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Staphylococcus aureus is a bacterium responsible for resistance to multiple drugs and the efflux system is widely studied among the resistance mechanisms developed by this species. The present study evaluates the inhibition of the MepA efflux pump by thiadiazine-derived compounds. For this purpose, thiadiazine-derived compounds (IJ-14 to IJ-20) were tested against S. aureus K2068 strains. Microdilution tests were initially conducted to assess the Minimum Inhibitory Concentration (MIC) of the compounds and their efflux pump inhibition activity. In addition, fluorimetry tests were performed using BrEt emission and tests were conducted to inhibit the expression of the mepA gene. This involved comparing the bacterial gene expression with the antibiotic alone to the gene expression after combining compounds (IJ-17 and IJ-20) with the antibiotic. Furthermore, membrane permeability assessment tests and in silico molecular docking tests were performed. It was observed that the IJ17 and IJ20 compounds exhibited direct activity against the tested strain. The IJ17 compound produced significant results in the gene inhibition tests, which was also evidenced through the membrane permeability alteration test. These findings suggest that thiadiazine-derived compounds have promising effects against one of the main resistance mechanisms, with the IJ17 compound presenting observable mechanisms of action.

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Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and 18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.

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BACKGROUND: This study investigated alternative, non-invasive methods for human papillomavirus (HPV) detection in head and neck cancers (HNCs). We compared two approaches: analyzing computed tomography (CT) scans with a Deep Learning (DL) model and using radiomic features extracted from CT images with machine learning (ML) models. METHODS: Fifty patients with histologically confirmed HNC were included. We first trained a modified ResNet-18 DL model on CT data to predict HPV status. Next, radiomic features were extracted from manually segmented regions of interest near the oropharynx and used to train four ML models (K-Nearest Neighbors, logistic regression, decision tree, random forest) for the same purpose. RESULTS: The CT-based model achieved the highest accuracy (90%) in classifying HPV status. Among the ML models, K-Nearest Neighbors performed best (80% accuracy). Weighted Ensemble methods combining the CT-based model with each ML model resulted in moderate accuracy improvements (70-90%). CONCLUSIONS: Our findings suggest that CT scans analyzed by DL models hold promise for non-invasive HPV detection in HNC. Radiomic features, while less accurate in this study, offer a complementary approach. Future research should explore larger datasets and investigate the potential of combining DL and radiomic techniques.

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Idiopathic inflammatory myopathies, especially antisynthetase syndrome, often appear outside of the muscles as interstitial lung disease (ILD). Another typical finding is the presence of mechanic's hands. The aim of the present study was to describe the clinical, functional, tomographic, and serological data of patients with ILD and mechanic's hands and their response to treatment and survival rates. This is a retrospective study of ILD with concurrent myopathy. Among the 119 patients initially selected, 51 had mechanic's hands. All the patients were screened for anti-Jo-1 antibodies. An expanded panel of myopathy autoantibodies was also performed in 27 individuals. Of the 51 patients, 35 had 1 or more antibodies. The most common were anti-Jo-1, anti-PL-7, and anti-PL-12, while of the associated antibodies, anti-Ro52 was present in 70% of the 27 tested individuals. A significant response to treatment was characterized by an increase in predicted forced vital capacity (FVC) of at least 5% in the last evaluation done after 6 to 24 months of treatment. A decrease in predicted FVC of at least 5%, the need for oxygen therapy, or death were all considered treatment failures. All patients were treated with corticosteroids, and 71% with mycophenolate. After 24 months, 18 patients had an increase in FVC, 11 had a decrease, and 22 remained stable. After a median follow-up of 58 months, 48 patients remained alive and three died. Patients with honeycombing on high-resolution chest tomography (log-rank = 34.65; P < .001) and a decrease in FVC ≥5% (log-rank = 18.28, P < .001) had a poorer survival rate. Patients with ILD and mechanic's hands respond well to immunosuppressive treatment.

