ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

Objective:To analyze the genetic variations and clinical phenotypic characteristics of epilepsy associated with CSNK2B gene mutations. Methods:A case series summary study.Clinical data of 15 epileptic children with CSNK2B gene mutations diagnosed and treated at the Third Affiliated Hospital of Zhengzhou University and the Peking University First Hospital from February 2016 to October 2023 were retrospectively analyzed.The clinical manifestations, genotypes, and electroencephalography (EEG) results were summarized. Results:Among the 15 children (8 boys and 7 girls), 14 cases had de novo mutations in the CSNK2B gene, and 1 case had hereditary variations.There were 5 missense variants, 4 splice-site variants, 3 frameshift variants, and 3 nonsense variants.Ten mutation sites had not been previously reported (c.326G>A/p.Cys109Tyr, c.485A>G/p.His162Arg, c.368-1G>A, c.464A>C/p.Asp155Ala, c.301T>G/p.Tyr101Asp, c.342T>A/p.Cys114*, c.198del/p.Asn67Thrfs*5, c.292-10T>G, c.573-574del/p.Lys191Asnfs*54, and c. 11C>G/p.Ser4*).The age of onset of seizures ranged from 14 days to 6 years, with 13 cases starting within 2 years old.The types of seizures included focal seizures in 9 cases, generalized tonic-clonic seizure (GTCS) in 5 cases, myoclonic seizures in 1 case, atonic seizures in 1 case, atypical absence seizures in 1 case, and epileptic seizures in 1 case.Three cases had multiple seizures, and 4 cases had cluster seizures.The EEG showed slow background activity in 1 case.Epileptiform discharges were observed in 13 cases during the interictal phase, including generalized discharges in 6 cases, multifocal discharges in 3 cases, and focal discharges in 5 cases.Two cases had normal EEG findings.Brain magnetic resonance imaging results were normal in 10 cases.The age of the last follow-up ranged from 1 year and 1 month to 13 years and 10 months.Seizures were controlled in 12 cases treated with 1 or 2 antiepileptic drugs, while seizures persisted in 2 cases treated with multiple antiepileptic drugs, and 1 case suffered no seizures for 1 year and 3 months, without antiepileptic drug treatment.Oxcarbazepine was effective in 5 cases (5/7), Valproate sodium was effective in 6 cases (6/8), and Levetiracetam was effective in 3 cases (3/9). Conclusions:CSNK2B gene mutations are mainly de novo mutations, and epilepsy triggered by them typically starts within 2 years of age.GTCS and focal seizures are the most common types.The seizures of most children are easily controlled with the effective treatment of Oxcarbazepine, Valproate sodium, and Levetiracetam.

This article firstly reviews the current application status of mobile health based on social networking media, mobile health applications, wearable devices in infertility patients′nursing. Then, this paper analyzes the application effects of mobile health in the five aspects of treatment process management, health education, medication management, lifestyle management and psychological care for infertility patients based on the literature. The existing problems are analyzed and prospected on this basis, in order to provide a reference for the application of mobile health to infertility patients′nursing in China.

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Background Heavy metal emissions from mining and smelting areas are a global problem, and health risks associated with heavy metal contamination of soils are of great concern. The long-term mining of the largest realgar mine in Asia has caused severe arsenic and other metal pollution to the surrounding rivers and soils. Objective To understand the levels of metal contamination and health risks in agricultural soils of villages surrounding the largest realgar mine in Asia, and to lay a good foundation for further necessary pollution control actions and decisions. Methods A field survey was conducted to collect soil samples according to the Technical rules for monitoring of environmental quality of farmland soil (NY/T 395-2012), and then inductively coupled plasma mass spectrometry was used to determine the contents of 28 heavy metals [cadmium (Cd), arsenic (As), lead (Pb), mercury (Hg), chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), beryllium (Be), selenium (Se), cobalt (Co), antimony (Sb), molybdenum (Mo), vanadium (V), barium (Ba), thallium (Tl), boron (B) , bismuth (Bi), lithium (Li), manganese (Mn), strontium (Sr), calcium (Ca), rubidium (Rb), iron (Fe), magnesium (Mg), aluminum (Al), kalium (K), and titanium (Ti)]. Geoaccumulation index, single factor pollution index, and Nemerow comprehensive index were used to evaluate the degree and characteristics of single metal pollution and combined pollution in soil, respectively. A health risk assessment model was used to evaluate the risks of metals in soil to human health. Results The results of geoaccumulation index calculation revealed that 22 heavy metals were enriched in the soil, and the indexes of target heavy metals from high to low were Cd > Se > Pb >Hg > As > Co> Ni > Cu > Zn > Bi > Sb > Mo > Be> Cr > Ba >V > Li > Sr> Mn> Rb > Ca> Tl . The single factor pollution indexes of 17 heavy metals from high to low were Be > Cd > B > Mo > V > As > Ni > Cu > Pb > Zn > Co > Se > Tl > Ba > Cr > Hg > Sb. The Nemerow comprehensive index indicated all sampling points were graded as severe pollution. The mean of total non-carcinogenic health risk values and the mean of carcinogenic health risk values for the target heavy metals in the area were higher than the threshold (1) and the maximum acceptable risk (1.0×10^–4), respectively. The total carcinogenic health risks for adults and children reached 1.1×10^–3 and 1.67×10^–3, respectively. The mean non-carcinogenic health risk values of As, Co, Cr, and Pb pollution were greater than 1, and the maximum non-cancer risk value of Sb for children was greater than 1. The mean carcinogenic risk values of Ni, As, and Cu exceeded 1.0×10^–4 for both adults and children, and the maximum carcinogenic risk values of Be and Cr for children were more than 1.0×10^–4. Conclusion The farmland soil around the hugest realgar mine in Asia is contaminated by multiple metals. The study soil is seriously polluted by Cd, Se, Pb, As, Hg, Be, B, Mo, V, Ni, Cu, Pb, Zn, Co, and Ba. The pollution of Ni, As, Cu, Cr, and Be is considered as carcinogenic hazards to health, while the pollution of As, Co, Cr, Pb, and Sb poses non-carcinogenic health risks. Our study findings show that the soil is polluted by Co and Group 1 carcinogen Be, which could cause health risks; although Cr and Sb have not reached severe pollution levels, there are certain health risks and also need attention.

