Objective To understand the current situation of nutrition literacy of primary and secondary school students in Shijingshan District of Beijing, and analyze its influencing factors, and to put forward targeted suggestions for improving the students’ nutrition literacy and promoting their healthy growth. Methods A multi-stage stratified cluster sampling method was used to select 2480 primary and secondary school students and their parents from 5 primary schools, 3 middle schools and 1 high school in Shijingshan District. The multivariate logistic regression model was used to analyze the factors influencing the attainment rate of nutrition literacy. Results The median score of nutrition literacy of 2480 primary and secondary school students from grades 1 to 12 was 77.86 (in hundred-mark system), the quartile range (IQR) was 16.96, and the attainment rate of nutrition literacy was 42.46%. The cognitive level (45.12%) was higher than the skill level (41.20%) among students from grades 3 to 12. In terms of skills, the attainment rate of food preparation was the lowest, at 30.38%. The scores of nutrition literacy of girls were higher than those of boys, and the scores of primary school students were higher than those of secondary school students. Students with different levels of caregiver’s education, family income, and family food environment had different scores of nutrition literacy, and the differences were statistically significant (P<0.05). Multivariate logistic regression analysis showed that the attainment rate of nutrition literacy was closely related to student’s gender and study stage, caregiver’s education level, and family food environment. Conclusion The nutrition literacy of primary and secondary school students in Shijingshan District still needs to be improved, especially in the aspect of skills. Targeted nutrition education should be carried out.

Objective To report the diagnosis of a canine distemper virus outbreak among a colony of cynomolgus monkeys at an experimental monkey farm in 2019. MethodsA total of 46 samples were collected from 21 diseased cynomolgus monkeys (exhibiting symptoms such as facial rash, skin scurf, runny nose, and diarrhea) and from one deceased monkey at an experimental monkey breeding farm in South China in late 2019, including serum, skin rash swabs, and anticoagulated whole blood, liver, lung, and skin tissues were submitted for testing. All submitted samples were tested for canine distemper virus gene fragments using real-time quantitative PCR, while immunohistochemical staining was performed to detect canine distemper virus nucleoprotein in lung tissues. The skin tissue of the deceased monkey was ground and sieved. The filtrate was inoculated into a monolayer MDCK cell line for virus isolation. Then, whole-genome sequencing was performed to identify the isolated virus. The Clustal Omega tool was used to align and analyze the homology of different Asian canine distemper virus isolates. A phylogenetic tree was constructed, followed by genetic evolutionary analysis. ResultsClinical retrospective analysis revealed that the diseased cynomolgus monkeys exhibited symptoms similar to those observed in cynomolgus monkeys infected with measles virus. Necropsy findings showed red lesions in the lungs and significant hemorrhage in the colonic mucosa. Real-time quantitative PCR detected canine distemper virus nucleic acid in the serum, skin rash swabs of the infected monkeys, and various tissue samples of the deceased monkey, all of which tested positive. Calculation based on the standard curve formula indicated the viral load was highest in the skin tissue. Immunohistochemical staining of the deceased monkey's lung tissue demonstrated aggregation of CDV nucleoprotein in alveolar epithelial cells, bronchi, and bronchioles. A CDV strain was isolated from the skin tissue of the deceased monkey. Phylogenetic analysis indicated that this strain shares the closest relationship (98.86%) with the Asian-1 type canine distemper virus strain CDV/dog/HCM/33/140816, previously identified in dogs in Vietnam. ConclusionBased on comprehensive analysis of clinical symptoms, nucleic acid detection, viral protein immunohistochemistry, and whole-genome sequencing results, the diagnosis confirms that the cynomolgus monkeys in this facility are infected with canine distemper virus. It is recommended to include canine distemper virus as a routine surveillance target in captive monkey populations. Additionally, this study provides a foundation for further research on the molecular biological characteristics of canine distemper virus.

