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Abstract Objective To develop the second evidence-based Brazilian Society of Rheumatology consensus for diagnosis and treatment of lupus nephritis (LN). Methods Two methodologists and 20 rheumatologists from Lupus Comittee of Brazilian Society of Rheumatology participate in the development of this guideline. Fourteen PICO questions were defined and a systematic review was performed. Eligible randomized controlled trials were analyzed regarding complete renal remission, partial renal remission, serum creatinine, proteinuria, serum creatinine doubling, progression to end-stage renal disease, renal relapse, and severe adverse events (infections and mortality). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to develop these recommendations. Recommendations required ≥82% of agreement among the voting members and were classified as strongly in favor, weakly in favor, conditional, weakly against or strongly against a particular intervention. Other aspects of LN management (diagnosis, general principles of treatment, treatment of comorbidities and refractory cases) were evaluated through literature review and expert opinion. Results All SLE patients should undergo creatinine and urinalysis tests to assess renal involvement. Kidney biopsy is considered the gold standard for diagnosing LN but, if it is not available or there is a contraindication to the procedure, therapeutic decisions should be based on clinical and laboratory parameters. Fourteen recommendations were developed. Target Renal response (TRR) was defined as improvement or maintenance of renal function (±10% at baseline of treatment) combined with a decrease in 24-h proteinuria or 24-h UPCR of 25% at 3 months, a decrease of 50% at 6 months, and proteinuria

Abstract In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as “positive” by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.

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.

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

Abstract Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions’ correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

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.

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.

The integration of Electric Vehicles (EVs) equipped with Vehicle-to-Grid (V2G) technology has been recognized as a promising solution to enhance the flexibility of modern power distribution systems. However, to make this a reality, there is a need for flexible, reliable, and highly scalable computing architectures that can coordinate the charging and discharging process of EV fleets while identifying the best trade-offs between the vehicle charging expectations and the grid requirements. This challenging problem has been formalized in this paper as a welfare maximization problem, by defining proper utility functions to quantify the marginal cost/benefits of the charging and discharging process. Then, a self-organizing solution scheme is proposed in the task of enabling the EVs fleet to solve the welfare maximization problem by only exchanging local non-sensitive information. The insight is to define a formal connection between the charging/discharging scheduling problem and the economic dispatch of thermal generators, hence exploiting all the vast literature on the theory of the marginal cost for the decentralized solution of the scheduling problem, which is one of the main contributions of this paper. Simulation results obtained on two case studies are presented and discussed in order to assess the effectiveness of the proposed technique in solving complex scheduling problems in realistic operation scenarios.

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.

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.

Over the last ten years, researchers’ efforts have aimed to replace the classic linear economy model with the circular economy model, favoring green chemical and industrial processes. From this point of view, biologically active molecules, coming from plants, flowers and biomass, are gaining considerable value. In this study, firstly we focus on the development of a green protocol to obtain the purification of anthocyanins from the flower of Callistemon citrinus, based on simulation and on response surface optimization methodology. After that, we utilize them to manufacture and add new properties to bioplastics belonging to class 3, based on modified polyvinyl alcohol (PVA) with increasing amounts from 0.10 to 1.00%. The new polymers are analyzed to monitor morphological changes, optical properties, mechanical properties and antioxidant and antimicrobial activities. Fourier transform infrared spectroscopy (FTIR) spectra of the new materials show the characteristic bands of the PVA alone and a modification of the band at around 1138 cm−1 and 1083 cm−1, showing an influence of the anthocyanins’ addition on the sequence with crystalline and amorphous structures of the starting materials, as also shown by the results of the mechanical tests. These last showed an increase in thickening (from 29.92 μm to approx. 37 μm) and hydrophobicity with the concomitant increase in the added anthocyanins (change in wettability with water from 14° to 31°), decreasing the poor water/moisture resistance of PVA that decreases its strength and limits its application in food packaging, which makes the new materials ideal candidates for biodegradable packaging to extend the shelf-life of food. The functionalization also determines an increase in the opacity, from 2.46 to 3.42 T%/mm, the acquisition of antioxidant activity against 2,2-diphenyl-1-picrylhdrazyl and 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) radicals and, in the ferric reducing power assay, the antimicrobial (bactericidal) activity against different Staphylococcus aureus strains at the maximum tested concentration (1.00% of anthocyanins). On the whole, functionalization with anthocyanins results in the acquisition of new properties, making it suitable for food packaging purposes, as highlighted by a food fresh-keeping test.

