AIM:To investigate the effect of apoptosis stimulation protein 2(ASPP2)on the development of traumatic proliferative vitreoretinopathy(PVR)in a rabbit model.METHODS:A total of 30 New Zealand white rabbits were selected, and the right eyes of all rabbits were inflicted with a scleral penetrating wound of approximately 6 mm. Then rabbits were randomly and evenly divided into experimental and control group. The experimental group received an intravitreal injection of 0.1 mL of ARPE-19 cell suspension transfected with lentivirus-ASPP2, while the control group received an intravitreal injection of 0.1 mL of ARPE-19 cell suspension transfected with negative control lentivirus. At 1, 2, 3, and 4 wk after PVR modeling, a handheld tonometer was used to measure the intraocular pressure. Moreover, fundus photography and ocular ultrasound examination were performed to detect the retinal proliferation. At 4 wk after modeling, hematoxylin-eosin staining was used to observe the morphological retinal changes, and Western blot was used to determine the protein expressions of ASPP2 and the epithelial-mesenchymal transition(EMT)marker Vimentin in the rabbit retinas.RESULTS:At 1, 2, 3, and 4 wk after modeling, there were no significant changes in intraocular pressure within the experimental and control group of rabbit eyes, either before or after PVR modeling, the success rate of PVR modeling in the experimental group was lower than that in the control group(P<0.05), and the retinal proliferation and structural disorder was less severe in the experimental group. At 4 wk after modeling, the retinal protein expression level of ASPP2 in the experimental group was significantly higher than that in the control group(t=3.193, P=0.033), while the Vimentin protein expression level was significantly lower in the experimental group(t=-3.599, P=0.023).CONCLUSION:ASPP2 may be involved in regulating the process of EMT in retinal pigment epithelial cells, thereby delaying the development and progression of traumatic PVR in rabbit eyes.

Chrysophanol(Chr)and physcion(Phy)are primary active ingredients of a well-known traditional Chinese medicine namely rhubarb(Chinese name:Dahuang),and their glucuronides have been revealed as the dominant forms presenting in rats after oral administration.Either Chr or Phy has two glycosylation sites,resulting in a pair of positional isomers for glucuronides of either compound(CG1&CG2 and PG1&PG2).To confirmatively identify these glucuronides,energy-resolved mass spectrometry(ER-MS)was used to pursue the fragmentation trajectories of the targeted fragment ions,and the resultant breakdown graphs that were described by the optimal collision energy(OCE)were expected to exhibit the differences of glycosidic bond cleavage between the isomers.Quantum chemical calculation was thereafter conducted to produce the bond dissociation energy(BDE)of the glycosidic bonds.The isomers were unambiguously identified through applying the positive correlation rule between OCE and BDE.Fortunately,the glucuronides of Chr and Phy in vivo were observed through liver microsomes incubationin vitro.ER-MS was utilized to collect the Gaussian-shaped breakdown graphs in response to the neutral loss of 176 Da,and the absolute values of OCE were compared between positional isomers.The results revealed that CG1(-32.31 eV)>CG2(-31.61 eV),and nonetheless,PG1(-30.00 eV)Chr-8-GluA(-48.787 kJ/mol),and nonetheless,Phy-1-GluA(-48.672 kJ/mol)

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

Amid the global wave of digital economy, China's medical artificial intelligence applications are rapidly advancing through technological innovation and policy support, while facing multifaceted evaluation and regulatory challenges. The dynamic algorithm evolution undermines the consistency of assessment criteria, multimodal systems lack unified evaluation metrics, and conflicts persist between data sharing and privacy protection. To address these issues, the China National Health Development Research Center has established a value assessment framework for artificial intelligence medical technologies, formulated the country's first technical guideline for clinical evaluation, and validated their practicality through scenario-based pilot studies. Furthermore, this paper proposes introducing a "regulatory sandbox" model to test technical compliance in controlled environments, thereby balancing innovation incentives with risk governance.
Artificial Intelligence/legislation & jurisprudence* ; China ; Humans ; Algorithms

Computational approaches, encompassing both physics-based and machine learning (ML) methodologies, have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities. The human dopamine (DA) transporter (hDAT) is the primary therapeutic target of numerous psychiatric medications. However, traditional hDAT-targeting drugs, which interact with the primary binding site, encounter significant limitations, including addictive potential and stimulant effects. In this study, we propose an integrated workflow combining virtual screening based on weighted holistic atom localization and entity shape (WHALES) descriptors with in vitro experimental validation to repurpose novel hDAT-targeting drugs. Initially, WHALES descriptors facilitated a similarity search, employing four benztropine-like atypical inhibitors known to bind hDAT's allosteric site as templates. Consequently, from a compound library of 4,921 marketed and clinically tested drugs, we identified 27 candidate atypical inhibitors. Subsequently, ADMETlab was employed to predict the pharmacokinetic and toxicological properties of these candidates, while induced-fit docking (IFD) was performed to estimate their binding affinities. Six compounds were selected for in vitro assessments of neurotransmitter reuptake inhibitory activities. Among these, three exhibited significant inhibitory potency, with half maximal inhibitory concentration (IC₅₀) values of 0.753 μM, 0.542 μM, and 1.210 μM, respectively. Finally, molecular dynamics (MD) simulations and end-point binding free energy analyses were conducted to elucidate and confirm the inhibitory mechanisms of the repurposed drugs against hDAT in its inward-open conformation. In conclusion, our study not only identifies promising active compounds as potential atypical inhibitors for novel therapeutic drug development targeting hDAT but also validates the effectiveness of our integrated computational and experimental workflow for drug repurposing.

