Fritillariae Cirrhosae Bulbus is the dried bulb of perennial herbaceous plants in the Fritillaria genus(Liliaceae family) and is a representative traditional Chinese medicinal material with distinctive regional characteristics. Clinically, it is widely used in the treatment of dry cough, bronchial asthma, and other respiratory diseases, possessing significant medicinal and economic value and being highly esteemed in TCM. Currently, Fritillariae Cirrhosae Bulbus primarily relies on wild harvesting. However, due to excessive collection, its wild resources have drastically declined, and all source species have been classified as category Ⅱ in the List of National Key Protected Wild Plants, exacerbating the supply-demand imbalance in the market. To mitigate this issue, large-scale cultivation through the domestication of wild Fritillariae Cirrhosae Bulbus has become an inevitable trend. However, its strict environmental requirements, low propagation efficiency, high seedling mortality, and immature cultivation techniques have severely hindered industrialization. This study investigates the domestication process of Fritillariae Cirrhosae Bulbus, focusing on seed propagation, seedling cultivation, and medicinal material production. It also reviews the species and distribution of wild resources, their endangered status, market supply-demand dynamics, and the historical and current development of domestication. The findings indicate that enhancing propagation efficiency, optimizing cultivation models, and distinguishing between seed propagation and medicinal material production are key measures to accelerate the industrialization of domesticated Fritillariae Cirrhosae Bulbus. This research aims to promote the industrialization of Fritillariae Cirrhosae Bulbus domestication and provide a reference model for the conservation and sustainable utilization of rare and endangered medicinal plant resources.
Fritillaria/chemistry* ; Endangered Species ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/economics* ; China

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective To explore the risk factors for 28-day mortality of sepsis-associated acute kidney injury(SA-AKI)patients and to develop a nomogram risk prediction model.Methods A retrospective cohort study was conducted,involving 184 patients with SA-AKI admitted to the intensive care unit(ICU)of the 940th Hospital of Joint Logistic Support Force of PLA between January 2017 and December 2022.Patients were categorized into survival(n=135)and non-survival(n=49)groups based on 28-day mortality.Clinical data were collected,and statistically significant risk factors were preliminarily screened.Multivariate stepwise logistic regression analysis was performed to identify independent risk factors for 28-day mortality of SA-AKI patients.A nomogram predictive model was constructed using these factors,and internally validated with the Bootstrap method.The receiver operating characteristic curve(ROC curve)was drawn,and the area under the ROC curve(AUC)was calculated to verify the predictive value and accuracy of the model.Results The 28-day mortality rate among 184 SA-AKI patients was 26.6％(49/184).Multivariate stepwise logistic regression analysis identified multiple organ dysfunction syndrome(MODS)(OR=16.393,95％CI 4.317-62.254,P＜0.001),high acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score(OR=1.097,95％CI 1.036-1.161,P=0.002),low oxygenation index(OR=0.992,95％CI 0.986-0.998,P=0.015),low neutrophil count(OR=0.912,95％CI 0.860-0.968,P=0.002)and low fibrinogen concentration(OR=0.733,95％CI 0.549-0.978,P=0.034)as independent risk factors.The prediction model equation was P=1/1+e-logit(P),logit(P)=-1.626+2.797×MODS+0.092×AP ACHE Ⅱ+(-0.311)×fibrinogen+(-0.092)×neutrophil count+(-0.008)×oxygenation index.Internal validation with 1000 Bootstrap resamples showed high consistency between predicted and actual values.ROC analysis showed an AUC of 0.911(95％CI 0.868-0.955,P＜0.05)for the model,with 93.9％sensitivity and 78.5％specificity at a cut-off of 0.194.The Hosmer-Lemeshow test confirmed good calibration(P=0.62),and decision-making curve analysis demonstrated clinical utility within the high-risk threshold range(0.1-0.9).Conclusions MODS,high APACHE Ⅱ score,low oxygenation index,low neutrophil count,and low fibrinogen concentration are independent risk factors for 28-day mortality in SA-AKI patients.The developed nomogram risk prediction model may provide important guidance for predicting 28-day mortality in SA-AKI patients.

Homo- or heterodimeric compounds that affect dimeric protein function through interaction between monomeric moieties and protein subunits can serve as valuable sources of potent and selective drug candidates. Here, we screened an in-house dimeric natural product collection, and panepocyclinol A (PecA) emerged as a selective and potent STAT3 inhibitor with profound anti-tumor efficacy. Through cross-linking C712/C718 residues in separate STAT3 monomers with two distinct Michael receptors, PecA inhibits STAT3 DNA binding affinity and transcription activity. Molecular dynamics simulation reveals the key conformation changes of STAT3 dimers upon the di-covalent binding with PecA that abolishes its DNA interactions. Furthermore, PecA exhibits high efficacy against anaplastic large T cell lymphoma in vitro and in vivo, especially those with constitutively activated STAT3 or STAT3^Y640F. In summary, our study describes a distinct and effective di-covalent modification for the dimeric compound PecA to disrupt STAT3 function.

