Objective To investigate the prevalence of soil-transmitted nematode human infections in Hubei Province from 2016 to 2023, so as to provide insights into formulation of soil-transmitted nematodiasis control strategies and evaluation of soil-transmitted nematodiasis control effectiveness. Methods There were 10 surveillance points established in Hubei Province for soil-transmitted nematodiasis each year from 2016 to 2023. The surveillance points were divided into eastern, western, southern, northern and central regions according to geographical locations, and one administrative village was sampled from a township in each region as a surveillance point. Then, at least 200 permanent residents at ages of over 3 years were randomly sampled at each point, with at least 1 000 individuals sampled. Soil-transmitted nematode infection was detected using a modified Kato-Katz thick smear method (two slides for one stool sample), and Enterobius vermicularis infection was additionally detected with a cellophane tape test among children at ages of 3 to 9 years. The prevalence of soil-transmitted nematode human infections was descriptively analyzed. Results A total of 80 547 individuals underwent surveillance for soil-transmitted nematodiasis in Hubei Province from 2016 to 2023, and a total of 262 individuals were detected with soil-transmitted nematode infections, including two cases with mixed infections, yielding an overall prevalence rate of 0.33% (262/80 547). The prevalence rates of hookworm, Ascaris lumbricoides, Trichuris trichiura, and E. vermicularis infections were 0.02% (20/80 547), 0.07% (55/80 547), 0.02% (15/80 547) and 0.22% (174/80 547), respectively, appearing a parasite species-specific prevalence rate (χ² = 250.235, P < 0.05), and the prevalence rates of soil-transmitted nematode infections were 0.50%(51/10271),0.37%(34/9120),0.39%(39/10099),0.40%(41/10168), 0.36% (37/10 234), 0.45% (46/10 211), 0.03% (3/10 238) and 0.11% (11/10 206) in Hubei Province from 2016 to 2023, respectively, showing a year-specific prevalence rate (χ² = 60.589, P < 0.05). Soil-transmitted nematode infections were detected across 36 surveillance points, and there was a significant difference in the prevalence of soil-transmitted nematode infections among surveillance points (0.02% to 3.30%, P < 0.001). In addition, the differences in prevalence rates of human soil-transmitted nematode infection among different age groups (χ² = 531.091, P < 0.05), occupations (P < 0.001), and education levels (P < 0.001) were all statistically significant. Among these, the children at ages of 3 to 9 years old (1.40%, 174/12 461), nursery-school children (1.69%, 90/5 312) and preschool-aged children (1.56%, 93/5 969) were relatively high. Conclusions The overall prevalence of soil-transmitted nematodiasis was low in Hubei Province from 2016 to 2023; however, the prevalence varied across regions and among populations, with E. vermicularis as the predominant species of soil-transmitted nematode. Soil-transmitted nematodiasis control should be given a high priority in key areas in Hubei Province, and preschool children, primary school students, and individuals with a low educational level are recommended as key target populations for health education and interventions.

To investigate the distribution and development of common allergens in children in Hangzhou area, and to provide an epidemiological basis for the prevention, diagnosis, and treatment of allergic diseases in this area. This study is a retrospective study, selecting 3 524 children who underwent allergen screening at DiAn Medical Laboratory Center (Hangzhou) Co., Ltd. from January 2022 to January 2024 as the research subjects, including 2 012 males and 1 512 females. Among them, there were 1 098 infants (0-1 year olds), 1 673 toddlers (1-3 year olds), and 753 children (3-14 year olds). Immunoblotting was used to detect 21 allergen-specific immunoglobulin E (sIgE) antibodies. The positive rates of various allergens were calculated according to gender, age, and other factors, and the epidemiological characteristics and trends were analyzed. The results showed that the overall positive rate of sIgE was 60.33% (2 126/3 524). The main inhaled allergens were Dermatophagoides pteronyssinus/Dermatophagoides farinae (992, 28.15%), dog dander (295, 8.37%) and cat dander (181, 5.13%). The main food allergens were milk (696, 19.75%), egg white (541, 15.36%) and shrimp (205, 5.83%). Boys had significantly higher sIgE positivity rates than girls for Dermatophagoides pteronyssinus/Dermatophagoides farinae(642, 31.90%, χ2=10.10, P=0.001), house dust(61, 3.02%, χ2=5.12, P=0.024), cat dander(124, 6.16%, χ2=4.06, P=0.044), mold combinations(103, 5.14%, χ2=7.51, P=0.006), and tree pollen combinations(42, 2.07%, χ2=5.44, P=0.020) (all P<0.05); With age, there was a significant increase in positivity rates for house dust mite/dust mite, house dust, mold combinations, milk, and beef (all P<0.05), and a significant decrease in positivity rates for cockroach consumption, egg whites, shrimp, crab, cod, lobster/scallop, and soybeans (all P<0.05), the positive rates of only ingestive allergens decreased significantly ( P<0.001), and the positive rates of only inhalant and mixed allergens increased significantly ( P<0.001). In conclusion,in the Hangzhou area, dust mites are the most common inhalant allergens among children with allergic diseases, while milk is the most common food allergen. Boys are more sensitive to inhalant allergens, and as children grow older, the positive rates for different allergens undergo significant changes. Dynamic monitoring of changes in specific IgE antibodies to various allergens can assist in the diagnosis, prevention, and treatment of allergic diseases.

