Pharmacologic profile of ITI-333: a novel molecule for treatment of substance use disorders.

RATIONALE: Medications are urgently needed to treat symptoms of drug withdrawal and mitigate dysphoria and psychiatric comorbidities that drive opioid abuse and relapse. ITI-333 is a novel molecule in development for treatment of substance use disorders, psychiatric comorbidities, and pain. OBJECTIVE: Characterize the preclinical profile of ITI-333 using pharmacological, behavioral, and physiological assays. METHODS: Cell-based assays were used to measure receptor binding and intrinsic efficacy of ITI-333; animal models were employed to assess effects on opioid reinstatement, precipitated oxycodone withdrawal, and drug abuse liability. RESULTS: In vitro, ITI-333 is a potent 5-HT2A receptor antagonist (Ki = 8 nM) and a biased, partial agonist at µ-opioid (MOP) receptors (Ki = 11 nM; lacking ß-arrestin agonism) with lesser antagonist activity at adrenergic α1A (Ki = 28 nM) and dopamine D1 (Ki = 50 nM) receptors. In vivo, ITI-333 blocks 5-HT2A receptor-mediated head twitch and MOP receptor-mediated effects on motor hyperactivity in mice. ITI-333 alone is a naloxone-sensitive analgesic (mice) which suppresses somatic signs of naloxone-precipitated oxycodone withdrawal (mice) and heroin cue-induced reinstatement responding without apparent tolerance or physical dependence after chronic dosing (rats). ITI-333 did not acutely impair gastrointestinal or pulmonary function (rats) and was not intravenously self-administered by heroin-maintained rats or rhesus monkeys. CONCLUSIONS: ITI-333 acts as a potent 5-HT2A receptor antagonist, as well a biased MOP receptor partial agonist with low intrinsic efficacy. ITI-333 mitigates opioid withdrawal/reinstatement, supporting its potential utility as a treatment for OUD.

Lion lords and sharing hyaenas: Carnivore guild dynamics around elephant carcasses.

Megaherbivore carcasses represent sporadic but energetically rewarding resources for carnivores, offering a unique opportunity to study coexistence dynamics between facultative scavengers. South African fenced protected areas, such as Madikwe Game Reserve (Madikwe hereafter), host viable populations of large carnivores and high densities of elephants, Loxodonta africana. However, high carnivore densities can lead to potentially fatal interspecific encounters and increased competition, particularly around high-quality trophic resources. This study explores the temporal partitioning and co-detection strategies of carnivores at six elephant carcasses in Madikwe, aiming to understand how the increased carrion biomass available at elephant carcasses influences coexistence dynamics. Camera traps were deployed to monitor carcasses during two periods (2019 and 2020), revealing occurrences of six carnivore species. Carnivores, particularly black-backed jackals, Lupulella mesomelas, (hereafter jackal), lions, Panthera leo, and spotted hyaenas, Crocuta crocuta, comprised 56.7% of carcass observations, highlighting their pivotal roles in scavenging dynamics. Pairwise co-detection analysis demonstrated consistent association and shared peak activity periods between jackals and spotted hyaenas, indicating potential resource sharing. However, the minimal co-detection rates between lions and other carnivores highlight their resource domination. There was some evidence of temporal partitioning between carnivores, with most species exhibiting earlier peaks in nocturnal activity to avoid temporal overlap with lions. This study emphasises the importance of elephant carcasses in the diet of multiple species and coexistence techniques utilised to exploit this ephemeral resource. As fenced protected areas become crucial for conserving intact carnivore guilds globally, further research into carnivore behavioural adaptations at carcasses is recommended to shed light on their coexistence strategies.

First record of Arionvulgaris Moquin-Tandon, 1855 (Arionidae) from Armenia.

Background: Arionvulgaris Moquin-Tandon, 1855 is amongst the fastest-spreading terrestrial slugs Europe-wide. In recent years, it has been recorded in Canada, Mexico and continues to expand eastwards into Eurasia. Renowned for its high invasiveness, combatting its swift spread creates significant challenges in organising effective preventative measures. New information: This study presents the first record of Arionvulgaris from Armenia, which is the second record of this species' invasion of the Caucasus. In 2022, a substantial population of A.vulgaris was observed close to the City of Stepanavan, which is also the first record in Armenia of the family Arionidae. How the species was introduced to Armenia remains unknown. Identification of Arionvulgaris was conducted, based on external and genital morphology and mitochondrial CO1 (cytochrome c oxidase subunit 1) gene sequencing, revealing notable similarities with Central European clades. Our results confirm the introduction and distribution of A.vulgaris to Armenia. Invasion of such species into Armenia will require additional monitoring and would be aided by further research on Armenia's mollusc fauna in the future.

