ObjectiveTo investigate somatization symptoms in adolescents during frequent earthquakes in Hefei， and to explore their correlation with earthquake experiences. MethodsA cross-sectional survey was used to select 324 adolescents in Hefei as the survey objects. The self-rating scale of somatization symptoms （SSS） and the fatigue intensity scale （FIS） were used to evaluate the somatization symptoms and fatigue degree of middle school students， and multivariate Logistic regression analysis was used to explore the related factors of somatization symptoms and fatigue among middle school students. ResultsA total of 324 adolescents were included， and the overall detection rate of somatization symptoms was 6.5%， and the detection rate of moderate or above fatigue was 20.1%. The results of regression analysis showed that adolescents who were concerned about the earthquake for a longer time （≥1 h） had a higher risk of somatization symptoms （OR=5.430， 95%CI： 1.547-19.058）， and adolescents who received pre-earthquake training had a lower degree of fatigue （OR=0.535， 95%CI： 0.292-0.981） （P<0.05）. ConclusionDuring the frequent earthquakes， adolescents have more somatization symptoms and fatigue. Therefore， it is crucial to enhance health education， reduce the emphasis on event-related reports， and implement earthquake prevention and disaster reduction training to improve the physical and mental health of adolescents.

Dry eye （DE） is a prevalent multifactorial disease of the ocular surface， clinically characterized by tear film homeostasis imbalance accompanied by related ocular surface symptoms. Specifically， the tear film is a thin liquid layer of tears covering the cornea and conjunctiva through blinking， while tear film homeostasis serves as the foundation for maintaining normal ocular surface structure and function. Insufficient tear secretion and excessive tear film evaporation lead to tear hyperosmolarity and the production of inflammatory mediators， disrupting tear film homeostasis and subsequently forming DE. Additionally， cascade reactions are triggered， resulting in a "vicious cycle of DE" that exacerbates the disease severity and prolongs its duration. Therefore， for DE treatment， it is crucial to restore tear film homeostasis and terminate this vicious cycle. Traditional Chinese medicine （TCM）， which differentiates and treats DE based on systemic conditions， often achieves favorable therapeutic outcomes， offering additional treatment options for DE. Studies have demonstrated that TCM can alleviate DE by regulating tear film homeostasis and terminating the vicious cycle. This review systematically summarizes recent basic experimental research in China and abroad on TCM in alleviating DE by regulating tear film homeostasis， aiming to provide a theoretical basis for clinical treatment and an insight for research design.

