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.

ObjectiveTo investigate the distribution of traditional Chinese medicine （TCM） syndrome elements in type 2 diabetes mellitus （T2DM） patients based on maximum body mass index （maxBMI） and explore their association with complication risks. MethodsA retrospective cross-sectional study was used to collect clinical data from hospitalized T2DM patients， extracting age， gender， smoking history， alcohol consumption history， duration of disease， HbA1c level， complications， and TCM syndromes， and extracting the syndrome elements of disease location and disease nature based on their TCM syndromes. MaxBMI was calculated by telephone survey of patients' self-reported maximum body weight； patients with maxBMI ≥24 kg/m² were classified into spleen-heat syndrome group， and those with maxBMI ＜24 kg/m² were classified into consumptive-heat syndrome group. The distribution of TCM syndrome types and syndrome elements of patients in the two groups were analysed. Then the propensity score matching method was used to balance the baseline characteristics between the two groups and compare the differences in the distribution of syndrome types and syndrome elements and the risk of macrovascular and microvascular complications between the two groups. ResultsAmong the 1178 T2DM patients， syndrome elements in spleen-heat patients （1034 cases） were primarily located in the spleen （351 cases， 33.95%）， liver （240 cases， 23.21%）， and stomach （139 cases， 13.44%）， while in consumptive-heat patients （144 cases）， they were concentrated in the spleen （57 cases， 39.58%）， liver （34 cases， 23.61%）， and kidneys （17 cases， 11.81%）； regarding syndrome elements of disease nature， spleen-heat patients were predominantly characterized by qi deficiency （481 cases， 46.52%）， phlegm （353 cases， 22.73%）， and dampness （241 cases， 23.31%）， whereas consumptive-heat patients showed more qi deficiency （84 cases， 58.33%） and yin deficiency （44 cases， 30.56%）. After propensity score matching， 132 cases were included in each group， and no statistically significant differences were observed in the distribution of syndrome elements of disease location between the two groups （P＞0.05）， but the phlegm element was significantly more prevalent in spleen-heat patients than in consumptive-heat patients （P = 0.006）. Regarding the risk of complications， spleen-heat patients had a significantly higher risk of developing macrovascular complications compared to consumptive-heat patients （OR=2.04， P=0.010）， while no significant differences were found between groups in the occurrence of microvascular complications （P＞0.05）. ConclusionThe spleen-heat T2DM patients show a more frequent syndrome element of disease nature of phlegm， and a higher risk of developing macrovascular complications compared to consumptive-heat patients.

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.

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.

Objective To identify co-expressed genes and potential comorbidity mechanisms between Alzheimer's disease(AD)and epilepsy with publicly available single-cell transcriptome sequencing data from human brains,fol-lowed by functional validation in APP/PS1 double transgenic AD mouse models expressing the chimerical Mo/HuAPP695swe amyloid precursor protein and mutant PS1-dE9 presenilin 1.Methods The single-cell transcriptome sequencing data of brain tissue from AD and epilepsy patients were collected from gene expression omnibus(GEO)database followed by cell clustering,differential expression analysis and gene ontology(GO)func-tional enrichment analysis using R-based tools such as Seurat and cluster Profiler and video electroencephalogram (vEEG)monitoring and Western blot experiments.Results A total of eight major brain cell types were identified,with neurons and glial cells exhibiting shared differentially expressed genes between AD and epilepsy.These co-ex-pressed genes were significantly clustered in pathways related to metal ion homeostasis,synaptic transmission,oxi-dative stress,and immune activation,which suggested common pathological mechanisms involving in synaptic dys-function and neuro-inflammation in both disorders.The vEEG recordings of APP/PS1 mouse model of AD showed 30％of mice exhibited high-frequency epileptic seizures,while 70％showed low-frequency seizure activity.Subse-quent validation in the prefrontal cortex of AD mice confirmed up-regulated expression of key molecular markers(HES5,c-FOS,and RPL10A)identified through single-cell sequencing analysis.Conclusions AD and epilepsy share gene co-expression profiles and functional pathways in specific cell types.The results of research provide a theoretical support for further elucidating their comorbidity mechanisms and developing targeted therapeutic strategy.

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.

