Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

[Objective] To explore the strategy of blood management in patients with massive transfusion in the frigid plateau region. [Methods] The treatment process of a patient with liver rupture in the frigid plateau region was analyzed, and the blood management strategy of the frigid plateau region was discussed in combination with the difficulties of blood transfusion and literature review. [Results] The preoperative complete blood count (CBC) test results of the patient were as follows: RBC 3.14×10¹²/L, Hb 10⁶ g/L, HCT 30.40%, PLT 115.00×10⁹/L; coagulation function: PT 18.9 s, FiB 1.31 g/L, DD > 6 μg/mL, FDP 25.86 μg/mL; ultrasound examination and imaging manifestations suggested liver contusion and laceration / intraparenchymal hematoma, splenic contusion and laceration, and massive blood accumulation in the abdominal cavity; it was estimated that the patient's blood loss was ≥ 2 000 mL, and massive blood transfusion was required during the operation; red blood cell components were timely transfused during the operation, and the blood component transfusion was guided according to the patient's CBC and coagulation function test results, providing strong support and guarantee for the successful treatment of the patient. The patient recovered well after the operation, and the CBC test results were as follows: RBC 4.32×10¹²/L, Hb 144 g/L, HCT 39.50%, PLT 329.00×10⁹/L; coagulation function: APTT 29.3 s, PT 12.1 s, FiB 2.728 g/L, DD>6 μg/mL, FDP 25.86 μg/mL. The patient was discharged after 20 days, and regular follow-up reexamination showed no abnormal results. [Conclusion] Individualized blood management strategy should comprehensively consider the patient’s clinical symptoms, the degree of hemoglobin decline, dynamic coagulation test results and existing treatment conditions. Efficient and reasonable patient blood management strategies can effectively improve the clinical outcomes of massive transfusion patients in the frigid plateau region.

OBJECTIVE To explore the effect and potential mechanism of the compatibility of Astragali Radix-Puerariae Lobatae Radix on ferroptosis of liver cells in type 2 diabetes mellitus （T2DM） insulin resistance （IR） rats. METHODS Sixty male SD rats were randomly divided into control group （12 rats） and modeling group （48 rats）. The modeling group was fed with a high- fat diet for 4 consecutive weeks and then given a one-time tail vein injection of 1% streptozotocin to establish T2DM IR model. The model rats were randomly divided into model group， the compatibility of Astragali Radix-Puerariae Lobatae Radix group [QG group， 4.05 g/（kg·d）， intragastric administration]， ferroptosis inhibitor ferrostatin-1 group [Fer-1 group， 5 mg/kg by intraperitoneal injection， once every other day]， the compatibility of Astragali Radix-Puerariae Lobatae Radix+ferroptosis inducer erastin group [QG+erastin group， 4.05 g/（kg·d） by intragastric administration+erastin 10 mg/（kg·d）， intraperitoneal injection]. After 4 weeks of intervention， serum fasting blood glucose （FBG） and fasting insulin （FINS） were measured in each group of rats， and homeostasis model assessment of insulin resistance （HOMA-IR） and the natural logarithm of insulin action index（IAI） were calculated； the serum levels of total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate transaminase （AST） and alanine transaminase （ALT）， Fe2+ and Fe content， glutathione （GSH）， malondialdehyde （MDA） and superoxide dismutase （SOD） levels， NADP+/NADPH ratio and reactive oxygen species （ROS） were determined. The pathological morphology of its liver tissue was observed； the protein expressions of glutathione peroxidase 4 （GPX4）， ferritin heavy chain 1 （FTH1）， long-chain acyl-CoA synthetase 3 （ACSL3）， ACSL4， ferritin mitochondrial （FTMT）， and cystine/glutamate anti-porter （xCT） in the liver tissue of rats were detected. RESULTS Compared with control group， the liver cells in the model group of rats showed disordered arrangement， swelling， deepened nuclear staining， and more infiltration of inflammatory cells， as well as a large number of hepatocyte vacuoles and steatosis； FBG （after medication）， the levels of TC， TG， LDL-C， AST， ALT， FINS， MDA and ROS， HOMA-IR， Fe2+ and Fe content， NADP+/NADPH ratio and protein expression of ACSL4 were significantly increased or up-regulated， while the levels of HDL-C， GSH and SOD， IAI， protein expressions of GPX4， FTH1， ACSL3， FTMT and xCT were significantly reduced or down-regulated （P＜0.01）. Compared with the model group， both QG group and Fer-1 group showed varying degrees of improvement in pathological damage of liver tissue and the levels of the above indicators， the differences in the changes of most indicators were statistically significant （P＜0.01 or P＜0.05）. Compared with QG group， the improvement of the above indexes of QG+erastin group had been reversed significantly （P＜0.01）. CONCLUSIONS The compatibility decoction of Astragali Radix-Puerariae Lobatae Radix can reduce the level of FBG in T2DM IR rats， and alleviate IR degree， ion overload and pathological damage of liver tissue. The above effects are related to the inhibition of ferroptosis.