Objective To compare the epidemic characteristics of seasonal influenza in Xicheng District, Beijing, between 2018-2019 and 2023-2024. Methods The present study was a cross-sectional study. The influenza cases and etiological surveillance data of Xicheng District, Beijing in 2018-2019 and 2023-2024, respectively, were analyzed to study the differences in the incidence rate of influenza cases in different ages, regions, occupations, and etiological detection results during the four years. Results There were significant differences in the incidence rates of influenza (χ²=37 038.65，P＜0.05) and the interval between visits (Z=468.692, P < 0.05) in 2018, 2019, 2023 and 2024. There were statistically significant differences in the incidence rates of influenza among different age groups, streets, and occupations during the four years (Z=75.646, 209.276, 72.132, 361.176, 43.570, all P < 0.05). Compared with 2018-2019, the growth rate of influenza in Xicheng District, Beijing in 2023-2024 was the highest in the 15-24 year-old group, Tianqiao Street, and housework and unemployed people. The difference in the interval between influenza visits among people of different age groups during these 4 years was statistically significant (P < 0.05). The growth rate of the interval between influenza visits in Xicheng District, Beijing in 2023-2024 was the highest in the 15-24 year-old group, while it showed a decrease in the 0-4 year-old group. The etiology in 2018-2019 was mainly H1N1 and H3N2 subtypes, and in 2023-2024 it was mainly H3N2 subtype and influenza B, and the peak values of H1N1, H3N2 subtype and influenza B in 2023-2024 were all higher than those in 2018-2019. Conclusion During the epidemic period, non-medical interventions have changed the transmission mode, seasonal peak and subtype distribution of influenza virus. As the epidemic has eased, the pattern of influenza has gradually rebounded.

Objective To explore the impact of meteorological factors on the incidence of scarlet fever in Xicheng District of Beijing, and to provide a scientific basis for formulating targeted prevention and control measures. Methods Daily scarlet fever incidence data and corresponding meteorological data from 2010 to 2019 in Xicheng District of Beijing were collected. Using year, month, and day of the week as time-stratified variables, a conditional logistic regression model with a time-stratified case-crossover design was employed to analyze the impact of different meteorological factors on the number of scarlet fever cases in Xicheng District, while controlling for long-term trends, seasonality and other confounding factors. Results From 2010 to 2019, a total of 3,195 cases of scarlet fever were reported in Xicheng District, with an average annual incidence rate of 24.17/100 000. The overall trend showed fluctuating decline, during which three incidence peaks occurred, with the highest incidence rate observed in 2011. No severe or fatal cases were reported. The incidence of scarlet fever exhibited a distinct seasonal bimodal distribution, primarily concentrated between April to June and November to January of the following year. The conditional logistic regression results showed that the average relative humidity and average temperature were positively correlated with scarlet fever cases (β=0.0203, β=0.0613, P＜0.001), while the average vapor pressure was negatively correlated with scarlet fever cases (β=-0.1468, P＜0.001). Increases average relative humidity and average temperature were risk factors for scarlet fever incidence (OR=1.0205，95%CI=1.0150 -1.0261；OR=1.0632，95%CI=1.0379 -1.0891). For every 1.00% increase in average relative humidity, the number of scarlet fever cases increased by 2.05% (1.50% –2.61%). Similarly, for every 1^。C rise in average temperature, the number of cases increased by 6.32% (3.79% – 8.91%). In contrast, an increase in average vapor pressure had a protective effect against scarlet fever (OR=0.8635,95%CI=0.8392-0.8885). For every 1 hPa increase in average vapor pressure, the number of scarlet fever cases decreased by 13.65% (11.15%–16.08%). Conclusion The average relative humidity, average temperature and average vapor pressure are the primary meteorological factors influencing the incidence of scarlet fever in Xicheng District of Beijing, and can be utilized as indicators for the prevention, control, surveillance and early warning of scarlet fever.

