With the development of brain-computer interface technology （BCI）， its potential to enhance human cognitive and physical abilities continues to emerge， while also stimulating the“superhuman”enhancement impulse to try to push the boundaries of treatment. Overmodification of the body governed by the pursuit of BCI“superhuman”augmentation may lead to a series of ethical body dilemmas， such as issues of bodily autonomy， the risk and benefit of the body medical dimension， and how to define the idealized body at the social level when the natural body and the BCI technology-enhanced body compete in the future. Therefore， it is necessary to take positive measures to promote the development of brain-computer interface technology in the process of continuous innovation and ethical responsibility.

ObjectiveTo explore the possible mechanisms of Shujin Jiannao Formula （舒筋健脑方） for cerebral palsy. MethodsThirty 7-day-old SD rats were randomly divided into normal group， model group， and Shujin Jiannao Formula group， with 10 rats in each group. The model group and Shujin Jiannao Formula group established a cerebral palsy model by the classic Rice-Vannucci method. After successful modeling， rats in Shujin Jiannao Formula group were given Shujin Jiannao Formula 16 g/（kg·d） by gavage， while the normal group and model group were given normal saline 10 ml/（kg·d） by gavage once a day. After one week of intervention， the rats' body weight was measured， and Zea-Longa scores， the righting reflex test， and the hindlimb suspension test were conducted for assessment； hematoxylin-eosin （HE） staining and Nissl staining were used to observe pathological changes in brain tissue， and the number of Nissl-positive neurons was counted； enzyme-linked immunosorbent assay （ELISA） was employed to measure levels of inflammatory cytokines in the brain tissue， specifically interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）； immunofluorescence was used to detect the expression levels of neurofilament protein 200 （NF200） and myelin basic protein （MBP） in brain tissue； Western Blot analysis was conducted to determine the protein levels of phosphoinositide 3-kinase （PI3K）， protein kinase B （Akt/PKB/Rac）， and mammalian target of rapamycin （mTOR） in brain tissue. ResultsCompared with the normal group， rats in the model group showed significantly higher Zea-Longa scores and lower scores in the hindlimb suspension test （P＜0.01）； pathological findings revealed loose structure in the cerebral cortex， hippocampal atrophy， and neuronal damage in brain tissue. Levels of IL-1β and TNF-α elevated， and the number of Nissl-stained positive neurons in the cortex and hippocampal CA1 region reduced， and immunofluorescence intensity of NF200 and MBP， as well as protein expression levels of PI3K and mTOR， significantly decreased （P＜0.05 or P＜0.01）. Compared with the model group， rats in Shujin Jiannao Formula group showed decreased Zea-Longa scores and increased hindlimb suspension test scores （P＜0.05）； pathological damage in brain tissue alleviated， levels of IL-1β and TNF-α reduced， the number of Nissl-stained positive neurons in the cortex and hippocampal CA1 region increased， and the immunofluorescence intensity of NF200 and MBP， as well as the protein levels of PI3K and mTOR， significantly elevated （P＜0.05 or P＜0.01）. There were no statistically significant differences among the groups in body weight， body-turning time， or AKT protein levels in brain tissue （P＞0.05）. ConclusionShujin Jiannao Formula can improve the neurological function of rats with cerebral palsy， exert neurorestorative effects， and its mechanism of action may be related to the reduction of inflammatory response in brain tissue and the activation of the PI3K/AKT/mTOR signaling pathway.

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 The varieties of chemical drugs prohibited for children in the 2020 edition of Chinese Pharmacopoeia were analyzed.Provide a technical basis for the development of a list of drugs prohibited by children.This is requirement of China's Children's Development Program(2021-2030).Methods Drug inserts are collected through websites such as"PharmaIntelligence",from which information on children's prohibitions is extracted,and drugs with consistent information on children's prohibitions are analyzed in the instructions of different manufacturers.Results Among the 1 741 kinds of chemicals,there are a total of 240 drugs with consistent information on the prohibition of children in the instructions of different manufacturers,of which 113 are labeled"prohibited"for children of different ages,53 are"not suitable"for children of different ages,38 are"not recommended""for children of different ages,and 36 are labeled with other children's prohibition related information.According to the classification of the clinical drug instructions involving 21 categories.Including anti-infective drugs(77),nonsteroidal anti-inflammatory drugs and anti-gout drugs(28),nervous system drugs(20),psychotropic drugs(16),digestive system drugs(16),endocrine system drugs(16),etc.Conclusion Children are at a special stage of growth and development,and the pharmacological and toxicological characteristics of drugs in children are different from those in adults.The risk-benefit ratio of drugs used in children needs continuous research to provide more detailed evidence for drug use and ensure the safety of drug use in children.

