ObjectiveTo develop an early - warning approach for influenza in Anhui province using the moving epidemic method （MEM）， offering a scientific basis for influenza prevention and control in the province. MethodsBased on the surveillance data reported weekly by influenza sentinel hospitals and network laboratories in the “China Influenza Surveillance Information System”， the weekly influenza virus detection positive rate （positive rate， PR） across the province was analyzed. The MEM was used to estimate the epidemic threshold and classification intensity threshold of influenza in Anhui province before and after the COVID-19 pandemic， respectively. The early warning effect of the MEM was evaluated using cross-validation. ResultsEpidemic and graded intensity thresholds of influenza were derived using PR and MEM. Prior to the COVID-19 pandemic， the starting， medium-intensity， high-intensity， extremely-high-intensity， and ending thresholds of influenza epidemics were 17.08%， 38.47%， 53.20%， 59.71%， and 12.34% respectively. At this point， the model exhibited sensitivity of 0.98， specificity of 0.86， Matthews correlation coefficient of 0.84， and Youden index of 0.84. After the COVID-19 pandemic， these thresholds were 25.61%， 43.81%， 63.29%， 74.46%， and 17.82% respectively. At this point， the model demonstrates sensitivity of 0.86， specificity of 0.92， Matthews correlation coefficient of 0.78， and Youden index of 0.77. ConclusionMEM shows good applicability in estimating the epidemic and graded intensity thresholds of influenza during the winter and spring in Anhui province. Both the influenza epidemic threshold and graded intensity thresholds after the COVID-19 pandemic are higher than those before the pandemic.

ObjectiveTo explore the impact of meteorological factors on influenza-like illness （ILI） cases in Anhui Province. MethodsILI surveillance data of Anhui Province from 2016 to 2023 were collected from the “China Influenza Surveillance Information System”， and meteorological data were obtained from Anhui Meteorological Center. R 4.2.1 was used to conduct Spearman rank correlation analysis between ILI cases and meteorological factors. A generalized additive model （GAM） was established to analyze the impact of meteorological factors on ILI cases. ResultsCorrelation analysis and GAM analysis showed that the average temperature and average relative humidity with a one-week lag had significant non-linear effects on ILI （P<0.05）. When the weekly average temperature was below 14 ℃， the number of ILI cases gradually increased as the temperature decreased. When the weekly average relative humidity was below 65%， the number of ILI cases gradually increased as the weekly average relative humidity decreased. The changes in ILI among the age groups 5-14， 15-24 and 25-59 years were influenced by meteorological factors， exhibiting a nonlinear relationship with weekly average temperature or weekly average relative humidity. ConclusionWeekly average temperature and weekly average relative humidity are the main factors affecting ILI in Anhui Province， and there is a lag effect， which can provide a reference for influenza surveillance and early warning.

Implementation science aims to facilitate the efficient dissemination and sustainable application of evidence based interventions, and has accordingly generated a variety of theories, models, and frameworks. Among these, the Exploration, Preparation, Implementation, and Sustainment (EPIS) frameworkfunctions both as a process model and a determinant framework, emphasizing the staged and iterative nature of implementation, and systematically explaining the influence of multilevel factors on implementation processes and outcomes through four dimensions: outer context, inner context, bridging factors, and innovation factors. The EPIS framework has been widely applied in health and public service settings internationally, yet its use in China based research remains relatively limited. This article systematically introduces the core components and application methods of the EPIS framework, and illustrates its value in identifying key influencing factors, developing implementation strategies, and optimizing implementation outcomes through case examples from the health sector, aiming to provide a methodological reference for implementation research and the promotion of evidence based interventions in China.

Objective: To evaluate the effectiveness of a family-school collaborative intervention on sleep health among fifth-grade primary school students, so as to provide scientific evidence for promoting sleep health among primary school students. Methods: From April to June 2024, a baseline survey was conducted on 342 fifth-grade students and their parents from one primary school in a district of Beijing, using the convenient sampling method. A semester of sleep health education was implemented from September to December 2024, covering cognitive, behavioral and environmental factors that affected timely bedtime; and the program took various forms, including student courses, popular-science articles for parents, and personalized feedback reports. Another primary school in the same area was conveniently selected as the control group, where 313 fifth-grade students and their parents received routine health education. The subjects were assessed for their baseline and end-point (March to June 2025) sleep conditions using sleep diaries (222 participants in the intervention group and 225 in the control group) and accelerometers (120 participants in the intervention group and 88 in the control group), respectively. The independent samples t-test and the Wilcoxon rank-sum test were used to compare characteristics between groups，and linear mixed models were employed to evaluate the intervention effects. Results: In the sleep diary evaluation, the intervention group had an earlier weekend wake up time and a smaller social jetlag than the control group at baseline ( t/Z =3.42, 7.21); in the accelerometer evaluation, the intervention group exhibited a smaller social jetlag than the control group at baseline ( Z =3.90) (all P <0.05). In the sleep diary evaluation, compared with the control group, the intervention group showed a reduced pre-to-post change in school-day bedtime ( β = -6.70), whereas the pre-to-post changes in school-day sleep duration, weekend wake-up time, weekend sleep duration, and average sleep duration were all increased at baseline and at the end ( β =8.41, 22.58, 27.03,13.74) (all P <0.05). In the accelerometer evaluation, compared with the control group, the intervention group showed reduced pre-to-post changes in school-day sleep onset time and school-day wake time ( β =-14.75, -6.24), whereas the pre-to-post change in school-day sleep duration increased at baseline and at the end ( β =8.01) (all P <0.05). Conclusion A family-school collaborative sleep health intervention may be an effective strategy for improving sleep among fifth-grade primary school students.

