Objective To investigate the clinical characteristics of heart failure with preserved ejection fraction(HFpEF)patients with normal N-terminal pro-brain natriuretic peptide(NT-proBNP).Methods A total of 116 HFpEF patients diagnosed at Binzhou Medical University Hospital form January 2022 to October 2024,along with 61 non-heart failure patients were selected as subjects.Using NT-proBNP diagnosis criteria,HFpEF patients were divided into normal NT-proBNP group(n=62)and elevated NT-proBNP group(n=54),with non-heart failure patients serving as control group(n=61).Clinical parameters including laboratory tests,echocardiography,and CT imaging were systematically compared among all three groups to characterize the clinical features of HFpEF patients with normal NT-proBNP.Results Compared with elevated NT-proBNP group,prevalence of complications,such as hypertension,diabetes,and atrial fibrillation and pulmonary hypertension incidence were lower in normal NT-proBNP group significantly.Left ventricular diastolic function indicators was superior in normal NT-proBNP group than that in elevated NT-proBNP group,with significant differences(P＜0.05).Imaging evaluations revealed that chest CT scans of the normal NT-proBNP group more frequently exhibited characteristic ground-glass opacities.Conclusion HFpEF patients with normal NT-proBNP display distinct clinical characteristics,including milder cardiac structural abnormalities,lower comorbidity burden,and unique imaging manifestations such as ground-glass opacities,which can provide reference for early clinical diagnosis.

Objective:To compare the efficacies of endovascular therapy (EVT) and standard medical therapy in acute ischemic stroke (AIS) patients aged ≥85 years, and analyze the independent influencing factors for poor prognosis of AIS patients after EVT.Methods:Sixty-nine AIS patients aged ≥85 years admitted to Department of Neurology, Xi'an Third Hospital from January 2018 to April 2024, including 40 accepted EVT and 28 accepted standard medicinal therapy, were enrolled. Modified Rankin scale (mRS) was used to evaluate the prognosis of the patients 90 days after onset. General data, prognosis and complications between the EVT group and standard medical therapy group were compared. General data, treatment processes and complications between patients with good prognosis and poor prognosis in the EVT group were compared. Multivariate Logistic regression was used to analyze the independent influencing factors for poor prognosis in AIS patients after EVT.Results:Compared with the standard medical therapy, the EVT group had significantly lower NIHSS score at discharge, greater improvement in NIHSS score (NIHSS score at admission-NIHSS score at discharge), lower mRS score 90 days after onset, higher good prognosis rate, lower mortality rate within 90 days of onset, and longer hospital stay ( P<0.05). In the EVT group, 11 patients (27.5%) had good prognosis and 29 patients (72.5%) had poor prognosis 90 days after onset. Compared with the good prognosis group, the poor prognosis group had significantly higher blood glucose level and lower Alberta Stroke Program Early CT Score (ASPECT) on admission ( P<0.05). Multivariate Logistic regression analysis showed that blood glucose on admission ( OR=2.363, 95% CI: 1.134-4.928, P=0.022) and ASPECT score on admission ( OR=0.273, 95% CI: 0.088-0.854, P=0.026) were independent influencing factors for poor prognosis in AIS patients after EVT. Conclusion:AIS patients aged ≥85 years received EVT have better prognosis compared with those accepted standard medical therapy; these patients with high glucose level and low ASPECT score on admission have poor prognosis.

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.

Infectious keratitis (IK) is a leading cause of blindness worldwide, primarily resulting from improper contact lens use, trauma, and a compromised immune response. The pathogenic microorganisms responsible for IK include bacteria, fungi, viruses, and Acanthamoeba. This review examines standard therapeutic agents for treating IK, including broad-spectrum empiric antibiotics for bacterial keratitis (BK), antifungals such as voriconazole and natamycin for fungal infections, and antiviral nucleoside analogues for viral keratitis (VK). Additionally, this review discusses therapeutic agents, such as polyhexamethylene biguanide (PHMB), for the treatment of Acanthamoeba keratitis (AK). The review also addresses emerging drugs and the challenges associated with their clinical application, including anti-biofilm agents that combat drug resistance and nuclear factor kappa-B (NF-κB) pathway-targeted therapies to mitigate inflammation. Furthermore, methods of Photodynamic Antimicrobial Therapy (PDAT) are explored. This review underscores the importance of integrating novel and traditional therapies to tackle drug resistance and enhance drug delivery, with the goal of advancing treatment strategies for IK.