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background Asthma poses a serious threat to children's growth, development, and mental health, thus there has been an increasing focus on the control of asthma morbidity in children and the assessment of its risk factors. A growing body of research has found that exposure to ambient air pollutants an significatly increase the risk of childhood asthma. Objective To understand the changes of ambient air pollutant concentrations in Nanjing and asthma outpatient visits to Nanjing Children's Hospital, and to quantitatively analyze the effects of exposure to different ambient air pollutants on children's asthma outpatient visits. Methods Daily data of ambient air pollutants fine particulate matter (PM_2.5), inhalable particle (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), meteorological factors (air temperature & relative humidity), and outpatient visits due to asthma in the hospital from January 1, 2019 to December 31, 2023 were collected, and a generalized additive model based on quasi poisson distributions was used to quantitatively analyze the short-term effects of ambient air pollutant exposure on outpatient visits due to asthma in the hospital. Results The annual average concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ in Nanjing from 2019 to 2023 did not exceed the national limits. For single-day lagged effects, the single-pollutant model showed that the effects of PM_2.5, PM₁₀, NO₂, and CO on children's asthma outpatient visits were greatest for every 10 units increase at lag0, with excess risk (ER) of 1.39% (95%CI: 0.65%, 2.14%), 1.46% (95%CI: 0.97%, 1.95%), 5.46% (95%CI: 4.36%, 6.57%), and 0.18% (95%CI: 0.11%, 0.26%), respectively, and SO₂ reached the maximum effect at lag1, with an ER of 23.15% (95%CI: 13.57%, 33.53%) for each 10 units increase in concentration. Different pollutants reached their maximum cumulative lag effects at different time. The PM₁₀, PM_2.5, SO₂, NO₂, and CO showed the largest cumulative lag effects at lag01, lag01, lag02, lag02, and lag03, respectively, with ERs of 1.35% (95%CI: 0.77%, 1.92%), 0.96% (95%CI: 0.10%, 1.83%), 28.50% (95%CI: 15.49%, 42.98%), 6.92% (95%CI: 5.53%, 8.33%), and 0.31% (95%CI: 0.20%, 0.42%), respectively. The influences of PM_2.5 and PM₁₀ on outpatient visits due to asthma in the hospital became more pronounced with advancing age, while the associations with NO₂, SO₂, and CO were weakened as children grew older. Conclusion Ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) can increase childhood asthma visits, and different pollutants have varied effects on the number of asthmatic children's visits at different ages.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE To evaluate the efficacy， safety and economy of inclisiran in the treatment of atherosclerotic cardiovascular disease with hypercholesterolemia. METHODS A rapid health technology assessment （HTA） approach was employed. HTA reports， systematic reviews（SR）/meta-analyses， and pharmacoeconomic studies related to inclisiran were systematically identified through comprehensive searches of Chinese and English databases， including PubMed， Embase， the Cochrane Library， CNKI and Wanfang database， supplemented by HTA institutional repositories. The search timeframe spanned from database inception to April 2025. The results of the studies were descriptively analysed and summarized through literature screening， data extraction and literature quality assessment. RESULTS The final analysis included 22 studies， comprising one HTA report， 15 SR/meta-analyses， and 6 pharmacoeconomic evaluations. Regarding therapeutic efficacy， compared with control group， inclisiran could significantly reduce the levels of low-density lipoprotein cholesterol， proprotein convertase subtilisin/kexin type 9， total cholesterol， triacylglycerol， apolipoprotein B， and lipoprotein（a）， increase the level of high-density lipoprotein cholesterol， and reduce the risk of adverse cardiovascular events. In terms of safety， the inclisiran group showed no significant difference compared with the control group in the risk of total adverse events， serious adverse events， or non-serious adverse events； however， an increased incidence of injection site reactions was observed， most of which were mild. In terms of cost-effectiveness， there were discrepancies in research conclusions both domestically and internationally. More studies indicated that inclisiran did not demonstrate cost-effectiveness advantage and would require an appropriate price reduction to meet cost-effectiveness criteria. CONCLUSIONS Inclisiran demonstrates favorable efficacy and acceptable safety in treating atherosclerotic cardiovascular disease with hypercholesterolemia， though its economic profile requires improvement.