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The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70–0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53–0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01–0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 . In a randomized clinical trial, alerts based on the detection of abnormalities in electrocardiograms using a deep learning algorithm reduced all-cause mortality at 90 days in patients admitted to hospital emergency or internal medicine departments.

A major open question in microbial community ecology is whether we can predict how the components of a diet collectively determine the taxonomic composition of microbial communities. Motivated by this challenge, we investigate whether communities assembled in pairs of nutrients can be predicted from those assembled in every single nutrient alone. We find that although the null, naturally additive model generally predicts well the family-level community composition, there exist systematic deviations from the additive predictions that reflect generic patterns of nutrient dominance at the family level. Pairs of more-similar nutrients (e.g. two sugars) are on average more additive than pairs of more dissimilar nutrients (one sugar–one organic acid). Furthermore, sugar–acid communities are generally more similar to the sugar than the acid community, which may be explained by family-level asymmetries in nutrient benefits. Overall, our results suggest that regularities in how nutrients interact may help predict community responses to dietary changes.

More than 98% of infant deaths occur in low- and middle-income countries (LMICs). Breastfeeding improves infant survival and protects against certain illnesses, such as diarrhea and pneumonia, which are leading causes of deaths in those <5 years of age in LMICs. The World Health Organization (WHO) recommends early initiation of breastfeeding within 1 hour of birth, exclusive breastfeeding up to 6 months of age, and continued breastfeeding up to 2 years of age. However, fewer than half of infants in LMICs are breastfed optimally to these standards. The objectives of this article are to describe the global epidemiology and health benefits of breastfeeding with particular focus on LMICs. We searched PubMed to identify original research articles on breastfeeding in LMICs and reviews related to the benefits of breastfeeding, with particular focus on articles published in the past 5 years. We used reports and data published by the WHO and the United Nations Children's Fund (UNICEF) related to global breastfeeding rates, targets, and programmatic initiatives. We used the Lives Saved Tool to estimate mortality related to breastfeeding practices. Less than half of infants globally receive early, exclusive, or continued breastfeeding. Certain high-risk groups, such as premature or HIV-exposed infants, face particular challenges and benefits related to breastfeeding. The WHO, UNICEF, and other global partners have developed a multipronged strategy to promote global breastfeeding, ranging from government-level advocacy to grassroots community support groups. Using the Lives Saved Tool, we estimate that nearly 200,000 lives of those <5 years of age could be saved in LMICs from 2020 to 2030 if early, exclusive, and continued breastfeeding rates were linearly increased from current rates to meet the WHO 2030 goals of 60% to 80% coverage. If this goal were exceeded and near-universal coverage were achieved, the number of lives would increase even further such that >820,000 lives per year could potentially be saved by universal breastfeeding. In this review, we delineate the health and economic benefit of breastfeeding in LMICs, discuss breastfeeding epidemiology in the global context, and describe targeted strategies to improve breastfeeding uptake.

The most recent WHO classification of endocrine and neuroendocrine tumours has brought about significant changes in the diagnosis and grading of these lesions. For instance, pathologists now have the ability to stratify subsets of thyroid and adrenal neoplasms using various histological features and composite risk assessment models. Moreover, novel recommendations on how to approach endocrine neoplasia involve additional immunohistochemical analyses, and the recognition and implementation of these key markers is essential for modernising diagnostic capabilities. Additionally, an improved understanding of tumour origin has led to the renaming of several entities, resulting in the emergence of terminology not yet universally recognised. The adjustments in nomenclature and prognostication may pose a challenge for the clinical team, and care providers might be eager to engage in a dialogue with the diagnosing pathologist, as treatment guidelines have not fully caught up with these recent changes. Therefore, it is crucial for a surgical pathologist to be aware of the knowledge behind the implementation of changes in the WHO classification scheme. This review article will delve into the most significant diagnostic and prognostic changes related to lesions in the parathyroid, thyroid, adrenal glands and the gastroenteropancreatic neuroendocrine system. Additionally, the author will briefly share his personal reflections on the clinical implementation, drawing from a couple of years of experience with these new algorithms.