Stigma Related to the Non-Medical Use and Diversion of Prescription Stimulant Drugs: Should We Care.

BACKGROUND: Non-medical use (NMU) and diversion of prescription stimulants are prevalent on college campuses. Diversion represents a primary source of acquisition for NMU among young adults. This study examined relationships between stigmatizing beliefs related to NMU and diversion of stimulant medications and engagement in these behaviors, as well as how such perceptions are associated with indicators of psychological distress among those who engage in these behaviors. METHODS: Young adults (N = 384) were recruited from a large US university to participate in this cross-sectional electronic survey-based study. Relationships between stigma variables and NMU and diversion were assessed. Among those who engage in NMU and diversion, we tested relationships between stigma variables and indicators of psychological distress, using validated instruments. RESULTS: Perceived social and personal stigmatic beliefs did not significantly predict NMU. However, perceived social and personal stigma of diversion significantly reduced diversion likelihood. For NMU, associations were found between stigma variables and indicators of psychological distress. Markedly, we found that as stigmatic perceptions of NMU increased, so did depressive, anxiolytic, and suicidal symptomatology among those who engage in NMU. CONCLUSIONS: Stigmatization does not deter NMU; however, stigmatization is positively associated with psychological harm among those who engage in NMU. Interventions should be developed to reduce stigmatization in order to improve psychological health among those who engage in NMU. Stigmatic perceptions of diversion were not predictive of psychological harm, though they are negatively associated with diversion behavior.

Co-occurring psychological distress and alcohol or other drug use among Indigenous Australians: Data from the National Aboriginal and Torres Strait Islander Health Survey.

OBJECTIVES: To determine the prevalence and demographic, social and health characteristics associated with co-occurring psychological distress symptoms, risky alcohol and/or substance use among a national sample of Aboriginal and Torres Strait Islander people aged 15 years or older. METHODS: This study uses secondary cross-sectional data from the 2018-19 National Aboriginal and Torres Strait Islander Health Survey (NATSIHS). Data were collected via face-to-face interviews with those living in private dwellings across Australia. Participants were Aboriginal and Torres Strait Islander people (n = 10,579) aged 15 years or older. Data pertaining to psychological distress, alcohol and substance use were obtained and weighted to represent the total population of Aboriginal and Torres Strait Islander people in Australia. RESULTS: A total of 20.3% participants were found to have co-occurring psychological distress, risky alcohol use and/or substance use, and 4.0% reported co-occurrence of all three conditions. Female participants in a registered marriage and fully engaged in study or employment had lower rates of co-occurring conditions. Poorer self-rated health, one or more chronic conditions and increased experiences of unfair treatment and physical harm in the past 12 months were associated with increased rates of co-occurring conditions. CONCLUSION: A range of potential risk and protective factors were identified for co-occurring psychological distress, risky alcohol and/or substance use among Aboriginal and Torres Strait Islander people. This information is critical for planning effective holistic strategies to decrease the burden of suffering imposed upon the individual, family and community members impacted by co-occurring conditions.

GSDMB/ORMDL3 Rare/Common Variants Are Associated with Inhaled Corticosteroid Response among Children with Asthma.

Inhaled corticosteroids (ICS) are efficacious in the treatment of asthma, which affects more than 300 million people in the world. While genome-wide association studies have identified genes involved in differential treatment responses to ICS in asthma, few studies have evaluated the effects of combined rare and common variants on ICS response among children with asthma. Among children with asthma treated with ICS with whole exome sequencing (WES) data in the PrecisionLink Biobank (91 White and 20 Black children), we examined the effect and contribution of rare and common variants with hospitalizations or emergency department visits. For 12 regions previously associated with asthma and ICS response (DPP10, FBXL7, NDFIP1, TBXT, GLCCI1, HDAC9, TBXAS1, STAT6, GSDMB/ORMDL3, CRHR1, GNGT2, FCER2), we used the combined sum test for the sequence kernel association test (SKAT) adjusting for age, sex, and BMI and stratified by race. Validation was conducted in the Biorepository and Integrative Genomics (BIG) Initiative (83 White and 134 Black children). Using a Bonferroni threshold for the 12 regions tested (i.e., 0.05/12 = 0.004), GSDMB/ORMDL3 was significantly associated with ICS response for the combined effect of rare and common variants (p-value = 0.003) among White children in the PrecisionLink Biobank and replicated in the BIG Initiative (p-value = 0.02). Using WES data, the combined effect of rare and common variants for GSDMB/ORMDL3 was associated with ICS response among asthmatic children in the PrecisionLink Biobank and replicated in the BIG Initiative. This proof-of-concept study demonstrates the power of biobanks of pediatric real-life populations in asthma genomic investigations.