Objective To explore the correlation and lag effect between meteorological factors and the incidence of scarlet fever in Xicheng District, Beijing, and to provide a theoretical basis for the surveillance, early warning，and scientific prevention and control of scarlet fever. Methods Based on the daily scarlet fever incidence data and concurrent meteorological data in Xicheng District, Beijing from 2010 to 2019, the distributed lag non-linear model (DLNM) was used to analyze the impact of meteorological factors on the incidence of scarlet fever. Results The risk of scarlet fever was the highest when the daily average temperature was 34.2℃ with a lag of 0 days (RR=1.175, 95% CI:1.006-1.372). The risk was the second highest when the daily average temperature was 2.2℃ with a lag of 12 days (RR=1.123, 95% CI:1.044 -1.209). The cumulative relative risk of scarlet fever was statistically significant when the daily average temperature ranged from 27.2℃ to 34.2℃, with the highest cumulative relative risk at 34.2℃ (RR=1.906, 95%CI:1.215-2.989). When the daily average relative humidity was 19.5% with a lag of 6 days, the risk of scarlet fever incidence was the highest (RR=1.022, 95% CI: 1.005-1.040). The cumulative relative risk was statistically significant when the daily average relative humidity ranged from 24.7% to 40.2%, with the highest cumulative relative risk at 27.3% (RR=1.170, 95% CI: 1.020-1.343). The risk of scarlet fever was the highest when the daily average vapor pressure was 1.2 hPa with a lag of 5 days (RR=1.029, 95%CI:1.008-1.050). The cumulative relative risk of scarlet fever was statistically significant when the daily average vapor pressure was 1.2-7.4 hPa, and the highest cumulative risk was when the daily average vapor pressure was 1.2 hPa (RR=1.362, 95% CI:1.022-1.815). Conclusion There is a nonlinear relationship between meteorological factors and the incidence of scarlet fever in Xicheng District, Beijing, with a certain lag effect. Daily average temperature (27.2-34.2℃), daily average relative humidity (28.6~36.3%) and daily average vapor pressure (1.2-7.4 hPa) increase the risk of scarlet fever. These factors can be used as indicators for the prevention, control, surveillance, and early warning of scarlet fever.

Objective:To assess the application effects of an energy-restricted diet combined with high-protein, high-dietary-fiber meal replacement powder and probiotics in overweight/obese adults.Methods:It was a randomized controlled trial. A consecutive sample of 150 overweight/obese adults who underwent physical examinations at the Health Care Center of the First Hospital of Hebei Medical University between November 2021 and March 2022. The participants were randomly assigned into the combined group, the high-protein group, and the common group (50 participants per group) using a random number table method. All three groups of subjects received weight loss health education, energy-restricted diet, and interventions with meal replacement powder and probiotics (or probiotic placebo). The combined group was given high-protein and high-dietary fiber meal replacement powder and probiotics. The high-protein group was given high-protein meal replacement powder and probiotic placebo. The common group was given ordinary meal replacement powder and probiotic placebo. The meal replacement powder was packaged in 35 g per bag, with main components of varying amounts of protein, fat, carbohydrates, vitamins, and trace elements. Both the probiotic powder and the probiotic placebo came in 2 g sachets. The primary components of probiotic powder were various Bifidobacterium, Lactobacillus and excipients, while the main component of probiotic placebo was excipients. The meal replacement powder and the probiotic powder or probiotic placebo were taken twice a day for a total of 12 weeks, one sachet of each time, followed by a 4-week follow-up. The body weight, body mass index, body fat mass, abdominal circumference and hip circumference were measured before the trial (week 0) and at the end of weeks 2, 4, 8, 12, and 16. The change rates of each indicator were calculated. Biochemical indicators, trace elements, and 25-hydroxyvitamin D levels were measured at the end of week 0, 4, 8, and 12. A product evaluation questionnaire was conducted at the end of week 12. A total of 19 cases dropped out due to various reasons. Finally, 46 cases in the combined group, 42 cases in the high-protein group, and 43 cases in the common group were included in the analysis. Paired-samples t test, Kruskal-Wallis H test, one-way analysis of variance, and Mann-Whitney U test were used to compare the differences in weight-loss and maintenance effects, safety and patient acceptance among the three