Heatstroke,especially in high-temperature and high-humidity environments,is a life-threatening acute heat-injury disease that seriously endangers human health.Timely and effective on-site treatment is crucial for patients'survival and prognosis.Early recognition,rapid assessment,and on-site cooling are the core of pre-hospital treatment of heatstroke.Currently,there is a lack of standardized application procedures for pre-hospital emergency care of heatstroke.Therefore,the"Expert Consensus on Pre-hospital Emergency Management of Heatstroke(2024 edition)"was initiated by the Expert Group on Heatstroke Prevention of the People's Liberation Army and developed in collaboration with experts from local pre-hospital emergency care,emergency departments,and intensive care units.This consensus focuses on heatstroke prevention,on-site and ambulance-based treatment,and early emergency room interventions,and puts forward 10 evidence-based recommendations,aiming to provide a reference for scientific and standardized pre-hospital emergency care of heatstroke.

Objective:To investigate the clinical efficacy of moxibustion(Mox)combined with low-dose tadalafil(TAD)in the treatment of diabetes mellitus-induced erectile dysfunction(DMED)with the syndrome of Qi deficiency and blood stasis.Meth-ods:According to the inclusion and exclusion criteria,we selected 90 patients with DMED for this trial and equally randomized them into a Mox,a TAD,and a Mox combined with TAD(Mox+TAD)group to be treated by mild Mox applied to the acupoints Zusanli,Sanyinjiao and Yinlingquan qd alt,oral medication with low-dose TAD at 5 mg per dose qd,and combination of the above two thera-pies,respectively,all for 4 weeks.We obtained from the patients their IIEF-5 scores,traditional Chinese medicine(TCM)symptoms scores,Erectile Hardness Scale(EHS)scores,corpus cavernosal hemodynamic indexes,and the peak systolic velocity(PSV),end diastolic velocity(EDV)and resistance index(RI)of the corpus cavernosal arteries before and after treatment,and compared them among the three groups.Results:The total effectiveness rate was significantly higher in the Mox+TAD(90.0％)than in the Mox(46.7％)and TAD groups(60.0％)(P＜0.05).Compared with the baseline,the IIEF-5 and EHS scores were increased,while the TCM symptoms scores decreased in all the three groups after treatment,more significantly in the Mox+TAD group than in the other two(P＜0.05).And the PSV and RI were remarkably increased,while the EDV decreased(P＜0.05)in all the three groups(P＜0.05)after treatment,with PSV even higher in the Mox+TAD than in the Mox and TAD groups(P＜0.05).Conclusion:Moxi-bustion combined with tadalafil has a definite efficacy and safety for the treatment of DMED,which can effectively improve the erectile function of the patients by increasing penile blood supply,benefiting qi and activating blood circulation.