Evolution of wing shape in geometrid moths: phylogenetic effects dominate over ecology.

Locomotory performance is an important determinant of fitness in most animals, including flying insects. Strong selective pressures on wing morphology are therefore expected. Previous studies on wing shape in Lepidoptera have found some support for hypotheses relating wing shape to environment-specific selective pressures on aerodynamic performance. Here, we present a phylogenetic comparative study on wing shape in the lepidopteran family Geometridae, covering 374 species of the northern European fauna. We focused on 11 wing traits including aspect ratio, wing roundness, and the pointedness of the apex, as well as the ratio of forewing and hindwing areas. All measures were taken from images available on the internet, using a combination of tools available in Fiji software and R. We found that wing shape demonstrates a phylogenetically conservative pattern of evolution in Geometridae, showing similar or stronger phylogenetic signal than many of its potential predictors. Several wing traits showed statistically significant associations with predictors such as body size, phenology, and preference for forest habitats. Overall, however, all of these associations remained notably weak, with no wing shape being excluded for any value of the predictors, including body size. We conclude that, in geometrids, wing traits do not readily respond to selective pressures optimizing aerodynamic performance of the moths in different environments. Selection on wing shape may nevertheless operate through other functions of the wings, with the effectiveness of crypsis at rest being a promising candidate for further studies.

Advancing the care of individuals with cancer through innovation & technology: Proceedings from the cardiology oncology innovation summit 2020 and 2021.

As cancer therapies increase in effectiveness and patients' life expectancies improve, balancing oncologic efficacy while reducing acute and long-term cardiovascular toxicities has become of paramount importance. To address this pressing need, the Cardiology Oncology Innovation Network (COIN) was formed to bring together domain experts with the overarching goal of collaboratively investigating, applying, and educating widely on various forms of innovation to improve the quality of life and cardiovascular healthcare of patients undergoing and surviving cancer therapies. The COIN mission pillars of innovation, collaboration, and education have been implemented with cross-collaboration among academic institutions, private and public establishments, and industry and technology companies. In this report, we summarize proceedings from the first two annual COIN summits (inaugural in 2020 and subsequent in 2021) including educational sessions on technological innovations for establishing best practices and aligning resources. Herein, we highlight emerging areas for innovation and defining unmet needs to further improve the outcome for cancer patients and survivors of all ages. Additionally, we provide actionable suggestions for advancing innovation, collaboration, and education in cardio-oncology in the digital era.

AI-based preeclampsia detection and prediction with electrocardiogram data.

Introduction: More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings. Methods: Ten-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis. Results: The UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00). Discussion: We conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.

Control of cell proliferation by memories of mitosis.

Mitotic duration is tightly constrained, and extended mitosis is characteristic of problematic cells prone to chromosome missegregation and genomic instability. We show here that mitotic extension leads to the formation of p53-binding protein 1 (53BP1)-ubiquitin-specific protease 28 (USP28)-p53 protein complexes that are transmitted to, and stably retained by, daughter cells. Complexes assembled through a Polo-like kinase 1-dependent mechanism during extended mitosis and elicited a p53 response in G1 that prevented the proliferation of the progeny of cells that experienced an approximately threefold extended mitosis or successive less extended mitoses. The ability to monitor mitotic extension was lost in p53-mutant cancers and some p53-wild-type (p53-WT) cancers, consistent with classification of TP53BP1 and USP28 as tumor suppressors. Cancers retaining the ability to monitor mitotic extension exhibited sensitivity to antimitotic agents.

Demographic patterns of walleye (Sander vitreus) reproductive success in a Wisconsin population.