Objective:To explore and model risk factors in patients with major adverse cardiovascular events (MACEs) after acute type A aortic dissection (ATAAD), and to develop and validate a personalized machine learning model to assess risk factors and predict MACEs in these patients.Methods:Clinical data of patients who attended Beijing Anzhen Hospital and underwent surgical treatment for ATAAD from January 2018 to October 2022 were retrospectively analyzed. Using MACEs as the endpoint, 70% of these patients were randomly divided into the training set and the remaining 30% into the validation set. LASSO regression was applied to explore key clinical variables in the training set. The optimal predictive model was selected from nine machine learning algorithms based on area under the curve. And Shapley Additive explanations was used to elucidate the predictive model. Results:Of the 481 patients included in this study, 135 (35.6%) patients experienced an endpoint event. By combining the results of the training and validation sets, when assessing the validity of the single model with the highest predictive accuracy for the outcome, it was shown that the logistic model (0.774, 95% CI: 0.717-0.830) was the most effective in the combined effect and had a high model accuracy (0.743, 95% CI: 0.720-0.766). According to the results of the LASSO, the factors most associated with postoperative MACEs were history of cerebrovascular disease, coronary artery involvement, shock status on admission to the operating room, FDP, PLT, CPB, ascending aortic clamping, and age. Conclusion:In this study, nine machine learning models were developed to predict the occurrence of postoperative MACEs in patients with acute type A aortic dissection. The logistic model performed significantly better compared to other algorithms. Our study successfully predicted postoperative MACES and identified the factors most associated with MACEs.

Objective:To explore the research hotspots and trends in the radiomics field based on bibliometric for breast cancer.Methods:Publications related to radiomics for breast cancer during January 2012 to June 2024 were retrieved from the Web of Science Core Collection databases.Then,the R software's package"bibliometric"tool,VOSviewer and CiteSpace softwares were used to conduct visual analysis of radiomics-related bibliometric for breast cancer,which involved to publication trends,countries,institutions,journals,authors,keywords,and highlight keywords.Results:A total of 683 radiomics-related literatures for breast cancer were obtained,and 518 articles were finally included after non-English literatures and non-Article literatures were excluded.China and Sun Yat-sen University emerged as the leading country(326 articles)and institution(83 articles).The top three journals with the most of published articles were respectively,and.Professor Mao Ning has the largest number of articles,followed by Pinker Katja and Liang Changhong.The results of keywords and highlight keywords showed that current radiomics researches for breast cancer mainly focused on the applications of magnetic resonance imaging(MRI),machine learning,deep learning,artificial intelligence,and radiogenomics in prediction for diseases'prognosis,and assessment for treatment's response.Conclusion:The applications of machine learning,deep learning and multi-parameter imaging belongs to hot-research fields in assessment for personalized treatment.Future research directions would more and more tend to integrate artificial intelligence and multi-omics analysis,so as to promote prognostic assessment and personalized treatment.

Objective To verify the efficacy of a domestic human DNA quantification kit based on real-time fluorescence quantitative PCR in detecting the total human DNA concentration,male DNA concen-tration in mixed male/female DNA samples,the degree of DNA degradation and inhibitor tolerance.Methods Samples with different concentrations,different male/female ratios,different concentrations of inhibitors,and different degradation degrees were tested using the domestic human DNA quantification kit based on real-time fluorescence quantitative PCR.This kit was compared with a similar product on the market and was applied to the detection of DNA from real cases.Results This human DNA quan-tification kit can effectively detect human DNA as low as 0.001 65 ng/μL,and 6.25 pg/μL of male DNA in mixed samples with a male-to-female ratio of 1∶15 000.Even when the sample contains as high as 400 ng/μL of humic acid or 1 000 μmol/L of hemin alone,the DNA concentration can still be accurately detected.The degradation index can effectively characterize the degradation degree of the sample.This kit has been successfully applied in forensic practice.Conclusion This human DNA quan-tification kit is accurate and reliable in detection.It can accurately reflect the degradation of DNA and inhibitor tolerance.It has good performance in quantitative accuracy,determination of the male/female ratio in mixed samples,and inhibitor tolerance.It has application potential in forensic case examination.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