Objective To observe the clinical efficacy and safety of dexmedetomidine injection combined with esketamine injection in the treatment of patients with oral squamous cell carcinoma radical resection.Methods Patients with oral squamous cell carcinoma radical resection were randomly divided into treatment and control groups.The treatment group will receive intravenous administration of 0.6 μg·kg-1 dexmedetomidine 10 minutes before anesthesia induction.Subsequently,anesthesia induction will be performed with intravenous administration of 0.5 mg·kg-1 esketamine.Anesthesia maintenance will be achieved with intravenous infusion of 0.25 mg·kg-1·h-1 esketamine and 0.3 μg·kg-1·h-1 dexmedetomidine used an infusion pump.The control group will receive intravenous administration of an equivalent volume of 0.9％NaCl 10 minutes before anesthesia induction.Anesthesia induction will then be performed with intravenous administration of 2.5-5.0 μg·kg-1 fentanyl.Anesthesia maintenance will involve intravenous infusion of 0.10-0.25 μg·kg-1·min-1 remifentanil used an infusion pump.The anesthesia effectiveness,analgesic effectiveness,hemodynamics and safety were compared between the two groups.Results Treatment group were enrolled 62 cases,1 case dropped out,and 61 cases were finally included in the statistical analysis.Control group were enrolled 61 cases,1 case dropped out,and 60 cases were finally included in the statistical analysis.The recovery room stay time of treatment and control groups was(25.97±4.52)and(18.39±3.64)min,the extubation time was(16.75±4.84)and(10.16±3.18)min,and the differences were statistically significant(all P＜0.05).After operation 24 h,visual analogue scores of treatment and control groups were(0.85±0.17)and(1.39±0.25)points,adrenocorticotropin levels were(60.07±7.13)and(72.64±9.81)pg·mL-1,cortisol levels were(481.20±49.15)and(539.94±57.77)nmol·L-1,and the differences were statistically significant(all P＜0.05).The mean arterial pressure at 30 min after anesthesia induction(T1)and at the end of surgery(T2)in treatment group were(82.34±4.98)and(86.57±4.18)mmHg,while those in control group were(77.25±7.16)and(76.02±6.29)mmHg;the heart rates of T1 and T2 in treatment groups were(64.08±4.19)and(66.45±4.83)time·min-1,while those in control group were(68.44±6.02)and(72.08±7.27)time·min-1;and the differences were statistically significant(all P＜0.05).The adverse drug reactions in two groups were nausea,vomiting,bradycardia and dizziness.The total incidences of adverse drug reactions in treatment and control groups were 8.20％and 15.00％,without significant difference(P＞0.05).Conclusion Dexmedetomidine injection combined with esketamine injection has a definitive analgesia efficacy in the treatment of patients with oral squamous cell carcinoma radical resection,which can significantly reduce stress responses,maintain hemodynamic stability,without increasing the incidence of adverse drug reactions.

Aim To investigate the effect of neogam-bogic acid(NGA)on inducing ferroptosis in osteosar-coma K7M2 cells and subcutaneous transplanted tumor mice and explore the underlying mechanism.Methods MTT assay was employed to detect the effect of NGA(1,2,4,8,16,32,64,128 μmol·L-1)on cell prolif-eration,and the IC50 value was calculated.Calcein AM assay was used to detect cell viability.Transwell was applied to detect cell invasion.TEM was utilized to ob-serve the mitochondria morphology.K7M2 cells were subjected to treat with ferroptosis inducers erastin(Era)and inhibitors ferrostatin-1(Fer-1)to assess the levels of MDA,GSH,Fe2+,and LDH.RT-qPCR and Western blot were used to detect the mRNA and protein expression of STAT3,GPX4,and SLC7A11.A transplanted tumor model was established and treated with NGA to assess the impact of it on tumor growth and ferroptosis in vivo.HE staining was applied to ana-lyze the pathological status of tumor tissues.Nile red fluorescence staining was applied to detect the level of lipid components in tumor tissues.Results The pro-liferation,viability and invasion ability of K7M2 cells were significantly reduced after treatment with NGA at different concentrations(P＜0.05),and typical fea-tures of ferroptosis such as decreased mitochondrial vol-ume and reduced mitochondrial spine were observed.Compared to the control,the expression of MDA,Fe2+and LDH significantly increased(P＜0.01),while the content of GSH significantly decreased(P＜0.01).The ferroptosis in osteosarcoma was enhanced by the erastin,while inhibited by ferrostatin-1.In terms of mechanism,NGA inhibited the mRNA and protein ex-pression levels of STAT3,GPX4 and SLC7A11(P＜0.05).In vivo experiments confirmed that NGA signif-icantly improved the pathological state of tumor tissues,inhibited tumor growth,and induced ferroptosis in os-teosarcoma tissue cells.Conclusion NGA induces ferroptosis in osteosarcoma cells both in vitro and in vi-vo by inhibiting the STAT3/GPX4/SLC7A11 signaling axis,thereby exerting an anti-osteosarcoma effect.