Hyperkalemia is one of the common ion metabolism disorders in clinical practice. Hyperkalemia is defined as serum potassium higher than 5.0 mmol/L according to the guidelines at home and abroad. Acute severe hyperkalemia can cause serious consequences, such as flaccid paralysis, fatal arrhythmia, and even cardiac arrest. The use of renin-angiotensin- aldosterone system inhibitors, β-blockers and diuretics, low-sodium and high-potassium diets, and the presence of related comorbidities increase the occurrence of hyperkalemia. Hyperkalemia risk exist in all clinical departments, but there is a lack of a standardization in the management of multi- department cooperation in hospital. Therefore, a number of domestic nephrology and cardiology department experts have discussed a management model for multi-department cooperation in hyperkalemia, formulating the management standard on hospital evaluation, early warning, diagnosis and treatment, and process. This can promote each department to more effectively participate in nosocomial hyperkalemia diagnosis and treatment, as well as the long-term management of chronic hyperkalemia, improving the quality of hyperkalemia management in hospital.

Objective:To analyze the predictive value of von Willebrand factor (vWF) for venous thromboembolism (VTE) of patients in intensive care unit (ICU) by using propensity score matching (PSM).Methods:Patients admitted to ICU of the Second Affiliated Hospital of Kunming Medical University from December 2020 to June 2022 who stayed in ICU for ≥72 hours and underwent daily bedside vascular ultrasound screening were included. Baseline data such as age, gender, primary disease, and chronic comorbidities were collected. Coagulation indexes before admission to ICU and 24 hours and 48 hours after ICU admission were collected, including prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), international normalized ratio (INR), fibrinogen (Fib), fibrin monomer (FM), vWF, D-dimer, antithrombin Ⅲ (ATⅢ), etc. Patients were divided into VTE group and non-VTE group according to whether they had VTE or not [diagnosis of VTE: patients underwent daily ultrasound screening of bedside blood vessels (both upper and lower limbs, visceral veins), and those suspected of having thrombosis were confirmed by ultrasonographer or pulmonary angiography]. Using PSM analysis method, the VTE group was used as the benchmark to conduct 1 : 1 matching of age, whether there was malignant tumor, whether there was infection, whether there was diabetes, and coagulation indicators before admission to ICU. Finally, the cases with balanced covariates between the two groups were obtained. The risk factors of VTE were analyzed by multivariate Logistic regression analysis. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of vWF in the occurrence of VTE in critically ill patients.Results:A total of 120 patients were enrolled, of which 18 (15.0%) were diagnosed with VTE within 72 hours after admission to ICU, and 102 (85.0%) were not found to have thrombus in ICU. Before PSM, there were significant differences in age, gender, proportion of malignant tumor and infection, and coagulation indexes between VTE group and non-VTE group. After PSM, 14 pairs were successfully matched, and the unbalanced covariables between the two groups reached equilibrium. Multivariate Logistic regression analysis showed that vWF was an independent risk factor for VTE at 48 hours after ICU admission in critically ill patients [odds ratio ( OR) = 1.165, 95% confidence interval (95% CI) was 1.000-1.025, P = 0.004]. ROC curve analysis showed that the area under the ROC curve (AUC) of vWF at 48 hours after ICU admission for predicting VTE was 0.782, 95% CI was 0.618-0.945, P = 0.007. When the optimal cut-off value was 312.12%, the sensitivity was 67.7% and the specificity was 93.0%. Conclusion:Dynamic monitoring of vWF is helpful to predict the occurrence of VTE in ICU patients, and vWF at 48 hours after ICU admission has certain value in predicting the occurrence of VTE.

Objective To analyze the causes of changes in the prevalence of respiratory diseases and the reason for changes in medical visit behavior of children in Zhejiang Province during the winter and spring seasons of 2019-2021, and to provide important reference for the allocation of hospital resources, implementation of hierarchical diagnosis and treatment, and epidemic prevention and control. Methods A retrospective study was conducted on 256 937 outpatient medical records from January 23rd to April 23rd of each year from 2019 to 2021 at the Children's Hospital Affiliated to Zhejiang University School of Medicine. Statistical methods were used for data analysis. Results A total of 256 937 cases were selected in the present study, including 157 000 cases in 2019, 22 192 cases in 2020, and 77 745 cases in 2021. The number of patients to the Children's Hospital of Zhejiang University School of Medicine from outside Hangzhou accounted for 41.74%, 14.36% , and 18.53% in 2019-2021, respectively. For 0~2 years old , 3~6 years old , and 7~14 years old groups , the percentages of patients with upper respiratory tract infections were 49.54%, 45.95%, and 46.74%, respectively ; with lower respiratory tract infections were 42.90% , 31.76% , and 22.95% ; with influenza were 2.23% , 3.15% and 4.09%; and with asthma were 1.37%, 5.08%, and 8.15%, respectively. Conclusion From 2019 to 2021, there have been significant changes in the total number of respiratory diseases in children, the proportion of disease types, and the proportion of children's geographical composition. It is necessary to continue to monitor children's respiratory diseases, grasp the dynamic changes in their medical visits in real time, adjust the hospital admission model , implement the graded treatment policy, and promote the prevention and control of respiratory diseases in children.

