OBJECTIVE: Plant-based diets are associated with a lower risk of chronic diseases. Large-scale proteomics can identify objective biomarkers of plant-based diets, and improve our understanding of the pathways that link plant-based diets to health outcomes. This study investigated the plasma proteome of four different plant-based diets [overall plant-based diet (PDI), provegetarian diet, healthful plant-based diet (hPDI), and unhealthful plant-based diet (uPDI)] in the Atherosclerosis Risk in Communities (ARIC) Study and replicated the findings in the Framingham Heart Study (FHS) Offspring cohort.
METHODS: ARIC Study participants at visit 3 (1993-1995) with completed food frequency questionnaire (FFQ) data and proteomics data were divided into internal discovery (n = 7690) and replication (n = 2543) data sets. Multivariable linear regression was used to examine associations between plant-based diet indices (PDIs) and 4955 individual proteins in the discovery sample. Then, proteins that were internally replicated in the ARIC Study were tested for external replication in FHS (n = 1358). Pathway overrepresentation analysis was conducted for diet-related proteins. C-statistics were used to predict if the proteins improved prediction of plant-based diet indices beyond participant characteristics.
RESULTS: In ARIC discovery, a total of 837 diet-protein associations (PDI = 233; provegetarian = 182; hPDI = 406; uPDI = 16) were observed at false discovery rate (FDR) < 0.05. Of these, 453 diet-protein associations (PDI = 132; provegetarian = 104; hPDI = 208; uPDI = 9) were internally replicated. In FHS, 167/453 diet-protein associations were available for external replication, of which 8 proteins (PDI = 1; provegetarian = 0; hPDI = 8; uPDI = 0) replicated. Complement and coagulation cascades, cell adhesion molecules, and retinol metabolism were over-represented. C-C motif chemokine 25 for PDI and 8 proteins for hPDI modestly but significantly improved the prediction of these indices individually and collectively (P value for difference in C-statistics<0.05 for all tests).
CONCLUSIONS: Using large-scale proteomics, we identified potential candidate biomarkers of plant-based diets, and pathways that may partially explain the associations between plant-based diets and chronic conditions.

Thirteen spider species belonging to the family Mysmenidae Petrunkevitch, 1928 are reported from Xishuangbanna Tropical Botanical Garden (XTBG), Menglun Township, Mengla County, Yunnan Province of China. One genus and five species are documented as new to science: Mengmenabanna gen. nov. et. sp. nov. (♂♀), Mengmenayulin sp. nov. (♀), Mosuheguomu sp. nov. (♂♀), Mysmenaluosuo sp. nov. (♂♀), and Mysmenadai sp. nov. (♀). One species is proposed as a new combination: Mosuzhengi (Lin & Li, 2008) comb. nov. (♂♀, ex Mysmena Simon, 1894). The females of Microdipoenamenglunensis (Lin & Li, 2008), Mysmenaarcilonga Lin & Li, 2008, Mysmenafurca Lin & Li, 2008, and Mysmenarostella Lin & Li, 2008 are described for the first time. Three known species are re-examined and photographed: Gaoligongataeniata Lin & Li, 2014, Mysmenabiangulata (Lin & Li, 2008), and Mysmenacornigera (Lin & Li, 2008). Morphological diagnoses and illustrations are provided for these thirteen mysmenid species.

