Abstract Conversational artificial intelligence (CAI), as an emerging digital tool, holds distinct potential for enhancing adolescents health literacy and fostering proactive health behaviors. In light of the increasing need for upstream prevention and early intervention in adolescent physical and mental health, the review systematically synthesizes current evidence on the role of CAI in shaping proactive health behaviors. It dissects the underlying mechanisms of CAI through a triple logic framework: technological context driven, psychological motivation activation, and behavior generation and maintenance.It also maps the evolving applications of CAI in areas such as psychological support and lifestyle interventions. Finally, it outlines future prospects, emphasizing the strengthening of agency awareness cultivation and human-computer collaborative interventions, aiming to provide a theoretical foundation and practical guidance for leveraging CAI to power adolescents in developing proactive health behaviors.

AIM:To investigate the therapeutic effects of bone marrow mesenchymal stem cell-derived exosomes(BMSC-Exos)on diabetic retinopathy(DR)in rats, with a focus on their ability to modulate oxidative stress and inflammatory responses through the TLR4/NF-κB signaling pathway.METHODS:Streptozotocin(STZ)-induced diabetic Sprague-Dawley(SD)rats were randomly divided into four groups: Group 1 [normal+phosphate-buffered saline(PBS)], Group 2(normal+Exos), Group 3(DR+PBS), and Group 4(DR+Exos). At the 8th week after modeling, BMSC-Exos or PBS were injected intravitreally. Retinal tissues were collected at the 16th week for histological analysis [hematoxylin and eosin(HE)staining], apoptosis detection(TUNEL). Oxidative stress markers [8-OHdG, superoxide dismutase(SOD), and glutathione(GSH)] were assessed, and inflammatory cytokines [enzyme-linked immunosorbent assay(ELISA)for interleukin(IL)-6 and tumor necrosis factor-alpha(TNF-α)], and molecular profiling [quantitative polymerase chain reaction(qPCR)for TLR4, NF-κB, and VEGF] were measured.RESULTS:The BMSC-Exos treatment significantly suppressed the activation of the TLR4/NF-κB pathway in DR rats(P<0.01), which was accompanied by reduced retinal vascular leakage(Evans blue assay, P<0.001), decreased apoptosis(TUNEL, P<0.05), and attenuated oxidative stress(elevated SOD and GSH, reduced 8-OHdG, P<0.05). The levels of inflammatory cytokines(IL-6 and TNF-α)were markedly decreased(P<0.01).CONCLUSION:BMSC-Exos alleviate DR by suppressing the TLR4/NF-κB axis, thus reducing oxidative damage, inflammation, and microvascular dysfunction. This research offers a novel therapeutic approach for early-stage DR.

Transarterial radioembolization (TARE) is a widely utilized therapeutic approach for hepatocellular carcinoma (HCC), however, the clinical implementation is constrained by the stringent preparation conditions of radioembolization agents. Herein, we incorporated the superstable homogeneous iodinated formulation technology (SHIFT), simultaneously utilizing an enhanced solvent form in a carbon dioxide supercritical fluid environment, to encapsulate radionuclides (such as ¹³¹I,¹⁷⁷Lu, or ¹⁸F) with lipiodol for the preparation of radiolipiodol. The resulting radiolipiodol exhibited exceptional stability and ultra-high labeling efficiency (≥99%) and displayed notable intratumoral radionuclide retention and in vivo stability more than 2 weeks following locoregional injection in subcutaneous tumors in mice and orthotopic liver tumors in rats and rabbits. Given these encouraging findings, ¹⁸F was authorized as a radiotracer in radiolipiodol for clinical trials in HCC patients, and showed a favorable tumor accumulation, with a tumor-to-liver uptake ratio of ≥50 and minimal radionuclide leakage, confirming the feasibility of SHIFT for TARE applications. In the context of transforming from preclinical to clinical screening, the preparation of radiolipiodol by SHIFT represents an innovative physical strategy for radionuclide encapsulation. Hence, this work offers a reliable and efficient approach for TARE in HCC, showing considerable promise for clinical application (ChiCTR2400087731).

OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers. METHODS: Based on the endoscopic appearance of peptic ulcers, color features were extracted to distinguish active bleeding (Forrest I) from non-bleeding ulcers (Forrest II and III). The edge and texture features were used to describe the morphology and appearance of the ulcers in different grades. By integrating deep features extracted from a deep learning network with manually extracted visual features, a multi-feature representation of endoscopic images was created to predict the risk of rebleeding of peptic ulcers. RESULTS: In a dataset consisting of 3573 images from 708 patients with Forrest classification, the proposed multi-feature fusion model achieved an accuracy of 74.94% in the 6-level rebleeding risk classification task, outperforming the experienced physicians who had a classification accuracy of 59.9% (P<0.05). The F1 scores of the model for identifying Forrest Ib, IIa, and III ulcers were 90.16%, 75.44%, and 77.13%, respectively, demonstrating particularly good performance of the model for Forrest Ib ulcers. Compared with the first model for peptic ulcer rebleeding classification, the proposed model had improved F1 scores by 5.8%. In the simplified 3-level risk (high-risk, low-risk, and non-endoscopic treatment) classification task, the model achieved F1 scores of 93.74%, 81.30%, and 73.59%, respectively. CONCLUSIONS The proposed multi-feature fusion model integrating deep features from CNNs with manually extracted visual features effectively improves the accuracy of rebleeding risk classification for peptic ulcers, thus providing an efficient diagnostic tool for clinical assessment of rebleeding risks of peptic ulcers.
Humans ; Deep Learning ; Peptic Ulcer ; Risk Assessment ; Peptic Ulcer Hemorrhage ; Recurrence

BACKGROUND: In the interim analysis of MONARCH plus, adding abemaciclib to endocrine therapy (ET) improved progression-free survival (PFS) and objective response rate (ORR) in predominantly Chinese postmenopausal women with HR+/HER2- advanced breast cancer (ABC). This study presents the final pre-planned PFS analysis. METHODS: In the phase III MONARCH plus study, postmenopausal women in China, India, Brazil, and South Africa with HR+/HER2- ABC without prior systemic therapy in an advanced setting (cohort A) or progression on prior ET (cohort B) were randomized (2:1) to abemaciclib (150 mg twice daily [BID]) or placebo plus: anastrozole (1.0 mg/day) or letrozole (2.5 mg/day) (cohort A) or fulvestrant (500 mg on days 1 and 15 of cycle 1 and then on day 1 of each subsequent cycle) (cohort B). The primary endpoint was PFS of cohort A. Secondary endpoints included cohort B PFS (key secondary endpoint), ORR, overall survival (OS), safety, and health-related quality of life (HRQoL). RESULTS: In cohort A (abemaciclib: n  = 207; placebo: n  = 99), abemaciclib plus a non-steroidal aromatase inhibitor improved median PFS vs . placebo (28.27 months vs . 14.73 months, hazard ratio [HR]: 0.476; 95% confidence interval [95% CI]: 0.348-0.649). In cohort B (abemaciclib: n  = 104; placebo: n  = 53), abemaciclib plus fulvestrant improved median PFS vs . placebo (11.41 months vs . 5.59 months, HR: 0.480; 95% CI: 0.322-0.715). Abemaciclib numerically improved ORR. Although immature, a trend toward OS benefit with abemaciclib was observed (cohort A: HR: 0.893, 95% CI: 0.553-1.443; cohort B: HR: 0.512, 95% CI: 0.281-0.931). The most frequent grade ≥3 adverse events in the abemaciclib arms were neutropenia, leukopenia, anemia (both cohorts), and lymphocytopenia (cohort B). Abemaciclib did not cause clinically meaningful changes in patient-reported global health, functioning, or most symptoms vs . placebo. CONCLUSIONS: Abemaciclib plus ET led to improvements in PFS and ORR, a manageable safety profile, and sustained HRQoL, providing clinical benefit without a high toxicity burden or reduced quality of life. TRIAL REGISTRATION ClinicalTrials.gov (NCT02763566).
Humans ; Female ; Fulvestrant/therapeutic use* ; Breast Neoplasms/metabolism* ; Aminopyridines/therapeutic use* ; Benzimidazoles/therapeutic use* ; Middle Aged ; Aromatase Inhibitors/therapeutic use* ; Aged ; Receptor, ErbB-2/metabolism* ; Adult ; Letrozole/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Anastrozole/therapeutic use*

