ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.

The ambiguity of etiology makes temporomandibular joint osteoarthritis (TMJOA) "difficult-to-treat". Emerging evidence underscores the therapeutic promise of exosomes in osteoarthritis management. Nonetheless, challenges such as low yields and insignificant efficacy of current exosome therapies necessitate significant advances. Addressing lower strontium (Sr) levels in arthritic synovial microenvironment, we studied the effect of Sr element on exosomes and miRNA selectively loading in synovial mesenchymal stem cells (SMSCs). Here, we developed an optimized system that boosts the yield of SMSC-derived exosomes (SMSC-EXOs) and improves their miRNA profiles with an elevated proportion of beneficial miRNAs, while reducing harmful ones by pretreating SMSCs with Sr. Compared to untreated SMSC-EXOs, Sr-pretreated SMSC-derived exosomes (Sr-SMSC-EXOs) demonstrated superior therapeutic efficacy by mitigating chondrocyte ferroptosis and reducing osteoclast-mediated joint pain in TMJOA. Our results illustrate Alix's crucial role in Sr-triggered miRNA loading, identifying miR-143-3p as a key anti-TMJOA exosomal component. Interestingly, this system is specifically oriented towards synovium-derived stem cells. The insight into trace element-driven, site-specific miRNA selectively loading in SMSC-EXOs proposes a promising therapeutic enhancement strategy for TMJOA.
MicroRNAs/metabolism* ; Mesenchymal Stem Cells/drug effects* ; Osteoarthritis/drug therapy* ; Exosomes/drug effects* ; Strontium/pharmacology* ; Synovial Membrane/cytology* ; Humans ; Animals ; Temporomandibular Joint Disorders/therapy* ; Temporomandibular Joint

Objective:To investigate personal exposures to nitrogen oxides(NOX)and nitrogen di-oxide(NO2)and the influence of baseline personal characteristics,living environment and daily activity patterns of the participants on the exposures among adults over 35 in Tianjin and Shanghai.Methods:In this panel study,91 healthy nonsmoking adults aged over 35 from Tianjin and Shanghai participated in our study.The study was conducted in summer and winter.The participants were followed for three times with an interval of at least two weeks.Only participants in Shanghai were followed once in winter because of the COVID-19 pandemic.Twenty-seven participants completed follow-up visits in both seasons.We measured their 24 h personal exposures to NOX and NO2and collected their baseline and time-activity in-formation through questionnaire/diary.The linear mixed model was used to analyze the associations be-tween potential influencing factors and personal NOX and NO2 exposure levels.Results:There were 349 follow-up visits with valid 24 h personal NO2 and NOX exposure measurements in the two cities.The ave-rage 24 h personal exposures to NO2 and NOX(volume fraction)in Tianjin participants were 18.0 x 10-9 and 26.2 × 10-9 in summer,and 31.0 x 10-9 and 54.9 x 10-9in winter,respectively;and the average 24 h personal exposures to NO2 and NOX in Shanghai participants were 38.7 x 10-9and 100.0x10-9in summer,and 45.5 x10-9 and 139.2 x 10-9 in winter,respectively.The results of univariate regression analysis showed that their personal NOX exposure levels were significantly associated with city,season,gender,average daily cooking times,and ambient NO2 concentrations measured at fixed-site monitoring stations.In addition to the above factors,the personal NOX exposure levels were also significantly associ-ated with educational level and the personal NO2 exposure levels were also significantly associated with passive smoking,average daily home time,cooking energy type,residential distance from main traffic road,and use of kitchen ventilators.Multivariate regression analysis showed that the personal exposure levels of NO2 and NOX were significantly lower in Tianjin than that in Shanghai,were significantly lower in summer than that in winter,and were significantly and positively associated with ambient NO2 concen-trations measured at fixed-site monitoring stations.In addition,personal NOX exposure levels were signifi-cantly lower in females than in males,and personal NO2 exposure levels were significantly positively asso-ciated with average daily cooking times and significantly inversely associated with average daily home time.For every interquartile range(IQR)increase(12.7 × 10-9)in ambient NO2,the personal NO2 exposure levels increased by 27.5％(95％CI:17.0％-38.9％),and personal NOX exposure levels in-creased by 16.1％(95％CI:7.1％-25.8％).Conclusion:Season,city and ambient NO2 concentra-tions are significant influencing factors of personal exposure levels of NO2and NOX At the same time,the personal exposures levels of NO2 are also affected by lifestyle factors.Our study provides scientific evi-dence for making precise air pollution control decisions and reducing the exposure levels of NOX in the population.

