The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the relationship between urinary cadmium levels and thyroid hormone levels in people aged 40-89 years old in selected areas of China.Methods:Based on the "Investigation of the Impact of Soil Quality of Agricultural Land on Human Health in Typical Areas" project from October 2019 to August 2020, a multi-stage stratified random sampling method was used to include 6 588 middle-aged and older adults aged 40-89. Demographic characteristics, dietary frequency and disease status were collected through the questionnaire and physical examination. Urinary cadmium and urinary creatinine were detected by random midstream urine. Fasting venous blood was collected for the detection of Triiodothyronine (T3) and Thyroxine (T4). The linear mixed effects model was used to explore the association of urine cadmium levels with thyroid hormone levels. Its dose-response relationship was explored by using the restricted cubic spline.Results:The age of the subjects was (63.48±12.18) years, with males accounting for 51.28%. The M ( Q 1,Q 3) of urinary cadmium level, T3 and T4 was 2.48 (1.36, 4.42) μg/g·creatinine, (1.96±0.51) nmol/L and (113.75±29.11) nmol/L, respectively. The linear mixed effects model showed that the changes of T3 and T4 were 0.027 (0.009, 0.044) nmol/L and 2.019 (1.084, 2.953) nmol/L for each one-unit increase (natural logarithm transformed) of urinary cadmium. The restricted cubic spline showed that there was a positive nonlinear association between urinary cadmium and T3 as well as T4 (all Pnonlinear<0.05). Conclusion:In selected areas of China, the urinary cadmium level of middle-aged and older adults aged 40-89 years is positively associated with T3 and T4.

Objective:To explore the relationship between urinary cadmium levels and thyroid hormone levels in people aged 40-89 years old in selected areas of China.Methods:Based on the "Investigation of the Impact of Soil Quality of Agricultural Land on Human Health in Typical Areas" project from October 2019 to August 2020, a multi-stage stratified random sampling method was used to include 6 588 middle-aged and older adults aged 40-89. Demographic characteristics, dietary frequency and disease status were collected through the questionnaire and physical examination. Urinary cadmium and urinary creatinine were detected by random midstream urine. Fasting venous blood was collected for the detection of Triiodothyronine (T3) and Thyroxine (T4). The linear mixed effects model was used to explore the association of urine cadmium levels with thyroid hormone levels. Its dose-response relationship was explored by using the restricted cubic spline.Results:The age of the subjects was (63.48±12.18) years, with males accounting for 51.28%. The M ( Q 1,Q 3) of urinary cadmium level, T3 and T4 was 2.48 (1.36, 4.42) μg/g·creatinine, (1.96±0.51) nmol/L and (113.75±29.11) nmol/L, respectively. The linear mixed effects model showed that the changes of T3 and T4 were 0.027 (0.009, 0.044) nmol/L and 2.019 (1.084, 2.953) nmol/L for each one-unit increase (natural logarithm transformed) of urinary cadmium. The restricted cubic spline showed that there was a positive nonlinear association between urinary cadmium and T3 as well as T4 (all Pnonlinear<0.05). Conclusion:In selected areas of China, the urinary cadmium level of middle-aged and older adults aged 40-89 years is positively associated with T3 and T4.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Aim To establish NCI-H446/EP for small cell lung cancer resistant cells resistant to cisplatin and etoposide, and to evaluate their biological characteristics and multidrug resistance. Methods Nude mice were subcutaneously inoculated with NCI-H446 cells of SCLC to construct an in vivo model of xenograft tumor, and were given first-line EP regimen treatment for SCLC, inducing drug resistance in vivo, and stripping tumor tissue in vitro culture to obtain drug-resistant cells. The resistance coefficient, cell doubling time, cell cycle distribution, expression of multidrug resistance gene (MDR1), and drug resistance-related protein were detected in vitro, and the drug resistance to cisplatin and etoposide in vivo were verified. Results Mice with NCI-H446 tumors acquired resistance after eight weeks' EP regimen treatment, and the drug-resistant cell line NCI-H446/EP was obtained by isolation and culture in vitro. The resistance factors of this cell line to cisplatin, etoposide, SN38 and doxorubicin were 12.01, 18.36, 65.4 and 10.12, respectively. Compared with parental cells, the proportion of NCIH446/EP cells in Q

