BACKGROUND:Degenerative scoliosis is defined as a condition that occurs in adulthood with a coronal cobb angle of the spine>10° accompanied by sagittal deformity and rotational subluxation,which often produces symptoms of spinal cord and nerve compression,such as lumbar pain,lower limb pain,numbness,weakness,and neurogenic claudication.The finite element method is a mechanical analysis technique for computer modelling,which can be used for spinal mechanics research by building digital models that can realistically restore the human spine model and design modifications. OBJECTIVE:To review the application of finite element method in the etiology and treatment of degenerative scoliosis. METHODS:The literature databases CNKI,PubMed,and Web of Science were searched for articles on the application of finite element method in degenerative scoliosis published before October 2023.Search terms were"finite element analysis,biomechanics,stress analysis,degenerative scoliosis,adult spinal deformity"in Chinese and English.Fifty-four papers were finally included. RESULTS AND CONCLUSION:(1)The biomechanical findings from the degenerative scoliosis model constructed using the finite element method were identical to those from the in vivo experimental studies,which proves that the finite element method has a high practical value in degenerative scoliosis.(2)The study of the etiology and treatment of degenerative scoliosis by the finite element method is conducive to the prevention of the occurrence of the scoliosis,slowing down the progress of the scoliosis,the development of a more appropriate treatment plan,the reduction of complications,and the promotion of the patients'surgical operation.(3)The finite element method has gradually evolved from a single bony structure to the inclusion of soft tissues such as muscle ligaments,and the small sample content is increasingly unable to meet the research needs.(4)The finite element method has much room for exploration in degenerative scoliosis.

BACKGROUND:Gradient artificial bone repair scaffolds can mimic unique anatomical features in musculoskeletal tissues,showing great potential for repairing injured musculoskeletal tissues. OBJECTIVE:To review the latest research advances in gradient artificial bone repair scaffolds for tissue engineering in the musculoskeletal system and describe their advantages and fabrication strategies. METHODS:The first author of the article searched the Web of Science and PubMed databases for articles published from 2000 to 2023 with search terms"gradient,bone regeneration,scaffold".Finally,76 papers were analyzed and summarized after the screening. RESULTS AND CONCLUSION:(1)As an important means of efficient and high-quality repair of skeletal system tissues,gradient artificial bone repair scaffolds are currently designed bionically for the natural gradient characteristics of bone tissue,bone-cartilage,and tendon-bone tissue.These scaffolds can mimic the extracellular matrix of native tissues to a certain extent in terms of structure and composition,thus promoting cell adhesion,migration,proliferation,differentiation,and regenerative recovery of damaged tissues to their native state.(2)Advanced manufacturing technology provides more possibilities for gradient artificial bone repair scaffold preparation:Gradient electrospun fiber scaffolds constructed by spatially differentiated fiber arrangement and loading of biologically active substances have been developed;gradient 3D printed scaffolds fabricated by layered stacking,graded porosity,and bio-3D printing technology;gradient hydrogel scaffolds fabricated by in-situ layered injections,simple layer-by-layer stacking,and freeze-drying method;and in addition,there are also scaffolds made by other modalities or multi-method coupling.These scaffolds have demonstrated good biocompatibility in vitro experiments,were able to accelerate tissue regeneration in small animal tests,and were observed to have significantly improved histological structure.(3)The currently developed gradient artificial bone repair scaffolds have problems such as mismatch of gradient scales,unclear material-tissue interactions,and side effects caused by degradation products,which need to be further optimized by combining the strengths of related disciplines and clinical needs in the future.

Subarachnoid hemorrhage (SAH) is a serious intracranial hemorrhage characterized by acute bleeding into the subarachnoid space. The effects of shikonin, a natural compound from the roots of Lithospermum erythrorhizon, on oxidative stress and blood–brain barrier (BBB) injury in SAH was evaluated in this study. A rat model of SAH was established by endovascular perforation to mimic the rupture of intracranial aneurysms. Rats were then administered 25 mg/kg of shikonin or dimethylsulfoxide after surgery. Brain edema, SAH grade, and neurobehavioral scores were measured after 24 h of SAH to evaluate neurological impairment. Concentrations of the oxidative stress markers superoxide dismutase (SOD), glutathione (GSH), and malondialdehyde (MDA) in the brain cortex were determined using the corresponding commercially available assay kits. Evans blue staining was used to determine BBB permeability. Western blotting was used to quantify protein levels of tight junction proteins zonula occludens-1, Occludin, and Claudin-5. After modeling, the brain water content increased significantly whereas the neurobehavioral scores of rats with SAH decreased prominently. MDA levels increased and the levels of the antioxidant enzymes GSH and SOD decreased after SAH. These changes were reversed after shikonin administration. Shikonin treatment also inhibited Evans blue extravasation after SAH. Furthermore, reduction in the levels of tight junction proteins after SAH modeling was rescued after shikonin treatment. In conclusion, shikonin exerts a neuroprotective effect after SAH by mitigating BBB injury and inhibiting oxidative stress in the cerebral cortex.

