With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Objective To understand the respiratory protection competency of staff in hospitals.Methods Staff from six hospitals of different levels and characteristics in Beijing were selected,including doctors,nurses,medical technicians,and servicers,to conduct knowledge assessment on respiratory protection competency.According to exposure risks of respiratory infectious diseases,based on actual cases and daily work scenarios,content of respira-tory protection competency assessment was designed from three aspects:identification of respiratory infectious di-seases,transmission routes and corresponding protection requirements,as well as correct selection and use of masks.The assessment included 6,6,and 8 knowledge points respectively,with 20 knowledge points in total,all of which were choice questions.For multiple-choice questions,full marks,partial marks,and no mark were given respective-ly if all options were correct,partial options were correct and without incorrect options,and partial options were correct but with incorrect options.Difficulty and discrimination analyses on question of each knowledge point was conducted based on classical test theory.Results The respiratory protection competency knowledge assessment for 326 staff members at different risk levels in 6 hospitals showed that concerning the 20 knowledge points,more than 60％participants got full marks for 6 points,while the proportion of full marks for other questions was relatively low.Less than 10％participants got full marks for the following 5 knowledge points:types of airborne diseases,types of droplet-borne diseases,conventional measures for the prevention and control of healthcare-associated infec-tion with respiratory infectious diseases,indications for wearing respirators,and indications for wearing medical protective masks.Among the 20 knowledge questions,5,1,and 14 questions were relatively easy,medium,and difficult,respectively;6,1,4,and 9 questions were with discrimination levels of ≥0.4,0.30-0.39,0.20-0.29,and ≤0.19,respectively.Conclusion There is still much room for hospital staff to improve their respiratory protection competency,especially in the recognition of diseases with different transmission routes and the indications for wearing different types of masks.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Eighteen compounds were isolated from the methanol extract of the fruits of Litsea cubeba by silica gel, ODS, Sephadex LH-20 column chromatography, semi-preparative RP-HPLC, chiral HPLC and recrystallization. Their structures were elucidated by spectroscopic analyses and by comparison with reported spectroscopic data and physicochemical properties, and the absolute configurations of the enantiomers were established by experimental and calculated electronic circular dichroism spectra. These compounds were identified as (+)-(R)-4-hydroxypiperitenone (1a), (-)-(S)-4-hydroxypiperitenone (1b), (3S,4S,6R)-3,6-dihydroxy-1-menthene (2), (4S,5R)-4-hydroxy-5-isopropyl-2-methylcyclohex-2-en-1-one (3), (R)-6-hydroxy-3-(2-hydroxypropan-2-yl)-6-methylcyclohex-2-enone (4), (4S,6R)-4-hydroxy-6-isopropyl-3-methylcyclohex-2-enone (5), (1R,3S,4R)-3-hydroxy isopulegol (6), subamone (7), (6S)-3,7-dimethyl-7-hydroxy-2(Z)-octen-6-olide (8), (6S)-6,7-dihydroxy-3,7-dimethyloct-2(Z)-enoate (9), holostylactone (10), sesamin (11), dimethylmatairesinol (12), p-hydroxybenzoylcarbinol (13), syringaldehyde (14), p-hydroxybenzaldehyde (15), 4-hydroxy-3-methoxybenzaldehyde (16), 5,4ʹ-dihydroxy-7-methoxyflavone (17). Among them, compounds 1a and 1b were a pair of new monoterpenoid enantiomers, 8 and 9 were new natural products, 2-7, 10, 11 and 17 were isolated from Litsea genus for the first time. The in vitro anti-inflammatory effects of compounds 1-17 were evaluated using lipopolysaccharide-stimulated RAW264.7 cells, the results showed that compound 14 exhibited significant anti-inflammatory activity with NO inhibitory rate of 66.27% at a concentration of 40 μmol·L^-1.

Objective To observe the clinical efficacy of Sanjiao Tiaoqi Acupuncture in the treatment of post-stroke respiratory dysfunction.Methods Seventy-two patients with post-stroke respiratory dysfunction were randomly divided into observation group and control group,36 cases in each group.The control group was given routine treatment,and the observation group was treated with Sanjiao Tiaoqi Acupuncture on the basis of the control group,both groups were treated for 14 consecutive days.After 2 weeks of treatment,the clinical efficacy of the two groups was evaluated,and the changes of white blood cell count(WBC),C-reactive protein(CRP)and clinical pulmonary infection score(CPIS)were observed before and after treatment.The changes of diaphragmatic activity were compared before and after treatment between the two groups.Results(1)After treatment,the WBC and CRP levels of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the WBC and CRP levels,with statistically significant differences(P＜0.05).(2)After treatment,the CPIS scores of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving CPIS scores,with a statistically significant difference(P＜0.05).(3)After treatment,the diaphragm mobility of patients in the two groups was significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving diaphragm mobility,and the difference was statistically significant(P＜0.05).(4)The total effective rate was 91.67％(33/36)in the observation group and 75.00％(27/36)in the control group.The efficacy of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).Conclusion Sanjiao Tiaoqi Acupuncture for post-stroke respiratory dysfunction can significantly promote the absorption of inflammatory factors in patients and improve diaphragm mobility,with remarkable clinical efficacy.