Objective To report the diagnosis of a canine distemper virus outbreak among a colony of cynomolgus monkeys at an experimental monkey farm in 2019. MethodsA total of 46 samples were collected from 21 diseased cynomolgus monkeys (exhibiting symptoms such as facial rash, skin scurf, runny nose, and diarrhea) and from one deceased monkey at an experimental monkey breeding farm in South China in late 2019, including serum, skin rash swabs, and anticoagulated whole blood, liver, lung, and skin tissues were submitted for testing. All submitted samples were tested for canine distemper virus gene fragments using real-time quantitative PCR, while immunohistochemical staining was performed to detect canine distemper virus nucleoprotein in lung tissues. The skin tissue of the deceased monkey was ground and sieved. The filtrate was inoculated into a monolayer MDCK cell line for virus isolation. Then, whole-genome sequencing was performed to identify the isolated virus. The Clustal Omega tool was used to align and analyze the homology of different Asian canine distemper virus isolates. A phylogenetic tree was constructed, followed by genetic evolutionary analysis. ResultsClinical retrospective analysis revealed that the diseased cynomolgus monkeys exhibited symptoms similar to those observed in cynomolgus monkeys infected with measles virus. Necropsy findings showed red lesions in the lungs and significant hemorrhage in the colonic mucosa. Real-time quantitative PCR detected canine distemper virus nucleic acid in the serum, skin rash swabs of the infected monkeys, and various tissue samples of the deceased monkey, all of which tested positive. Calculation based on the standard curve formula indicated the viral load was highest in the skin tissue. Immunohistochemical staining of the deceased monkey's lung tissue demonstrated aggregation of CDV nucleoprotein in alveolar epithelial cells, bronchi, and bronchioles. A CDV strain was isolated from the skin tissue of the deceased monkey. Phylogenetic analysis indicated that this strain shares the closest relationship (98.86%) with the Asian-1 type canine distemper virus strain CDV/dog/HCM/33/140816, previously identified in dogs in Vietnam. ConclusionBased on comprehensive analysis of clinical symptoms, nucleic acid detection, viral protein immunohistochemistry, and whole-genome sequencing results, the diagnosis confirms that the cynomolgus monkeys in this facility are infected with canine distemper virus. It is recommended to include canine distemper virus as a routine surveillance target in captive monkey populations. Additionally, this study provides a foundation for further research on the molecular biological characteristics of canine distemper virus.

Objective:To explore the influencing factors related to olfactory dysfunction secondary to different types of chronic rhinosinusitis（CRS）. Methods:A retrospective analysis was conducted on 185 CRS patients treated at the Department of Otolaryngology-Head and Neck Surgery of Shanxi Provincial People's Hospital from July 2023 to July 2024. Based on the presence or absence of nasal polyps, CRS was divided into two groups: chronic rhinosinusitis with nasal polyps（CRSwNP） and chronic rhinosinusitis without nasal polyps（CRSsNP）. Further, based on whether olfactory dysfunction was present, the CRSwNP and CRSsNP groups were divided into subgroups with olfactory dysfunction and normal olfaction. General data, laboratory tests, and modified sinus CT scores were compared between the subgroups. Logistic regression analysis was conducted to identify independent influencing factors based on the results of univariate analysis combined with clinical significance, and two nomogram models were established. The area under the curve of the receiver operating characteristic（ROC） curve, calibration curves, and decision curve analysis were used to assess the diagnostic performance, calibration, and clinical utility of the predictive model. Results:The proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region were identified as independent influencing factors in the CRSwNP group; the proportion of blood monocytes and modified CT scores of the bilateral posterior region were independent influencing factors in the CRSsNP group. The nomogram prediction model showed good diagnostic performance, calibration, and clinical utility in both the CRSwNP and CRSsNP groups. Conclusion:Olfactory dysfunction in CRSwNP patients is closely related to the proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region, while olfactory dysfunction in CRSsNP patients is closely related to the proportion of blood monocytes and modified CT scores of the bilateral posterior region. Moreover, the predictive model established in this study demonstrates good clinical performance and can be used for early identification and risk prediction of olfactory dysfunction secondary to CRS.
