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Background: Increasingly valuable, sophisticated, and powerful aircraft are developed and incorporated into the armed forces. Technological advances are notable for increasing physiological load during the flight, making human resources a limiting factor in such operations. It is necessary to understand the physiological demands to which aircraft pilots are exposed, especially combat pilots. Objective: This study aimed to present: i. the physiological consequences on the human body due to high accelerative forces during high-performance flights; ii. the efficiency of the Anti-G Straining Maneuver (AGSM) countermeasure to such consequences; and iii. the state of the art about surface electromyography's uses (sEMG) in AGSM. Method: It was conducted a bibliographic search in the PubMed database using the keywords “anti-G effort maneuver” and “electromyography” and their synonyms. After this, a narrative review of the results was performed. This research model allows a comprehensive discussion about the topic, enabling an in-depth contextualization in a prosperous field of study. These characteristics are suitable for detecting literature gaps and directing the research discussion. Discussion: There is a consensus in the literature that AGSM is the most effective resource to prevent loss of consciousness induced by G-load (G-LOC). The sEMG is an affordable and useful tool for AGSM training, especially given the unavailability of human centrifuges. The current literature on the application of sEMG in the context of AGSM training is limited, presenting a diversity of objectives, methodology, and analyzed parameters. Conclusion: The use of sEMG as real-time biofeedback can improve the pilot's muscle control skills during AGSM.

Muitos são os riscos ocupacionais inerentes a aviação que podem estar associados ao desenvolvimento de desconfortos musculoesqueléticos e lesões. A atividade operacional do piloto na cabine da aeronave é complexa, é necessário que ele execute tarefas simultâneas, expondo-os a riscos físicos além das demandas inerentes da aviação operacional militar. Objetivos: Descrever a prevalência de desconforto musculoesquelético em pilotos militares de transporte da Força Aérea Brasileira e suas possíveis associações com os parâmetros biométricos, horas de voo e frequência de prática de atividade física. Método: Trata-se de um estudo observacional descritivo, com amostra composta por pilotos militares de aeronave de transporte. Como instrumentos foram utilizados questionários de autopreenchimento para caracterização da história ocupacional, perfil de atividade física e histórico de desconforto físico. Resultados: Um total de 26 pilotos foram incluídos no estudo. A frequência de atividade física em média 4,3 ± 1,59 vezes por semana, 46,15% dos sujeitos foram classificados como muito ativos. Na avaliação de desconforto, os segmentos corporais mais frequentes foram: costas inferior com 73,1%, seguido da cervical com 42,2 % e pescoço com 34,6 % dos resultados. Conclusão: Os desconfortos da coluna vertebral, cervical e lombar, foram os com maior prevalência observados na população analisada, com a intensidade dor variando de leve a desconforto intenso. Apesar da amostra ter sido caracterizada como jovem e ativa quanto a prática de atividade física, a presença de desconforto se fez presente.

Background & Aims: Biliary abnormalities in autoimmune hepatitis (AIH) and interface hepatitis in primary biliary cholangitis (PBC) occur frequently, and misinterpretation may lead to therapeutic mistakes with a negative impact on patients. This study investigates the use of a deep learning (DL)-based pipeline for the diagnosis of AIH and PBC to aid differential diagnosis. Methods: We conducted a multicenter study across six European referral centers, and built a library of digitized liver biopsy slides dating from 1997 to 2023. A training set of 354 cases (266 AIH and 102 PBC) and an external validation set of 92 cases (62 AIH and 30 PBC) were available for analysis. A novel DL model, the autoimmune liver neural estimator (ALNE), was trained on whole-slide images (WSIs) with H&E staining, without human annotations. The ALNE model was evaluated against clinico-pathological diagnoses and tested for interobserver variability among general pathologists. Results: The ALNE model demonstrated high accuracy in differentiating AIH from PBC, achieving an area under the receiver operating characteristic curve of 0.81 in external validation. Attention heatmaps showed that ALNE tends to focus more on areas with increased inflammation, associating such patterns predominantly with AIH. A multivariate explainable ML model revealed that PBC cases misclassified as AIH more often had ALP values between 1 × upper limit of normal (ULN) and 2 × ULN, coupled with AST values above 1 × ULN. Inconsistency among general pathologists was noticed when evaluating a random sample of the same cases (Fleiss’s kappa value 0.09). Conclusions: The ALNE model is the first system generating a quantitative and accurate differential diagnosis between cases with AIH or PBC. Impact and implications: This study demonstrates the significant potential of the autoimmune liver neural estimator model, a transformer-based deep learning system, in accurately distinguishing between autoimmune hepatitis and primary biliary cholangitis using digitized liver biopsy slides without human annotation. The scientific justification for this work lies in addressing the challenge of differentiating these conditions, which often present with overlapping features and can lead to therapeutic mistakes. In addition, there is need for quantitative assessment of information embedded in liver biopsies, which are currently evaluated on qualitative or semi-quantitative methods. The results of this study are crucial for pathologists, researchers, and clinicians, providing a reliable diagnostic tool that reduces interobserver variability and improves diagnostic accuracy of these conditions. Potential methodological limitations, such as the diversity in scanning techniques and slide colorations, were considered, ensuring the robustness and generalizability of the findings.

