Objective To elucidate the causal relationships between colorectal cancer (CRC) and prevalent psychiatric disorders through a two-sample Mendelian randomization approach. Methods Utilizing publicly available genome-wide association study data, we explored the connections between CRC and various psychiatric disorders, including depression, anxiety, bipolar disorder, and schizophrenia. We applied three statistical analyses: inverse variance weighting, MR-Egger, and median weighting. Sensitivity analyses were conducted to ensure the reliability and validity of the results. Results Inverse variance weighting analysis showed no significant links between CRC and depression (P=0.090), anxiety (P=0.099), or schizophrenia (P=0.899). Conversely, a significant inverse relationship was found with bipolar disorder (P=0.010). Conclusion No causal connection exists between CRC and the psychiatric conditions of depression, anxiety, or schizophrenia. However, CRC may have a causal association with a reduced risk of bipolar disorder, further supporting the existence of the gut-brain axis.

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

Objective To compare the in vitro degradation rate,cytotoxicity,in vivo osteogenic ability,and in vivo biotoxicity of a new type of biodegradable implant material Zn-2Cu-xSr(x=0,0.5,and 1 wt.％)alloy and evaluate its application prospects in the orthopedic field.Methods The in vitro degradation ability of Zn,Zn-2Cu,Zn-2Cu-0.5Sr,Zn-2Cu-1Sr,and Ti materials was evaluated by pH moni-toring and material degradation rate.Cell proliferation experiment and cytoskeleton fluorescence staining were used to compare the in vitro cell compatibility of Zn,Zn-2Cu,Zn-2Cu-0.5Sr,Zn-2Cu-1Sr,and blank control.Micro-computed tomography(CT)scanning and bone min-eral density detection were used to evaluate the in vivo osteogenic ability of Zn,Zn-2Cu,Zn-2Cu-0.5Sr,and Zn-2Cu-1Sr.HE staining was used to compare the in vivo biotoxicity of Zn-2Cu-1Sr and blank control.Results There were no statistical differences in the pH values between the Zn,Zn-2Cu,Zn-2Cu-0.5Sr,Zn-2Cu-1Sr,and Ti groups(P＞0.05).The degradation rate in the Zn-2Cu-0.5Sr group was signifi-cantly lower than that in the Zn-2Cu-1Sr and Zn-2Cu groups(P＜0.05).The cell proliferation rate in the blank control group was signifi-cantly lower than that in the Zn-2Cu-0.5Sr and Zn-2Cu-1Sr groups(P＜0.05).The bone mineral density in the Zn-2Cu-1Sr group was significantly higher than that in the Zn-2Cu and Zn-2Cu-0.5Sr groups(P＜0.05).HE staining showed no abnormal or pathological mor-phology in the rats'heart,liver,lung,and kidney tissues in the Zn-2Cu-1Sr group.Conclusion Adding trace amounts of Cu and Sr to Zn can reduce the degradation rate and improve the biocompatibility and osteogenic ability of the material.

Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

Objective To compare the in vitro degradation rate,cytotoxicity,in vivo osteogenic ability,and in vivo biotoxicity of a new type of biodegradable implant material Zn-2Cu-xSr(x=0,0.5,and 1 wt.％)alloy and evaluate its application prospects in the orthopedic field.Methods The in vitro degradation ability of Zn,Zn-2Cu,Zn-2Cu-0.5Sr,Zn-2Cu-1Sr,and Ti materials was evaluated by pH moni-toring and material degradation rate.Cell proliferation experiment and cytoskeleton fluorescence staining were used to compare the in vitro cell compatibility of Zn,Zn-2Cu,Zn-2Cu-0.5Sr,Zn-2Cu-1Sr,and blank control.Micro-computed tomography(CT)scanning and bone min-eral density detection were used to evaluate the in vivo osteogenic ability of Zn,Zn-2Cu,Zn-2Cu-0.5Sr,and Zn-2Cu-1Sr.HE staining was used to compare the in vivo biotoxicity of Zn-2Cu-1Sr and blank control.Results There were no statistical differences in the pH values between the Zn,Zn-2Cu,Zn-2Cu-0.5Sr,Zn-2Cu-1Sr,and Ti groups(P＞0.05).The degradation rate in the Zn-2Cu-0.5Sr group was signifi-cantly lower than that in the Zn-2Cu-1Sr and Zn-2Cu groups(P＜0.05).The cell proliferation rate in the blank control group was signifi-cantly lower than that in the Zn-2Cu-0.5Sr and Zn-2Cu-1Sr groups(P＜0.05).The bone mineral density in the Zn-2Cu-1Sr group was significantly higher than that in the Zn-2Cu and Zn-2Cu-0.5Sr groups(P＜0.05).HE staining showed no abnormal or pathological mor-phology in the rats'heart,liver,lung,and kidney tissues in the Zn-2Cu-1Sr group.Conclusion Adding trace amounts of Cu and Sr to Zn can reduce the degradation rate and improve the biocompatibility and osteogenic ability of the material.

