Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

Objective:To explore the application effects of augmented reality (AR) technology combined with mobile interactive learning in cardiopulmonary resuscitation (CPR) teaching for nursing students on internship.Methods:We assigned 150 nursing interns between June 2022 and November 2022 to receive conventional CPR teaching (control group) and 150 nursing interns between November 2022 and March 2023 to receive CPR teaching based on AR technology combined with mobile interactive learning (observation group). The two groups were compared for the following indicators: time spent on individual procedures and practice error rate during rescue simulation and post-training attitude; theoretical knowledge, CPR practice score, and CPR self-efficacy; and CPR knowledge retention levels at 15 days, 1 month, and 3 months after training. SPSS 23.0 was used to perform the t-test, analysis of variance, chi-square test, and Fisher's exact test. Results:After training, the observation group showed a significantly shorter time spent on each procedure, a significantly lower practice error rate (8.00% vs. 16.67%), and significantly higher post-training attitude scores than the control group (all P<0.05). After training, the observation group had significantly higher scores than the control group in the theoretical score [(78.81±5.48) points vs. (66.66±5.23) points, P<0.05], compression depth [(5.62±1.06) cm vs. (5.16±0.94) cm, P<0.05], the degree of chest rebound [(98.73±7.82)% vs. (85.67±7.06)%, P<0.05], and self-efficacy scores ( P<0.05), which were all increased compared with pre-training values; and the observation group had significantly lower scores than the control group in compression frequency [(118.73±10.43) times/min vs. (126.19±10.79) times/min, P<0.05] and ventilation volume [(608.94±56.49) mL/time vs. (673.77±57.76) mL/time, P<0.05], which were all decreased compared with pre-training values. At 15 days, 1 month, and 3 months after training, the knowledge retention levels of nursing students were declined in both groups, and were significantly lower in the control group than in the observation group ( P<0.05). Conclusions:The implementation of CPR teaching based on AR technology combined with mobile interactive learning for undergraduate nursing interns can improve their attitude towards CPR training and self-efficacy during practice, reduce practice time and errors, and enhance cognitive ability and practical ability for CPR, which is a feasible and effective method for CPR training.

Objective To investigate the relationship between the levels of serum neuron-specific enolase(NSE)and tumor necrosis factor alpha(TNF-α)and the attack stage and prognosis of children with epilepsy secondary to viral encephalitis(VE).Methods A total of 96 children with VE admitted to the People's Hospital Affiliated to Inner Mongolia Medical University from January 2015 to January 2020 were selected as the research subjects,and they were divided into the control group(children with VE,n=30)and the observation group(children with epilepsy secondary to VE,n=66).In addition,30 healthy children who underwent physical examination in the hospital during the same period were selected as the health group.The levels of serum NSE and TNF-α in the health group,control group and observation group were compared.Children in the observation group were further divided into the 24h relapse group(n=48)and 24h non-relapse group(n=18)according to the attack of disease within 24h after admission.The levels of serum NSE and TNF-α were compared between the two groups.Pearson correlation was used to analyze the relationship between the levels of NSE,TNF-α and the attack stage of epilepsy secondary to VE.Children in the observation group were divided into the good prognosis group(n=45)and the poor prognosis group(n=21)according to the Glasgow Outcome Scale score at discharge.The serum levels of NSE,TNF-α and other possible prognostic factors were compared between the two groups.Multivariate logistic regression analysis was used to explore the prognostic factors of children with epilepsy secondary to VE,and the predictive value of serum NSE and TNF-αlevels on the prognosis of children with epilepsy secondary to VE was analyzed by drawing the receiver operating characteristic curve.Results The level of serum NSE in the control group was significantly higher than that in the health group(P＜0.05),and there was no significant difference in the level of serum TNF-α between the control group and the health group(P＞0.05).The serum levels of NSE and TNF-α in the observation group were significantly higher than those in the control group(P＜0.05).The serum levels of NSE and TNF-α in the observation group were significantly higher than those in the control group(P＜0.05).The levels of serum NSE and TNF-α in the 24 h relapse group were significantly higher than those in the 24 h non-relapse group(P＜0.05).The proportion of severe abnormal EEG,severe abnormal brain images and complicated respiratory failure,and serum levels of c-reactive protein,NSE and TNF-α in the good prognosis group were lower than those in the poor prognosis group(P＜0.05);there were no significant differences in sex,age,body mass,brain injury site,fever,hypokalemia,hyponatremia,previous convulsions,stress hyperglycemia,complicated organ dysfunction,viral infection,first episode of epilepsy,Glasgow Coma Scale score at admission,duration of convulsion,length of hospital stay,white blood cell count,aspartate transaminase,creatine kinase and cardiac troponin levels between the two groups(P＞0.05).The results of multivariate logistic regression analysis showed that complicated respiratory failure,serum NSE and TNF-α levels were correlated with the prognosis of children with epilepsy secondary to VE(P＜0.05).The area under the curve(AUC)of serum NSE and TNF-α levels in predicting the prognosis of children with epilepsy secondary to VE was 0.724(95％confidence interval:0.672-0.776)and 0.689(95％confidence interval:0.637-0.734),respectively,with a sensitivity of 82.22％and 75.56％and a specificity of 76.19％and 71.43％;the AUC of the combination of the two in predicting the prognosis of children with epilepsy secondary to VE was 0.826(95％confidence interval:0.774-0.873),with a sensitivity of 73.33％and a specificity of 80.95％.Conclusion The serum levels of NSE and TNF-α are abnormally high in children with epilepsy secondary to VE.Both of them are factors affecting the prognosis of children with epilepsy secondary to VE,showing a good predictive value for the prognosis of epilepsy secondary to VE.