intervention groups, and to analyze the application effect of the energy-restricted diet combined with high-protein and high-dietary fiber meal replacement powder plus probiotics in overweight/obese adults. Results:Among the 131 overweight/obese adults included in the analysis, there were 57 males and 74 females, with a mean age of (37.30±8.33) years. By the end of the week 12, the body mass index [26.87(25.77, 30.38) vs 29.61(27.96, 33.09) kg/m2; 27.10(24.70, 31.37) vs 29.40(27.20, 34.17) kg/m2; 27.98(26.43, 30.12) vs 29.88(28.22, 31.93) kg/m2] and body fat masses [22.15(17.70, 30.15) vs 30.75(25.63, 35.40) kg; 23.35(19.12, 28.70) vs 29.45(26.20, 37.05) kg; 26.80(24.10, 31.60) vs 30.00(26.00, 34.70) kg] in the combined group, the high-protein group and the common group were all lower than those at baseline (week 0) (all P<0.05). At the end of the week 12, the change rates of body fat mass and body mass index in the combined group were both higher than those in the high-protein group and the common group [(25.98%±9.58%) vs (23.88%±11.15%) and (9.35%±11.00%), 9.29%(7.23%, 11.58%) vs 7.96% (5.51%, 10.92%) and 5.77% (2.68%, 10.03%)] (all P<0.05). At the end of the week 12, the body fat mass in the combined group and the high-protein group were both lower than that in the common group [22.15(17.70, 30.15), 23.35(19.12, 28.70) vs 26.80(24.10, 31.60) kg] (both P<0.05). At the end of the week 12, the decreased values of uric acid and high-sensitivity C-reactive protein in the combined group were both higher than those in the high-protein group and the common group [17.15(13.02, 23.45) vs 1.50(0.22, 28.60) and 4.20(0.15, 19.95) μmol/L, 0.43(0.24, 0.60) vs 0.21(0.06, 0.43) and 0.28(-0.04, 0.88) mg/L](both P<0.05). No serious adverse events were observed during the intervention period and at the end of the intervention. In the product evaluation questionnaire, the combined group scored higher than the high-protein group and the common group on items such as usage frequency, taste, satiety, willingness to continue use, willingness to recommend to others, and willingness to purchase [4(3, 4) vs 3(3, 4) and 3(2, 4) points, 4(3, 4) vs 3(3, 4) and 3(2, 4) points, 4(3, 4) vs 3(3, 4) and 3(3, 3) points, 4(3, 4) vs 3(3, 4) and 3(3, 4) points, 4(3, 4) vs 3(3, 4) and 3(3, 3) points, 3(3, 4) vs 3(3, 4) and 3(2, 3) points] (all P<0.05). Conclusion:An energy-restricted diet combined with high-protein, high-dietary-fiber meal replacement powder and probiotics demonstrates superior weight-loss and weight-maintenance effects in overweight/obese adults, with high safety and great user acceptability.

Objective:To explore the factors influencing diarrhea after nasal feeding in patients with severe neurological illness, construct a nomogram model and verify its predictive performance.Methods:A retrospective study was conducted to select 797 nasal feeding patients admitted to the Neurological Intensive Care Unit of the Tianjin First Central Hospital from May 2020 to November 2023 as study subjects. Patients were divided into a modeling set ( n=558) and a validation set ( n=239) in a 7∶3 ratio. Influencing factors were screened based on univariate analysis, correlations between variables were detected by multicollinearity diagnostics, and then risk factors were identified using binomial Logistic regression, and a nomogram model was created. The predictive capacity of the model was assessed using the area under the receiver operating characteristic curve ( AUC), the degree of fit of the model was evaluated using the Hosmer-Lemeshow test and calibration curves were plotted, and the practical application value of the model in clinical practice was appraised by means of clinical decision curve analysis. Results:Binomial Logistic regression analysis showed that Glasgow Coma Scale score ( OR=0.891), Acute Physiology and Chronic Health Evaluation Ⅱ score ( OR=1.063), sedative-analgesic ( OR=0.326), albumin level ( OR=0.856), days of antibiotic use ( OR=3.338), and acid suppressants ( OR=3.260 ) were the factors influencing diarrhea after nasal feeding in patients with severe neurological illness ( P<0.05). In the validation set, the AUC for the nomogram model of the risk of diarrhea after nasal feeding in patients with severe neurological illness was 0.82 [95% CI (0.77, 0.87) ], with a sensitivity of 0.72 and a specificity of 0.72. Clinical decision curve showed nomogram model had high application value. Conclusions:The constructed nomogram model has good predictive performance and can help healthcare professionals in neurocritical care specialties to identify patients at high risk of diarrhea at an early stage, thus informing the development of individualized prevention strategies in clinical practice.

