Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Combined seawater Hf and Nd isotope compositions have been applied as reliable tracers of water mass mixing and weathering input changes.The establishment of standardized synchronous extraction methods for Fe-Mn oxyhydroxide in sediments,which serve as effective carriers for isotopic signals of ancient seawater,is a prerequisite and key for paleoceanography research.The research material of this study was the surface sediments of the Western Arctic Ocean obtained from China's 7th Arctic Scientific Expedition,and a reliable simultaneous Nd-Hf isotope extraction method in Fe-Mn oxyhydroxide fraction was established through systematic optimization of pretreatment procedures and key extraction parameters.This research focused on analyzing the effects of pretreatment(Milli-Q water pre-wash)and key extraction parameters(leaching time,solution/solid ratios,concentration of EDTA-2Na,and concentration of mixed solution of hydroxyamine hydrochloride(HH)and acetic acid(HAc))on element extraction efficiency and isotopic composition.The experimental results indicated that the Milli-Q water pre-wash was not necessary,Fe-Mn oxyhydroxide extraction could be directly performed.Nd extraction amount in Fe-Mn oxyhydroxide fraction was not sensitive to changes in extraction conditions,while the Hf extraction amount was significantly affected by leaching time,solution/solid ratios and concentration of EDTA-2Na.The optimal conditions for synchronously extracting Nd-Hf isotopes were as follows:adding 20 mL of a mixed solution of 0.005 mol/L HH+1.5%HAc+0.03 mol/L EDTA-2Na per gram of sample,and reaction at room temperature for 3 h under shaking.Through systematic verification of 87Sr/86Sr ratio,εNd value,εHf value,Al/Nd ratio and Al/Hf ratio,it was confirmed that this method could effectively avoid interferences from residual components and accurately obtain Nd-Hf isotope signals of ancient seawater preserved in sediments.The standardized method established in this work provided reliable technical support for reconstructing water mass mixing between the Arctic,North Atlantic,and North Pacific Oceans and riverine input histories.It also provided important reference for the extraction methods of Nd and Hf isotopes from marine sources in sediments from other sea areas around the world.

Objective To establish a rapid screening and analysis method for etomidate and its ana-logues using a portable mass spectrometer equipped with a thermal desorption-atmospheric pressure chemical ionization source-linear ion trap.Methods A 10 μL aliquot of a standard solution at a con-centration of 1 μg/mL was taken,and after the solvent evaporated,the sample was inserted into the in-let of the portable mass spectrometer for detection.By adjusting the collision-induced dissociation pa-rameters,the molecular ion peak and fragment ion peak information of the standard were obtained and used to establish a reference database.In addition,the method was applied to 29 seized liquid and plant samples.Results A screening system for etomidate and its analogues was established based on the portable mass spectrometer and the corresponding mass spectrometry library.The system enables qualitative screening analysis by identifying primary protonated molecular ions and secondary product ions of etomidate and its analogues.The limits of detection for etomidate and its 12 analogues ranged from 0.1 to 10 μg/mL.Etomidate and its analogues were detected in all 29 liquid and plant samples.However,this method could not distinguish between isomeric imidazole esters,such as isopropoxate and propoxate.Additionally,when testing 2-SH-etomidate,there was a false positive for the detection of etomidate.Conclusion This study established a rapid screening method for etomidate and its ana-logues using a portable mass spectrometer.The method combines the high sensitivity of mass spectrome-try with the on-site applicability of portable devices,significantly improving detection efficiency and meeting the on-site detection needs of etomidate and its analogues.

Intestinal flora imbalance is closely related to the pathogenesis of rheumatoid arthritis (RA). The existing studies have explored the monomer components such as tripterygium glycosides, total glycosides of Chaenomeles speciosa, and triterpenoid saponins of Clematis, Chinese materia medica such as Tripterygium wilfordii, Caulis Sinomenii, Radix Paeoniae Alba, Fructus Gardeniae, Fructus Chebulae, Radix Ginseng, Radix et Rhizoma Rhei, Rhizoma Atractylodis Macrocephalae, Pterostilbene, and Ginger, as well as the mechanisms of Danggui Sini Decoction, Danggui Niantong Decoction, Duhuo Jisheng Decoction, Yunpi Jiedu Tongluo Qushi Decoction, Qingre Huoxue Decoction, Compound Fengshining, Qingre Yangyin Chushi Decoction, Aconitum Decoction, Zhijing Powder, Jinwu Jiangu Capsule, and Fermented Chinese Medicine Qushi Chubi Decoction in intervening RA by regulating intestinal flora, suggesting that Chinese materia medica can restore intestinal homeostasis, reduce joint inflammation and play a role in the prevention and treatment of RA by regulating immune response, improving intestinal mucosal barrier and regulating intestinal metabolites.

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.

Traumatic brain injury(TBI)is a serious condition characterized by high rates of mortality and disability.Deaths caused by severe TBI usually occur within the first few hours after the injury.Timely and effective management of TBI during pre-hospital and emergency treatment phases is crucial for improving patients'prognosis.To address this issue,the Emergency Physicians Branch of the Chinese Medical Doctor Association,the Emergency Medicine Professional Committee of the People's Liberation Army,the Beijing Emergency Medicine Society,and the Chinese Emergency Medicine Specialist Association have jointly selected national experts in emergency medicine and neurosurgery to formulate the"Chinese Expert Consensus on the Pre-hospital and Emergency Diagnosis and Treatment of Adult Traumatic Brain Injury".This consensus consists of two parts focusing on pre-hospital and emergency diagnosis and treatment of TBI,with 24 recommendations put forward to guide relevant clinical practices in pre-hospital and emergency management.

Strangles,caused by Streptococcus equi subsp.equi,remains one of the most prevalent and high-incidence infectious diseases in intensive donkey farms,posing a significant threat to the healthy development of the donkey industry.Vaccination serves as an effective measure for the pre-vention and control of the disease,however,there is currently no attenuated vaccine against this disease in China.To provide a candidate strain for the development of a live attenuated strangles vaccine,this study focused on a wild-type S.equi subsp.equi strain isolated from donkeys.Using homologous recombination gene knockout technology,the aroA gene(encoding 5-enolpyru-vylshikimate-3-phosphate synthase)and the sag A gene(encoding the precursor of streptolysin S toxin)were sequentially deleted to construct a double-gene-deletion strain(ΔsagA/aroA).The virulence and key biological characteristics of the mutant strain were systematically evaluated.TheΔsagA/aroA strain was successfully generated,exhibiting complete loss of hemolytic activity and maintaining stable genetic inheritance over 60 consecutive passages.Electron microscopy revealed that the mutant retained morphological characteristics compared to the wild-type strain,and its growth rate was significantly slower(P＜0.000 1).Virulence assessment using a challenge dose of 1× 105 CFU/0.2 mL(the minimum fully lethal dose of the wild-type strain)demonstrated markedly attenuated virulence in the mutant.Immunization trials with 1 ×104 CFU/0.2 mL of theΔsagA/aroA strain revealed a increase in ELISA antibody titers by day 7 post-vaccination,and higher levels at days 14 and 21.Notably,antibody levels in the experimental group were significant-ly higher than those in the control group(P＜0.000 1).These findings confirm that the double-gene-deletion strain S.equi subsp.equi ΔsagA/aroA exhibits reduced virulence while retaining im-munogenicity,which suggested it can be used as a promising candidate strain for further develop-ment of a live attenuated strangles vaccine.