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.

OBJECTIVE: To evaluate the short-and medium-term efficacy of posterior medial branch block in the treatment of persistent pain after percutaneous vertebral augmentation. METHODS: From January 2018 to January 2023, a total of 1, 062 patients with osteoporotic vertebral compression fractures underwent percutaneous vertebral augmentation. Among them, 32 elderly patients who experienced persistent low back pain after surgery and subsequently received posterior medial branch block and cryoablation were included. Six patients died during follow-up, leaving 26 patients for final analysis (1 male, 25 females). The mean age was (82.96±5.66) years (ranged, 76 to 94 years). The mean body mass index was (23.76±3.08) kg·m^-2(ranged 18.1 to 27.2 kg·m^-2). The bone mineral density T-value ranged from -2.5 to -4.3 with a mean of (-3.09±0.56). The mean volume of bone cement injected was 6.00 (5.38, 7.00) ml. Fracture locations were T₁₁ (2 cases), T₁₂ (7 cases), L₁ (10 cases), L₂ (6 cases), and L₃ (1 case). The mean interval from vertebral augmentation to block treatment was (7.12±2.22) months (rangd 6 to 12 months). The vertebral augmentation procedures were percutaneous kyphoplasty(PKP) in 12 cases and percutaneous vertebroplasty (PVP) in 14 cases. At the 2nd week, 3rd month, and 6th month after the block, the numerical rating scale(NRS), Oswestry disability index(ODI), patient satisfaction, and pain relief rate at the 6th month were evaluated. Relationships between pain relief rate at the 6th month after the last treatment and possible influencing factors were analyzed. RESULTS: Compared with X-ray films after percutaneous vertebral augmentation, the X-ray films before block showed an increase in kyphotic angle and vertebral compression rate, with statistically significant differences(P<0.05). At the 2nd week, 3rd month, and 6th month after posterior medial branch block and cryoablation, NRS and ODI scores were significantly lower than before the block(P<0.05). Among the 26 patients, 5 received additional cryoablation. At the 6th month after the last treatment, 19 patients reported excellent or good satisfaction. Univariate binary Logistic analysis showed all P>0.05, and no independent factor affecting final satisfaction or pain relief at 6 months after the last treatment was identified. CONCLUSION Posterior medial branch block(with cryoablation) can effectively improve short-and medium-term symptoms and function in patients with persistent axial low back pain after percutaneous vertebral augmentation for osteoporotic vertebral fractures.
Humans ; Male ; Female ; Aged ; Spinal Fractures/surgery* ; Aged, 80 and over ; Osteoporotic Fractures/surgery* ; Vertebroplasty/adverse effects* ; Nerve Block/methods*

Objective:To construct a tripartite game model involving the government,the public,and the pharmaceutical industry alliance during public health emergencies,revealing the dynamic mechanisms of health-related information quality regulation and exploring effective strategies to optimize the informa-tion dissemination environment through reward-punishment mechanisms.Methods:Based on evolutionary game theory,a tripartite evolutionary game model was established,integrating strategy spaces,payoff functions,and parameter definitions for each stakeholder.The pharmaceutical industry alliance's strate-gies included publishing high-or low-quality information(α),the public's strategies encompassed ration-al analysis or passive response(β),and the government's strategies involved regulatory enforcement or inaction(γ).Key parameters,such as economic benefits(Iyy),regulatory costs(Czf),penalties(Fyy),and incentives(Pyy),were quantified to reflect real-world scenarios.Replicator dynamic equa-tions and Jacobian matrices were derived to analyze the stability of equilibrium points,while MATLAB 2016a simulations were conducted to validate the model under varying initial conditions(e.g.,Iyy=100,150,200;Pyy=0,20,35;Fyy=0,10,20).Sensitivity analyses examined the impact of critical pa-rameters on system evolution,by 50 iterative simulations to observe convergence patterns.Results:The study revealed three key findings:(1)Public rational discernment(β)significantly influenced the phar-maceutical industry's strategy.Simulations demonstrated that increasing Iqz(benefits of information acqui-sition)reduced Cqz(cognitive costs),elevating β from 0.4 to 0.8 and driving α(high-quality information probability)to stabilize at 1.(2)Government regulatory intensity(γ)correlated positively with the so-cial hazards of low-quality information.When Fyy+Pyy＞Iyy,speculative behaviors decreased,achieving equilibrium at α=1.(3)Dual stable equilibria emerged:a high-quality equilibrium(α=1,β=1,γ=0)with lower regulatory costs and a low-quality equilibrium(α=0,β=0,γ=1)associated with higher social risks.Phase diagrams illustrated path dependency,where initial α＜0.5 led to the low-quality equilibrium unless dynamic penalties(Fyy＞20)and incentives(Pyy＞30)were enforced.Conclusion:A"carrot-stick"collaborative governance framework is proposed,emphasizing categorized regulation,AI-enabled auditing,and dynamic penalty systems.Future research should integrate emotional utility func-tions to address irrational decision-making impacts,thereby enhancing the adaptability of health informa-tion regulatory systems.