ObjectiveTo investigate the mechanisms of Runmu Dihuang decoction （RMDHD） in treating perimenopausal dry eye with liver-kidney Yin deficiency syndrome based on the silent information regulator 3 （SIRT3）/hypoxia-inducible factor-1α （HIF-1α）/nuclear factor-κB （NF-κB） signaling pathway. MethodsSixty female Sprague-Dawley rats were randomly divided into six groups （n=10 per group）： Sham operation group， model group， sodium hyaluronate eye drop group， and low-， medium-， and high-dose RMDHD groups （5.625， 11.25， 22.50 g·kg^-1）. Except for the sham operation group， all rats underwent bilateral ovariectomy and were administered 0.1% benzalkonium chloride eye drops combined with long-term chronic irritation to establish a perimenopausal dry eye model with liver-kidney Yin deficiency syndrome. Drug administration began in the 11th week after modeling and continued for 21 days. General conditions， screen-grip test scores， tear secretion volume， tear film breakup time （TFBUT）， and corneal fluorescein staining were recorded. Serum levels of reactive oxygen species （ROS）， follicle-stimulating hormone （FSH）， estradiol （E₂）， and progesterone （PROG） were measured by enzyme-linked immunosorbent assay （ELISA）. Pathological changes in the lacrimal glands， corneas， and uteri were observed using hematoxylin-eosin （HE） staining. Protein expression levels of SIRT3， HIF-1α， phosphorylated NF-κB p65 （p-NF-κB p65）， and total NF-κB p65 in the lacrimal glands were detected by Western blot. The expression of inflammatory cytokines interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α） in the lacrimal glands was assessed by immunohistochemistry （IHC）. ResultsAfter model establishment， no significant differences were observed among the groups except the sham operation group. Compared with the sham operation group， the other groups exhibited slowed movement， dull responses， increased irritability， reduced body weight， elevated rectal temperature， decreased screen-grip test scores， reduced tear secretion， and significantly shortened TFBUT （P<0.05）. After treatment， compared with the model group， the sodium hyaluronate eye drop group and all RMDHD groups showed improved general conditions， significantly increased tear secretion （P<0.05）， prolonged TFBUT （P<0.05）， and elevated screen-grip test scores （P<0.05）. Serum ROS and FSH levels were significantly decreased， while E₂ and PROG levels were significantly increased （P<0.05）. Pathological damage to the cornea， lacrimal glands， and uterus was ameliorated. In addition， protein expression levels of SIRT3 and HIF-1α in the lacrimal glands were significantly upregulated （P<0.05）， whereas the expression of p-NF-κB p65， IL-1β， and TNF-α was significantly downregulated （P<0.05）. ConclusionRMDHD increases tear secretion and TFBUT， improves lacrimal gland and corneal injury， and alleviates dry eye symptoms in a perimenopausal dry eye rat model with liver-kidney Yin deficiency syndrome. The underlying mechanism may be related to regulation of the SIRT3/HIF-1α/NF-κB signaling pathway， inhibition of oxidative stress and inflammatory responses， and reduction of ocular surface tissue damage.

To report two cases of psychiatric symptoms associated with zonisamide, an antiepileptic drug, and raise clinical awareness of this potential adverse effect. Two male patients with epilepsy treated with zonisamide were retrospectively analyzed. Case 1 (25 years old) developed acute emotional and behavioral abnormalities (e.g., insomnia, aggression, incoherent speech) after switching from sodium valproate to zonisamide (200 mg/day). Case 2 (48 years old) had long-term zonisamide use (≥5 years) with persistent treatment-resistant psychotic symptoms (e.g., delusions, command hallucinations). Clinical courses, medication adjustments, and symptom responses were documented. In Case 1, psychiatric symptoms resolved after discontinuing zonisamide and switching to sodium valproate, with improved mood stability and reduced impulsivity. In Case 2, despite escalating antipsychotic medications (risperidone, clozapine), psychotic symptoms persisted, likely due to ongoing zonisamide use. Both cases highlighted zonisamide’s potential to exacerbate or induce psychiatric manifestations, possibly via mechanisms involving sodium/calcium channel inhibition and neurotransmitter dysregulation (e.g., dopamine, serotonin). Zonisamide can cause or worsen psychiatric symptoms, particularly in vulnerable individuals. Clinicians should monitor for mental health changes during zonisamide treatment and consider drug discontinuation or substitution with alternative antiepileptics (e.g., sodium valproate) if psychiatric adverse effects emerge. Awareness of this association is crucial to avoid misdiagnosis and optimize epilepsy management.