Objective:To investigate the feasibility and effectiveness of three-dimensional visualization technology in the precise diagnosis and treatment of locally advanced differentiated thyroid cancer(DTC).Methods:A retrospective analysis was conducted on the clinical data of 196 patients with locally advanced DTC treated at the 960th Hospital of the PLA from December 2021 to August 2023. The cohort included 71 male and 125 female patients, with a mean age of 43.7 years (rangd from 18 to 77 years). All patients underwent neck-enhanced CT scans and were divided into two groups: the study group( n=102), which underwent preoperative three-dimensional visualization of CT data, and the control group( n=94), which did not. Baseline data for both groups were matched using SPSS27.0 with 1∶1 propensity score matching (PSM) and the caliper value was 0.02. A total of 49 patients were included in each group, including 35 first-time surgeries and 14 reoperation. Among the 70 first-time surgeries, 29 patients underwent robotic surgery and 41 underwent open surgery. Among the 28 reoperations, 4 underwent robotic surgery and 24 underwent open surgery. In the study group, three-dimensional visual models were used to comprehensively evaluate the tumor and metastatic lesion size, spatial location, and adjacent relationship with surrounding organs, surgical treatments were guided by these models, whereas the control group relied on two-dimensional imaging for guidance. The clinical data were statistically analyzed using SPSS27.0. Results:All operations were successfully completed. There were no statistical differences in baseline data between the two groups( P>0.05). Among first-time surgeries, the study group showed shorter operation times [175(145, 200) min vs 205(182, 249) min, P<0.001], a lower incidence of postoperative chyle leak (0 vs 8.57%, P=0.027), a higher rate of robotic surgery (48.57% vs 34.28%, P=0.225), a greater number of harvested lymph nodes [46(40, 62) vs 37(28, 56), P=0.032], a greater number of cervical lymph node metastasis[15(7, 22) vs 5(1, 14), P=0.004] and a larger diameter of metastasis lymph nodes[12(10, 16) mm vs 4(1, 10) mm, P<0.001]. There were no significant differences in intraoperative blood loss, postoperative drainage days and incidence of hypoparathyroidism( P>0.05). During the reoperation, the study group had shorter operation times[103.5(95.0, 122.5) min vs 146.50(133.25, 172.25) min, P<0.001], less intraoperative blood loss[12.50(8.75, 22.50) mL vs 30.00(17.50, 35.00) mL, P=0.021], fewer postoperative drainage days[5.00(4.00, 6.00) d vs 6.00(5.00, 7.25) d, P=0.016] and a lower incidence of hypoparathyroidism(7.14% vs 42.86%, P=0.038).The robotic surgery rate was higher in the study group (21.42% vs 7.14%, P=0.596). There were no significant differences in lymph node dissection numbers, metastatic lymph node counts, or chyle leak incidences between the two groups ( P>0.05). No acute bleeding or incision infection occurred in any patient postoperatively. Conclusion:Three-dimensional visualization technology is an effective preoperative assessment method for evaluating the resectability of tumors and metastases lesions in locally advanced DTC. It enhances the accuracy and safety of surgery for locally advanced DTC.

Objective:To investigate the feasibility and safety of implementing a domestic vehicle-mounted remote mobile robotic surgical system in thyroid surgery applications, integrated with 5G communication technology.Methods:Using the main system located on the vehicle-mounted mobile robot operating platform of the 960th Hospital of PLA Joint Logistics Support Force and the slave system of Weifang Traditional Chinese Hospital, the remote radical thyroidectomy 5G communication technology, and analyze the clinical and information transmission data of two female patients who underwent remote mobile robot thyroid cancer surgery on October 21, 2024 at Weifang Traditional Chinese Medicine Hospital.Results:The remote radical thyroidectomy was conducted by the robosurgeons utilizing a vehicle-mounted mobile robotic surgical system, and the procedure was successfully completed without necessitating intermediate open surgery. The operation durations for patient 1 and patient 2 were 135 minutes and 108 minutes, respectively, with 7 and 13 lymph nodes dissected, respectively. The average delay in surgical data transmission was recorded at 61.9 milliseconds, with no instances of signal interruption or frame loss. The procedure proceeded smoothly, without any jamming, and the audio and video transmissions were consistently clear. Follow up for 21 days after surgery showed no complications such as hoarseness, skin damage, or lymphatic fistula.Conclusion:The implementation of a vehicle-mounted remote mobile robotic surgery system for thyroid surgery has demonstrated safety and feasibility. Furthermore, the utilization of the 5G network offers rapid data transmission and minimal latency, closely approximating the therapeutic efficacy of traditional robotic thyroidectomy.

