Search

Introdução: O futebol exige dos atletas um grande rendimento físico, implicando uma possível elevação de lesões e microtraumas musculares. Objetivo: Avaliar marcadores bioquímicos, imunológicos e hematológicos para predizer lesões musculares em atletas quando submetidos a um jogo de futebol. Métodos: Foram incluídos no estudo atletas do Esporte Clube São José de Porto Alegre, que disputaram 70 minutos de uma partida de futebol, com idade de15 e 20 anos. Foram coletadas amostras de sangue para análise dos marcadores em cinco momentos: Pré-temporada (PT), Pré-jogo (PJ), 24 horas pós-jogo (P24H), 48 horas pós-jogo (P48H) e 72 horas pós-jogo (P72H). Foram avaliados o hemograma, citocinas inflamatórias (IL-1β, IL-6, CXCL-8 e TNF-α), marcadores bioquímicos creatina quinase (CK), lactato desidrogenase (LDH), eletrólitos sanguíneos, cálcio, magnésio, sódio e potássio. Resultados: Os atletas apresentaram significância estatística (p

Made available in DSpace on 2022-04-29T08:31:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-09-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19. Department of Internal Medicine Medical School Universidade Federal de Minas Gerais Telehealth Center University Hospital Universidade Federal de Minas Gerais Institute for Health Technology Assessment IATS/ CNPq)., Rua Ramiro Barcelos, 2359. Prédio 21 | Sala 507 Department of Statistics Universidade Federal de Minas Gerais Center for Research and Graduate Studies in Business Administration Universidade Federal de Minas Gerais Universidade Federal de São João del-Rei Infectious Diseases Department Vall d'Hebron University Hospita Universitat Autònoma de Barcelona Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor Grupo Hospitalar Conceição Pontifícia Universidade Católica do Rio Grande do Sul RGS) Hospital São Lucas PUCRS Rede Mater Dei de Saúde Hospital Márcio Cunha Universidade do Sul de Santa Catarina UNISUL Dissertare Scientific Advice SOS Cardio Hospital Internal Medicine Department University Hospital Universidade Federal de Minas Gerais Faculdade de Medicina de Botucatu Universidade Estadual Paulista Júlio de Mesquita Filho Hospital das Clínicas da Faculdade de Medicina de Botucatu Universidade Federal do Rio Grande do Sul Hospital Bruno Born Research Center of Vale do Taquari. Hospital Mãe de Deus Hospital Universitário de Canoas Mucuri Medical School FAMMUC Universidade Federal dos Vales do Jequitinhonha e Mucuri – UFVJM Hospital Santa Rosalia Hospital Metropolitano Doutor Célio de Castro Hospital Moinhos de Vento Hospital Unimed BH Hospital Risoleta Tolentino Neves Post-graduation Center Medical School Universidade Federal de Minas Gerais Hospital Metropolitano Odilon Behrens Hospital Eduardo de Menezes Universidade FUMEC Hospital Julia Kubitschek Hospital Universitário de Santa Maria Departamento de Medicina Interna Universidade Federal de Santa Maria Hospital São João de Deus Hospital Regional Antônio Dias Faculdade Ciências Médicas de Minas Gerais Hospital de Clínicas de Porto Alegre Universidade Federal do Rio Grande do Sul Faculdade de Ciências Humanas de Curvelo Hospital João XXIII Av. Professor Alfredo Balena Hospital Santo Antônio Hospital Universitário Ciências Médicas Vall d'Hebron University Hospital PROSICS Barcelona Instituto René Rachou-FIOCRUZ Minas. Universidade Federal da Fronteira Sul Hospital Regional do Oeste Pontifícia Universidade Católica de Minas Gerais Hospital Tacchini Centro Universitário de Patos de Minas. RPatos de Minas Hospital Semper Hospital Santa Cruz Universidade de Santa Cruz Fundação Hospitalar do Estado de Minas Gerais – FHEMIG. Universidade Federal de Ouro Preto Hospital das Clínicas da Universidade Federal de Pernambuco Universidade Federal de Pernambuco Centro de Ciências Médicas Centro Universitário de Belo Horizonte UniBH) Erasmus MC University Medical Center Rotterdam Department of Cardiology Faculdade de Medicina de Botucatu Universidade Estadual Paulista Júlio de Mesquita Filho Hospital das Clínicas da Faculdade de Medicina de Botucatu

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.

