Objective: To compare the effects of height-desk-chair matching on the viewing distance of primary school students before and after intervention, so as to provide scientific basis for the hygiene management of desks and chairs. Methods: From April to June 2025, a random cluster sampling method was used to select 141 third grade students from three classes equipped with adjustable desks and chairs in a primary school in Hefei City for a height-desk-chair matching intervention study. The height of students desks and chairs was adjusted according to the standard height and height range specified in the Functional Sizes and Technical Requirements of Chairs and Tables for Educational Institutions (GB/T 3976-2014), with an intervention period of one week. Before and after the intervention, eye use data were measured by using the electronic smart device "Cloud Clip", while collecting data on vision data viewing distance, time spent using eyes at close range and outdoor time, desk and chair height, and physical examination. Linear regression analysis was used to investigate the factors related to viewing distance before the intervention of height-desk-chair matching, and a paired t-test was used to analyze the difference in viewing distance before and after the intervention. A mixed effects model was used to explore the effect of height desk and chair adaptation intervention on viewing distance. Results: The compliance rates for desk and chair adjustments before and after the intervention were 1.4% and 18.4%, respectively, with a statistically significant difference ( χ 2=22.84, P <0.01). The viewing distance increased from (30.48±5.01) cm before intervention to (32.06±5.75) cm post intervention, with a statistically significant difference ( t=4.57, P <0.01). The proportion of students meeting the viewing distance standard increased from 33.3% to 51.1%. The linear mixedeffects model results indicated that the association between height appropriate desk and chair interventions and viewing distance was statistically significant, regardless of whether covariates such as time spent using eyes at close range and outdoor time were adjusted ( β=-1.58, 95%CI = -2.25 to -0.91; β=-1.14, 95%CI =-1.85 to -0.43, both P <0.05). Conclusion Height adjusted desks and chairs, which can effectively increase the viewing distance for primary school students, has positive implications for improving healthy eye care behaviors among children and adolescents.

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.

Objective To elucidate the causal relationships between colorectal cancer (CRC) and prevalent psychiatric disorders through a two-sample Mendelian randomization approach. Methods Utilizing publicly available genome-wide association study data, we explored the connections between CRC and various psychiatric disorders, including depression, anxiety, bipolar disorder, and schizophrenia. We applied three statistical analyses: inverse variance weighting, MR-Egger, and median weighting. Sensitivity analyses were conducted to ensure the reliability and validity of the results. Results Inverse variance weighting analysis showed no significant links between CRC and depression (P=0.090), anxiety (P=0.099), or schizophrenia (P=0.899). Conversely, a significant inverse relationship was found with bipolar disorder (P=0.010). Conclusion No causal connection exists between CRC and the psychiatric conditions of depression, anxiety, or schizophrenia. However, CRC may have a causal association with a reduced risk of bipolar disorder, further supporting the existence of the gut-brain axis.

Alzheimer’s disease (AD), a progressive neurodegenerative disorder and the leading cause of dementia in the elderly, is characterized by severe cognitive decline, loss of daily living abilities, and neuropsychiatric symptoms. This condition imposes a substantial burden on patients, families, and society. Despite extensive research efforts, the complex pathogenesis of AD, particularly the early mechanisms underlying cognitive dysfunction, remains incompletely understood, posing significant challenges for timely diagnosis and effective therapeutic intervention. Among the various cellular components implicated in AD, GABAergic interneurons have emerged as critical players in the pathological cascade, playing a pivotal role in maintaining neural network integrity and function in key brain regions affected by the disease. GABAergic interneurons represent a heterogeneous population of inhibitory neurons essential for sustaining neural network homeostasis. They achieve this by precisely modulating rhythmic oscillatory activity (e.g., theta and gamma oscillations), which are crucial for cognitive processes such as learning and memory. These interneurons synthesize and release the inhibitory neurotransmitter GABA, exerting potent control over excitatory pyramidal neurons through intricate local circuits. Their primary mechanism involves synaptic inhibition, thereby modulating the excitability and synchrony of neural populations. Emerging evidence highlights the significant involvement of GABAergic interneuron dysfunction in AD pathogenesis. Contrary to earlier assumptions of their resistance to the disease, specific subtypes exhibit vulnerability or altered function early in the disease process. Critically, this impairment is not merely a consequence but appears to be a key driver of network hyperexcitability, a hallmark feature of AD models and potentially a core mechanism underlying cognitive deficits. For instance, parvalbumin-positive (PV⁺) interneurons display biphasic alterations in activity. Both suppressing early hyperactivity or enhancing late activity can rescue cognitive deficits, underscoring their causal role. Somatostatin-positive (SST⁺) neurons are highly sensitive to amyloid β-protein (Aβ) dysfunction. Their functional impairment drives AD progression via a dual pathway: compensatory hyperexcitability promotes Aβ generation, while released SST-14 forms toxic oligomers with Aβ, collectively accelerating neuronal loss and amyloid deposition, forming a vicious cycle. Vasoactive intestinal peptide-positive (VIP⁺) neurons, although potentially spared in number early in the disease, exhibit altered firing properties (e.g., broader spikes, lower frequency), contributing to network dysfunction (e.g., in CA1). Furthermore, VIP release induced by 40 Hz sensory stimulation (GENUS) enhances glymphatic clearance of Aβ, demonstrating a direct link between VIP neuron function and modulation of amyloid pathology. Given their central role in network stability and their demonstrable dysfunction in AD, GABAergic interneurons represent promising therapeutic targets. Current research primarily explores three approaches: increasing interneuron numbers (e.g., improving cortical PV⁺ interneuron counts and behavior in APP/PS1 mice with the antidepressant citalopram; transplanting stem cells differentiated into functional GABAergic neurons to enhance cognition), enhancing neuronal activity (e.g., using low-dose levetiracetam or targeted activation of specific molecules to boost PV⁺ interneuron excitability, restoring neural network γ‑oscillations and memory; non-invasive neuromodulation techniques like 40 Hz repetitive transcranial magnetic stimulation (rTMS), GENUS, and minimally invasive electroacupuncture to improve inhibitory regulation, promote memory, and reduce Aβ), and direct GABA system intervention (clinical and animal studies reveal reduced GABA levels in AD-affected brain regions; early GABA supplementation improves cognition in APP/PS1 mice, suggesting a therapeutic time window). Collectively, these findings establish GABAergic interneuron intervention as a foundational rationale and distinct pathway for AD therapy. In conclusion, GABAergic interneurons, particularly the PV⁺, SST⁺, and VIP⁺ subtypes, play critical and subtype-specific roles in the initiation and progression of AD pathology. Their dysfunction significantly contributes to network hyperexcitability, oscillatory deficits, and cognitive decline. Understanding the heterogeneity in their vulnerability and response mechanisms provides crucial insights into AD pathogenesis. Targeting these interneurons through pharmacological, neuromodulatory, or cellular approaches offers promising avenues for developing novel, potentially disease-modifying therapies.