O presente trabalho aborda o tema gestão em saúde, mais especificamente a telemedicina e os seus impactos no custo do atendimento em saúde. Busca-se como objetivo: levantar e apresentar o impacto do uso da telemedicina sobre o custo do atendimento em saúde. Para tanto, realizou-se uma pesquisa exploratória e descritiva, por meio de uma revisão integrativa realizada nas bases de dados Medline, SciELO, LILACS e no buscador acadêmico Google Scholar. Como resultado foram analisados 62 artigos que contribuem ao apontar que o uso da telemedicina/telessaúde apresentou redução nos custos do atendimento à saúde, ao aumentar o custo-efetividade, o custo-utilidade e gerar uma redução de gastos, sendo uma ferramenta promissora na prestação da assistência à saúde, gerando benefícios tanto ao paciente quanto à instituição.

Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.

Stéphanie Gonvers,1,2 Sebastiao N Martins-Filho,3 André Hirayama,4 Julien Calderaro,5 Rebecca Phillips,3 Emilie Uldry,1,2 Nicolas Demartines,1,2 Emmanuel Melloul,1,2 Young Nyun Park,6 Valérie Paradis,7 Swan N Thung,8 Venancio Alves,4 Christine Sempoux,2,9 Ismail Labgaa1,2 1Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland; 2Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland; 3Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada; 4Department of Pathology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil; 5Department of Pathology, APHP, Henri Mondor University Hospital, Creteil, Val-de-Marne, France; 6Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea; 7Department of Pathology, APHP, Beaujon University Hospital, Clichy, France; 8Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 9Department of Pathology, Lausanne University Hospital (CHUV), Lausanne, SwitzerlandCorrespondence: Ismail Labgaa, Department of Visceral Surgery, University Hospital of Lausanne (CHUV), Rue du Bugnon 46, CH-1011, Lausanne, Switzerland, Tel +41 0 79 556 68 47, Email ismail.labgaa@chuv.chAbstract: The macroscopic appearance of a tumor such as hepatocellular carcinoma (HCC) may be defined as its phenotype which is de facto dictated by its genotype. Therefore, macroscopic characteristics of HCC are unlikely random but rather reflect genomic traits of cancer, presumably acting as a valuable source of information that can be retrieved and exploited to infer prognosis. This review aims to provide a comprehensive overview of the available data on the prognostic value of macroscopic characterization in HCC. A total of 57 studies meeting eligible criteria were identified, including patients undergoing liver resection (LR; 47 studies, 83%) or liver transplant (LT; 9 studies, 16%). The following macroscopic variables were investigated: tumor size (n = 42 studies), number of nodules (n = 28), vascular invasion (n = 24), bile duct invasion (n = 6), growth pattern (n = 15), resection margin (n = 11), tumor location (n = 6), capsule (n = 2) and satellite (n = 1). Although the selected studies provided insightful data with notable prognostic performances, a lack of standardization and substantial gaps were noted in the report and the analysis of gross findings. This topic remains incompletely covered. While the available studies underscored the value of macroscopic variables in HCC prognostication, important lacks were also observed. Macroscopic characterization of HCC is likely an underexploited source of prognostic factors that must be actively explored by future multidisciplinary research. Keywords: liver cancer, HCC, prognostication, gross, survival, recurrence

Protein import and genome replication are essential processes for mitochondrial biogenesis and propagation. The J-domain proteins Pam16 and Pam18 regulate the presequence translocase of the mitochondrial inner membrane. In the protozoan Trypanosoma brucei, their counterparts are TbPam16 and TbPam18, which are essential for the procyclic form (PCF) of the parasite, though not involved in mitochondrial protein import. Here, we show that during evolution, the 2 proteins have been repurposed to regulate the replication of maxicircles within the intricate kDNA network, the most complex mitochondrial genome known. TbPam18 and TbPam16 have inactive J-domains suggesting a function independent of heat shock proteins. However, their single transmembrane domain is essential for function. Pulldown of TbPam16 identifies a putative client protein, termed MaRF11, the depletion of which causes the selective loss of maxicircles, akin to the effects observed for TbPam18 and TbPam16. Moreover, depletion of the mitochondrial proteasome results in increased levels of MaRF11. Thus, we have discovered a protein complex comprising TbPam18, TbPam16, and MaRF11, that controls maxicircle replication. We propose a working model in which the matrix protein MaRF11 functions downstream of the 2 integral inner membrane proteins TbPam18 and TbPam16. Moreover, we suggest that the levels of MaRF11 are controlled by the mitochondrial proteasome.

