Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

BACKGROUND:Cervical spondylosis is a chronic degenerative disease that has become one of the most common and frequent diseases threatening human health.At present,the initial diagnosis of the cervical spine and its surrounding structures mainly relies on the interpretation of medical images by radiologists,which not only requires a high level of technical requirements for operators,but also has the disadvantages of strong subjectivity,high labor intensity,and low efficiency.With the rapid development of artificial intelligence technology,its powerful data processing and image recognition capabilities have shown broad application prospects in the medical field.Deep learning has also made certain progress in the research of spinal diseases.OBJECTIVE:To summarize the current status and research progress in the application of artificial intelligence technology in cervical spine imaging images in recent years,evaluating the performance of artificial intelligence models as well as future trends and challenges to be overcome.METHODS:The first author searched the relevant articles in WanFang,CNKI,and PubMed in June 2024.The Chinese search terms were"artificial intelligence,deep learning,cervical spine."English serach terms were"artificial intelligence,Al,cervical vertebrae,cervical."Finally,101 articles were included and analyzed.RESULTS AND CONCLUSION:(1)Artificial intelligence technology can realize automatic segmentation of cervical vertebrae and measurement of curvature change by segmentation,classification,landmarks recognition of medical image parts,detect cervical vertebral fracture,nerve root,and spinal cord type cervical spondylosis,identify cervical spine ossification of posterior longitudinal ligament,and predict post-surgery related risk factors and cervical vertebra maturation classification.(2)Although artificial intelligence technology has shown great potential in the field of cervical spine research,it is still in the early stages of exploration and rapid development,with unlimited room for development and innovation.

With the growing emphasis on maternal and child oral health, the significance of managing oral health across preconception, pregnancy, and infancy stages has become increasingly apparent. Oral health challenges extend beyond affecting maternal well-being, exerting profound influences on fetal and neonatal oral development as well as immune system maturation. This expert consensus paper, developed using a modified Delphi method, reviews current research and provides recommendations on maternal and child oral health management. It underscores the critical role of comprehensive oral assessments prior to conception, diligent oral health management throughout pregnancy, and meticulous oral hygiene practices during infancy. Effective strategies should be seamlessly integrated across the life course, encompassing preconception oral assessments, systematic dental care during pregnancy, and routine infant oral hygiene. Collaborative efforts among pediatric dentists, maternal and child health workers, and obstetricians are crucial to improving outcomes and fostering clinical research, contributing to evidence-based health management strategies.
Humans ; Pregnancy ; Female ; Infant ; Consensus ; Mouth Diseases/therapy* ; Pregnancy Complications/therapy* ; Oral Health ; Infant, Newborn ; Delphi Technique ; Oral Hygiene

AIM To establish an HPLC-MS/MS method for the simultaneous residue determination of 11 plant growth regulators(PGRs)in Renshen Guben preparations,and to conduct a risk assessment.METHODS The analysis was performed on a 40 ℃ thermostatic ACQUITY UPLC ? Waters HSS T3 column(2.1 mm×100 mm,1.8 μm),with the mobile phase of acetonitrile-0.1％formic acid(containing 5 mmol/L ammonium formate)flowing at 0.30 mL/min in a gradient elution manner,and electro spray ionization was employed in both positive and negative ion scanning,with multiple reaction monitoring mode.The chronic and acute exposure risk values of the detected PGRs were calculated and assessed based on residue levels,health guidance values,and exposure estimates.RESULTS Eleven PGRs exhibited good linear relationships within their own ranges(R2 ≥ 0.990),whose average recoveries were 70.0％-120.0％,with RSDs all below 12.0％.In both oral liquid and pill forms,mepiquat chloride showed the highest average residue levels,while sodium 5-nitroguaiacolate exhibited the highest acute risk value(0.765 7,0.908 1)and chronic risk value(0.023 1,0.027 0).CONCLUSION Although PGRs residues are detected in Renshen Guben preparations,all levels remained within safe limits.

