants such as polychlorinated biphenyls (PCBs) may impair platelet function through oxidative stress. Objective To investigate the differential effects of single and mixed exposure to PCBs on platelet parameters among individuals with normal glucose tolerance (NGT), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM), and to evaluate the potential modifying role of a healthy lifestyle. Methods This study included 2249 participants (including 707 with NGT, 759 with IFG, and 783 with T2DM). Plasma PCB concentrations were measured using triple quadrupole gaschromatography-tandem mass spectrometry. Generalized linear regression was used to assess the associations between individual PCB congeners and platelet parameters. Quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR) models were used to evaluate the overall effects of PCBs mixture exposure on platelet parameters across different glycemic states, as well as its interaction with healthy lifestyle score (HLS). Results Generalized linear regression analyses showed significant differences in the effects of PCBs on platelet parameters across different glycemic states (P<0.05). After adjusting for confounders, PCBs mixture exposure was significantly associated with lower platelet counts (PLT) in individuals with NGT (b=−10.60, 95%CI: −16.48, −4.71) and IFG (b=−12.91, 95%CI: −18.90, −6.92), whereas no significant association was observed in individuals with T2DM (P=0.051). Mean platelet volume (MPV) and platelet-large cell ratio (P-LCR) increased significantly with higher PCBs exposure levels across all three groups (P<0.05). BKMR analysis showed a positive association between PCBs mixture exposure and P-LCR, with the strongest association observed in the NGT group. Furthermore, a significant interaction was observed between HLS and PCBs mixture exposure, and a higher HLS attenuated the effects of PCBs on P-LCR. Conclusion Glycemic glycemic states may modify the effects of PCBs on platelets. Individuals with NGT appear more sensitive to PCBs exposure, whereas the T2DM state may attenuate this effect. Moreover, healthy lifestyles, including not smoking, moderate alcohol consumption, maintaining moderate-to-high physical activity, a healthy diet, and an appropriate body mass index (BMI), may mitigate the adverse effects of most PCBs on platelet parameters.

ObjectiveTo systematically collate the randomized controlled trials （RCTs） on the intervention of traditional Chinese medicine （TCM） in chronic kidney disease （CKD） and analyze the characteristics of clinical trials and the current situation of outcome indicators， thus providing references for constructing a core outcome set （COS） for CKD. MethodsThe RCTs on the intervention of TCM in CKD were retrieved from CNKI， Wanfang Data， VIP， SinoMed， PubMed， Cochrane Library， EMbase， and Web of Science with the time interval from January 2022 to December 2024. The risk of bias was evaluated， and the outcome indicators were qualitatively analyzed. ResultsA total of 242 RCTs were included， which reported 238 outcome indicators， with the cumulative frequency of 2 724 times. These outcome indicators can be classified into seven domains： TCM syndromes and diseases （270 times， accounting for 9.90%）， symptoms and signs （218 times， 8.00%）， physical and chemical examinations （1 609 times， 59.07%）， quality of life （29 times， 1.06%）， long-term prognosis （6 times， 0.22%）， economic evaluation （0 times）， and safety events （535 times， 19.64%）. The results demonstrated that the RCTs of TCM intervention for CKD had several shortcomings such as the lack of a basis for sample size estimation， lack of standardization in TCM syndrome differentiation and classification， room for improvement in methodological quality， flaws in the design of the timing of efficacy measurement， absence of long-term prognosis evaluation， imbalance in the selection of surrogate and endpoint indicators， disconnection between overall evaluation and individualized assessment， lack of TCM characteristic indicators， and insufficient reporting of safety indicators. ConclusionAt present， the design of outcome indicators in the RCTs for TCM intervention in CKD is still not standardized. It is recommended that future research endeavors to enhance methodological quality， establish a standardized COS for TCM intervention in CKD， and thereby propel the high-quality development of TCM.