Objective To explore the influencing factors of stroke-associated pneumonia(SAP)in patients with acute ischemic stroke(AIS)complicated with type 2 diabetes mellitus(T2DM)and to establish a related predictive model.Methods A total of 125 patients with AIS complicated with T2DM from September 2021 to September 2023 were divided into SAP group(n=28)and non-SAP group(n=97)according to the diagnostic criteria of SAP.The clinical data of two groups of patients were collected,the etiology of SAP group was analyzed,and the influencing factors of SAP were discussed.The related predictive model was established,and the ROC curve was used to test the application value of the model.Results The main pathogens in SAP group were gram-negative bacilli(73.9％).There were significant differences in disturbance of consciousness,indwelling gastric tube,acid inhibitor,glycosylated hemoglobin,fasting blood glucose,albumin,D-dimer,neutrophil/lymphocyte ratio(NLR),platelet/lymphocyte ratio between SAP group and non-SAP group(all P＜0.05).Logistic analysis showed that disturbance of consciousness,indwelling gastric tube,use of acid suppressants,glycosylated hemoglobin and NLR were independent risk factors for SAP(all P＜0.05).ROC analysis showed that the predictive model predicted the area under the SAP curve was 0.948(95％CI:0.911-0.986).The best cutoff value was 0.217,the sensitivity was 0.893 and the specificity was 0.866.Conclusions Gram-negative bacilli are the main pathogens of SAP in AIS patients with T2DM.Disturbance of consciousness,indwelling gastric tube,use of acid suppressants,glycosylated hemoglobin and NLR are independent risk factors for SAP.The model based on these factors has high predictive value.

Aim To explore the mechanism and mate-rial basis of Shenkang injection(SKI)in the treatment of renal fibrosis(RF)by bioinformatics and in vitro experiments.Methods The differentially expressed genes of RF were screened by GEO database.With the help of CMAP database,based on the similarity princi-ple of gene expression profile,the drugs that regulated RF were repositioned,and then the components of SKI potential treatment RF were screened by molecular fin-gerprint similarity analysis.At the same time,the core targets and pathways of SKI regulating RF were predic-ted based on network pharmacology.Finally,it was verified by molecular docking and cell experiments.Results Based on the GEO database,two RF-related data sets were screened,and CMAP was relocated to three common RF therapeutic drugs(saracatinib,da-satinib,pp-2).Molecular fingerprint similarity analysis showed that RF therapeutic drugs had high structural similarity with five SKI components such as salvianolic acid B and hydroxysafflor yellow A.Molecular docking results showed that salvianolic acid B,hydroxysafflor yellow A and other components had good binding abili-ty with MMP1 and MMP13,which were the core targets of SKI-regulated potential treatment of RF.Network pharmacology analysis suggested that the core targets of SKI were mainly enriched in signaling pathways such as Relaxin and AGE-RAGE.Cell experiments showed that SKI could significantly reduce the mRNA expres-sion levels of AGER,NFKB1,COL1A1,SERPINE1,VEGFC in AGE-RAGE signaling pathway and MMP1 and MMP13 in Relaxin signaling pathway in RF model cells,and significantly increase the mRNA expression level of RXFP1.Conclusions SKI can play a role in the treatment of RF by regulating Relaxin and AGE-RAGE signaling pathways,and its material basis may be salvianolic acid B,hydroxysafflor yellow A and other components.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To investigate the influence of different delivery techniques,treatment sites and dose-volume algorithms on the results of three-dimensional dosimetric verification for intensity-modulated radiation therapy(IMRT)plans and the importance of individualized quality assurance(QA)evaluation standard for radiotherapy plans.Methods Totally 350 tumor patients receiving radiotherapy at some hospital from January 2017 to February 2022 had their three-dimensional dosimetric verification results of IMRT plans selected retrospectively and underwent data collection with COMPASS system,and then were grouped in terms of delivery technique(fixed-beam IMRT and volumetric modulated arc therapy),treatment site(neck,chest and abdomen)and dose-volume algorithm(anisotropic analytical algorithm and collapsed cone convolution algorithm).All the groups were compared based on the 3％/2 mm criterion with regard to the Gamma pass rate of 10％prescription dose area(GP10％),Gamma pass rate(GP50％)and mean Gamma index(μGI5o％)of 50％prescription dose area,dose of 95％target volume(D95％)and its mean dose(Dmean),parotid gland mean dose(Dmean),dose of 1％spinal cord volume(D1％),dose of 1％brain stem volume(D1％)of head and neck radiotherapy plan,heart and lung mean dose(Dmean)and dose of 1％spinal cord volume(D1％)of chest radiotherapy plan and bladder,rectum and femur mean dose of abdomen radiotherapy plan(Dmean).SPSS 26.0 software was used for statistical analysis.Results For different delivery techniques,significant differences were found in all the QA results except GP50％of abdomen radiotherapy plan(P＜0.05).For different treatment sites,the differences were statistically significant between the QA results of head and neck radiotherapy plan and abdomen plan and between those of chest radio-therapy plan and abdomen radiotherapy plan(P＜0.05),while were not significant between the QA results of head and neck radiotherapy plan and chest radiotherapy plan(P＞0.05).For different dose-volume algorithms,the QA results had significant differences except D5％of abdomen radiotherapy planning target volume and Dmean and D1％of chest radiotherapy PTV(P＜0.05).Conclusion Dosimetric verification results vary depending on the delivery technique,treatment site and dose-volume algorithm.Statistical process control recommended by AAPM TG-218 report may be involved in to establish individualized QA standard for radiotherapy plans in case universal action limits are not appropriate.[Chinese Medical Equipment Journal,2024,45(11):54-59]

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.