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The waters of Aotearoa New Zealand span over 4.2 million km2 of the South Pacific Ocean and harbour a rich diversity of seafloor-associated taxa. Due to the immensity and remoteness of the area, there are significant gaps in the availability of data that can be used to quantify and map the distribution of seafloor and demersal biodiversity, limiting effective management. In this study, we describe the development and accessibility of an online atlas of seabed biodiversity that aims to fill these gaps. Species distribution models were developed for 579 taxa across four taxonomic groups: demersal fish, reef fish, subtidal invertebrates and macroalgae. Spatial layers for taxa distribution based on habitat suitability were statistically validated and then, as a further check, evaluated by taxonomic experts to provide measures of confidence to guide the future use of these layers. Spatially explicit uncertainty (SD) layers were also developed for each taxon distribution. We generated layer-specific metadata, including statistical and expert evaluation scores, which were uploaded alongside the accompanying spatial layers to the open access database Zenodo. This database provides the most comprehensive source of information on the distribution of seafloor taxa for Aotearoa New Zealand and is thus a valuable resource for managers, researchers and the public that will guide the management and conservation of seafloor communities. The atlas of seabed biodiversity for Aotearoa New Zealand is freely accessible via the open-access database Zenodo under https://doi.org/10.5281/zenodo.7083642 (Stephenson et al., 2022).

Smallholder farming systems in southern Africa are characterized by low-input management and integrated livestock and crop production. Low yields and dry-season feed shortages are common. To meet growing food demands, sustainable intensification (SI) of these systems is an important policy goal. While mixed crop–livestock farming may offer greater productivity, it implies trade-offs between feed supply, soil nutrient replenishment, soil carbon accumulation, and other ecosystem functions (ESFs) and ecosystem services (ESSs). Such settings require a detailed system understanding to assess the performance of prevalent management practices and identify potential SI strategies. Models can evaluate different management scenarios on extensive spatiotemporal scales and help identify suitable management strategies. Here, we linked the process-based models APSIM (Agricultural Production Systems sIMulator) for cropland and aDGVM2 (Adaptive Dynamic Global Vegetation Model) for rangeland to investigate the effects of (i) current management practices (minimum input crop–livestock agriculture), (ii) an SI scenario for crop production (with dry-season cropland grazing), and (iii) a scenario with separated rangeland and cropland management (livestock exclusion from cropland) in two representative villages of the Limpopo Province, South Africa, for the period from 2000 to 2010. We focused on the following ESFs and ESSs provided by cropland and rangeland: yield and feed provision, soil carbon storage, cropland leaf area index (LAI), and soil water. Village surveys informed the models of farming practices, livelihood conditions, and environmental circumstances. We found that modest SI measures (small fertilizer quantities, weeding, and crop rotation) led to moderate yield increases of between a factor of 1.2 and 1.6 and reduced soil carbon loss, but they sometimes caused increased growing-season water limitation effects. Thus, SI effects strongly varied between years. Dry-season crop residue grazing reduced feed deficits by approximately a factor of 2 compared with the rangeland-only scenario, but it could not fully compensate for the deficits during the dry-to-wet season transition. We expect that targeted deficit irrigation or measures to improve water retention and the soil water holding capacity may enhance SI efforts. Off-field residue feeding during the dry-to-wet season transition could further reduce feed deficits and decrease rangeland grazing pressure during the early growing season. We argue that integrative modeling frameworks are needed to evaluate landscape-level interactions between ecosystem components, evaluate the climate resilience of landscape-level ecosystem services, and identify effective mitigation and adaptation strategies.

Malaria is caused by infection with protozoan parasites of the Plasmodium genus, which is part of the phylum Apicomplexa. Most organisms in this phylum contain a relic plastid called the apicoplast. The apicoplast genome is replicated by a single DNA polymerase (apPOL), which is an attractive target for anti-malarial drugs. We screened small-molecule libraries (206,504 compounds) using a fluorescence-based high-throughput DNA polymerase assay. Dose/response analysis and counter-screening identified 186 specific apPOL inhibitors. Toxicity screening against human HepaRG human cells removed 84 compounds and the remaining were subjected to parasite killing assays using chloroquine resistant P. falciparum parasites. Nine compounds were potent inhibitors of parasite growth and may serve as lead compounds in efforts to discover novel malaria drugs.

