ObjectiveTo analyze the protective effect of different types of influenza vaccines (InfV) against influenza A among the individuals aged between 3‒17 years old, and to provide a scientific basis for the prevention and control of influenza in the future. MethodsA retrospective cohort study was conducted to collect data on the incidence and InfV vaccination of the individuals aged between 3‒17 years during the influenza epidemic season from 2022 to 2023. Vaccine effectiveness (VE) was calculated, and a log-binomial regression model was used to calculate the corrected VE. ResultsThe incidence rate of influenza in InfV vaccinated and un-vaccinated groups was 7.32% (1 937/ 26 446) and 9.65% (4 421/45 837), respectively. After adjusting for age and gender factors, the unadjusted VE (95%CI) was 54.57% (52.24%‒56.78%). The unadjusted VE (95%CI) was 53.66% (50.36%‒56.74%) for males and 55.60% (52.24%‒58.72%) for females, respectively. The unadjusted VE (95%CI) for the age group of 3‒ years, 6‒ years, 9‒ years, 12‒ years, and 15‒17 years were 64.08% (60.89%‒67.01%), 57.40% (53.71%‒60.80%), 57.77% (52.49%‒62.47%), 24.36% (9.49%‒36.79%), and 24.09% (-17.59%‒51.00%), respectively. The unadjusted VE (95%CI) for quadrivalent split-virion inactivated influenza vaccine, trivalent split-virion inactivated influenza vaccine, trivalent subunit influenza vaccine, and trivalent live attenuated influenza vaccine were 53.84% (51.32%‒56.24%), 62.17% (56.28%‒67.26%), 79.83% (69.94%‒86.46%), and 31.59% (19.07%‒42.18%), respectively. ConclusionThe InfV used during the 2022‒2023 influenza season had a good protective effect against influenza A among the individuals aged between 3‒17 years old, especially in those aged between 3‒11 years old.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Air Pollution/analysis* ; Environmental Exposure/analysis* ; China/epidemiology* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Monitoring

Objective:To analyze retinal vascular parameters and distribution characteristics in Chinese population via the fully automated quantitative measurement of retinal vascular morphological parameters based on artificial intelligence technology.Methods:A cross-sectional study was performed.A total of 1 842 patients without fundus diseases who visited Beijing Tongren Hospital from January 2011 to December 2021 were included.Standardized questionnaires, blood draws and ophthalmologic examinations of enrolled subjects were conducted.Color fundus photographs centered on the optic disk of one eye of patients were collected, and a deep learning-based semantic segmentation network ResNet101-Unet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters.The main measurement indexes included retinal vascular branching angle, vascular fractal dimension, average vascular caliber, and average vascular tortuosity.To compare different retinal parameters between sexes, the correlation between the above parameters and ocular factors such as best corrected visual acuity, intraocular pressure, and axial length, as well as systemic factors such as sex, age, hypertension, diabetes mellitus, and cardiovascular disease was analyzed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Beijing Tongren Hospital, Capital Medical University (No.20001220). Written informed consent was obtained from each subject.Results:The model established in this study achieved an accuracy over 0.95 for both vascular and optic disk segmentation.The vascular branching angle, vascular fractal dimension, average vascular caliber, and average vascular tortuosity were (51.023±11.623)°, 1.573(1.542, 1.592), 64.124(60.814, 69.053)μm, (0.001 062±0.000 165)°, respectively.Compared with females, males had larger vascular branching angle, smaller average vascular caliber and smaller vascular tortuosity, and the differences were statistically significant (all at P<0.05). The average vascular caliber increased by 1.142 μm in people with cardiovascular disease compared to people without cardiovascular disease ( B=1.142, P=0.029, 95% CI: 0.116-2.167). The average vascular tortuosity was positively correlated with hypertension ( B=3.053×10 -5, P=0.002, 95% CI: 1.167×10 -5-4.934×10 -5) and alcohol consumption ( B=1.036×10 -5, P=0.014, 95% CI: 0.211×10 -5-1.860×10 -5) and negatively correlated with hyperlipidemia ( B=-2.422×10 -5, P=0.015, 95% CI: -4.382×10 -5-0.462×10 -5). For each 1-mm increase in axial length, there was a decrease of 0.004 in vessel fractal dimension ( B=-0.004, P<0.001, 95% CI: -0.006--0.002), a decrease of 0.266 μm in the average vessel caliber ( B=-0.266, P=0.037, 95% CI: -0.516--0.016), and a decrease of -2.45×10 -5° in the average vessel tortuosity ( B=-2.45×10 -5, P<0.001, 95% CI: -0.313×10 -5--0.177×10 -5). For each 1.0 increase in BCVA, there was an increase of 3.992° in the vascular branch angle ( B=3.992, P=0.004, 95% CI: 1.283-6.702), an increase of 0.090 in vascular fractal dimension ( B=0.090, P<0.001, 95% CI: 0.078-0.102) and a decrease of 14.813 μm in the average vascular diameter ( B=-14.813, P<0.001, 95% CI: -16.474--13.153). Conclusions:A model for retinal vascular segmentation is successfully constructed.Retinal vessel parameters are associated with sex, age, systemic diseases, and ocular factors.