Objective:To investigate the acute effects of persistent high exposure to atmospheric fine particulate matter (PM 2.5) on residents' outpatient visits for respiratory diseases. Methods:We collected daily outpatient records from 92 hospitals in 13 cities across the Beijing-Tianjin-Hebei region, along with daily PM 2.5, nitrogen dioxide (NO 2), and meteorological data from 2013 to 2018. Five persistent high PM 2.5 exposure scenarios were defined in terms of daily mean PM 2.5 concentrations (>75 μg/m 3 and >150 μg/m 3), duration (≥2 days and ≥3 days), and whether or not there was concurrent exposure to high levels of NO 2 (daily mean NO 2 concentration >50 μg/m 3). A two-stage statistical analysis strategy based on a generalized linear model was applied to conduct a time-series analysis to assess the exposure-response relationship between persistent high PM 2.5 exposure scenarios and residents' outpatient visits for a variety of respiratory diseases, and to estimate excess outpatient visits. Results:During the period, M ( Q1, Q3) PM 2.5 and NO 2 concentrations were 61.2 (42.3, 95.1) μg/m 3 and 40.2 (31.4, 54.4) μg/m 3, respectively, and the daily respiratory disease outpatient visits were 57 (52, 66) cases. When compared with non-permanent high PM 2.5 exposure periods, exposure scenarios with PM 2.5 >75 μg/m 3 and lasting for ≥2 days caused an increased risk of outpatient visits for respiratory diseases by 2.10% (95% CI: 1.44%-2.77%), and resulted in 43 787 (95% CI: 30 025-57 757) excess visits; in this scenario, the concurrent exposure to high levels of NO 2 had a greater acute effect on respiratory disease visits than the absence of exposure to high levels of NO 2 ( P<0.001). The risk of respiratory disease visits increased substantially by 4.41% (95% CI: 3.15%-5.68%) when the daily mean PM 2.5 concentration exceeded 150 μg/m 3 for ≥2 days. Subgroup disease analyses showed that scenarios with daily mean PM 2.5 concentrations exceeding 75 μg/m 3 for ≥3 days caused a significant increase in the risk of lower respiratory tract infections, chronic lower respiratory disease, and asthma visits. Conclusions:Sustained persistent high PM 2.5 exposure increases the risk of outpatient visits for various respiratory diseases; concurrent exposure to high concentrations of NO 2 leads to a greater risk of visiting the clinic, suggesting that the prevention and control of PM 2.5 pollution should be synchronized with the control of mobile source emissions, to synergistically manage the compound pollution of PM 2.5 and NO 2 in the atmosphere.

OBJECTIVE: To assess the value of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score combined with PSA density (PSAD) in the diagnosis of clinically significant prostate cancer (CSPCa) in the PSA grey zone by MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy. METHODS: This retrospective study included 327 male patients with total PSA (tPSA) levels of 4-10 μg/L undergoing MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy in our hospital between January 2021 and December 2023. According to the pathological results, we divided the patients into a CSPCa (n = 44) and a non-CSPCa group (n = 283), collected their clinical and imaging data, and subjected them to statistical analysis. RESULTS: The age, tPSA level, PSAD and PI-RADS score were significantly higher, while the free PSA (fPSA) level, f/tPSA ratio and prostate volume remarkably lower in the CSPCa than in the non-CSPCa group (P<0.05). The areas under the curve (AUCs) of PSAD, PI-RADS score and their combination were 0.772, 0.730 and 0.801, with sensitivities of 63.63%, 70.45% and 72.73%, and specificities of 84.10%, 75.62% and 83.75%, respectively (P<0.01). With PSAD 0.2 μg/(ml·cm3) as the best cut-off value and based on the PI-RADS scores, the patients were divided into two groups for analysis. In the patients with PI-RADS scores 2 and 5, the AUCs were 0.534 and 0.643, with sensitivities of 16.67% and 63.64%, and specificities of 85.14% and 64.29%, with no statistically significant differences (P= 0.784, P= 0.228), and in those with PI-RADS scores 3 and 4, the AUCs were 0.794 and 0.843, with sensitivities of 57.14% and 80.00%, and specificities of 87.14% and 81.82%, with statistically significant differences (P= 0.009, P<0.001). CONCLUSION PI-RADS v2.1 score combined with PSAD can effectively improve the diagnostic efficiency of CSPCa in the PSA grey zone by MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy and serve as a guide for selection of prostate biopsy.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Retrospective Studies ; Prostate-Specific Antigen ; Magnetic Resonance Imaging ; Image-Guided Biopsy ; Prostate/pathology* ; Aged ; Middle Aged

Objective To explore the current status of medical technology anxiety experienced by elder-ly patients during the use of digital healthcare technology and its influencing factors.Methods A convenience sampling method was used to select 552 elderly patients from 10 hospitals in Liaoning Province as study sub-jects.A cross-sectional survey was conducted using the technology anxiety scale,ehealth literacy scale,self-ef-ficacy scale,and family APGAR index.Results The smart healthcare medical technology anxiety scale score for older patients was(44.93±14.30)points,and the ehealth literacy scale score was(25.29±9.61)points.Smart healthcare medical technology anxiety in older patients was negatively correlated with ehealth literacy,self-efficacy,and family care index(r=-0.299,-0.336,-0.304,P<0.01).Multiple linear regression showed that age,education level,living situation,monthly income,household registration,presence of chronic disease,ehealth literacy,self-efficacy,and family care index were influencing factors for smart healthcare medi-cal technology anxiety in older patients(P<0.05),collectively explaining 35.8%of the variance.Conclusion Ol-der patients exhibit a moderate-to-high level of smart healthcare medical technology anxiety,while their ehealth litera-cy remains at a low level.