OBJECTIVE: To estimate the social and economic burden of Downs syndrome for patients and their families residing in Changsha, China. METHODS: An 160-item self-administered questionnaire was designed and distributed to the primary caregivers of the patients in March 2020. A total of 81 eligible participants had completed the questionnaire, among which 20 were excluded for incomplete data. A patient perspective was taken to estimate the economic burden of the disease. The social impact of the disease on the patient's family was evaluated through questions adapted from the Stanford Psychological Wellbeing (PWB) Scale. RESULTS: The estimated life-course cost of a Downs syndrome patient in Changsha is 4 985 659 RMB, with the patient and caregiver's loss of income taking the greater proportion. In addition, as the majority of the patients' primary caregivers, female caregivers experienced not only considerable financial hardship caused by the care provision, but also a significant amount of psychological pressure and social discrimination. CONCLUSION Increased level of social welfare for the patients and social support for their female caregivers are essential for reducing economic burden and improving their quality of life in the area. In addition, prenatal screening and diagnosis for Downs syndrome are important for reducing both the social and economic burden of the disease by preventing its occurrence.
Caregivers ; China ; Down Syndrome ; Female ; Financial Stress ; Humans ; Quality of Life ; Social Discrimination ; Surveys and Questionnaires

OBJECTIVE: To compare the clinical application and health economic values of non-invasive prenatal testing (NIPT) and second trimester serum screening (STSS). METHODS: A retrospective analysis was carried out on 54 026 singleton pregnant women undergoing NIPT and STSS from March 1, 2018 to December 31, 2019 in Changsha Maternal and Child Health Care Hospital. For pregnant women with high-risk results of NIPT, prenatal diagnosis and follow-up of pregnancy outcomes were conducted. The data was grouped to 4 screening models, and their cost-benefit was analyzed. RESULTS: The sensitivity, specificity and positive predictive value of NIPT were all higher than STSS. Screening models 1 to 4 have prevented the birth of 71, 29, 52 and 54 patients with Down syndrome, respectively. The safety index of screening models 1 to 4 were 0.0036, 0.3944, 02215 and 0.1281, respectively. When the price of NIPT was decreased to 600 RMB, the cost-benefit of the screening models 1 to 4 was 0.46, 0.65, 0.44 and 0.40 million RMB, respectively. CONCLUSION NIPT has a better detection performance than STSS. When the price of NIPT is 600 RMB, screening model 1 has the best screening effect and the highest accuracy, safety index and health economical value.
Child ; China ; Cost-Benefit Analysis ; Down Syndrome/diagnosis* ; Female ; Humans ; Pregnancy ; Prenatal Diagnosis/methods* ; Retrospective Studies

Focal cortical dysplasia (FCD) is one of the most common causes of drug-resistant epilepsy. Dysmorphic neurons are the major histopathological feature of type II FCD, but their role in seizure genesis in FCD is unclear. Here we performed whole-cell patch-clamp recording and morphological reconstruction of cortical principal neurons in postsurgical brain tissue from drug-resistant epilepsy patients. Quantitative analyses revealed distinct morphological and electrophysiological characteristics of the upper layer dysmorphic neurons in type II FCD, including an enlarged soma, aberrant dendritic arbors, increased current injection for rheobase action potential firing, and reduced action potential firing frequency. Intriguingly, the upper layer dysmorphic neurons received decreased glutamatergic and increased GABAergic synaptic inputs that were coupled with upregulation of the Na⁺-K⁺-Cl^- cotransporter. In addition, we found a depolarizing shift of the GABA reversal potential in the CamKII-cre::PTEN^flox/flox mouse model of drug-resistant epilepsy, suggesting that enhanced GABAergic inputs might depolarize dysmorphic neurons. Thus, imbalance of synaptic excitation and inhibition of dysmorphic neurons may contribute to seizure genesis in type II FCD.
Animals ; Drug Resistant Epilepsy/surgery* ; Epilepsy/pathology* ; Malformations of Cortical Development/pathology* ; Malformations of Cortical Development, Group I ; Mice ; Neurons/pathology* ; Seizures/pathology*

Animals ; Carrier Proteins/metabolism* ; Histidine/metabolism* ; Humans ; Methylation ; Methyltransferases/metabolism* ; Neoplasm Proteins/metabolism* ; Neoplasms/metabolism* ; Protein Processing, Post-Translational