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Abstract This study explores the social, economic and environmental dimensions of human‐wildlife conflict (HWC) in four districts of Bhutan. Looking beyond the common unidimensional approach to the evaluation of HWCs, it documents the multifaceted nature of HWCs through a social‐ecological system (SES) lens. We carried out a mixed method analysis of HWC based on 96 interviews consisting of qualitative and quantitative data and eight focus group discussions conducted in four districts representing four regions of Bhutan. Our case study finding reveals that HWC scenarios are complex and there is spatial variation in both social and ecological factors that contribute to HWC. In some areas, ecological aspects such as the composition and behaviour of wildlife species exacerbate the conflict, while in other regions the co‐occurrence of wildlife and livestock in the same ‘wild’ landscape drives HWC. The socio‐economic dimensions of HWC are largely driven by attitudes, tolerance, and behaviour of individuals underpinned by multiple factors. We propose that the SES approach improves our understanding of key social, economic and ecological factors, their interactions, and the feedback that either exacerbates or ameliorates HWC in contested landscapes. Read the free Plain Language Summary for this article on the Journal blog.

Background: Hospitalisation can be a traumatic experience, where inpatients are exposed to an abundance of physical and psychological stressors. Evidence suggests that these hospital-related stressors negatively impact health: a phenomenon known as post-hospital syndrome. The current study aimed to identify hospital-related stressors, and to develop and provide initial validation for a new measure of in-hospital stress.Methods: Measure development occurred in three stages: (i) semi-structured interviews, (ii) item generation, and (iii) pilot testing. Twenty-one patients were interviewed regarding their recent hospital experiences, and a list of hospital-related stressors was produced. These stressors were compiled into a questionnaire and piloted on 200 recent inpatients to provide initial evidence of internal consistency and construct validity.Results: Stressors identified from the interviews captured all relevant questions from three previous hospital stress measures, plus 12 more. The most reported stressor was ‘poor sleep’. These hospital-related stressors were developed into 67 questions, forming the Hospital Stress Questionnaire (HSQ). The HSQ showed excellent internal consistency and construct validity, and correlated with feelings of vulnerability and being unprepared to go home.Conclusion: The HSQ is a promising self-report tool for measuring in-hospital stress. Future research ought to investigate its psychometric properties further in larger and more diverse samples. The measure has potential to be used to monitor patient risk of post-hospital syndrome.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Abstract Background The opioid crisis continues in full force, as physicians and caregivers are desperate for resources to help patients with opioid use and chronic pain disorders find safer and more accessible non-opioid tools. Main body The purpose of this article is to review the current state of the opioid epidemic; the shifting picture of cannabinoids; and the research, policy, and current events that make opioid risk reduction an urgent public health challenge. The provided table contains an evidence-based clinical framework for the utilization of cannabinoids to treat patients with chronic pain who are dependent on opioids, seeking alternatives to opioids, and tapering opioids. Conclusion Based on a comprehensive review of the literature and epidemiological evidence to date, cannabinoids stand to be one of the most interesting, safe, and accessible tools available to attenuate the devastation resulting from the misuse and abuse of opioid narcotics. Considering the urgency of the opioid epidemic and broadening of cannabinoid accessibility amidst absent prescribing guidelines, the authors recommend use of this clinical framework in the contexts of both clinical research continuity and patient care.

In biliary tract cancer HER2 alterations correlate with poor prognosis. Here, the authors present the results of a phase II clinical trial reporting the efficacy and safety of the tyrosine kinase inhibitor neratinib in patients with HER2-mutation positive advanced biliary tract cancers.