OBJECTIVE To study the effects of sophoranone （SOP） on the biological behavior of nasopharyngeal carcinoma CNE-1 cells and mitogen-activated protein kinase （MAPK） signaling pathway. METHODS CNE-1 cells were divided into blank group and SOP low-， medium- and high-concentration groups （SOP-L group， SOP-M group， SOP-H group， 25， 50 and 100 μmol/L）. The number of invasive cells， the number of migratory cells， and the apoptosis rate of cells were detected. The expression levels of mitogen-activated protein kinase kinase （MEK）， extracellular signal-regulated kinase 1 （ERK1）， ERK2， and c-Jun N-terminal kinase （JNK） mRNA， as well as phosphorylation levels of ERK， JNK， and p38 mitogen-activated protein kinase （abbreviated as “p38”） proteins in cells were all detected. Additionally， cells were divided into blank group， SOP high-concentration group （SOP- H group， 100 μmol/L）， SOP high-concentration combined with p38 inhibitor group （SOP-H+SB group， 100 μmol/L SOP+10 μmol/L SB）， and SOP high-concentration combined with JNK inhibitor group （SOP-H+SP group， 100 μmol/L SOP+10 μmol/L SP）. The number of invasive cells， cell migration rate， and the protein phosphorylation levels of JNK and p38 in cells， as well as the protein expression levels of matrix metalloproteinase-9（MMP-9）， proliferating cell nuclear antigen Ki67， and cleaved-caspase-3 were measured. RESULTS Compared with the blank group， SOP for each concentration could significantly decrease the number of invasive cells， the number of migratory cells， and mRNA expressions of MEK， ERK1， ERK2 （except for the SOP-L group） and JNK， but increase the apoptosis rate of cells and phosphorylation levels of ERK， JNK， and p38 proteins （P＜0.05）. Compared with the SOP-H group， the protein phosphorylation levels of p38 and JNK， and the protein expression of cleaved-caspase-3 were decreased significantly in SOP-H+SB group and SOP-H+SP group， while the number of invasive cells， cell migration rate， and the protein expression levels of MMP-9 and Ki67 were all increased significantly （P＜0.05）. CONCLUSIONS SOP can inhibit the proliferation， migration and invasion of CNE-1 cells， and induce the apoptosis， the mechanisms of which may be associated with promoting the phosphorylation of proteins related to the MAPK signaling pathway.

Objective: To analyze the association of participation in non-sports extracurricular tutoring classes with the prevalence of screening myopia, axial length (AL) and axial length to corneal radius ratio (AL/CR) among primary school students, so as to provide evidences for formulating myopia prevention and control policies. Methods: In December 2024, combination of convenience and cluster sampling method was used to select 2 273 students from two primary schools in Hefei City, Anhui Province. Ophthalmic examinations and questionnaire surveys were conducted to obtain information on myopia, AL, AL/CR and participation in various types of extracurricular tutoring. A binary Logistic regression model was used to analyze the association between non-sports tutoring and screening myopia, and multiple linear regression models were used to examine the associations between non-sports tutoring and AL and AL/CR. Results: Among the surveyed students, the participation rate in non-sports extracurricular tutoring classes was 64.9% , and the overall prevalence of screening myopia was 39.1%. The average AL and AL/CR were (23.60± 1.01 ) mm and (3.00±0.12), respectively. Univariate analysis showed that students who attended non-sports, music, or academic tutoring classes for ≥2 h per week had higher risks of screening myopia and greater AL/CR values than non-participants (screening myopia: OR =1.38, 1.82, 1.55; AL/CR: β =0.01, 0.03, 0.03; all P <0.05). After adjusting for sex, grade, and participation in sports tutoring, multivariate analysis indicated that participation in non-sports and musical instrument tutoring classes for ≥2 h per week remained significantly associated with higher risks of screening myopia ( OR =1.26, 1.49, both P <0.05). Multiple linear regression showed that participation in musical instrument tutoring for ≥2 h per week was positively correlated with AL ( β=0.14, P < 0.05). Conclusions Participation in non-sports extracurricular tutoring is common among primary school students. Attending non-sports tutoring classes for ≥2 h per week increases the risk of screening myopia.