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Asymmetrical rates of cladogenesis and extinction abound in the tree of life, resulting in numerous minute clades that are dwarfed by larger sister groups. Such taxa are commonly regarded as phylogenetic relicts or "living fossils" when they exhibit an ancient first appearance in the fossil record and prolonged external morphological stasis, particularly in comparison to their more diversified sister groups. Due to their special status, various phylogenetic relicts tend to be well-studied and prioritized for conservation. A notable exception to this trend is found within Amblypygi ("whip spiders"), a visually striking order of functionally hexapodous arachnids that are notable for their antenniform first walking leg pair (the eponymous "whips"). Paleoamblypygi, the putative sister group to the remaining Amblypygi, is known from Late Carboniferous and Eocene deposits but is survived by a single living species, Paracharon caecusHansen (1921), that was last collected in 1899. Due to the absence of genomic sequence-grade tissue for this vital taxon, there is no global molecular phylogeny for Amblypygi to date, nor a fossil-calibrated estimation of divergences within the group. Here, we report a previously unknown species of Paleoamblypygi from a cave site in Colombia. Capitalizing upon this discovery, we generated the first molecular phylogeny of Amblypygi, integrating ultraconserved element sequencing with legacy Sanger datasets and including described extant genera. To quantify the impact of sampling Paleoamblypygi on divergence time estimation, we performed in silico experiments with pruning of Paracharon. We demonstrate that the omission of relicts has a significant impact on the accuracy of node dating approaches that outweighs the impact of excluding ingroup fossils, which bears upon the ancestral range reconstruction for the group. Our results underscore the imperative for biodiversity discovery efforts in elucidating the phylogenetic relationships of "dark taxa," and especially phylogenetic relicts in tropical and subtropical habitats. The lack of reciprocal monophyly for Charontidae and Charinidae leads us to subsume them into one family, Charontidae, new synonymy.

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OBJECTIVES: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities. METHODS: We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique called Generative Blending Augmentation (GBA). GBA leverages a SinGAN model to learn representative generative features from a single training image to diversify realistically tumor appearances. This way, we compensate for image synthesis errors, subsequently improving the generalization power of a downstream segmentation model. The proposed augmentation is further combined to an iterative self-training procedure leveraging pseudo labels at each pass. RESULTS: The proposed solution ranked first for vestibular schwannoma (VS) segmentation during the validation and test phases of the MICCAI CrossMoDA 2022 challenge, with best mean Dice similarity and average symmetric surface distance measures. CONCLUSION AND SIGNIFICANCE: Local contrast alteration of tumor appearances and iterative self-training with pseudo labels are likely to lead to performance improvements in a variety of segmentation contexts.

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Chronic heart failure continues to be one of the main causes of impairment in the functioning and quality of life of people who suffer from it, as well as one of the main causes of mortality in our country and around the world. Mexico has a high prevalence of risk factors for developing heart failure, such as high blood pressure, diabetes, and obesity, which makes it essential to have an evidence-based document that provides recommendations to health professionals involved in the diagnosis and treatment of these patients. This document establishes the clinical practice guide (CPG) prepared at the initiative of the Mexican Society of Cardiology (SMC) in collaboration with the Iberic American Agency for the Development and Evaluation of Health Technologies, with the purpose of establishing recommendations based on the best available evidence and agreed upon by an interdisciplinary group of experts. This document complies with international quality standards, such as those described by the US Institute of Medicine (IOM), the National Institute of Clinical Excellence (NICE), the Intercollegiate Network for Scottish Guideline Development (SIGN) and the Guidelines International Network (G-I-N). The Guideline Development Group was integrated in a multi-collaborative and interdisciplinary manner with the support of methodologists with experience in systematic literature reviews and the development of CPG. A modified Delphi panel methodology was developed and conducted to achieve an adequate level of consensus in each of the recommendations contained in this CPG. We hope that this document contributes to better clinical decision making and becomes a reference point for clinicians who manage patients with chronic heart failure in all their clinical stages and in this way, we improve the quality of clinical care, improve their quality of life and reducing its complications.