BACKGROUND: Conducting high-quality stroke trials is complex and costly. Often these trials compete for the attention of researchers and the availability of patients. Enrolling patients in more than one study concurrently has the potential to accelerate recruitment into individual studies. DISCOVERY is a multicenter, inception cohort study of cognitive impairment and dementia following ischemic or hemorrhagic stroke. At the request of site investigators, a DISCOVERY committee reviews individual studies for approval of possible concurrent co-enrollment into DISCOVERY. The purpose of this report is to summarize the characteristics and outcomes of studies reviewed by committee for possible co-enrollment.
METHODS: This analysis covers studies reviewed from 07/01/2020 to 04/26/2022 by the Site Management Committee (SMC) of the DISCOVERY Recruitment and Retention Core. Characterization of each study included study type, number and length of follow-up visits, and whether there were protocol-required blood draws, brain imaging studies, or cognitive tests. Studies were scored for patient burden and scientific overlap with Discovery. The primary outcome was SMC approval to co-enroll.
RESULTS: 59 studies were reviewed, and 69.5% (n = 41, 21 clinical trials; 20 observational studies) were found by the SMC to be appropriate for co-enrollment. Higher patient burden and greater scientific overlap with DISCOVERY reduced the rates of approval for co-enrollment.
CONCLUSIONS: A large number of diverse stroke studies are being run concurrently across the DISCOVERY study network, however, about two-thirds of the studies were considered appropriate for consideration of co-enrollment. Future studies should study how co-enrollment might improve trial network efficiency.

BACKGROUND: Knowledge graphs are a common form of knowledge representation in biomedicine and many other fields. We developed an open biomedical knowledge graph-based system termed Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ROBOKOP consists of both a front-end user interface and a back-end knowledge graph. The ROBOKOP user interface allows users to posit questions and explore answer subgraphs. Users can also posit questions through direct Cypher query of the underlying knowledge graph, which currently contains roughly 6 million nodes or biomedical entities and 140 million edges or predicates describing the relationship between nodes, drawn from over 30 curated data sources.
OBJECTIVE: We aimed to apply ROBOKOP to survey data on workplace exposures and immune-mediated diseases from the Environmental Polymorphisms Registry (EPR) within the National Institute of Environmental Health Sciences.
METHODS: We analyzed EPR survey data and identified 45 associations between workplace chemical exposures and immune-mediated diseases, as self-reported by study participants (n= 4574), with 20 associations significant at P<.05 after false discovery rate correction. We then used ROBOKOP to (1) validate the associations by determining whether plausible connections exist within the ROBOKOP knowledge graph and (2) propose biological mechanisms that might explain them and serve as hypotheses for subsequent testing. We highlight the following three exemplar associations: carbon monoxide-multiple sclerosis, ammonia-asthma, and isopropanol-allergic disease.
RESULTS: ROBOKOP successfully returned answer sets for three queries that were posed in the context of the driving examples. The answer sets included potential intermediary genes, as well as supporting evidence that might explain the observed associations.
CONCLUSIONS: We demonstrate real-world application of ROBOKOP to generate mechanistic hypotheses for associations between workplace chemical exposures and immune-mediated diseases. We expect that ROBOKOP will find broad application across many biomedical fields and other scientific disciplines due to its generalizability, speed to discovery and generation of mechanistic hypotheses, and open nature.

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10-20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Stomach cancer is one of the leading causes of cancer death worldwide, despite its incidence and mortality falling in many places. The discovery in 1984 that a bacterial infection with Helicobacter pylori could cause stomach and duodenal ulcers prompted work in its role in causing gastritis, and led to the first prospective study in 1991 by Forman et al., showing that infection with H.pylori increased the risk of stomach cancer in those infected by almost three-fold. Prior to then, it was hypothesized that stomach was caused by poor diets. While diets may still play a role, the falls in stomach cancer incidence have been associated with reductions in population prevalence of H. pylori. Discovery of the link was accelerated by the use of stored sera from other unrelated studies, and the use of serological assays. Since those discoveries the treatment landscape of gastric disorders has changed significantly, with a rapid uptake of antibiotic and proton pump inhibitors (triple) therapies in those who are H. pylori positive. Over time we have seen falls in gastric cancer, peptic and duodenal ulcers and in many of the procedures previously used to cure peptic ulcer disease, such as vagotomies and gastrectomies. Further still, an oral vaccine against H. pylori, first trialled in China, holds much promise of being the third vaccine against a cancer causing infection. If successful this would lead to a further reduction in H. pylori related conditions, and ultimately gastric cancer, an otherwise lethal disease.