Skin microbiome disturbance linked to drought-associated amphibian disease.

The onset of global climate change has led to abnormal rainfall patterns, disrupting associations between wildlife and their symbiotic microorganisms. We monitored a population of pumpkin toadlets and their skin bacteria in the Brazilian Atlantic Forest during a drought. Given the recognized ability of some amphibian skin bacteria to inhibit the widespread fungal pathogen Batrachochytrium dendrobatidis (Bd), we investigated links between skin microbiome health, susceptibility to Bd and host mortality during a die-off event. We found that rainfall deficit was an indirect predictor of Bd loads through microbiome disruption, while its direct effect on Bd was weak. The microbiome was characterized by fewer putative Bd-inhibitory bacteria following the drought, which points to a one-month lagged effect of drought on the microbiome that may have increased toadlet susceptibility to Bd. Our study underscores the capacity of rainfall variability to disturb complex host-microbiome interactions and alter wildlife disease dynamics.

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions.

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

Flux balance analysis of the ammonia-oxidizing bacterium Nitrosomonas europaea ATCC19718 unravels specific metabolic activities while degrading toxic compounds.

The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants (e.g., aromatic hydrocarbons such as benzene and toluene) enable it to thrive under various harsh environmental conditions. Constraint-based metabolic models constructed from genome sequences enable quantitative insight into the central and specialized metabolism within a target organism. These genome-scale models have been utilized to understand, optimize, and design new strategies for improved bioprocesses. Reduced modeling approaches have been used to elucidate Nitrosomonas europaea metabolism at a pathway level. However, genome-scale knowledge about the simultaneous oxidation of ammonia and pollutant metabolism of N. europaea remains limited. Here, we describe the reconstruction, manual curation, and validation of the genome-scale metabolic model for N. europaea, iGC535. This reconstruction is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. Calculated flux distributions under different trophic conditions show that several key pathways are affected by the type of carbon source available, including central carbon metabolism and energy production.

Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US.

Individuals participating in biobanks and other large research projects are increasingly asked to provide broad consent for open-ended research use and widespread sharing of their biosamples and data. We assessed willingness to participate in a biobank using different consent and data sharing models, hypothesizing that willingness would be higher under more restrictive scenarios. Perceived benefits, concerns, and information needs were also assessed. In this experimental survey, individuals from 11 US healthcare systems in the Electronic Medical Records and Genomics (eMERGE) Network were randomly allocated to one of three hypothetical scenarios: tiered consent and controlled data sharing; broad consent and controlled data sharing; or broad consent and open data sharing. Of 82,328 eligible individuals, exactly 13,000 (15.8%) completed the survey. Overall, 66% (95% CI: 63%-69%) of population-weighted respondents stated they would be willing to participate in a biobank; willingness and attitudes did not differ between respondents in the three scenarios. Willingness to participate was associated with self-identified white race, higher educational attainment, lower religiosity, perceiving more research benefits, fewer concerns, and fewer information needs. Most (86%, CI: 84%-87%) participants would want to know what would happen if a researcher misused their health information; fewer (51%, CI: 47%-55%) would worry about their privacy. The concern that the use of broad consent and open data sharing could adversely affect participant recruitment is not supported by these findings. Addressing potential participants' concerns and information needs and building trust and relationships with communities may increase acceptance of broad consent and wide data sharing in biobank research.

A close look at above ground biomass of a large and heterogeneous Seasonally Dry Tropical Forest - Caatinga in North East of Brazil.

