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Detecting VHE prompt emission from binary neutron-star mergers: ET and CTA synergies

The current generation of very-high-energy $gamma-$ray (VHE; E above 30 GeV) detectors (MAGIC and H.E.S.S.) have recently demonstrated the ability to detect the afterglow emission of GRBs. However, the GRB prompt emission, typically observed in the 10 keV-10 MeV band, has so far remained undetected at higher energies. Here, we investigate the perspectives of multi-messenger observations to detect the prompt emission of short GRBs in VHE. Considering binary neutron star mergers as progenitors of short GRBs, we evaluate the joint detection efficiency of the Cherenkov Telescope Array (CTA) observing in synergy with the third generation of gravitational wave detectors, such as the Einstein Telescope (ET) and Cosmic Explorer (CE). In particular, we evaluate the expected capabilities to detect and localize gravitational wave events in the inspiral phase and to provide an early warning alert able to drive the VHE search. We compute the amount of possible joint detections by considering several observational strategies, and demonstrate that the sensitivities of CTA make the detection of the VHE emission possible even if it is several orders fainter than the one observed at 10 keV-10 MeV. We discuss the results in terms of possible scenarios of production of VHE photons from binary neutron star mergers by considering GRB prompt and afterglow emissions

Malnutrition in COVID-19 survivors: prevalence and risk factors

Background: Nutritional status is a critical factor throughout COVID-19 disease course. Malnutrition is associated with poor outcomes in hospitalized COVID-19 patients. Aim: To assess the prevalence of malnutrition and identify its associated factors in COVID-19 survivors. Methods: Study cohort included 1230 COVID-19 survivors aged 18-86 attending a post-COVID-19 outpatient service. Data on clinical parameters, anthropometry, acute COVID-19 symptoms, lifestyle habits were collected through a comprehensive medical assessment. Malnutrition was assessed according to Global Leadership Initiative on Malnutrition (GLIM) criteria. Results: Prevalence of malnutrition was 22% at 4-5 months after acute disease. Participants who were not hospitalized during acute COVID-19 showed a higher frequency of malnutrition compared to those who needed hospitalization (26% versus 19%, p < 0.01). Malnutrition was found in 25% COVID-19 survivors over 65 years of age compared to 21% younger participants (p < 0.01). After multivariable adjustment, the likelihood of being malnourished increased progressively and independently with advancing age (Odds ratio [OR] 1.02; 95% CI 1.01-1.03) and in male participants (OR 5.56; 95% CI 3.53-8.74). Malnutrition was associated with loss of appetite (OR 2.50; 95% CI 1.73-3.62), and dysgeusia (OR 4.05; 95% CI 2.30-7.21) during acute COVID-19. Discussion: In the present investigation we showed that malnutrition was highly prevalent in a large cohort of COVID-19 survivors at 4-5 months from acute illness. Conclusions: Our findings highlight the need to implement comprehensive nutritional assessment and therapy as an integral part of care for COVID-19 patients

Science with the Einstein Telescope: a comparison of different designs

The Einstein Telescope (ET), the European project for a third-generation gravitational-wave detector, has a reference configuration based on a triangular shape consisting of three nested detectors with 10 km arms, where in each arm there is a `xylophone' configuration made of an interferometer tuned toward high frequencies, and an interferometer tuned toward low frequencies and working at cryogenic temperature. Here, we examine the scientific perspectives under possible variations of this reference design. We perform a detailed evaluation of the science case for a single triangular geometry observatory, and we compare it with the results obtained for a network of two L-shaped detectors (either parallel or misaligned) located in Europe, considering different choices of arm-length for both the triangle and the 2L geometries. We also study how the science output changes in the absence of the low-frequency instrument, both for the triangle and the 2L configurations. We examine a broad class of simple `metrics' that quantify the science output, related to compact binary coalescences, multi-messenger astronomy and stochastic backgrounds, and we then examine the impact of different detector designs on a more specific set of scientific objectives.Comment: 197 pages, 72 figure

