ObjectiveBased on the established characteristic profiles， quantitative analysis of multiple components， and chemometric analysis of Taraxacum mongolicum， the quality of different T. mongolicum germplasms was evaluated at the chemical level， thereby providing a reference for the screening of high-quality germplasms and the rational utilization of wild resources. MethodsAn ultra-performance liquid chromatography （UPLC） was employed to establish characteristic profiles. Principal component analysis （PCA） and partial least squares-discriminant analysis （PLS-DA） were then adopted to screen and comprehensively rank marker compounds. ResultsThe UPLC fingerprint of T. mongolicum germplasm identified 13 chromatographic peaks corresponding to gallic acid， coumaric acid， neochlorogenic acid， monocaffeoyltartaric acid， chlorogenic acid， cryptochlorogenic acid， caffeic acid， p-coumaric acid， cichoric acid， luteoloside， isochlorogenic acid B， isochlorogenic acid A， and isochlorogenic acid C. Combined with chemometric analysis such as PCA and PLS-DA， eight core markers （cichoric acid， luteoloside， cryptochlorogenic acid， isochlorogenic acid B， chlorogenic acid， caffeic acid， isochlorogenic acid C， and isochlorogenic acid A） were screened for distinguishing wild and cultivated germplasms. Additionally， eight core markers （cichoric acid， caffeic acid， luteoloside， chlorogenic acid， cryptochlorogenic acid， isochlorogenic acid A， monocaffeoyltartaric acid， and neochlorogenic acid） were selected for the evaluation and screening of different T. mongolicum germplasms. ConclusionThis study establishes a UPLC analysis method capable of simultaneously determining 13 characteristic components in T. mongolicum， such as cichoric acid and chlorogenic acid， as well as their precursor compound contents in the biosynthetic pathway. Based on the above methods， three T. mongolicum germplasms （PGY-004， PGY-009， and PGY-010） with promising medicinal potential are selected for subsequent research on variety breeding. The present study provides a reference for quality control of Taraxacum mongolicum， germplasm screening， and the rational development and utilization of wild resources.

Osteoporosis is a common senile bone metabolism disease， clinically characterized by decreased bone mass， destruction of bone microstructure， increased bone fragility， and easy fracture. It tends to occur in the elderly and postmenopausal women， seriously threatening the quality of life and physical and mental health of the elderly. At present， the treatment of osteoporosis is mainly based on oral western medicines， such as calcium， Vitamin D， and bisphosphonates. Still， there are drawbacks such as a long medication cycle and many adverse reactions. In recent years， due to the advantages of multi-component， multi-pathway， and multi-target， some traditional Chinese medicines and effective ingredients can regulate the osteogenic and osteoclastic differentiation process in both directions and are widely used in the prevention and treatment of osteoporosis. Hippophae rhamnoides is a commonly used herbal medicine， and its fruits are rich in flavonoids， polyphenols， fatty acids， vitamins， and trace elements， which have been proven to have a good anti-osteoporosis effect. Isorhamnetin is the main effective ingredient of Hippophae rhamnoides fruits， which has many pharmacological effects such as anti-inflammation， anti-oxidative stress， anti-aging， and anti-tumor. Studies have shown that isorhamnetin can participate in the regulation of bone metabolism and has a good anti-osteoporosis effect. However， the pharmacological effects and related mechanisms of isorhamnetin against osteoporosis have not been systematically summarized. Therefore， this paper reviewed the pharmacological effects and related mechanisms of isorhamnetin against osteoporosis by referring to relevant literature to provide more basis for the development and application of isorhamnetin.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

Objective:To observe the effect of isokinetic sensorimotor training on the hand function of stroke survivors.Methods:Forty-two stroke survivors with hand dysfunction were randomly divided into an isokinetic group of 22 and a control group of 20. Both groups were given sensorimotor training in addition to routine drug treatment and rehabilitation therapy, but the isokinetic group was additionally provided with sensorimotor training based on isokinetic techniques for 45 minutes daily, 5 days a week for 4 consecutive weeks. Before and after the intervention, both groups were evaluated using the Semmes-Weinstein monofilament examination (SWME), their two-point discrimination (2-PD) was documented, proprioception of their wrist joints was quantified, and the Fugl-Meyer upper extremity assessment (FMA-UE) and the simplified upper limb function assessment (STEF) were applied.Results:In both groups after treatment, there was a significant improvement in the SWME scores and 2-PD distance of the index finger and the thenar, and there was a significant decrease in the angle of motion perception (at 30° of flexion). The average FMA-UE and STEF scores of both groups had improved. After the treatment, the SWME scores of the index finger and the thenar, as well as well as the average FMA-UE and STEF scores of the isokinetic group were significantly higher than the control group′s averages. Angle of motion perception was also significantly superior.Conclusions:Sensorimotor training based on isokinetic techniques can significantly improve touch, motion sense, gross motor function and the fine motor ability of stroke survivors.

Narrative is the cornerstone of interpersonal relationships and life safety. However， its important value in daily life， school education， health management， personal happiness， career development， and other aspects has been ignored. The narrative ecology of families， schools， hospitals， workplaces， and elderly care institutions is worrying， the narrative connection between parent-child and intergenerational is broken， the narrative nature of adolescents is ignored， the narrative demands of patients are neglected， narrative relationships in the workplace are indifferent， and the narrative capital of the elderly is idle. These issues have resulted in serious social problems， such as depression and suicide among adolescents， conflicts between doctors and patients， workplace and life burnout among middle-aged people， and the inability of the elderly to achieve healthy aging， which have become a “stumbling block” to the realization of holistic health. Advocating the construction of narrative ecology and interpersonal narrative connections is an important measure of achieving holistic health. Taking the “narrative concept” as the overall framework， and based on the research， education， and practice carried out by the Alliance of Narrative Medicine in Higher Education Institutions， this paper proposed that actively build China’s narrative system of life and health， to enable narrative play an active and dynamic role in the construction of narrative ecology in different spaces， such as the families， the schools， the hospitals， the workplaces， and the elderly care institutions， as well as practically improve the quality of life of the people.

