Objective To investigate the current level of public awareness regarding nuclear emergency evacuation around a nuclear power plant, analyze the influencing factors, and propose suggestions and countermeasures based on the results. Methods In July 2024, according to the survey protocol and questionnaire developed by the National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, a field-based centralized online questionnaire was administered. A total of 854 residents living near the nuclear power plant were included as survey participants. An analysis of variance was used to compare the impact of different factors on the public knowledge of nuclear radiation and awareness of nuclear emergency evacuation, while the chi-square test was employed to compare differences between groups. Results A total of 854 questionnaires were collected in this study. The survey revealed that the levels of public knowledge about nuclear radiation and awareness of nuclear emergency evacuation around the power plant were relatively low, with average objective awareness rates of 51% and 47%, respectively. In terms of age, the 30-45 years old group had the highest average score, while the group aged 60 and above had the lowest. Regarding education level, the group with primary school education or below had the lowest average score, whereas those with junior college or undergraduate education scored the highest. The internet (73.7%) was the primary source of emergency information for the public, followed by television (61.7%). The majority of the public (85.0%) expressed trust in the government during evacuation and were willing to follow governmental evacuation arrangements. The main reason for this willingness was the belief that the government could provide sufficient emergency supplies. Conclusion The surveyed population exhibited low levels of knowledge regarding nuclear radiation and awareness of nuclear emergency evacuation, with generally low awareness rates. Awareness levels were influenced by factors such as sex, age, educational background, and distance from the nuclear power plant. To enhance public awareness, it is necessary to strengthen science communication related to nuclear radiation and public protective actions in nuclear emergencies. Targeted dissemination strategies with high communication effectiveness, accessibility, and public acceptance should be adopted to gradually enhance public awareness of nuclear radiation and nuclear emergency protective actions.

Objective:To investigate the risk factors of lower extremity deep venous thrombosis (LEDVT) in patients with sepsis during hospitalization in intensive care unit (ICU), and to construct a nomogram prediction model of LEDVT in sepsis patients in the ICU based on the critical care scores combined with inflammatory markers, and to validate its effectiveness in early prediction.Methods:726 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2015 to December 2021 were retrospectively included as the training set to construct the prediction model. In addition, 213 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2022 to June 2023 were retrospectively included as the validation set to verify the performance of the prediction model. Clinical data of patients were collected, such as demographic information, vital signs at the time of admission to the ICU, underlying diseases, past history, various types of scores within 24 hours of admission to the ICU, the first laboratory indexes of admission to the ICU, lower extremity venous ultrasound results, treatment, and prognostic indexes. Lasso regression analysis was used to screen the influencing factors for the occurrence of LEDVT in sepsis patients, and the results of Logistic regression analysis were synthesized to construct a nomogram model. The nomogram model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve, clinical impact curve (CIC) and decision curve analysis (DCA).Results:The incidence of LEDVT after ICU admission was 21.5% (156/726) in the training set of sepsis patients and 21.6% (46/213) in the validation set of sepsis patients. The baseline data of patients in both training and validation sets were comparable. Lasso regression analysis showed that seven independent variables were screened from 67 parameters to be associated with the occurrence of LEDVT in patients with sepsis. Logistic regression analysis showed that the age [odds ratio ( OR) = 1.03, 95% confidence interval (95% CI) was 1.01 to 1.04, P < 0.001], body mass index (BMI: OR = 1.05, 95% CI was 1.01 to 1.09, P = 0.009), venous thromboembolism (VTE) score ( OR = 1.20, 95% CI was 1.11 to 1.29, P < 0.001), activated partial thromboplastin time (APTT: OR = 0.98, 95% CI was 0.97 to 0.99, P = 0.009), D-dimer ( OR = 1.03, 95% CI was 1.01 to 1.04, P < 0.001), skin or soft-tissue infection ( OR = 2.53, 95% CI was 1.29 to 4.98, P = 0.007), and femoral venous cannulation ( OR = 3.72, 95% CI was 2.50 to 5.54, P < 0.001) were the independent influences on the occurrence of LEDVT in patients with sepsis. The nomogram model was constructed by combining the above variables, and the ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the occurrence of LEDVT in patients with sepsis was 0.793 (95% CI was 0.746 to 0.841), and the AUC in the validation set was 0.844 (95% CI was 0.786 to 0.901). The calibration curve showed that its predicted probability was in good agreement with the actual probabilities were in good agreement, and both CIC and DCA curves suggested a favorable net clinical benefit. Conclusion:The nomogram model based on the critical illness scores combined with inflammatory markers can be used for early prediction of LEDVT in ICU sepsis patients, which helps clinicians to identify the risk factors for LEDVT in sepsis patients earlier, so as to achieve early treatment.