Background: To describe success and failure (S&F) after lumbar spine surgery in terms that are preciseand that are equally understandable across the entire health ecosystem. Methods: Back and leg pain (NPRS scale) and disability (Oswestry Disability Index –ODI) of patients operated were prospectively recorded before and 6 to 12 months after the procedure. Satisfaction was recorded in the postoperative period. Initially, patients were classified as Satisfied or Unsatisfied.Optimal cutoff values for disability and pain were estimated with ROC curves. Satisfied and Unsatisfied groups were subdivided into four categories based on a combination of satisfaction, disability and pain: Success, Incomplete success, Incomplete failure and Failure. These categories were translated into operational definitions with simple verbal terms, according to previously described norms of numeric-to-verbal equivalence. Results: 486 consecutive patients were recruited from May 2019 to February 2021, with a drop-out rate of 16.4%; Preoperative PROMs mean values were ODI 42.2 (+ 16.4), NPRS back 6.6 (+ 2.6) and NPRS leg 6.2 (+ 2.9) points; 19.3% were classified as Unsatisfied and cutoff values for disability (ODI=28; AUC 0.79) and pain (NPRS=6; AUC 0.79) were defined. Satisfied and Unsatisfied groups were further subdivided based on concordance or non-concordance with the discrimination cutoff values: Success (59.6%) - satisfied with pain and disability levels concordant (NPRS ≤5, AND ODI ≤27); Incomplete success (20.4%) - satisfied with pain and disability levels non-concordant (NPRS ≥6 AND/OR ODI ≥28); Incomplete failure (7.1%) – unsatisfied with pain and disability levels non-concordant (NPRS ≤5 AND/OR ODI ≤27); and Failure (12.4%) - unsatisfied with pain and disability levels concordant (NPRS ≥6 AND ODI ≥28). After that, groups were translated to: Success - patients are satisfied and present no or only mild to tolerable pain, and no or borderline disability; Incomplete Success – patients are satisfied despite levels of pain and/or disability worse than the ideal for success; Incomplete Failure – patients are not satisfied despite levels of pain and/or disability better than expected for failure; Failure – patients are unsatisfied and present moderate to severe pain and disability.Conclusion: It is possible to report S&F after surgery for degenerative disc disease of the lumbar spine with precise and meaningful operational definitions focused on the experience of the patient.

Scientific data regarding the prevalence of COVID-19 neurological manifestations and prognosis in Latin America countries is still lacking. Therefore, the study aims to understand neurological manifestations of SARS-CoV 2 infection and outcomes in the Brazilian population.This study is part of the Brazilian COVID-19 Registry, a multicentric cohort, including data from 37 hospitals. For the present analysis, patients were grouped according to the presence of reported symptoms (i.e., headache; anosmia and ageusia; syncope and dizziness) vs. clinically-diagnosed neurological manifestations (clinically-defined neurological syndrome: neurological signs or diagnoses captured by clinical evaluation) and matched with patients without neurological manifestations by age, sex, number of comorbidities, hospital of admission, and whether or not patients had underlying neurological disease.From 6,635 hospitalized patients with COVID-19, 30.8% presented reported neurological manifestations, 10.3% were diagnosed with a neurological syndrome and 60.1% did not show any neurological manifestations. In patients with reported symptoms, the most common ones were headache (20.7%), ageusia (11.1%) and anosmia (8.0%). In patients with neurological syndromes, acute encephalopathy was the most common diagnosis (9.7%). In the matched analysis, patients with neurological syndromes presented more cases of septic shock (17.0 vs. 13.0%, p = 0.045), intensive care unit admission (45.3 vs. 38.9%, p = 0.023), and mortality (38.7 vs. 32.6%, p = 0.026; and 39.2 vs. 30.3%, p 0.001) when compared to controls.COVID-19 in-hospital patients with clinically defined neurological syndromes presented a higher incidence of septic shock, ICU admission and death when compared to controls.

The majority prognostic scores proposed for early assessment of coronavirus disease 19 (COVID-19) patients are bounded by methodological flaws. Our group recently developed a new risk score - ABC2SPH - using traditional statistical methods (least absolute shrinkage and selection operator logistic regression - LASSO). In this article, we provide a thorough comparative study between modern machine learning (ML) methods and state-of-the-art statistical methods, represented by ABC2SPH, in the task of predicting in-hospital mortality in COVID-19 patients using data upon hospital admission. We overcome methodological and technological issues found in previous similar studies, while exploring a large sample (5,032 patients). Additionally, we take advantage of a large and diverse set of methods and investigate the effectiveness of applying meta-learning, more specifically Stacking, in order to combine the methods' strengths and overcome their limitations. In our experiments, our Stacking solutions improved over previous state-of-the-art by more than 26% in predicting death, achieving 87.1% of AUROC and MacroF1 of 73.9%. We also investigated issues related to the interpretability and reliability of the predictions produced by the most effective ML methods. Finally, we discuss the adequacy of AUROC as an evaluation metric for highly imbalanced and skewed datasets commonly found in health-related problems.