The sensitive scalp is a complex syndrome characterised by abnormal nerve sensations, such as stinging, itching, burning, and tightness, and diminished tolerance to physical or chemical stimuli that would not typically cause such unpleasant responses, with its pathology involving physical barrier destruction at the stratum corneum level, neural hyperreactivity and sensory nerve sensitization, and local immunological dysregulation and chronic low‑grade inflammation. The homeostasis of the scalp microenvironment plays a crucial regulatory role in maintaining the hair follicle growth cycle, yet this “soil” factor has long been underappreciated in hair loss research, which has traditionally centered on intrinsic follicular defects, such as genetic predisposition or androgen-driven mechanisms. In this review, we summarized the potential mechanisms underlying the imbalance in the “barrier-neural-immune” network in sensitive scalps. Sensitive scalp is further manifested as impaired skin barrier, including increased transepidermal water loss and abnormal lipid secretion. These alterations are accompanied by increased surface pH, excessive secretion of sebum rich in free fatty acids, reduced microbial diversity, and dysbiosis characterized by a reduction in Cutibacterium and an increase in Staphylococcus colonization. Mechanistically, we also analyzed how neurogenic inflammation, mediated by substances such as substance P (SP) and calcitonin gene-related peptide (CGRP), along with persistent low-grade inflammation, disrupts the microenvironment of the hair follicle stem cell niche through mechanisms involving mechanosensitive ion channels (e.g., Piezo1), oxidative stress, and cytokine cascades. When the barrier is compromised, the weakened stratum corneum may facilitate the penetration of external stimuli, sensitizing cutaneous nerve endings. Those hyperresponsive nerves then fire off neuropeptides like substance P and CGRP. These peptides activate mast cells and other immune cells via the MrgprB2 receptor, prompting degranulation and a wave of pro-inflammatory mediators that kick off neurogenic inflammation. At the same time, oxidative stress and inflammatory cytokines drive a persistent low-grade inflammation that directly damages the hair follicle stem cell niche. Physical compression from shrinking hair shafts activates Piezo1 channels, setting off calcium influx and Caspase‑3‑dependent apoptotic signaling. We primarily considered the interactions between inflammatory signaling and the sensitization of the androgen receptor (AR) pathway, as well as the inhibition of the Wnt/β‑catenin pathway, which may subsequently contribute to hair follicle miniaturization and disruption of the growth cycle. The inflammatory microenvironment appears to boost AR expression in dermal papilla cells, making them more sensitive to dihydrotestosterone (DHT). This heightened sensitivity may promote the expression of transforming growth factor-β1 (TGF-β1), TGF-β2, and Dickkopf-1 (DKK1), which suppress the Wnt/β‑catenin pathway—essential for follicle growth and regeneration—potentially accelerating follicular miniaturization and stalling the growth cycle. Coupled with direct inflammatory apoptosis, oxidative stress, and circadian disruption, these routes may collectively speed up hair follicle stem cell depletion and diffuse hair thinning. These findings point to the need for more mechanistic and translational research to test the proposed pathways and explore therapies that target the microenvironment in sensitive scalp-related hair loss. Ultimately, we hope to provide theoretical support for the intervention of hair loss by restoring the steady state of the scalp microenvironment and combining with new precision delivery technologies such as microneedles.

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.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

Objective: To investigate the effects of Zuogui Jiangtang Jieyu Formula (ZGJTJYF) on histone H3 lysine 18 lactylation (H3K18la) in the hippocampus of rats with diabetes mellitus complicated with depression (DD) and predict the regulatory genes of H3K18la. Methods: Male Sprague-Dawley rats were divided into control, model, and positive drug (metformin [0.18 g/kg] and fluoxetine [1.8 mg/kg]) groups, and the three groups were treated with high, medium, and low ZGJTJYF doses (20.52, 10.26, and 5.13 g/kg, respectively), with 10 rats per group. After treatment, the forced swimming and water maze tests were performed to assess depressive-like behaviors and cognitive function. An enzyme-linked immunosorbent assay was used to measure blood insulin, glycosylated hemoglobin, lactate levels, and lactate content in the hippocampus. Western blotting was used to detect H3K18la expression in the hippocampus. Cleavage Under Targets and lagmentation(CUT&Tag) experiments targeted hippocampal H3K18la epigenetic modification regions to analyze the transcription factors bound by H3K18la. Kyoto Encyclopedia of Genes and Genomes and Protein-Protein Interaction networks were constructed to identify key pathways and target genes regulated by H3K18la. Results: Compared with the normal group, the model group rats showed prolonged immobility time in the forced swim test, increased escape latency in the water maze experiment, decreased target quadrant distance ratio (P<0.01), increased serum lactate content, and decreased lactate content in hippocampal homogenate (P<0.01), as well as decreased H3K18la protein expression in the hippocampus (P<0.01). Compared with the model group, ZGJTJYF reduced the immobility time in the forced swim test and the escape latency in the water maze test (P<0.01), while the distance ratio in the target quadrant increased (P<0.01) in model rats. Lowered fasting blood glucose, insulin, and glycosylated hemoglobin levels (P<0.05, P<0.01) were also observed. ZGJTJYF also increased the lactate content and H3K18la protein expression in hippocampal homogenate (P<0.05, P<0.01). The DNA sequences bound by H3K18la were predominantly enriched at the transcription start sites. ZGJTJYF modulated H3K18la-associated pathways, including cell adhesion junctions, tumor growth factor-beta (TGF-β) signaling, stem cell pluripotency regulation, mitogen-activated protein kinase(MAPK) signaling pathway, and insulin resistance, leading to the identification of 12 target genes. Conclusion ZGJTJYF enhances hippocampal lactate levels and H3K18la modification in DD rats, which may regulate neural cell interactions, neurogenic stem cell function, TGF-β signaling, MAPK signaling, and insulin resistance pathways.