La insuficiencia cardiaca crónica sigue siendo unas de las principales causas de afectación en el funcionamiento y en la calidad de vida de las personas que la presentan, así como una de las primeras causas de mortalidad en nuestro país y en todo el mundo. México tiene una alta prevalencia de factores de riesgo para desarrollar insuficiencia cardiaca, tales como hipertensión arterial, diabetes y obesidad, lo que hace imprescindible contar con un documento basado en la evidencia que brinde recomendaciones a los profesionales de la salud involucrados en el diagnóstico y el tratamiento de estos pacientes. Este documento establece la guía de práctica clínica (GPC) elaborada por iniciativa de la Sociedad Mexicana de Cardiología (SMC) en colaboración con la Agencia Iberoamericana de Desarrollo y Evaluación de Tecnologías en Salud, con la finalidad de establecer recomendaciones basadas en la mejor evidencia disponible y consensuadas por un grupo interdisciplinario y multicolaborativo de expertos. Cumple con estándares internacionales de calidad, como los descritos por el Institute of Medicine de los Estados Unidos de América (IOM), el National Institute of Clinical Excellence (NICE) del Reino Unido, la Intercollegiate Network for Scottish Guideline Development (SIGN) de Escocia y la Guidelines International Network (G-I-N). El grupo de desarrollo de la guía se integró de manera interdisciplinaria con el apoyo de metodólogos con experiencia en revisiones sistemáticas de la literatura y en el desarrollo de GPC. Se llevó a cabo y se condujo metodología de panel Delphi modificado para lograr un nivel de consenso adecuado en cada una de las recomendaciones contenidas en esta GPC. Esperamos que este documento contribuya para la mejor toma de decisiones clínicas y se convierta en un punto de referencia para los clínicos que manejan pacientes con insuficiencia cardiaca crónica en todas sus etapas clínicas, y de esta manera logremos mejorar la calidad en la atención clínica, aumentar la calidad de vida de los pacientes y disminuir las complicaciones de la enfermedad.

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Background: Segmental surgical resection is a frequently indicated procedure to treat aggressive mandibular tumors. One of the most important complications derived from this technique is permanent paresthesia of the inferior alveolar nerve (IAN), which significantly affects the quality of life of patients who experience it. This could be avoided through maneuvers that preserve the IAN. The objective of this paper is to review the main techniques for IAN preservation and to present 2 cases with the technique used by the author. Material and Methods: A systematic review was performed according to the PRISMA guidelines, apropos of two clinical cases reported in this study. The MEDLINE/PubMed and Scopus databases were searched. Several variables were considered and are presented in detail in the form of tables and figures. In addition, 2 case reports with NAI preservation techniques are presented. Results: 13 articles were finally obtained for analysis. 127 patients were evaluated, reporting mandibular resections associated with various pathologies. Various surgical techniques were used, all with the same goal of maintaining the IAN. In most of the patients, the maintenance of sensitivity was achieved, which was verified with different methods. Conclusions: Preservation of the IAN in maxillofacial surgical procedures where surgical resection of the mandibular bone has been performed is an alternative that has demonstrated successful results in terms of reducing postoperative sequelae and is currently positioned as a necessary and feasible procedure. (AU)

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Mesiodens, which emerge towards the nasal cavity, often require consultation in maxillofacial practice. Typically accessed through wide palatal flaps with ostectomy, this method involves limited visibility and poses the risk of damaging the roots and apex of adjacent dental structures. This study advocates a minimally invasive technique that involves vestibulotomy between the central incisors, facilitating direct and rapid access through nasal floor dissection, minimizing comorbidities. A systematic review was performed, following the PRISMA guidelines, apropos on ten clinical cases reported in this study. The MEDLINE/Pubmed and Web of Science databases were searched. Several variables were considered and are presented comprehensively in tables and figures. Additionally, 10 case reports with mesiodens in the maxilla were submitted to surgical treatment using a minimally invasive intraoral transnasal disinclusion. The initial literature search resulted in 37 articles, of which 9 met the inclusion criteria for the analysis. Regarding postoperative complications, no bone exposure, incisor root damage, extensive surgical approach, palatal or vestibular hematoma, or palatal necrosis was observed. However, 10% experienced superficial damage to the nasopalatine neurovascular, while 80% and 20% presented mild and moderate postoperative facial edema, respectively. Hypoesthesia in 20% of patients recovered in the first week, 40% in the first month and 40% at 3 months. The minimally invasive intraoral, transnasal, non-endoscopic approach emerges as a safe and predictable alternative to conventional surgical techniques. Presumes minimal postoperative complications, mitigating the risk of excessive bone removal and damage to adjacent structures.

