⁶³Ni is predominantly generated through neutron activation in nuclear reactors and is classified as a pure beta-emitting radionuclide with a half-life of 101.1 a. During decay, ⁶³Ni emits a beta ray with an energy of 65.87 keV. ⁶³Ni can be used in the manufacture of beta radiation sources, which are utilized as reference and working sources for beta activity measurement and beta energy response calibration. Additionally, it is used in electron capture detectors for chromatography, ionization sources in electron tubes, and electron capture probes in gas chromatography. These instruments have extensive applications in food safety, public health and epidemic prevention, soil pollution monitoring, and security. ⁶³Ni is an artificial radionuclide not commonly found in the natural environment under normal conditions. However, the ⁶³Ni generated during routine operations of nuclear power plants, as well as residual materials and wastes contaminated with ⁶³Ni during plant decommissioning, may be released into the environment through liquid effluents or solid wastes. This can pose potential radiation risks to both the public and the environment. Hence, it is necessary to monitor the activity concentration of ⁶³Ni. Currently, reports on this subject are limited in China, and there is a lack of established standards for the determination of ⁶³Ni in nuclear power plants. This article reviews the global literature on the pretreatment and purification measurement processes of ⁶³Ni. The merits and demerits are summarized for pretreatment methods such as acid leaching, mixed acid digestion, ashing acid leaching/dissolution, and alkali fusion, and for separation and purification methods like solvent extraction, precipitation, and extraction chromatography. The article also highlights the advantages of measurement using liquid scintillation counters. This review provides a reference for the establishment of the determination method of ⁶³Ni in liquid effluents and solid wastes from nuclear power plants.

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.

Objective To preliminarily design a wide-energy-spectrum CR-39 solid-state nuclear track individual neutron dosimeter with different energy sections. Methods The thickness of the converter was optimized using the Monte Carlo SRIM program to broaden the energy range of the dosimeter. The self-made wide-energy-spectrum CR-39 individual neutron dosimeter was calibrated using ²⁴¹Am-Be, ²⁵²Cf, and thermal neutron sources to evaluate its dosimetric performance, including linearity, energy response, and neutron energy resolution. Results The linear correlation coefficient of the measurement system exceeded 0.98. The relative deviations of the energy response were 35.0% for blank section and 42.0% for polyethylene section, falling within the range of −50% to + 100% and meeting the monitoring requirements. The detection sensitivity for thermal neutron dose was 67 137.2 tr·cm⁻²·mSv⁻¹, and the detection sensitivity for thermal neutron fluence was 0.98 × 10⁻³ tr·n⁻¹, demonstrating good thermal neutron detection capability. Conclusion The self-made wide-energy-spectrum CR-39 individual neutron dosimeter fundamentally meets the requirements for individual neutron dose monitoring and is suitable for individual neutron dose monitoring in the energy range of thermal neutrons (up to approximately 15 MeV).

ObjectiveTo investigate the mechanisms of mitochondrial dynamics and metabolic reprogramming in the treatment of diabetic nephropathy （DN） by Shenxiao Tongluo prescription via the peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α）/sirtuin-3 （SIRT3）/hypoxia-inducible factor-1α （HIF-1α） signaling pathway. MethodsSixty-five SD rats were randomized into a sham group （10 rats） and a modeling group （55 rats）， and the modeling rats underwent left nephrectomy and intraperitoneal injection of streptozotocin （35 mg·kg^-1） to prepare a DN model. After successful modeling， the rats were randomized into model， empagliflozin （10 mg·kg^-1）， and low-， medium-， and high-dose （7.656， 15.312， 30.624 g·kg^-1， respectively） Shenxiao Tongluo prescription groups. The urine microalbumin （UmAlb）， blood urea nitrogen （BUN）， and serum creatinine （SCr） levels of rats in each group were assessed after continuous gavage for 8 weeks. The corresponding kits were used to measure the levels of lactate， superoxide dismutase （SOD）， and malondialdehyde （MDA） in the kidney tissue. Hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff staining were performed to observe the pathological changes in the kidney tissue. Transmission electron microscopy was employed to observe mitochondrial morphology. Immunohistochemistry was employed to determine the expression levels of dynamin-related protein 1 （DRP1） and pyruvate kinase M2 （PKM2） in the kidney tissue. Western blot was adopted to assess the protein levels of PGC-1α， SIRT3， HIF-1α， dynamin-related protein 1 （Drp1）， optic atrophy 1 （OPA1）， hexokinase 2 （HK2）， and pyruvate kinase M2 （PKM2） in the kidney tissue. ResultsCompared with the sham group， the model group showed elevated levels of UmAlb， BUN， SCr， lactate， and MDA， decreased SOD level （P<0.05）， glomerular hypertrophy， thickening of the mesangial basement membrane， vacuolar degeneration of renal tubular epithelial cells， and infiltration of renal interstitial inflammatory cells， oval mitochondria with disordered， blurred or disappearing cristae， down-regulated protein levels of PGC-1α， SIRT3， and OPA1， and up-regulated protein levels of HIF-1α， DRP1， HK2， and PKM2 （P<0.05）. Compared with the model group， the treatment in all the groups increased the body weight， lowered the levels of GLU， UmAlb， BUN， and MDA， raised the level of SOD， alleviated the pathological damage in the kidney tissue and mitochondrial damage， up-regulated the expression of PGC-1α， SIRT3， and OPA1， and down-regulated the expression of HIF-1α， DRP1， and PKM2 （P<0.05）. Empagliflozin and Shenxiao Tongluo prescription at medium and high doses lowered the levels of SCr and lactate and down-regulated the expression of HK2 （P<0.05）， which had no statistical significance in the low-dose Shenxiao Tongluo prescription group. ConclusionShenxiao Tongluo prescription may regulate mitochondrial dynamics and metabolic reprogramming by activating the PGC-1α/SIRT3/HIF-1α pathway， thereby alleviating oxidative damage in the kidney tissue and delaying the progression of DN.