Humans ; Sinusitis/complications* ; Chronic Disease ; Retrospective Studies ; Olfaction Disorders/etiology* ; Nasal Polyps/complications* ; Rhinitis/complications* ; Female ; Male ; Logistic Models ; Middle Aged ; Smell ; Adult ; ROC Curve ; Nomograms ; Eosinophils ; Tomography, X-Ray Computed

OBJECTIVE: To investigate the therapeutic potential of octaarginine (R8)-modified essential oil from Fructus Alpiniae zerumbet (EOFAZ) lipid microspheres (EOFAZ@^R8LM) for cardiovascular therapy. METHODS: EOFAZ@^R8LM was developed by leveraging the volatilization of EOFAZ and integrating it with the oil phase of LM, followed by surface modification with cell-penetrating peptide R8 to target the site of vascular endothelial injury. The therapeutic effects of this formulation in alleviating lipopolysaccharide-induced vascular endothelial inflammation were evaluated by assessing mitochondrial membrane potential (MMP), intracellular reactive oxygen species (ROS) levels, as well as inflammatory factors interleukin-6 (IL-6) and interleukin-1β (IL-1β) levels. RESULTS: EOFAZ@^R8LM effectively delivered EOFAZ to the site of injury and specifically targeted the mitochondria in vascular endothelial cells, thereby ameliorating mitochondrial dysfunction through regulation of MMP and reduction of intracellular ROS levels. Moreover, it attenuated the expression levels of IL-6 and IL-1β, exerting protective effects on the vascular endothelium. CONCLUSION Our findings highlight the significant therapeutic potential of EOFAZ@^R8LM in cardiovascular therapy, providing valuable insights for developing novel dosage forms utilizing EOFAZ for effective treatment against cardiovascular diseases.

Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, overshadowing its influence on the biological effects of formulations. In this study, we reevaluated the delivery effect of CH using widely applied lipid microspheres (LM) as a model DDS. We conducted comprehensive investigations into the impact of CH on the distribution, cell uptake, and protein corona (PC) of LM at sites of cardiovascular inflammatory injury. The results demonstrated that moderate CH promoted the accumulation of LM at inflamed cardiac and vascular sites without exacerbating damage while partially mitigating pathological damage. Then, the slow cellular uptake rate observed for CH@LM contributed to a prolonged duration of drug efficacy. Network pharmacology and molecular docking analyses revealed that CH depended on LM and exerted its biological effects by modulating peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in vascular endothelial cells and estrogen receptor alpha (ERα) protein levels in myocardial cells, thereby enhancing LM uptake at cardiovascular inflammation sites. Proteomics analysis unveiled a serum adsorption pattern for CH@LM under inflammatory conditions showing significant adsorption with CH metabolism-related apolipoprotein family members such as apolipoprotein A-V (Apoa5); this may be a major contributing factor to their prolonged circulation in vivo and explains why CH enhances the distribution of LM at cardiovascular inflammatory injury sites. It should be noted that changes in cell types and physiological environments can also influence the biological behavior of formulations. The findings enhance the conceptualization of CH and LM delivery, providing novel strategies for investigating prescription factors' bioactivity.

OBJECTIVE: To explore the preventive effect of transcutaneous phrenic nerve stimulation on ventilator-induced diaphragmatic dysfunction (VIDD) in patients requiring invasive mechanical ventilation. METHODS: A randomized controlled trial was conducted. The patients requiring invasive mechanical ventilation admitted to the intensive care unit (ICU) of Jiaxing First Hospital from November 2022 to December 2023 were enrolled. Participants were randomized into the control group and the observation group using a random number table. The control group was given ICU standardized nursing intervention, including turning over and slapping the back, raising the head of the bed, sputum aspiration on demand, aerosol inhalation, oral care, and monitoring of airbag pressure and gastric retention, the observation group was given additional transcutaneous phrenic nerve stimulation intervention on the basis of ICU standardized nursing intervention. The stimulation intensity was set to 