Background Scientific data regarding the prevalence of COVID-19 neurological manifestations and prognosis in Latin America countries is still lacking. Therefore, the study aims to understand neurological manifestations of SARS-CoV 2 infection in the Brazilian population and its association with patient outcomes, such as in-hospital mortality. Methods This study is part of the Brazilian COVID-19 Registry, a multicentric COVID-19 cohort, including data from 37 Brazilian hospitals. For the analysis, patients were grouped according to the presence of self-reported vs. clinically-diagnosed neurological manifestations and matched with patients without neurological manifestations by age, sex, number of comorbidities, hospital, and whether or not patients ha neurological underlying disease. Results From 7,232 hospitalized patients with COVID-19, 27.8% presented self-reported neurological manifestations, 9.9% were diagnosed with a clinically-defined neurological syndrome and 1.2% did not show any neurological symptoms. In patients with self-reported symptoms, the most common ones were headache (19.3%), ageusia (10.4%) and anosmia (7.4%). Meanwhile, in the group with clinically-defined neurological syndromes, acute encephalopathy was the most common diagnosis (10.5%), followed by coma (0.6%1) and seizures (0.4%). Men and younger patients were more likely to self-report neurological symptoms, while women and older patients were more likely to develop a neurological syndrome. Patients with clinically-defined neurological syndromes presented a higher prevalence of comorbidities, as well as lower oxygen saturation and blood pressure at hospital admission. In the paired analysis, it was observed that patients with clinically-defined neurological syndromes were more likely to require ICU admission (46.9 vs. 37.9%), mechanical ventilation (33.4 vs. 28.2%), to develop acute heart failure (5.1 vs. 3.0%, p=0.037) and to die (40.7 vs. 32.3%, p

BackgroundIt is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19. The study aims to compare clinical characteristics and outcomes of COVID-19 patients with and without hypothyroidism.MethodsThe study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis, including data collected from 37 hospitals. Matching for age, sex, number of comorbidities and hospital was performed to select the patients without hypothyroidism for the paired analysis.ResultsFrom 7,762 COVID-19 patients, 526 had previously diagnosed hypothyroidism (50%) and 526 were selected as matched controls. The median age was 70 (interquartile range 59.0-80.0) years-old and 68.3% were females. The prevalence of underlying comorbidities were similar between groups, except for coronary and chronic kidney diseases, that had a higher prevalence in the hypothyroidism group (9.7% vs. 5.7%, p=0.015 and 9.9% vs. 4.8%, p=0.001, respectively). At hospital presentation, patients with hypothyroidism had a lower frequency of respiratory rate > 24 breaths per minute (36.1% vs 42.0%; p=0.050) and need of mechanical ventilation (4.0% vs 7.4%; p=0.016). D-dimer levels were slightly lower in hypothyroid patients (2.3 times higher than the reference value vs 2.9 times higher; p=0.037). In-hospital management was similar between groups, but hospital length-of-stay (8 vs 9 days; p=0.029) and mechanical ventilation requirement (25.4% vs. 33.1%; p=0.006) were lower for patients with hypothyroidism. There was a trend of lower in-hospital mortality in patients with hypothyroidism (22.1% vs. 27.0%; p=0.062).ConclusionIn this large Brazilian COVID-19 Registry, patients with hypothyroidism had a lower requirement of mechanical ventilation, and showed a trend of lower in-hospital mortality. Therefore, hypothyroidism does not seem to be associated with a worse prognosis, and should not be considered among the comorbidities that indicate a risk factor for COVID-19 severity.

ObjectiveTo provide a thorough comparative study among state-of-the-art machine learning methods and statistical methods for determining in-hospital mortality in COVID-19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods.Materials and MethodsDe-identified data were obtained from COVID-19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID-19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross-validation procedure, from which we assessed performance and interpretability metrics.ResultsThe Stacking of machine learning models improved over the previous state-of-the-art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macro F1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the ‘why’.ConclusionThe best results were obtained using the meta-learning ensemble model – Stacking. State-of the art explainability techniques such as SHAP-values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine-learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions.