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Introduction: Dengue virus infection is a global health problem lacking specific therapy, requiring an improved understanding of DENV immunity and vaccine responses. Considering the recent emerging of new dengue vaccines, here we performed an integrative systems vaccinology characterization of molecular signatures triggered by the natural DENV infection (NDI) and attenuated dengue virus infection models (DVTs). Methods and results: We analyzed 955 samples of transcriptomic datasets of patients with NDI and attenuated dengue virus infection trials (DVT1, DVT2, and DVT3) using a systems vaccinology approach. Differential expression analysis identified 237 common differentially expressed genes (DEGs) between DVTs and NDI. Among them, 28 and 60 DEGs were up or downregulated by dengue vaccination during DVT2 and DVT3, respectively, with 20 DEGs intersecting across all three DVTs. Enriched biological processes of these genes included type I/II interferon signaling, cytokine regulation, apoptosis, and T-cell differentiation. Principal component analysis based on 20 common DEGs (overlapping between DVTs and our NDI validation dataset) distinguished dengue patients by disease severity, particularly in the late acute phase. Machine learning analysis ranked the ten most critical predictors of disease severity in NDI, crucial for the anti-viral immune response. Conclusion: This work provides insights into the NDI and vaccine-induced overlapping immune response and suggests molecular markers (e.g., IFIT5, ISG15, and HERC5) for anti-dengue-specific therapies and effective vaccination development.

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Autosomal-dominant polycystic kidney disease is a prevalent genetic disorder characterized by the development of renal cysts, leading to kidney enlargement and renal failure. Accurate measurement of total kidney volume through polycystic kidney segmentation is crucial to assess disease severity, predict progression and evaluate treatment effects. Traditional manual segmentation suffers from intra- and inter-expert variability, prompting the exploration of automated approaches. In recent years, convolutional neural networks have been employed for polycystic kidney segmentation from magnetic resonance images. However, the use of Transformer-based models, which have shown remarkable performance in a wide range of computer vision and medical image analysis tasks, remains unexplored in this area. With their self-attention mechanism, Transformers excel in capturing global context information, which is crucial for accurate organ delineations. In this paper, we evaluate and compare various convolutional-based, Transformers-based, and hybrid convolutional/Transformers-based networks for polycystic kidney segmentation. Additionally, we propose a dual-task learning scheme, where a common feature extractor is followed by per-kidney decoders, towards better generalizability and efficiency. We extensively evaluate various architectures and learning schemes on a heterogeneous magnetic resonance imaging dataset collected from 112 patients with polycystic kidney disease. Our results highlight the effectiveness of Transformer-based models for polycystic kidney segmentation and the relevancy of exploiting dual-task learning to improve segmentation accuracy and mitigate data scarcity issues. A promising ability in accurately delineating polycystic kidneys is especially shown in the presence of heterogeneous cyst distributions and adjacent cyst-containing organs. This work contribute to the advancement of reliable delineation methods in nephrology, paving the way for a broad spectrum of clinical applications.

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BACKGROUND: Thiadiazines are heterocyclic compounds that contain two nitrogen atoms and one sulfur atom in their structure. These synthetic molecules have several relevant pharmacological activities, such as antifungal, antibacterial, and antiparasitic. OBJECTIVES: The present study aimed to evaluate the possible in vitro and in silico interactions of compounds derived from thiadiazines. METHODS: The compounds were initially synthesized, purified, and confirmed through HPLC methodology. Multi-drug resistant bacterial strains of Staphylococcus aureus 10 and Pseudomonas aeruginosa 24 were used to evaluate the direct and modifying antibiotic activity of thiadiazine derivatives. ADMET assays (absorption, distribution, metabolism, excretion, and toxicity) were conducted, which evaluated the influence of the compounds against thousands of macromolecules considered as bioactive targets. RESULTS: There were modifications in the chemical synthesis in carbon 4 or 3 in one of the aromatic rings of the structure where different ions were added, ensuring a variability of products. It was possible to observe results that indicate the possibility of these compounds acting through the cyclooxygenase 2 mechanism, which, in addition to being involved in inflammatory responses, also acts by helping sodium reabsorption. The amine group present in thiadiazine analogs confers hydrophilic characteristics to the substances, but this primary characteristic has been altered due to alterations and insertions of other ligands. The characteristics of the analogs generally allow easy intestinal absorption, reduce possible hepatic toxic effects, and enable possible neurological and anti-inflammatory action. The antibacterial activity tests showed a slight direct action, mainly of the IJ23 analog. Some compounds were able to modify the action of the antibiotics gentamicin and norfloxacin against multi-drug resistant strains, indicating a possible synergistic action. CONCLUSIONS: Among all the results obtained in the study, the relevance of thiadiazine analogs as possible coadjuvant drugs in the antibacterial, anti-inflammatory, and neurological action with low toxicity is clear. Need for further studies to verify these effects in living organisms is not ruled out.