Objective To study the death status of pancreatic cancer among residents in Xuhui district, Shanghai, from 1992 to 2021, and analyze its trends of change, so as to provide evidence for the prevention and treatment of pancreatic cancer. Methods Based on the database of Shanghai death registration system from 1992 to 2021, the crude mortality rate, standardized mortality rate, age-specific mortality rate and other indicators of pancreatic cancer among registered residents in Xuhui district were calculated. The Joinpoint software was used to analyze the trends of average annual percent change (AAPC) of pancreatic cancer mortality rate, and the age-period-cohort model was used to analyze the age effect, period effect and birth cohort effect pairs significant changes in pancreatic cancer mortality. Results In 2021, the mortality rate of pancreatic cancer in Xuhui district, Shanghai, ranked fourth among malignant tumors, and the winning rate and world standard rate of the whole population, males and females were 8.34/100 000 (8.81/100 000, 7.98/100 000) and 7.28/100 000 (7.69/100 000, 6.96/100 000), respectively, with males higher than females. AAPC of crude mortality rate and the standardized (6) mortality rate were higher in males than that in females. The age-specific mortality rate increased with the increase of age, and the highest mortality rate was found in 60-84 years old group. The age-period-cohort model showed that from 1992 to 2021, the annual net shift of pancreatic cancer mortality among the whole population, male and female residents in Xuhui district, Shanghai, was 1.22%, 1.58%, 1.15% (P=0.20, 0.19, 0.45) respectively, and the time trend was not significant. From the perspective of age effect, the risk of death from pancreatic cancer in the whole population and with age deviation in males had an obvious trend with increasing age (P<0.05), while the age effect in females had no obvious trend. From the perspective of period effect, no period deviation was significant in the whole population, males and females (P>0.05). In terms of cohort effects, there were significant differences in the whole population and the male cohort deviations(P<0.05). No significant cohort effect was observed in the female population. Conclusions The mortality rate of pancreatic cancer among registered residents in Xuhui district, Shanghai from 1992 to 2021, was on the rise, especially in the 60-84 years old group and male. The prevention and control of pancreatic cancer needs to develop effective epidemic prevention measures for corresponding populations.

As the cornerstone of modern medical diagnosis,pathology is facing multiple challenges such as workforce shortages,strong diagnostic subjectivity,and inefficient workflows.With advantages in image recognition,pattern analysis,and big data processing,artificial intelligence(AI)is increasingly being integrated into the field of pathological diagnosis,driving its transition toward digitization and intelligence.This article systematically reviews the development of AI in pathology,from early supervised learning validation to weakly supervised learning overcoming annotation bottlenecks,and the recent rise of self-supervised and multimodal foundation models.It demonstrates the broad applications of AI in improving diagnostic consistency,optimizing workflows,and predicting molecular features and prognoses.AI not only enhances the objectivity and efficiency of pathological diagnosis but also promotes the development of emerging interdisciplinary fields such as computational pathomics,providing strong support for precision medicine.Although challenges such as data standardization and regulatory approval remain in clinical implementation,the deep integration of AI and pathology is ushering in a new era of human-machine collaboration and intelligent diagnostics.

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Objective To explore and identify the nutritional/inflammatory indicator with the highest predictive potential for overall survival(OS)in postoperative tumor patients so as to provide guidance for postoperative rehabilitation of tumor patients.Methods Data from 3 191 surgical patients were collected,including 15 nutritional/inflammatory indicators.The maximum selection rank statistic method was used to calculate the optimal cut-off values for continuous indicators.The Kaplan-Meier method was used to assess OS,and Cox proportional hazards models were used to analyze the association between the aforementioned 15 indicators and survival.The predictive value of these 15 indicators was evaluated with receiver operating characteristic(ROC)curves and C-index.Results Multivariate analysis showed that all 15 indicators were significantly associated with poorer OS in surgical patients(P＜0.05 for all).Time-dependent area under the curve(AUC)and C-index analysis indicated that 3 indicators with the highest predictive potential in OS in postoperative tumor patients were the nutritional risk index(NRI)(C-index:0.597),C-reactive protein-to-albumin ratio(CAR)(C-index:0.587),and C-reactive protein-to-lymphocyte ratio(CLR)(C-index:0.587).The optimal cut-off value for NRI was determined to be 104.31(i.e.,NRI＜104.31 suggests malnutrition)with the maximum selection rank statistic method,the optimal cut-off value for CAR to be 0.05(i.e.,CAR≥0.05 suggests a strong inflammatory response,often accompanied by malnutrition),and the optimal cut-off value for CLR to be 1.18(i.e.,CLR≥1.18 suggests a strong inflammatory response).Subgroup analysis indicated that NRI,CAR,and CLR had good correlation with tumor staging,and there were significant differences between tumor node metastasis(TNM)Ⅲ/Ⅳ stage patients and TNM Ⅰ/Ⅱ stage patients when there was a strong inflammatory response or malnutrition.Conclusion In postoperative tumor patients,NRI,CLR,and CAR have high prognostic value.Combining these with the patient's clinical stage,it enables more precise guidance for clinical diagnosis and treatment strategies.