Perimenopausal depression （PMD） is an affective disorder that occurs in women during the transition from sexual maturity to old age. It can induce various complications， such as insomnia and cognitive decline. The etiology of PMD is complex. Although multiple hypotheses have been proposed， there is still no unified theory that fully explains its pathogenesis. Research into its mechanisms relies heavily on animal experiments， and establishing reliable animal models is crucial for experimental studies. Appropriate animal models can better simulate human pathophysiological states， rapidly evaluate the efficacy and safety of drugs and intervention methods， grasp the essence of the disease， and uncover its intrinsic connections， thereby exploring more advanced intervention strategies. However， there is a lack of systematic review and summarization of literature related to model construction. Additionally， traditional Chinese medicine （TCM）， adhering to the principles of ''syndrome differentiation and treatment'' and ''holistic concept''， has shown significant efficacy in treating PMD. In recent years， research exploring and analyzing its therapeutic mechanisms has been increasing. Therefore， to gain a clearer and more comprehensive understanding of PMD animal modeling methods and the mechanisms of TCM， this paper reviewed Chinese and English literature on PMD animal models and mechanisms of TCM in PMD treatment. It summarized the construction methods of single-factor and multi-factor PMD models， and discussed the advantages and disadvantages of each modeling approach. Furthermore， it delved into the mechanisms of TCM intervention in PMD， revealing that TCM formulas primarily exert their effects by regulating the hypothalamic-pituitary-adrenal axis， hypothalamic-pituitary-ovarian axis， gut-brain axis， cell signaling pathways， neural circuits， hormone levels， and neurotransmitter levels. This review aims to provide a reference for future research in this field. In summary， by summarizing the progress in the methods for PMD animal model construction and the mechanisms of TCM， the paper seeks to offer new insights into the mechanistic research of TCM intervention in PMD.

Objective To understand the status of body image disturbance and its influencing factors in patients with ankylosing spondylitis (AS), so as to provide a scientific basis for the clinical management of AS. Methods A total of 353 AS patients admitted from January 2022 to December 2024 were selected as research subjects. Chinese version of Body Image Disturbance Questionnaire (BIDQ) was used to investigate the body image disturbance in AS patients. Single factor analysis was performed by t test and analysis of variance, and multiple factors were analyzed by multivariate linear regression. Results The total score of BIDQ in 342 AS patients was (25.01±4.22). Multivariate linear regression analysis results showed that self-paid medical expense, nighttime VAS score and negative emotion PANAS score could positively predict body image disturbance in AS patients (standardized regression coefficient=0.413, 0.413, 0.460, P<0.05), and PSSS score, positive emotion PANAS score and exercise management CDSSM score could negatively predict body image disturbance (standardized regression coefficient=-0.245, -0.134, -0.247, P<0.05). Conclusion The body image disturbance in AS patients is worthy of clinical attention. Nighttime pain, negative emotion and self-paid medical treatment can increase the risk of body image disturbance. Positive emotion, social support and high self-management level of exercise behavior can reduce the formation of body image disturbance, which can provide new ideas for clinical management of AS patients.

ObjectiveTo investigate the risk factors for acute kidney injury （AKI） after liver transplantation， and to establish and validate a risk prediction model， and to provide a basis for early identification of high-risk patients and intervention in clinical practice. MethodsA single-center retrospective study was conducted， and clinical data were collected from 162 patients who received liver transplantation in Liver Transplantation Center of The First Hospital of Shanxi Medical University from March 2020 to June 2025. The patients were divided into AKI group with 69 patients and non-AKI group with 93 patients according to the diagnostic criteria for AKI established by the Kidney Disease： Improving Global Outcomes organization and the presence or absence of AKI within 7 days after surgery. The independent-samples t test was used for comparison of normally distributed continuous data between groups， while the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. The univariate differential analysis was used to obtain the factors associated with AKI after liver transplantation， and the multivariate logistic regression analysis was used to identify the independent risk factors and establish a nomogram model； the Bootstrap method with 1 000 repeated samples was used to perform internal validation of the model. The dataset was randomly divided into a training set and a validation set at a ratio of 7∶3， and the receiver operating characteristic （ROC） curve， the calibration curve， and decision curve analysis （DCA） were used to assess the discriminatory ability， calibration， and clinical applicability of the predictive model. ResultsBody mass index （BMI） （odds ratio ［OR］=1.281， 95% confidence interval ［CI］： 1.037　—　1.582， P=0.022）， serum creatinine （OR=1.097， 95%CI： 1.020　—　1.181， P=0.013）， intraoperative blood loss （OR=1.005， 95%CI： 1.002　—　1.009， P=0.004）， and cold ischemia time （OR=0.984， 95%CI： 0.976　—　0.991， P<0.001） were independent risk factors for the development of AKI after liver transplantation. The nomogram prediction model established based on the above factors had an area under the ROC curve （AUC） of 0.964 （95%CI： 0.931　—　0.997）， with an optimal cutoff value of 0.319， a sensitivity of 0.971， and a specificity of 0.903. In the training set （n=113）， the nomogram had an AUC of 0.969 （95% CI： 0.933　—　0.971）， while in the validation set （n=49）， the nomogram had an AUC of 0.941 （95%CI： 0.855　—　0.944）. The calibration curve showed good consistency between the predicted incidence rate and the actual incidence rate， and DCA showed that it had good net clinical benefit. ConclusionBMI， serum creatinine， cold ischemia time， and intraoperative blood loss are independent risk factors for the development of AKI after liver transplantation， and the nomogram prediction model established based on these factors performs well and has a good value in predicting the development of AKI after liver transplantation.