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Multi-modal medical image segmentation is a crucial task in oncology that enables the precise localization and quantification of tumors. The aim of this work is to present a meta-analysis of the use of multi-modal medical Transformers for medical image segmentation in oncology, specifically focusing on multi-parametric MR brain tumor segmentation (BraTS2021), and head and neck tumor segmentation using PET-CT images (HECKTOR2021). The multi-modal medical Transformer architectures presented in this work exploit the idea of modality interaction schemes based on visio-linguistic representations: (i) single-stream, where modalities are jointly processed by one Transformer encoder, and (ii) multiple-stream, where the inputs are encoded separately before being jointly modeled. A total of fourteen multi-modal architectures are evaluated using different ranking strategies based on dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) metrics. In addition, cost indicators such as the number of trainable parameters and the number of multiply-accumulate operations (MACs) are reported. The results demonstrate that multi-path hybrid CNN-Transformer-based models improve segmentation accuracy when compared to traditional methods, but come at the cost of increased computation time and potentially larger model size.

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Rabies is an ancient neuroinvasive viral (genus Lyssavirus, family Rhabdoviridae) disease affecting approximately 59,000 people worldwide. The central nervous system (CNS) is targeted, and rabies has a case fatality rate of almost 100% in humans and animals. Rabies is entirely preventable through proper vaccination, and thus, the highest incidence is typically observed in developing countries, mainly in Africa and Asia. However, there are still cases in European countries and the United States. Recently, demographic, increasing income levels, and the coronavirus disease 2019 (COVID-19) pandemic have caused a massive raising in the animal population, enhancing the need for preventive measures (e.g., vaccination, surveillance, and animal control programs), postexposure prophylaxis, and a better understanding of rabies pathophysiology to identify therapeutic targets, since there is no effective treatment after the onset of clinical manifestations. Here, we review the neuroimmune biology and mechanisms of rabies. Its pathogenesis involves a complex and poorly understood modulation of immune and brain functions associated with metabolic, synaptic, and neuronal impairments, resulting in fatal outcomes without significant histopathological lesions in the CNS. In this context, the neuroimmunological and neurochemical aspects of excitatory/inhibitory signaling (e.g., GABA/glutamate crosstalk) are likely related to the clinical manifestations of rabies infection. Uncovering new links between immunopathological mechanisms and neurochemical imbalance will be essential to identify novel potential therapeutic targets to reduce rabies morbidity and mortality.

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Advanced sequencing technologies have expedited resolution of higher-level arthropod relationships. Yet, dark branches persist, principally among groups occurring in cryptic habitats. Among chelicerates, Solifugae ("camel spiders") is the last order lacking a higher-level phylogeny and have thus been historically characterized as "neglected [arachnid] cousins". Though renowned for aggression, remarkable running speed, and xeric adaptation, inferring solifuge relationships has been hindered by inaccessibility of diagnostic morphological characters, whereas molecular investigations have been limited to one of 12 recognized families. Our phylogenomic dataset via capture of ultraconserved elements sampling all extant families recovered a well-resolved phylogeny, with two distinct groups of New World taxa nested within a broader Paleotropical radiation. Divergence times using fossil calibrations inferred that Solifugae radiated by the Permian, and most families diverged prior to the Paleogene-Cretaceous extinction, likely driven by continental breakup. We establish Boreosolifugae new suborder uniting five Laurasian families, and Australosolifugae new suborder uniting seven Gondwanan families using morphological and biogeographic signal.

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Dengue virus (DENV) infection manifests as a febrile illness with three distinct phases: early acute, late acute, and convalescent. Dengue can result in clinical manifestations with different degrees of severity, dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. Interferons (IFNs) are antiviral cytokines central to the anti-DENV immune response. Notably, the distinct global signature of type I, II, and III interferon-regulated genes (the interferome) remains uncharacterized in dengue patients to date. Therefore, we performed an in-depth cross-study for the integrative analysis of transcriptome data related to DENV infection. Our systems biology analysis shows that the anti-dengue immune response is characterized by the modulation of numerous interferon-regulated genes (IRGs) enriching, for instance, cytokine-mediated signaling (e.g., type I and II IFNs) and chemotaxis, which is then followed by a transcriptional wave of genes associated with cell cycle, also regulated by the IFN cascade. The adjunct analysis of disease stratification potential, followed by a transcriptional meta-analysis of the interferome, indicated genes such as IFI27, ISG15, and CYBRD1 as potential suitable biomarkers of disease severity. Thus, this study characterizes the landscape of the interferome signature in DENV infection, indicating that interferome dynamics are a crucial and central part of the anti-dengue immune response.