Objective To assess the feasibility and imaging outcomes of unilateral biportal endoscopic technique in the treatment of lumbar foraminal stenosis through contralateral approach.Methods The clinical data of 33 patients with lumbar foraminal stenosis treated with unilateral biportal endoscopic technique from January 2021 to July 2022 were retrospectively analyzed.There were 17 males and 16 females;age ranging from 34 to 72 years old with an average of(56.00±7.89)years old;operation time and perioperative complications were recorded;visual analogue scale(VAS)of pain was recorded,to evaluate the degree of low back pain and lower extremity pain,and Oswestry disability index(ODI)to evaluate the lumbar spine func-tion.At the latest follow-up,the modified Macnab score was used to evaluate the clinical efficacy.Results All patients success-fully completed the operation.The operation time ranged from 47 to 65 minutes,with an average of(56.10±5.19)minutes.The postoperative follow-up ranged from 12 to 18 months,with an average of(14.9±2.3)months.The VAS of low back and lower extermity pain before operation were(7.273±1.442)and(7.697±1.447)scores,ODI was(69.182±9.740)％.Postoperative lumbocrural pain VAS were(3.394±0.966)and(2.818±0.727)scores,ODI was(17.30±4.78)％.At the latest follow-up,VAS of back and lower extermity pain was(2.788±0.650)and(2.394±0.704)scores,ODI was(14.33±350)％.There were signifi-cant differences in VAS of low back and lower extremity pain and ODI before and after operation(P＜0.05).At the latest follow-up,according to the modified Macnab criteria,24 patients got excellent result,5 as good,2 as fair,and 2 as poor.Conclusion Unilateral biportal endoscopic treatment of lumbar foraminal stenosis through the contralateral approach is a safe and efficient method,with few complications,quick postoperative recovery,and satisfactory clinical outcomes.During the follow-up period,no iatrogenic lumbar instability was observed.