This work is focused on characterizing and understanding the aboveground biomass of Caatinga in a semiarid region in northeastern Brazil. The quantification of Caatinga biomass is limited by the small number of field plots, which are inadequate for addressing the biome's extreme heterogeneity. Satellite-derived biomass products can address spatial and temporal changes but they have not been validated for seasonally dry tropical forests. Here we combine a compilation of published field phytosociological observations with a new 30m spatial resolution satellite biomass product. Both data were significantly correlated, satellite estimates consistently captured the wide variability of the biomass across the different physiognomies (2-272 Mg/ha). Based on the satellite product we show that in year 2000 about 50 percent of the region had very low biomass (<2 Mg/ha) and that the majority of the biomass (86%) is concentrated in only 27% of the area. Our work confirm other estimates of biomass 39 Mg/ha (9-61 Mg/ha) and carbon 0.79 PgC. The satellite products together with ground based estimates has the potential to improve forest management in Caatinga and other seasonally dry tropical forests through improved approximation of spatial variability, how they relate to climate, and support numerical modeling experiments in semiarid regions.

Double ovarian tumour in the mare: Case report.

Ovarian tumours in mares represent 2.5% to 6% of the most frequent neoplasms found in the equine species, with a higher chance of benignity. This study aims to describe a case of two different tumours found in the same ovary of a mare that presented clinical signs of suppressed oestrous cycle during 5 years. After unilateral ovariectomy, the ovary was sent to the histopathology examination which determined a mixed tumour of granulosa cell and leiomyosarcoma. After treatment, the mare returned to oestrus and got pregnant in the next season.

Physical exercise alters the activation of downstream proteins related to BDNF-TrkB signaling in male Wistar rats with epilepsy.

There are a considerable number of studies concerning the behavioral effects of physical exercise on the epileptic brain; however, the intracellular signaling mechanisms involved remain unclear. We investigated the effects of aerobic exercise on hippocampal levels of brain-derived neurotrophic factor (BDNF), expression of its receptor tropomyosin receptor kinase B (TrkB), and activation of intracellular proteins related to BDNF-TrkB signaling in male Wistar rats with pilocarpine-induced epilepsy. Thirty days after the first spontaneous seizure, rats from the exercise group undertook a 30-day physical exercise program on the treadmill. Thereafter, BDNF levels, expression of TrkB, and activation of intracellular proteins were quantified by enzyme-linked immunosorbent assay, Western blotting, and multiplex assay, respectively. Statistical analyses were conducted using nonparametric tests. Rats with epilepsy presented decreased BDNF levels compared with control rats. BDNF levels increased significantly in the exercise group compared with the epileptic and control groups. Expression of full-length and truncated TrkB was increased in rats with epilepsy, and physical exercise restored its expression to control levels. RAC-alpha serine/threonine-protein kinase, mammalian target of rapamycin, and extracellular signal-regulated kinase activation were reduced in rats with epilepsy, and exercise increased activation compared with control and epilepsy groups. Increased cAMP response element binding protein activation was observed in the exercise group compared with the epilepsy group. Our findings indicate that the beneficial effects of exercise in the epileptic brain can be in part related to alterations in the activation of proteins related to the BDNF-TrkB signaling pathway.

Resistance Exercise Reduces Seizure Occurrence, Attenuates Memory Deficits and Restores BDNF Signaling in Rats with Chronic Epilepsy.

Epilepsy is a disease characterized by recurrent, unprovoked seizures. Cognitive impairment is an important comorbidity of chronic epilepsy. Human and animal model studies of epilepsy have shown that aerobic exercise induces beneficial structural and functional changes and reduces the number of seizures. However, little is yet understood about the effects of resistance exercise on epilepsy. We evaluated the effects of a resistance exercise program on the number of seizures, long-term memory and expression/activation of signaling proteins in rats with epilepsy. The number of seizures was quantified by video-monitoring and long-term memory was assessed by an inhibitory avoidance test. Using western blotting, multiplex and enzyme-linked immunosorbent assays, we determined the effects of a 4-week resistance exercise program on IGF-1 and BDNF levels and ERK, CREB, mTOR activation in the hippocampus of rats with epilepsy. Rats with epilepsy submitted to resistance exercise showed a decrease in the number of seizures compared to non-exercised epileptic rats. Memory deficits were attenuated by resistance exercise. Rats with epilepsy showed an increase in IGF-1 levels which were restored to control levels by resistance exercise. BDNF levels and ERK and mTOR activation were decreased in rats with epilepsy and resistance exercise restored these to control levels. In conclusion, resistance exercise reduced seizure occurrence and mitigated memory deficits in rats with epilepsy. These resistance exercise-induced beneficial effects can be related to changes in IGF-1 and BDNF levels and its signaling protein activation. Our findings indicate that the resistance exercise might be included as complementary therapeutic strategy for epilepsy treatment.