White-matter tracts play a pivotal role in transmitting sensory and motor information, facilitating interhemispheric communication and integrating different brain regions. Meanwhile, sensorimotor disturbance is a common symptom in patients with major depressive disorder (MDD). However, the role of aberrant sensorimotor white-matter system in MDD remains largely unknown. Herein, we investigated the topological structure alterations of white-matter morphological brain networks in 233 MDD patients versus 257 matched healthy controls (HCs) from the DIRECT consortium. White-matter networks were derived from magnetic resonance imaging (MRI) data by combining voxel-based morphometry (VBM) and three-dimensional discrete wavelet transform (3D-DWT) approaches. Support vector machine (SVM) analysis was performed to discriminate MDD patients from HCs. The results indicated that the network topological changes in node degree, node efficiency, and node betweenness were mainly located in the sensorimotor superficial white-matter system in MDD. Using network nodal topological properties as classification features, the SVM model could effectively distinguish MDD patients from HCs. These findings provide new evidence to highlight the importance of the sensorimotor system in brain mechanisms underlying MDD from a new perspective of white-matter morphological network.
Humans ; Depressive Disorder, Major/pathology* ; White Matter/pathology* ; Male ; Female ; Support Vector Machine ; Adult ; Magnetic Resonance Imaging ; Middle Aged ; Case-Control Studies ; Sensorimotor Cortex ; Brain

Objective The fully automatic segmentation of glioma and its subregions is fundamental for computer-aided clinical diagnosis of tumors.In the segmentation process of brain magnetic resonance imaging(MRI),convolutional neural networks with small convolutional kernels can only capture local features and are ineffective at integrating global features,which narrows the receptive field and leads to insufficient segmentation accuracy.This study aims to use dilated convolution to address the problem of inadequate global feature extraction in 3D-UNet.Methods 1)Algorithm construction:A 3D-UNet model with three pathways for more global contextual feature extraction,or 3DGE-UNet,was proposed in the paper.By using publicly available datasets from the Brain Tumor Segmentation Challenge(BraTS)of 2019(335 patient cases),a global contextual feature extraction(GE)module was designed.This module was integrated at the first,second,and third skip connections of the 3D UNet network.The module was utilized to fully extract global features at different scales from the images.The global features thus extracted were then overlaid with the upsampled feature maps to expand the model's receptive field and achieve deep fusion of features at different scales,thereby facilitating end-to-end automatic segmentation of brain tumors.2)Algorithm validation:The image data were sourced from the BraTs 2019 dataset,which included the preoperative MRI images of 335 patients across four modalities(T1,T1ce,T2,and FLAIR)and a tumor image with annotations made by physicians.The dataset was divided into the training,the validation,and the testing sets at an 8∶1∶1 ratio.Physician-labelled tumor images were used as the gold standard.Then,the algorithm's segmentation performance on the whole tumor(WT),tumor core(TC),and enhancing tumor(ET)was evaluated in the test set using the Dice coefficient(for overall effectiveness evaluation),sensitivity(detection rate of lesion areas),and 95%Hausdorff distance(segmentation accuracy of tumor boundaries).The performance was tested using both the 3D-UNet model without the GE module and the 3DGE-UNet model with the GE module to internally validate the effectiveness of the GE module setup.Additionally,the performance indicators were evaluated using the 3DGE-UNet model,ResUNet,UNet++,nnUNet,and UNETR,and the convergence of these five algorithm models was compared to externally validate the effectiveness of the 3DGE-UNet model.Results 1)In internal validation,the enhanced 3DGE-UNet model achieved Dice mean values of 91.47%,87.14%,and 83.35%for segmenting the WT,TC,and ET regions in the test set,respectively,producing the optimal values for comprehensive evaluation.These scores were superior to the corresponding scores of the traditional 3D-UNet model,which were 89.79%,85.13%,and 80.90%,indicating a significant improvement in segmentation accuracy across all three regions(P＜0.05).Compared with the 3D-UNet model,the 3DGE-UNet model demonstrated higher sensitivity for ET(86.46%vs.80.77%)(P＜0.05),demonstrating better performance in the detection of all the lesion areas.When dealing with lesion areas,the 3DGE-UNet model tended to correctly identify and capture the positive areas in a more comprehensive way,thereby effectively reducing the likelihood of missed diagnoses.The 3DGE-UNet model also exhibited exceptional performance in segmenting the edges of WT,producing a mean 95%Hausdorff distance superior to that of the 3D-UNet model(8.17 mm vs.13.61 mm,P＜0.05).However,its performance for TC(8.73 mm vs.7.47 mm)and ET(6.21 mm vs.5.45 mm)was similar to that of the 3D-UNet model.2)In the external validation,the other four algorithms outperformed the 3DGE-UNet model only in the mean Dice for TC(87.25%),the mean sensitivity for WT(94.59%),the mean sensitivity for TC(86.98%),and the mean 95%Hausdorff distance for ET(5.37 mm).Nonetheless,these differences were not statistically significant(P＞0.05).The 3DGE-UNet model demonstrated rapid convergence during the training phase,outpacing the other external models.Conclusion The 3DGE-UNet model can effectively extract and fuse feature information on different scales,improving the accuracy of brain tumor segmentation.

