Dasabuvir and Ombitasvir/Paritaprevir/Ritonavir with or without Ribavirin in Patients with HIV-HCV Coinfection. Real Life Interim Analysis of an Italian Multicentre Compassionate Use Program

Background and Aims: An HCV cure is now possible in a large proportion of HIV-HCV patient. We present real life results of a compassionate use program promoted by SIMIT (Infectious and Tropical Diseases Italian Society) of Dasabuvir and Ombitasvir/Paritaprevir/Ritonavir ± Ribavirin for 12 weeks in 213 HIV-HCV genotype 1 patients. Data on efficacy and tolerability of this strategy in HIV patients have been reported until now only in 43 non cirrhotic HIV subjects

Socio-demographic and clinical predictors of post-acute, mid-and long-term psychological sequelae of COVID-19: a two-year cross-sectional investigation on 1317 patients at the University Hospital of Verona

Background: The present paper focuses on socio-demographics, clinical variables, and the distance from the infection in predicting the long-term psycho-social consequences of COVID-19. Methods: Patients were screened with a cross-sectional design at the Psychological Service of the University Hospital of Verona (Italy) at 3, 6, 12, and 18 months after their SARS-CoV-2 infection. The assessment was part of the Horizon 2020-funded ORCHESTRA Project and included the Hospital Anxiety and Depression Scale (HADS), the Short Form Health Survey 36 (SF-36), the Impact of Event Scale-Revised (IES-R), and ad-hoc questions measuring pre-post COVID-19 changes on psycho-social dimensions (sleep quality, nutrition, level of autonomy, work, social relationships, emotional wellbeing). Results: Between June 2021 and June 2023, we evaluated 1317 patients (mean age 56.6 ± 14.8 years; 48% male): 35% at three months, 40% at 6, 20% at 12, and 5% at 18 months after the infection. Thirty-five percent were hospitalized due to COVID-19. Overall, 16% reported some form of clinically significant mental distress following the infection (HADS-TOT), with 13% and 6%, respectively, experiencing anxiety (HADS-Anxiety) and depressive symptoms (HADS-Depression). Four percent testified post-traumatic symptoms. The SF-36 scale revealed that 16% and 17% of subjects had physical or psychological deterioration in quality of life, respectively. The regression analyses showed that females experienced higher levels of anxiety and depression compared to males, along with worse mental and physical quality of life and pre-post infection changes in nearly all the investigated psycho-social dimensions. Younger people felt more anxiety and had a reduced mental quality of life than their older counterparts, who, in turn, had poorer scores in terms of autonomy and physical functioning. Hospitalized patients had lower levels of self-sufficiency, social relationships, and work than non-hospitalized people. The latter were more anxious and reported a lower physical quality of life. Finally, patients evaluated for the first time at 12- and 18 months showed a more significant impairment in mental and physical quality of life than those assessed at three months. Conclusions: Our data show that COVID-19 psychological sequelae tend to persist over time, still needing clinical attention and intervention planning, especially for females

Haemolytic anaemia in an HIV-infected patient with severe falciparum malaria after treatment with oral artemether-lumefantrine

Intravenous (i.v.) artesunate is now the recommended first-line treatment of severe falciparum malaria in adults and children by WHO guidelines. Nevertheless, several cases of haemolytic anaemia due to i.v. artesunate treatment have been reported. This paper describes the case of an HIV-infected patient with severe falciparum malaria who was diagnosed with haemolytic anaemia after treatment with oral artemether-lumefantrine