Plasma is integral to haemostatic resuscitation after injury, but the timing of administration remains controversial. Anticipating approval of lyophilised plasma by the US Food and Drug Administration, the US Department of Defense funded trials of prehospital plasma resuscitation. We investigated use of prehospital plasma during rapid ground rescue of patients with haemorrhagic shock before arrival at an urban level 1 trauma centre. The Control of Major Bleeding After Trauma Trial was a pragmatic, randomised, single-centre trial done at the Denver Health Medical Center (DHMC), which houses the paramedic division for Denver city. Consecutive trauma patients in haemorrhagic shock (defined as systolic blood pressure [SBP] ≤70 mm Hg or 71–90 mm Hg plus heart rate ≥108 beats per min) were assessed for eligibility at the scene of the injury by trained paramedics. Eligible patients were randomly assigned to receive plasma or normal saline (control). Randomisation was achieved by preloading all ambulances with sealed coolers at the start of each shift. Coolers were randomly assigned to groups 1:1 in blocks of 20 according to a schedule generated by the research coordinators. If the coolers contained two units of frozen plasma, they were defrosted in the ambulance and the infusion started. If the coolers contained a dummy load of frozen water, this indicated allocation to the control group and saline was infused. The primary endpoint was mortality within 28 days of injury. Analyses were done in the as-treated population and by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01838863. From April 1, 2014, to March 31, 2017, paramedics randomly assigned 144 patients to study groups. The as-treated analysis included 125 eligible patients, 65 received plasma and 60 received saline. Median age was 33 years (IQR 25–47) and median New Injury Severity Score was 27 (10–38). 70 (56%) patients required blood transfusions within 6 h of injury. The groups were similar at baseline and had similar transport times (plasma group median 19 min [IQR 16–23] vs control 16 min [14–22]). The groups did not differ in mortality at 28 days (15% in the plasma group vs 10% in the control group, p=0·37). In the intention-to-treat analysis, we saw no significant differences between the groups in safety outcomes and adverse events. Due to the consistent lack of differences in the analyses, the study was stopped for futility after 144 of 150 planned enrolments. During rapid ground rescue to an urban level 1 trauma centre, use of prehospital plasma was not associated with survival benefit. Blood products might be beneficial in settings with longer transport times, but the financial burden would not be justified in an urban environment with short distances to mature trauma centres. US Department of Defense.