Reducing zoonotic influenza A virus (IAV) risk in the United States necessitates mitigation of IAV in exhibition swine. We evaluated the effectiveness of shortening swine exhibitions to

The Didymos Reconnaissance and Asteroid Camera for OpNav (DRACO) was the only instrument on board NASA’s Double Asteroid Redirection Test spacecraft. DRACO had a 2628.326 mm focal length Ritchey–Chrétien telescope with a 208.28 mm aperture and a 0.29° field of view (FOV). The camera used a front-side-illuminated complementary metal–oxide–semiconductor (CMOS) sensor, BAE CIS2521F, with 2048 × 2048 illuminated pixels. DRACO worked with an onboard image processor to support autonomous targeting and ensure impact with Dimorphos, the secondary asteroid in the 65803 Didymos system, on 2022 September 26. This paper covers the calibration and in-flight performance of DRACO, including descriptions of ground and in-flight activities, instrument characterization, the calibration pipeline, and delivered Planetary Data System (PDS) products. DRACO exhibited low read noise and dark current that enhanced the instrument’s sensitivity, although the sensor’s nonlinearity and popcorn noise in global shutter mode required careful characterization and compensation to optimize terminal navigation performance. DRACO had a relatively circular point-spread function of less than two binned pixels across the full FOV. The combined calibration efforts achieved an absolute radiometric calibration (in both rolling and global shutter modes) of better than 20% and a relative calibration across an image of

Abstract Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.

Abstract Background While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as ‘mild’, ‘moderate’ or ‘severe’ based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. Methods We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (

Measurement(s) diet Technology Type(s) DNA metabarcoding diet analysis Sample Characteristic - Organism Phoca vitulina Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.19077809

Introduction: Individuals with focal segmental glomerular sclerosis (FSGS) typically undergo kidney biopsy only once, which limits the ability to characterize kidney cell gene expression over time. Methods: We used single-cell RNA sequencing (scRNA-seq) to explore disease-related molecular signatures in urine cells from subjects with FSGS. We collected 17 urine samples from 12 FSGS subjects and captured these as 23 urine cell samples. The inflammatory signatures from renal epithelial and immune cells were evaluated in bulk gene expression data sets of FSGS and minimal change disease (MCD) (The Nephrotic Syndrome Study Network [NEPTUNE] study) and an immune single-cell data set from lupus nephritis (Accelerating Medicines Partnership). Results: We identified immune cells, predominantly monocytes, and renal epithelial cells in the urine. Further analysis revealed 2 monocyte subtypes consistent with M1 and M2 monocytes. Shed podocytes in the urine had high expression of marker genes for epithelial-to-mesenchymal transition (EMT). We selected the 16 most highly expressed genes from urine immune cells and 10 most highly expressed EMT genes from urine podocytes as immune signatures and EMT signatures, respectively. Using kidney biopsy transcriptomic data from NEPTUNE, we found that urine cell immune signature and EMT signature genes were more highly expressed in FSGS biopsies compared with MCD biopsies. Conclusion: The identification of monocyte subsets and podocyte expression signatures in the urine samples of subjects with FSGS suggests that urine cell profiling might serve as a diagnostic and prognostic tool in nephrotic syndrome. Furthermore, this approach may aid in the development of novel biomarkers and identifying personalized therapies targeting particular molecular pathways in immune cells and podocytes.

Abstract Triple Negative Breast Cancer (TNBC) accounts for 15–20% of all breast cancer cases, yet is responsible for a disproportionately high percentage of breast cancer mortalities. Thus, there is an urgent need to identify novel biomarkers and therapeutic targets based on the molecular events driving TNBC pathobiology. Estrogen receptor beta (ERβ) is known to elicit anti-cancer effects in TNBC, however its mechanisms of action remain elusive. Here, we report the expression profiles of ERβ and its association with clinicopathological features and patient outcomes in the largest cohort of TNBC to date. In this cohort, ERβ was expressed in approximately 18% of TNBCs, and expression of ERβ was associated with favorable clinicopathological features, but correlated with different overall survival outcomes according to menopausal status. Mechanistically, ERβ formed a co-repressor complex involving enhancer of zeste homologue 2/polycomb repressive complex 2 (EZH2/PRC2) that functioned to suppress oncogenic NFκB/RELA (p65) activity. Importantly, p65 was shown to be required for formation of this complex and for ERβ-mediated suppression of TNBC. Our findings indicate that ERβ+ tumors exhibit different characteristics compared to ERβ− tumors and demonstrate that ERβ functions as a molecular switch for EZH2, repurposing it for tumor suppressive activities and repression of oncogenic p65 signaling.

