Animal experiments are essential tools in biomedical research, serving as a bridge between basic research and clinical trials. Systematic reviews and meta-analyses (SRs/MAs) of animal experiments are crucial methods for integrating evidence from animal experiment, which can facilitate the translation of findings into clinical research, reduce translational risks, and promote resource integration in basic research. With the continuous development of the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology, its application in SRs/MAs of animal experiments has gained increasing attention. This article first outlines the principles and specific applications of the GRADE methodology in SRs/MAs of animal experiments, including qualitative descriptive systematic reviews, meta-analyses, and network meta-analyses. It then deeply analyzes the misuse of the GRADE methodology in practice, including incorrect evidence grading, improper classification of evidence, misapplication in qualitative systematic reviews, inconsistencies between the documentation of the upgrading and downgrading process and results, and inappropriate use for making recommendations. Furthermore, this article comprehensively discusses the factors influencing the grading of evidence certainty in SRs/MAs of animal experiments, including the impact of bias risk, indirectness, inconsistency, imprecision, and publication bias on evidence downgrading, as well as the role of large effect sizes and cross-species consistency in evidence upgrading. Finally, in response to the issues discussed, improvement strategies are proposed, including further research and optimization of the GRADE methodology for SRs/MAs of animal experiments, the development of reporting guidelines tailored to the characteristics of SRs/MAs in animal experiment research, and enhanced professional training for researchers in the GRADE methodology. This article aims to improve the quality of evidence in SRs/MAs of animal experiments, strengthen their reliability in clinical decision-making, and promote the more efficient translation of findings from animal experiment research into clinical practice.

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.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

OBJECTIVE: Based on the PTEN-induced hypothetical kinase 1 (PINK1)/Parkin pathway, the effect of acupuncture pretreatment on the expression of mitochondrial autophagy-related proteins in gastrocnemius muscle tissue of rats with exercise-induced muscle damage (EIMD) was observed, and the underlying mechanism of acupuncture pretreatment for the prevention and treatment of EIMD was explored. METHODS: Of 88 SD male rats, aged 6 weeks, 8 rats were randomly selected as a blank group, and the remaining 80 rats were randomized into a model group and an acupuncture pretreatment group, with 40 rats in each group. Either the model group or the acupuncture pretreatment group was subdivided randomly into 5 subgroups with 8 rats in each one according to the time points of sample collection, 0 h, 12 h, 24 h, 48 h and 72 h after modeling. An intermittent downhill running centrifugal exercise was carried out on an animal experimental treadmill to establish the EIMD model in the model group and the acupuncture pretreatment group. The rats in the acupuncture pretreatment group received acupuncture at "Guanyuan" (CV6) and bilateral "Zusanli" (ST36), once a day for 20 min each time, for 7 consecutive days before EIMD model preparation. Transmission electron microscopy was used to observe the ultrastructure of gastrocnemius muscle tissue in each group. The contents of malondialdehyde (MDA), superoxide dismutase (SOD) and catalase (CAT) in serum were detected by ELISA. Western blot was used to detect the protein expression of PINK1, Parkin, sequestosome 1 (p62) and microtubule-associated protein light chain 3B (LC3B) in rat gastrocnemius muscle tissue. Real-time PCR was adopted to detect the mRNA expression of PINK1, Parkin, p62 and LC3B in rat gastrocnemius muscle tissue. RESULTS: Compared with the blank group, the mitochondria of gastrocnemius muscles showed obvious swelling in the 0 h, 12 h, 24 h, and 48 h model subgroups , autophagosomes were formed in the 12 h and 24 h model subgroups, and the mitochondrial morphology returned to normal gradually in the 72 h model subgroup. The serum MDA contents of rats in 5 model subgroups increased (P<0.01, P<0.05). The contents of SOD and CAT in the subgroups of 0 h, 12 h, 24 h and 48 h decreased (P<0.05, P<0.01). The protein and mRNA expression levels of PINK1, Parkin and LC3B in gastrocnemius muscle tissue of rats in 0 h, 12 h and 24 h subgroups were elevated (P<0.01); and the protein and mRNA expression levels of p62 in the 0 h, 12 h, 24 h and 48 h subgroups were reduced (P<0.01, P<0.05). Compared with the model subgroup at the same time point, the myofibril damage and the degree of mitochondrial swelling were mild in each acupuncture pretreatment subgroup, and the numbers of autophagosomes were fewer. The contents of MDA in the acupuncture pretreatment subgroups decreased at 0 h, 12 h, 24 h, and 48 h (P<0.05, P<0.01). The contents of SOD and CAT in the 12 h acupuncture pretreatment subgroup increased (P<0.05, P<0.01). The protein and mRNA expression levels of PINK1 and Parkin in the 0 h, 12 h, and 24 h acupuncture pretreatment subgroups decreased (P<0.01, P<0.05). The protein and mRNA expression levels of LC3B in the 12 h acupuncture pretreatment subgroup decreased (P<0.01), and that of p62 in the 0 h and 24 h acupuncture pretreatment subgroups increased (P<0.01, P<0.05). CONCLUSION The intermittent downhill running centrifugal exercise induces the excessive mitochondrial autophagy. Acupuncture pretreatment may attenuate EIMD, and the underlying mechanism is related to the regulation of PINK1/Parkin signaling pathway expression, reducing oxidative stress damage in skeletal muscle cells, and inhibiting mitochondrial autophagy overactivation.
Animals ; Ubiquitin-Protein Ligases/genetics* ; Male ; Rats ; Acupuncture Therapy ; Protein Kinases/genetics* ; Rats, Sprague-Dawley ; Mitophagy ; Humans ; Muscle, Skeletal/metabolism* ; Physical Conditioning, Animal ; Muscular Diseases/physiopathology* ; Signal Transduction

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: To analyze the characteristics and safety risks of preservatives and sweeteners in beverages sold near schools in Anshun City, so as to provide a evidence for formulating targeted regulatory strategies in campus. Methods: From December 2023 to July 2024, 834 beverage samples were collected from sales points near primary and secondary schools in Xixiu District and four surrounding townships of Anshun City by a stratified random sampling method. High performance liquid chromatography was used to detect three preservatives (sorbic acid, benzoic acid and dehydroacetic acid) and four sweeteners (sodium saccharin, acesulfame-K, aspartame, and neotame). Differences were analyzed using the Chi-square test. Results: The overall exceedance rate of preservative was 8.6% (72 samples), with dehydroacetic acid showing the highest exceedance rate (7.0%, 58 samples), significantly higher than sorbic acid (0.6%, 5 samples) and benzoic acid (0.4%, 3 samples) ( χ 2=90.85, P <0.01). The overall exceedance rate of sweetener was 10.4% (87 samples), with sodium saccharin having the highest exceedance rate ( 6.2 %, 52 samples),significantly higher than neotame (2.8%, 23 samples), acesulfame-K (0) and aspartame (0) ( χ 2=262.04, P <0.01). Potential risks were identified due to the co occurrence of multiple additive exceedances, including 0.7% (6 samples) for mixed preservatives and 1.6% (13 samples) for mixed sweetener. No statistically significant differences were found in preservative (7.2%, 26 samples) or sweetener (12.3%, 44 samples) exceedance rates between micro enterprises and large, medium and small enterprises ( χ 2=2.67, 5.16, both P >0.05). Conclusion Systemic misuse risk of food additives in beverages sold near school necessitates a risk traceability based regulatory framework, with emphasis on standardizing enterprise production practices and strengthening oversight of sales outlets near campuses.

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.