Objective: To investigate the epidemiological characteristics and spatial-temporal clustering of severe fever with thrombocytopenia syndrome (SFTS) in Huai'an City, Jiangsu Province from 2011 to 2024, so as to provide a basis for optimizing local SFTS prevention and control strategies, and identifying high-risk areas and key populations. Methods: Data on SFTS incidence and deaths in Huai'an City from 2011 to 2024 were collected from the Infectious Disease Reporting Information System of the Chinese Disease Prevention and Control Information System. The reported incidence, mortality, and fatality rates were calculated. Descriptive analysis was performed on temporal, population, and regional distribution. The average annual percent change (AAPC) was used to analyze the trend in the reported incidence of SFTS. Global and local spatial autocorrelation analyses were employed to examine the spatial distribution patterns and spatial association patterns of SFTS incidence while spatio-temporal scanning analyses was used to assess the spatial-temporal clustering of SFTS. Results: A total of 337 SFTS cases were reported in Huai'an City from 2011 to 2024, with the reported incidence rising from 0.17/100 000 to 1.88/100 000. There were 20 deaths, with an average annual mortality of 0.03/100 000, and a fatality rate of 5.93%. The incidence showed obvious seasonality, with a peak in May and June (148 cases, accounting for 43.92%). Spring and summer accounted for 107 cases (31.75%) and 159 cases (47.18%), respectively. The reported SFTS cases were mainly male, farmers, and individuals aged ≥41 years, accounting for 56.38%, 79.23%, and 96.74%, respectively. The population distribution of death cases was basically consistent with that of incident cases. Xuyi County was a high-incidence area, with a total of 332 reported cases, accounting for 98.52%. All death cases were reported in this county. Spatial autocorrelation analyses revealed a positive spatial correlation in SFTS incidence from 2019 to 2024, with Moran's I values ranging from 0.214 to 0.336 (all P<0.05). Heqiao Town, Tianquanhu Town, and Guiwu Town in Xuyi County were identified as high-high clustering areas. Spatio-temporal scanning analyses showed that cluster 1 was consistent with the high-high clustering areas, with an aggregation time from the second quarter of 2019 to the second quarter of 2022. Conclusions From 2011 to 2024, the reported incidence of SFTS in Huai'an City showed an upward trend, with a high incidence in spring and summer. Males, farmers, and the middle-aged and elderly population were the key populations for prevention and control. Xuyi County was the key area for prevention and control.

Objective: To investigate the status and influencing factors of nutritional health knowledge awareness among primary and middle school students in Wenzhou City, Zhejiang Province, so as to provide a basis for carrying out targeted nutrition health education for students. Methods: The stratified cluster random sampling method was used to select students from 48 schools across 12 counties (cities and districts) in Wenzhou City as survey subjects from October 2023 to February 2024. Basic information, parental information, and nutritional health knowledge were collected using the Questionnaire on nutritional health knowledge for School-Age Children. The awareness rate of nutritional health knowledge was analyzed. Multivariable logistic regression model was used to analyze the influencing factors for nutritional health knowledge awareness among primary and middle school students. Results: The survey included 4 405 boys, accounting for 51.42%, and 4 161 girls, accounting for 48.58%. The sample consisted of 2 497 pupils in Grades 1-3 (29.15%), 2 591 pupils in Grades 4-6 (30.25%), 1 739 junior high school students (20.30%), and 1 739 senior high school students (20.30%). The median score of nutrition health knowledge was 75.00 (interquartile range, 19.00), and the awareness rate was 4 524 (52.81%). Multivariable logistic regression analysis showed that girls (OR=1.317, 95%CI: 1.198-1.447), primary school students (Grades 1-3, OR=7.830, 95%CI: 6.444-9.513; Grades 4-6, OR=1.276, 95%CI: 1.066-1.528), and those whose mothers had an educational level above junior high school (senior high school/technical secondary school/technical school/junior college, OR=1.188, 95%CI: 1.044-1.352; bachelor' s degree or above, OR=1.194, 95%CI: 1.024-1.392) had a higher likelihood of nutrition and health knowledge awareness. Conversely, students who were overweight or obese (OR=0.798, 95%CI: 0.671-0.950), lived in school (OR=0.763, 95%CI: 0.650-0.895), and had a daily outdoor activity duration of <30 minutes (OR=0.641, 95%CI: 0.570-0.721) had a lower likelihood of nutritional health knowledge awareness. Conclusions The awareness rate of nutritional health knowledge among primary and middle school students in Wenzhou City needs to be improved, and is mainly influenced by gender, educational stage, body mass index, mother' s educational level, and outdoor activity duration. It is recommended to implement differentiated health education strategies and build a "family-school-community" linkage mechanism to improve students' nutrition health literacy.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

