Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning.

Background: Cerebral small vessel disease lacks specific clinical manifestations, and extraction of valuable features from multimodal images is expected to improve its diagnostic accuracy. In this study, we used deep learning techniques to segment cerebral small vessel disease imaging markers in multimodal magnetic resonance images and analyze them with clinical risk factors. Methods and results: We recruited 211 lacunar stroke patients and 83 control patients. The patients' cerebral small vessel disease markers were automatically segmented using a V-shaped bottleneck network, and the number and volume were calculated after manual correction. The segmentation results of the V-shaped bottleneck network for white matter hyperintensity and recent small subcortical infarction were in high agreement with the ground truth (DSC>0.90). In small lesion segmentation, cerebral microbleed (average recall=0.778; average precision=0.758) and perivascular spaces (average recall=0.953; average precision=0.923) were superior to lacunar infarct (average recall=0.339; average precision=0.432) in recall and precision. Binary logistic regression analysis showed that age, systolic blood pressure, and total cerebral small vessel disease load score were independent risk factors for lacunar stroke (P<0.05). Ordered logistic regression analysis showed age was positively correlated with cerebral small vessel disease load score and total cholesterol was negatively correlated with cerebral small vessel disease score (P<0.05). Conclusion: Lacunar stroke patients exhibited higher cerebral small vessel disease imaging markers, and age, systolic blood pressure, and total cerebral small vessel disease score were independent risk factors for lacunar stroke patients. V-shaped bottleneck network segmentation network based on multimodal deep learning can segment and quantify various cerebral small vessel disease lesions to some extent.

Effects of organochlorine pesticides on human and rat 17ß-hydroxysteroid dehydrogenase 1 activity: Structure-activity relationship and in silico docking analysis.

The objective of this study was to examine the effect of 11 organochlorine pesticides on human and rat 17ß-Hydroxysteroid dehydrogenase 1 (17ß-HSD1) in human placental and rat ovarian microsome and on estradiol production in BeWo cells. The results showed that the IC50 values for endosulfan, fenhexamid, chlordecone, and rhothane on human 17ß-HSD1 were 21.37, 73.25, 92.80, and 117.69 µM. Kinetic analysis revealed that endosulfan acts as a competitive inhibitor, fenhexamid as a mixed/competitive inhibitor, chlordecone and rhothane as a mixed/uncompetitive inhibitor. In BeWo cells, all insecticides except endosulfan significantly decreased estradiol production at 100 µM. For rats, the IC50 values for dimethomorph, fenhexamid, and chlordecone were 11.98, 36.92, and 109.14 µM. Dimethomorph acts as a mixed inhibitor, while fenhexamid acts as a mixed/competitive inhibitor. Docking analysis revealed that endosulfan and fenhexamid bind to the steroid-binding site of human 17ß-HSD1. On the other hand, chlordecone and rhothane binds to a different site other than the steroid and NADPH-binding site. Dimethomorph binds to the steroid/NADPH binding site, and fenhexamid binds to the steroid binding site of rat 17ß-HSD1. Bivariate correlation analysis showed a positive correlation between IC50 values and LogP for human 17ß-HSD1, while a slight negative correlation was observed between IC50 values and the number of HBA. ADMET analysis provided insights into the toxicokinetics and toxicity of organochlorine pesticides. In conclusion, this study identified the inhibitory effects of 3-4 organochlorine pesticides and binding mechanisms on human and rat 17ß-HSD1, as well as their impact on hormone production.

Screening of miRNAs in White Blood Cell as a Radiation Biomarkers for Rapid Assessment of Acute Radiation Injury.

Accidental radiation exposure is a threat to human health that necessitates effective clinical diagnosis. Suitable biomarkers are urgently needed for early assessment of exposure dose. Existing technologies being used to assess the extent of radiation have notable limitations. As a radiation biomarker, miRNA has the advantages of simple detection and high throughput. In this study, we screened for miRNAs with dose and time dependent responses in peripheral blood leukocytes via miRNA sequencing in establishing the animal model of acute radiation injury. Four radiation-sensitive and stably expressed miRNAs were selected out in the 24 h group of leukocyte miRNAs: mmu-miR-130b-5p, mmu-miR-148b-5p, mmu-miR-184-3p, mmu-miR-26a-2-3p, and five were screened in the 48 h group of leukocyte miRNAs: mmu-miR-130b-5p, mmu-miR-423-5p, mmu-miR-676-3p, mmu-miR-150-5p, mmu-miR-342-3p.The correlation curves between their expression and irradiation dose were plotted. Then, the results were validated by RT-qPCR in mouse peripheral blood. As a result, mmu-miR-150-5p and mmu-miR-342-3p showed the highest correlation at 48h after irradiation, and mmu-miR-130b-5p showed good correlation at both 24 h and 48 h after irradiation. In a conclusion, the miRNAs that are sensitive to ionizing radiation with dose dependent effects were selected out, which have the potential of forming a rapid assessment scheme for acute radiation injury.