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


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


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


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


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


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.

Cover page of Long gun violence in California versus Texas: How legislation can reduce firearm violence.

Long gun violence in California versus Texas: How legislation can reduce firearm violence.


INTRODUCTION: Long guns (LGs) are uniquely implicated in firearm violence and mass shootings. On 1/1/2019 California (CA) raised the minimum age to purchase LGs from 18 to 21. This study aimed to evaluate the incidence of LG violence in CA vs. Texas (TX), a state with rising firearm usage and fewer LG regulations, hypothesizing decreased LG firearm incidents in CA vs increased rates in TX after CA LG legislation. METHODS: A retrospective analysis of the Gun Violence Archive (2015-2021) was performed. An additional analysis of all firearm incidents within TX and CA was performed. CA and TX census data were used to calculate incidents of LG violence per 10,000,000 people. The primary outcome was the number of LG-related firearm incidents. Median yearly rates of LG violence per 10,000,000 people were compared for pre (2015-2018) vs post (2019-2021) CA LG legislation (Senate Bill 1100 (SB1100). RESULTS: Median LG incidents decreased in CA post-SB1100 (4.21 vs 1.52, p < 0.001) by nearly 64 %, whereas any gun firearm violence was similar pre vs post-SB1100 (77.0 vs 74.5 median incidents, p = 0.89). In contrast, median LG incidents increased after SB1100 (4.34 vs 5.17 median incidents, p = 0.011) by nearly 35 % in TX, with any gun incidents increasing by nearly 53 % (83.48 vs 127.46, p < 0.001). CONCLUSION: CA LG firearm incidents decreased following SB 1100 legislation whereas the incidence in TX increased during this same time. Meanwhile, the incidence of any firearm violence remained similar in CA but increased in TX. This suggests the sharp decline in CA LG incidents may be related to SB1100. Accordingly, increasing the age to purchase a LG from 18 to 21 at a federal level may help curtail LG violence nationally.

Cover page of Impact of Treatment Delay in Head and Neck Mucosal Melanoma on Overall Patient Survival.

Impact of Treatment Delay in Head and Neck Mucosal Melanoma on Overall Patient Survival.


Objectives  Head and neck mucosal melanoma (HNMM) is a rare malignancy with high mortality. This study evaluates the impact of treatment delays on overall survival in HNMM. Design/Setting/Participants  A retrospective review of patients with surgically managed HNMM treated with adjuvant radiation was performed from the 2004-2016 National Cancer Database. Main Outcome Measures  Durations of diagnosis-to-treatment initiation (DTI), surgery-to-radiotherapy initiation (SRT), duration of radiotherapy (RTD), surgery-to-immunotherapy initiation (SIT), diagnosis-to-treatment end (DTE), and total treatment package (TTP) were calculated. Results  A total of 1,011 patients (50.7% female, 90.5% Caucasian) met inclusion criteria. Median DTI, SRT, RTD, SIT, DTE, and TTP were 30, 49, 41, 102, 119, and 87 days, respectively. Only longer DTE was associated with decreased mortality (hazard ratio, 0.720; 95% confidence interval, 0.536-0.965; p  = 0.028). Conclusion  DTI, SRT, RTD, SIT, and TTP do not significantly affect overall survival in patients with HNMM who undergo surgery and adjuvant radiation. Longer DTE is associated with improved survival in this population. Level of Evidence  4.

Cover page of It matters what you see: Graphic media images of war and terror may amplify distress.

It matters what you see: Graphic media images of war and terror may amplify distress.


Media exposure to graphic images of violence has proliferated in contemporary society, particularly with the advent of social media. Extensive exposure to media coverage immediately after the 9/11 attacks and the Boston Marathon bombings (BMB) was associated with more early traumatic stress symptoms; in fact, several hours of BMB-related daily media exposure was a stronger correlate of distress than being directly exposed to the bombings themselves. Researchers have replicated these findings across different traumatic events, extending this work to document that exposure to graphic images is independently and significantly associated with stress symptoms and poorer functioning. The media exposure-distress association also appears to be cyclical over time, with increased exposure predicting greater distress and greater distress predicting more media exposure following subsequent tragedies. The war in Israel and Gaza, which began on October 7, 2023, provides a current, real-time context to further explore these issues as journalists often share graphic images of death and destruction, making media-based graphic images once again ubiquitous and potentially challenging public well-being. For individuals sharing an identity with the victims or otherwise feeling emotionally connected to the Middle East, it may be difficult to avoid viewing these images. Through a review of research on the association between exposure to graphic images and public health, we discuss differing views on the societal implications of viewing such images and advocate for media literacy campaigns to educate the public to identify mis/disinformation and understand the risks of viewing and sharing graphic images with others.

Cover page of Sensitivity of Easterly QBO’s Boreal Winter Teleconnections and Surface Impacts to SSWs

Sensitivity of Easterly QBO’s Boreal Winter Teleconnections and Surface Impacts to SSWs


Abstract: The quasi-biennial oscillation (QBO) is thought to influence boreal winter surface conditions over Asia and around the North Atlantic. Confirming if these responses are robust is complicated by the QBO having multiple pathways to influence surface conditions as well as internal variability. The reanalysis record suggests that sudden stratospheric warmings (SSWs), breakdowns of the polar vortex that can elicit persistent surface impacts, are more frequent during easterly QBO (EQBO). Hence, this modulated frequency of SSWs may account for some of the EQBO surface responses. However, many climate models do not reproduce this QBO–SSW relationship, perhaps because it is noise or because the model QBOs are deficient. We circumvent these issues by using an ensemble of fixed boundary condition branched simulations in which a realistic EQBO is prescribed in control simulations previously devoid of a QBO, allowing us to isolate the transient atmospheric response to EQBO. Imposing EQBO accelerates the tropical upper-tropospheric wind, shifts the subtropical jet poleward, and attenuates the polar vortex. Interestingly, the latter is not entirely dependent on the statistically significant increase in SSW frequency due to EQBO. Corroborating observations, EQBO is associated with warmer surface temperatures over Asia and negative North Atlantic Oscillation (NAO) conditions. We then subsample the branched/control simulations based on which EQBO members have SSWs. The negative NAO response is primarily associated with more frequent SSWs, while the Asia warming develops irrespective of SSWs. These results have implications for wintertime predictability and clarify the pairing of particular QBO teleconnections with certain surface impacts. Significance Statement: The QBO is one of the few parts of the Earth system that is predictable months in advance and that also elicits global effects on surface temperature, circulation, and precipitation. Unfortunately, climate models and operational forecast systems do not simulate the QBO well and it is not always clear how robust the global impacts of the QBO are. Here, we impose the QBO in idealized model simulations, which modulates wintertime surface temperature and precipitation over Asia, the North Atlantic, Europe, and Africa in a manner consistent with observations. This work substantiates the importance of climate and forecast models properly simulating the QBO.