Objective To construct a predictive model for the survival of transplant kidneys after kidney transplantation. Methods The clinical data of 366 kidney transplant recipients and donors were retrospectively analyzed, and the recipients were divided into low-risk group (n=101), medium-risk group (n=189), and high-risk group (n=76) based on the kidney donor profile index (KDPI). Each group was further divided into Remuzzi score ≤3 group and Remuzzi score >3 group based on time-zero biopsy Remuzzi scores. Kaplan-Meier method was used to analyze the survival of transplant kidneys. Univariate and multivariate Cox regression analyses were performed to identify risk factors affecting long-term survival after kidney transplantation. A predictive model for transplant kidney survival was established and a nomogram was drawn. The predictive performance of the model was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Results The median KDPI was 65%, and the median Remuzzi score was 3. The 5-year survival rate of transplant kidneys was 83.5%. Kaplan-Meier survival curves showed that in the KDPI medium-risk and KDPI high-risk groups, the subgroup with lower Remuzzi score had a higher survival rates of transplant kidneys than the subgroup with higher Remuzzi score. Univariate and multivariate Cox regression analyses showed that KDPI, Remuzzi score, and donor’s age were independent risk factors for transplant kidney loss (all P<0.05). The ROC curve showed that the AUC of the nomogram prediction model established based on independent risk factors for the 1, 3 and 5-year survival rates of transplant kidneys were 0.91, 0.93 and 0.94 for the training set, and 0.89, 0.85 and 0.88 for the validation set. Calibration curves shows good consistency between the training and validation sets of the model. Conclusions The nomogram predictive model based on KDPI, time-zero biopsy Remuzzi score and donor’s age has good predictive value for transplant kidney survival.

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Nanoparticle delivery systems have good application prospects in the field of precision therapy, but the preparation process of nanomaterial has problems such as short in vivo circulation time, easy recognition, and clearance by the immune system in the body. In recent years, biomimetic nanoparticle delivery systems mediated by natural cell membranes have become a research hotspot to address these issues. The natural membrane biomimetic nanoparticle delivery system cleverly integrates the advantages of natural biofilm "autologous" and "artificial" functional carriers by using endogenous cell membranes to modify the surface of nanocarriers, endowing them with characteristics such as tumor targeting, low immunogenicity, and long blood circulation. Currently, biomimetic nanoparticle delivery systems have been used in the treatment of malignant tumors, cardiovascular diseases, bacterial infections, and other diseases. This paper analyzes the development status and current research hotspots of natural cell membrane camouflaged biomimetic nanoparticle delivery systems mainly reviews the latest research progress of red blood cell membrane camouflaged biomimetic nanoparticle delivery systems in the field of disease treatment in recent years. It focuses on exploring the advantages, future development prospects, and limitations of biomimetic nanoparticle delivery systems based on red blood cell membrane camouflage in improving drug delivery, to provide a reference for the in-depth research and development of this system.

ObjectiveTo develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures. MethodsA retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsAmong the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT. ConclusionThe GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.

ObjectiveTo observe the clinical effect of garlic moxibustion with herbal medicinals in treating allergic rhinitis （AR） with latent heat in lung meridian and explore the potential mechanisms. MethodsA total of 70 AR patients with the latent heat in lung meridian were randomly divided into loratadine group （35 cases） and moxibustion group （35 cases）. The loratadine group was treated with loratadine tablets orally， 10mg each time， once a day， one week as a course for 4 courses. The moxibustion group received garlic moxibustion with herbal medicinals once a day， 7 times as one course， for a total of 4 courses. The primary outcome was total nasal symptom score （TNSS）， measured before and after treatment， and at the 1-month follow-up. Secondary outcomes included traditional Chinese medicine （TCM） syndrome score， immunoglobulin E （IgE） level and eosinophils （EOS） level before and after 4 weeks of treatment. Efficacy was evaluated based on the changes in TNSS after treatment. Adverse reactions and events during treatment were observed， and safety evaluation was performed. ResultsA total of 33 patients from each group were included in the final analysis. The moxibustion group had a total effective rate of 93.94% （31/33）， significantly higher than the 75.76% （25/33） of the loratadine group （P＜0.05）. After treatment， both groups showed a significant reduction in TNSS， TCM syndrome score， IgE and EOS levels （P＜0.05 or P＜0.01）， and more reductions were seen in the moxibustion group （P＜0.05）. During follow-up， the moxibustion group also had a lower TNSS than the loratadine group （P＜0.05）. During treatment， one patient in the moxibustion group experienced shortness of breath， and one patient in the loratadine group experienced headache. Both symptoms were mild and improved after symptomatic treatment， allowing them to continue the treatment. ConclusionGarlic moxibustion with herbal medicinals can alleviate nasal inflammatory symptoms， reduce allergic reactions in AR patients， and effectively control AR flare-ups and related inflammatory responses， superior to oral loratadine alone and of good safety.