ObjectiveTo analyze the research hotspots and development trends in the field of spinal cord stimulation (SCS) for spinal cord injury (SCI). MethodsLiterature about SCS for SCI was retrieve from the Web of Science (WOS) Core Collection database, with a time range from January, 1999 to July, 2025. VOSviewer 1.6.20 and CiteSpace 6.4.R2 were used to analyze the annual publication volume, countries, authors, institutions, journals and keywords. ResultsA total of 636 literatures were included. From 1999 to 2025, the overall publication trend in this field showed an upward trajectory, with recent years fluctuating but tending to stabilize. The country with the most publications was the United States (429 papers), followed by Russia (98 papers) and China (70 papers). The institution with the highest number of publications was the University of California, Los Angeles (76 papers), the author with the most publications was V. Reggie Edgerton (70 papers), and the journal with the most publications was Journal of Clinical Medicine (31 papers). The most frequently cited study focused on exploring the combination of epidural spinal cord stimulation with task-specific training to restore motor function in patients with complete SCI. Keyword analysis showed that the research hotspots in this field were mainly focused on neuroregulation mechanisms, recovery of motor and autonomic nervous dysfunction, artificial intelligence, closed-loop stimulation and brain-computer interface technology innovations. In recent years, the research focus gradually shifted from basic mechanisms to personalized and precise multifunctional rehabilitation strategies. ConclusionThe field of SCS for SCI has undergone phases of basic mechanism exploration and clinical application expansion. Current research hotspots and future trends focus primarily on the development of new stimulation paradigms and combined innovative technologies.

Objective To investigate the characteristics of attentional bias towards cancer-related stimuli in breast cancer patients with fear of cancer recurrence(FCR)under subthreshold and suprathreshold stimulus conditions.Methods A total of 94 female breast cancer patients admitted in First Affiliated Hospital of Army Medical University from September 2022 to March 2023 were recruited to complete the Fear of Cancer Recurrence Inventory-Short Form(FCRI-SF)and the dot-probe task.According to the FCRI-SF cut-off score of 13,they were divided into clinical and non-clinical FCR groups,with 47 cases in each group.One-sample t-test and repeated-measures ANOVA were used to statistically analyze the subjects in the 2 groups in terms of attentional bias score,attentional orienting score and attentional disengagement difficulty score.Results The patients in the clinical FCR group showed a significant attentional bias toward cancer-related negative words(P<0.05).Under the subthreshold stimulus condition,its main component was attentional orienting to cancer-related negative words(P<0.05).In the suprathreshold stimulus condition,the main components were attentional orienting to neutral words matched by cancer-related negative words and difficulty in attentional disengagement from cancer-related negative words as well as attentional avoidance for cancer-related positive words(P<0.05).Conclusion Breast cancer patients with clinical FCR have an attentional bias toward cancer-related negative stimuli.Reducing their attention to cancer-related negative stimuli may be an effective measure to reduce FCR level in the patients.

As a new generation of time-of-flight mass spectrometry,multiple-reflection time-of-flight mass spectrometry(MR-TOF-MS)has been increasingly applied in the fields such as nuclear physics,chemistry,and biology due to its ultra-high resolution and rapid analysis capabilities.However,the analytical performance of MR-TOF-MS largely depends on the ion bunch state entering the mass analyzer.In this study,a rectangular ion trap(RIT)was developed,designed and processed using printed circuit board technology,as an ion accumulating and focusing device for MR-TOF mass analyzer.Compared to traditional ion traps composed of two sets of planar electrodes,this RIT had higher voltage utilization efficiency,resulting in more efficient ion collection and focusing.The ions were cooled to a sufficiently small bunch for precise mass measurement with MR-TOF-MS mass spectrometry in only 1 ms of cooling time in the RIT,then orthogonally ejected to the MR-TOF mass spectrometer for mass analysis.Experimental results indicated that the working cycle,ion flux,and ion focusing state of the RIT fully met the requirements of the MR-TOF mass analyzer.When coupled with the MR-TOF mass analyzer,the RIT enabled MR-TOF-MS to achieve a mass resolution of 1.5×105.

