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Disruption of gut barrier integrity and host–microbiome interactions underlie MASLD severity in patients with type-2 diabetes mellitus

(2024)

Aberration of the "gut-liver axis" contributes to the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Here, we use multi-omics to analyze the gut microbiota composition and metabolic profile of patients with type-2 diabetes mellitus (T2DM). T2DM patients were screened for liver disease by blood tests, ultrasound, and liver stiffness measurements. Stool microbiota was analyzed by 16S rRNA gene sequencing; metabolomic profiling by Nuclear Magnetic Resonance spectroscopy and Ultra-High Performance-Mass Spectrometry. Microbiome and metabolic signatures were analyzed in the whole cohort and in matched subsets to identify signatures specific for steatosis (MASLD±) or fibrosis (Fibrosis±). Gut permeability was assessed in-vitro using monolayers of MDCK cells and trans-epithelial electric resistance (TEER). Cytokine profile was assessed in serum and stools.Overall, 285 patients were enrolled: 255 serum, 252 urine and 97 stool samples were analyzed. Anaeroplasma and Escherichia/Shigella ASVs were higher, while Butyricicoccus ASVs were lower in those with normal liver. In MASLD±, Butyricicoccus ASV was significantly higher in those with steatosis. In the Fibrosis±, Butyricicoccus ASV was significantly lower in those with fibrosis. Glycochenodeoxycholic acid-3-sulfate (G-UDCA-3S) appeared to be higher in MASLD with fibrosis. Fecal water from patients with MASLD and fibrosis caused the greatest drop in the TEER vs those with normal liver; this was reversed with protease inhibitors. Finally, fecal IL-13 was lower in MASLD with fibrosis. We identified microbiome signatures which were specific for steatosis and fibrosis and independent of other metabolic risk factors. Moreover, we conclude that protease-related gut permeability plays a role in those MASLD patients with fibrosis, and that disease progression is linked to a gut-liver axis which is at least partially independent of T2DM.

Deep Generative Models for Fast Photon Shower Simulation in ATLAS

(2024)

Abstract: The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Software Performance of the ATLAS Track Reconstruction for LHC Run 3

(2024)

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

Cover page of Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI

(2024)

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients’ well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

Use of herbal medication in the perioperative period: Potential adverse drug interactions

(2024)

Use of herbal medications and supplements has experienced immense growth over the last two decades, with retail sales in the USA exceeding $13 billion in 2021. Since the Dietary Supplement Health and Education Act (DSHEA) of 1994 reduced FDA oversight, these products have become less regulated. Data from 2012 shows 18% of U.S. adults used non-vitamin, non-mineral natural products. Prevalence varies regionally, with higher use in Western states. Among preoperative patients, the most commonly used herbal medications included garlic, ginseng, ginkgo, St. John's wort, and echinacea. However, 50-70% of surgical patients fail to disclose their use of herbal medications to their physicians, and most fail to discontinue them preoperatively. Since herbal medications can interact with anesthetic medications administered during surgery, the American Society of Anesthesiologists (ASA) and the American Association of Nurse Anesthetists (AANA) recommend stopping herbal medications 1-2 weeks before elective surgical procedures. Potential adverse drug effects related to preoperative use of herbal medications involve the coagulation system (e.g., increasing the risk of perioperative bleeding), the cardiovascular system (e.g., arrhythmias, hypotension, hypertension), the central nervous system (e.g., sedation, confusion, seizures), pulmonary (e.g., coughing, bronchospasm), renal (e.g., diuresis) and endocrine-metabolic (e.g., hepatic dysfunction, altered metabolism of anesthetic drugs). During the preoperative evaluation, anesthesiologists should inquire about the use of herbal medications to anticipate potential adverse drug interactions during the perioperative period.

Measurement of the Z boson invisible width at s = 13 TeV with the ATLAS detector

(2024)

A measurement of the invisible width of the Z boson using events with jets and missing transverse momentum is presented using 37 fb−1 of 13 TeV proton–proton data collected by the ATLAS detector in 2015 and 2016. The ratio of Z→inv to Z→ℓℓ events, where inv refers to non-detected particles and ℓ is either an electron or a muon, is measured and corrected for detector effects. Events with at least one energetic central jet with pT≥110 GeV are selected for both the Z→inv and Z→ℓℓ final states to obtain a similar phase space in the ratio. The invisible width is measured to be 506±2(stat.)±12(syst.) MeV and is the single most precise recoil-based measurement. The result is in agreement with the most precise determination from LEP and the Standard Model prediction based on three neutrino generations.

Cover page of Linguistic Features of Secondary School Writing: Can Natural Language Processing Shine a Light on Differences by Sex, English Language Status, or Higher Scoring Essays?

Linguistic Features of Secondary School Writing: Can Natural Language Processing Shine a Light on Differences by Sex, English Language Status, or Higher Scoring Essays?