Case 1: A 27-year-old female with ALK-positive anaplastic large cell lymphoma/leukemia; Case 2: A 27-year-old male with acute myeloid leukemia; Case 3: A 56-year-old male with myelodysplastic syndrome. These three patients underwent haploid hematopoietic stem cell transplantation and experienced severe oral mucosal inflammation, nausea, vomiting, diarrhea, and other symptoms over a long period, which significantly restricted eating. Neurological and psychiatric symptoms appeared at 50, 38, and 50 days following transplantation, respectively. The diagnosis of Wernicke encephalopathy was made by head magnetic resonance imaging, whereas the condition improved significantly after intravenous infusion of vitamin B 1.

To meet the emerging demands of clinical medicine development and transformation of doctor-patient relationship models,enhance medical students'theoretical knowledge and practical application level of medical humanities,as well as improve the educational experience,the medical humanities teaching team of Harbin Medical University introduced and innovated the workshop teaching model in teaching.By summarizing the basic connotation of workshop teaching mode and its innovation in medical humanities education,taking the"doctor-patient communication"course of Harbin Medical University as an example,this paper shared the specific practice and application of workshop teaching mode in course teaching from three aspects,including preparation,implementation,and evaluation and feedback of teaching,as well as explored its innovation and application prospects in medical humanities education,with a view to providing valuable references for the organic integration of medical humanities education and clinical professional education,professional theory and practice.

ObjectiveTo determine the optimal dose of Dachengqi Decoction （大承气汤， DCQD） for the treatment of acute intestinal obstruction （AIO） through a randomized， double-blind， dosage parallel controlled， multi-center clinical trial， and to providee evidence support for the reasonable dosage of DCQD in clinical practice. MethodsBased on the commonly used clinical dose of DCQD， three different groups were set up， including low-dose group which used Dahuang （Radix et Rhizoma Rhei） 12 g， Houpo （Cortex Magnoliae Officinalis） 9 g， Zhishi （Fructus Aurantii Immaturus） 9 g， and Mangxiao （Natrii Sulfas） 4.5 g， medium-dose group using Dahuang 36 g， Houpo 27 g， Zhishi 27 g， Mangxiao 13.5 g， and high-dose group using Dahuang 60 g， Houp0 45 g， Zhishi 45 g and Mangxiao 22.5 g. Initially， 149 AIO patients with Yangming （阳明） bowel excess syndrome were randomly assigned to three groups using a stratified randomization method， and both the patients and the doctors were blinded. In addition to conventional western medicine treatment， each group was given 12 bags of granules made from the raw herbs of DCQD at different doses， taken orally or injected through a gastric catheter once every 6 hours， 3 bags each time， for 3 consecutive days. After treatment， the indicators of the three groups of patients with primary AIO and non-primary AIO were evaluated respectively， and the full analysis set （FAS） and per-protocol set （PPS） were used for analysis. The primary outcomes were the time to recover voluntary bowel movements and voluntary flatulence. The secondary outcomes were the ideal rate of spontaneous defecation and the ideal rate of spontaneous flatus. The occurrence of adverse events during the study was recorded and analyzed using the safety analysis set （SS）. ResultsA total of 91 patients with primary AIO and 58 patients with non-primary AIO were included in the FAS and SS analysis， while 80 primary AIO patients and 56 non-primary AIO patients were included in the PPS analysis. Both FAS and PPS analysis showed significant differences in the time to recover voluntary bowel movements and voluntary flatulence among primary AIO patients in different dose groups of DCQD （P＜0.01）， and the high- and medium-dose groups assumed less time than the low-dose group （P＜0.05）. There was no statistically significant difference in the ideal rate of spontaneous defecation and spontaneous flatus among the three groups （P＞0.05）. And consistent results were seen in the non-primary AIO patients among the three groups. Five adverse events occurred in primary AIO patients （3 in the low-dose group， 1 in the medium-dose group， and 1 in the high-dose group）， mainly manifested as abdominal distension and abdominal pain， and there was no statistically significant difference in the incidence of adverse events （P＞0.05）. No adverse events occurred in patients with non-primary AIO. ConclusionDCQD， as an effective treatment for patients with AIO， is commonly used at a medium dose for patients with primary AIO and at a high dose for patients with non-primary AIO. The therapeutic advantage is mainly reflected in the shorter time to recover spontaneous defecation and spontaneous flatulence and the improvement of intestinal function.

The article presents the diagnosis and treatment of an imported case with severe Plasmodium falciparum malaria, and the effect of plasma exchange combined with continuous renal replacement therapy. Severe P. falciparum malaria is characterized by complex clinical symptoms and multiple complications, and plasma exchange combined with continuous renal replacement therapy has a satisfactory therapeutic efficacy for severe P. falciparum malaria.