Objective To grasp the main environmental factors affecting the growth of Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao;To predict the distribution of suitable areas of Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao in Shanxi Province;To provide references for the rational distribution of the resources in Shanxi Province.Methods This study utilized the sample point longitude and latitude information collected in the"Fourth Survey of Chinese Materia Medica Resources"database in Shanxi Province.The data were supplemented by searching the China Digital Herbarium and retrieving related literature records.347 sample points distribution data and environmental factors were added to the MaxEnt model.The main environmental factors and contribution rates affecting the geographical distribution of Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao were screened out.The ArcGIS software was used to divide the ecological suitable area of Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao in Shanxi Province.Results The area under the ROC curve of the established MaxEnt model was 0.909,indicating that the model prediction results were accurate.The model screened 19 environmental factors.Among them,climate factor was the most important environmental factor,followed by biological factor and topographic factor,and soil factor had the least influence.The potential suitable areas of Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao in Shanxi Province were mainly distributed in the northern mountainous areas,presenting a trend of gradually decreasing suitability levels from north to south.Under the current climate conditions,the most suitable area for Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao in Shanxi Province was 15 424 km2,the suitable area was 19 856 km2,the sub suitable area was 59 436 km2,and the unsuitable area was 61 894 km2.Conclusion Based on MaxEnt model and ArcGIS software,this study predicts the distribution of suitable areas of Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao in Shanxi Province,which has certain reference value for the protection and rational distribution of Astragalus membranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao resources in Shanxi Province.

This paper proposed to understand the pathogenesis and provide syndrome differentiated treatment for anxiety and depression-related dry eyes from the perspective of the liver and the lung， in order to provide ideas for treatment of this disease with traditional Chinese medicine. It is believed that the occurrence and development of anxiety and depression-related dry eyes is related to the ethereal qi and blood damage and blocked circulation of qi and blood. The liver and the lung are the main located zang-fu （脏腑） organs of the disease， and the qi movement， sweat pores， meridians and collaterals abnormalities of the liver and the lung are the pathological basis. The basic pathogenesis is disharmony of the liver and the lung， loss nourishment of eyes， and loss calm of the mind. In clinical practice， the root treatment is to restore the functions of the liver governing ascent and the lung governing descent， and to open up the sweat pores， meridians and collaterals， while the branch treatment is to promote the production of body fluids， nourish yin and calm the mind. Both the root and the branch causes are treated to restore the physiological functions， and Danzhi Xiaoyao Powder （丹栀逍遥散） combined with Shengmai Powder （生脉散） with modification is often used as the basic prescription.