Harvest in walleye Sander vitreus fisheries is size-selective and could influence phenotypic traits of spawners; however, contributions of individual spawners to recruitment are unknown. We used parentage analyses using single nucleotide polymorphisms to test whether parental traits were related to the probability of offspring survival in Escanaba Lake, Wisconsin. From 2017 to 2020, 1339 adults and 1138 juveniles were genotyped and 66% of the offspring were assigned to at least one parent. Logistic regression indicated the probability of reproductive success (survival of age-0 to first fall) was positively (but weakly) related to total length and growth rate in females, but not age. No traits analyzed were related to reproductive success for males. Our analysis identified the model with the predictors' growth rate and year for females and the models with year and age and year for males as the most likely models to explain variation in reproductive success. Our findings indicate that interannual variation (i.e., environmental conditions) likely plays a key role in determining the probability of reproductive success in this population and provide limited support that female age, length, and growth rate influence recruitment.

Ex vivo lung perfusion in donation after circulatory death: A post hoc analysis of the Normothermic Ex Vivo Lung Perfusion as an Assessment of Extended/Marginal Donors Lungs trial.

OBJECTIVE: Donation after circulatory death (DCD) donors offer the ability to expand the lung donor pool and ex vivo lung perfusion (EVLP) further contributes to this ability by allowing for additional evaluation and resuscitation of these extended criteria donors. We sought to determine the outcomes of recipients receiving organs from DCD EVLP donors in a multicenter setting. METHODS: This was an unplanned post hoc analysis of a multicenter, prospective, nonrandomized trial that took place during 2011 to 2017 with 3 years of follow-up. Patients were placed into 3 groups based off procurement strategy: brain-dead donor (control), brain-dead donor evaluated by EVLP, and DCD donors evaluated by EVLP. The primary outcomes were severe primary graft dysfunction at 72 hours and survival. Secondary outcomes included select perioperative outcomes, and 1-year and 3-years allograft function and quality of life measures. RESULTS: The DCD EVLP group had significantly higher incidence of severe primary graft dysfunction at 72 hours (P = .03), longer days on mechanical ventilation (P < .001) and in-hospital length of stay (P = .045). Survival at 3 years was 76.5% (95% CI, 69.2%-84.7%) for the control group, 68.3% (95% CI, 58.9%-79.1%) for the brain-dead donor group, and 60.7% (95% CI, 45.1%-81.8%) for the DCD group (P = .36). At 3-year follow-up, presence observed bronchiolitis obliterans syndrome or quality of life metrics did not differ among the groups. CONCLUSIONS: Although DCD EVLP allografts might not be appropriate to transplant in every candidate recipient, the expansion of their use might afford recipients stagnant on the waitlist a viable therapy.

Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy.

BACKGROUND: This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy. OBJECTIVE: This study aimed to assess the efficacy of an artificial intelligence-based heart failure detection model for peripartum cardiomyopathy detection. STUDY DESIGN: We first built a deep-learning model for heart failure detection using retrospective data at the University of Tennessee Health Science Center. Cases were adult and nonpregnant female patients with a heart failure diagnosis; controls were adult nonpregnant female patients without heart failure. The model was then tested on an independent cohort of pregnant women at the University of Tennessee Health Science Center with or without peripartum cardiomyopathy. We also tested the model in an external cohort of pregnant women at Atrium Health Wake Forest Baptist. Key outcomes were assessed using the area under the receiver operating characteristic curve. We also repeated our analysis using only lead I electrocardiogram as an input to assess the feasibility of remote monitoring via wearables that can capture single-lead electrocardiogram data. RESULTS: The University of Tennessee Health Science Center heart failure cohort comprised 346,339 electrocardiograms from 142,601 patients. In this cohort, 60% of participants were Black and 37% were White, with an average age (standard deviation) of 53 (19) years. The heart failure detection model achieved an area under the curve of 0.92 on the holdout set. We then tested the ability of the heart failure model to detect peripartum cardiomyopathy in an independent University of Tennessee Health Science Center cohort of pregnant women and an external Atrium Health Wake Forest Baptist cohort of pregnant women. The independent University of Tennessee Health Science Center cohort included 158 electrocardiograms from 115 patients; our deep-learning model achieved an area under the curve of 0.83 (0.77-0.89) for this data set. The external Atrium Health Wake Forest Baptist cohort involved 80 electrocardiograms from 43 patients; our deep-learning model achieved an area under the curve of 0.94 (0.91-0.98) for this data set. For identifying peripartum cardiomyopathy diagnosed ≥10 days after delivery, the model achieved an area under the curve of 0.88 (0.81-0.94) for the University of Tennessee Health Science Center cohort and of 0.96 (0.93-0.99) for the Atrium Health Wake Forest Baptist cohort. When we repeated our analysis by building a heart failure detection model using only lead-I electrocardiograms, we obtained similarly high detection accuracies, with areas under the curve of 0.73 and 0.93 for the University of Tennessee Health Science Center and Atrium Health Wake Forest Baptist cohorts, respectively. CONCLUSION: Artificial intelligence can accurately detect peripartum cardiomyopathy from electrocardiograms alone. A simple electrocardiographic artificial intelligence-based peripartum screening could result in a timelier diagnosis. Given that results with 1-lead electrocardiogram data were similar to those obtained using all 12 leads, future studies will focus on remote screening for peripartum cardiomyopathy using smartwatches that can capture single-lead electrocardiogram data.

