ObjectiveTo investigate the changing trend of hemoglobin （Hb） and related influencing factors in patients with liver failure after artificial liver support system （ALSS） therapy. MethodsA total of 106 patients with liver failure who were hospitalized and received ALSS therapy in our hospital from January to December 2018 were enrolled and analyzed in terms of clinical data and red blood cell parameters such as Hb， mean corpuscular volume （MCV）， mean corpuscular hemoglobin （MCH）， mean corpuscular hemoglobin concentration （MCHC）， and red blood cell distribution width-coefficient of variation （RDW-CV）. A one-way repeated-measures analysis of variance was used for comparison of continuous data with repeated measurement between groups， and the paired t-test was used for comparison between two groups. The Kruskal-Wallis H test was used for comparison of continuous data with skewed distribution between multiple groups， the Mann-Whitney U test was used for further comparison between two groups. Univariate and multivariate linear regression analyses were used to identify the influencing factors for the reduction in Hb after ALSS therapy. ResultsThe 106 patients with liver failure received 606 sessions of ALSS therapy， and Hb was measured for 402 sessions before and after treatment. There was a significant reduction in Hb after ALSS therapy in the patients with liver failure （97.49±20.51 g/L vs 109.38±20.22 g/L， t=32.764， P<0.001）. Longitudinal observation was further performed for 14 patients with liver failure， and the results showed that the level of Hb was 108.50±21.61 g/L before the last session of ALSS therapy， with certain recovery compared with the level of Hb （103.14±19.15 g/L） on the second day after ALSS， and there was an increase in Hb on day 3 （102.57±21.73 g/L） and day 7 （105.57±22.04 g/L） after surgery. The level of Hb in patients with liver failure on the second day after ALSS decreased with the increase in the number of ALSS sessions （F=8.996， P<0.001）， while MCV and MCH gradually increased with the increase in the number of ALSS sessions （F=9.154 and 13.460， P=0.004 and P<0.001）， and RDW-CV first gradually increased and then gradually decreased （F=4.520， P=0.032）； MCHC showed fluctuations with no clear trend （F=0.811， P=0.494）. The multivariate linear regression analysis showed that the duration of ALSS therapy， the mode of ALSS therapy， and initial treatment were independent risk factors for the reduction in Hb after ALSS therapy. ConclusionALSS therapy can influence the level of peripheral blood Hb in patients with liver failure， and patient blood management should be strengthened for patients with liver failure who are receiving ALSS therapy.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

19 cinnamamide/ester-triazole compounds were designed, synthesized and evaluated for their anti-Alzheimer's disease (AD) activity. Among them, compound 4f displayed excellent anti-β-amyloid protein (Aβ)-mediated cytotoxicity (EC₅₀ = 2.03 ± 2.45 μmol·L^-1) in APPswe cells and acetylcholinesterase inhibiton (IC₅₀ = 4.88 ± 4.70 μmol·L^-1). Further study indicated that, at dosages of (1, 5 and 25 mg·kg^-1), compound 4f was effective in improving spatial learning and memory deficits in Aβ_1-42-impaired mice, which was achieved by promoting the nonamyloidogenic signaling and inhibiting the amyloidogenic pathway, along with the suppression of Aβ-induced Tau phosphorylation. All animal experiments in this study were approved by the Experimental Animal Care and Use Committee of the Institute of Medicinal Biotechnology (IMB-20220908D701). In conclusion, compound 4f holds promise as a lead candidate for AD treatment, and the present study lays the foundation for its subsequent development.