OBJECTIVE: To observe the therapeutic effects and underlying mechanism of baicalin against colon cancer. METHODS: The effects of baicalin on the proliferation and growth of colon cancer cells MC38 and CT26. WT were observed and predicted potential molecular targets of baicalin for colon cancer therapy were studied by network pharmacology. Furthermore, molecular docking and drug affinity responsive target stability (DARTS) analysis were performed to confirm the interaction between potential targets and baicalin. Finally, the mechanisms predicted by in silico analyses were experimentally verified in-vitro and in-vivo. RESULTS: Baicalin significantly inhibited proliferation, invasion, migration, and induced apoptosis in MC38 and CT26 cells (all P<0.01). Additionally, baicalin caused cell cycle arrest at the S phase, while the G₀/G₁ phase was detected in the tiny portion of the cells. Subsequent network pharmacology analysis identified 6 therapeutic targets associated with baicalin, which potentially affect various pathways including 39 biological processes and 99 signaling pathways. In addition, molecular docking and DARTS predicted the potential binding of baicalin with cyclin dependent kinase inhibitor 2A (CDKN2A), protein kinase B (AKT), caspase 3, and mitogen-activated protein kinase (MAPK). In vitro, the expressions of CDKN2A, MAPK, and p-AKT were suppressed by baicalin in MC38 and CT26 cells. In vivo, baicalin significantly reduced the tumor size and weight (all P<0.01) in the colon cancer mouse model via inactivating p-AKT, CDKN2A, cyclin dependent kinase 4, cyclin dependent kinase 2, interleukin-1, tumor necrosis factor α, and activating caspase 3 and mouse double minute 2 homolog signaling (all P<0.05). CONCLUSION Baicalin suppressed the CDKN2A protein level to prevent colon cancer and could be used as a therapeutic target for colon cancer.
Flavonoids/pharmacology* ; Colonic Neoplasms/prevention & control* ; Animals ; Cell Line, Tumor ; Molecular Docking Simulation ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Cyclin-Dependent Kinase Inhibitor p16/metabolism* ; Mice ; Mice, Inbred BALB C ; Cell Movement/drug effects* ; Humans ; Gene Expression Regulation, Neoplastic/drug effects* ; Cell Cycle Checkpoints/drug effects*

OBJECTIVES: To study the role of plasma exchange combined with continuous blood purification in the treatment of refractory Kawasaki disease shock syndrome (KDSS). METHODS: A total of 35 children with KDSS who were hospitalized in the Department of Pediatric Intensive Care Unit, Hunan Children's Hospital, from January 2019 to August 2022 were included as subjects. According to whether plasma exchange combined with continuous veno-venous hemofiltration dialysis was performed, they were divided into a purification group with 12 patients and a conventional group with 23 patients. The two groups were compared in terms of clinical data, laboratory markers, and prognosis. RESULTS: Compared with the conventional group, the purification group had significantly shorter time to recovery from shock and length of hospital stay in the pediatric intensive care unit, as well as a significantly lower number of organs involved during the course of the disease (P<0.05). After treatment, the purification group had significant reductions in the levels of interleukin-6, tumor necrosis factor-α, heparin-binding protein, and brain natriuretic peptide (P<0.05), while the conventional group had significant increases in these indices after treatment (P<0.05). After treatment, the children in the purification group tended to have reductions in stroke volume variation, thoracic fluid content, and systemic vascular resistance and an increase in cardiac output over the time of treatment. CONCLUSIONS Plasma exchange combined with continuous veno-venous hemofiltration dialysis for the treatment of KDSS can alleviate inflammation, maintain fluid balance inside and outside blood vessels, and shorten the course of disease, the duration of shock and the length of hospital stay in the pediatric intensive care unit.
Humans ; Child ; Plasma Exchange ; Mucocutaneous Lymph Node Syndrome/therapy* ; Continuous Renal Replacement Therapy ; Renal Dialysis ; Plasmapheresis ; Shock

