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.

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.

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.

BACKGROUND: Previous evidence showed that ambient air pollution and cardiovascular mortality are related. However, there is a lack of evidence towards the modification effect of long-term lifestyle on the association between short-term ambient air pollution and death from cardiovascular events. METHOD: A total of 14,609 death from major adverse cardiovascular events (MACE) were identified among the China Kadoorie Biobank participants from 2013 to 2018. Ambient air pollution exposure including particulate matter 2.5 (PM_2.5), SO₂, NO₂, CO, and O₃ from the same period were obtained from space-time model reconstructions based on remote sensing data. Case-crossover design and conditional logistic regression was applied to estimate the effect of short-term exposure to air pollutants on MACE mortality. RESULTS: We found MACE mortality was significantly associated with PM_2.5 (relative percent increase 2.91% per 10 µg/m³ increase, 95% CI 1.32-4.53), NO₂ (5.37% per 10 µg/m³ increase, 95% CI 1.56-9.33), SO₂ (6.82% per 10 µg/m³ increase, 95% CI 2.99-10.80), and CO (2.24% per 0.1 mg/m³ increase, 95% CI 1.02-3.48). Stratified analyses indicated that drinking was associated with elevated risk of MACE mortality with NO₂ and SO₂ exposure; physical inactivity was associated with higher risk of death from MACE when exposed to PM_2.5; and people who had balanced diet had lower risk of MACE mortality when exposed to CO and NO₂. CONCLUSIONS The study results showed that short-term exposure to ambient PM_2.5, NO₂, SO₂, and CO would aggravate the risk of cardiovascular mortality, yet healthy lifestyle conduct might mitigate such negative impact to some extent.
Humans ; Cardiovascular Diseases/epidemiology* ; China/epidemiology* ; Male ; Female ; Air Pollution/adverse effects* ; Middle Aged ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Exposure/adverse effects* ; Life Style ; Aged ; Adult ; Risk Factors ; Cross-Over Studies ; East Asian People

Recent clinical trials have demonstrated a protective effect in using traditional Chinese medicine Tongxinluo (TXL) capsule to treat atherosclerosis. However, clinical evidence of the effects of TXL treatment on coronary plaque vulnerability is unavailable. In response, we developed this study to investigate the hypothesis that on the basis of statin therapy, treatment with TXL capsule may stabilize coronary lesions in patients with acute coronary syndrome (ACS). The TXL-CAP study was an investigator-initiated, randomized, double-blind clinical trial conducted across 18 medical centers in China. Patients with ACS aging from 18 to 80 years old who had a non-intervened coronary target lesion with a fibrous cap thickness (FCT) < 100 μm and lipid arc > 90° as defined by optical coherence tomography (OCT) were recruited. A total of 220 patients who met the selection criteria but did not meet the exclusion criteria will be finally recruited and randomized to receive treatment with TXL (n = 110) or placebo (n = 110) for a duration of 12 months. The primary endpoint was the difference in the minimum FCT of the coronary target lesion between TXL and placebo groups at the end of the 12-month follow-up. Secondary endpoints included: (1) changes of the maximum lipid arc and length of the target plaque, and the percentage of lipid, fibrous, and calcified plaques at the end of the 12-month period; (2) the incidence of composite cardiovascular events and coronary revascularization within the 12 months; (3) changes in the grade and scores of the angina pectoris as assessed using the Canadian Cardiovascular Society (CCS) grading system and Seattle angina questionnaire (SAQ) score, respectively; and (4) changes in hs-CRP serum levels. The results of the TXL-CAP trial will provide additional clinical data for revealing whether TXL capsules stabilizes coronary vulnerable plaques in Chinese ACS patients.

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 establish a rat model of perimenopausal dry eye with liver and kidney yin deficiency from the perspective of"measuring evidence by prescription"in Chinese medicine.Methods Thirty SPF-grade female SD rats were divided randomly into sham-operated,model,and Qiju Dihuang soup groups(n=10).Rats in the latter two groups underwent bilateral ovariectomy and were given 0.1％benzalkonium chloride eye drops combined with provocation for 10 weeks to establish a model of perimenopausal dry eye with liver and kidney yin deficiency.After modeling,rats in the Qiju Dihuang soup group were gavaged with Qiju Dihuang soup at a dose of 8.37 g/(kg·d),rats in the sham-operated and model groups were gavaged with saline at a rate of 1 mL/100(g·d)for 21 d.The general condition,retinal screen test scores,tear secretion,time of tear film rupture,and corneal fluorescein staining were observed and recorded in each group.Serum levels of follicle-stimulating hormone(FSH),estradiol(E2),and progesterone(PROG)were measured by enzyme-linked immunosorbent assay.Pathological damage to the cornea and lacrimal glands were detected by hematoxylin/eosin staining.Expression levels of the inflammatory factors interleukin(IL)-1β and tumor necrosis factor(TNF)-a in the cornea were detected by immunohistochemistry.Results Compared with the sham-operated group,rats in the model group showed behavioral signs of slow action and reaction,irritability,body mass loss,and increased anal temperature(P＜0.05),decreased retinal screen test scores(P＜0.05),tear secretion(P＜0.05),and time of tear film rupture(P＜0.05),and pathological damage to the cornea and lacrimal glands.FSH levels increased and E2 and PROG levels decreased(P＜0.05)and expression levels of IL-1 and TNF-α increased in the model group compared with the sham group.All the above indexes were significantly improved in the Qiju Dihuang soup group compared with the model group.Conclusions From the perspective of"measuring evidence by formula"in Chinese medicine,we successfully established a rat model of perimenopausal dry eye with liver-kidney yin deficiency syndrome,which provides a theoretical and experimental basis for future systematic and in-depth research on the mechanism of perimenopausal dry eye with formula and evidence.

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.