Objective:To explore the current situation and influencing factors of quality of life of septic patients in intensive care unit (ICU) after discharge, and to provide theoretical basis for clinical early psychological intervention and continuity of care.Methods:A prospective observational study was conducted. The septic patients who were hospitalized in the department of critical care medicine of the Affiliated Hospital of Jining Medical University and discharged with improvement from January 1 to December 31, 2022 were selected as the research objects. The demographic information, basic diseases, infection site, vital signs at ICU admission, severity scores of the condition within 24 hours after ICU admission, various biochemical indexes, treatment process, and prognostic indexes of all the patients were recorded. All patients were assessed by questionnaire at 3 months of discharge using the 36-item short-form health survey scale (SF-36 scale), the activities of daily living scale (ADL scale), and the Montreal cognitive assessment scale (MoCA scale). Multiple linear regression was used to analyze the factors influencing the quality of life of septic patients after discharge from the hospital.Results:A total of 200 septic patients were discharged with improvement and followed up at 3 months of discharge, of which 150 completed the questionnaire. Of the 150 patients, 57 had sepsis and 93 had septic shock. The total SF-36 scale score of septic patients at 3 months of discharge was 81.4±23.0, and the scores of dimensions were, in descending order, role-emotional (83.4±23.0), mental health (82.9±23.6), bodily pain (82.8±23.3), vitality (81.6±23.2), physical function (81.4±23.5), general health (81.1±23.3), role-physical (79.5±27.0), and social function (78.8±25.2). There was no statistically significant difference in the total SF-36 scale score between the patients with sepsis and septic shock (82.6±22.0 vs. 80.7±23.6, P > 0.05). Incorporating the statistically significant indicators from linear univariate analysis into multiple linear regression analysis, and the results showed that the factors influencing the quality of life of septic patients at 3 months after discharge included ADL scale score at 3 months after discharge [ β= 0.741, 95% confidence interval (95% CI) was 0.606 to 0.791, P < 0.001], length of ICU stay ( β= -0.209, 95% CI was -0.733 to -0.208, P = 0.001), duration of mechanical ventilation ( β= 0.147, 95% CI was 0.122 to 0.978, P = 0.012), total dosage of norepinephrine ( β= -0.111, 95% CI was -0.044 to -0.002, P = 0.028), mean arterial pressure (MAP) at ICU admission ( β= -0.102, 95% CI was -0.203 to -0.007, P = 0.036) and body weight ( β= 0.097, 95% CI was 0.005 to 0.345, P = 0.044). Conclusions:The quality of life of patients with sepsis at 3 months after discharge is at a moderately high level. The influencing factors of the quality of life of patients with sepsis at 3 months after discharge include the ADL scale score at 3 months after discharge, the length of ICU stay, the duration of mechanical ventilation, the total dosage of norepinephrine, MAP at ICU admission and body weight, and healthcare professionals should enhance the treatment and care of the patients during their hospitalization based on the above influencing factors, and pay attention to early psychological intervention and continued care for such patients.