OBJECTIVE: Early bolting of Saposhnikovia divaricata has seriously hindered its medicinal value and sustainable development of resources. The molecular mechanism of bolting and flowering of S. divaricata is still unclear and worth of research. In our study, we explored the transcriptome of the genes related to the bolting and flowering of S. divaricata. METHODS: The transcriptome library was constructed, sequenced, assembled and annotated from the bolting and unbolting leaves of S. divaricata by high-throughput sequencing at the bud and flowering stage. Focus on the pathways related to bolting and flowering in plants, and exploring genes. The expression of seven candidate genes was verified by real-time fluorescence quantitative PCR (qRT-PCR). RESULTS: Transcriptome results showed that 249 889 422 high-quality clean reads were obtained. A total of 67 866 unigenes were assembled with an average length of 948.1 bp. Trinity de Novo assembly produced 67 866 unigenes with an average length of 948.1 bp. Among 993 differentially expressed genes, 484 genes were significantly up-regulated and 509 genes were down-regulated in the SdM group. A total of 79 GO terms were significantly enriched for differentially expressed genes. KEGG results showed that 11 154 unigenes were enriched in 89 pathways. And 21 candidate genes related to bolting and flowering of S. divaricata were excavated. The qRT-PCR results showed that expression trends of HDA9, PHYB, AP2, TIR1, Hsp90, CaM, and IAA7 were consistent with transcriptomic sequencing results. In addition, RNA-seq had identified 10 740 transcription factors and classified them into 58 families by their conserved domains. Further studies showed that the transcription factors regulating the flowering of S. divaricata were mainly distributed in the NAC, MYB_related, HB-other, ARF, and AP2 families. CONCLUSION Based on the results of this study, it was found that the plant hormone signal transduction pathway was one of the decisive factors to control bolting and flowering. Among them, auxin related genes IAA and TIR1 are the key genes in the bolting and flowering process of S. divaricata.

Objective:To explore any effect of mechanical vibration on the expression of estrogen and brain-derived neurotrophic factor (BDNF) in ovariectomized rats with an osteoporotic fracture.Methods:Thirty 3-month-old female Wistar rats were divided randomly into a control group, an ovariectomy group and a vibration group, each of 10. Fractures were induced in the rats of all three groups. Twenty minutes of whole-body vertical vibration was applied to the vibration group at a frequency of 35Hz, 5 days a week for 6 weeks. After 2 and 6 weeks the fracture healing of each group was evaluated using X-rays, the levels of hippocampal estrogen were measured using enzyme-linked immunosorbent assays and fracture-end BDNF was quantified by immunoblotting.Results:After 2 and 6 weeks of vibration the average fracture healing in the vibration group was significantly greater than in the other 2 groups. The average estrogen content in the hippocampus of the vibration group was significantly higher than in the other 2 groups after both 2 and 6 weeks, while the average BDNF content in their fracture ends was significantly lower. The BDNF expression at the fracture end was significantly correlated with the fracture healing.Conclusion:Mechanical vibration can promote the expression of estrogen and BDNF in the hippocampus and accelerate fracture healing in osteoporotic rats.