ObjectiveTo investigate the mechanisms of mitochondrial dynamics and metabolic reprogramming in the treatment of diabetic nephropathy （DN） by Shenxiao Tongluo prescription via the peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α）/sirtuin-3 （SIRT3）/hypoxia-inducible factor-1α （HIF-1α） signaling pathway. MethodsSixty-five SD rats were randomized into a sham group （10 rats） and a modeling group （55 rats）， and the modeling rats underwent left nephrectomy and intraperitoneal injection of streptozotocin （35 mg·kg^-1） to prepare a DN model. After successful modeling， the rats were randomized into model， empagliflozin （10 mg·kg^-1）， and low-， medium-， and high-dose （7.656， 15.312， 30.624 g·kg^-1， respectively） Shenxiao Tongluo prescription groups. The urine microalbumin （UmAlb）， blood urea nitrogen （BUN）， and serum creatinine （SCr） levels of rats in each group were assessed after continuous gavage for 8 weeks. The corresponding kits were used to measure the levels of lactate， superoxide dismutase （SOD）， and malondialdehyde （MDA） in the kidney tissue. Hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff staining were performed to observe the pathological changes in the kidney tissue. Transmission electron microscopy was employed to observe mitochondrial morphology. Immunohistochemistry was employed to determine the expression levels of dynamin-related protein 1 （DRP1） and pyruvate kinase M2 （PKM2） in the kidney tissue. Western blot was adopted to assess the protein levels of PGC-1α， SIRT3， HIF-1α， dynamin-related protein 1 （Drp1）， optic atrophy 1 （OPA1）， hexokinase 2 （HK2）， and pyruvate kinase M2 （PKM2） in the kidney tissue. ResultsCompared with the sham group， the model group showed elevated levels of UmAlb， BUN， SCr， lactate， and MDA， decreased SOD level （P<0.05）， glomerular hypertrophy， thickening of the mesangial basement membrane， vacuolar degeneration of renal tubular epithelial cells， and infiltration of renal interstitial inflammatory cells， oval mitochondria with disordered， blurred or disappearing cristae， down-regulated protein levels of PGC-1α， SIRT3， and OPA1， and up-regulated protein levels of HIF-1α， DRP1， HK2， and PKM2 （P<0.05）. Compared with the model group， the treatment in all the groups increased the body weight， lowered the levels of GLU， UmAlb， BUN， and MDA， raised the level of SOD， alleviated the pathological damage in the kidney tissue and mitochondrial damage， up-regulated the expression of PGC-1α， SIRT3， and OPA1， and down-regulated the expression of HIF-1α， DRP1， and PKM2 （P<0.05）. Empagliflozin and Shenxiao Tongluo prescription at medium and high doses lowered the levels of SCr and lactate and down-regulated the expression of HK2 （P<0.05）， which had no statistical significance in the low-dose Shenxiao Tongluo prescription group. ConclusionShenxiao Tongluo prescription may regulate mitochondrial dynamics and metabolic reprogramming by activating the PGC-1α/SIRT3/HIF-1α pathway， thereby alleviating oxidative damage in the kidney tissue and delaying the progression of DN.

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:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Acute lateral ankle sprain (ALAS) is one of the most common sport injuries, with high incidence, recurrence and disability rates. Currently, exercise rehabilitation-based non-surgical treatment is the primary management approach for ALAS. However, there remain improper practices such as excessive immobilization or uncontrolled activity, which contribute to recurrent sprains and chronic ankle instability, significantly impairing patients′ athletic function and quality of life. To standardize the non-surgical management of ALAS, improve the cure rates, and reduce the recurrence and disability rates, Chinese Sports Rehabilitation Medicine Training Project of Chinese Medical Association, Foot and Ankle Basics and Orthopedics Group, Orthopedic Branch of Chinese Medical Doctor Association, and Sports Medicine Branch of Jiangsu Medical Association organized relevant experts to formulate Expert consensus on non-surgical treatment for acute lateral ankle sprain ( version 2025), following the principles of scientific vigor, practicality, and innovation. Thirteen recommendations were proposed for standardized treatment protocols across different healing phases, aiming to provide references for standard management of ALAS and improve the therapeutic outcomes.