Objective:To assess the application effects of an energy-restricted diet combined with high-protein, high-dietary-fiber meal replacement powder and probiotics in overweight/obese adults.Methods:It was a randomized controlled trial. A consecutive sample of 150 overweight/obese adults who underwent physical examinations at the Health Care Center of the First Hospital of Hebei Medical University between November 2021 and March 2022. The participants were randomly assigned into the combined group, the high-protein group, and the common group (50 participants per group) using a random number table method. All three groups of subjects received weight loss health education, energy-restricted diet, and interventions with meal replacement powder and probiotics (or probiotic placebo). The combined group was given high-protein and high-dietary fiber meal replacement powder and probiotics. The high-protein group was given high-protein meal replacement powder and probiotic placebo. The common group was given ordinary meal replacement powder and probiotic placebo. The meal replacement powder was packaged in 35 g per bag, with main components of varying amounts of protein, fat, carbohydrates, vitamins, and trace elements. Both the probiotic powder and the probiotic placebo came in 2 g sachets. The primary components of probiotic powder were various Bifidobacterium, Lactobacillus and excipients, while the main component of probiotic placebo was excipients. The meal replacement powder and the probiotic powder or probiotic placebo were taken twice a day for a total of 12 weeks, one sachet of each time, followed by a 4-week follow-up. The body weight, body mass index, body fat mass, abdominal circumference and hip circumference were measured before the trial (week 0) and at the end of weeks 2, 4, 8, 12, and 16. The change rates of each indicator were calculated. Biochemical indicators, trace elements, and 25-hydroxyvitamin D levels were measured at the end of week 0, 4, 8, and 12. A product evaluation questionnaire was conducted at the end of week 12. A total of 19 cases dropped out due to various reasons. Finally, 46 cases in the combined group, 42 cases in the high-protein group, and 43 cases in the common group were included in the analysis. Paired-samples t test, Kruskal-Wallis H test, one-way analysis of variance, and Mann-Whitney U test were used to compare the differences in weight-loss and maintenance effects, safety and patient acceptance among the three intervention groups, and to analyze the application effect of the energy-restricted diet combined with high-protein and high-dietary fiber meal replacement powder plus probiotics in overweight/obese adults. Results:Among the 131 overweight/obese adults included in the analysis, there were 57 males and 74 females, with a mean age of (37.30±8.33) years. By the end of the week 12, the body mass index [26.87(25.77, 30.38) vs 29.61(27.96, 33.09) kg/m2; 27.10(24.70, 31.37) vs 29.40(27.20, 34.17) kg/m2; 27.98(26.43, 30.12) vs 29.88(28.22, 31.93) kg/m2] and body fat masses [22.15(17.70, 30.15) vs 30.75(25.63, 35.40) kg; 23.35(19.12, 28.70) vs 29.45(26.20, 37.05) kg; 26.80(24.10, 31.60) vs 30.00(26.00, 34.70) kg] in the combined group, the high-protein group and the common group were all lower than those at baseline (week 0) (all P<0.05). At the end of the week 12, the change rates of body fat mass and body mass index in the combined group were both higher than those in the high-protein group and the common group [(25.98%±9.58%) vs (23.88%±11.15%) and (9.35%±11.00%), 9.29%(7.23%, 11.58%) vs 7.96% (5.51%, 10.92%) and 5.77% (2.68%, 10.03%)] (all P<0.05). At the end of the week 12, the body fat mass in the combined group and the high-protein group were both lower than that in the common group [22.15(17.70, 30.15), 23.35(19.12, 28.70) vs 26.80(24.10, 31.60) kg] (both P<0.05). At the end of the week 12, the decreased values of uric acid and high-sensitivity C-reactive protein in the combined group were both higher than those in the high-protein group and the common group [17.15(13.02, 23.45) vs 1.50(0.22, 28.60) and 4.20(0.15, 19.95) μmol/L, 0.43(0.24, 0.60) vs 0.21(0.06, 0.43) and 0.28(-0.04, 0.88) mg/L](both P<0.05). No serious adverse events were observed during the intervention period and at the end of the intervention. In the product evaluation questionnaire, the combined group scored higher than the high-protein group and the common group on items such as usage frequency, taste, satiety, willingness to continue use, willingness to recommend to others, and willingness to purchase [4(3, 4) vs 3(3, 4) and 3(2, 4) points, 4(3, 4) vs 3(3, 4) and 3(2, 4) points, 4(3, 4) vs 3(3, 4) and 3(3, 3) points, 4(3, 4) vs 3(3, 4) and 3(3, 4) points, 4(3, 4) vs 3(3, 4) and 3(3, 3) points, 3(3, 4) vs 3(3, 4) and 3(2, 3) points] (all P<0.05). Conclusion:An energy-restricted diet combined with high-protein, high-dietary-fiber meal replacement powder and probiotics demonstrates superior weight-loss and weight-maintenance effects in overweight/obese adults, with high safety and great user acceptability.