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 investigate the expression of the circular RNA circ-PHC3 in cervical cancer tissues and its regulatory mechanisms in the proliferation,migration,and invasion of cervical cancer cells.Methods The expression levels of circ-PHC3 in cervical cancer tissues and adjacent non-tumor tissues were analyzed using the GEO database.The correlation between circ-PHC3 expression and the clinical stage as well as prognosis of cervical cancer patients was also evaluated.The expression of circ-PHC3 in cervical cancer cell lines HCC94,C33A,HeLa,HCC1106,and SiHa was detected by real-time quantitative polymerase chain reaction(qRT-PCR).The cell line with the highest circ-PHC3 expression was selected for transfection with a circ-PHC3 inhibitor.The interaction between circ-PHC3 and miR-1179 was validated using a dual luciferase reporter gene assay.The expression levels of miR-1179 in transfected cells were further assessed by qRT-PCR.Functional assays,including colony formation,flow cytometry,wound healing,and Transwell assays,were conducted to evaluate cell proliferation,cell cycle progression,migration,and invasion,respectively.Western blot analysis was performed to determine the expression of key proteins associated with proliferation,migration,and invasion in circ-PHC3-modulated cells.Finally,in vivo experiments were carried out to investigate the impact of circ-PHC3 silencing on the growth and metastasis of cervical cancer cells in animal models.Results The expression level of circ-PHC3 in cervical cancer tissues was significantly higher than that in adjacent normal tissues(P<0.01).Furthermore,circ-PHC3 expression was significantly associated with the clinical stage of cervical cancer(P<0.01).Patients with high circ-PHC3 expression exhibited a notably lower survival rate compared to those with low circ-PHC3 expression(P<0.01).In cervical cancer cell lines including HCC94,C33A,HeLa,HCC1106,and SiHa,circ-PHC3 expression was markedly upregulated(all P<0.01),with the highest expression observed in HCC1106 cells(P<0.01).Circ-PHC3 was found to directly interact with miR-1179(P<0.01),and silencing circ-PHC3 significantly increased miR-1179 expression(P<0.01).Transfection of HCC1106 cells with a circ-PHC3 inhibitor significantly suppressed cell proliferation,migration,and invasion(all P<0.01),and induced cell cycle arrest(P<0.01);these effects were partially reversed by co-transfection with a miR-1179 inhibitor(all P<0.05).In HCC1106 cells with circ-PHC3 knockdown,the expression levels of key proteins associated with proliferation,migration,and invasion—Cyclin E,CDK2,MMP-9,and N-cadherin—were significantly reduced(all P<0.01),and this reduction was partially attenuated by miR-1179 inhibition(all P<0.01).In vivo experiments further demonstrated that circ-PHC3 knockdown significantly inhibited tumor growth and metastasis of HCC1106 cells(all P<0.01).Conclusions Circ-PHC3 is highly expressed in cervical cancer tissues,and its overexpression is significantly correlated with poor prognosis in patients with cervical cancer.Knockdown of circ-PHC3 upregulates the expression of miR-1179 and suppresses the proliferation,migration,and invasion of cervical cancer cells.