Effects of different physical exercise programs on susceptibility to pilocarpine-induced seizures in female rats.

In epilepsy, the most common serious neurological disorder worldwide, several investigations in both humans and animals have shown the effectiveness of physical exercise programs as a complementary therapy. Among the benefits demonstrated, regular exercise can decrease the number of seizures as well as improve cardiovascular and psychological health in people with epilepsy. While many studies in animals have been performed to show the beneficial effects of exercise, they exclusively used male animals. However, females are also worthy of investigation because of their cyclical hormonal fluctuations and possible pregnancy. Considering the few animal studies concerning seizure susceptibility and exercise programs in females, this study aimed to verify whether exercise programs can interfere with seizure susceptibility induced by pilocarpine in adult female Wistar rats. Animals were randomly divided into three groups: control, forced, and voluntary (animals kept in a cage with a wheel). After the final exercise session, animals received a pilocarpine hydrochloride (350 mg/kg i.p.; Sigma) injection to induce seizures. To measure the intensity of pilocarpine-induced motor signs, we used a scale similar to that developed by Racine (1972) in the kindling model. During a 4-h period of observation, we recorded latency for first motor signs, latency for reaching SE, number of animals that developed SE, and intensity of pilocarpine-induced motor signs. No difference was observed among groups in latency for first motor signs and in the number of animals that developed SE. Although the voluntary group presented more intense motor signs, an increased latency for developing SE was observed compared with that in forced and control groups. Our behavioral results are not enough to explain physiological and molecular pathways, but there are mechanisms described in literature which may allow us to propose possible explanations. Voluntary exercise increased latency to SE development. Further investigation is necessary to elucidate the pathways involved in these results, while more studies should be performed regarding gender specific differences.

An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research.

Several AI-aided screening tools have emerged to tackle the ever-expanding body of literature. These tools employ active learning, where algorithms sort abstracts based on human feedback. However, researchers using these tools face a crucial dilemma: When should they stop screening without knowing the proportion of relevant studies? Although numerous stopping rules have been proposed to guide users in this decision, they have yet to undergo comprehensive evaluation. In this study, we evaluated the performance of three stopping rules: the knee method, a data-driven heuristic, and a prevalence estimation technique. We measured performance via sensitivity, specificity, and screening cost and explored the influence of the prevalence of relevant studies and the choice of the learning algorithm. We curated a dataset of abstract collections from meta-analyses across five psychological research domains. Our findings revealed performance differences between stopping rules regarding all performance measures and variations in the performance of stopping rules across different prevalence ratios. Moreover, despite the relatively minor impact of the learning algorithm, we found that specific combinations of stopping rules and learning algorithms were most effective for certain prevalence ratios of relevant abstracts. Based on these results, we derived practical recommendations for users of AI-aided screening tools. Furthermore, we discuss possible implications and offer suggestions for future research.

Impact of the 'Health on Wheels' (HoW) strategy on COVID-19 vaccination coverage in hard-to-reach communities in Alta Verapaz, Guatemala, 2022.

