Artificial intelligence(AI)has emerged as a cutting-edge technology leading the future and is a key engine for China's development.In the innovation and research of medical devices,AI has provided critical support in the areas of intelligent diagnostic assistance,intelligent therapeutic assis-tance,intelligent monitoring,life support,et al.Ma-chine learning-enabled device software functions(ML-DSFs)have become an essential component of many medical devices.Recently,the United States Food and Drug Administration(FDA)released a draft guidance titled"Marketing Submission Rec-ommendations for a Predetermined Change Con-trol Plan for Artificial Intelligence/Machine Learn-ing(AI/ML)-Enabled Device Software Functions(Draft)."that aimed to provide a forward-looking approach to foster the development of ML medical devices.By supporting iterative updates through modifications,this approach ensures the continu-ous safety and effectiveness of the devices.This guidance represents the latest in regulatory direc-tion and is especially beneficial for enhancing the quality and efficiency of clinical trials for AI prod-ucts.Therefore,we plan to provide a detailed intro-duction and interpretation of the guidance,with the aim of learning from international advanced regulatory concepts and experiences to promote the development of ML-DSFs with more profound international influence.

OBJECTIVE: To explore the mechanism of crocin, a major active component of Crocus sativus (Zanghonghua), in regulating amyloid beta (Aβ) generation, endoplasmic reticulum (ER) stress, and autophagy in neuronal cells, with potential therapeutic applications in Alzheimer's disease (AD). METHODS: Mouse neuroblastoma Neuron2a (N2a) cells stably transfected with the human amyloid precursor protein (APP) Swedish mutant was used as a cellular model for AD (N2a/APP). Control cells were vector transfected (N2a/vector). The effects of 3 different doses of crocin on reactive oxygen species (ROS) generation, cytosolic calcium, and apoptosis were evaluated by flow cytometry. Aβ levels were determined by enzyme-linked immunosorbent assay. APP processing and ER stress proteins expressions were determined by Western blot. Autophagosome formation was evaluated by autophagy detection kit and confocal microscope. RESULTS: Crocin inhibited APP expression in N2a/APP cells and promoted α-cleavage of APP processing, while modestly reduced beta-secretase 1 (BACE1) and presenilin 1 (PS1, P<0.05 or P<0.01). ER stress markers, including the binding immunoglobulin protein/78-kD glucose-regulated protein (Bip/GRP78) and C/EBP homologous protein (CHOP), were elevated in N2a/APP cells compared to N2a/vector cells (P<0.05). Crocin could effectively reduce the levels of ER stress (P<0.05 or P<0.01). In addition, crocin enhanced autophagy by promoting formation of autophagosome (P<0.05 or P<0.01). CONCLUSION Crocin significantly inhibited Aβ generation by promoting α-cleavage of APP processing, inhibiting ER stress-associated unfolded protein response, and regulating autophagy.
Endoplasmic Reticulum Stress/drug effects* ; Autophagy/drug effects* ; Animals ; Endoplasmic Reticulum Chaperone BiP ; Mice ; Amyloid beta-Peptides/metabolism* ; Amyloid beta-Protein Precursor/metabolism* ; Carotenoids/pharmacology* ; Humans ; Cell Line, Tumor ; Reactive Oxygen Species/metabolism* ; Apoptosis/drug effects* ; Calcium/metabolism*

Tumor cell-intrinsic programmed death-ligand 1 (PD-L1) signals mediate tumor initiation, progression and metastasis, but their effects in ameloblastoma (AM) have not been reported. In this comprehensive study, we observed marked upregulation of PD-L1 in AM tissues and revealed the robust correlation between elevated PD-L1 expression and increased tumor growth and recurrence rates. Notably, we found that PD-L1 overexpression markedly increased self-renewal capacity and promoted tumorigenic processes and invasion in hTERT⁺-AM cells, whereas genetic ablation of PD-L1 exerted opposing inhibitory effects. By performing high-resolution single-cell profiling and thorough immunohistochemical analyses in AM patients, we delineated the intricate cellular landscape and elucidated the mechanisms underlying the aggressive phenotype and unfavorable prognosis of these tumors. Our findings revealed that hTERT⁺-AM cells with upregulated PD-L1 expression exhibit increased proliferative potential and stem-like attributes and undergo partial epithelial‒mesenchymal transition. This phenotypic shift is induced by the activation of the PI3K-AKT-mTOR signaling axis; thus, this study revealed a crucial regulatory mechanism that fuels tumor growth and recurrence. Importantly, targeted inhibition of the PD-L1-PI3K-AKT-mTOR signaling axis significantly suppressed the growth of AM patient-derived tumor organoids, highlighting the potential of PD-L1 blockade as a promising therapeutic approach for AM.
Ameloblastoma/metabolism* ; Humans ; B7-H1 Antigen/metabolism* ; Neoplasm Recurrence, Local/pathology* ; Signal Transduction ; Cell Proliferation ; Up-Regulation ; TOR Serine-Threonine Kinases/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Telomerase/metabolism* ; Jaw Neoplasms/metabolism* ; Epithelial-Mesenchymal Transition ; Animals ; Cell Line, Tumor ; Female ; Male