Objective To observe the safety and effectiveness of single dose intravenous infusion of tranexamic acid(TX-A)in dual level posterior lumbar interbody fusion(PLIF),and to explore the changes and trends in perioperative white blood cell(WBC),erythrocyte sedimentation rate(ESR),and C-reactive protein(CRP).Methods Between October 2020 and September 2022,46 patients with lumbar degenerative disease were treated with dual level PLIF,including 18 males and 28 females,with an average age of(60.24±10.68)years old,from 34 to 80 years old.They were divided into observation group and control group according to different treatment methods.There were 28 patients in the observation group,including 12 males and 16 females,with an average age of(61.04±9.03)years old.There were 3 cases with lumbar disc herniation(LDH),lumbar spinal stenosis(LSS)18 cases,lumbar spondylolisthesis(LS)7 cases.TXA(1 g/100 ml)was administered intravenously 15 min before skin incision after general anesthesia.The control group consisted of 18 patients,including 6 males and 12 females,with an average age of(59.00±13.04)years old.There were 5 cases with LDH,LSS 9 cases,LS 4 cases,and TXA was not used.The operation time,intraoperative bleeding volume,postoperative drainage volume,postoperative deep vein thrombosis(DVT),postoperative hospital stay,postoperative activated partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen(FIB),platelet(PLT),red blood cell(RBC),hemoglobin(HB),hematocrit(HCT),the first day,the fourth day,the seventh day and the last tested after operation WBC,ESR and CRP were recorded.Results The postop-erative wounds of the patients healed well and there was no DVT.46 patients were followed up from 3 to 6 months.The intraop-erative blood loss was 400.0(300.0,500.0)ml and the postoperative drainage was 260.0(220.0,450.0)ml in the observation group,which were lower than the control group[600.0(400.0,1000.0)ml,395.0(300.0,450.0)ml],P＜0.05.There was no significant difference between the two groups in operation time,postoperative hospital stay,postoperative APTT,PT,TT,FIB,PLT,RBC,HB,HCT,and postoperative WBC,ESR and CRP at different times(P＞0.05).Conclusion Single dose intravenous infusion of TXA can reduce the blood loss of bi-segmental PLIF,and has no significant effect on WBC,ESR and CRP after op-eration.

Objective:To construct and validate a nomogram model for predicting the risk of 28-day mortality in sepsis patients.Methods:A retrospective cohort study was conducted. 281 sepsis patients admitted to the department of intensive care unit (ICU) of the 940th Hospital of the Joint Logistics Support Force of PLA from January 2017 to December 2022 were selected as the research subjects. The patients were divided into a training set (197 cases) and a validation set (84 cases) according to a 7∶3 ratio. The general information, clinical treatment measures and laboratory examination results within 24 hours after admission to ICU were collected. Patients were divided into survival group and death group based on 28-day outcomes. The differences in various data were compared between the two groups. The optimal predictive variables were selected using Lasso regression, and univariate and multivariate Logistic regression analyses were performed to identify factors influencing the mortality of sepsis patients and to establish a nomogram model. Receiver operator characteristic curve (ROC curve), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the nomogram model.Results:Out of 281 cases of sepsis, 82 cases died with a mortality of 29.18%. The number of patients who died in the training and validation sets was 54 and 28, with a mortality of 27.41% and 33.33% respectively. Lasso regression, univariate and multivariate Logistic regression analysis screened for 5 independent predictors associated with 28-day mortality. There were use of vasoactive drugs [odds ratio ( OR) = 5.924, 95% confidence interval (95% CI) was 1.244-44.571, P = 0.043], acute physiology and chronic health evaluation Ⅱ (APACHEⅡ: OR = 1.051, 95% CI was 1.000-1.107, P = 0.050), combined with multiple organ dysfunction syndrome (MODS: OR = 17.298, 95% CI was 5.517-76.985, P < 0.001), neutrophil count (NEU: OR = 0.934, 95% CI was 0.879-0.988, P = 0.022) and oxygenation index (PaO 2/FiO 2: OR = 0.994, 95% CI was 0.988-0.998, P = 0.017). A nomogram model was constructed using the independent predictive factors mentioned above, ROC curve analysis showed that the AUC of the nomogram model was 0.899 (95% CI was 0.856-0.943) and 0.909 (95% CI was 0.845-0.972) for the training and validation sets respectively. The C-index was 0.900 and 0.920 for the training and validation sets respectively, with good discrimination. The Hosmer-Lemeshoe tests both showed P > 0.05, indicating good calibration. Both DCA and CIC plots demonstrate the model's good clinical utility. Conclusions:The use of vasoactive, APACHEⅡ score, comorbid MODS, NEU and PaO 2/FiO 2 are independent risk factors for 28-day mortality in patients with sepsis. The nomogram model based on these 5 indicators has a good predictive ability for the occurrence of mortality in sepsis patients.

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.