Dryland rock detention structures increase herbaceous vegetation cover and stabilize shrub cover over 10 years but do not directly affect soil fertility.

Rock detention structures (RDS) such as check dams, gabions, and one rock dams are commonly used to mitigate erosion impacts in dryland ephemeral stream channels. RDS increase local water infiltration and floodplain connectivity, reduce sediment transport, and enhance vegetation growth and establishment. In addition to increasing overall vegetation cover, RDS may also buffer against a cycle of vegetation growth and collapse during years of extremely variable precipitation, helping to maintain stable cover. Although widely employed by land managers, success as reported in scientific literature varies, especially with regard to RDS effects on vegetation and soil fertility. We present the results of a 10-year field experiment in southeastern Arizona, USA, designed in collaboration with local land practitioners to measure local in-channel effects of RDS. Over 10 years, cover of herbaceous vegetation (forbs and grasses) doubled from 11 % to 22 % in channels treated with RDS, but did not significantly increase in untreated control channels. Shrub cover in treated channels was significantly less variable than in control channels over time. We analyzed the complex relationships between RDS, vegetation cover, and soil fertility using structural equation modeling (SEM), which represented conditions of the tenth year alone. SEM revealed that RDS did not directly affect soil fertility, as measured by total soil nitrogen, total soil carbon, soil organic matter, microbial richness, and potential nutrient cycling capacity. Notably, SEM did not yield the same trends as temporal monitoring, possibly because our structural equation models could not capture change over time. This discrepancy highlights the need for long-term, frequent monitoring of aboveground and belowground conditions to evaluate treatment success on a management scale. Overall, installing rock detention structures in ephemeral channels in arid and semiarid regions is a low-cost, feasible way to increase channel sediment aggradation, forb, and grass cover; stabilize shrub cover; and combat dryland degradation.

Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease.

INTRODUCTION: Acute kidney injury (AKI) is common among hospitalized patients with sickle cell disease (SCD) and contributes to increased morbidity and mortality. Early identification and management of AKI is essential to preventing poor outcomes. We aimed to predict AKI earlier in patients with SCD using a machine-learning model that utilized continuous minute-by-minute physiological data. METHODS: A total of6,278 adult SCD patient encounters were admitted to inpatient units across five regional hospitals in Memphis, TN, over 3 years, from July 2017 to December 2020. From these, 1,178 patients were selected after filtering for data availability. AKI was identified in 82 (7%) patient encounters, using the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The remaining 1,096 encounters served as controls. Features derived from five physiological data streams, heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from bedside monitors were used. An XGBoost classifier was used for classification. RESULTS: Our model accurately predicted AKI up to 12 h before onset with an area under the receiver operator curve (AUROC) of 0.91 (95% CI [0.89-0.93]) and up to 48 h before AKI with an AUROC of 0.82 (95% CI [0.80-0.83]). Patients with AKI were more likely to be female (64.6%) and have history of hypertension, pulmonary hypertension, chronic kidney disease, and pneumonia than the control group. CONCLUSION: XGBoost accurately predicted AKI as early as 12 h before onset in hospitalized SCD patients and may enable the development of innovative prevention strategies.

Salient Beliefs Associated with Unsanctioned Adult Cannabis Use in an Evolving Socio-Political Environment.