Abstract Previous reports indicate a potential role for signal transducer and activator of transcription 3 (STAT3) in amyloid-β (Aβ) processing and neuritic plaque pathogenesis. In the present study, the impact of STAT3 inhibition on cognition, cerebrovascular function, amyloid pathology, oxidative stress, and neuroinflammation was studied using in vitro and in vivo models of Alzheimer’s disease (AD)-related pathology. For in vitro experiments, human brain vascular smooth muscle cells (HBVSMC) and human brain microvascular endothelial cells (HBMEC) were used, and these cultured cells were exposed to Aβ peptides followed by measurement of activated forms of STAT3 expression and reactive oxygen species (ROS) generation. Further, 6 months old 5XFAD/APOE4 (5XE4) mice and age-matched negative littermates were used for in vivo experiments. These mice were treated with STAT3 specific inhibitor, LLL-12 for 2 months followed by neurobehavioral and histopathological assessment. In vitro experiments showed exposure of cerebrovascular cells to Aβ peptides upregulated activated forms of STAT3 and produced STAT3-mediated vascular oxidative stress. 5XE4 mice treated with the STAT3-specific inhibitor (LLL-12) improved cognitive functions and functional connectivity and augmented cerebral blood flow. These functional improvements were associated with a reduction in neuritic plaques, cerebral amyloid angiopathy (CAA), oxidative stress, and neuroinflammation. Reduction in amyloid precursor protein (APP) processing and attenuation of oxidative modification of lipoprotein receptor related protein-1 (LRP-1) were identified as potential underlying mechanisms. These results demonstrate the broad impact of STAT3 on cognitive functions, parenchymal and vascular amyloid pathology and highlight the therapeutic potential of STAT3 specific inhibition for treatment of AD and CAA.

Abstract Background Fabry disease (FD) is a rare genetic disorder characterized by glycosphingolipid accumulation and progressive damage across multiple organ systems. Due to its heterogeneous presentation, the condition is likely significantly underdiagnosed. Several approaches, including provider education efforts and newborn screening, have attempted to address underdiagnosis of FD across the age spectrum, with limited success. Artificial intelligence (AI) methods present another option for improving diagnosis. These methods isolate common health history patterns among patients using longitudinal real-world data, and can be particularly useful when patients experience nonspecific, heterogeneous symptoms over time. In this study, the performance of an AI tool in identifying patients with FD was analyzed. The tool was calibrated using de-identified health record data from a large cohort of nearly 5000 FD patients, and extracted phenotypic patterns from these records. The tool then used this FD pattern information to make individual-level estimates of FD in a testing dataset. Patterns were reviewed and confirmed with medical experts. Results The AI tool demonstrated strong analytic performance in identifying FD patients. In out-of-sample testing, it achieved an area under the receiver operating characteristic curve (AUROC) of 0.82. Strong performance was maintained when testing on male-only and female-only cohorts, with AUROCs of 0.83 and 0.82 respectively. The tool identified small segments of the population with greatly increased prevalence of FD: in the 1% of the population identified by the tool as at highest risk, FD was 23.9 times more prevalent than in the population overall. The AI algorithm used hundreds of phenotypic signals to make predictions and included both familiar symptoms associated with FD (e.g. renal manifestations) as well as less well-studied characteristics. Conclusions The AI tool analyzed in this study performed very well in identifying Fabry disease patients using structured medical history data. Performance was maintained in all-male and all-female cohorts, and the phenotypic manifestations of FD highlighted by the tool were reviewed and confirmed by clinical experts in the condition. The platform’s analytic performance, transparency, and ability to generate predictions based on existing real-world health data may allow it to contribute to reducing persistent underdiagnosis of Fabry disease.

Abstract Background External ventricular drain (EVD)-related infections (EVDIs) are feared complications that are difficult to rapidly and correctly diagnose, which can lead to unnecessary treatment with broad-spectrum antibiotics. No readily available diagnostic parameters have been identified to reliably predict or identify EVDIs. Moreover, intraventricular hemorrhage is common and affect cerebrospinal fluid (CSF) cellularity. The relationship between leukocytes and erythrocytes is often used to identify suspected infection and triggers the use of antibiotics pending results of cultures, which may take days. Cell count based surveillance diagnostics assumes a homogeneous distribution of cells in the CSF. Given the intraventricular sedimentation of erythrocytes on computed tomography scans this assumption may be erroneous and could affect diagnostics. Aims To evaluate the consistency of cell counts in serially sampled CSF from EVDs, with and without patient repositioning, to assess the effect on infection diagnostics. Methods We performed a prospective single-center study where routine CSF sampling was followed by a second sample after 10 min, allocated around a standard patient repositioning, or not. Changes in absolute and pairwise cell counts and ratios were analyzed, including mixed regression models. Results Data from 51 patients and 162 paired samples were analyzed. We observed substantial changes in CSF cellularity as the result of both resampling and repositioning, with repositioning found to be an independent predictor of bidirectional cellular change. Glucose and lactate levels were affected, however clinically non-significant. No positive CSF cultures were seen during the study. Thirty percent (30%) of patients changed suspected EVDI status, as defined by the cell component of local and national guidelines, when resampling after repositioning. Conclusions CSF cell counts are not consistent and are affected by patient movement suggesting a heterogeneity in the intraventricular space. The relationship between leukocytes and erythrocytes was less affected than absolute changes. Importantly, cell changes are found to increase with increased cellularity, often leading to changes in suspected EVDI status. Faster and more precise diagnostics are needed, and methods such as emerging next generation sequencing techniques my provide tools to more timely and accurately guide antibiotic treatment. Trial Registration NCT04736407, Clinicaltrials.gov, retrospectively registered 2nd February 2021.