Objective To evaluate the value of coronary CT angiography(CCTA)radiomics machine learning(ML)model combined with pericoronary fat attenuation index(FAI)for predicting coronary plaques progression.Methods Totally 194 patients with CCTA showing coronary plaques and received at least one CCTA review afterwards were retrospectively collected.The annual change value of total plaque burden(△TPB/y)was calculated based on the first and last CCTA to assess plaque progression.All patients were categorized into non-progressive(△TPB/y＜median △TPB/y)and progressive(△ TPB/y≥median △ TPB/y)groups.The patients were divided into training set(n=155)and validation set(n=39)at the ratio of 8∶2.Univariate and multivariate logistic regression analyses were used to screen clinical and primary CCTA related factors for plaque progression,and CCTA model was constructed.Radiomics features were extracted and screened based on primary CCTA to build ML models using random forest(RF),Gaussian process(GP),partial least squares discriminant analysis(PLS-DA),quadratic discriminant analysis(QDA)and support vector machine(SVM)algorithms.The effectiveness of all models was verified in validation set and the optimal ML model was selected.And its combination with CCTA model constructed combined model.The efficacy of each model for predicting coronary plaques progression was evaluated.Results Of 194 cases,97 were in progressive group and 97 were in non-progressive group.The training set included 77 cases of plaques progression and 78 of plaques non-progression,and the validation set included 20 of plaques progression and 19 of plaques non-progression.FAI was the independent predictor of plaque progression(OR=1.08,P＜0.001)and CCTA model was constructed.Ten optimal radiomics features based on training set were selected to build RF,GP,PLS-DA,QDA and SVM models.The area under the curve(AUC)of RF model in training set and validation set were both high,was considered as the optimal ML model.The AUC of CCTA,RF and combined models in training set was 0.684,0.847 and 0.861,respectively,while was 0.629,0.768 and 0.821 in validation set,respectively.Conclusion CCTA radiomics ML model combined with FAI could effectively predict coronary plaques progression.

BACKGROUND:Cervical spondylosis is a chronic degenerative disease that has become one of the most common and frequent diseases threatening human health.At present,the initial diagnosis of the cervical spine and its surrounding structures mainly relies on the interpretation of medical images by radiologists,which not only requires a high level of technical requirements for operators,but also has the disadvantages of strong subjectivity,high labor intensity,and low efficiency.With the rapid development of artificial intelligence technology,its powerful data processing and image recognition capabilities have shown broad application prospects in the medical field.Deep learning has also made certain progress in the research of spinal diseases.OBJECTIVE:To summarize the current status and research progress in the application of artificial intelligence technology in cervical spine imaging images in recent years,evaluating the performance of artificial intelligence models as well as future trends and challenges to be overcome.METHODS:The first author searched the relevant articles in WanFang,CNKI,and PubMed in June 2024.The Chinese search terms were"artificial intelligence,deep learning,cervical spine."English serach terms were"artificial intelligence,Al,cervical vertebrae,cervical."Finally,101 articles were included and analyzed.RESULTS AND CONCLUSION:(1)Artificial intelligence technology can realize automatic segmentation of cervical vertebrae and measurement of curvature change by segmentation,classification,landmarks recognition of medical image parts,detect cervical vertebral fracture,nerve root,and spinal cord type cervical spondylosis,identify cervical spine ossification of posterior longitudinal ligament,and predict post-surgery related risk factors and cervical vertebra maturation classification.(2)Although artificial intelligence technology has shown great potential in the field of cervical spine research,it is still in the early stages of exploration and rapid development,with unlimited room for development and innovation.

AIM To establish an HPLC-MS/MS method for the simultaneous residue determination of 11 plant growth regulators(PGRs)in Renshen Guben preparations,and to conduct a risk assessment.METHODS The analysis was performed on a 40 ℃ thermostatic ACQUITY UPLC ? Waters HSS T3 column(2.1 mm×100 mm,1.8 μm),with the mobile phase of acetonitrile-0.1％formic acid(containing 5 mmol/L ammonium formate)flowing at 0.30 mL/min in a gradient elution manner,and electro spray ionization was employed in both positive and negative ion scanning,with multiple reaction monitoring mode.The chronic and acute exposure risk values of the detected PGRs were calculated and assessed based on residue levels,health guidance values,and exposure estimates.RESULTS Eleven PGRs exhibited good linear relationships within their own ranges(R2 ≥ 0.990),whose average recoveries were 70.0％-120.0％,with RSDs all below 12.0％.In both oral liquid and pill forms,mepiquat chloride showed the highest average residue levels,while sodium 5-nitroguaiacolate exhibited the highest acute risk value(0.765 7,0.908 1)and chronic risk value(0.023 1,0.027 0).CONCLUSION Although PGRs residues are detected in Renshen Guben preparations,all levels remained within safe limits.

