Health economics evidence plays an important role in linking clinical value evidence with health resource allocation decisions in the development of clinical practice guidelines. It can not only effectively balance clinical effectiveness and economic feasibility but also avoid forming "idealized" recommendations that are detached from the affordability of the healthcare system or the burden-bearing capacity of patients. To promote guideline developers to use health economics evidence more standardizedly and fully, this paper conducts an in-depth analysis of the current application status, existing challenges, access channels, and application processes of health economics evidence in current guidelines, and on this basis, puts forward considerations and suggestions for strengthening and standardizing the application of health economics evidence in China's clinical practice guidelines.

Objective: This study compared the effects of personalized exercise versus acupuncture on multidimensional cognitive function in individuals at high risk for stroke with mild cognitive impairment (MCI). Methods: A randomized controlled trial enrolled 200 stroke-risk adults aged 50−80 years. Ninety participants diagnosed with MCI (MMSE, HIS, CDR criteria) were randomly assigned to exercise (n = 30), acupuncture (n = 30), or control (n = 30) groups. Interventions lasted 6 months. Cognitive outcomes (MMSE, Raven’s Progressive Matrices, Digit Symbol Substitution Test, Animal Fluency, Rey-Osterrieth test, Stroop test) were assessed before and after intervention. Multivariate regression identified risk factors for MCI. ANOVA was used for group comparisons. Results: Hypertension (aOR = 1.5) and diabetes (aOR = 1.3) were significant risk factors for MCI. After intervention, the exercise group showed the largest MMSE improvement (Δ = 5.0), compared with acupuncture (Δ = 3.0) and control (Δ = 1.0) (p < 0.001). Exercise produced greater gains in non-verbal reasoning (31.8% vs. 26.1% in acupuncture, p < 0.01). Acupuncture more effectively enhanced processing speed and attention (DSST: Δ = 26 vs. Δ = 20 in exercise, p = 0.03) and executive function (Stroop interference time: Δ = 16 vs. Δ = 15 seconds in exercise, p = 0.04). Both interventions significantly improved verbal fluency (p < 0.001), with larger benefits in those with baseline MMSE ≤ 20. Conclusion Hypertension and diabetes are key risk factors for MCI in stroke-prone individuals. Exercise yields greater improvement in global cognition and memory, whereas acupuncture is more effective for enhancing attention and executive function. Both modalities benefit verbal fluency, particularly in lower-functioning participants.

Based on ancient and modern literature， this paper systematically elucidates the concept， physiological characteristics， and reproductive significance of tiangui （天癸）， and further reviews its theoretical connotation throughout the entire process of male reproductive function， covering its onset， maturation， maintenance， disorder， and decline. In terms of its physiological foundation， tiangui is rooted in kidney essence and regulates male reproductive function through the coordination of zang-fu （脏腑） organs. It possesses characteristics of being time-limited， rhythmic， relatively independent， targeted， and functionally diverse. The occurrence of male reproductive diseases is closely related to the abnormality of tiangui， which primarily manifests as tiangui deficiency and dysregulation of tiangui timing. In clinical practice， treatment should follow the principle of "supplementing its deficiency and regulating its timing"， so as to restore the operating rhythm of tiangui.

Aim To clarify the mechanism by which Qishen Yixin Granules improved microcirculation vas-cular endothelial dysfunction(VED)in mice,through activating the Nrf2/HO-1 signaling pathway to regulate oxidative stress.Methods C57 mice were randomly divided into six groups:blank group,model group,pos-itive drug group,and low-,medium-,and high-dose groups of Qishen Yixin Granules.The VED model was established by long-term infusion of Ang Ⅱ combined with a high-fat diet.Each treatment group received the corresponding drug intervention.After four weeks of drug intervention,cardiac function was assessed by echocardiography.Carstairs staining was used to ob-serve the formation of microthrombi in myocardial tis-sue.The micro vascular ischemia was evaluated by Hei-denhain staining.The ultrastructure of endothelial cells was observed by electron microscopy.The levels of EMPs,ROS,NO,ET-1,TF,TM,VWF,and TXA2 in serum were measured by ELISA.The expression levels of MDA,SOD,and GSH-Px in mouse heart tissue were determined by chemical methods.Cardiac microvascu-lar density and the expression of Nrf2,Keap1,and HO-1 proteins were detected by Immunohistochemical stai-ning.The protein expressions of Keap1,cytoplasmic Nrf2,nuclear Nrf2,and HO-1 in myocardial tissue were detected by Western blot.Results Qishen Yixin Granules could effectively improve the cardiac function of mice,alleviate the damage of endothelial cells and endothelial function.They could up-regulate serum NO levels and the activities of antioxidant enzymes SOD and GSH-Px,while down-regulating the expression of ROS and vascular inflammatory injury factors such as ET-1,VWF,TXA2,TF,TM,and EMPs.Qishen Yixin Granules also increased the positive counts of CD34,Nrf2,and HO-1,as well as microvessel density.Fur-thermore,they inhibited the expression of MDA,Keap1,and cytoplasmic Nrf2 protein in myocardial tis-sue,while increasing the expression of nuclear proteins HO-1 and Nrf2.Conclusions Qishen Yixin Granules may inhibit oxidative stress and inflammatory response by regulating the Nrf2/HO-1 signaling pathway,thereby improving vascular endothelial damage and cardiac function in VED mice.