ObjectiveTo identify the main chemical constituents of Anmeidan （AMD） and to explore the mechanism of AMD in regulating the extracellular signal-regulated kinase 1/2 （ERK1/2）/mitogen-activated protein kinase （MAPK）-interacting serine/threonine-protein kinase （MNK）/eukaryotic translation initiation factor 4E （eIF4E） signaling pathway to improve circadian rhythm disturbances in insomnia rats. MethodsThe main chemical constituents of AMD were identified using ultra-high-performance liquid chromatography-linear ion trap-electrostatic orbital trap mass spectrometry （UPLC-LTQ/Orbitrap/MS） in combination with reference standards. Sixty male Sprague-Dawley （SD） rats were randomly divided into control， model， melatonin， and AMD low-， medium-， and high-dose groups， with 10 rats in each group. Except for the control group， all rats were administered p-chlorophenylalanine via intraperitoneal injection to establish an insomnia model. The activity-rest rhythm of rats was assessed using the open field test and circadian rhythm test. Hematoxylin-eosin （HE） staining and Nissl staining were used to observe structural changes in hypothalamic neurons. Immunofluorescence， real-time quantitative polymerase chain reaction （Real-time PCR）， and Western blot analysis were employed to detect mRNA and protein expression levels of ERK1/2， MNK， and eIF4E in the hypothalamus. ResultsA total of 50 chemical components， including flavonoids， phenylpropanoids， triterpenoid saponins， alkaloids， and lignans， were identified in AMD. Compared with the control group， the model group exhibited significantly increased total distance traveled， average speed， central area residence time， and cumulative rearing time （P<0.01）， as well as prolonged cumulative activity time and total activity time in both light and dark phases （P<0.01）. Hypothalamic neurons in the model group were sparsely arranged， reduced in number， and exhibited nuclear disappearance or nucleolar rupture， with a significantly increased apoptosis index （P<0.01）. The cytoplasm appeared turbid， Nissl body staining was lighter， and the Nissl body apoptosis index was significantly increased （P<0.01）. The mRNA expression levels of ERK1/2， MNK， and eIF4E were significantly decreased （P<0.01）， along with a significant reduction in protein expression levels of ERK1/2， phosphorylated ERK1/2 （p-ERK1/2）， MNK， phosphorylated MNK （p-MNK）， eIF4E， and phosphorylated eIF4E （p-eIF4E） （P<0.01）. Compared with the model group， the total distance， average speed， central area residence time and body upright cumulative time of the AMD high-dose group were significantly reduced （P<0.01）. The total distance， average speed and body upright cumulative time of the AMD medium-dose group were significantly reduced （P<0.01）. The cumulative time of light activity and total time of activity in each dose group of AMD were significantly shortened （P<0.01）. The cumulative time of dark activity in the high-dose group of AMD was prolonged （P<0.01）. The neurons in the middle and high dose groups of AMD were closely arranged， the number of neurons increased， and the apoptosis index of hypothalamic cells decreased significantly （P<0.05， P<0.01）. The cytoplasm of the low， middle and high dose groups of AMD was clear， the color of Nissl body became darker， and the apoptosis index of Nissl body decreased significantly （P<0.01）. The expression of ERK1/2， MNK and eIF4E mRNA and protein in the hypothalamus of the middle and high dose groups of AMD increased significantly （P<0.05， P<0.01）. ConclusionAMD primarily contains 50 chemical constituents， including flavonoids， phenylpropanoids， and triterpenoid saponins. It exhibits a "synergistic enhancement" effect through multiple components and multiple pathways to improve insomnia. AMD ameliorates circadian rhythm disturbances in p-chlorophenylalanine-induced insomnia rats by upregulating ERK1/2/MNK/eIF4E signaling pathway-related proteins.

BACKGROUND:In recent years,with the rapid development of biomedicine,the study of brain aging and exosomes has attracted more and more attention,but there is no bibliometrics analysis in this field. OBJECTIVE:To objectively analyze domestic and foreign literature on brain aging and exosomes in the past 15 years,to summarize the research status,hot spots,and development trends in this field. METHODS:Using the core database of Web of Science as a search platform,we downloaded the literature on brain aging and exosomes published from the establishment of the database to December 28,2022,and analyzed the data from the aspects of country or region,institution,author,keywords,and co-cited literature using CiteSpace 6.1.R6 visualization software. RESULTS AND CONCLUSION:A total of 1 045 research articles were included,and the number of publications on brain aging and exosomes research both domestically and internationally was showing an increasing trend year by year.The United States ranked first with 429 papers,and China ranked second with 277 papers.Louisiana State University ranked first with 16 articles.Professor Lukiw Walter J from Louisiana State University in the United States was the author with the highest number of publications,and Professor Bartel DP from the Massachusetts Institute of Technology was the author with the most citations.The most prolific Journal was the International Journal of Molecular Sciences.Alzheimer's disease,microRNA,gene expression,extracellular vesicles,exosomes,oxidative stress,and biomarkers are the most relevant terms.According to the research on hot topics,biomarkers have become a new research hotspot.The above results indicate that the research on brain aging and exosomes has gradually increased in the past 15 years.The research direction has gradually shifted from the initial exploration of the expression of miRNAs in central nervous system diseases related to brain aging to the search for biomarkers that can identify and diagnose neurodegenerative diseases.The study of exocrine miRNAs to protect central nervous system from damage has emerged as promising therapeutic strategy.