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Glycoengineering was carried out in the mammalian cell line CHO for the production of pro-tein-based drugs.Firstly,the genome sequence of the Rosa26 locus of CHO cells was determined,the gRNA sequences were designed,and the landing pad was integrated into the Rosa26 locus of CHO cells by CRISPR/Cas9 technology.Three targeting vectors co-expressed by glycosyltransferases,which are β-1,4 galactosyltransferase(B4GALT1),α-2,6-sialyltransferase 1(ST6GAL1)and N-acetaminoglycosyl-transferase Ⅲ(GnT Ⅲ),were constructed by overlapping PCR and seamless ligation technology,and the three glycosyltransferase genes were integrated into the CHO Rosa26 locus by Cre enzyme-mediated cassette exchange technology.PCR confirmed that three glycosyltransferases had been successfully site-directed integrated into the Rosa26 site.The mRNA expression levels of the three glycosyltransferases were more than 50 000-fold by qRT-PCR,and the protein expression levels of the three glycosyltrans-ferases were more than 4-fold via western blotting(P＜0.001).A CHO-engineered cell line with three glycosyltransferases integrated into Rosa26 site was successfully constructed.

Objective To identify high-risk factors for healthcare-associated infection(HAI)in patients in inten-sive care units(ICUs),and develop a quick response(QR)code-based APP prediction tool.Methods Information of inpatients in general ICUs of three hospitals in Guizhou Province from January to December 2024 were collected.Risk factors were analyzed with a logistic regression model.QR code-based APP was constructed and validated.Results A total of 1 782 patients in general ICUs of three hospitals in Guizhou Province in 2024 were included in the analysis,out of which 410 were HAI cases,and the incidence of HAI was 23.01％.Multivariate logistic regre-ssion analysis results of HAI in ICU inpatients showed that regional gross domestic product(GDP)≥58 685 Yuan,performing pathogen culture during this hospitalization,history of diabetes mellitus,history of cancer,length of hospital stay ≥7 days before infection,and duration of persistent fever＞5 days before infection were independent risk factors for HAI in ICU patients(all P＜0.05).The discrimination of the model(area under the receiver operating characteristic curve[AUC]of 0.841),calibration(Brier score of 0.129),and clinical effectiveness(net benefit of 11.4％when the risk threshold was 5％-74％)all performed well.Conclusion The QR code-based APP prediction tool is of great significance for scientific research transformation and precise HAI control.