The authors present a microwave imaging system that can operate in continuous transmit-receive mode. Using an array of transmitters, a single receiver and a reconstruction matrix that correlate random time patterns with the captured signal, they demonstrate real-time imaging and tracking through a wall.

Transient simulation of complex converter topologies is a challenging problem, especially in detailed analysis tools like SPICE. Transistor models presented for SPICE are often evaluated by accuracy, with less consideration for the computational cost of model elements. In order to optimize models for application simulations, this research quantifies the relative simulation performance of modeling approaches and contextualizes the results with regard to accuracy. It is well established that the primary contributor to semiconductor dynamic behavior is the voltage-dependent interelectrode capacitances. Therefore, this study isolates these model components to resolve their influence on model accuracy and run-time. Both the voltage-dependencies modeled, and the mathematic formulation chosen strongly influence the accuracy of interelectrode capacitance models. In addition to these factors, the specific implementation chosen within SPICE also determines simulation performance. Through careful evaluation of these factors, this study offers specific recommendations for optimal implementations of interelectrode capacitances in SPICE.

Atopic disorders, including atopic dermatitis, food and environmental allergies, and asthma, are increasingly prevalent diseases. Atopic disorders are often associated with eosinophilia, driven by T helper type 2 (Th2) immune responses, and triggered by disrupted barrier function leading to abnormal immune priming in a susceptible host. Immune deficiencies, in contrast, occur with a significantly lower incidence, but are associated with greater morbidity and mortality. A subset of atopic disorders with eosinophilia and elevated IgE are associated with monogenic inborn errors of immunity (IEI). In this review, we discuss current knowledge of IEI that are associated with atopy and the lessons these immunologic disorders provide regarding the fundamental mechanisms that regulate type 2 immunity in humans. We also discuss further mechanistic insights provided by animal models.

Objective: To describe mortality, healthcare resource utilization (HRU), and costs among Medicare beneficiaries with primary Clostridioides difficile infection (pCDI) or recurrent CDI (rCDI), with and without sepsis. Methods: We conducted a retrospective observational study of 100% Medicare Fee-for-Service claims from adults aged ⩾ 65 years with ⩾1 CDI episode between 1 January 2009 and 31 December 2017. Patients were continuously enrolled in Medicare Parts A/B/D 12 months before and up to 12 months after pCDI. ICD-9/10 codes defined CDI using ⩾1 inpatient claim, or ⩾1 outpatient claim plus ⩾1 claim for CDI treatment. The pCDI episode ended after 14 days without a CDI claim. rCDI episodes started within 8 weeks from the end of a previous CDI episode. ICD-9/10 codes identified all-cause sepsis over 12 month follow-up. Results: Of 497,489 CDI patients, 41.0% ( N = 203,888) had sepsis; 57.7% with sepsis died versus 32.4% without sepsis. Among patients with pCDI only ( N = 345,893) or ⩾1 rCDI ( N = 151,596), 39.2% and 45.1% suffered sepsis, respectively. All-cause hospitalizations were frequent for all cohorts (range: 81–99%). Among patients who died, those with sepsis versus without had more-frequent intensive care unit (ICU) use (pCDI: 29% versus 15%; rCDI: 65% versus 34%), longer hospital stays (pCDI: 12 versus 10 days; rCDI: 12 versus 9 days), and higher per-patient-per-month costs (pCDI: $34,841 versus $22,753; rCDI: $42,269 versus $25,047). In both cohorts, sepsis patients who survived had higher total costs and all-cause HRU than those without sepsis. All p