Objective To investigate the ameliorative effects and mechanisms of six types of tea(green tea,cyan tea,red tea,white tea,black tea and yellow tea)on metabolic disorders in obesity mice induced by high-fat diet(HFD).Methods Four-week-old male C57BL/6J mice were randomly divided into 8 groups with 7 mice per group.An HFD-induced obese mouse model was established,and the mice in control group maintained on standard diet followed by intragastric administration of different teas for 5 weeks.The body weight,liver weight ratio,fasting blood glucose,and lipid profile of the mice were measured to assess glucose and lipid metabolism.Serum inflammatory factors including IL-6,tumor necrosis factor-alpha(TNF-α)and oxidative stress markers[malondialdehyde(MDA)and superoxide dismutase(SOD)were measured.Additionally,liver histopathology and the expression of key glycolipid metabolism-related genes,adenosine monophosphate-activated protein kinase(AMPK)and carnitine palmitoyltransferase 1(CPT-1),were analyzed to explore underlying mechanisms.Results Cyan tea significantly suppressed weight gain,demonstrating superior weight control.White tea markedly reduced fasting blood glucose levels and decreased the area under the curve of oral glucose tolerance test(OGTT)and insulin tolerance test(ITT),indicating synergistic improvements in glucose metabolism and insulin sensitivity.Yellow tea exhibited exceptional anti-inflammatory and antioxidant effects,reducing hepatic IL-6 and MDA while enhancing SOD activity.Green tea activated the lipid oxidation pathway by upregulating AMPK/CPT-1 expression.All kinds of tea significantly attenuated hepatic lipid droplet accumulation.Conclusion All six types of tea alleviated metabolic disorders by reducing hepatic fat content in obesity mice.However,different types of tea exert their unique effects on improving metabolic disorders through differential mechanisms such as glucose metabolism regulation,lipid oxidation,and anti-inflammatory and antioxidant actions.

OBJECTIVE To explore the diagnostic value of combined detection of microRNA(miRNA)and alpha-fetoprotein(AFP),protein induced by vitamin K absence or antagonist-Ⅱ(PIVKA-Ⅱ)in ascites and serum ex-tracellular vesicles(EVs)for hepatitis B virus(HBV)-related primary hepatocellular carcinoma(HCC).METHODS From Nov.2023 to Nov.2024,41 patients with liver cancer and 26 patients with liver cirrhosis who underwent ascites placement or ascites concentration and reinfusion procedures at the Fifth Medical Center of Chi-nese PLA General Hospital were selected as study subjects.Ascites and serum samples were collected.Real-time quantitative reverse transcription polymerase chain reaction(qRT-PCR)was used to detect the expression levels of miR-21,miR-125a,miR-150 and miR-200a in EVs.Chemiluminescence was used to measure the levels of AFP and PIVKA-Ⅱ in ascites,serum and EVs from ascites and serum.An artificial neural network was utilized to con-struct a combined diagnostic model of serum and ascites markers.RESULTS The area under the curve(AUC)for distinguishing HCC from liver cirrhosis using a combination of serum and other indicators was 0.933.The AUC for distinguishing HCC from liver cirrhosis using a combination of ascites and other indicators was 0.912.By screening all detected indicators using an artificial neural network and incorporating indicators with a relative im-portance＞0.5 into the diagnostic model,the model included four indicators:ascites AFP,ascites EVs miR-21,ascites EVs miR-200a and serum EVs miR-200a.This model had a sensitivity of 80.77％,a specificity of 87.80％and an AUC of 0.960 for distinguishing HCC from liver cirrhosis patients.CONCLUSION The combined diagnos-tic markers of miRNA,AFP and PIVKA-Ⅱ in ascites and serum-derived EVs have good application value in the diagnosis of HCC.

Objective:To establish a highly sensitive indirect competitive ELISA(icELISA)method for detecting ribavirin in chicken.Methods:Based on the obtained monoclonal antibodies against ribavirin,a chessboard test was employed to determine the optimal working concentration of artificial antigen and antibody,and then established an icELISA method.Furthermore,performance of the detection method was evaluated.Results:The established icELISA method has a linear range of 0.44～32.71 ng/ml,IC50 of which was 3.78 ng/ml,and the limit of detection(LOD)was 0.20 ng/ml.Except for specific reaction with ribavirin,there were no cross reactions with other antiviral drugs.Recovery rate of sample spiking was between 91.60%and 100.76%,and coefficient of variation was between 7.29%and 10.63%.Conclusion:A highly sensitive and specific icELISA method for detection of ribavirin has been estab-lished,which can be used to determine the residue of ribavirin in chicken.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.