Objective To investigate the expression of the circular RNA circ-PHC3 in cervical cancer tissues and its regulatory mechanisms in the proliferation,migration,and invasion of cervical cancer cells.Methods The expression levels of circ-PHC3 in cervical cancer tissues and adjacent non-tumor tissues were analyzed using the GEO database.The correlation between circ-PHC3 expression and the clinical stage as well as prognosis of cervical cancer patients was also evaluated.The expression of circ-PHC3 in cervical cancer cell lines HCC94,C33A,HeLa,HCC1106,and SiHa was detected by real-time quantitative polymerase chain reaction(qRT-PCR).The cell line with the highest circ-PHC3 expression was selected for transfection with a circ-PHC3 inhibitor.The interaction between circ-PHC3 and miR-1179 was validated using a dual luciferase reporter gene assay.The expression levels of miR-1179 in transfected cells were further assessed by qRT-PCR.Functional assays,including colony formation,flow cytometry,wound healing,and Transwell assays,were conducted to evaluate cell proliferation,cell cycle progression,migration,and invasion,respectively.Western blot analysis was performed to determine the expression of key proteins associated with proliferation,migration,and invasion in circ-PHC3-modulated cells.Finally,in vivo experiments were carried out to investigate the impact of circ-PHC3 silencing on the growth and metastasis of cervical cancer cells in animal models.Results The expression level of circ-PHC3 in cervical cancer tissues was significantly higher than that in adjacent normal tissues(P<0.01).Furthermore,circ-PHC3 expression was significantly associated with the clinical stage of cervical cancer(P<0.01).Patients with high circ-PHC3 expression exhibited a notably lower survival rate compared to those with low circ-PHC3 expression(P<0.01).In cervical cancer cell lines including HCC94,C33A,HeLa,HCC1106,and SiHa,circ-PHC3 expression was markedly upregulated(all P<0.01),with the highest expression observed in HCC1106 cells(P<0.01).Circ-PHC3 was found to directly interact with miR-1179(P<0.01),and silencing circ-PHC3 significantly increased miR-1179 expression(P<0.01).Transfection of HCC1106 cells with a circ-PHC3 inhibitor significantly suppressed cell proliferation,migration,and invasion(all P<0.01),and induced cell cycle arrest(P<0.01);these effects were partially reversed by co-transfection with a miR-1179 inhibitor(all P<0.05).In HCC1106 cells with circ-PHC3 knockdown,the expression levels of key proteins associated with proliferation,migration,and invasion—Cyclin E,CDK2,MMP-9,and N-cadherin—were significantly reduced(all P<0.01),and this reduction was partially attenuated by miR-1179 inhibition(all P<0.01).In vivo experiments further demonstrated that circ-PHC3 knockdown significantly inhibited tumor growth and metastasis of HCC1106 cells(all P<0.01).Conclusions Circ-PHC3 is highly expressed in cervical cancer tissues,and its overexpression is significantly correlated with poor prognosis in patients with cervical cancer.Knockdown of circ-PHC3 upregulates the expression of miR-1179 and suppresses the proliferation,migration,and invasion of cervical cancer cells.

Objective To investigate the clinical characteristics of heart failure with preserved ejection fraction(HFpEF)patients with normal N-terminal pro-brain natriuretic peptide(NT-proBNP).Methods A total of 116 HFpEF patients diagnosed at Binzhou Medical University Hospital form January 2022 to October 2024,along with 61 non-heart failure patients were selected as subjects.Using NT-proBNP diagnosis criteria,HFpEF patients were divided into normal NT-proBNP group(n=62)and elevated NT-proBNP group(n=54),with non-heart failure patients serving as control group(n=61).Clinical parameters including laboratory tests,echocardiography,and CT imaging were systematically compared among all three groups to characterize the clinical features of HFpEF patients with normal NT-proBNP.Results Compared with elevated NT-proBNP group,prevalence of complications,such as hypertension,diabetes,and atrial fibrillation and pulmonary hypertension incidence were lower in normal NT-proBNP group significantly.Left ventricular diastolic function indicators was superior in normal NT-proBNP group than that in elevated NT-proBNP group,with significant differences(P＜0.05).Imaging evaluations revealed that chest CT scans of the normal NT-proBNP group more frequently exhibited characteristic ground-glass opacities.Conclusion HFpEF patients with normal NT-proBNP display distinct clinical characteristics,including milder cardiac structural abnormalities,lower comorbidity burden,and unique imaging manifestations such as ground-glass opacities,which can provide reference for early clinical diagnosis.

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.