Probabilistic assessment of the exposure risk to dietary perchlorate in the Chinese population from general and key areas

ObjectiveTo assess the exposure and health risks from perchlorates in food and tea among Chinese residents grouped by key and common areas.MethodsPerchlorate content data for food and tea in China were collected from the literature. Combined with data from the Chinese Total Diet Study in 2015 and using the hazard quotient， the estimated dietary exposure to perchlorate in different areas was assessed and compared using several health-based guidance values.ResultsThe highest mean perchlorate content was 175.54 μg/kg in tea. The results of the probability assessment showed that vegetables were critical exposure pathways in the Chinese population， accounting for 79.40% of total daily perchlorate intake. The 99% daily perchlorate intake of Chinese populations was lower than the provisional maximum tolerable daily intake （PMTDI， 10 μg/kg∙BW） set by the Joint FAO/WHO. However， the daily perchlorate intake of 4.96% and 26.57% of the residents in two areas far exceeded the temporary tolerable daily intake （tTDI， 0.7 μg/kg∙BW） set by China. In tea， 95% of daily perchlorate intake was lower than the PMTDI， but the daily perchlorate intakes of 57.47% and 77.94% of the tea drinkers in the two areas exceeded the tTDI.ConclusionThe risk of exposure to perchlorate via dietary intake in the Chinese population is generally low； however， there are key areas where the exposure is significantly higher than those in other areas owing to high local pollution

Serum CRP interacts with SPARC and regulate immune response in severe cases of COVID-19 infection

Serum C-reactive protein (CRP) has been found elevated during COVID-19 infection, and associated with systematic inflammation as well as a poor clinical outcome. However, how did CRP participated in the COVID-19 pathogenesis remains poorly understood. Here, we report that serum C-reactive protein (CRP) levels are correlated with megakaryocyte marker genes and could regulate immune response through interaction with megakaryocytes. Molecular dynamics simulation through ColabFold showed a reliable interaction between monomeric form of CRP (mCRP) and the secreted protein acidic and rich in cysteine (SPARC). The interaction does not affect the physiological activities of SPARC while would be disturbed by pentamerization of CRP. Interplay between SPARC and mCRP results in a more intense immune response which may led to poor prognosis. This study highlights the complex interplay between inflammatory markers, megakaryocytes, and immune regulation in COVID-19 and sheds light on potential therapeutic targets

Molecular Cloning and Expression Analysis of the Endogenous Cellulase Gene MaCel1 in Monochamus alternatus

The purpose of this study was to characterize the endogenous cellulase gene MaCel1 of Monochamus alternatus, which is an important vector of Bursaphelenchus xylophilus, a pine wood nematode, which causes pine wilt disease (PWD). In this study, MaCel1 was cloned by rapid amplification of cDNA end (RACE), and its expression analyzed by RT-qPCR (real-time quantitative PCR detecting). A total of 1778 bp of cDNA was obtained. The encoding region of this gene was 1509 bp in length, encoding a protein containing 502 amino acids with a molecular weight of 58.66 kDa, and the isoelectric point of 5.46. Sequence similarity analysis showed that the amino acids sequence of MaCel1 had high similarity with the beta-Glucosinolate of Anoplophora glabripennis and slightly lower similarity with other insect cellulase genes (GH1). The beta-D-Glucosidase activity of MaCel1 was 256.02 +/- 43.14 U/L with no beta-Glucosinolate activity. MaCel1 gene was widely expressed in the intestine of M. alternatus. The expression level of MaCel1 gene in male (3.46) and female (3.51) adults was significantly higher than that in other developmental stages, and the lowest was in pupal stage (0.15). The results will help reveal the digestive mechanism of M. alternatus and lay the foundation for controlling PWD by controlling M. alternatus

Gut Bacterial Communities of Lymantria xylina and Their Associations with Host Development and Diet

The gut microbiota of insects has a wide range of effects on host nutrition, physiology, and behavior. The structure of gut microbiota may also be shaped by their environment, causing them to adjust to their hosts; thus, the objective of this study was to examine variations in the morphological traits and gut microbiota of Lymantria xylina in response to natural and artificial diets using high-throughput sequencing. Regarding morphology, the head widths for larvae fed on a sterilized artificial diet were smaller than for larvae fed on a non-sterilized host-plant diet in the early instars. The gut microbiota diversity of L. xylina fed on different diets varied significantly, but did not change during different development periods. This seemed to indicate that vertical inheritance occurred in L. xylina mutualistic symbionts. Acinetobacter and Enterococcus were dominant in/on eggs. In the first instar larvae, Acinetobacter accounted for 33.52% of the sterilized artificial diet treatment, while Enterococcus (67.88%) was the predominant bacteria for the non-sterilized host-plant diet treatment. Gut microbe structures were adapted to both diets through vertical inheritance and self-regulation. This study clarified the impacts of microbial symbiosis on L. xylina and might provide new possibilities for improving the control of these bacteria

Probiotics are beneficial for liver cirrhosis: a systematic review and meta-analysis of randomized control trials

IntroductionGut dysbiosis may play a pivotal role in the pathogenesis of cirrhosis and the severity of complications. Numerous studies have investigated the probiotics as treatments for cirrhosis. However, there is still a lack of definitive evidence confirming the beneficial effects of probiotics on cirrhosis.MethodsDatabases including PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched for randomized controlled trials that compared the effects of probiotic intervention and control treatments, including placebo, no treatment, and active control, on cirrhosis, published from inception to February 2024. Outcomes included hepatic encephalopathy (HE) reversal, safety and tolerability of probiotics, liver function, quality of life, and other cirrhotic-related outcomes. A meta-analysis was conducted to synthesize evidence.ResultsThirty studies were included. The quantitative synthesis results showed that compared with the control group, probiotics significantly reverse minimal hepatic encephalopathy (MHE) (risk ratio [RR] 1.54, 95% confidence interval [CI] 1.03 to 2.32) and improve HE (RR 1.94, 95% CI 1.24 to 3.06). Additionally, probiotics demonstrated higher safety and tolerability by causing a lower incidence of serious adverse events (RR 0.71, 95% CI 0.58 to 0.87). Probiotics could potentially improve liver function by reducing the Model for End-Stage Liver Disease (MELD) scores (standardized mean difference [SMD] -0.57, 95% CI -0.85 to −0.30), and displayed favorable changes in quality of life (SMD 0.51, 95% CI 0.27 to 0.75) and gut flora (SMD 1.67, 95% CI 1.28 to 2.06).ConclusionThis systematic review and meta-analysis offers compelling evidence that probiotics are beneficial for cirrhosis by demonstrating reversal of HE, potential for liver function improvements, enhancements in quality of life, and regulation of gut dysbiosis. Furthermore, the apparent safety profile suggests that probiotics are a promising intervention for treating cirrhosis.Clinical trial registration numberCRD42023478380

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io