COVID-19 is a systemic disease caused by the etiologic agent SARS-CoV-2, first reported in Hubei Province in Wuhan, China, in late 2019. The SARS-CoV-2 virus has evolved over time with distinct transmissibility subvariants from ancestral lineages. The clinical manifestations of the disease vary according to their severity and can range from asymptomatic to severe. Due to the rapid evolution to a pandemic, epidemiological studies have become essential to understand and effectively combat COVID-19, as the incidence and mortality of this disease vary between territories and populations. This study correlated epidemiological data on the incidence and mortality of COVID-19 with frequencies of important SNPs in GWAS studies associated with the susceptibility and mortality of this disease in different populations. Our results indicated significant correlations for 11 genetic variants (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026). Of these 11 variants, 7 (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate, these variants were more frequent in EUR populations, suggesting that this population is more susceptible to COVID-19. The rs2176724 variant was inversely related to incidence rates; therefore, the higher the frequency of the allele is, the lower the incidence rate. This variant was more frequent in the AFR population, which suggests a protective factor against SARS-CoV-2 infection in this population. The variants rs10774671, rs4804803, and rs7528026 showed a significant relationship with mortality rates. SNPs rs10774671 and rs4804803 were inversely related to mortality rates and are more frequently present in the AFR population. The rs7528026 variant, which is more frequent in the AMR population, was positively related to mortality rates. The study has the potential to identify and correlate the genetic profile with epidemiological data, identify populations that are more susceptible to severe forms of COVID-19, and relate them to incidence and mortality.

Background: Current guidelines do not indicate any comprehensive management of hepatic hypervascular incidentalomas (HVIs) discovered in hepatocellular carcinoma (HCC) patients during intra-arterial therapies (IATs). This study aims to evaluate the prognostic value of HVIs detected on per-interventional cone beam computed tomography (CBCT) during IAT for HCC in patients waiting for liver transplantation (LT). Material and methods: In this retrospective single-institutional study, all liver-transplanted HCC patients between January 2014 and December 2018 who received transarterial chemoembolization (TACE) or radioembolization (TARE) before LT were included. The number of ≥10 mm HCCs diagnosed on contrast-enhanced pre-interventional imaging (PII) was compared with that detected on per-interventional CBCT with a nonparametric Wilcoxon test. The correlation between the presence of an HVI and histopathological criteria associated with poor prognosis (HPP) on liver explants was investigated using the chi-square test. Tumor recurrence (TR) and TR-related mortality were investigated using the chi-square test. Recurrence-free survival (RFS), TR-related survival (TRRS), and overall survival (OS) were assessed according to the presence of HVI using Kaplan–Meier analysis. Results: Among 63 included patients (average age: 59 ± 7 years, H/F = 50/13), 36 presented HVIs on per-interventional CBCT. The overall nodule detection rate of per-interventional CBCT was superior to that of PII (median at 3 [Q1:2, Q3:5] vs. 2 [Q1:1, Q3:3], respectively, p < 0.001). No significant correlation was shown between the presence of HVI and HPP (p = 0.34), TR (p = 0.095), and TR-related mortality (0.22). Kaplan–Meier analysis did not show a significant impact of the presence of HVI on RFS (p = 0.07), TRRS (0.48), or OS (p = 0.14). Conclusions: These results may indicate that the treatment plan during IAT should not be impacted or modified in response to HVI detection.

Coronavirus disease 2019 (COVID-19) is an infection caused by SARS-CoV-2. Genome-wide association studies (GWASs) have suggested a strong association of genetic factors with the severity of the disease. However, many of these studies have been completed in European populations, and little is known about the genetic variability of indigenous peoples’ underlying infection by SARS-CoV-2. The objective of the study is to investigate genetic variants present in the genes AQP3, ARHGAP27, ELF5L, IFNAR2, LIMD1, OAS1 and UPK1A, selected due to their association with the severity of COVID-19, in a sample of indigenous people from the Brazilian Amazon in order to describe potential new and already studied variants. We performed the complete sequencing of the exome of 64 healthy indigenous people from the Brazilian Amazon. The allele frequency data of the population were compared with data from other continental populations. A total of 66 variants present in the seven genes studied were identified, including a variant with a high impact on the ARHGAP27 gene (rs201721078) and three new variants located in the Amazon Indigenous populations (INDG) present in the AQP3, IFNAR2 and LIMD1 genes, with low, moderate and modifier impact, respectively.