Objective: To explore the relationship between sleep quality and executive function among middle school students, so as to provide theoretical support for the promotion of adolescents physical and mental health development. Methods: From September to December 2023, 5 713 junior and senior high school students aged 13 to 18 were selected by stratified cluster random sampling method from Shanghai, Suzhou, Taiyuan, Wuyuan, Xingyi, and Urumqi. Pittsburgh Sleep Quality Index was used to conduct sleep quality survey. And conduct executive function was tested on middle school students, including inhibitory function, refresh function and conversion function tests. Spearman correlation and linear regression were used to analyze the relationship between sleep quality and executive function of middle school students. Results: The total Pittsburgh Sleep Quality Index (PSQI) score of boys was [4.0(2.0,6.0)] and that of girls was [5.0(3.0,6.0)], and the difference was statistically significant ( Z=-10.90, P < 0.01 ). The total PSQI score of boys was positively correlated with both 2-back reaction time and conversion function of executive function ( r =0.04, 0.04); the total PSQI score of girls was negatively correlated with 2-back reaction time ( r =-0.04) ( P <0.05). After controlling for variables such as mental health, physical activity and nutritional status,linear regression analyses showed that PSQI total score of middle school students was positively correlated with the inhibitory function and the conversion function response time [ B (95% CI )=1.28(0.21-2.34), 7.62(2.34-12.90), P <0.05]; the associations of total PSQI scores among middle school students with both 2-back and 1-back response time were not statistically significant [ B (95% CI )=-5.88(-16.14-4.37), 8.05( -3.39 -19.50), P >0.05]. Conclusion Positive correlations are observed on sleep quality with inhibitory and conversion functions of executive function among middle school students.

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.

Autoimmune hepatitis （AIH） is a chronic inflammatory liver disease. The pathogenesis of AIH remains unclear， but it is mainly autoimmune injury caused by the breakdown of autoimmune tolerance due to the abnormal activation of the immune system， while the specific molecular mechanism remains unknown. Recent studies have shown that abnormal amino acid metabolism plays an important role in the development and progression of AIH. This article reviews the research advances in amino acid metabolic reprogramming in AIH， in order to provide a theoretical basis for amino acid metabolism as a new target for the clinical diagnosis and treatment of AIH.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Astragali Radix (AR), a traditional Chinese medicine (TCM), has demonstrated therapeutic efficacy against various diseases, including cardiovascular conditions, over centuries of use. While doxorubicin serves as an effective chemotherapeutic agent against multiple cancers, its clinical application remains constrained by significant cardiotoxicity. Research has indicated that AR exhibits protective properties against doxorubicin-induced cardiomyopathy (DIC); however, the specific bioactive components and underlying mechanisms responsible for this therapeutic effect remain incompletely understood. This investigation seeks to identify the protective bioactive components in AR against DIC and elucidate their mechanisms of action. Through network medicine analysis, astragaloside IV (AsIV) and formononetin (FMT) were identified as potential cardioprotective agents from 129 AR components. In vitro experiments using H9c2 rat cardiomyocytes revealed that the AsIV-FMT combination (AFC) effectively reduced doxorubicin-induced cell death in a dose-dependent manner, with optimal efficacy at a 1∶2 ratio. In vivo, AFC enhanced survival rates and improved cardiac function in both acute and chronic DIC mouse models. Additionally, AFC demonstrated cardiac protection while maintaining doxorubicin's anti-cancer efficacy in a breast cancer mouse model. Lipidomic and metabolomics analyses revealed that AFC normalized doxorubicin-induced lipid profile alterations, particularly by reducing fatty acid accumulation. Gene knockdown studies and inhibitor experiments in H9c2 cells demonstrated that AsIV and FMT upregulated peroxisome proliferator activated receptor γ coactivator 1α (PGC-1α) and PPARα, respectively, two key proteins involved in fatty acid metabolism. This research establishes AFC as a promising therapeutic approach for DIC, highlighting the significance of multi-target therapies derived from natural herbals in contemporary medicine.
Animals ; Doxorubicin/adverse effects* ; Saponins/administration & dosage* ; Isoflavones/pharmacology* ; Rats ; Cardiomyopathies/prevention & control* ; Mice ; Fatty Acids/metabolism* ; Myocytes, Cardiac/metabolism* ; Triterpenes/administration & dosage* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Humans ; Cardiotonic Agents/administration & dosage* ; Mice, Inbred C57BL ; Cell Line ; Astragalus Plant/chemistry* ; Astragalus propinquus

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*