BACKGROUND: In April 2022, after a year of COVID-19 vaccination, there were large differences in coverage between urban and rural areas in Guatemala. To address barriers in rural communities, the "Health on Wheels" (HoW) strategy was implemented. The strategy deployed mobile brigades with a dedicated team of health workers and a culturally sensitive health promotion plan in selected communities in 15 districts in Alta Verapaz, a health area with low COVID-19 vaccination uptake and a high-level of COVID-19 vaccine hesitancy. This study evaluates the impact of the HoW strategy. METHODS: We measured the relative increase in COVID-19 doses administered prior and during the HoW implementation period in the 190 intervened communities and compared to 188 communities without the intervention. Communities were grouped by health district and the impact analyses were stratified by number of COVID-19 vaccine dose (1st, 2nd, and 3rd doses) and history of vaccine hesitancy. RESULTS: The increase in 1st, 2nd, and 3rd dose-COVID-19 vaccination coverage between before and during HoW implementation was 2.4, 2.2 and 2.6 times higher in intervened communities (20 %, 21 % and 37 % increase in 1st, 2nd and 3rd dose, respectively) than in non-intervened communities (8 %, 10 % and 14 % increase in 1st, 2nd and 3rd dose respectively). For the 1st dose, increase in dose administration was 2.9 times higher in intervened communities (n = 24) with hesitancy (24 % increase) compared to non-intervened communities (n = 188) without hesitancy (8 % increase). CONCLUSION: The deployment of mobile brigades with a dedicated team of vaccinators and culturally sensitive health promotion through the HoW strategy successfully accelerated the increase in COVID-19 vaccination coverage in rural communities in Guatemala.

A primer on synthesizing individual participant data obtained from complex sampling surveys: A two-stage IPD meta-analysis approach.

The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its performance compared with other approaches, dealing with the complexities of the primary and meta-analytic data has received little attention, particularly when IPD are drawn from complex sampling surveys. Complex sampling surveys often feature clustering, stratification, and multistage sampling to obtain nationally or internationally representative data from a target population. Furthermore, IPD from these studies is likely to provide more than one effect size. To address these complexities, we propose a two-stage meta-analytic approach that generates model-based effect sizes in Stage 1 and synthesizes them in Stage 2. We present a sequence of steps, illustrate their implementation, and discuss the methodological decisions and options within. Given its flexibility to deal with the complex nature of the primary and meta-analytic data and its ability to combine multiple IPD sets or IPD with aggregated data, the proposed two-stage approach opens up new analytic possibilities for synthesizing knowledge from complex sampling surveys. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Pre- and Postoperative Levels of Carcinoembryonic Antigen in Microsatellite Stable Versus Instable Colon Cancer: a Retrospective Analysis.

PURPOSE: The prognosis of microsatellite stable (MSS) versus instable (MSI) tumors is an ongoing matter of debate, with differences in expression of carcinoembryonic antigen (CEA) in these two tumor subsets being inconsistently reported to date. The aim of this study was to investigate CEA expression in the context of clinical parameters in MSS and MSI tumors. METHODS: Clinical, pathological, and biochemical parameters of colon cancer patients who underwent curative surgery were documented in a database and compared between MSS and MSI cases. The pre- to postoperative trend of CEA was analyzed. Survival was assessed using the Kaplan-Meier (log rank) test. RESULTS: One hundred sixty-nine patients were included in the study. Compared to those with MSS tumors, there was a higher proportion of preoperatively elevated CEA among those with MSI tumors (p = 0.067). Median CEA values decreased over the pre- to postoperative course with MSS (p = 0.01) but not MSI (p = 0.093) tumors. The distribution of N classification differed between MSS and MSI tumors (p = 0.014). Patients with MSI tumors had superior survival. CONCLUSION: Despite the better prognosis, MSI tumors are associated with increases in CEA. Our findings shed light on discrepancies related to the prognostic evaluation of MSI tumors. Furthermore, in follow-up of colorectal cancers, CEA measurements should be interpreted differently for MSI and MSS tumors.

Checklist of the fish fauna of the Munim River Basin, Maranhão, north-eastern Brazil.