Micro/nanoplastics (MNPs), recognized as emerging environmental pollutants, are widely distributed in natural environments. Due to their small particle size and significant migratory capacity, MNPs can infiltrate diverse environmental matrices, then invade and accumulate in the organism via the skin, respiration, and digestion. Recently, concerns have grown over the detrimental effects and potential toxicity of MNPs on reproductive health. This review summarized published epidemiological and toxicological studies related to MNPs exposure and their effects on reproductive health. Firstly, this review critically examined the current landscape of epidemiological evidence and found that MNPs (e.g., polystyrene, polypropylene, polyvinyl chloride, polyethylene, etc.) are present in various biological specimens from both males and females, and their presence may be associated with an increased risk of reproductive disorders. Secondly, extensive toxicological studies revealed that MNPs exposure induces reproductive health damage through mechanisms such as disrupting the microstructure of reproductive organs and altering molecular-level expressions. Oxidative stress, inflammatory responses, and apoptosis are identified as potential links between MNPs exposure and reproductive damage. Finally, this review addressed the prevalent shortcomings in existing studies and proposed future directions to tackle the challenges posed by MNPs-induced reproductive harm. These insights aim to inform strategies for safeguarding public reproductive health and ecological security, providing a scientific foundation for mitigating risks associated with MNPs pollution.

OBJECTIVE To investigate the effects and mechanism of Wenpi tongluo kaiqiao formula （WPTL） against neuronal necroptosis in Alzheimer’s disease （AD） mice based on the Z-DNA binding protein 1 （ZBP1）/mixed lineage kinase domain-like protein （MLKL） signaling pathway. METHODS Forty APP/PS1 transgenic AD mice were randomly divided into model group， WPTL low-dose （WPTL-L） group （10.4 g/kg， calculated by the raw medicine）， WPTL high-dose （WPTL-H） group （20.8 g/kg， calculated by the raw medicine） and donepezil hydrochloride group （3 mg/kg）， with 10 mice in each group； another 10 C57BL/6J mice were selected as normal control group. Intragastric administration， once a day， for 30 consecutive days. Twenty-four hours after the last administration， Morris water maze test was performed to evaluate learning and memory abilities； the pathological morphology of hippocampal tissues was observed； the serum levels of tumor necrosis factor-α （TNF-α） and interleukin-4 （IL-4） were determined； the expressions of amyloid precursor protein （APP）， Tau protein， and ZBP1/MLKL signaling pathway-related proteins in hippocampal tissues were detected； the positive expression of phosphorylated receptor-interacting protein kinase 3 （p-RIPK3） in the neurons of hippocampal tissues and mRNA expression of ZBP1 were measured in hippocampal tissues. RESULTS Compared with normal control group， the escape latency of mice in model group was prolonged significantly on day 3 to 5 （P＜0.05）， the times of crossing platform reduced significantly （P＜0.05）， and obvious pathological changes were observed in the hippocampal tissue. The level of TNF- α， the expressions of APP， p-Tau and ZBP1， the phosphorylation levels of RIPK1， RIPK3 and MLKL， the fluorescence intensity of p-RIPK3 as well as the mRNA expression of ZBP1 were significantly increased （P＜0.05）， while the serum level of IL-4 was decreased significantly （P＜0.05）. Compared with model group， above indexes were reversed significantly in administration groups （P＜0.05）， and pathological damage of hippocampal tissue was alleviated. CONCLUSIONS WPTL can inhibit the ZBP1/MLKL signaling pathway， reduce neuronal necroptosis in AD mice， and inhibit inflammatory responses， thereby improving learning and spatial memory abilities in AD mice.

To summarize the current status in the application of digital and intelligent technologies in the field of pharmacovigilance and to provide reference to the selection and development of methods for pharmacovigilance of rare diseases.

Rheumatoid arthritis （RA） is a chronic autoimmune disease characterized by synovitis as its pathological basis. Although current therapeutic drugs can alleviate symptoms， they are often accompanied by a high risk of side effects. In recent years， the use of flavonoids from traditional Chinese medicine （TCM） in the treatment of RA has garnered significant attention. Studies have shown that the mechanisms by which flavonoids treat RA include inhibiting the release of pro-inflammatory factors， regulating multiple cellular signaling pathways， alleviating oxidative stress， modulating immune system functions， inhibiting bone destruction， and suppressing angiogenesis. Due to their notable anti-inflammatory， antioxidant， and immunomodulatory activities， flavonoids hold promise as potential therapeutic agents for RA. A substantial number of articles in this field have been published. By reviewing Chinese and international literature and applying bibliometric and visual analysis using CiteSpace， this paper explored research hotspots and frontiers in this field， systematically reviewed the structures and anti-RA mechanisms of TCM flavonoids， provided a theoretical basis for their use in RA treatment and clinical applications， and offered new perspectives and references for the discovery of novel TCM-based anti-RA drugs.