The gut microbiota as an early predictor of COVID-19 severity

Several studies reported alterations of the human gut microbiota (GM) during COVID-19. To evaluate the potential role of the GM as an early predictor of COVID-19 at disease onset, we analyzed gut microbial samples of 315 COVID-19 patients that differed in disease severity. We observed significant variations in microbial diversity and composition associated with increasing disease severity, as the reduction of short-chain fatty acid producers such as Faecalibacterium and Ruminococcus, and the growth of pathobionts as Anaerococcus and Campylobacter. Notably, we developed a multi-class machine-learning classifier, specifically a convolutional neural network, which achieved an 81.5% accuracy rate in predicting COVID-19 severity based on GM composition at disease onset. This achievement highlights its potential as a valuable early biomarker during the first week of infection. These findings offer promising insights into the intricate relationship between GM and COVID-19, providing a potential tool for optimizing patient triage and streamlining healthcare during the pandemic.IMPORTANCEEfficient patient triage for COVID-19 is vital to manage healthcare resources effectively. This study underscores the potential of gut microbiota (GM) composition as an early biomarker for COVID-19 severity. By analyzing GM samples from 315 patients, significant correlations between microbial diversity and disease severity were observed. Notably, a convolutional neural network classifier was developed, achieving an 81.5% accuracy in predicting disease severity based on GM composition at disease onset. These findings suggest that GM profiling could enhance early triage processes, offering a novel approach to optimizing patient management during the pandemic.Efficient patient triage for COVID-19 is vital to manage healthcare resources effectively. This study underscores the potential of gut microbiota (GM) composition as an early biomarker for COVID-19 severity. By analyzing GM samples from 315 patients, significant correlations between microbial diversity and disease severity were observed. Notably, a convolutional neural network classifier was developed, achieving an 81.5% accuracy in predicting disease severity based on GM composition at disease onset. These findings suggest that GM profiling could enhance early triage processes, offering a novel approach to optimizing patient management during the pandemic

Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort

Background Lack of specific definitions of clinical characteristics, disease severity, and risk and preventive factors of post-COVID-19 syndrome (PCS) severely impacts research and discovery of new preventive and therapeutics drugs.Methods This prospective multicenter cohort study was conducted from February 2020 to lune 2022 in 5 countries, enrolling SARS-CoV-2 out-and in-patients followed at 3-, 6-, and 12-month from diagnosis, with assessment of clinical and biochemical features, antibody (Ab) response, Variant of Concern (VoC), and physical and mental quality of life (QoL).Outcome of interest was identification of risk and protective factors of PCS by clinical phenotype, setting, severity of disease, treatment, and vaccination status. We used SF-36 questionnaire to assess evolution in QoL index during follow-up and unsupervised machine learning algorithms (principal component analysis, PCA) to explore symptom clusters. Severity of PCS was defined by clinical phenotype and QoL. We also used generalized linear models to analyse the impact of PCS on QoL and associated risk and preventive factors. CT registration number: NCT05097677.Findings Among 1796 patients enrolled, 1030 (57%) suffered from at least one symptom at 12-month. PCA identified 4 clinical phenotypes: chronic fatigue-like syndrome (CFs: fatigue, headache and memory loss, 757 patients, 42%), respiratory syndrome (REs: cough and dyspnoea, 502, 23%); chronic pain syndrome (CPs: arthralgia and myalgia, 399, 22%); and neurosensorial syndrome (NSs: alteration in taste and smell, 197, 11%). Determinants of clinical phenotypes were different (all comparisons p < 0.05): being female increased risk of CPs, NSs, and CFs; chronic pulmonary diseases of REs; neurological symptoms at SARS-CoV-2 diagnosis of REs, NSs, and CFs; oxygen therapy of CFs and REs; and gastrointestinal symptoms at SARS-CoV-2 diagnosis of CFs. Early treatment of SARS-CoV-2 infection with monoclonal Ab (all clinical phenotypes), corticosteroids therapy for mild/severe cases (NSs), and SARS-CoV-2 vaccination (CPs) were less likely to be associated to PCS (all comparisons p < 0.05). Highest reduction in QoL was detected in REs and CPs (43.57 and 43.86 vs 57.32 in PCS-negative controls, p < 0.001). Female sex (p < 0.001), gastrointestinal symptoms (p = 0.034) and renal complications (p = 0.002) during the acute infection were likely to increase risk of severe PCS (QoL <50). Vaccination and early treatment with monoclonal Ab reduced the risk of severe PCS (p = 0.01 and p = 0.03, respectively).Interpretation Our study provides new evidence suggesting that PCS can be classified by clinical phenotypes with different impact on QoL, underlying possible different pathogenic mechanisms. We identified factors associated to each clinical phenotype and to severe PCS. These results might help in designing pathogenesis studies and in selecting high-risk patients for inclusion in therapeutic and management clinical trials

Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage

Objective: To use Multi-Criteria Decision Analysis (MCDA) to determine weights for eleven criteria in order to prioritize COVID-19 non-critical patients for admission to hospital in healthcare settings with limited resources. Methods: The MCDA was applied in two main steps: specification of criteria for prioritizing COVID-19 patients (and levels within each criterion); and determination of weights for the criteria based on experts\u2019 knowledge and experience in managing COVID-19 patients, via an online survey. Criteria were selected based on available COVID-19 evidence with a focus on low- and middle-income countries (LMICs). Results: The most important criteria (mean weights, summing to 100%) are: PaO2 (16.3%); peripheral O2 saturation (15.9%); chest X-ray (14.1%); Modified Early Warning Score-MEWS (11.4%); respiratory rate (9.5%); comorbidities (6.5%); living with vulnerable people (6.4%); body mass index (5.6%); duration of symptoms before hospital evaluation (5.4%); CRP (5.1%); and age (3.8%). Conclusions: At the beginning of a new pandemic, when evidence for disease predictors is limited or unavailable and effective national contingency plans are difficult to establish, the MCDA prioritization model could play a pivotal role in improving the response of health systems

Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage

Objective: To use Multi-Criteria Decision Analysis (MCDA) to determine weights for eleven criteria in order to prioritize COVID-19 non-critical patients for admission to hospital in healthcare settings with limited resources. Methods: The MCDA was applied in two main steps: specification of criteria for prioritizing COVID-19 patients (and levels within each criterion); and determination of weights for the criteria based on experts’ knowledge and experience in managing COVID-19 patients, via an online survey. Criteria were selected based on available COVID-19 evidence with a focus on low- and middle-income countries (LMICs). Results: The most important criteria (mean weights, summing to 100%) are: PaO2 (16.3%); peripheral O2 saturation (15.9%); chest X-ray (14.1%); Modified Early Warning Score-MEWS (11.4%); respiratory rate (9.5%); comorbidities (6.5%); living with vulnerable people (6.4%); body mass index (5.6%); duration of symptoms before hospital evaluation (5.4%); CRP (5.1%); and age (3.8%). Conclusions: At the beginning of a new pandemic, when evidence for disease predictors is limited or unavailable and effective national contingency plans are difficult to establish, the MCDA prioritization model could play a pivotal role in improving the response of health systems

Computational tools for the reliability assessment and the engineering design of procedures and devices in bariatric surgery

Bariatric surgery is the most effective intervention for severe obesity, as one of the most serious health problem worldwide. Laparoscopic adjustable gastric banding is one of the principal technique. Nonetheless, side effects are frequent and weight-loss is not always successful. Non-optimal intervention design, surgery invasiveness and general anesthesia are the principal cause of this situation. A more advanced approach is required, integrating bioengineering and medical competences, aiming to engineering design the procedure, to improve efficacy and to reduce the need for anesthesia. Computational methods can be exploited to evaluate stomach functionality after surgery and to interpret mechano-biological processes, aiming at the optimal design of the intervention. Results from coupled experimental and computational activities are here reported, showing the potentialities of the engineering approach. Endoscopic surgery should minimize invasiveness and anesthetic requirement, but previously proposed techniques demonstrate marginal efficacy. Procedural consistent advances are required, as devices designed to provide endoscopic gastric banding. Preliminary results from computational activities are proposed, again to show the capabilities of the engineering approach to mimic and to optimize the overall surgical procedure

Post-caesarean section surgical site infections at a Tanzanian tertiary hospital: a prospective observational study

Few data are available on the determinants and characteristics of post-caesarean section (CS) surgical site infections (SSIs) in resource-limited settings. We conducted a prospective observational cohort study to evaluate the rates, determinants, and microbiological characteristics of post-CS SSI at the Dodoma Regional Referral Hospital (DRRH) Gynaecology and Obstetrics Department in Tanzania. Spanning a three-month period, all pregnant women who underwent CS were enrolled and followed up for 30 days. SSI following CS occurred in 224 (48%) women. Only 10 (2.1%) women received pre-incision antibiotic prophylaxis. Urgent intervention is needed to prevent and control infections and contain the rising rate of post-CS SSI at the DRRH. (C) 2016 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved