OBJECTIVE: To investigate the effect of irradiation dose rate of ⁶⁰Co γ-ray on hematopoietic and immune cells in total body irradiation (TBI) mice. METHODS: After TBI with 8 Gy ⁶⁰Co γ-ray at three irradiation dose rates of 0.027, 0.256 and 0.597 Gy/min, the survival and change of body weight of C57BL/6J mice were observed within 30 days. The peripheral blood parameters were examined at each time point within 30 days post-irradiation. The hematopoietic stem/progenitor cell counts of mice were examined on the 10^th and 30^th day post-irradiation by flow cytometry, as well as the proportions of immune cells in peripheral blood, bone marrow and spleen of mice on the 30^th day post-irradiation. RESULTS: After TBI with 8 Gy ⁶⁰Co γ-ray, the 30-day survival rate of high dose-rate group was 0, which was significantly lower than 90% of medium dose-rate group and 100% of low dose-rate group (both P < 0.001). The peripheral blood parameters of all three groups showed a sharp decline → low value → gradually recovering trend. The count of white blood cell, neutrophil, lymphocyte, red blood cell, platelet and hemoglobin level in the high dose-rate and medium dose-rate groups were significantly lower than those in the low dose-rate group on day 7-18 post-irradiation (all P < 0.05), but there were no significant differences between the high dose-rate and medium dose-rate groups (P >0.05). On the 10^th day after irradiation, the proportion and number of bone marrow hematopoietic stem/progenitor cells (including LK, LSK, LT-HSC, ST-HSC, and MPP cells) in the low dose-rate and medium dose-rate groups were significantly decreased compared to those in the normal group (all P < 0.05), but there were no significant differences between the two groups (P >0.05). On the 30^th day after irradiation, LSK, LT-HSC, ST-HSC and MPP cells in the low dose-rate group recovered to normal levels, while those in the medium dose-rate group were still significantly lower than those in the low dose-rate group (all P < 0.001). The results of bone marrow and peripheral immune cell tests on the 30^th day after irradiation showed that the ratios of T and B lymphocytes in the low dose-rate and medium dose-rate groups were reduced compared to that in the normal group (both P < 0.05), while the ratio of neutrophils was increased (P < 0.01). The trend of changes in the spleen and peripheral blood was consistent. CONCLUSION The degree of hematopoietic and immune cell damage in mice after TBI with 8 Gy ⁶⁰Co γ-ray is related to the dose rate, and low dose-rate irradiation can reduce the damage in the animal model. Therefore, choosing the appropriate dose rate of irradiation is a key factor in establishing an objective and reliable experimental animal model of irradiation.
Animals ; Mice ; Whole-Body Irradiation ; Gamma Rays ; Mice, Inbred C57BL ; Hematopoietic Stem Cells/radiation effects* ; Cobalt Radioisotopes ; Dose-Response Relationship, Radiation ; Male

Maternal health during pregnancy has a direct impact on the risk and severity of neurodevelopmental disorders (NDDs) in the offspring, especially in the case of drug exposure. However, little progress has been made to assess the risk of drug exposure during pregnancy due to ethical constraints and drug use factors. We collected and manually curated sub-pathways and pathways (sub-/pathways) and drug information to propose an analytical framework for predicting drug candidates. This framework linked sub-/pathway activity and drug response scores derived from gene transcription data and was applied to human fetal brain development and six NDDs. Further, specific and pleiotropic sub-/pathways/drugs were identified using entropy, and sex bias was analyzed in conjunction with logistic regression and random forest models. We identified 19 disorder-associated and 256 regionally pleiotropic and specific candidate drugs that targeted risk sub-/pathways in NDDs, showing temporal or spatial changes across fetal development. Moreover, 5443 differential drug-sub-/pathways exhibited sex-biased differences after filling in the gender labels. A user-friendly NDDP visualization website ( https://ndd-lab.shinyapps.io/NDDP ) was developed to allow researchers and clinicians to access and retrieve data easily. Our framework overcame data gaps and identified numerous pleiotropic and specific candidates across six disorders and fetal developmental trajectories. This could significantly contribute to drug discovery during pregnancy and can be applied to a wide range of traits.
Humans ; Female ; Pregnancy ; Neurodevelopmental Disorders/metabolism* ; Male ; Prenatal Exposure Delayed Effects ; Fetal Development/drug effects* ; Drug Discovery/methods* ; Brain/metabolism*

AIM To explore the clinical effects of Supplemented Baihe Gujin Decoction on elderly patients with postoperative pulmonary infection following non-small cell lung cancer surgery.METHODS Ninety-two patients were randomly assigned into control group(46 cases)for 1-week intervention of conventional treatment,and observation group(46 cases)for 1-week intervention of both Supplemented Baihe Gujin Decoction and conventional treatment.The changes in clinical effects,TCM syndrome scores,immune function indices(CD3+,CD4+,CD8+,CD4+/CD8+),inflammatory indices(CRP,PCT,TNF-α),serum indices(sTREM-1,CD40L,NLR)and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,CD8+,inflammatory indices,serum indices(P＜0.05),and increased CD3+,CD4+,CD4+/CD8+(P＜0.05),especially for the observation group(except for CD4+,CD8+)(P＜0.05).CONCLUSION For the elderly patients with postoperative pulmonary infection following non-small cell lung cancer surgery,Supplemented Baihe Gujin Decoction can safely and effectively relieve clinical symptoms,enhance immune functions,reduce serum sTREM-1,CD40L levels and NLR,and control inflammatory responses.

The study was aimed at identifying the diversity of tick species in selected areas of Qinghai,to analyze the genetic differentiation characteristics of tick-borne spotted fever group rickettsiae(SFGR),and to provide the theoretical basis for SFGR prevention and control in the region.The 16S rRNA gene was used for molecular biological identification of 446 collected tick samples,and the infection characteristics of SFGR in tick samples were determined according to the SFGR outer membrane protein A(ompA)gene.Haplotype analysis,phylogenetic tree construction,and estimation of differentiation times for SFGR were conducted in DNASP v6,IQ-tree v2.2.0,and BEAST v2.7.4 software.The obtained 446 tick samples belonged to three categories:(1)Haemaphy-salis spp.,including Haemaphysalis qinghaiensis(n=192)and H.danieli(n=37);(2)Dermacentor spp.,including Dermacentor ever-estianus(n=121),D.nuttalli(n=55),and D.silvarum(n=36);and(3)Hyalomma marginatum(n=5).Rickettsia raoultii was de-tected in D.everestianus,D.silvarum,D.nuttalli,H.qinghaiensis,and H.danieli,with infection rates of 95.9%,80.6%,69.1%,4.1%,and 2.7%,respectively.R.sibirica subsp.sibirica BJ-90 was found only in D.silvarum and D.nuttalli,with infection rates of 5.6%and 1.8%,respectively.The Candidatus R.gannanii F107 was found in H.danieli and H.qinghaiensis,with infection rates of 16.2%and 7.8%,respectively.Ca.R.hongyuanensis was detected only in H.qinghaiensis,with a prevalence of 16.3%.The prevalence of R.aeschlimannii was 20%and 2.7%in Hy.marginatum and H.danieli,respectively.Haplotype and nucleotide polymorphism analy-ses revealed 13 haplotypes in R.raoultii,with haplotype H13 as the dominant haplotype(42/192);seven haplotypes in Ca.R.ganna-nii F107,with haplotype H4 as the dominant haplotype(4/18);and three haplotypes in Ca.R.hongyuanensis,with haplotype H1 as the dominant haplotype(11/13).The phylogenetic tree indicated that the sequences of R.raoultii in selected areas of Qinghai and R.rhipicephali clustered into one branch;Ca.R.hongyuanensis and Ca.R.gannanii F107 clustered into one branch;and R.sibirica subsp.sibirica BJ-90 clustered into one branch with R.sibirica.Estimates of differentiation time revealed that the mean differentiation time for the six Rickettsia was approximately 2 000 Mya(95%CI:1 999.08-2 001.02 Mya).The tick species distributed in selected ar-eas of Qinghai are diverse,and this study provides the first report of Hy.marginatum in Qinghai Province.SFGR significantly varied in prevalence among tick species and showed high genetic diversity.