Objective To understand the distribution of M protein gene (emm) of group A streptococcus (GAS) infections among children in Xicheng District of Beijing. Methods Throat swab samples from scarlet fever and pharyngeal infection cases were collected in sentinel hospitals of Xicheng District, Beijing from 2011 to 2024. GAS strains were isolated and identified, and emm gene was amplified and sequenced by PCR to determine the genotype. The differences in emm genotype between different groups were compared. Results A total of 3 130 throat swab samples were collected, and 400 GAS strains were isolated, with a positive rate of 12.78%. The highest positive rate was 19.93% in 2011. The positive rate of scarlet fever (45.71%) was higher than that of pharyngeal infection (6.14%) (P<0.001). There were 391 emm gene positive strains, and the differences in the positive rate of emm gene among different cases were statistically significant (P<0.001). A total of 7 genotypes and 27 gene subtypes were detected. Among different groups, the emm genotypes were mainly emm12 and emm1. The emm gene subtypes were mainly emm12.00 and emm1.00. Except for some years, the genotypes and their subtypes were dominated by emm12 and emm12.00, and the distribution differences of the two major genotypes and their subtypes were statistically significant from 2011 to 2019 (P<0.001). There were differences in genotypes and subtypes among different age groups (P=0.002). Conclusion The dominant types of emm genes in group A streptococcus infections among children were emm12 and emm1 in Xicheng District of Beijing from 2011 to 2024, and the dominant gene subtypes were emm12.00 and emm1.00. It is necessary to comprehensively strengthen the monitoring of the epidemic situation and genotype, timely grasp the distribution and variation of emm gene.

Objective: To analyze the epidemiological characteristics of dengue fever outbreaks in schools from Guangzhou in 2024, so as to provide a reference for formulating targeted prevention and control policies and measures. Methods: By using the National Infectious Disease Surveillance Information Reporting Management System to obtain information on dengue fever cases in Guangzhou from January 1st to December 31st, 2024. Descriptive data analysis was conducted on the temporal distribution, regional distribution, and school distribution of dengue fever outbreaks in schools. A mediation effect model was used to analyze the mediating effect of the time between onset and reporting between the type of school and the occurrence of recurrent cases. Results: In 2024,12.41% (385 cases) of dengue fever cases in Guangzhou involved schools, with 300 schools affected. Among these, 16 schools (5.33%) reported cluster outbreaks, and 24 schools (8.00%) reported secondary cases. The first dengue case at the school was reported at 26 July and the last case was reported at 4 December, the peak reporting period for cases was October 7 to November 3. The incidence of secondary cases in schools in central urban areas (5.19%) was lower than that in suburban schools ( 17.39 %), and the difference was statistically significant ( χ 2=9.15, P <0.01). The time from onset to reporting partially mediated the relationship between school type and the occurrence of recurrent cases ( β=0.23, P <0.05), accounting for 21.50% of the total effect. Conclusions Dengue fever is a key infectious disease facing schools in Guangzhou during summer and autumn. Surveillance of dengue fever outbreaks in schools should be strengthened during the peak season to reduce the risk of cluster outbreaks and the occurrence of secondary cases.