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 systematically analyze the functional system and construction requirements for infection prevention and control('infection control system'in short)in medical institutions so as to facilitate the effective,standardized and practical construction of the infection control system.METHODS The questionnaires were de-signed based on the relevant criteria and literatures that were released in China with the combination of expect con-sultant and were distributed to experts or professionals involving multiple fields such as hospital infection manage-ment,clinical medical treatment and information technology.The questionnaires were recycled,summarized and analyzed.RESULTS The list of functions of the infection control system(consultative draft)was formulated after review of literatures and expert consultation,including fundamental functions such as data management,case sur-veillance and intervention feedback as well as the advanced functions like target surveillance,occupational protec-tion and interconnection.The surveyed subjects agreed unanimously after the questionnaire survey that all of the function modules and elements enlisted were important,the average score of importance was more than 4 points,the score of coefficient of variable(CV)for importance of the function modules was less than 0.25,indicating that there was high consistency in the opinions among the surveyed subjects.The element of tracing and epidemiologi-cal survey function was adopted and added according to the feedback suggestions from some of the subjects;two function elements including data query and clinical interaction were revised,and the list of function requirements for the infection control systems was finally defined.CONCLUSION The requirements for functions of the infection control system that are determined in the study can provide important bases and data support for the research and standardized development of future infection control system.

Objective:To investigate the clinical efficacy of moxibustion(Mox)combined with low-dose tadalafil(TAD)in the treatment of diabetes mellitus-induced erectile dysfunction(DMED)with the syndrome of Qi deficiency and blood stasis.Meth-ods:According to the inclusion and exclusion criteria,we selected 90 patients with DMED for this trial and equally randomized them into a Mox,a TAD,and a Mox combined with TAD(Mox+TAD)group to be treated by mild Mox applied to the acupoints Zusanli,Sanyinjiao and Yinlingquan qd alt,oral medication with low-dose TAD at 5 mg per dose qd,and combination of the above two thera-pies,respectively,all for 4 weeks.We obtained from the patients their IIEF-5 scores,traditional Chinese medicine(TCM)symptoms scores,Erectile Hardness Scale(EHS)scores,corpus cavernosal hemodynamic indexes,and the peak systolic velocity(PSV),end diastolic velocity(EDV)and resistance index(RI)of the corpus cavernosal arteries before and after treatment,and compared them among the three groups.Results:The total effectiveness rate was significantly higher in the Mox+TAD(90.0％)than in the Mox(46.7％)and TAD groups(60.0％)(P＜0.05).Compared with the baseline,the IIEF-5 and EHS scores were increased,while the TCM symptoms scores decreased in all the three groups after treatment,more significantly in the Mox+TAD group than in the other two(P＜0.05).And the PSV and RI were remarkably increased,while the EDV decreased(P＜0.05)in all the three groups(P＜0.05)after treatment,with PSV even higher in the Mox+TAD than in the Mox and TAD groups(P＜0.05).Conclusion:Moxi-bustion combined with tadalafil has a definite efficacy and safety for the treatment of DMED,which can effectively improve the erectile function of the patients by increasing penile blood supply,benefiting qi and activating blood circulation.

Retrieving keyframes most relevant to text from small intestine videos with given labels can efficiently and accurately locate pathological regions. However, training directly on raw video data is extremely slow, while learning visual representations from image-text datasets leads to computational inconsistency. To tackle this challenge, a small bowel video keyframe retrieval based on multi-modal contrastive learning (KRCL) is proposed. This framework fully utilizes textual information from video category labels to learn video features closely related to text, while modeling temporal information within a pretrained image-text model. It transfers knowledge learned from image-text multimodal models to the video domain, enabling interaction among medical videos, images, and text data. Experimental results on the hyper-spectral and Kvasir dataset for gastrointestinal disease detection (Hyper-Kvasir) and the Microsoft Research video-to-text (MSR-VTT) retrieval dataset demonstrate the effectiveness and robustness of KRCL, with the proposed method achieving state-of-the-art performance across nearly all evaluation metrics.
Humans ; Video Recording ; Intestine, Small/diagnostic imaging* ; Machine Learning ; Image Processing, Computer-Assisted/methods* ; Algorithms

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.