After the Three Mile Island, Chernobyl, and Fukushima nuclear accidents, numerous issues were exposed during the emergency evacuation process, such as insufficiently detailed emergency plans, lack of specific evacuation route schemes, inadequate preparation of emergency protective materials, and delays in emergency response decision-making. Additionally, these accidents revealed serious issues with the emergency evacuation of vulnerable populations. In particular, during the Fukushima nuclear accident, the lack of resource support led to deteriorating health and fatalities among hospital patients and elderly residents in nursing homes near the nuclear power plant during emergency evacuation. To learn from the experiences and lessons of public protection actions in emergency evacuations during major nuclear accidents, the government should enhance the guidance of nuclear emergency evacuation plans, increase the quality of emergency training and exercises, and improve their specificity and continuity, as well as establish an efficient nuclear emergency rescue response and decision-making mechanism. For vulnerable populations in nuclear emergency evacuations, the government should consider updating the nuclear emergency plans and disaster preparedness material reserves of medical facilities (such as designated treatment hospitals) and elderly facilities (such as nursing homes) within the emergency protection action areas of nuclear power plants in a timely manner. This will ensure that these institutions have the capacity to provide initial evacuation and necessary support for vulnerable populations in disaster situations.

Objective: To determine the current prevalence of health risk behaviors among college students in Henan Province, and to conduct an in depth analysis of the impact of cumulative ecological risks on health risk behaviors, so as to provide scientific basis for promoting healthy development of adolescents. Methods: Using a multi stage stratified cluster sampling method, 9 743 college students from six universities in Henan Province were included as the research subjects from April to June 2023. A questionnaire survey was conducted using the College Student Cumulative Ecological Risk Scale and the China Urban Adolescent Health Related Behavior Survey Questionnaire (University Version). Data were analyzed by descriptive statistical analysis, Chi square test and binary Logistic regression. Results: The reporting rates of unhealthy eating behavior, unhealthy weight loss behaviors, lack of physical activity, daily risk behaviors, negative emotions, current smoking behavior current drinking behaviors, Internet addiction emotions and dangerous sexual behaviors among college students in Henan Province were 40.2%, 39.5%, 76.0%, 13.7%, 28.1%, 11.3%, 12.7%, 5.9% and 2.2%, respectively. The reporting rates of negative emotions, current smoking behaviors, current drinking behaviors, dangerous sexual behaviors and daily risk behaviors of college students were higher in boys than in girls ( χ 2=44.00, 995.20, 902.49, 121.95, 103.09, P <0.05). In terms of reporting rates of unhealthy diet, unhealthy weight loss and lack of exercise behavior, girls were higher than boys ( χ 2=107.59, 13.01, 145.83, P <0.05). Cumulative ecological risk was positively correlated with overall health risk behaviors. For every unit increase in the cumulative ecological risk index, the risk of health risk behaviors among college students increased by 48%. Conclusions The prevalence of health risk behaviors among college students is relatively common. It should adrocate for a healthy lifestyle, reduce the cumulative ecological risk and the occurrence of health risk behaviors to promote the healthy development of adolescents.

Objective:To analyze and explore the independent risk factors of 28-day mortality in patients with septic myocardial injury.Methods:A retrospective cohort study was conducted to collect clinical data of 505 patients diagnosed with sepsis related myocardial injury admitted to the intensive care unit (ICU) of the Affiliated Hospital of Jining Medical University from January 2015 to December 2020. According to the 28-day survival status of patients, they were divided into survival group and death group. COX multivariate regression analysis was used to analyze the influencing factors of the 28-day mortality rate of sepsis related myocardial injury patients, and receiver operating characteristic (ROC) curves were drawn to evaluate the effectiveness of independent risk factors in predicting the 28-day mortality rate of sepsis related myocardial injury patients.Results:A total of 505 patients with sepsis myocardial injury were included, of which 282 survived on 28 days and 223 died, with a mortality rate of 44.16%. COX multivariate regression analysis showed that Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, blood lactate (LAC), oxygenation index (PaO 2/FiO 2), admission heart rate, and albumin were independent risk factors for sepsis associated myocardial injury mortality at 28 days (all P<0.05). ROC curve analysis showed that the area under the ROC curve (AUC) of SOFA score was 0.766 2, and the 95% confidence interval (95% CI) was 0.724 5-0.807 9; The predictive value of 28-day mortality in sepsis associated myocardial injury patients was superior to APACHE Ⅱ score, LAC, PaO 2/FiO 2, admission heart rate, and albumin [The AUC values were 0.754 1(0.711 5-0.796 7), 0.752 6(0.710 1-0.795 1), 0.697 0(0.649 7-0.744 2), 0.623 2(0.573 7-0.672 7), and 0.620 3(0.570 8-0.669 7), respectively]. Conclusions:SOFA score, APACHE Ⅱ score, LAC, PaO 2/FiO 2, admission heart rate, and albumin are independent risk factors for the 28-day mortality rate of sepsis related myocardial injury. Clinical practice should identify these factors early, intervene early, and improve patient prognosis.