ObjectivesThe COVID-19 pandemic has resulted in widespread morbidity and mortality with the consequences expected to be felt for many years. Significant variation exists in the care even of similar patients with COVID-19, including treatment practices within and between institutions. Outcome measures vary among clinical trials on the same therapies. Understanding which therapies are of most value is not possible unless consensus can be reached on which outcomes are most important to measure. Furthermore, consensus on the most important outcomes may enable patients to monitor and track their care, and may help providers to improve the care they offer through quality improvement. To develop a standardised minimum set of outcomes for clinical care, the International Consortium for Health Outcomes Measurement (ICHOM) assembled a working group (WG) of 28 volunteers, including health professionals, patients and patient representatives.DesignA list of outcomes important to patients and professionals was generated from a systematic review of the published literature using the MEDLINE database, from review of outcomes being measured in ongoing clinical trials, from a survey distributed to patients and patient networks, and from previously published ICHOM standard sets in other disease areas. Using an online-modified Delphi process, the WG selected outcomes of greatest importance.ResultsThe outcomes considered by the WG to be most important were selected and categorised into five domains: (1) functional status and quality of life, (2) mental functioning, (3) social functioning, (4) clinical outcomes and (5) symptoms. The WG identified demographic and clinical variables for use as case-mix risk adjusters. These included baseline demographics, clinical factors and treatment-related factors.ConclusionImplementation of these consensus recommendations could help institutions to monitor, compare and improve the quality and delivery of care to patients with COVID-19. Their consistent definition and collection could also broaden the implementation of more patient-centric clinical outcomes research.

**Background:** The economic impact associated with the treatment strategies of coronavirus disease-2019 (COVID-19) patients by hospitals and health-care systems in Brazil is unknown and difficult to estimate. This research describes the investments made to absorb the demand for treatment and the changes in occupation rates and billing in Brazilian hospitals. **Methods:** This research covers the initial findings of “COVID-19 hospital costs and the proposition of a bundled reimbursement strategy for the health-care system,” which includes 10 hospitals. The chief financial officer, the chief medical officer, and hospital executives of each participating hospital provided information regarding investments attributed to COVID-19 patient treatment. The analysis included variations in occupation rates and billing from 2019 to 2020 observed in each institution, and the investments for medical equipment, individual protection materials and building construction per patient treated. **Results:** The majority of hospitals registered a decrease in hospitalization rates and revenue from 2019 to 2020. For intensive care units (ICUs), the mean occupancy rate ranged from 88% to 83%, and for wards, it ranged from 85% to 73%. Monthly average revenue decreased by 10%. The mean hospital investment per COVID-19 inpatient was I$6800 (standard deviation 7664), with the purchase of ventilators as the most common investment. For this item, the mean, highest and lowest acquisition cost per ventilator were, respectively, I$31 468, I$48 881 and I$17 777. **Conclusion:** There was significant variability in acquisition costs and investments by institution for responding to the COVID-19 pandemic. These findings highlight the importance of continuing microeconomic studies for a comprehensive assessment of hospital costs. Only with more detailed analyses, will it be possible to define and drive sustainable strategies to manage and reimburse COVID-19 treatment in health-care systems.

ObjectiveTo develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones.DesignCohort studySettingThe Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.ParticipantsConsecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay.Main outcome measuresIn-hospital mortalityResultsMedian (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (https://abc2sph.com/).ConclusionsWe designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.Summary boxesWhat is already known on this topic?Rapid scoring systems may be very useful for fast and effective assessment of COVID-19 patients in the emergency department.The majority of available scores have high risk of bias and lack benefit to clinical decision making.Derivation and validation studies in low- and middle-income countries, including Latin America, are scarce.What this study addsABC2-SPH employs seven well defined variables, routinely assessed upon hospital presentation: age, number of comorbidities, blood urea nitrogen, C reactive protein, Spo2/FiO2 ratio, platelets and heart rate.This easy-to-use risk score identified four categories at increasing risk of death with a high level of accuracy, and displayed better discrimination ability than other existing scores.A free web-based calculator is available and may help healthcare practitioners to estimate the expected risk of mortality for patients at hospital presentation.