Objective: To determine the cost of achieving a live birth after first transfer using highly purified human menotropin (HP-hMG) or recombinant follicle-stimulating hormone (FSH) for controlled ovarian stimulation in predicted high-responder patients in the Menopur in Gonadotropin-releasing hormone Antagonist Single Embryo Transfer–High Responder (MEGASET-HR) trial. Design: Cost minimization analysis of trial results. Setting: Thirty-one fertility centers. Patient(s): Six hundred and nineteen women with serum antimüllerian hormone ≥5 ng/mL. Intervention(s): Controlled ovarian stimulation with HP-hMG or recombinant FSH in a gonadotropin-releasing hormone (GnRH) antagonist assisted reproduction cycle where fresh transfer of a single blastocyst was performed unless ovarian response was excessive whereupon all embryos were cryopreserved and patients could undergo subsequent frozen blastocyst transfer within 6 months of randomization. Main Outcome Measure(s): Mean cost of achieving live birth after first transfer (fresh or frozen). Result(s): First-transfer efficacy, defined as live birth after first fresh or frozen transfer, was 54.5% for HP-hMG and 48.0% for recombinant FSH (difference 6.5%). Average cost to achieve a live birth after first transfer (fresh or frozen) was lower with HP-hMG compared with recombinant FSH. For fresh transfers, the cost was lower with HP-hMG compared with recombinant FSH. The average cost to achieve a live birth after first frozen transfer was also lower in patients treated with HP-hMG compared with recombinant FSH. Conclusion(s): Treatment of predicted high-responders with HP-hMG was associated with lower cost to achieve a live birth after first transfer compared with recombinant FSH. Clinical Trial Registration Number: NCT02554279.

Abstract Predator–prey interactions are critical in understanding how communities function. However, we need to describe intraspecific variation in diet to accurately depict those interactions. Harbor seals (Phoca vitulina) are an abundant marine predator that prey on species of conservation concern. We estimated intrapopulation feeding diversity (variation in feeding habits between individuals of the same species) of harbor seals in the Salish Sea. Estimates of feeding diversity were examined relative to sex, month, and location using a novel approach that combined molecular techniques, repeated cross‐sectional sampling of scat, and a specialization metric (within‐individual consistency in diet measured by the Proportional Similarity Index (PSi)). Based on 1,083 scat samples collected from five haul‐out sites during four nonsequential years, we quantified diet using metabarcoding techniques and determined the sex of the scat depositor using a molecular assay. Results suggest that intrapopulation feeding diversity was present. Specialization was high over short periods (24–48 hr, PSi = 0.392, 95% CI = 0.013, R = 100,000) and variable in time and space. Females showed more specialization than males, particularly during summer and fall. Additionally, demersal and benthic prey species were correlated with more specialized diets. The latter finding suggests that this type of prey likely requires specific foraging strategies and that there are trade‐offs between pelagic and benthic foraging styles for harbor seals. This differential feeding on prey species, as well as between sexes of harbor seals, indicates that predator–prey interactions in harbor seals are complex and that each sex may have a different impact on species of conservation concern. As such, describing intrapopulation feeding diversity may unravel hitherto unknown complex predator–prey interactions in the community.

Abstract Researchers have increasingly begun to use consumer wearables or wrist-worn smartwatches and fitness monitors for measurement of cardiovascular psychophysiological processes related to mental and physical health outcomes. These devices have strong appeal because they allow for continuous, scalable, unobtrusive, and ecologically valid data collection of cardiac activity in “big data” studies. However, replicability and reproducibility may be hampered moving forward due to the lack of standardization of data collection and processing procedures, and inconsistent reporting of technological factors (e.g., device type, firmware versions, and sampling rate), biobehavioral variables (e.g., body mass index, wrist dominance and circumference), and participant demographic characteristics, such as skin tone, that may influence heart rate measurement. These limitations introduce unnecessary noise into measurement, which can cloud interpretation and generalizability of findings. This paper provides a brief overview of research using commercial wearable devices to measure heart rate, reviews literature on device accuracy, and outlines the challenges that non-standardized reporting pose for the field. We also discuss study design, technological, biobehavioral, and demographic factors that can impact the accuracy of the passive sensing of heart rate measurements, and provide guidelines and corresponding checklist handouts for future study data collection and design, data cleaning and processing, analysis, and reporting that may help ameliorate some of these barriers and inconsistencies in the literature.

The APOBEC mutational signature is prevalent in different tumour types. Here, using HPV16- positive cervical samples, the authors show that the signature is more prevalent in the viral genome of benign or clearing HPV16 infections compared to the viral genomes of the more advanced precancerous lesions or cervical cancer.