Background: The changing cannabis legal climate in the United States has been associated with increased prevalence of use, more favorable general attitudes, and cultural norms toward its use among young adults. The purpose of this study was to conceptualize salient beliefs governing illicit cannabis use within a state where medical use is legal and adult use remains illegal. Methods: Following an initial qualitative pilot, a prospective study (baseline and a 30-day follow-up) that utilized an electronic survey design was conducted. Students at a large US university (N = 514) completed a baseline assessment of cannabis use and Theory of Planned Behavior (TPB) constructs, and 169 completed the follow-up assessing actual behavior. Results: Roughly 60% reported illicit cannabis use. Attitude and subjective norms accounted for 57% of the variation in intention for cannabis use in the next 30 days, while intention and perceived behavioral control (PBC) explained 51% of the variation in prospective use. The majority of salient beliefs identified exhibited moderate to strong correlations with intention and behavior among the larger sample. Close friends and partners were identified as important referents, outcome expectations of relaxation, improved social functioning, and more enjoyment, as well as environmental factors, such as proximity to use, safe utilization location, and continued illegality of adult use exhibited strong associations to intention and behavior. Conclusions: The practical utility of the salient belief measures identified herein presents a useful foundation for interventions aimed at reducing illicit cannabis utilization among young adults.

A novel record of African wild dogs (Lycaon pictus) in Vwaza Marsh Wildlife Reserve, Malawi.

Most African wild dog (Lycaon pictus) populations are in decline and, due to habitat fragmentation and conflict rates in areas of higher anthropogenic land-use, are primarily restricted to protected areas. As a species that occurs at low densities, with a strict reproductive social structure, wild dogs rely on long-range dispersal to facilitate colonization, reproduction, and pack formation. In Malawi, large carnivores have been subject to widespread population decline and several protected areas have a reduced large carnivore guild, including the loss of resident wild dog populations. Here, during a biodiversity monitoring camera trap survey, we captured a novel record of wild dogs in Vwaza Marsh Wildlife Reserve (Vwaza), Malawi. The 11 photographic captures of potentially three individual wild dogs represent the first documented evidence of the species in Vwaza and the first record since an unconfirmed report in 2011. We hypothesize that this group of wild dogs moved into Vwaza through the Malawi-Zambia Transfrontier Conservation Area (MZTFCA), with the MZTFCA linking protected areas in Malawi with the Luangwa Valley, Zambia. The evidence provided here, and similar documentation in Kasungu National Park, Malawi, show that large carnivores can potentially move through the MZTFCA into protected areas in Malawi. We argue that the MZTFCA provides an important dispersal corridor that could help facilitate the recolonization of wild dogs, and other large carnivores, in Malawian protected areas. However, further research is needed to assess the permeability and status of the MZTFCA corridor into Malawi. We show that camera traps can be useful to document novel records of rare species and can be used to inform conservation management planning.

Mechanical Circulatory Support Devices in the Elderly.

As the field of mechanical circulatory support (MCS) continues to advance and resuscitation protocols are being refined, elderly patients previously not considered for MCS are now being supported. MCS devices can broadly be classified based on the duration of support into temporary or durable devices. Although mortality is higher in the elderly, carefully selected patients, MCS support can be valuable and lead to excellent recovery. Age itself should not preclude patients from being candidates for MCS because we must not restrict the progress of science in medicine for any age.

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.

Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months-1 year, 6 months-3 years, and 6 months-5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

Selective deconstruction of mixed plastics by a tailored organocatalyst.

Plastic represents an essential material in our society; however, a major imbalance between their high production and end-of-life management is leading to unrecovered energy, economic hardship, and a high carbon footprint. The adoption of plastic recycling has been limited, mainly due to the difficulty of recycling mixed plastics. Here, we report a versatile organocatalyst for selective glycolysis of diverse consumer plastics and their mixed waste streams into valuable chemicals. The developed organocatalyst selectively deconstructs condensation polymers at a specific temperature, and additives or other polymers such as polyolefin or cellulose can be readily separated from the mixed plastics, providing a chemical recycling path for many existing mixed plastics today. The Life Cycle Assessment indicates that the production of various condensation polymers from the deconstructed monomers will result in a significant reduction in greenhouse gas emissions and energy input, opening a new paradigm of plastic circularity toward a net-zero carbon society.