Background The Autism Spectrum Quotient is a popular autism screening tool recommended for identifying potential cases of autism. However, many women with autism demonstrate a different presentation of traits to those currently captured by screening measures and assessment methods, such as the Autism Spectrum Quotient. Aims Different models of the Autism Spectrum Quotient have been proposed in the literature, utilising different items from the original 50-item scale. Within good-fitting models, the current study aimed to explore whether these items assess autistic traits similarly across men and women. Method Seventeen Autism Spectrum Quotient models were identified from the literature. Using the responses of a large sample of adults from the UK general population (5246 women, 1830 men), confirmatory factor analysis was used to evaluate the fit of each model. Measurement invariance with respect to gender, adjusting for age, was explored in the 11 model frameworks that were found to have satisfactory fit to our data. Results It emerged that only two items were gender invariant (non-biased), whereas for the remaining items, the probability of endorsement was influenced by gender. In particular, women had a higher probability of endorsing items relating to social skills and communication. Conclusions If the items of the Autism Spectrum Quotient indeed reflect autism-related traits, those items should be rephrased to ensure they do not present a gender-related bias. This is vital for ensuring more timely diagnoses and support for all people with autism.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) persists throughout the world with over 65 million registered cases of survivors with post-COVID-19 sequelae, also known as LongCOVID-19 (LongC). LongC survivors exhibit various symptoms that span multiple organ systems, including the nervous system. To search for neurological markers of LongC, we investigated the soluble biomolecules present in the plasma and the proteins associated with plasma neuronal-enriched extracellular vesicles (nEVs) in 33 LongC patients with neurological impairment (nLongC), 12 COVID-19 survivors without any LongC symptoms (Cov), and 28 pre-COVID-19 healthy controls (HC). COVID-19 positive participants were infected between 2020 and 2022, not hospitalized, and were vaccinated or unvaccinated before infection. IL-1β was significantly increased in both nLongC and Cov and IL-8 was elevated in only nLongC. Both brain-derived neurotrophic factor and cortisol were significantly elevated in nLongC and Cov compared to HC. nEVs from people with nLongC had significantly elevated protein markers of neuronal dysfunction, including amyloid beta 42, pTau181 and TDP-43. This study shows chronic peripheral inflammation with increased stress after COVID-19 infection. Additionally, differentially expressed nEV neurodegenerative proteins were identified in people recovering from COVID-19 regardless of persistent symptoms.

In kilonovae, freshly synthesized r -process elements imprint features on optical spectra, as observed in AT2017gfo, the counterpart to the GW170817 binary neutron star merger. However, measuring the r -process compositions of the merger ejecta is computationally challenging. Vieira et al. introduced Spectroscopic r -process Abundance Retrieval for Kilonovae ( SPARK ), a software tool to infer elemental abundance patterns of the ejecta and associate spectral features with particular species. Previously, we applied SPARK to the 1.4-day spectrum of AT2017gfo and inferred its abundance pattern for the first time, characterized by electron fraction Y _e = 0.31, a substantial abundance of strontium, and a dearth of lanthanides and heavier elements. This ejecta is consistent with wind from a remnant hypermassive neutron star and/or accretion disk. We now extend our inference to spectra at 2.4 and 3.4 days and test the need for multicomponent ejecta, where we stratify the ejecta in composition. The ejecta at 1.4 and 2.4 days is described by the same single blue component. At 3.4 days, a new redder component with lower Y _e = 0.16 and a significant abundance of lanthanides emerges. This new redder component is consistent with dynamical ejecta and/or neutron-rich ejecta from a magnetized accretion disk. As expected from photometric modeling, this component emerges as the ejecta expands, the photosphere recedes, and the earlier bluer component dims. At 3.4 days, we find an ensemble of lanthanides, with the presence of cerium most concrete. This presence of lanthanides has important implications for the contribution of kilonovae to the r -process abundances observed in the Universe.

Kilonovae are likely a key site of heavy r -process element production in the Universe, and their optical/infrared spectra contain insights into both the properties of the ejecta and the conditions of the r -process. However, the event GW170817/AT2017gfo is the only kilonova so far with well-observed spectra. To understand the diversity of absorption features that might be observed in future kilonovae spectra, we use the TARDIS Monte Carlo radiative transfer code to simulate a suite of optical spectra spanning a wide range of kilonova ejecta properties and r -process abundance patterns. To identify the most common and prominent absorption lines, we perform dimensionality reduction using an autoencoder, and we find spectra clusters in the latent space representation using a Bayesian Gaussian Mixture model. Our synthetic kilonovae spectra commonly display strong absorption by strontium _38 Sr ii , yttrium _38 Y ii , and zirconium _40 Zr i–ii , with strong lanthanide contributions at low electron fractions ( Y _e ≲ 0.25). When a new kilonova is observed, our machine-learning framework will provide context on the dominant absorption lines and key ejecta properties, helping to determine where this event falls within the larger “zoo” of kilonovae spectra.