Rheumatoid arthritis （RA） is a chronic autoimmune disease characterized by synovitis as its pathological basis. Although current therapeutic drugs can alleviate symptoms， they are often accompanied by a high risk of side effects. In recent years， the use of flavonoids from traditional Chinese medicine （TCM） in the treatment of RA has garnered significant attention. Studies have shown that the mechanisms by which flavonoids treat RA include inhibiting the release of pro-inflammatory factors， regulating multiple cellular signaling pathways， alleviating oxidative stress， modulating immune system functions， inhibiting bone destruction， and suppressing angiogenesis. Due to their notable anti-inflammatory， antioxidant， and immunomodulatory activities， flavonoids hold promise as potential therapeutic agents for RA. A substantial number of articles in this field have been published. By reviewing Chinese and international literature and applying bibliometric and visual analysis using CiteSpace， this paper explored research hotspots and frontiers in this field， systematically reviewed the structures and anti-RA mechanisms of TCM flavonoids， provided a theoretical basis for their use in RA treatment and clinical applications， and offered new perspectives and references for the discovery of novel TCM-based anti-RA drugs.

OBJECTIVE To investigate the intervention effect and its potential mechanism of Yifei xuanfei jiangzhuo formula by inhibiting mitochondrial fission in a vascular dementia （VaD） model rats. METHODS VaD rat model was established by bilateral common carotid artery ligation. The experimental animals were randomly divided into sham operation group （SHAM）， model group （MOD），Yifei xuanfei jiangzhuo formula low-dose group （YFXF-L）， Yifei xuanfei jiangzhuo formula high-dose group （YFXF-H）， and Donepezil hydrochloride group （positive control）， with 9 animals in each group. After 30 days of intervention， the spatial learning memory ability was assessed by Morris water maze experiment； HE staining was used to observe histopathological changes in CA1 area of hippocampus； ELISA was used to detect the levels of serum inflammatory factors [interleukin-1β （IL-1β） and IL-4]； Western blot was used to detect the expressions of heat shock protein 90 （HSP90）/mixed lineage kinase domain-like protein （MLKL）/dynamin-related protein 1 （Drp1） pathway-related proteins， mitochondrial fusion proteins （MFN1， MFN2）， and adenosine triphosphate synthase 5A （ATP5A） in hippocampal tissues. The immunohistochemistry was used to detect the level of phosphorylated MLKL （p-MLKL）； real-time fluorescence quantitative PCR was adopted to detect mRNA expressions ofHSP90， MFN1， MFN2 and ATP5A. RESULTS Compared with SHAM group， the escape latency of rats in the MOD group was significantly prolonged， the number of crossing the platform was significantly reduced， and the hippocampal tissues showed typical neuronal damage characteristics， the positive expression level of p-MLKL and the serum level of IL-1β significantly increased， while the serum level of IL-4 significantly decreased， the protein and mRNA expression of HSP90， as well as the protein expressions of p-MLKL/MLKL and p-Drp1（Ser616）/Drp1 were all significantly increased in hippocampal tissue， the protein and mRNA expressions of MFN1， MFN2 and ATP5A， and protein expression of p-Drp1（Ser637）/Drp1 were all significantly decreased （P＜0.05）. After the intervention of Yifei xuanfei jiangzhuo formula， above indicators in each treatment group were all significantly reversed （P＜0.05）. CONCLUSIONS Yifei xuanfei jiangzhuo formula may alleviate neuronal damage and neuroinflammatory responses in VaD rats by regulating the HSP90/MLKL/Drp1 signaling pathway， inhibiting mitochondrial fission， thereby maintaining mitochondrial dynamic balance and improving mitochondrial function.

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.