Objective To investigate the change in choroid plexus(CP)volume in Parkinson's disease(PD)patients with different cognitive states and its correlation with structural volumes of other brain regions.Methods A cross-sectional study was conducted on 48 PD patients admitted in Department of Neurosurgery of the First Affiliated Hospital of Army Medical University between May 2023 and April 2024,and on 35 healthy controls(HC)recruited through a physical exam center.According to the results of Mini-Mental State Examination(MMSE),the patients were divided into PD with cognitive impairment(PD-CI)group(n=27)and PD with normal cognitive function(PD-NC)group(n=21).3.0T magnetic resonance imaging(MRI)was performed using MPRAGE sequences,and CP volume and volumes of other brain regions were obtained using FreeSurfer 6.0 software.The CP volume was adjusted by calculating the ratio of its volume to estimated total intracranial volume(eTIV).After controlling for confounders,partial correlation analysis was used to assess the relationship between the CPV/eTIV ratio and the volumes of other brain regions as well as cognitive scale scores.Additionally,multiple linear regression analysis was performed to further explore the relationship between CPV and cognitive function in the PD-CI group.Results Compared to the HC group,the CPV in the PD-CI group was significantly larger(P=0.029).In the PD-CI group,the CPV/eTIV ratio showed significant positive correlations with the volume of the lateral ventricles(r=0.689,P=0.001),the volume of the third ventricle(r=0.592,P=0.006),the volume of cerebrospinal fluid(CSF)(r=0.508,P=0.022),and white matter hyperintensities(WMH)(r=0.486,P=0.030),but was negatively correlated with the volume of the caudate nucleus(r=-0.530,P=0.016),the volume of the thalamus(r=-0.477,P=0.033),and the MMSE scores(r=-0.483,P=0.031).But in the PD-NC group,the CPV/eTIV ratio was only positively correlated with CSF volume(r=0.571,P=0.021).Multiple linear regression analysis indicated that the CPV/eTIV ratio and MMSE scores remained significantly negatively correlated in the PD-CI group(β=-0.388,P=0.046).Conclusion Cognitive impairment in PD patients may be closely associated with the change in CP volume,suggesting that the volume can serve as a potential imaging marker in assessment of cognitive impairment in PD patients.

Objective To investigate the epidemiological characteristics and effectiveness of emergency responses to epidemic foci in Guangzhou City in 2024, so as to optimization of the dengue fever control strategy in Guangzhou City. Methods All data pertaining to dengue fever cases in Guangzhou City in 2024 were collected from the National Notifiable Infectious Disease Surveillance Information Reporting System. The temporal, spatial and population distributions of dengue fever cases and sources of infections were descriptively analyzed, and the effectiveness of emergency responses to epidemic foci of dengue fever was evaluated through standard space index (SSI), the interval from disease onset to case reporting and the percentage of isolation in hospital. Results A total of 3 656 dengue fever cases were reported in Guangzhou City in 2024, including 3 102 local cases and 554 imported cases. Of all cases, 67.86% (2 481 cases) occurred at ages of 20 to 59 years, and the three most common occupations included housework/unemployment (793 cases, 21.69%), business servants (744 cases, 20.35%) and retirees (669 cases, 18.30%). The peak of dengue fever epidemics was concentrated during the period from the 39th to the 45th weeks in 2024, when a total of 2 317 local cases were reported, accounting for 74.69% of all local cases in 2024. Dengue fever cases were reported across all 11 districts in Guangzhou City in 2024, with local cases concentrated in Baiyun District (754 cases, 24.31%), Liwan District (398 cases, 12.83%), Panyu District (365 cases, 11.77%), Haizhu District (332 cases, 10.70%) and Tianhe District (328 cases, 10.57%). Imported dengue fever cases were predominantly domestically imported (492 cases, 88.81%), with the majority imported from Foshan City (377 cases), and overseas imported cases were predominantly imported from southeastern Asian countries. The mean proportion of case isolation in hospital was 9.16% (284/3 102), and the mean interval from disease onset to case reporting was (3.99 ± 2.70) days, while the percentages of mosquito density meeting the required standard were 61.68% (462/ 749) and 66.32% (126/190) on the 4th and 7th day of emergency responses to epidemic foci, respectively. Conclusions The prevention and control cycle of dengue fever in Guangzhou in 2024 took longer than in previous years, with a larger scale of the epidemic. Although some progress has been made in epidemic management, there are still problems such as unsustainable mosquito vector control and low hospitalization isolation rates for cases. Further optimization of control measures in mosquito vector control, case monitoring and management is required to improve the effectiveness of dengue fever control measures.