This study aimed to explore the molecular mechanism of rubioncolin C(RuC) in inhibiting gastric cancer(GC). AGS and MGC803 cell lines were selected as cellular models. After treating the cells with RuC at different concentrations, the effects of RuC on the proliferation ability of GC cells were assessed using the CCK-8 method, real-time cellular analysis(RTCA), and colony formation assays. Transmission electron microscopy was used to observe subcellular structural changes. Immunofluorescence was applied to detect LC3 fluorescent foci. Acridine orange staining was used to evaluate the state of intracellular lysosomes. Western blot was employed to detect the expression of autophagy-related proteins LC3Ⅱ, P62, and lysosomal cathepsin D(CTSD). The SuperPred online tool was used to predict the target proteins that bound to RuC, and molecular docking analysis was conducted to identify the interaction sites between RuC and CTSD. The drug affinity responsive target stability(DARTS) assay was performed to detect the direct binding interaction between RuC and CTSD. The results showed that RuC significantly inhibited the proliferation and colony formation of GC cells at low concentrations, with 24-hour half-maximal inhibitory concentrations(IC_(50)) of 3.422 and 2.697 μmol·L~(-1) for AGS and MGC803 cells, respectively. After 24 hours of treatment with RuC at concentrations of 1, 2, and 3 μmol·L~(-1), the colony formation rates for AGS cells were 61.0%±1.5%, 28.0%±0.5%, and 18.2%±0.5%, respectively, while the rates for MGC803 cells were 56.0%±0.5%, 23.3%±1.0%, and 11.8%±1.0%, all of which were significantly reduced. Transmission electron microscopy revealed that RuC promoted an increase in autophagosome formation in GC cells. Immunofluorescence detection showed that LC3 fluorescent foci of GC cells increased with the increase in RuC dose. RuC up-regulated the expression of autophagy-related proteins LC3Ⅱ and P62 in GC cells. Acridine orange staining indicated that RuC altered the acidic environment of lysosomes. SuperPred online prediction identified CTSD as a potential target protein of RuC. Western blot analysis revealed that RuC induced the up-regulation of the inactive precursor of CTSD in GC cells. CTSD activity assays indicated that RuC reduced the activity of CTSD. Molecular docking simulations found that RuC bound to the substrate-binding region of CTSD, forming hydrogen bonds with the Tyr205 and Asp231 residues. Microscale thermophoresis and DARTS assays further confirmed that RuC directly bound to CTSD. In summary, RuC inhibits lysosomal activity by targeting and down-regulating the expression of CTSD, thereby inducing autophagosome accumulation in GC cells.
Humans ; Stomach Neoplasms/enzymology* ; Cathepsin D/chemistry* ; Cell Line, Tumor ; Molecular Docking Simulation ; Cell Proliferation/drug effects* ; Autophagosomes/metabolism* ; Autophagy/drug effects*

Objective To identify high-risk factors for healthcare-associated infection(HAI)in patients in inten-sive care units(ICUs),and develop a quick response(QR)code-based APP prediction tool.Methods Information of inpatients in general ICUs of three hospitals in Guizhou Province from January to December 2024 were collected.Risk factors were analyzed with a logistic regression model.QR code-based APP was constructed and validated.Results A total of 1 782 patients in general ICUs of three hospitals in Guizhou Province in 2024 were included in the analysis,out of which 410 were HAI cases,and the incidence of HAI was 23.01％.Multivariate logistic regre-ssion analysis results of HAI in ICU inpatients showed that regional gross domestic product(GDP)≥58 685 Yuan,performing pathogen culture during this hospitalization,history of diabetes mellitus,history of cancer,length of hospital stay ≥7 days before infection,and duration of persistent fever＞5 days before infection were independent risk factors for HAI in ICU patients(all P＜0.05).The discrimination of the model(area under the receiver operating characteristic curve[AUC]of 0.841),calibration(Brier score of 0.129),and clinical effectiveness(net benefit of 11.4％when the risk threshold was 5％-74％)all performed well.Conclusion The QR code-based APP prediction tool is of great significance for scientific research transformation and precise HAI control.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective To investigate the prevalence of common diseases among primary and secondary school students in Yichang City from 2019 to 2022, and to provide a scientific basis for formulating effective intervention measures in the future. Methods By random cluster sampling , 7 schools in urban areas and 5 schools in suburban counties were selected to screen common diseases such as myopia, dental caries, obesity and abnormal spinal curvature. Descriptive epidemiological methods were employed for statistical analysis. Results A total of 17 023 primary and secondary school students were screened from 2019 to 2022. The overall detection rate of common diseases from high to low was myopia (54.12%), caries (36.75%), overweight (15.17%), obesity (11.88%), malnutrition (5.80%), and abnormal spinal curvature (3.49%). The detection rates of myopia and abnormal curvature of the spine showed an increasing trend with years and school stages, while the detection rates of malnutrition and dental caries showed a decreasing trend with years and school stages. The detection rates of overweight and obesity showed no trend difference with years, and the detection rates of obesity showed a decreasing trend with school stages. The rates of myopia, overweight and obesity were higher in urban areas than those in suburban counties, and the rate of dental caries was higher in suburban counties than that in urban areas. The prevalence of overweight, obesity, and malnutrition in boys was higher than that in girls. The prevalence of myopia and dental caries in girls was higher than that in boys. The above differences were statistically significant (all P<0.05). Conclusion Myopia, dental caries, obesity, and abnormal curvature of the spine are the current focus of the prevention and treatment of common diseases in students. There are great differences between different regions, school stages, and genders. The ^“tripartite linkage^” of schools, families, and communities should be achieved with the joint efforts of the education and health departments to actively take targeted intervention measures to reduce the prevalence.