Objective:To study the application effect of the Hazard Analysis and Critical Control Point(HACCP)system in risk assessment and dynamic management of operational safety of medical equipment in surgery rooms.Methods:Based on the HACCP principle,hazard analysis and risk assessment were conducted on the operational safety of equipment in surgery room,and the critical control points were determined,and measures of monitor and management were formulated to conduct whole-process dynamic management for equipment in surgery room.A total of 120 medical equipment in use in the surgery room of Shaanxi Provincial Cancer Hospital from January 2022 to January 2024 were selected.According to random number table method,they were divided into conventional management mode group(60 equipment that received conventional management mode)and dynamic management mode group(60 equipment that received the mode of risk assessment and dynamic management based on HACCP system for operational safety of medical equipment in surgery rooms)to receive different managements.The effect,operational quality,the score of management standardization,and management level for equipment in managing and controlling risk of safety of equipment were compared between two management modes.Results:The average pressure-related injury,electrical injury,extravasation of body fluid,and incidence of unqualified cleaning and disinfection of equipment in the dynamic management mode group were respectively(3.01±1.01)%,(2.65±0.78)%,(1.65±0.64)%and(1.15±0.16)%,all of which were lower than those in the conventional management mode group,and the differences were statistically significant(t=16.311,13.434,12.018,12.629,P<0.05).The average utilization rate,cost-benefit amplification and social benefit amplification of the equipment in the dynamic management mode group were respectively(95.36±2.87)%,(7.98±1.32)%and(10.68±2.36)%,all of which were higher than those in the conventional management mode group,and the average operating rate in the dynamic management mode group was(9.87±1.54)%,which was lower than that in the conventional management mode group,and the differences of the above indicators were significant(t=7.657,15.567,10.354,12.466,P<0.05).The scores of the standard degrees of use and operation,disinfection and sterilization,operation and maintenance,and repairing fault of equipment in the dynamic management mode group were higher than those in the conventional management mode group,and the differences of the above data between two groups were statistically significant(t=16.289,11.415,13.396,9.420,P<0.05),respectively.The rate of standard placement,the rate of reasonable recording and integrity rate of equipment in the dynamic management mode group were higher than those in the conventional management mode group,and the failure rate was lower than that of the conventional management mode group,and the differences of the above indicators between the two groups were statistically significant(x2=6.708,7.070,8.491,13.333,P<0.05).Conclusion:The risk assessment and dynamic management based on HACCP system for operational safety of medical equipment in surgery room can improve the management effect for equipment in surgery room,and reduce the operational risk of equipment,and realize the whole-process management and control for medical equipment in surgery room.

Approximately 20% to 30% of the global workforce is engaged in shift work. As a significant cause of circadian disruption, shift work is closely associated with an increased risk for periodontitis. Nevertheless, how shift work-related circadian disruption functions in periodontitis remains unknown. Herein, we employed a simulated shift work model constructed by controlling the environmental light-dark cycles and revealed that shift work-related circadian disruption exacerbated the progression of experimental periodontitis. RNA sequencing and in vitro experiments indicated that downregulation of the core circadian protein brain and muscle ARNT-like protein 1 (BMAL1) and activation of the Gasdermin D (GSDMD)-mediated pyroptosis were involved in the pathogenesis of that. Mechanically, BMAL1 regulated GSDMD-mediated pyroptosis by suppressing NOD-like receptor protein 3 (NLRP3) inflammasome signaling through modulating nuclear receptor subfamily 1 group D member 1 (NR1D1), and inhibiting Gsdmd transcription via directly binding to the E-box elements in its promoter. GSDMD-mediated pyroptosis accelerated periodontitis progression, whereas downregulated BMAL1 under circadian disruption further aggravated periodontal destruction by increasing GSDMD activity. And restoring the level of BMAL1 by circadian recovery and SR8278 injection alleviated simulated shift work-exacerbated periodontitis via lessening GSDMD-mediated pyroptosis. These findings provide new evidence and potential interventional targets for circadian disruption-accelerated periodontitis.
Pyroptosis/physiology* ; ARNTL Transcription Factors/metabolism* ; Animals ; Periodontitis/etiology* ; Mice ; Phosphate-Binding Proteins/metabolism* ; Shift Work Schedule/adverse effects* ; Intracellular Signaling Peptides and Proteins/metabolism* ; Mice, Inbred C57BL ; Male ; Disease Models, Animal ; Gasdermins

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,of-fering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accu-racy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.