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Age is a significant risk factor for the coronavirus disease 2019 (COVID-19) severity due to immunosenescence and certain age-dependent medical conditions (e.g., obesity, cardiovascular disorder, and chronic respiratory disease). However, despite the well-known influence of age on autoantibody biology in health and disease, its impact on the risk of developing severe COVID-19 remains poorly explored. Here, we performed a cross-sectional study of autoantibodies directed against 58 targets associated with autoimmune diseases in 159 individuals with different COVID-19 severity (71 mild, 61 moderate, and 27 with severe symptoms) and 73 healthy controls. We found that the natural production of autoantibodies increases with age and is exacerbated by SARS-CoV-2 infection, mostly in severe COVID-19 patients. Multiple linear regression analysis showed that severe COVID-19 patients have a significant age-associated increase of autoantibody levels against 16 targets (e.g., amyloid ß peptide, ß catenin, cardiolipin, claudin, enteric nerve, fibulin, insulin receptor a, and platelet glycoprotein). Principal component analysis with spectrum decomposition and hierarchical clustering analysis based on these autoantibodies indicated an age-dependent stratification of severe COVID-19 patients. Random forest analysis ranked autoantibodies targeting cardiolipin, claudin, and platelet glycoprotein as the three most crucial autoantibodies for the stratification of severe COVID-19 patients ≥50 years of age. Follow-up analysis using binomial logistic regression found that anti-cardiolipin and anti-platelet glycoprotein autoantibodies significantly increased the likelihood of developing a severe COVID-19 phenotype with aging. These findings provide key insights to explain why aging increases the chance of developing more severe COVID-19 phenotypes.

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Rabies is a fatal viral zoonosis caused by rabies virus (RABV). RABV infects the central nervous system and triggers acute encephalomyelitis in both humans and animals. Endemic in the Brazilian Northeast region, RABV emergence in distinct wildlife species has been identified as a source of human rabies infection and as such, constitutes a public health concern. Here, we performed post-mortem RABV analyses of 144 encephalic tissues from bats sampled from January to July 2022, belonging to 15 different species. We identified phylogenetically distinct RABV from Phyllostomidae and Molossidae bats circulating in Northeastern Brazil. Phylogenetic clustering revealed the close evolutionary relationship between RABV viruses circulating in bats and variants hosted in white-tufted marmosets, commonly captured to be kept as pets and linked to human rabies cases and deaths in Brazil. Our findings underline the urgent need to implement a phylogenetic-scale epidemiological surveillance platform to track multiple RABV variants which may pose a threat to both humans and animals.

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[This corrects the article DOI: 10.1016/j.heliyon.2022.e11368.].

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G protein-coupled receptors (GPCR) are involved in various physiological and pathophysiological processes. Functional autoantibodies targeting GPCRs have been associated with multiple disease manifestations in this context. Here we summarize and discuss the relevant findings and concepts presented in the biennial International Meeting on autoantibodies targeting GPCRs (the 4th Symposium), held in Lübeck, Germany, 15-16 September 2022. The symposium focused on the current knowledge of these autoantibodies' role in various diseases, such as cardiovascular, renal, infectious (COVID-19), and autoimmune diseases (e.g., systemic sclerosis and systemic lupus erythematosus). Beyond their association with disease phenotypes, intense research related to the mechanistic action of these autoantibodies on immune regulation and pathogenesis has been developed, underscoring the role of autoantibodies targeting GPCRs on disease outcomes and etiopathogenesis. The observation repeatedly highlighted that autoantibodies targeting GPCRs could also be present in healthy individuals, suggesting that anti-GPCR autoantibodies play a physiologic role in modeling the course of diseases. Since numerous therapies targeting GPCRs have been developed, including small molecules and monoclonal antibodies designed for treating cancer, infections, metabolic disorders, or inflammatory conditions, anti-GPCR autoantibodies themselves can serve as therapeutic targets to reduce patients' morbidity and mortality, representing a new area for the development of novel therapeutic interventions.

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