10 U, the pulse frequency was set to 40 Hz, and the stimulation frequency was set to 12 times/min. Transcutaneous phrenic nerve stimulation was administered once a day for 30 minutes each time, for a total of 5 days. Diaphragm thickening fraction (DTF) and arterial blood gas parameters on days 1, 3, and 5 of intervention were compared between the two groups. After 5 days of intervention, other parameters including the incidence of VIDD, duration of mechanical ventilation, and length of ICU stay were compared. RESULTS: A total of 120 patients requiring invasive mechanical ventilation were enrolled, with 16 dropouts (dropout rate was 13.33%). Ultimately, 51 patients in the control group and 53 patients in the observation group were analyzed. Baseline characteristics, including gender, age, body mass index (BMI), acute physiology and chronic health evaluation II (APACHE II) score, albumin (Alb), hemoglobin (Hb), and disease type, showed no significant differences between the two groups. DTF in both groups gradually increased over duration of intervention [DTF on days 1, 3, and 5 in the control group was (20.83±2.33)%, (21.92±1.27)%, and (23.93±2.33)%, respectively, and that in the observation group was (20.89±1.96)%, (22.56±1.64)%, and (25.34±2.38)%, respectively], with more significant changes in DTF in the observation group, showing time effects (F_time = 105.975, P < 0.001), intervention effects (F_intervention = 7.378, P = 0.008), and interaction effects (F_interaction = 3.322, P = 0.038). Arterial blood gas parameters did not differ significantly before intervention between the groups, but after 5 days of intervention, arterial partial pressure of oxygen (PaO₂) in the observation group was significantly higher than that in the control group [mmHg (1 mmHg≈0.133 kPa): 100.72±15.75 vs. 93.62±15.54, P < 0.05], and arterial partial pressure of carbon dioxide (PaCO₂) was significantly lower than that in the control group (mmHg: 36.53±3.10 vs. 37.69±2.02, P < 0.05). At 5 days of intervention, the incidence of VIDD in the observation group was significantly lower than that in the control group [15.09% (8/53) vs. 37.25% (19/51), P < 0.05], and both duration of mechanical ventilation and length of ICU stay were significantly shorter than those in the control group [duration of mechanical ventilation (days): 7.93±2.06 vs. 8.77±1.76, length of ICU stay (days): 9.64±2.35 vs. 11.01±2.01, both P < 0.05]. CONCLUSIONS Transcutaneous phrenic nerve stimulation can improve diaphragmatic and respiratory function in patients receiving invasive mechanical ventilation, reduce the incidence of VIDD, and shorten the duration of mechanical ventilation and length of ICU stay.
Humans ; Transcutaneous Electric Nerve Stimulation ; Respiration, Artificial/adverse effects* ; Diaphragm/physiopathology* ; Phrenic Nerve ; Intensive Care Units ; Male ; Female ; Middle Aged

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Objective To construct an intelligent segmentation and T-stage diagnostic model for esophageal cancer based on the DAEUnet and ConvNeXt networks using transfer learning.Methods Dicom raw data from 126 patients diagnosed with esophageal cancer between January 2018 and April 2022 were collected,including 100 cases from Department of Thoracic Surgery at the First Affiliated Hospital of Army Medical University and 26 cases from the Department of Thoracic Surgery at Shanxi Cancer Hospital.After data augmentation,a total of 60 275 images were obtained.The DAEUnet esophageal cancer intelligent segmentation network was built,and on this basis,3 classification networks,ConvNeXt,Swin Transformer,and ResNet were constructed for T-stage diagnosis of esophageal cancer.Results The Dice similarity coefficient(DSC)for esophageal cancer intelligent segmentation using the DAEUnet network was 0.82,and the DSC value of the esophagus,aorta,normal esophagus,mediastinal lymph nodes,and heart was 72.4％,87.5％,79.3％,60.5％ and 96.8％,respectively.Among the 3 T-stage diagnosis models for esophageal cancer,the ConvNeXt model performed the best,with a precision value for T1～T4 stages of 0.65,0.727,0.889 and 0.92,respectively,and an AUC value of 0.892,which were superior to the ResNet and Swin Transformer networks.Conclusion The proposed DAEUnet and ConvNeXt-based intelligent segmentation and T-stage diagnosis model for esophageal cancer improves T-stage accuracy and treatment efficiency.