This article reported a case of pyruvate dehydrogenase E1-α deficiency suggested by abnormal brain development during prenatal ultrasound imaging. Prenatal ultrasound revealed a mild enlargement of bilateral cerebral ventricles and the possibility of intracranial hemorrhage in the fetus at 25 +1 weeks of gestation. MRI showed the fetus with absent corpus callosum, enlarged bilateral cerebral ventricles and paraventricular cysts. After genetic counseling and careful consideration, the couple opted for pregnancy termination. To clarify the cause of the disease, whole-exome sequencing was performed on the fetal skin to detect possible variants, and which revealed a frameshift mutation c.924_930dup(p.R311Gfs*5) in exon 10 of the PDHA1 gene. Sanger sequencing confirmed the mutation was a de novo pathogenic variant, indicating that the fetus was affected by pyruvate dehydrogenase E1-α deficiency.

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Familial primary nocturnal enuresis (FPNE) is common in clinical practice and has shown an obvious familial aggregation that is associated with genetic factors.It has been found that chromosomes 4, 8, 12, 13 and 22 are related to the inheritance of enuresis. PRDM13 and EDNRB genes are related to the pathogenesis of enuresis, but the specific functions remain unclear.FPNE accounts for a high proportion in patients with refractory enuresis.Compared with other types of primary enuresis, FPNE is not difficult to be diagnosed, as long as the related family members have enuresis, it can be diagnosed as FPNE.Due to treatment difficulties, FPNE easily lasts into adulthood, serving as a type of intractable enuresis.Therefore, early diagnosis and active intervention should be made for children with FPNE.In this review, the epidemiology, pathogenesis, diagnosis and treatment of FPNE were summarized, aiming to provide references for improving the clinical diagnosis and treatment of FPNE.

Objective:To construct a nomogram model predicting the occurrence of acute kidney injury (AKI) in patients with sepsis in the intensive care unit (ICU), and to verify its validity for early prediction.Methods:Sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to December 2021 were retrospectively included, and those who met the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Univariate and multivariate logistic regression models were used to identify independent risk factors for AKI in patients with sepsis, and a nomogram was constructed based on the independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model.Results:741 patients with sepsis were included in the study, 335 patients developed AKI within 7 d of ICU admission, with an AKI incidence of 45.1%. Randomization was performed in the training set ( n=519) and internal validation set ( n=222). Multivariate logistic analysis revealed that acute physiology and chronic health status score Ⅱ, sequential organ failure score, serum lactate, calcitoninogen, norepinephrine dose, urea nitrogen, and neutrophil percentage were independent factors influencing the occurrence of AKI, and a nomogram model was constructed by combining these variables. In the training set, the AUC of the nomogram model ROC was 0.875 (95% CI: 0.767-0.835), the calibration curve showed consistency between the predicted and actual probabilities, and the DCA showed a good net clinical benefit. In the internal validation set, the nomogram model had a similar predictive value for AKI (AUC=0.871, 95% CI: 0.734-0.854). Conclusions:A nomogram model constructed based on the critical care score at admission combined with inflammatory markers can be used for the early prediction of AKI in sepsis patients in the ICU. The model is helpful for clinicians early identify AKI in sepsis patients.