Objective: To investigate the reversal of drug resistance of drug-resistant hepatoma cell line HepG2/DDP by emetine and its mechanism. Methods : The drug-resistant hepatoma cell line HepG2/DDP was established by high-dose cisplatin(DDP) shock combined with low-dose continuous induction. Reverse virtual screening, molecular docking, surface plasmon resonance(SPR) and Western blot assays were employed to explore the target of emetine. The target was validated by transfection assayin vivo. The inhibitory effect of emetine combined with DDP on the proliferation of HepG2/DDP cells was detected by cytotoxicity assay. The sensitization effect of emetine was verified by flow cytometry, and the expression of the apoptosis related proteins BCL2, Bax and Cleaved-caspase-3 were analyzed by Western blot. Results : Emetine enhanced the sensitivity of HepG2/DDP cells to DDP and reduced the resistance index(RI) from 3.69 to 0.93. Reverse virtual screening, molecule docking and SPR results showed that emetine can stably bind to PARP-1. Western blot assays showed that emetine had a potent enzymatic inhibitory activity against PARP-1in vivo. Furthermore, emetine's potentiation of DDP in HEPG2/DDP cells nearly disappeared when the cells were transfected with siRNAs against PARP-1. Flow cytometry showed that emetine could enhance the proapoptotic effect of DDP on HepG2/DDP cells. Conclusion Emetine can enhance the sensitivity of HepG2/DDP cells to DDP, and its mechanism may be related to its inhibition of PARP-1.

Objective To explore the application of propofol combined with small dose fentanyl in manual reduction anesthesia in orthopedics.MethodsOctober 2014 to August 2016 in Huzhou central hospital treated 120 cases of acute long bone closed fracture and ankle, shoulder dislocation and other patients, efficient analysis anesthesia, surgery time, patient recovery time, propofol is added volume and total volume, and detection 3min, 1min, 3min, 5min and waking SpO2, HR, MAP, RR values.After administration before it is administered.ResultsAnesthetic effect experimental group total efficiency is higher, the difference was statistically significant (P<0.05);after simultaneous administration of all patients 1min, 3min, 5min signs were lower than prodrug 3min signs, the difference statistically significant (P<0.05), after clear signs of its former administration 3min with no significant difference;experimental group recovery time, propofol bolus of propofol than with total control were lower, the difference was statistically significant (P<0.05);and the two groups were similar to the operation time, the difference was not statistically significant.ConclusionIn summary, propofol combined with entanyl anesthesia for patients to take the treatment of orthopedic manual reduction obviously.

Objective To study the potential efficacy of transplanted bone marrow-derived mesenchymal stem cells (MSCs) in treating and repairing the acute lung injury in animal models. Methods MSCs were isolated from mouse bone marrow, cultrued and amplified in vitro. The lipopolysaccharide (LPS) was inhaled through postnasal tract to cause acute lung injury in mice and the MSCs labeled by Brdu were administrated via vein into the mice. The migration and differention of the cells were identified by immunostaining and double immunostaining. The pathological changes, pulmonary edema index and the content of IL-1β in lung homogenate were used to accese the therapeutical effect of MSCs. Results The cultured MSCs dispalyed a positive CD44 and a negative CD34. The Brdu-labeled cells were detected in the lungs of the recipient 4 days after transplantation, indicating its origin of MSCs. Theses cells also exhibited characteristics of aveolar epithelials, expressing the cytokeratin-the marker of epithelium. Compared with the injuried ones, the mice treated with MSCs showed a decreased pulmonary edema in-dex and IL-1β content in the lung homogenate. Conclusion These data suggest a therapeutical effects of MSCs in treating and repairing the mouse acute lung injury.