This study aimed to evaluate the effects of prepartum supplementation levels on the productive and nutritional performance and metabolic responses of Nellore cows in a grazing system. Forty-four pregnant Nellore cows multiparous with an initial mean gestation time of 230 days, average initial body weight (BW) of 541 ± 19 kg, and body condition score (BCS) of 5.5 ± 1.7 were used and distributed in a completely randomized design, with four treatments and 11 cows per treatment. The treatments evaluated consisted of four supplementation levels in the prepartum period with a duration of 60 days, according to the following scheme: 0.0 (Control = CON); 0.5 (LOW); 1.0 (Medium = MED) and; 1.5 (High = HIG) kg per animal day-1 of supplement. The supplement was composed of wheat meal and urea, and formulated to contain 22% of crude protein (CP). A positive linear effect (P < 0.05) was observed on the intake of CP and CP:digested organic matter (DOM) ratio as supplementation levels were increased. In addition, a quadratic effect (P < 0.05) was detected on the intake of neutral detergent fiber corrected for ash and protein (apNDF), digested neutral detergent fiber, and DOM with increasing supplementation levels in the prepartum period, in which MED cows had higher intakes compared to CON, LOW, and HIG cows. Prepartum supplementation levels did not affect (P > 0.10) the average daily gain in the prepartum or postpartum periods and, BCS of the dams, and BW of calves at birth or 45 days of age. In the prepartum period, supplementation levels did not affect (P > 0.10) the blood concentrations of total proteins, albumin, globulins, glucose, cholesterol, triglycerides, non-esterified fatty acids, and beta-hydroxybutyrate in the experimental cows. In conclusion, the provision of 1.0 kg of supplement per animal per day during the last 60 days of pregnancy improves prepartum nutritional performance and postpartum milk production of grazing beef cows.

Abstract Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.

The power and transportation sectors contribute to more than 66% of global carbon emissions. Decarbonizing these sectors is critical for achieving a zero-carbon economy by mid-century and mitigating the most severe impacts of climate change. Battery packs, which enable energy storage in electric vehicles, are a key component of electrified transport systems. The production of these batteries has significantly increased in recent years to meet rising demand, and this trend is expected to continue. However, current traction batteries exhibit lower energy density compared to fossil fuels. As a result, accurate battery models that balance computational complexity and precision are essential for designing high-performance energy storage systems. This paper provides a comprehensive review of the most used electrical models for lithium-ion batteries in traction applications, as reported in the technical literature. By exploring the strengths and limitations of different modeling approaches, this paper aims to offer valuable insights into their practical applicability for the electrification of transportation systems. Additionally, this paper discusses the primary methods employed to derive the values of the electrical components within these models. Finally, it examines the key parameters—such as temperature, state of charge, and aging—that significantly influence the component values. Ultimately, it guides researchers and practitioners in selecting the most suitable modeling approach for their specific needs.

Clean hydrogen (H2) use (i.e., produced using either renewable or low-carbon energy sources) can help decarbonize energy-intensive industries, the transport sector, and the power sector. The European regulatory framework establishes that the production of green H2 must be supported either by the electricity grid through a power purchase agreement (PPA) or by intermittent renewable energy source (RES) plants owned by the hydrogen producer. Although the issue of the optimization of hydrogen production costs has already been approached, constraints related to the current regulatory framework and the modeling of nonlinear electrolyzer efficiency still represent open problems. In this paper, a mixed-integer linear programming (MILP) problem, assuming as the objective function the overall cost minimization of the allowed energy mix for green H2 production, is formulated. Two approaches are compared: in the first one, electrolyzers can only operate at 100% load, whereas the second one allows for more flexible electrolyzer scheduling, by enabling partial-load working operations. The simulation results of several scenarios considering different H2 production targets, forecasted RES production, and cost for PPAs demonstrate the effectiveness of the proposed methodology.

Breakthrough COVID-19 (occurring in fully vaccinated people) has been described. Data on its characteristics among immune-mediated rheumatic disease (IMRD) patients are scarce. This study describes breakthrough COVID-19 occurring in IMRD patients participating in the SAFER-study, a Brazilian multicentric cohort evaluating the safety, effectiveness, and immunogenicity of SARS-CoV-2 vaccines in patients with autoimmune diseases. A descriptive analysis of the population and a binary logistic regression model were performed to evaluate the predictors of COVID-19-related hospitalization. A p-value < 0.05 was significant. The included 160 patients were predominantly females (83.1%), with a mean (SD) age of 40.23 (13.19) years. The patients received two (19%), three (70%), or four (11%) vaccine doses. The initial two-dose series was mainly with ChAdOx1 (Oxford/AstraZeneca) (58%) or BBIBP-CorV (Sinopharm-Beijing) (34%). The first booster (n = 150) was with BNT162b2 (BioNtech/Fosun Pharma/Pfizer) (63%) or ChAdOx1 (29%). The second booster (n = 112) was with BNT162b2 (40%) or ChAdOx1 (26%). The COVID-19 hospitalization rate was 17.5%. IMRD moderate/high activity (OR: 5.84; CI: 1.9–18.5; p = 0.002) and treatment with corticosteroids (OR: 2.94; CI: 1.02–8.49; p = 0.0043) were associated with higher odds of hospitalization, while increasing the number of vaccine doses was protective (OR: 0.37; CI: 0.15–0.9; p = 0.032). These findings, along with previous reassuring results about the safety of the COVID-19 vaccines, argue in favor of booster vaccination in IMRD patients.