Objective To systematically evaluate the efficacy and safety of traditional Chinese medicine (TCM) compound in treating pulmonary nodules, providing evidence-based medical evidence for TCM intervention in pulmonary nodules. Methods Computer search of PubMed, CNKI, Wanfang, VIP, and SinoMed was conducted to select randomized controlled trials (RCTs) of TCM compound intervention in pulmonary nodules, with the retrieval time from the inception to November 29, 2023. The Cochrane bias risk assessment tool was used to evaluate the quality of the included studies, and Review Manager 5.4 was used for Meta-analysis. Results A total of 18 RCTs were included, covering 8 provinces across the country, with a total sample size of 1301 patients. The TCM compounds used in the included studies all incorporated the method of dissolving phlegm and dissipating nodules. There was a high risk of bias uncertainty in the included studies. Meta-analysis results suggested that TCM compound could significantly reduce the diameter of pulmonary nodules [MD=−1.41, 95%CI (−1.70, −1.13), P<0.001], decrease the number of nodules [MD=−0.37, 95%CI (−0.73, −0.01), P=0.05], alleviate clinical symptoms [MD=−4.84, 95%CI (−6.04, −3.64), P<0.001], and improve lung function [forced expiratory volume in one second (FEV1), MD=0.55, 95%CI (0.09, 1.01), P=0.02; FEV1/forced vital capacity, MD=6.12, 95%CI (4.47, 7.78), P<0.001]. However, there was no statistically significant difference in the probability of malignancy between the experimental group and the control group [MD=−0.01, 95%CI (−0.01, 0.00), P=0.09]. Conclusion TCM compound can significantly reduce the diameter of pulmonary nodules, decrease the number of nodules, alleviate clinical symptoms, and improve lung function, but future multicenter, large-sample, high-quality RCTs are still needed to further explore and verify this conclusion.

ObjectiveTo analyze the characteristics and life loss of injury-related deaths among the registered residents in Anji County from 2015 to 2024, and to provide a reference for conducting targeted injury prevention and control efforts. MethodsData on injury-related deaths among the registered residents in Anji County from 2015 to 2024 were collected from the Chronic Disease Surveillance and Management System of Zhejiang Province. Indicators including the crude mortality rate (CMR), standardized mortality rate (SMR), proportion, potential years of life lost (PYLL), average years of life lost (AYLL) and potential years of life lost rate (PYLLR) were calculated. The annual trends in injury mortality were evaluated using average annual percent change (AAPC). ResultsFrom 2015 to 2024, a total of 3 800 injury deaths were reported in Anji County, accounting for 11.94% of all deaths and ranking as the fourth leading cause of death. The CMR was 80.65/100 000 and showed an upward trend (AAPC=3.53%, P=0.003), while the SMR was 50.68/100 000 with no significant trend (AAPC=-1.47%, P=0.196). The CMR was higher in males (95.03/100 000) than in females (66.49/100 000). The top five causes of injury-related deaths were accidental falls, traffic accidents, suicide, drowning and accidental poisoning. The top two injury-related causes of death varied by age group: for the 0‒<15 age group, they were accidental falls, traffic accidents (with drowning tied for second place); for the 15‒<45 age group, they were traffic accidents and suicide; for the 45‒<65 age group, they were traffic accidents and accidental falls; and for the 65‒105 age group, they were accidental falls and traffic accidents. From 2015 to 2024, the total PYLL due to injury deaths was 47 464.00 person-years, the AYLL was 12.49 years, and the PYLLR was 10.07‰. ConclusionFrom 2015 to 2024, the injury crude mortality rate in Anji County was relatively high and showed an upward trend. Targeted interventions based on demographic characteristics and injury types should be implemented to reduce injury mortality.

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 analyze the distribution characteristics of 13 common respiratory pathogens in children in Zibo area from 2020 to 2022.Methods A total of 3091 hospitalized children with respiratory infections admitted to Zibo Maternal and Child Health Hospital from January 2020 to December 2022 were selected as the subjects.Throat swabs or bronchoalveolar lavage fluid samples were collected from the patients,and 13 common respiratory pathogens were tested to analyze the distribution differences among different genders,ages,and seasons.Results Among 3091 pediatric patients,1794 were found to be infected with pathogens.The top three pathogens were Mycoplasma pneumoniae,rhinovirus,and respiratory syncytial virus(RSV).The single infection rate was 47.75％,while the mixed infection rate was 10.28％,with the most common scenario being a mixed infection of two pathogens.There were statistically significant differences in the pathogen profiles across different age groups(P＜0.001):infants had the highest detection rate of RSV,young children were primarily infected with rhinovirus,preschool and school-age children were predominantly infected with Mycoplasma pneumoniae.Seasonal distribution showed that the highest positive rate was in autumn,while the lowest was in spring(P＜0.05).In spring,the main pathogens were rhinovirus and Mycoplasma pneumoniae;in summer,they were rhinovirus and parainfluenza virus;in autumn,they were Mycoplasma pneumoniae and RSV;and in winter,the detection rates of Mycoplasma pneumoniae and influenza B virus were higher.Conclusion From 2020 to 2022,Mycoplasma pneumoniae,rhinovirus and RSV were the main pathogens of children's respiratory tract infection in Zibo area,and there were significant differences in the distribution of pathogens among different ages and seasons.

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

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 in-vestigations 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 inflam-mation 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 circu-lation 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 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.