OBJECTIVE To systematically analyze the trends in the volume and value of out-licensing deals by Chinese biopharmaceutical companies over the past two decades， and to quantitatively identify factors associated with deal value， thereby providing an evidence base for policymakers and corporate managers. METHODS Based on the PharnexCloud database， information on out-licensing deals disclosed by Chinese biopharmaceutical companies between January 1， 2005 and December 31， 2025 was collected， and executed deals with disclosed specific financial terms were included for analysis. Descriptive statistical analyses of deal value， product characteristics and deal characteristics were performed using Stata SE 18.0； with the natural logarithm of deal value as the dependent variable， stepwise multiple regression models were constructed to examine the associations of product and deal characteristics with deal value. RESULTS A total of 210 out-licensing deals were included， with a cumulative deal value of approximately 214.151 billion dollars and a mean deal value of 1.020 billion dollars . The trading market expanded rapidly from 2020 onward， with the annual deal value reaching a record 95.687 billion dollars in 2025. Multiple regression analysis showed that the deal value of products at the drug discovery stage was significantly lower than that at the preclinical stage [regression coefficient （ β ） ＝－1.995， 95% confidence interval （CI）： －3.256 to －0.734， P ＜0.05 ] ； the deal value of oncology products was significantly higher than that of other therapeutic areas （ β ＝0.828， 95%CI： 0.224 to 1.431， P ＜0.05）； global licensing （ β ＝1.690， 95%CI： 1.107 to 2.273， P ＜0.05） and co-development arrangements （ β ＝0.849， 95%CI： 0.084 to 1.615， P ＜0.05） were significantly associated with higher deal value. CONCLUSIONS Out-licensing deals by Chinese biopharmaceutical companies have achieved significant growth in both volume and value over the past two decades. Oncology products， global licensing， and co-development arrangements are significantly and positively associated with higher deal value.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

Objective To construct the training program for the digital and intelligent capabilities of specialized nurses in the Central Sterile Supply Department(CSSD),and conduct preliminary practice to provide talent support for the intelligent development of CSSD.Methods From February to April 2024,based on the core technologies of digital intelligence and related core capabilities,a training program for digital intelligence-related competencies of CSSD specialized nurses was constructed using literature review and the Delphi expert consultation method.From July to August 2024,the program was initially implemented in the training of CSSD specialized nurses.The nurses'information competency before and after the training was compared,and the nurses' satisfaction with the digital intelligence-related training program was assessed.Results This study conducted 2 rounds of expert consultation via questionnaire.The effective recovery rate of the questionnaires in both rounds was 100％.The expert authority coefficients were 0.790 and 0.800,respectively,and the variation coefficients ranged from 0 to 0.229 and 0 to 0.105.Ultimately,a training program for the digital-related competencies of CSSD specialty nurses was established,which includes 4 components:training objectives,training content,training methods,and assessment methods.Specifically,there were 3 indicators at the first level and 14 at the second level for training objectives,6 indicators at the first level and 32 at the second level for training content,and 6 indicators at the first level for training methods and assessment methods.After the implementation of the training program,the information competency of the nurses in all dimensions and the total score were significantly higher than those before training(P＜0.05).Moreover,the average scores for the training content,training methods,and assessment methods were all above 3 points,indicating a high overall satisfaction among the nurses.Conclusion The construction process of the training program for the digital and intelligent capabilities of CSSD specialty nurses is scientific and reliable.The content is highly practical and distinctive in its specialty.The training methods and assessment approaches are diverse.This program can enhance nurses' information competency and provide a reference for the implementation of digital and intelligent training for CSSD specialty nurses.

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 develop a training program for general practitioners (GPs) on carotid plaque screening using portable intelligent point-of-care ultrasonography (POCUS).Methods:A draft training program for GPs on carotid plaque screening using POCUS was initially formulated through focus group discussions involving experts in ultrasonography and general practice. Two rounds of Delphi consultation were conducted with 15 ultrasound experts from 6 secondary and tertiary public hospitals in Shanghai in February and April 2023. The analytic hierarchy process (AHP) was applied to assign weights to the indicators in the final program.Results:The final program consisted of 4 first-level indicators, 10 second-level indicators, and 47 third-level indicators. Among the first-level indicators,"training objects" had the lowest weight (7.69%), while the other three indicators were equally weighted (30.77% each). The top 3 third-level indicators by combined weight were "mastering key diagnostic points of carotid plaque"(10.26%),"mastering carotid plaque screening using portable POCUS"(6.84%), and "primary care hands-on training sessions"(6.39%).Conclusion:A structured training program for GPs has been successfully developed for carotid plaque screening using portable intelligent POCUS, comprising 4 first-level, 10 second-level, and 47 third-level indicators.