OBJECTIVE To evaluate the effect of clinical pharmacists participating in the treatment of chronic heart failure （CHF） based on the clinical pharmacy pathway （CPP）. METHODS Totally 226 CHF patients recruited from August 24th， 2024 to March 14th， 2025， were divided into an observation group and a control group based on the random number table method， with 113 cases in each group. All patients were treated with conventional therapy. The observation group was additionally given CPP management （including pharmaceutical care during hospitalization， the formulation of individualized discharge medication regimens， and pharmaceutical follow-up after discharge）. The cardiac function parameters at admission， at discharge， at 3 and 6 months after discharge， drug use at 6 months after discharge， economic indicators， as well as the readmission rate and mortality rate at 6 months after discharge were compared between the two groups. Morisky Medication Adherence Scale-8 Items （MMAS-8）， Somatic Self-rating Scale （SSS） and Patient Health Questionnaire-9 （PHQ-9） scores were compared at admission， at discharge and at 3 and 6 months after discharge. RESULTS Six months after discharge， 24 patients dropped out. Eventually， 104 patients in the observation group and 98 patients in the control group completed the study. Compared with at admission， New York Heart Association （NYHA） cardiac functional classification， left ventricular ejection fraction （LVEF） and N -terminal pro-B-type natriuretic peptide （NT-proBNP） of both groups of patients at discharge as well as at 3 and 6 months after discharge were significantly improved； moreover， the improvements at 3 and 6 months after discharge were significantly better than those at discharge. Meanwhile， the above indexes （except for NYHA cardiac functional classification at discharge， NT-proBNP and NYHA cardiac functional classification at 3 months after discharge） of the observation group at discharge， at 3 and 6 months after discharge were significantly better than the control group （ P ＜0.05）. The utilization rates of angiotensin converting enzyme inhibitor （ACEI）/angiotensin Ⅱ receptor blocker （ARB）/angiotensin receptor neprilysin inhibitor （ARNI）， the proportion of β-blockers reaching the target dose， the utilization rate of sodium-glucose linked transporter 2 inhibitor （SGLT2i）， and the proportion of SGLT2i reaching the target dose in the observation group were significantly higher than the control group （ P ＜0.05）， and the proportion of drugs and readmission rate were significantly lower than the control group （ P ＜0.05）. Compared with at admission， MMAS-8 scores of the patients in the observation group at discharge， at 3 and 6 months after discharge were significantly increased， while SSS and PHQ-9 scores were significantly lowered （ P ＜0.05）. And all the above scores gradually decreas ed with the extension of discharge time （ P ＜0.05）. CONCLUSIONS Clinical pharmacists can utilize CPP to significantly improve patients’ cardiac function， medication adherence， somatic symptoms and depression. Additionally， they can significantly improve the utilization rates of ACEI/ARB/ARNI and SGLT2i， as well as the proportion of target doses of β-blockers and SGLT2i， while simultaneously reducing readmission rates.

ObjectiveTo assess the cost-effectiveness of human immunodeficiency virus (HIV) prevention and control strategies across different high-risk populations, investment levels, and allocation proportions in an area, thereby providing a reference for optimizing resource allocation in acquired immunodeficiency syndrome (AIDS) prevention and control. MethodsDemographic, epidemiological, and clinical progression data of the target population in an area from 2018 to 2024 were collected, along with the input costs and intervention coverage of HIV-related projects. The Optima HIV model was utilized to perform fitting and prediction, whereby the allocation of resources to optimized target populations and program interventions was modeled under varying future investment scenarios to predict the impacts on the reduction of new HIV infections and HIV-related deaths. ResultsUnder the scenario of maintaining the current level of intervention input for HIV key populations, new HIV infections and related deaths in the region were predicted to be controlled at a low level by 2030. In terms of intervention input for HIV key populations, it is suggested that appropriately increasing the intervention input for key HIV populations will further reduce new HIV infections and HIV-related deaths in the region. However, when the total input increases to 1.75 times the baseline level, the marginal effect of input will be saturated. Regarding structural adjustments in investment and considering both the current total investment scenario and 1.75 times the total investment scenario, it is predicted that further reductions in regional HIV new infections and HIV-related deaths can be achieved, provided that the intervention input for key populations (including men who have sex with men, MSM) is increased, while concurrently intensifying the proportion of intervention measures such as condom promotion to form optimized intervention portfolios. ConclusionIn the field of HIV/ AIDS prevention and control, sustained commitment to intervention investment, with a strategic focus on interventions for key populations and intensified implementation of critical intervention measures, will effectively improve the epidemiological impacts of HIV/AIDS prevention and control efforts.