Objective To utilize single-cell and peripheral blood transcriptomic data from 3D brain organoids,combined with machine learning,to analyze the role of mitochondrial autophagy genes in schizophrenia(SCZ).Methods By integrating two machine learning algorithms,we identified differentially expressed mitochondrial autophagy-related genes between schizophrenia patients and healthy controls using peripheral blood RNA sequencing data.The relationship between mitophagy gene,immune cells and inflammatory factors was further explored.Comprehensive single-cell analysis was used to explore the signaling pathways and specific transcription factors based on mitophagy genes.Results Using machine learning,seven key mitophagy genes expressed in schizophrenia patients were identified.Based on Mitoscore analysis,at the single-cell level,neurons with high mitochondrial autophagy activity(Mitohigh_Neuron)formed new interactions with endothelial cells via the SPP1 signaling pathway.Conclusion This study identified two subtypes of mitophagy and seven key mitophagy genes in schizophrenia,providing new insights into the pathogenesis of the disease.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:We aimed to develop a novel visualized model based on nomogram to predict postoperative overall survival.Methods:This was a multicenter, retrospective, observational cohort study, including participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery from 11 medical centers in China from August 1, 2015 to June 30, 2018. Baseline characteristics, histopathological data and nutritional status, as assessed using Nutrition Risk Screening 2002 (NRS 2002) score and the scored Patient-Generated Subjective Global Assessment, were collected. The least absolute shrinkage and selection operator regression and Cox regression were used to identify variables to be included in the predictive model. Internal and external validations were performed.Results:There were 681 and 127 patients in the training and validation cohorts, respectively. A total of 188 deaths were observed over a median follow-up period of 59 (range: 58 to 60) months. Two independent predictors of NRS 2002 and Tumor-Node-Metastasis (TNM) stage were identified and incorporated into the prediction nomogram model together with the factor of age. The model's concordance index for 1-, 3- and 5-year overall survival was 0.696, 0.724, and 0.738 in the training cohort and 0.801, 0.812, and 0.793 in the validation cohort, respectively.Conclusions:In this study, a new nomogram prediction model based on NRS 2002 score was developed and validated for predicting the overall postoperative survival of patients with gastric colorectal cancer. This model has good differentiation, calibration and clinical practicability in predicting the long-term survival rate of patients with gastrointestinal cancer after radical surgery.

Objective:To investigate the clinical efficacy of 595-nm pulsed dye laser (PDL) in the treatment of port-wine stains (PWS) in infants and toddlers.Methods:A retrospective analysis was conducted based on clinical data from 155 infants and toddlers with PWS treated with 595-nm PDL at West China Hospital of Sichuan University from January 2013 to October 2023, and the efficacy was evaluated according to pre- and post-treatment photographs. The children were grouped according to gender, age, lesion color, lesion area, lesion sites, and number of treatment sessions, separately, and the differences were analyzed between different groups. Further analysis was conducted to determine factors affecting efficacy of PDL for PWS. Adverse reactions after treatment were recorded. The Mann-Whitney U test and Kruskal-Wallis H test were used to analyze unidirectional ordered R × C contingency table data, the Bonferroni approach was used for multiple comparisons, and multivariate ordered logistic regression analysis was performed for multifactorial analysis. Results:After the treatment with 595-nm PDL, 135 infants and toddlers with PWS showed good response, with an overall response rate of 87.1%. Univariate analysis indicated that the efficacy was associated with the lesion area ( P = 0.016) and the number of treatment sessions ( P < 0.001), but not with age ( P = 0.340), gender ( P = 0.164), lesion color ( P = 0.530), or lesion sites ( P = 0.077), and the smaller the lesion area, the more the treatment sessions, the better the therapeutic effect. Multivariate ordered logistic regression analysis further confirmed the correlations of efficacy with lesion area ( P = 0.010) and number of treatment sessions ( P < 0.001). Adverse reactions occurred in 5 (3.2%) cases of PWS, including 2 (1.3%) of hypopigmentation, 2 (1.3%) of hyperpigmentation, and 1 (0.6%) of scar formation. Conclusion:The 595-nm PDL was safe and effective for the treatment of PWS in infants and toddlers with few adverse reactions, making it a reliable therapeutic option.