Objective:To investigate the expression of Neurexophilin 4(NXPH4)in hepatocellular carcinoma(HCC)and its clinical significance and function.Methods:Retrieved LIHC project data(mRNA expression profiles and clinical records)from TCGA,analyzed differential NXPH4 expression in HCC versus adjacent non-tumor tissues,and investi-gated correlations between NXPH4 expression and clinicopathological characteristics/prognostic outcomes in HCC.Using GOEA and GSEA to investigate potential biological functions of NXPH4 in hepatocellular carcinoma.Results:NXPH4 exhibited significant upregulation in 23 cancer types(P<0.05),with significant associations to advanced HCC progression markers including TNM stage(P<0.05),histologic grade(P<0.05),and vascular invasion(P<0.05).Clinically,elevated NXPH4 expression correlated with reduced OS(HR=1.64,95%CI:1.15-2.33,P=0.006)and DSS(HR=1.88,95%CI:1.19-2.96,P=0.007).The immune infiltration results showed that NXPH4 expression was significantly correlated with Th2 cells and Th17 cells(all P values<0.05).Furthermore,NXPH4 expression was positively correlated with the levels of several immune checkpoint markers:TIGIT(r=0.265),PD-1(r=0.297),CTLA-4(r=0.302),and LAG-3(r=0.179,all P<0.001).Gene enrichment analysis revealed NXPH4 was significantly enriched in:pattern specification process(P<0.001);receptor ligand activity(P<0.001);collagen formation(P=0.009);activation of matrix metalloproteinases(P<0.001);neuro-active ligand receptor interactions(P<0.001);and ALK2 signaling(P=0.039).Conclusion:NXPH4 is associated with clinical pathological staging and poor prognosis in HCC patients;NXPH4 is associated with oncogenic pathways and im-mune infiltration,and has high value in predicting patient prognosis and immunotherapy.

The scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

Iron is one of the essential trace elements for the human body,which is crucial for the growth and development of newborns,especially premature infants.It participates in the generation of hemoglobin,affects the activity of various enzymes,and subsequently affects neurometabolism,neurochemistry,neuroanatomy,and gene/protein composition,thereby having a lasting impact on the development of the central nervous system.This article reviews the research progress on the relationship between iron metabolism and neurodevelopment in premature infants in recent years,aiming to provide scientific basis for clinical management and preventive intervention of premature infants.

Objective To design a centrifugal agtomization principle-based disinfection and disinsection device for plateau areas.Methods A disinfection and disinsection device was developed based on centrifugal atomization principle,which was composed of a spraying body,lifting and rotating mechanisms,an electrical control system,a spray tank and a sprayer carriage.The spraying body consisted of spraying components,a motor,an air blower,a fan and etc,and the spraying components adopted the structural form of multi-layer disc stacking to realize the atomization of liquid medicine;the lifting mechanism implemented up-and-down adjustment of the air blower through an electric actuartor,and the rotating mechanism executed left-and-right adjustment of the air blower thrgouth the worm gear motor and limit switch;the electrical control system was made up of a power source,electrical devices,a distribution system and a control system,in which a lead-acid battery pack was used for power supply and a DGUS touch screen and a DCS001 controller were involved in the control system;the spray tank was formed of a tank body,a needle valve,a water pump,float switch,etc;the sprayer carriage comprised a frame,a pusher,two sealing plates and four wheels.Some disc samples with different diameters were trial produced,and comparison experiments were carried out to investigate the relationship between disc diameter,motor speed,spray flow rate and atomization effect;a prototype was manufactured for performance testing of the device.Results The disc diameter and motor speed were inversely proportional to the droplet size;the spray flow rate was directly proportional to the droplet size,when the spray flow rate increased by 60%,the increment of the droplet size was restrained within 2%to 11%.The prototype test results indicated that the device could be used for ultra-low-volume disinfection and insecticide in plateau areas,with the D50 and D90 of the droplet size being 31 and 48 μm,respectively.Conclusion The device developed gains advantages in atomiztion,and meets the requirements for epidemic prevention,disinfection and disinsection in plateau areas.[Chinese Medical Equipment Journal,2025,46(4):35-39]

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the expression of Neurexophilin 4(NXPH4)in hepatocellular carcinoma(HCC)and its clinical significance and function.Methods:Retrieved LIHC project data(mRNA expression profiles and clinical records)from TCGA,analyzed differential NXPH4 expression in HCC versus adjacent non-tumor tissues,and investi-gated correlations between NXPH4 expression and clinicopathological characteristics/prognostic outcomes in HCC.Using GOEA and GSEA to investigate potential biological functions of NXPH4 in hepatocellular carcinoma.Results:NXPH4 exhibited significant upregulation in 23 cancer types(P<0.05),with significant associations to advanced HCC progression markers including TNM stage(P<0.05),histologic grade(P<0.05),and vascular invasion(P<0.05).Clinically,elevated NXPH4 expression correlated with reduced OS(HR=1.64,95%CI:1.15-2.33,P=0.006)and DSS(HR=1.88,95%CI:1.19-2.96,P=0.007).The immune infiltration results showed that NXPH4 expression was significantly correlated with Th2 cells and Th17 cells(all P values<0.05).Furthermore,NXPH4 expression was positively correlated with the levels of several immune checkpoint markers:TIGIT(r=0.265),PD-1(r=0.297),CTLA-4(r=0.302),and LAG-3(r=0.179,all P<0.001).Gene enrichment analysis revealed NXPH4 was significantly enriched in:pattern specification process(P<0.001);receptor ligand activity(P<0.001);collagen formation(P=0.009);activation of matrix metalloproteinases(P<0.001);neuro-active ligand receptor interactions(P<0.001);and ALK2 signaling(P=0.039).Conclusion:NXPH4 is associated with clinical pathological staging and poor prognosis in HCC patients;NXPH4 is associated with oncogenic pathways and im-mune infiltration,and has high value in predicting patient prognosis and immunotherapy.

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

Depression is a prevalent mental and emotional disor-der that often results in significant emotional disturbances,cog-nitive dysfunction,and memory impairments.It is characterized by a high incidence rate,a substantial disability burden,and limited therapeutic efficacy.Currently,the long-term use of medications for the treatment of depression can result in a range of adverse reactions,highlighting the urgent need to explore no-vel approaches that can effectively alleviate depressive symptoms while minimizing side effects.Curcumin,a natural polyphenolic compound derived from the rhizome of turmeric,demonstrates considerable potential in the prevention and treatment of depres-sion,owing to its diverse array of biological activities.In recent years,numerous studies have investigated the use of curcumin for the treatment of depression.This article aims to provide a comprehensive review of the mechanisms of action underlying curcumin's efficacy in treating depression.Specifically,it focu-ses on its ability to improve neurotransmitter imbalances,restore neural plasticity,alleviate neural damage,mitigate dysfunction of the hypothalamic-pituitary-adrenal(HPA)axis,regulate in-flammatory factors and neuroinflammatory signaling pathways,and inhibit oxidative stress.This review is intended to offer in-sights and methodological references for basic research on curcu-min,as well as for the development of novel therapeutic agents for the treatment of depression.

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control