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.

OBJECTIVES: To explore the mediating role of sleep duration in the relationship between depression symptoms and myopia among middle school students. METHODS: This study was a cross-sectional research conducted using a stratified cluster random sampling method. A total of 1 728 middle school students were selected from two junior high schools and two senior high schools in certain urban areas and farms of the Xinjiang Production and Construction Corps. Questionnaire surveys and vision tests were conducted among the students. Spearman analysis was used to analyze the correlation between depression symptoms, sleep duration, and myopia. The Bootstrap method was employed to investigate the mediating effect of sleep duration between depression symptoms and myopia. RESULTS: The prevalence of myopia in the overall population was 74.02% (1 279/1 728), with an average sleep duration of (7.6±1.0) hours. The rate of insufficient sleep was 83.62% (1 445/1 728), and the proportion of students exhibiting depression symptoms was 25.29% (437/1 728). Correlation analysis showed significant negative correlations between visual acuity in both eyes and sleep duration with depressive emotions as measured by the Center for Epidemiologic Studies Depression Scale (with correlation coefficients of -0.064, -0.084, and -0.199 respectively; P<0.01), as well as with somatic symptoms and activities (with correlation coefficients of -0.104, -0.124, and -0.233 respectively; P<0.01) and interpersonal relationships (with correlation coefficients of -0.052, -0.059, and -0.071 respectively; P<0.05). The correlation coefficients for left and right eye visual acuity and sleep duration were 0.206 and 0.211 respectively (P<0.001). Sleep duration exhibited a mediating effect between depression symptoms and myopia (indirect effect=0.056, 95%CI: 0.029-0.088), with the mediating effect value for females (indirect effect=0.066, 95%CI: 0.024-0.119) being higher than that for males (indirect effect=0.042, 95%CI: 0.011-0.081). CONCLUSIONS Sleep duration serves as a partial mediator between depression symptoms and myopia in middle school students.
Humans ; Myopia/etiology* ; Male ; Female ; Depression/physiopathology* ; Cross-Sectional Studies ; Sleep ; Adolescent ; Students ; Child ; Time Factors ; Sleep Duration

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.

Creutzfeldt-Jakob disease(CJD)is a rare neurodegenerative disorder characterized by abnor-malities in the prion protein(PrP),the most common form of human prion disease.Although Genome-Wide Association Studies(GWAS)have identified numerous risk genes for CJD,the mechanisms under-lying these risk loci remain poorly understood.This study aims to elucidate novel genetically prioritized candidate proteins associated with CJD in the human brain through an integrative analytical pipeline.Uti-lizing datasets from Protein Quantitative Trait Loci(pQTL)(NpQTL1=152,NpQTL2=376),expres-sion QTL(eQTL)(N=452),and the CJD GWAS(NCJD=4 110,NControls=13 569),we imple-mented a systematic analytical pipeline.This pipeline included Proteome-Wide Association Study(PWAS),Mendelian randomization(MR),Bayesian colocalization,and Transcriptome-Wide Associa-tion Study(TWAS)to identify novel genetically prioritized candidate proteins implicated in CJD patho-genesis within the brain.Through PWAS,we identified that the altered abundance of six brain proteins was significantly associated with CJD.Two genes,STX6 and PDIA4,were established as lead causal genes for CJD,supported by robust evidence(False Discovery Rate＜0.05 in MR analysis;PP4/(PP3+PP4)≥0.75 in Bayesian colocalization).Specifically,elevated levels of STX6 and PDIA4 were asso-ciated with an increased risk of CJD.Additionally,TWAS demonstrated that STX6 and PDIA4 were asso-ciated with CJD at the transcriptional level.