COVID-19 atypical Parsonage-Turner syndrome: a case report

Background Neurological manifestations of Sars-CoV-2 infection have been described since March 2020 and include both central and peripheral nervous system manifestations. Neurological symptoms, such as headache or persistent loss of smell and taste, have also been documented in COVID-19 long-haulers. Moreover, long lasting fatigue, mild cognitive impairment and sleep disorders appear to be frequent long term neurological manifestations after hospitalization due to COVID-19. Less is known in relation to peripheral nerve injury related to Sars-CoV-2 infection. Case presentation We report the case of a 47-year-old female presenting with a unilateral chest pain radiating to the left arm lasting for more than two months after recovery from Sars-CoV-2 infection. After referral to our post-acute outpatient service for COVID-19 long haulers, she was diagnosed with a unilateral, atypical, pure sensory brachial plexus neuritis potentially related to COVID-19, which occurred during the acute phase of a mild Sars-CoV-2 infection and persisted for months after resolution of the infection. Conclusions We presented a case of atypical Parsonage-Turner syndrome potentially triggered by Sars-CoV-2 infection, with symptoms and repercussion lasting after viral clearance. A direct involvement of the virus remains uncertain, and the physiopathology is unclear. The treatment of COVID-19 and its long-term consequences represents a relatively new challenge for clinicians and health care providers. A multidisciplinary approach to following-up COVID-19 survivors is strongly advised

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

The Sex-Specific Detrimental Effect of Diabetes and Gender-Related Factors on Pre-admission Medication Adherence Among Patients Hospitalized for Ischemic Heart Disease: Insights From EVA Study

Background: Sex and gender-related factors have been under-investigated as relevant determinants of health outcomes across non-communicable chronic diseases. Poor medication adherence results in adverse clinical outcomes and sex differences have been reported among patients at high cardiovascular risk, such as diabetics. The effect of diabetes and gender-related factors on medication adherence among women and men at high risk for ischemic heart disease (IHD) has not yet been fully investigated.Aim: To explore the role of sex, gender-related factors, and diabetes in pre-admission medication adherence among patients hospitalized for IHD.Materials and Methods: Data were obtained from the Endocrine Vascular disease Approach (EVA) (ClinicalTrials.gov Identifier: NCT02737982), a prospective cohort of patients admitted for IHD. We selected patients with baseline information regarding the presence of diabetes, cardiovascular risk factors, and gender-related variables (i.e., gender identity, gender role, gender relations, institutionalized gender). Our primary outcome was the proportion of pre-admission medication adherence defined through a self-reported questionnaire. We performed a sex-stratified analysis of clinical and gender-related factors associated with pre-admission medication adherence.Results: Two-hundred eighty patients admitted for IHD (35% women, mean age 70), were included. Around one-fourth of the patients were low-adherent to therapy before hospitalization, regardless of sex. Low-adherent patients were more likely diabetic (40%) and employed (40%). Sex-stratified analysis showed that low-adherent men were more likely to be employed (58 vs. 33%) and not primary earners (73 vs. 54%), with more masculine traits of personality, as compared with medium-high adherent men. Interestingly, women reporting medication low-adherence were similar for clinical and gender-related factors to those with medium-high adherence, except for diabetes (42 vs. 20%, p = 0.004). In a multivariate adjusted model only employed status was associated with poor medication adherence (OR 0.55, 95%CI 0.31–0.97). However, in the sex-stratified analysis, diabetes was independently associated with medication adherence only in women (OR 0.36; 95%CI 0.13–0.96), whereas a higher masculine BSRI was the only factor associated with medication adherence in men (OR 0.59, 95%CI 0.35–0.99).Conclusion: Pre-admission medication adherence is common in patients hospitalized for IHD, regardless of sex. However, patient-related factors such as diabetes, employment, and personality traits are associated with adherence in a sex-specific manner

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