nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species.

Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field.

The Identification and Gene Mapping of Spotted Leaf Mutant spl43 in Rice.

Our study investigates the genetic mechanisms underlying the spotted leaf phenotype in rice, focusing on the spl43 mutant. This mutant is characterized by persistent reddish-brown leaf spots from the seedling stage to maturity, leading to extensive leaf necrosis. Using map-based cloning, we localized the responsible locus to a 330 Kb region on chromosome 2. We identified LOC_Os02g56000, named OsRPT5A, as the causative gene. A point mutation in OsRPT5A, substituting valine for glutamic acid, was identified as the critical factor for the phenotype. Functional complementation and the generation of CRISPR/Cas9-mediated knockout lines in the IR64 background confirmed the central role of OsRPT5A in controlling this trait. The qPCR results from different parts of the rice plant revealed that OsRPT5A is constitutively expressed across various tissues, with its subcellular localization unaffected by the mutation. Notably, we observed an abnormal accumulation of reactive oxygen species (ROS) in spl43 mutants by examining the physiological indexes of leaves, suggesting a disruption in the ROS system. Complementation studies indicated OsRPT5A's involvement in ROS homeostasis and catalase activity regulation. Moreover, the spl43 mutant exhibited enhanced resistance to Xanthomonas oryzae pv. oryzae (Xoo), highlighting OsRPT5A's role in rice pathogen resistance mechanisms. Overall, our results suggest that OsRPT5A plays a critical role in regulating ROS homeostasis and enhancing pathogen resistance in rice.

Osteoblast-specific down-regulation of NLRP3 inflammasome by aptamer-functionalized liposome nanoparticles improves bone quality in postmenopausal osteoporosis rats.

Rationale: NLRP3 inflammasome is critical in the development and progression of many metabolic diseases driven by chronic inflammation, but its effect on the pathology of postmenopausal osteoporosis (PMOP) remains poorly understood. Methods: We here firstly examined the levels of NLRP3 inflammasome in PMOP patients by ELISA. Then we investigated the possible mechanisms underlying the effect of NLRP3 inflammasome on PMOP by RNA sequencing of osteoblasts treated with NLRP3 siRNA and qPCR. Lastly, we accessed the effect of decreased NLRP3 levels on ovariectomized (OVX) rats. To specifically deliver NLRP3 siRNA to osteoblasts, we constructed NLRP3 siRNA wrapping osteoblast-specific aptamer (CH6)-functionalized lipid nanoparticles (termed as CH6-LNPs-siNLRP3). Results: We found that the levels of NLRP3 inflammasome were significantly increased in patients with PMOP, and were negatively correlated with estradiol levels. NLRP3 knock-down influenced signal pathways including immune system process, interferon signal pathway. Notably, of the top ten up-regulated genes in NLRP3-reduced osteoblasts, nine genes (except Mx2) were enriched in immune system process, and five genes were related to interferon signal pathway. The in vitro results showed that CH6-LNPs-siNLRP3 was relatively uniform with a dimeter of 96.64 ± 16.83 nm and zeta potential of 38.37 ± 1.86 mV. CH6-LNPs-siNLRP3 did not show obvious cytotoxicity and selectively delivered siRNA to bone tissue. Moreover, CH6-LNPs-siNLRP3 stimulated osteoblast differentiation by activating ALP and enhancing osteoblast matrix mineralization. When administrated to OVX rats, CH6-LNPs-siNLRP3 promoted bone formation and bone mass, improved bone microarchitecture and mechanical properties by decreasing the levels of NLRP3, IL-1ß and IL-18 and increasing the levels of OCN and Runx2. Conclusion: NLRP3 inflammasome may be a new biomarker for PMOP diagnosis and plays a key role in the pathology of PMOP. CH6-LNPs-siNLRP3 has potential application for the treatment of PMOP.

Prevalence and risk factors for human papillomavirus infection among Chinese ethnic women in southern of Yunnan, China

ABSTRACT Background: Dai is a major Chinese ethnic minority group residing in rural areas of the southern part of Yunnan. However, no data exist on the Human Papillomavirus (HPV) prevalence and genotype distribution among Dai women. Method: A total of 793 participants (Dai = 324, Han = 251, other ethnic = 218) were included in this study. PCR was performed to detect the HPV-positive samples, and genotyping was performed with an HPV Geno-Array. Result: The overall HPV prevalence was very low among Dai women compared to the others. The prevalence of high-risk-HPV infections was significantly higher (p = 0.001) among other ethnic women (22.0%) than that among Han (13.1%) and Dai women (7.1%). The overall HPV, high-risk-HPV, single and multiple infection prevalence among rural women were 12.9%, 12.1%, 12.3%, and 0.5%, respectively. HPV-16 (5.5%) was shown to be the most prevalent genotype, followed by HPV-52 (2.6%) and HPV-58 (2.4%). Urban women had relatively higher rates of overall HPV (16.0%), high-risk-HPV (14.1%), single genotype (11.9%), and multiple genotype (4.1%) infections. In urban women, HPV-52 (3.6%) was the most prevalent genotype, followed by HPV-39 (2.7%) and HPV-16 (1.2%). In the urban area, HPV prevalence was highest in women aged <29 years, but declined with increasing age. However, in rural women, the highest HPV prevalence was observed among women at older age (>50 years). Education and smoking habit were significantly associated with HPV infection among both rural and urban area women (p < 0.001). Conclusion: The prevalence and genotype distribution of HPV varied among ethnic women in urban and rural area of Yunnan Province.