Objective:To construct univariate and multivariate relapse free survival (RFS) prediction models for breast cancer patients with diabetes mellitus type 2 (T2DM) and to compare and select the model with higher predictive performance.Methods:A total of 912 breast cancer patients treated at the First Affiliated Hospital of Dalian Medical University from January 2010 to December 2016 were included, of which 202 patients had T2DM and 710 patients did not. Kaplan-Meier survival curve was drawn based on whether patients had T2DM, and log-rank test was performed based on whether patients had T2DM. All patients were randomly divided into a training set ( n=640) and a validation set ( n=272) at a ratio of 7∶3. Univariate and multivariate Cox proportional risk regression models were used to analyze RFS in breast cancer patients with the survival package. The "rms" package was employed to construct univariate and multivariate RFS prediction models for breast cancer patients with T2DM. Clinical decision curves and calibration curves were used to validate the models. The receiver operator characteristic (ROC) curve was used to compare and analyze the prediction performance of the two models. Results:There were no statistically significant differences between the training set and the validation set patients in terms of age, T2DM, surgical approach, axillary management methods, T stage, N stage, molecular sub-type, estrogen receptor (ER) 1, ER2, progesterone receptor (PR) , ER and PR consistency, Ki67, human epidermal growth factor receptor 2 (HER2) (all P>0.05) . There was a statistically significant difference in histological grade ( χ2=7.59, P=0.022) . Survival analysis showed that the 5-year RFS rate was 83.7% in patients with T2DM and 92.3% in patients without T2DM ( χ2=16.61, P<0.001) . Univariate analysis revealed that age ( HR=1.04, 95% CI: 1.03-1.06, P<0.001) , T2DM ( HR=2.31, 95% CI: 1.49-3.55, P<0.001) , surgical approach ( HR=2.39, 95% CI: 1.20-4.77, P=0.013) , axillary management methods ( HR=2.62, 95% CI: 1.72-3.98, P<0.001) , T stage (T 2: HR=2.13, 95% CI: 1.36-3.31, P<0.001; T 3: HR=6.90, 95% CI: 3.35-14.22, P<0.001) , N stage (N 2: HR=3.87, 95% CI: 2.12-7.07, P<0.001; N 3: HR=8.61, 95% CI: 4.71-15.75, P<0.001) , molecular sub-type (Luminal B: HR=2.74, 95% CI: 1.17-6.36, P=0.019; HER2 +: HR=3.64, 95% CI: 1.38-9.58, P=0.009; TNBC: HR=4.40, 95% CI: 1.71-11.34, P=0.002) , ER1 (>10%: HR=0.57, 95% CI: 0.37-0.90, P=0.016) , ER2 ( HR=0.57, 95% CI: 0.37-0.89, P=0.015) , and PR ( HR=0.56, 95% CI: 0.37-0.86, P=0.008) were all factors influencing RFS in breast cancer patients. Multivariate analysis demonstrated that age ( HR=1.04, 95% CI: 1.02-1.06, P<0.001) , T2DM ( HR=1.82, 95% CI: 1.16-2.85, P=0.009) , T stage (T 2: HR=1.60, 95% CI: 1.01-2.54, P=0.046; T 3: HR=2.64, 95% CI: 1.22-5.72, P=0.014) , N stage (N 2: HR=3.72, 95% CI: 2.01-6.88, P<0.001; N 3: HR=5.34, 95% CI: 2.78-10.25, P<0.001) , and ER1 (>10%: HR=0.63, 95% CI: 0.39-0.99, P=0.046) were independent factors influencing RFS in breast cancer patients. Based on the 10 and 5 variables with P<0.05 in the univariate and multivariate analyses respectively, the nomograms of the univariate and multivariate prediction models were constructed to evaluate the influence of factors such as T2DM on the postoperative RFS of breast cancer patients. Clinical decision curves and calibration curves indicated that both models had high predictive value for RFS in breast cancer patients, and the predictive results were highly consistent with the actual observed results. ROC curve analysis showed that there was no statistically significant difference in the area under the curve (AUC) of the two models for predicting the RFS rates of breast cancer patients in the training set and validation set at 36, 60, and 84 months (all P>0.05) , indicating that the predictive efficacy of the two models was comparable. The multivariate model is more suitable for clinical application because it uses fewer variables. Conclusions:Breast cancer patients with T2DM have poorer prognosis. Age, T2DM, T stage, N stage, and ER1 are independent factors influencing postoperative RFS in breast cancer patients. The multi-factor prediction model of RFS in breast cancer patients based on T2DM is more suitable for clinical application due to its higher predictive efficacy and fewer variables.