Neutralizing antibodies elicited by prior infection or vaccination are likely to be key for future protection of individuals and populations against SARS-CoV-2. Moreover, passively administered antibodies are among the most promising therapeutic and prophylactic anti-SARS-CoV-2 agents. However, the degree to which SARS-CoV-2 will adapt to evade neutralizing antibodies is unclear. Using a recombinant chimeric VSV/SARS-CoV-2 reporter virus, we show that functional SARS-CoV-2 S protein variants with mutations in the receptor-binding domain (RBD) and N-terminal domain that confer resistance to monoclonal antibodies or convalescent plasma can be readily selected. Notably, SARS-CoV-2 S variants that resist commonly elicited neutralizing antibodies are now present at low frequencies in circulating SARS-CoV-2 populations. Finally, the emergence of antibody-resistant SARS-CoV-2 variants that might limit the therapeutic usefulness of monoclonal antibodies can be mitigated by the use of antibody combinations that target distinct neutralizing epitopes. The new coronavirus, SARS-CoV-2, which causes the disease COVID-19, has had a serious worldwide impact on human health. The virus was virtually unknown at the beginning of 2020. Since then, intense research efforts have resulted in sequencing the coronavirus genome, identifying the structures of its proteins, and creating a wide range of tools to search for effective vaccines and therapies. Antibodies, which are immune molecules produced by the body that target specific segments of viral proteins can neutralize virus particles and trigger the immune system to kill cells infected with the virus. Several technologies are currently under development to exploit antibodies, including vaccines, blood plasma from patients who were previously infected, manufactured antibodies and more. The spike proteins on the surface of SARS-CoV-2 are considered to be prime antibody targets as they are accessible and have an essential role in allowing the virus to attach to and infect host cells. Antibodies bind to spike proteins and some can block the virus’ ability to infect new cells. But some viruses, such as HIV and influenza, are able to mutate their equivalent of the spike protein to evade antibodies. It is unknown whether SARS-CoV-2 is able to efficiently evolve to evade antibodies in the same way. Weisblum, Schmidt et al. addressed this question using an artificial system that mimics natural infection in human populations. Human cells grown in the laboratory were infected with a hybrid virus created by modifying an innocuous animal virus to contain the SARS-CoV-2 spike protein, and treated with either manufactured antibodies or antibodies present in the blood of recovered COVID-19 patients. In this situation, only viruses that had mutated in a way that allowed them to escape the antibodies were able to survive. Several of the virus mutants that emerged had evolved spike proteins in which the segments targeted by the antibodies had changed, allowing these mutant viruses to remain undetected. An analysis of more than 50,000 real-life SARS-CoV-2 genomes isolated from patient samples further showed that most of these virus mutations were already circulating, albeit at very low levels in the infected human populations. These results show that SARS-CoV-2 can mutate its spike proteins to evade antibodies, and that these mutations are already present in some virus mutants circulating in the human population. This suggests that any vaccines that are deployed on a large scale should be designed to activate the strongest possible immune response against more than one target region on the spike protein. Additionally, antibody-based therapies that use two antibodies in combination should prevent the rise of viruses that are resistant to the antibodies and maintain the long-term effectiveness of vaccines and therapies.

The leishmaniases are a group of four vector-borne neglected tropical diseases caused by 20 species of protozoan parasites of the genus Leishmania and transmitted through a bite of infected female phlebotomine sandflies. Endemic in over 100 countries, the four types of leishmaniasis-visceral leishmaniasis (VL) (known as kala-azar), cutaneous leishmaniasis (CL), mucocutaneous leishmaniasis (MCL), and post-kala-azar dermal leishmaniasis (PKDL)-put 1.6 billion people at risk. In Kenya, the extent of leishmaniasis research has not yet been systematically described. This knowledge is instrumental in identifying existing research gaps and designing appropriate interventions for diagnosis, treatment, and elimination. This study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to determine the state of leishmaniases research in Kenya and identify research gaps. We searched seven online databases to identify articles published until January 2022 covering VL, CL, MCL, and/or PKDL in Kenya. A total of 7,486 articles were found, of which 479 underwent full-text screening, and 269 met our eligibility criteria. Most articles covered VL only (n = 141, 52%), were published between 1980 and 1994 (n = 108, 39%), and focused on the theme of \"vectors\" (n = 92, 34%). The most prevalent study types were \"epidemiological research\" (n = 88, 33%) tied with \"clinical research\" (n = 88, 33%), then \"basic science research\" (n = 49, 18%) and \"secondary research\" (n = 44, 16%). While some studies still provide useful guidance today, most leishmaniasis research in Kenya needs to be updated and focused on prevention, co-infections, health systems/policy, and general topics, as these themes combined comprised less than 4% of published articles. Our findings also indicate minimal research on MCL (n = 1, <1%) and PKDL (n = 2, 1%). We urge researchers to renew and expand their focus on these neglected diseases in Kenya.

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

Old English Ecotheology examines the impact of environmental crises on early medieval English theology and poetry. Like their modern counterparts, theologians at the turn of the first millennium understood the interconnectedness of the Earth community, and affirmed the independent subjectivity of other-than-humans. The author argues for the existence of a specific Old English ecotheology, and demonstrates the influence of that theology on contemporaneous poetry. Taking the Exeter Book as a microcosm of the poetic corpus, she explores the impact of early medieval apocalypticism and environmental anxiety on Old English wisdom poems, riddles, elegies, and saints' lives.