Objective:To summarise the main points of operation during thyroid surgery using bilateral axillo-breast approach (BABA) with a domestic robot and to analyse the learning curve.Methods:From May. 2023 to Oct. 2023, we retrospectively analysed the clinical data of 38 patients who underwent thyroid surgery with domestically manufactured robot via BABA attending the Department of Thyroid and Breast Surgery of the 960th Hospital of PLA Joint Logistics Support Force, proposed and constructed learning curves using cumulative and analytical methods to study the minimum number of learning cases required for the loading time of assistants and surgeon-in-charge with da Vinci surgical experience to transition from the initial learning stage to the proficiency stage. The age, BMI, surgical time (loading time, operating time), postoperative drainage time, and number of lymph nodes dissected were compared among patients at different stages of the learning curve.Results:A total of 38 thyroidectomy patients successfully underwent surgery, with an average surgical time of (176.61 ± 47.98) minutes. After 18 cases, the assistant's loading time shortened and stabilized at (42.20 ± 3.44) minutes. The operator reached peak proficiency at the 18th case (loading time, operating time), which was considered the dividing point between the learning phase and the proficiency phase. During the learning phase, the average surgical time was (209.72 ± 49.28) minutes, operation time: (165.44 ± 49.93) minutes, while in the proficiency phase, the average surgical time decreased to 146.80 ± 18.34 minutes, operation time: (104.60 ± 19.01) minutes. There were no statistically significant differences in baseline characteristics such as age, BMI, postoperative drainage time, or the number of central compartment lymph nodes dissected between the two phases ( P > 0.05) . Conclusion:Over the results of this study, it was shown that for robosurgeons with experience in da Vinci robotic surgery, the transition from the learning stage to the proficiency stage could be considered after crossing the learning curve for 18 cases, and that assistants could become proficient in loading techniques after 18 procedures.

Objective:To explore the relationship between trait anger and depression among college students, as well as the mediating roles of perceived stress and emotion-oriented coping strategies.Methods:From March to April 2022, a total of 2 037 college students were surveyed by the trait anger scale, the perceived stress scale, the coping inventory for stressful situations, and the 9-item patient health questionnaire.The collected data were analyzed by SPSS 27.0 software and AMOS 24.0 statistical software packages.Results:The score of trait anger among college students was (14.78±3.56), the score of perceived stress was (10.02±4.77), the score of emotion-oriented coping was (14.43±3.38), and the score of depression was 2.00(0, 9.00). Correlation analysis showed that depression was significantly and positively correlated with temperamental anger, reactive anger, perceived stress, and emotion-oriented coping ( r=0.303-0.467, all P<0.01). Temperamental anger and reactive anger were significantly and positively correlated with both perceived stress and emotion-oriented coping ( r=0.279-0.332, all P<0.01). Emotion-oriented coping was significantly and positively correlated with perceived stress ( r=0.397, P<0.01). Mediation effect analysis showed that the total effect of trait anger on depression was 0.420. The direct effect of trait anger on depression was 0.241, accounting for 57.38% of the total effect. The mediating effects of perceived stress and emotion-oriented coping were 0.145 and 0.024, accounting for 34.52% and 5.72% of the total effect, respectively. The chain mediating effect of perceived stress and emotion-oriented coping was 0.010, accounting for 2.38% of the total effect. Conclusion:Trait anger can significantly and positively predict depression among college students, and it can also positively predict the level of depression among college students through the chain mediation effect of perceived stress and emotion orientation.

Gliomas, with their infiltrative growth and malignant potential, present considerable therapeutic challenges. Maximal safe resection is essential for optimizing patient survival and quality of life. Despite this, existing intraoperative visualization techniques suffer from limited sensitivity and accuracy. Label-free Raman spectroscopy has emerged as a non-invasive, rapidly diagnostic, and highly accurate technique. It accurately diagnoses tumors, identifies infiltration margins, and classifies molecular subtypes, enhancing the precision of glioma surgery. This review summarizes the latest advances and applications of label-free Raman spectroscopy in glioma surgery.

Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.