Transient simulation of complex converter topologies is a challenging problem, especially in detailed analysis tools like SPICE. Much of the recent literature on SPICE transistor modeling ignores the requirements of application designers and instead emphasizes detail, physical accuracy, and complexity. While these advancements greatly improve model accuracy, they also serve to increase computational complexity, making the resulting models less attractive to application designers. While some authors depart from this trend and present models which emphasize simulation speed, their results and analysis are limited to qualitative observation. This research develops a methodology to quantify the computational cost of model features and competitively benchmark models against each other. Additionally, it reviews recently published SiC mosfet models and presents a trade study on several candidate models likely to fare well in complex application simulations. Finally, this study also identifies key considerations which should be carried forward into future model design.

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale-Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%- 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE and the development of ordinal dynamic prediction models.

Depression is a debilitating disorder associated with poor health outcomes, including increased comorbidity and early mortality. Despite the advent of new digital health interventions, few have been tested among patients with more severe forms of depression. As such, in an intent-to-treat study we examined whether 218 patients with at least moderately severe depressive symptoms (PHQ-9 ≥ 15) experienced significant reductions in depressive symptoms after participation in a therapist-supported, evidence-based mobile health (mHealth) program, Meru Health Program (MHP). Patients with moderately severe and severe depressive symptoms at pre-program assessment experienced significant decreases in depressive symptoms at end-of treatment (mean [standard deviation] PHQ-9 reduction = 8.30 [5.03], Hedges' g = 1.64, 95% CI [1.44, 1.85]). Also, 34% of patients with at least moderately severe depressive symptoms at baseline and 29.9% of patients with severe depressive symptoms (PHQ-9 ≥ 20) at baseline responded to the intervention at end-of-treatment, defined as experiencing ≥50% reduction in PHQ-9 score and a post-program PHQ-9 score lower than 10. Limitations include use lack of a control group and no clinical diagnostic information. Future randomized trials are warranted to test the MHP as a scalable solution for patients with more severe depressive symptoms.

Background Although stress is a risk factor for mental and physical health problems, it can be difficult to assess, especially on a continual, non-invasive basis. Mobile sensing data, which are continuously collected from naturalistic smartphone use, may estimate exposure to acute and chronic stressors that have health-damaging effects. This initial validation study validated a mobile-sensing collection tool against assessments of perceived and lifetime stress, mental health, sleep duration, and inflammation. Methods Participants were 25 well-characterized healthy young adults ( M age = 20.64 years, SD = 2.74; 13 men, 12 women). We collected affective text language use with a custom smartphone keyboard. We assessed participants’ perceived and lifetime stress, depression and anxiety levels, sleep duration, and basal inflammatory activity (i.e. salivary C-reactive protein and interleukin-1β). Results Three measures of affective language (i.e. total positive words, total negative words, and total affective words) were strongly associated with lifetime stress exposure, and total negative words typed was related to fewer hours slept (all large effect sizes: r = 0.50 – 0.78). Total positive words, total negative words, and total affective words typed were also associated with higher perceived stress and lower salivary C-reactive protein levels (medium effect sizes; r = 0.22 – 0.32). Conclusions Data from this initial longitudinal validation study suggest that total and affective text use may be useful mobile sensing measures insofar as they are associated with several other stress, mental health, behavioral, and biological outcomes. This tool may thus help identify individuals at increased risk for stress-related health problems.

Digesta were collected from the intestines of seven species of bottom‐feeding fish to better understand the role of incidental ingestion of sediment in exposing fish to inorganic contaminants. A composite sediment tracer variable, based on concentrations of Co, Cr, Ni, Ti, V, and Y in digesta and in sediment, was calculated to estimate sediment content of digesta. Concentration factors (mg/kg in digesta divided by mg/kg in sediment) of eight elements of interest were linearly regressed on this tracer variable. The relative importance of sediment ingestion to oral exposure was quantified. Zinc, Cd, and Cu were ingested mainly from sediment‐free food. Arsenic, Cr, Ni, Al, and Pb, in contrast, were ingested mainly from sediment. As an example, 93% of the Ni in digesta from a brown bullhead (Ameiurus nebulosus) was from sediment and only 7% from food. Regressions of Al and Pb in digesta of suckers (Catostomidae) suggested an additional oral source, possibly from oxides coating biotic or abiotic surfaces. Overall, concentrations of 12 of 21 elements studied were positively correlated with sediment content (p < 0.005). Including sediment ingestion as a pathway for bottom‐feeding fish is essential for accurately estimating exposures in toxicological studies. Environ Toxicol Chem 2024;43:1036–1046. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Objective: Elimination of food calories as heat could help redress the excess accumulation of metabolic energy exhibited as obesity. Prior studies have focused on the induction of thermogenesis in beige and brown adipose tissues as the application of this principle, particularly because the β-adrenergic environment associated with thermogenic activation has been shown to have positive health implications. The counterpoint to this strategy is the regulation of heat loss; we propose that mammals with inefficient heat conservation will require more thermogenesis to maintain body temperature. Methods: Surface temperature thermography and rates of trans-epidermal water loss were integrated to profile the total heat transfer of genetically-engineered and genetically variable mice. Results: These data were incorporated with energy expenditure data to generate a biophysical profile to test the significance of increased rates of evaporative cooling. Conclusions: We show that mouse skins vary considerably in their heat retention properties, whether because of naturally occurring variation (SKH-1 mice), or genetic modification of the heat-retaining lipid lamellae (SCD1, DGAT1 or Agouti Ay obese mice). In particular, we turn attention to widely different rates of evaporative cooling as the result of trans-epidermal water loss; higher rates of heat loss by evaporative cooling leads to increased demand for thermogenesis. We speculate that this physiology could be harnessed to create an energy sink to assist with strategies aimed at treating metabolic diseases. Keywords: Trans-epidermal water loss, Evaporative cooling, Epidermal barrier, Syndecan-1, Obesity, Mouse skin, Thermogenesis, Dermal white adipose tissue, Energy expenditure, Brown adipose tissue