Background: The Maranhão State harbours great fish diversity, but some areas are still undersampled or little known, such as the Munim River Basin in the northeast of the State. This lack of knowledge is critical when considering anthropogenic impacts on riverine systems especially in the face of major habitat destruction. These pressing threats mean that a comprehensive understanding of diversity is critical and fish checklists extremely relevant. Therefore, the present study provides a checklist of the fish species found in the Munim River Basin, Maranhão State, north-eastern Brazil, based on collected specimens. New information: A total of 123 species were recorded for the Munim River Basin, with only two non-native species, Oreochromisniloticus and Colossomamacropomum, showing that the fish assemblage has relatively high ecological integrity. In addition, 29 species could not be identified at the species level, indicating the presence of species that are probably new to science in the Basin. A predominance of species belonging to the fish orders Characiformes and Siluriformes, with Characidae being recovered as the most species-rich family (21 species) agrees with the general pattern for river basins in the Neotropical Region. The total fish diversity was estimated by extensive fieldwork, including several sampling gears, carried out in different seasons (dry and rainy) and exploring different environments with both daily and nocturnal sampling, from the Basin's source to its mouth. A total of 84 sites were sampled between 2010 and 2022, resulting in 12 years of fieldwork. Fish assemblages were distinct in the Estuary and Upper river basin sections and more similar in the Lower and Middle sections indicating environmental filtering processes. Species were weakly nested across basin sections, but unique species were found in each section (per Simpsons Index). High variability of species richness in the Middle river basin section is likely due to microhabitat heterogeneity supporting specialist fish communities.

One year follow-up on a randomized study investigating serratus anterior muscle and pectoral nerves type I block to reduced neuropathic pain descriptors after mastectomy.

Breast cancer is the second most common diagnosed type of cancer in women. Chronic neuropathic pain after mastectomy occurs frequently and is a serious health problem. In our previous single-center, prospective, randomized controlled clinical study, we demonstrated that the combination of serratus anterior plane block (SAM) and pectoral nerve block type I (PECS I) with general anesthesia reduced acute postoperative pain. The present report describes a prospective follow-up study of this published study to investigate the development of chronic neuropathic pain 12 months after mastectomy by comparing the use of general anesthesia alone and general anesthesia with SAM + PECS I. Additionally, the use of analgesic medication, quality of life, depressive symptoms, and possible correlations between plasma levels of interleukin (IL)-1 beta, IL-6, and IL-10 collected before and 24 h after surgery as predictors of pain and depression were evaluated. The results showed that the use of SAM + PECS I with general anesthesia reduced numbness, hypoesthesia to touch, the incidence of patients with chronic pain in other body regions and depressive symptoms, however, did not significantly reduce the incidence of chronic neuropathic pain after mastectomy. Additionally, there was no difference in the consumption of analgesic medication and quality of life. Furthermore, no correlation was observed between IL-1 beta, IL-6, and IL-10 levels and pain and depression. The combination of general anesthesia with SAM + PECS I reduced the occurrence of specific neuropathic pain descriptors and depressive symptoms. These results could promote the use of SAM + PECS I blocks for the prevention of specific neuropathic pain symptoms after mastectomy.Registration of clinical trial: The Research Ethics Board of the Hospital Sirio-Libanes/Brazil approved the study (CAAE 48721715.0.0000.5461). This study is registered at Registro Brasileiro de Ensaios Clinicos (ReBEC), and ClinicalTrials.gov, Identifier: NCT02647385.

The Design of a Data Management System for a Multicenter Palliative Care Cohort Study.

CONTEXT: Prospective cohort studies of individuals with serious illness and their family members, such as children receiving palliative care and their parents, pose challenges regarding data management. OBJECTIVE: To describe the design and lessons learned regarding the data management system for the Pediatric Palliative Care Research Network's Shared Data and Research (SHARE) project, a multicenter prospective cohort study of children receiving pediatric palliative care (PPC) and their parents, and to describe important attributes of this system, with specific considerations for the design of future studies. METHODS: The SHARE study consists of 643 PPC patients and up to two of their parents who enrolled from April 2017 to December 2020 at seven children's hospitals across the United States. Data regarding demographics, patient symptoms, goals of care, and other characteristics were collected directly from parents or patients at 6 timepoints over a 24-month follow-up period and stored electronically in a centralized location. Using medical record numbers, primary collected data was linked to administrative hospitalization data containing diagnostic and procedure codes and other data elements. Important attributes of the data infrastructure include linkage of primary and administrative data; centralized availability of multilingual questionnaires; electronic data collection and storage system; time-stamping of instrument completion; and a separate but connected study administrative database used to track enrollment. CONCLUSIONS: Investigators planning future multicenter prospective cohort studies can consider attributes of the data infrastructure we describe when designing their data management system.