Objective: To explore the effect of orienteering exercises on the improvement of children s executive function, and to explore the relationship between executive function and orienteering intervention to provide theoretical support. Methods: Forty children from the fourth grade of Zhonghai the First Experimental Primary School in Changchun City were selected as the experimental subjects, 20 as the experimental group (10 males, 10 females) and 20 as the control group (10 males, 10 females). The functional changes of executive function subfunctions (inhibitory function, conversion function, refresh function) before and after orienteering exercises intervention were measured by More odd shifting, 1 back and Flanker. Results: After intervention, the inhibitory function, conversion function and refresh function were significantly decreased in the experimental group (10.29±15.99, 295.19±189.76, 642.85±220.78)ms compared with before intervention (25.62±10.18, 616.04±287.92, 1 051.25±275.00)ms (F=12.52, 20.76, 20.89, P<0.01), while there was no significant change in the control group (P>0.05). In this study, neither gender main effect nor interaction between sex × group, sex × time and sex × time × group were found (P>0.05). Conclusion Orienteering exercises can significantly improve children s executive function, which are not vaired by sex.

Objective To prepare quercetin ( QUE) loaded chitosan nanoparticles ( CS-NPs), evaluate its physicochemical properties and antioxidation activity in vitro.Methods Quercetin chitosan nanoparticles were prepared by ionic crosslinking method and self-assembly method.The preparation method was optimized using entrapment efficiency (EE), drug loading (DL) and size as indexes.The best formulation and preparation conditions were optimized by orthogonal test based on single-factor test, evaluation indicator as particle size and EE.The physicochemical properties of the obtained QUE-CS-NPs were characterized by the following methods: the transmission electron microscope (TEM), dynamic light scattering (DLS) analysis for morphology, size distribution and Zeta potential.In vitro release behavior in 0.5% SDS solution was evaluated by dialysis tube method.In vitro antioxidant activity assays were performed by evaluating the abilities of the microspheres for hydroxide radicals and superoxide anions .Results TEM results revealed QUE-CS-NPs with round and uniform.Particle-size analysis showed that the diameters and Zeta potential of the QUE-CS-NPs were (282.9 ±20) nm and (30.5 ±2) mV, with uniform distribution (polydispersity below 0.185).DL and EE of QUE-CS-NPs were (8.81 ±0.65) %and (80.02 ±1.04) %, respectively.QUE-CS-NPs showed extended administration times with 66.2% cumulative release within 72 h.QUE-CS-NPs showed pronounced antioxidant activity and a concentration dependent, even more substantial than that of pure QUE.Conclusion QUE-CS-NPs show a good size, sustain release effect and pronounce antioxidant activity.

OBJECTIVETo study the anti-tumor and immunity activity of toad coat (Chantui), which is a new officinal part of Bufo bufo gargarizans.

METHODThe tumor weight of S180, H22, Lewis lung cancer cell inoculated in mice were compared between the groups of mice, fed with toad coat, and those which were not (control group). The average longevity of the mice with HCA fed with toad coat was also compared with the control group. The T lymphocyte transformation and NK cell killing activity were tested and compared with the control group. The condition of the mice which were fed with great dosage of Chantui (16 g x kg(-1) x d(-1)) was observed.

RESULTThe tumor weight was remarkably reduced in the groups which were fed with toad coat compared with the control group. Tests show that toad coat can raise the activity of both T lymphocyte and NK cell. There was no obvious side-effect when the mice were fed with great dosage of toad coat.

CONCLUSIONThe results show that toad coat has a strong inhibitory activity against tumors inoculated in mice and a strong enhancement of immune activity, so it could be viewed as a new valuable safe medicinal source.

Animals ; Antineoplastic Agents ; administration & dosage ; Bufo bufo ; Cell Line, Tumor ; Female ; Killer Cells, Natural ; immunology ; Male ; Mice ; Mice, Inbred C57BL ; Neoplasms ; drug therapy ; immunology ; pathology ; Random Allocation ; Skin ; chemistry ; T-Lymphocytes ; immunology ; Tumor Burden ; drug effects