Background: The World Health Organization published the 2019 Integrated Care for Older People (ICOPE) framework to guide, assess, and promote the intrinsic capacity (IC) of older adults, referring to their physical and mental health. This study aims to investigate the relationship between IC and frailty among older adults. Methods: This cross-sectional study was conducted in a medical center in Taiwan in 2021. Two hundred ten patients over 65 admitted to the geriatric ward were invited to participate. The questionnaire included an IC measure, Fried Frailty Scale, and demographic items. The IC measure was ascertained using the six domains of ICOPE (cognition, mobility, nutrition, visual, hearing, and depressive symptoms). The Fried Frailty Scale was used to categorize participants as robust (Fried Frailty Scale=0), prefrail (Fried Frailty Scale=1-2), or frail (Fried Frailty Scale ≥3). Multinomial logistic regression was used to analyze the association between individual ICOPE domains and frailty stages, while adjusting for confounders. Results: Among the participants, 39.0% were prefrail, and 28.6% were frail. Limited mobility and depressive symptoms were significantly associated with prefrail (adjusted odds ratio [aOR]=4.44, 95% confidence interval [CI] 1.82–10.82; aOR=8.41, 95% CI 1.75–40.37) and frail (aOR=11.57, 95% CI 3.63–36.93; aOR=13.77, 95% CI 2.62–72.49) individuals, respectively. Malnutrition (aOR=4.01, 95% CI 1.18–13.62) and hearing loss (aOR=4.37, 95% CI 1.09–19.66) were significantly associated with frail older adults. Conclusion Limited mobility and depressive symptoms occurring at the prefrail stage could be used as assessment items for early detection of prefrail.

ObjectiveTo investigate the effect of Homebox A6 （HOXA6） on the proliferation， invasion， metastasis， and apoptosis of HepG2 hepatoma cells and its association with the PI3K/AKT signaling pathway. MethodsHepG2 hepatoma cells were cultured， and HOXA6 overexpression plasmid and siRNA were constructed and transfected into cells. The cells were randomly divided into empty plasmid group， HOXA 6 overexpression group， siRNA negative control group， and siRNA HOXA6 interference group. CCK8 assay was used to measure cell proliferation， Transwell assay was used to observe cell invasion， and wound healing assay was used to observe cell migration （related proteins TIMP3， MMP9， and MMP3）. Flow cytometry was used to measure cell apoptosis （related proteins BAX and BCL2）， the BCA method was used to measure protein concentration， and Western Blot was used to measure the expression of related proteins. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the SNK-q test was used for further comparison between two groups. ResultsCompared with the empty plasmid group， HOXA6 overexpression significantly promoted the proliferation， invasion， and migration of HepG2 hepatoma cells （all P<0.001）， and there was a significant reduction in the protein expression of TIMP3 （P<0.001）， while there were significant increases in the expression levels of MMP9 and MMP3 （both P<0.001）. Compared with the siRNA negative control group， HOXA6 interference significantly inhibited the proliferation， invasion， and migration of HepG2 hepatoma cells （all P<0.001）， and there was a significant increase in the protein expression of TIMP3 （P<0.001）， while there were significant reductions in the expression levels of MMP9 and MMP3 （both P<0.001）. Flow cytometry showed that compared with the empty plasmid group， HOXA6 overexpression inhibited the apoptosis of HepG2 hepatoma cells （P<0.001）， with a significant reduction in the expression of the apoptosis-related protein BAX and a significant increase in the expression of BCL2 （both P<0.001）. Compared with siRNA negative control group， HOXA6 interference promoted the apoptosis of HepG2 hepatoma cells （P<0.001）， with a significant increase in the expression of BAX and a significant reduction in the expression of BCL2 （both P<0.001）. Compared with the empty plasmid group， the HOXA6 overexpression group had significantly higher ratios of p-AKT/AKT and p-PI3K/PI3K （both P<0.001）， and compared with the siRNA negative control group， the siRNA HOXA6 interference group had significantly lower ratios of p-AKT/AKT and p-PI3K/PI3K （both P<0.001）. ConclusionHOXA6 can promote the proliferation， invasion， and metastasis of HepG2 hepatoma cells and inhibit their apoptosis by activating the PI3K/AKT signaling pathway through phosphorylation.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective To investigate the clinical and radiological features of osteofibrous dysplasia(OFD),and to improve the diagnostic accuracy of OFD and reduce the misdiagnosis rate.Methods A retrospective analysis was conducted on the clinical and radiological data of 21 patients with OFD confirmed by surgical pathology.Results Among the 21 patients,the imaging manifestations showed that the lesions were mainly located within the anterior cortex of the tibia,presenting as cystic expansile bone destruction with corti-cal expansion and thinning,protruding into the medullary cavity.Within the lesions,unequal thickness linear bony septations were visible,resembling soap-bubble changes,along with patchy ground-glass density shadow.A sclerotic rim was observed surrounding the lesions,with a clear boundary from normal tissue.No obvious soft tissue mass or periosteal reaction was noted in all cases.On MRI,the majority of the lesions exhibited prolonged T,and T2 signals,with linear hypointense separations observed within the lesions.No significant edema was noted in the surrounding bone and soft tissues,although a minor degree of bone marrow edema was present.All patients underwent surgical treatment.Fifteen patients were followed up for review,ranging from 3 to 38 months postoperatively.Among them,9 patients had no recurrence,while 6 patients experienced recurrence,manifesting as new lytic bone destruction in the surgical area.Conclusion The radiological manifestations of OFD are characteristic,and an accurate diagnosis can be made for typical cases in combination with the patient's age,location of the lesion and clinical presentation.Due to the high recurrence rate after OFD sur-gery,regular follow-up is recommended.

Modern education emphasizes the improvement of the comprehensive quality of the educated,and medical ed-ucation has particularly high requirements for students'practical abilities.Therefore,it is necessary to continuously analyze and optimize the education work when conditions permit.This paper first introduces the"Trinity Teaching"model and the college student medical technology skills competition.Based on this,it analyzes the advantages and disadvantages of the current training for college student medical technology skills competition,the value of applying the"Trinity Teaching"model,and explores its specific application methods,including the use of group work mode and building a large-scale teaching service system to serve medical teaching work.