Validation of the Pediatric Sequential Organ Failure Assessment Score and Evaluation of Third International Consensus Definitions for Sepsis and Septic Shock Definitions in the Pediatric Emergency Department.

Importance: Pediatric sepsis definitions have evolved, and some have proposed using the measure used in adults to quantify organ dysfunction, a Sequential Organ Failure Assessment (SOFA) score of 2 or more in the setting of suspected infection. A pediatric adaptation of SOFA (pSOFA) showed excellent discrimination for mortality in critically ill children but has not been evaluated in an emergency department (ED) population. Objective: To delineate test characteristics of the pSOFA score for predicting in-hospital mortality among (1) all patients and (2) patients with suspected infection treated in pediatric EDs. Design, Setting, and Participants: This retrospective cohort study took place from January 1, 2012, to January 31, 2020 in 9 US children's hospitals included in the Pediatric Emergency Care Applied Research Network (PECARN) Registry. The data was analyzed from February 1, 2020, to April 18, 2022. All ED visits for patients younger than 18 years were included. Exposures: ED pSOFA score was assigned by summing maximum pSOFA organ dysfunction components during ED stay (each 0-4 points). In the subset with suspected infection, visit meeting criteria for sepsis (suspected infection with a pSOFA score of 2 or more) and septic shock (suspected infection with vasoactive infusion and serum lactate level >18.0 mg/dL) were identified. Main Outcomes and Measures: Test characteristics of pSOFA scores of 2 or more during the ED stay for hospital mortality. Results: A total of 3 999 528 (female, 47.3%) ED visits were included. pSOFA scores ranged from 0 to 16, with 126 250 visits (3.2%) having a pSOFA score of 2 or more. pSOFA scores of 2 or more had sensitivity of 0.65 (95% CI, 0.62-0.67) and specificity of 0.97 (95% CI, 0.97-0.97), with negative predictive value of 1.0 (95% CI, 1.00-1.00) in predicting hospital mortality. Of 642 868 patients with suspected infection (16.1%), 42 992 (6.7%) met criteria for sepsis, and 374 (0.1%) met criteria for septic shock. Hospital mortality rates for suspected infection (599 502), sepsis (42 992), and septic shock (374) were 0.0%, 0.9%, and 8.0%, respectively. The pSOFA score had similar discrimination for hospital mortality in all ED visits (area under receiver operating characteristic curve, 0.81; 95% CI, 0.79-0.82) and the subset with suspected infection (area under receiver operating characteristic curve, 0.82; 95% CI, 0.80-0.84). Conclusions and Relevance: In a large, multicenter study of pediatric ED visits, a pSOFA score of 2 or more was uncommon and associated with increased hospital mortality yet had poor sensitivity as a screening tool for hospital mortality. Conversely, children with a pSOFA score of 2 or less were at very low risk of death, with high specificity and negative predictive value. Among patients with suspected infection, patients with pSOFA-defined septic shock demonstrated the highest mortality.

Low microbiome diversity in threatened amphibians from two biodiversity hotspots.

Microbial diversity positively influences community resilience of the host microbiome. However, extinction risk factors such as habitat specialization, narrow environmental tolerances, and exposure to anthropogenic disturbance may homogenize host-associated microbial communities critical for stress responses including disease defense. In a dataset containing 43 threatened and 90 non-threatened amphibian species across two biodiversity hotspots (Brazil's Atlantic Forest and Madagascar), we found that threatened host species carried lower skin bacterial diversity, after accounting for key environmental and host factors. The consistency of our findings across continents suggests the broad scale at which low bacteriome diversity may compromise pathogen defenses in species already burdened with the threat of extinction.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data.

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ.

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH3). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.