Since its discovery six decades ago, the predatory bacterium Bdellovibrio bacteriovorus has sparked recent interest as a potential remedy to the antibiotic resistance crisis. Here we give a comprehensive historical overview from discovery to progressive developments in microscopy and molecular mechanisms. Research on B. bacteriovorus has moved from curiosity to a new model organism, revealing over time more details on its physiology and fascinating predatory life cycle with the help of a variety of methods. Based on recent findings in cryo-electron tomography, we recapitulate on the intricate molecular details known in the predatory life cycle including how this predator searches for its prey bacterium, to how it attaches, grows, and divides all from within the prey cell. Finally, the newly developed B. bacteriovorus progeny leave the prey cell remnants in the exit phase. While we end with some unanswered questions remaining in the field, new imaging technologies and quantitative, systematic advances will likely help to unravel them in the next decades.

Protected areas (PA) to conserve wildlife are the cornerstone of biodiversity conservation but they can also result in increased human-wildlife conflict (HWC), which poses a serious challenge to jointly achieving sustainable development goals of food security and biodiversity conservation, particular in regions with high conservation values and subsistence farmers. In the Himalayan Kingdom of Bhutan, expanding PAs and other conservation efforts have led to increased wildlife populations that are causing more damage to crop and livestock and impacting on the livelihoods of subsistence farmers and herders. In this study, we used a social-ecological systems framework to quantify the intensity this impact and associated economic losses with identified wildlife species and compared differences between livelihood types (crop farming versus livestock husbandry) and land tenure (inside versus outside protected areas). Results indicated that Meso-scale wildlife species that are not the focus of conservation caused higher economic losses. Approximately 43% of total economic loss through crop depredation was attributed to wild pig (Sus scrofa) and 56% of the total economic loss through livestock predation was caused by wild dogs (Cuon alpinus). Losses borne by respondents whose livelihoods depend mainly on livestock were significantly higher, with a mean loss equivalent to US$1328 per household per annum, than those depending on crop production (US$171 per household per annum). Economic losses incurred through crop and livestock depredation were significantly higher for the respondents residing inside PAs, which is attributed by those households to a perceived increase in wildlife populations because of conservation policies. Interventions for prevention and mitigation of these impacts should recognize these varying unintended effects of wildlife and be better targeted at groups living in different parts of the landscape. These include expanding compensation scheme to losses caused by wild dogs and pigs, supporting ecotourism ventures within PAs to diversify income options and introducing control measures for these animals.

Background Hospital inpatients are exposed to high levels of stress during hospitalisation that may increase susceptibility to major adverse health events post-hospitalisation (known as post-hospital syndrome). However, the existing evidence base has not been reviewed and the magnitude of this relationship remains unknown. Therefore, the aim of the current systematic review and meta-analysis was to: 1) synthesise existing evidence and to determine the strength of the relationship between in-hospital stress and patient outcomes, and 2) determine if this relationship differs between (i) in-hospital vs post-hospital outcomes, and (ii) subjective vs objective outcome measures. Methods A systematic search of MEDLINE, EMBASE, PsychINFO, CINAHL, and Web of Science from inception to February 2023 was conducted. Included studies reported a measure of perceived and appraised stress while in hospital, and at least one patient outcome. A random-effects model was generated to pool correlations (Pearson’s r), followed by sub-group and sensitivity analyses. The study protocol was preregistered on PROSPERO (CRD42021237017). Results A total of 10 studies, comprising 16 effects and 1,832 patients, satisfied the eligibility criteria and were included. A small-to-medium association was found: as in-hospital stress increased, patient outcomes deteriorated (r = 0.19; 95% CI: 0.12–0.26; I2 = 63.6; p < 0.001). This association was significantly stronger for (i) in-hospital versus post-hospital outcomes, and (ii) subjective versus objective outcome measures. Sensitivity analyses indicated that our findings were robust. Conclusions Higher levels of psychological stress experienced by hospital inpatients are associated with poorer patient outcomes. However, more high-quality, larger scale studies are required to better understand the association between in-hospital stressors and adverse outcomes.