Abstract Widespread geographic movement and extensive comingling of exhibition swine facilitates the spread and transmission of infectious pathogens. Nasal samples were collected from 2862 pigs at 102 exhibitions and tested for five pathogens. At least one pathogen was molecularly detected in pigs at 63 (61.8%) exhibitions. Influenza A virus was most prevalent and was detected in 498 (17.4%) samples. Influenza D virus was detected in two (0.07%) samples. More than one pathogen was detected in 165 (5.8%) samples. Influenza A virus remains a top threat to animal and human health, but other pathogens may be disseminated through the exhibition swine population.

Abstract Background In traumatic brain injury (TBI) patients desmopressin administration may induce rapid decreases in serum sodium and increase intracranial pressure (ICP). Aim In an international multi-centre study, we aimed to report changes in serum sodium and ICP after desmopressin administration in TBI patients. Methods We obtained data from 14 neurotrauma ICUs in Europe, Australia and UK for severe TBI patients (GCS ≤ 8) requiring ICP monitoring. We identified patients who received any desmopressin and recorded daily dose, 6-hourly serum sodium, and 6-hourly ICP. Results We studied 262 severe TBI patients. Of these, 39 patients (14.9%) received desmopressin. Median length of treatment with desmopressin was 1 [1–3] day and daily intravenous dose varied between centres from 0.125 to 10 mcg. The median hourly rate of decrease in serum sodium was low (− 0.1 [− 0.2 to 0.0] mmol/L/h) with a median period of decrease of 36 h. The proportion of 6-h periods in which the rate of natremia correction exceeded 0.5 mmol/L/h or 1 mmol/L/h was low, at 8% and 3%, respectively, and ICPs remained stable. After adjusting for IMPACT score and injury severity score, desmopressin administration was independently associated with increased 60-day mortality [HR of 1.83 (1.05–3.24) (p = 0.03)]. Conclusions In severe TBI, desmopressin administration, potentially representing instances of diabetes insipidus is common and is independently associated with increased mortality. Desmopressin doses vary markedly among ICUs; however, the associated decrease in natremia rarely exceeds recommended rates and median ICP values remain unchanged. These findings support the notion that desmopressin therapy is safe.

Swine exhibited at agricultural fairs in North America have been the source of repeated zoonotic influenza A virus transmission, which creates a pathway for influenza pandemic emergence. We investigated the effect of using either live-attenuated influenza virus or killed influenza virus vaccines as prefair influenza vaccination of swine on zoonotic influenza transmission risk.

Recycling of wastewater provides a substantial solution to the global issue of water scarcity and high water use in aquaculture. However, this sustainable way of wastewater use has not been given much attention and exploration. This study focused on the consumer preference for fish grown in treated wastewater as well as the effect of aeration on the growth performance and economic benefit of African catfish (Clarias gariepinus) grown in treated wastewater. Two hundred (200) respondents from two communities (Chirapatre and Gyinyase) near the wastewater treatment plant in Kumasi were interviewed to determine their willingness to accept and pay for African catfish grown in treated wastewater. For the growth trial, a total of 600 fish (of average initial weight 39.12g) were stocked in two maturation ponds with 4 h (3:00am–7:00am) of aeration daily. The trial lasted for 12 weeks and variables monitored included the survival, growth performance (weight gain, specific growth rate, and yield) and water quality. Fish cultured in non-aerated wastewater ponds (NWFPs) under similar conditions as in aerated wastewater-fed ponds (AWFPs) served as control. The results indicated most important considerations for consumers in their choice of fish to consume were in order of importance; food safety, freshness of fish, taste and packaging. The proximity of consumers to the treatment plant, the price of fish, religion, and age and whether or not they were fish consumers affected their willingness to pay for African catfish grown in the treated wastewater significantly. For the growth trial, dissolved oxygen concentrations in the aerated ponds were significantly higher than in the NWFPs and this led to more than a doubling of the growth rates in the African catfish grown in the AWFPs (189.10g ± 11.32) as compared to the NWFPs (90.70g ± 11.59). The pond aeration improved fish growth significantly (p < 0.0098). On economic benefit, the aerated system yielded profits of 618.83 (€103.13) as compared to a loss of 104.99 (€17.50), which was incurred in the non-aerated ponds. Education of the consumers on the process of wastewater treatment and establishment of food safety guidelines will therefore be recommended to increase consumer interest in consuming fish from the treated wastewater.