Objective To evaluate the value of coronary CT angiography(CCTA)radiomics machine learning(ML)model combined with pericoronary fat attenuation index(FAI)for predicting coronary plaques progression.Methods Totally 194 patients with CCTA showing coronary plaques and received at least one CCTA review afterwards were retrospectively collected.The annual change value of total plaque burden(△TPB/y)was calculated based on the first and last CCTA to assess plaque progression.All patients were categorized into non-progressive(△TPB/y＜median △TPB/y)and progressive(△ TPB/y≥median △ TPB/y)groups.The patients were divided into training set(n=155)and validation set(n=39)at the ratio of 8∶2.Univariate and multivariate logistic regression analyses were used to screen clinical and primary CCTA related factors for plaque progression,and CCTA model was constructed.Radiomics features were extracted and screened based on primary CCTA to build ML models using random forest(RF),Gaussian process(GP),partial least squares discriminant analysis(PLS-DA),quadratic discriminant analysis(QDA)and support vector machine(SVM)algorithms.The effectiveness of all models was verified in validation set and the optimal ML model was selected.And its combination with CCTA model constructed combined model.The efficacy of each model for predicting coronary plaques progression was evaluated.Results Of 194 cases,97 were in progressive group and 97 were in non-progressive group.The training set included 77 cases of plaques progression and 78 of plaques non-progression,and the validation set included 20 of plaques progression and 19 of plaques non-progression.FAI was the independent predictor of plaque progression(OR=1.08,P＜0.001)and CCTA model was constructed.Ten optimal radiomics features based on training set were selected to build RF,GP,PLS-DA,QDA and SVM models.The area under the curve(AUC)of RF model in training set and validation set were both high,was considered as the optimal ML model.The AUC of CCTA,RF and combined models in training set was 0.684,0.847 and 0.861,respectively,while was 0.629,0.768 and 0.821 in validation set,respectively.Conclusion CCTA radiomics ML model combined with FAI could effectively predict coronary plaques progression.

ObjectiveTo observe the therapeutic effect of BD-77 by nebulized inhalation on animal models of various respiratory viral infections and investigate the mechanism of broad-spectrum antiviral action of BD-77 using proteomics. MethodThe influenza virus H1N1/FM1 experiment used ICR mice and divided them into a normal group， model group， Tamiflu group， and BD-77 groups of 75 and 37.5 g·L^-1 for inhalation of 20 min and 25 min. Human coronavirus 229E and OC43 experiment divided the BALB/c mice into a normal group， model group， chloroquine phosphate group， and BD-77 groups of 75， 37.5， 18.75， and 9.375 g·L^-1， with 10 mice in each group. Influenza virus H1N1/FM1 and human coronaviruses 229E and OC43 infection-induced pneumonia models were used to detect mouse lung index， and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the viral load in lung tissue. Enzyme-linked immunosorbent assay （ELISA） was used to detect related inflammatory factors in lung tissue， and proteomics analysis was performed on the lung tissue of OC43-infected mice. ResultCompared with that in the normal group， the lung index of mice in each infection group was significantly increased （P<0.01）， and viral nucleic acid could be detected in the lung tissue of mice infected with human coronaviruses 229E and OC43. The levels of interleukin-6 （IL-6）， IL-10， and tumor necrosis factor-α （TNF-α） in the lung tissue of mice infected with human coronavirus 229E were all significantly increased （P<0.01）. BD-77 could significantly reduce the lung index of mice infected with influenza virus H1N1/FM1 and human coronaviruses 229E and OC43 （P<0.05， P<0.01）， cut down the viral load in the lungs of mice infected with human coronaviruses 229E and OC43 （P<0.01）， and lower the contents of IL-6， IL-10， and TNF-α in the lung tissue of mice infected with human coronavirus 229E （P<0.01）. Proteomics analysis of the lung tissue of OC43-infected mice showed that BD-77 regulated the AMPK signaling pathway， TNF signaling pathway， NOD-like signaling pathway， IL-17 signaling pathway， Forkhead box protein O （FoxO） signaling pathway， transforming growth factor-β （TGF-β） signaling pathway， and other signaling pathways. ConclusionNebulized inhalation of BD-77 is effective in treating pneumonia caused by influenza virus H1N1/FM1 and human coronaviruses 229E and OC43 infection in mice and may exert its antiviral effects by regulating the balance of cellular metabolism， enhancing the immune function of the host， and attenuating inflammatory responses.

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.