To investigate the distribution and development of common allergens in children in Hangzhou area, and to provide an epidemiological basis for the prevention, diagnosis, and treatment of allergic diseases in this area. This study is a retrospective study, selecting 3 524 children who underwent allergen screening at DiAn Medical Laboratory Center (Hangzhou) Co., Ltd. from January 2022 to January 2024 as the research subjects, including 2 012 males and 1 512 females. Among them, there were 1 098 infants (0-1 year olds), 1 673 toddlers (1-3 year olds), and 753 children (3-14 year olds). Immunoblotting was used to detect 21 allergen-specific immunoglobulin E (sIgE) antibodies. The positive rates of various allergens were calculated according to gender, age, and other factors, and the epidemiological characteristics and trends were analyzed. The results showed that the overall positive rate of sIgE was 60.33% (2 126/3 524). The main inhaled allergens were Dermatophagoides pteronyssinus/Dermatophagoides farinae (992, 28.15%), dog dander (295, 8.37%) and cat dander (181, 5.13%). The main food allergens were milk (696, 19.75%), egg white (541, 15.36%) and shrimp (205, 5.83%). Boys had significantly higher sIgE positivity rates than girls for Dermatophagoides pteronyssinus/Dermatophagoides farinae(642, 31.90%, χ2=10.10, P=0.001), house dust(61, 3.02%, χ2=5.12, P=0.024), cat dander(124, 6.16%, χ2=4.06, P=0.044), mold combinations(103, 5.14%, χ2=7.51, P=0.006), and tree pollen combinations(42, 2.07%, χ2=5.44, P=0.020) (all P<0.05); With age, there was a significant increase in positivity rates for house dust mite/dust mite, house dust, mold combinations, milk, and beef (all P<0.05), and a significant decrease in positivity rates for cockroach consumption, egg whites, shrimp, crab, cod, lobster/scallop, and soybeans (all P<0.05), the positive rates of only ingestive allergens decreased significantly ( P<0.001), and the positive rates of only inhalant and mixed allergens increased significantly ( P<0.001). In conclusion,in the Hangzhou area, dust mites are the most common inhalant allergens among children with allergic diseases, while milk is the most common food allergen. Boys are more sensitive to inhalant allergens, and as children grow older, the positive rates for different allergens undergo significant changes. Dynamic monitoring of changes in specific IgE antibodies to various allergens can assist in the diagnosis, prevention, and treatment of allergic diseases.

Objective:To investigate the effects and underlying mechanism of ionizing radiation on the adipogenic of mesenchymal stem cells(MSCs).Methods:Mouse MSCs were cultured in vitro and treated with 2 Gy and 6 Gy radiation with 60Co,and the radiation dose rate was 0.98 Gy/min.Bulk RNA-seq was performed on control and irradiated MSCs.The changes of adipogenic differentiation and oxidative stress pathways of MSC were revealed by bioinformatics analysis.Oil Red O staining was used to detect the adipogenic differentiation ability of MSCs in vitro,and real-time fluorescence quantitative PCR(qPCR)was used to detect the expression differences of key regulatory factors Cebpa,Lpl and Pparg after radiation treatment.At the same time,qPCR and Western blot were used to detect the effect of inhibition of Nrf2,a key factor of antioxidant stress pathway,on the expression of key regulatory factors of adipogenesis.Moreover,the species conservation of the irradiation response of human bone marrow MSCs and mouse MSC was determined by qPCR.Results:Bulk RNA-seq suggested that ionizing radiation promotes adipogenic differentiation of MSCs and up-regulation of oxidative stress-related genes and pathways.The results of Oil Red O staining and qPCR showed that ionizing radiation promoted the adipogenesis of MSCs,with high expression of Cebpa,Lpl and Pparg,as well as oxidative stress-related gene Nrf2.Nrf2 pathway inhibitors could further enhance the adipogenesis of MSCs in bone marrow after radiation.Notably,the similar regulation of oxidative pathways and enhanced adipogenesis post irradiation were observed in human bone marrow MSCs.In addition,irradiation exposure led to up-regulated mRNA expression of interleukin-6 and down-regulated mRNA expression of colony stimulating factor 2 in human bone marrow MSCs.Conclusion:Ionizing radiation promotes adipogenesis of MSCs in mice,and oxidative stress pathway participates in this effect,blocking Nrf2 further promotes the adipogenesis of MSCs.Additionally,irradiation activates oxidative pathways and promotes adipogenic differentiation of human bone marrow MSCs.