Background: Secondary transports of patients suffering from traumatic brain injury (TBI) may result in a delayed management and neurosurgical intervention, which is potentially detrimental. The aim of this study was to study the effect of triaging and delayed transfers on outcome, specifically studying time to diagnostics and neurosurgical management.Methods: This was a retrospective observational cohort study of TBI patients in need of neurosurgical care, 15 years and older, in the Stockholm Region, Sweden, from 2008 throughout 2014. Data were collected from pre-hospital and in-hospital charts. Known TBI outcome predictors, including the protein biomarker of brain injury S100B, were used to assess injury severity. Characteristics and outcomes of direct trauma center (TC) and those of secondary transfers were evaluated and compared. Functional outcome, using the Glasgow Outcome Scale, was assessed in survivors at 6–12 months after trauma. Regression models, including propensity score balanced models, were used for endpoint assessment.Results: A total of n = 457 TBI patients were included; n = 320 (70%) patients were direct TC transfers, whereas n = 137 (30%) were secondary referrals. In all, n = 295 required neurosurgery for the first 24 h after trauma (about 75% of each subgroup). Direct TC transfers were more severely injured (median Glasgow Coma Scale 8 vs. 13) and more often suffered a high energy trauma (31 vs. 2.9%) than secondary referrals. Admission S100B was higher in the TC transfer group, though S100B levels 12–36 h after trauma were similar between cohorts. Direct or indirect TC transfer could be predicted using propensity scoring. The secondary referrals had a shorter distance to the primary hospital, but had later radiology and surgery than the TC group (all p < 0.001). In adjusted multivariable analyses with and without propensity matching, direct or secondary transfers were not found to be significantly related to outcome. Time from trauma to surgery did not affect outcome.Conclusions: TBI patients secondary transported to a TC had surgical intervention performed hours later, though this did not affect outcome, presumably demonstrating that accurate pre-hospital triaging was performed. This indicates that for selected patients, a wait-and-see approach with delayed neurosurgical intervention is not necessarily detrimental, but warrants further research.

Objective: Clostridioides difficile infection and recurrent C. difficile infection result in substantial economic burden and healthcare resource use. Sepsis and bowel surgery are known to be serious complications of C. difficile infection. This study evaluated clinical complications in patients with C. difficile infection and recurrent C. difficile infection during a 12-month period following the primary C. difficile infection. Methods: A retrospective analysis of commercial claims data from the IQVIA PharMetrics Plus™ database was conducted for patients aged 18–64 years with an index C. difficile infection episode requiring inpatient stay or an outpatient visit for C. difficile infection followed by a C. difficile infection treatment. Each C. difficile infection episode ended after a 14-day C. difficile infection-claim-free period was observed. Recurrent C. difficile infection was defined as a further C. difficile infection episode within an 8-week window following the claim-free period. Clinical complications were documented over 12 months of follow-up and stratified by the number of recurrent C. difficile infection episodes (0 rCDI, 1 rCDI, 2 rCDI, and 3+ rCDI). Results: In total, 46,571 patients with index C. difficile infection episode were included. During the 6-month pre-index, the mean (standard deviation) baseline Charlson comorbidity index score, by increasing the recurrent C. difficile infection group, was 1.2 (1.9), 1.5 (2.2), 1.8 (2.3), and 2.3 (2.5). During the 12-month follow-up, sepsis occurred in 16.5%, 27.3%, 33.1%, and 43.3% of patients, and subtotal colectomy or diverting loop ileostomy was performed in 4.6%, 7.3%, 8.9%, and 10.5% of patients, respectively, by increasing the recurrent C. difficile infection group. Conclusions: Reduction in recurrent C. difficile infection is an important step to reduce the burden of serious clinical complications, and new treatments are needed to reduce C. difficile infection recurrence.