OBJECTIVES: To study the clinical characteristics of plastic bronchitis (PB) in children and investigate the the risk factors for recurrence of PB. METHODS: This was a retrospective analysis of medical data of children with PB who were hospitalized in Children's Hospital of Chongqing Medical University from January 2012 to July 2022. The children were divided into a single occurrence of PB group and a recurrent PB group and the risk factors for recurrence of PB were analyzed. RESULTS: A total of 107 children with PB were included, including 61 males (57.0%) and 46 females (43.0%), with a median age of 5.0 years, and 78 cases (72.9%) were over 3 years old. All the children had cough, 96 children (89.7%) had fever, with high fever in 90 children. Seventy-three children (68.2%) had shortness of breath, and 64 children (59.8%) had respiratory failure. Sixty-six children (61.7%) had atelectasis and 52 children (48.6%) had pleural effusion. Forty-seven children (43.9%) had Mycoplasma pneumoniae infection, 28 children (26.2%) had adenovirus infection, and 17 children (15.9%) had influenza virus infection. Seventy-one children (66.4%) had a single occurrence of PB, and 36 cases (33.6%) had recurrent occurrence of PB (≥2 times). Multivariate logistic regression analysis showed that involvement of ≥2 lung lobes (OR=3.376) under bronchoscopy, continued need for invasive ventilation after initial removal of plastic casts (OR=3.275), and concomitant multi-organ dysfunction outside the lungs (OR=2.906) were independent risk factors for recurrent occurrence of PB (P<0.05). CONCLUSIONS Children with pneumonia accompanied by persistent high fever, shortness of breath, respiratory failure, atelectasis or pleural effusion should be highly suspected with PB. Involvement of ≥2 lung lobes under bronchoscopy, continued need for invasive ventilation after initial removal of plastic casts, and concomitant multi-organ dysfunction outside the lungs may be risk factors for recurrent occurrence of PB.
Female ; Male ; Child ; Humans ; Child, Preschool ; Multiple Organ Failure ; Retrospective Studies ; Bronchitis/etiology* ; Dyspnea ; Pleural Effusion ; Pulmonary Atelectasis ; Plastics ; Respiratory Insufficiency

OBJECTIVES: To explore a new method for electroencephalography (EEG) background analysis in neonates with hypoxic-ischemic encephalopathy (HIE) and its relationship with clinical grading and head magnetic resonance imaging (MRI) grading. METHODS: A retrospective analysis was performed for the video electroencephalography (vEEG) and amplitude-integrated electroencephalography (aEEG) monitoring data within 24 hours after birth of neonates diagnosed with HIE from January 2016 to August 2022. All items of EEG background analysis were enrolled into an assessment system and were scored according to severity to obtain the total EEG score. The correlations of total EEG score with total MRI score and total Sarnat score (TSS, used to evaluate clinical gradings) were analyzed by Spearman correlation analysis. The total EEG score was compared among the neonates with different clinical gradings and among the neonates with different head MRI gradings. The receiver operating characteristic (ROC) curve and the area under thecurve (AUC) were used to evaluate the value of total EEG score in diagnosing moderate/severe head MRI abnormalities and clinical moderate/severe HIE, which was then compared with the aEEG grading method. RESULTS: A total of 50 neonates with HIE were included. The total EEG score was positively correlated with the total head MRI score and TSS (r_s=0.840 and 0.611 respectively, P<0.001). There were significant differences in the total EEG score between different clinical grading groups and different head MRI grading groups (P<0.05). The total EEG score and the aEEG grading method had an AUC of 0.936 and 0.617 respectively in judging moderate/severe head MRI abnormalities (P<0.01) and an AUC of 0.887 and 0.796 respectively in judging clinical moderate/severe HIE (P>0.05). The total EEG scores of ≤6 points, 7-13 points, and ≥14 points were defined as mild, moderate, and severe EEG abnormalities respectively, which had the best consistency with clinical grading and head MRI grading (P<0.05). CONCLUSIONS The new EEG background scoring method can quantitatively reflect the severity of brain injury and can be used for the judgment of brain function in neonates with HIE.
Infant, Newborn ; Humans ; Hypoxia-Ischemia, Brain/diagnostic imaging* ; Retrospective Studies ; Brain Injuries ; Electroencephalography ; ROC Curve