Objective:To investigate the predictive value of microRNA-1(miR-1),brain natriuretic peptide(BNP)and ischemia-modified albumin(IMA)for unstable angina pectoris(UAP).Methods:We enrolled 237 UAP patients admitted to Xining Second People's Hospital between June 2018 and December 2020 as UAP group.Another 86 healthy subjects undergoing physical examination simultaneously were enrolled as control group.MiR-1 expression,BNP and IMA levels were measured.General data between UAP group and control group,serum miR-1 expression,BNP and IMA levels among different Braunwald class and prognosis were compared.Receiver operat-ing characteristic(ROC)curve was employed to analyze predictive value of miR-1,BNP,IMA and their combina-tion for prognosis in UAP patients.Results:Compared with participants in the control group,those in UAP group had significant higher serum miR-1 expression[(1.80±0.59)vs.(0.93±0.11)],BNP[(107.34±37.46)pg/ml vs.(52.31±10.64)pg/ml]and IMA[(79.76±19.29)g/L vs.(53.16±6.43)g/L](P＜0.001 all).As Braun-wald class increased(class Ⅰ～Ⅲ),serum miR-1 expression,BNP and IMA levels elevated(P＜0.001 all).Com-pared with patients in favorable outcome group,those in unfavorable outcome group had significant higher serum miR-1 expression[(2.31±0.54)vs.(1.53±0.41)],BNP[(147.03±29.63)pg/ml vs.(85.95±19.46)pg/ml]and IMA[(97.24±15.35)g/L vs.(70.35±13.88)g/L](P＜0.001 all).ROC curve indicated that AUC of com-bined detection for predicting unfavorable outcome in UAP patients was 0.925(95％CI 0.884～0.955),which was significantly higher than miR-1(AUC=0.880,95％CI 0.831～0.918),BNP(AUC=0.863,95％CI 0.813～0.904)and IMA(AUC=0.900,95％CI 0.854～0.935)alone(Z=2.884,3.130,2.090,P＜0.05 or＜0.01).Conclusion:MiR-1 expression,BNP and IMA levels significantly increase in UAP patients,and they are associated with the severity of disease.Combined detection has good predictive value for unfavorable outcome in UAP pa-tients.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To investigate the predictive value of microRNA-1(miR-1),brain natriuretic peptide(BNP)and ischemia-modified albumin(IMA)for unstable angina pectoris(UAP).Methods:We enrolled 237 UAP patients admitted to Xining Second People's Hospital between June 2018 and December 2020 as UAP group.Another 86 healthy subjects undergoing physical examination simultaneously were enrolled as control group.MiR-1 expression,BNP and IMA levels were measured.General data between UAP group and control group,serum miR-1 expression,BNP and IMA levels among different Braunwald class and prognosis were compared.Receiver operat-ing characteristic(ROC)curve was employed to analyze predictive value of miR-1,BNP,IMA and their combina-tion for prognosis in UAP patients.Results:Compared with participants in the control group,those in UAP group had significant higher serum miR-1 expression[(1.80±0.59)vs.(0.93±0.11)],BNP[(107.34±37.46)pg/ml vs.(52.31±10.64)pg/ml]and IMA[(79.76±19.29)g/L vs.(53.16±6.43)g/L](P＜0.001 all).As Braun-wald class increased(class Ⅰ～Ⅲ),serum miR-1 expression,BNP and IMA levels elevated(P＜0.001 all).Com-pared with patients in favorable outcome group,those in unfavorable outcome group had significant higher serum miR-1 expression[(2.31±0.54)vs.(1.53±0.41)],BNP[(147.03±29.63)pg/ml vs.(85.95±19.46)pg/ml]and IMA[(97.24±15.35)g/L vs.(70.35±13.88)g/L](P＜0.001 all).ROC curve indicated that AUC of com-bined detection for predicting unfavorable outcome in UAP patients was 0.925(95％CI 0.884～0.955),which was significantly higher than miR-1(AUC=0.880,95％CI 0.831～0.918),BNP(AUC=0.863,95％CI 0.813～0.904)and IMA(AUC=0.900,95％CI 0.854～0.935)alone(Z=2.884,3.130,2.090,P＜0.05 or＜0.01).Conclusion:MiR-1 expression,BNP and IMA levels significantly increase in UAP patients,and they are associated with the severity of disease.Combined detection has good predictive value for unfavorable outcome in UAP pa-tients.

The study was aimed at identifying the diversity of tick species in selected areas of Qinghai,to analyze the genetic differentiation characteristics of tick-borne spotted fever group rickettsiae(SFGR),and to provide the theoretical basis for SFGR prevention and control in the region.The 16S rRNA gene was used for molecular biological identification of 446 collected tick samples,and the infection characteristics of SFGR in tick samples were determined according to the SFGR outer membrane protein A(ompA)gene.Haplotype analysis,phylogenetic tree construction,and estimation of differentiation times for SFGR were conducted in DNASP v6,IQ-tree v2.2.0,and BEAST v2.7.4 software.The obtained 446 tick samples belonged to three categories:(1)Haemaphy-salis spp.,including Haemaphysalis qinghaiensis(n=192)and H.danieli(n=37);(2)Dermacentor spp.,including Dermacentor ever-estianus(n=121),D.nuttalli(n=55),and D.silvarum(n=36);and(3)Hyalomma marginatum(n=5).Rickettsia raoultii was de-tected in D.everestianus,D.silvarum,D.nuttalli,H.qinghaiensis,and H.danieli,with infection rates of 95.9%,80.6%,69.1%,4.1%,and 2.7%,respectively.R.sibirica subsp.sibirica BJ-90 was found only in D.silvarum and D.nuttalli,with infection rates of 5.6%and 1.8%,respectively.The Candidatus R.gannanii F107 was found in H.danieli and H.qinghaiensis,with infection rates of 16.2%and 7.8%,respectively.Ca.R.hongyuanensis was detected only in H.qinghaiensis,with a prevalence of 16.3%.The prevalence of R.aeschlimannii was 20%and 2.7%in Hy.marginatum and H.danieli,respectively.Haplotype and nucleotide polymorphism analy-ses revealed 13 haplotypes in R.raoultii,with haplotype H13 as the dominant haplotype(42/192);seven haplotypes in Ca.R.ganna-nii F107,with haplotype H4 as the dominant haplotype(4/18);and three haplotypes in Ca.R.hongyuanensis,with haplotype H1 as the dominant haplotype(11/13).The phylogenetic tree indicated that the sequences of R.raoultii in selected areas of Qinghai and R.rhipicephali clustered into one branch;Ca.R.hongyuanensis and Ca.R.gannanii F107 clustered into one branch;and R.sibirica subsp.sibirica BJ-90 clustered into one branch with R.sibirica.Estimates of differentiation time revealed that the mean differentiation time for the six Rickettsia was approximately 2 000 Mya(95%CI:1 999.08-2 001.02 Mya).The tick species distributed in selected ar-eas of Qinghai are diverse,and this study provides the first report of Hy.marginatum in Qinghai Province.SFGR significantly varied in prevalence among tick species and showed high genetic diversity.

AIM To explore the clinical effects of Supplemented Baihe Gujin Decoction on elderly patients with postoperative pulmonary infection following non-small cell lung cancer surgery.METHODS Ninety-two patients were randomly assigned into control group(46 cases)for 1-week intervention of conventional treatment,and observation group(46 cases)for 1-week intervention of both Supplemented Baihe Gujin Decoction and conventional treatment.The changes in clinical effects,TCM syndrome scores,immune function indices(CD3+,CD4+,CD8+,CD4+/CD8+),inflammatory indices(CRP,PCT,TNF-α),serum indices(sTREM-1,CD40L,NLR)and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,CD8+,inflammatory indices,serum indices(P＜0.05),and increased CD3+,CD4+,CD4+/CD8+(P＜0.05),especially for the observation group(except for CD4+,CD8+)(P＜0.05).CONCLUSION For the elderly patients with postoperative pulmonary infection following non-small cell lung cancer surgery,Supplemented Baihe Gujin Decoction can safely and effectively relieve clinical symptoms,enhance immune functions,reduce serum sTREM-1,CD40L levels and NLR,and control inflammatory responses.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.