Objective:A retrospective analysis was conducted on the clinical characteristics and polysomnography (PSG) features of patients with obstructive sleep apnea (OSA) of different ages.Methods:From January 2015 to March 2024, the patients who underwent overnight PSG monitoring at the Sleep Respiratory Disease Diagnosis and Treatment Center, Department of Otolaryngology, Head and Neck Surgery, Sixth Medical Center of the PLA General Hospital were sequentially enrolled.A total of 4 396 patients[aged from 18 to 97(46.04±12.60)years] with OSA who met the criteria were finally enrolled and divided into the youth group (18-44 years old, n=2 099), middle-aged group (45-59 years old, n=1 641), and elderly group (≥60 years old, n=656).The differences in general condition, Epworth sleepiness Scale (ESS) score, rapid eye movement sleep (REM) sleep time in total sleep time, micro-awakening index, apnea hypopnea index (AHI), minimum oxygen saturation at night (LSpO 2), oxygen hypoxia index (ODI) and so on were compared.Multivariate Logistic regression was used to analyze the relationship between age stratification and different severity of OSA (mild 5≤AHI≤15, moderate 1530, Times per hour). Results:The neck circumference [(39.11±3.71) cm], body mass index [BMI, (26.63±3.70) kg/m2], total sleep time [(386.13±66.09) min], the percentage of deep sleep in total sleep time [(3.05±4.80)%], the percentage of REM sleep time in total sleep time [(16.73±6.83)%], apnea-hypopnea index [AHI, (31.17±19.20) events/h], oxygen desaturation index [ODI, (46.57±33.54) events/h], microarousal index [(14.82±12.27) events/h], and Epworth Sleepiness Scale (ESS) score (8.29±5.92) in the elderly group were all lower than those in the young group [neck circumference (41.36±3.37) cm, BMI (28.55±4.21) kg/m2, total sleep time [(424.67±62.21) min], the percentage of deep sleep in total sleep time (5.89±6.79)%, the percentage of REM sleep time in total sleep time (17.95±6.67)%, AHI (39.55±27.89) events/h, ODI (61.88±34.60) events/h, microarousal index (22.77±19.31) events/h, ESS score (9.63±5.82)] and the middle-aged group [neck circumference (40.75±3.59) cm, BMI (27.89±3.55)kg/m2, total sleep time [(410.98±63.02)min], the percentage of deep sleep in total sleep time (3.59±5.11)%, the percentage of REM sleep time in total sleep time (17.47±6.63)%, AHI (36.01±23.63) events/h, ODI (57.20±34.75) events/h, microarousal index (19.23±15.59) events/h, ESS score (9.98±6.11)], with F values of 102.62, 62.15, 95.87, 95.94, 8.71, 29.60, 49.72, 59.11, and 19.03, respectively, and all P values were <0.001. The proportion of females [29.57% (94/656)], the proportion of patients with hypertension [37.20% (244/656)], and the nocturnal LSpO 2 [(79.09±9.88)%] in the elderly group were all higher than those in the young group [proportion of females [6.48% (136/2 099)], proportion of patients with hypertension 18.10% (380/2 099), nocturnal LSpO 2(75.19±12.90)%] and the middle-aged group [proportion of females [15.84%(260/1 641)], proportion of patients with hypertension 33.09% (543/1 641), nocturnal LSpO 2(76.15±12.44)%], with F values of 242.62, 150.90, and 25.05, respectively, and all P values were <0.001.There were no significant differences in neck circumference, percentage of REM sleep time in total sleep time, AHI and LSpO 2 at night between young and middle-aged groups ( P>0.05). After adjusting for gender, neck circumference, BMI, and hypertension confounders, the risk of severe OSA in the older group was increased by 27.4% compared with the younger group ( OR=1.274, 95% CI=1.036-1.566, P<0.05). Further stratified analysis showed that neck circumference and BMI were independent factors for severe OSA in the young and middle-aged groups ( P<0.01), while, only neck circumference was independently associated with severe OSA in the elderly group (OR=1.119, 95% CI=1.039-1.207, P<0.01). Conclusion:The severity of OSA in young and middle-aged patients is more serious than that in elderly patients. Obesity is an important factor affecting the degree of OSA in young and middle-aged patients. After controlling for confounding factors such as obesity, the risk of severe OSA in elderly patients is higher than that in young patients, which is considered to be related to factors such as pharyngeal collapse and relaxation of pharyngeal muscles.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.