Previous studies that assessed risk factors for venous thromboembolism (VTE) in COVID-19 patients have shown inconsistent results. Our aim was to investigate VTE predictors by both logistic regression (LR) and machine learning (ML) approaches, due to their potential complementarity. This cohort study of a large Brazilian COVID-19 Registry included 4120 COVID-19 adult patients from 16 hospitals. Symptomatic VTE was confirmed by objective imaging. LR analysis, tree-based boosting, and bagging were used to investigate the association of variables upon hospital presentation with VTE. Among 4,120 patients (55.5% men, 39.3% critical patients), VTE was confirmed in 6.7%. In multivariate LR analysis, obesity (OR 1.50, 95% CI 1.11-2.02); being an ex-smoker (OR 1.44, 95% CI 1.03-2.01); surgery ≤ 90 days (OR 2.20, 95% CI 1.14-4.23); axillary temperature (OR 1.41, 95% CI 1.22-1.63); D-dimer ≥ 4 times above the upper limit of reference value (OR 2.16, 95% CI 1.26-3.67), lactate (OR 1.10, 95% CI 1.02-1.19), C-reactive protein levels (CRP, OR 1.09, 95% CI 1.01-1.18); and neutrophil count (OR 1.04, 95% CI 1.005-1.075) were independent predictors of VTE. Atrial fibrillation, peripheral oxygen saturation/inspired oxygen fraction (SF) ratio and prophylactic use of anticoagulants were protective. Temperature at admission, SF ratio, neutrophil count, D-dimer, CRP and lactate levels were also identified as predictors by ML methods. By using ML and LR analyses, we showed that D-dimer, axillary temperature, neutrophil count, CRP and lactate levels are risk factors for VTE in COVID-19 patients.

Mesocestoides corti (syn. vogae) is a useful model for developmental studies of platyhelminth parasites of the Cestoda class, such as Taenia spp. or Echinococcus spp. It has been used in studies to characterize cestode strobilation, i.e. the development of larvae into adult worms. So far, little is known about the initial molecular events involved in cestode strobilation and, therefore, we carried out a study to characterize newly synthesized (NS) proteins upon strobilation induction. An approach based on bioorthogonal noncanonical amino acid tagging and mass spectrometry was used to label, isolate, identify, and quantify NS proteins in the initial steps of M. corti strobilation. Overall, 121 NS proteins were detected exclusively after induction of strobilation, including proteins related to development pathways, such as insulin and notch signaling. Metabolic changes that take place in the transition from the larval stage to adult worm were noted in special NS protein subsets related to developmental processes, such as focal adhesion, cell leading edge, and maintenance of location. The data shed light on mechanisms underlying early steps of cestode strobilation and enabled identification of possible developmental markers. We also consider the use of developmental responsive proteins as potential drug targets for developing novel anthelmintics. Biological significance Larval cestodiases are life-threatening parasitic diseases that affect both man and domestic animals worldwide. Cestode parasites present complex life cycles, in which they undergo major morphological and physiological changes in the transition from one life-stage to the next. One of these transitions occurs during cestode strobilation, when the mostly undifferentiated and non-segmented larval or pre-adult form develops into a fully segmented and sexually differentiated (strobilated) adult worm. Although the proteomes of bona fide larvae and strobialted adults have been previously characterized for a few cestode species, little is still known about the dynamic of protein synthesis during the early steps of cestode strobilation. Now, the assessment of newly synthesized (NS) proteins within the first 48 h of strobilation the model cestode M. corti allowed to shed light on molecular mechanisms that are triggered by strobilation induction. The functional analyses of this repertoire of over a hundred NS proteins pointed out to changes in metabolism and activation of classical developmental signaling pathways in early strobilation. Many of the identified NS proteins may become valuable cestode developmental markers and their involvement in vital processes make them also good candidate targets for novel anthelmintic drugs.

INTRODUCTION AND OBJECTIVE:Despite comparable outcomes of the different treatment options of localized prostate cancer (LPC), the side effects, the impact on quality of life (QoL) and the cost are ...

The effect of omega-3 polyunsaturated fatty acids has been the subject of intense study in many diseases; meanwhile, our understanding regarding the underlying mechanisms of omega-3 fatty acids against Trypanosoma evansi infection is still limited. A common feature of T. evansi infection is anemia. Erythrophagocytosis occurs due to direct traumatic actions of protozoa on the red blood cells, but the exact mechanisms of anemia are not yet fully understood. During infection, there is an increase in reactive oxygen species; some are highly toxic and rapidly detoxified by various cellular enzymatic and nonenzymatic mechanisms. For this purpose, the aim of this study was to evaluate the effect of fish oil (rich in omega-3) in protecting erythrocyte membranes from oxidative stress during experimental infection with T. evansi. For that, levels of membrane lipid peroxidation were analyzed and the activities of glutathione peroxidase (GPx), glutathione reductase, and catalase were also investigated. The level of lipid peroxidation increased in infected rats compared with the control, and differences were found in infected rats supplemented with fish oil 30 days before infection when compared with infected rats supplemented with fish oil after infection. Only the activity of GPx was changed in infected groups with a significantly reduced (p