(2024)

This article provides three major contributions to the literature: we provide granular information on the development of student argumentative writing across secondary school; we replicate the MacArthur et al. model of Natural Language Processing (NLP) writing features that predict quality with a younger group of students; and we are able to examine the differences for students across language status. In our study, we sought to find the average levels of text length, cohesion, connectives, syntactic complexity, and word-level complexity in this sample across Grades 7-12 by sex, by English learner status, and for essays scoring above and below the median holistic score. Mean levels of variables by grade suggest a developmental progression with respect to text length, with the text length increasing with grade level, but the other variables in the model were fairly stable. Sex did not seem to affect the model in meaningful ways beyond the increased fluency of women writers. We saw text length and word level differences between initially designated and redesignated bilingual students compared to their English-only peers. Finally, we see that the model works better with our higher scoring essays and is less effective explaining the lower scoring essays.

Cover page of Parental Preconception Adversity and Offspring Mental Health in African Americans and Native Americans in the United States: A Systematic Review.

Parental Preconception Adversity and Offspring Mental Health in African Americans and Native Americans in the United States: A Systematic Review.

(2024)

This systematic review examines the impact of parental preconception adversity on offspring mental health among African Americans (AAs) and Native Americans (NAs), two populations that have experienced historical trauma and currently experience ethnic/racial mental health disparities in the United States. PsycINFO, PubMed, CINAHL, Scopus, and Web of Science were searched for studies that included at least two generations of AAs or NAs from the same family, measured parental preconception adversity and their offsprings mental health, and examined the association between these variables. Over 3,200 articles were screened, and 18 articles representing 13 unique studies were included in this review. Among the studies with samples that included AAs (n = 12, 92%), 10 (83%) reported a significant association between parental preconception adversity and adverse offspring mental health. The only study with a sample of NAs (n = 1, 8%) also reported a significant association between these variables. Although the literature suggests that parental preconception adversity is associated with offspring mental health among AAs and NAs, it must be interpreted in the context of the small number of studies on this topic and the less-than-ideal samples utilized-just one study included a sample of NAs and several studies (n = 6, 46%) used multi-ethnic/racial samples without testing for ethnic/racial disparities in their results. A more rigorous body of literature on this topic is needed as it may help explain an important factor underlying ethnic/racial mental health disparities, with important implications for interventions and policy.

Cover page of Racial differences in baroreflex function: Implications for the cardiovascular conundrum.

Racial differences in baroreflex function: Implications for the cardiovascular conundrum.

(2024)

STUDY OBJECTIVE: African Americans (AAs) show early signs of vascular dysfunction paired with elevated blood pressure (BP) and total peripheral resistance (TPR), which is thought to underlie their increased rates of cardiovascular health complications relative to European Americans (EAs). AAs paradoxically have higher cardiac vagal tone, indexed by heart rate variability (HRV), which is cardio-protective. This paradox has been termed the Cardiovascular Conundrum. The physiological mechanism underlying this phenomenon is not well understood. We examined race differences in baroreflex function, which might be an important mechanism underlying the Cardiovascular Conundrum. DESIGN: Participants completed a 5-minute baseline period where resting cardiac metrics were assessed. SETTING: Laboratory. PARTICIPANTS: 130 college-aged individuals (54 women, 57 AAs). MAIN OUTCOME MEASURES: Baroreflex function was indexed as baroreflex sensitivity (BRS; the magnitude of changes in cardiovascular activity in accordance with BP changes) and effectiveness (BEI; the ratio of BP changes that elicit changes in cardiovascular activity) in the cardiac, vascular, and myocardial limbs. RESULTS AND CONCLUSIONS: Results showed AAs to have higher HRV and cardiac BRS in comparison to EAs, suggesting the baroreflex is more sensitive to correcting the heart period for changes in BP among AAs compared to EAs. However, AAs showed lower vascular BEI relative to EAs, suggesting less effective control of TPR. In sum, lower BEI in the vascular branch might be an important mechanism underlying the Cardiovascular Conundrum (i.e., higher HRV and BP) and by extension, health disparities in cardiovascular diseases between AAs and EAs.

Cover page of Overcoming barriers to improved decision-making for battery deployment in the clean energy transition.

Overcoming barriers to improved decision-making for battery deployment in the clean energy transition.

(2024)

Decarbonization plans depend on the rapid, large-scale deployment of batteries to sufficiently decarbonize the electricity system and on-road transport. This can take many forms, shaped by technology, materials, and supply chain selection, which will have local and global environmental and social impacts. Current knowledge gaps limit the ability of decision-makers to make choices in facilitating battery deployment that minimizes or avoids unintended environmental and social consequences. These gaps include a lack of harmonized, accessible, and up-to-date data on manufacturing and supply chains and shortcomings within sustainability and social impact assessment methods, resulting in uncertainty that limits incorporation of research into policy making. These gaps can lead to unintended detrimental effects of large-scale battery deployment. To support decarbonization goals while minimizing negative environmental and social impacts, we elucidate current barriers to tracking how decision-making for large-scale battery deployment translates to environmental and social impacts and recommend steps to overcome them.