BACKGROUND: The effects of acupuncture have varied in different randomized controlled trials (RCTs), and there are many factors that influence treatment effect of acupuncture in different outcomes, with conflicting results. OBJECTIVE: To identify factors and their impact on the treatment effect of acupuncture in different outcomes. METHODS: Acupuncture RCTs were searched from 7 databases including Medline (PubMed), Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, Wanfang Database, VIP Database, and China Biology Medicine disc between January 1st, 2015 and December 31st, 2019. Eligible studies must compare acupuncture to no acupuncture, sham acupuncture, or waiting lists, and report at least 1 patient-important outcome. A multi-level meta-regression was conducted using a 3-level robust mixed model and univariate analyses were performed for all independent variables, even those excluded from the multivariable model due to collinearities. We used thresholds of 0.2 and 0.4 for the difference of standardized mean differences (SMDs), categorising them as small (<0.2), moderate (0.2-0.4), or large (>0.4) effects. RESULTS: The pain construct analysis involved 211 effect estimates from 153 studies and 14 independent variables. High-frequency acupuncture treatment sessions produced larger effects compared to low-frequency sessions [large magnitude, the difference of adjusted SMDs 0.46, 95% confidence interval (CI) 0.07 to 0.84; P=0.02]. The non-pain symptoms construct analysis comprised 323 effect estimates from 231 studies and 15 independent variables. Penetrating acupuncture showed moderately larger effects when compared to non-penetrating acupuncture (0.30, 95% CI 0.06 to 0.53; P=0.01). The function construct analysis included 495 effect estimates from 274 studies and 14 independent variables. Penetrating acupuncture and the flexible acupuncture regimen showed moderately larger effects, compared to non-penetrating acupuncture and fixed regimen, respectively (0.40, 95% CI 0 to 0.80; P=0.05; 0.29, 95% CI 0.06 to 0.53; P=0.01). CONCLUSIONS High-frequency acupuncture sessions appear to be a more effective approach to managing painful symptoms. Penetrating acupuncture demonstrated greater effect in relieving non-painful symptoms. Both penetrating acupuncture type and flexible acupuncture regimen were linked to significant treatment effects in function outcomes. Future studies should consider the factors that are significantly associated with the effects of acupuncture in patient-important outcomes.
Humans ; Randomized Controlled Trials as Topic ; Acupuncture Therapy/methods* ; Pain ; Pain Management ; China

To establish a method for determining 26 inorganic elements in earthworm polypeptide and determine the elemental content in different batches of earthworm polypeptide, microwave digestion method was used to pre-treat the samples, and ICP-MS method was used to determine the content of 26 elements in different batches of earthworm polypeptide. The linear relationships of 26 elements were good in the range of 0-1 000 μg·L^-1, with R² greater than 0.999, precision RSD 0.21%-2.71%, repeatability RSD 0.19%-4.69%, stability RSD 0.11%-4.24%, and recovery rates of 82.41%-116.16%. The data was plotted using Origin 2022 software to characterize the distribution of elements content. SPSS 27.0 was used for principal component analysis, and SIMCA 14.1 software was used for OPLS-DA analysis. The results showed that among the 26 elements, the higher content of earthworm polypeptide was K, Na, and Ca, followed by Zn, Fe, Al, B and other trace elements, In, Sc, Co, Pb, Bi and other elements had little or no detectable content, and there were differences in the content of polypeptide in different batches. This study provides a theoretical basis for the production quality control, quality evaluation and drug efficacy application of earthworm polypeptide through the determination and analysis of the elements and content of earthworm polypeptide.

A novel series of 2-aryl substituted benzothiopyranone compounds was designed and synthesized based on our previously obtained benzothiopyranone scaffold with significant antituberculosis activity. All target compounds were evaluated for their antimycobacterial activity and preliminary druggability was subsequently investigated for some selected compounds with good activity. The results indicated that most compounds showed good activity against Mycobacterium tuberculosis H₃₇Rv. Among them, compounds 8g, 8h, 8q and 9f showed potent activity with MIC ranged from 0.2 to 0.4 μg·mL^-1. Furthermore, some active compounds exhibited low cytotoxicity and cardiotoxicity risk. It is worth noting that compounds 8h and 8q with good liver microsome stability and low inhibition of CYPs 3A4/5 and 2C9 were suitable for combination drug regimen to treat tuberculosis.