Objective:To detect the expression levels of long non-coding RNAs(lncRNA)in elderly patients with pulmonary tuberculosis(PTB)and those with non-tuberculous lung diseases(non-TB), and to assess the performance of these lncRNA in the differential diagnosis of PTB.Methods:A total of 300 elderly patients with suspected PTB were recruited from Beijing Chest Hospital between January 2024 and September 2024, and were further divided into the PTB group and the non-TB lung disease group based on the results of mycobacterium tuberculosis(MTB)pathogenicity testing.Peripheral blood mononuclear cells were isolated using a lymphocyte separation solution, and RNA was extracted using the TRIzol method.Nine lncRNAs, previously identified as differentially expressed in PTB through our group's microarray analysis, were selected and detected by real-time fluorescence quantitative polymerase chain reaction to evaluate the expression levels of these lncRNAs between the PTB and non-TB lung disease groups.The overall patients were randomly divided into training and validation sets in a 7∶3 ratio.Lasso regression was employed to select the characteristic variables, and a random forest algorithm was then used to construct the lncRNA diagnostic portfolio.Receiver operating characteristic(ROC)curves were generated to evaluate the diagnostic performance of individual lncRNAs and the combined panel in differentiating elderly patients with PTB from those with other non-TB lung diseases.Results:A total of 201 cases were included, with 105 confirmed elderly patients diagnosed with PTB(52.2%)and 96 elderly patients suffering from non-TB lung disease(47.8%).Compared to the elderly patients with non-TB lung disease, the expression levels of ENST00000417346.1, ENST00000620744.1, lncRNA PWP1, ENST00000583184.1, lncRNA ABHD17B, ENST00000607464.1, ENST00000516057.1, and NR_003000 were significantly downregulated in the PTB patients, whereas the expression level of lncRNA BCL2L10 was significantly upregulated in the PTB patients.ROC analysis revealed that the area under the curve(AUC)for each lncRNA ranged from 0.659 to 0.848.The diagnostic panel, which included NR_003000, ENST00000607464.1, ENST00000583184.1, and ENST00000620744.1 as determined by Lasso analysis, exhibited AUC values of 0.917 and 0.906 in the training and validation sets, respectively.The performance of this panel was superior to that of each individual lncRNA.Conclusions:The random forest model, which incorporates NR_003000, ENST00000607464.1, ENST00000583184.1, and ENST00000620744.1, demonstrates potential in differentiating between PTB and non-TB lung diseases.

Objective:To explore and model risk factors in patients with major adverse cardiovascular events (MACEs) after acute type A aortic dissection (ATAAD), and to develop and validate a personalized machine learning model to assess risk factors and predict MACEs in these patients.Methods:Clinical data of patients who attended Beijing Anzhen Hospital and underwent surgical treatment for ATAAD from January 2018 to October 2022 were retrospectively analyzed. Using MACEs as the endpoint, 70% of these patients were randomly divided into the training set and the remaining 30% into the validation set. LASSO regression was applied to explore key clinical variables in the training set. The optimal predictive model was selected from nine machine learning algorithms based on area under the curve. And Shapley Additive explanations was used to elucidate the predictive model. Results:Of the 481 patients included in this study, 135 (35.6%) patients experienced an endpoint event. By combining the results of the training and validation sets, when assessing the validity of the single model with the highest predictive accuracy for the outcome, it was shown that the logistic model (0.774, 95% CI: 0.717-0.830) was the most effective in the combined effect and had a high model accuracy (0.743, 95% CI: 0.720-0.766). According to the results of the LASSO, the factors most associated with postoperative MACEs were history of cerebrovascular disease, coronary artery involvement, shock status on admission to the operating room, FDP, PLT, CPB, ascending aortic clamping, and age. Conclusion:In this study, nine machine learning models were developed to predict the occurrence of postoperative MACEs in patients with acute type A aortic dissection. The logistic model performed significantly better compared to other algorithms. Our study successfully predicted postoperative MACES and identified the factors most associated with MACEs.