A deep learning-based method for recognizing the minutiae in fingerprint,as well as a Python programming-based evaluation system for quantifying the evidence value of fingerprint was proposed.Firstly,latent fingerprints,which were developed using a series of fluorescent nanomaterials synthesized by chemical methods,were used as unknown fingerprint(UKFP),while ink impressed fingerprints were used as known fingerprint(KFP).Then,the bifurcations and terminations in minutiae were recognized using the improved YOLOv8 deep learning model.After that,the similarity index(Sim.)of UKFP vs KFP were calculated by analyzing the angle similarity factor(α)and the curve similarity factor(β)between UKFP and KFP,meanwhile,the sensitivity index(Sen.)were calculated by analyzing the fineness factor(γ)between UKFP and KFP.The evidence value(EV)of fingerprint was thus obtained by the combination of Sim.and Sen..The calculation formulas for above evaluation factors(i.e.α,β and γ),evaluation indexes(i.e.Sim.and Sen.),and EV were also put forward.Finally,the evaluation system for quantifying the evidence value of fingerprint was established,the feasibility and reliability of this system were verified,and the external factors that impacted on Sim.,Sen.,and EV were investigated in detail.The Python-based evaluation system for quantifying the evidence value of fingerprint could achieve the goals objectively,comprehensively,accurately and efficiently,exhibiting easy operability,high efficiency,responsiveness and reliability.This research was expected to provide beneficial references for quantitatively evaluating and thoroughly developing the evidence value.

The hydroxyl value and conjugated linoleic acid content of dehydrated castor oils are two important indicators that reflect its properties.Thus,monitoring the two indicators can better realize the industrial production of high-quality dehydrated castor oils.However,traditional chemical measurement methods have many disadvantages in determination of the two indictors,including large reagent consumption,long determination time,and cannot achieve rapid monitoring.Fourier transform infrared spectrometry(FTIR)is a new and non-destructive detection method that is low-cost and can achieve rapid detection.In this work,FTIR technique was employed to collect spectral information of dehydrated castor oils and analyze the relationship between FTIR spectral information and hydroxyl value and conjugated linoleic acid content,and a rapid detection method for detecting the property indicators of dehydrated castor oils was thus established.FTIR scanning was performed on dehydrated castor oils with a hydroxyl value of 21.9-161.4 mg KOH/g and a conjugated linoleic acid content of less than 37.5%.Among different preprocessing methods,orthogonal scatter correction(OSC)could improve the prediction accuracy of the resulting model,by which the optimal modeling data segments for hydroxyl value and conjugated linoleic acid content were 3200-3800 cm-1 and 800-1200 cm-1,respectively,and the optimal modeling method was partial least squares(PLS).The coefficients of determination of the optimal models for hydroxyl value and conjugated linoleic acid content were all above 0.99.

A method for simultaneous determination of 21 kinds of Aconitum alkaloids(ATS)in biological specimens and infused liquor using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)was developed.The biological samples were pretreated with methanol-acetonitrile(1∶2,V/V)for protein precipitation,while infused liquors were diluted 100-fold with acetonitrile,followed by centrifugation,and filtration by a 0.22-μm membrane.Chromatographic separation was carried out on an EC-C18 column using gradient elution with the mixture of 10 mmol/L ammonium acetate and 0.2%formic acid as mobile phase A and acetonitrile as mobile phase B.With this method,all the analytes were separated within 9.5 min.The samples were detected in positive ESI mode with dynamic multiple reaction monitoring(MRM)and quantified via external standard calibration.The results showed that the concentrations of the analytes in the range of 2-1000 ng/mL had excellent linearity(R2>0.9992)with the peak area.The developed method was successfully used for detection of 21 kinds of aconitum alkaloids,with limits of detection of 0.5-2 ng/mL,quantification limits of 2-6 ng/mL,intra/inter-day precision≤6.0%,spiked recoveries of 89.4%-100.9%,extraction recoveries of 74.2%-104.4%,and matrix effects ranging from-11.1%to 9.2%in blood/urine.The method was applied to detection of 12 samples from 4 fatal aconite poisoning cases,and all 21 kinds of ATS with total alkaloid concentrations of 0.04-4.18 μg/mL in blood and 154.96-422.83 μg/mL in medicinal liquors were detected.Tissue distribution revealed that the order of concentrations from highest to lowest is as follows:urine(157.22 μg/mL)>gastric contents(51.37 μg/mL)>kidney(21.6 μg/g)>whole blood(4.18 μg/mL)>liver(0.03 μg/g).This method showed many advantages such as simple pretreatment,low detection limits,accurate quantification,broad analyte coverage,and superior anti-interference capability in complex matrices,proving ideal for forensic and toxicological analysis of aconitum alkaloids.

The faithful segregation of genetic material to daughter cells is a fundamental biological process and a prerequisite for autonomous construction of robustly self-replicating artificial cells.Artificial cells are mimic cell structures with part or whole functions of normal cells.The complexity of eukaryotic chromosome segregation machinery has limited its applications in the field of synthetic biology.In contrast,the plasmid segregation machineries employed by prokaryotes are relatively simple and can provide valuable approaches for the bottom-up construction of complex artificial cells.This review aimed to comprehensively analyze the origin species and working mechanism of three bacterial plasmid segregation systems,namely ParABS,ParMRC,and TubZRC systems.The current researches on plasmid segregation both in bacterial and artificial cellular systems were summarized,and the future directions of this field were also proposed.