MrgprA3 neurons drive cutaneous immunity against helminths through selective control of myeloid-derived IL-33.

Skin uses interdependent cellular networks for barrier integrity and host immunity, but most underlying mechanisms remain obscure. Herein, we demonstrate that the human parasitic helminth Schistosoma mansoni inhibited pruritus evoked by itch-sensing afferents bearing the Mas-related G-protein-coupled receptor A3 (MrgprA3) in mice. MrgprA3 neurons controlled interleukin (IL)-17+ γδ T cell expansion, epidermal hyperplasia and host resistance against S. mansoni through shaping cytokine expression in cutaneous antigen-presenting cells. MrgprA3 neuron activation downregulated IL-33 but induced IL-1ß and tumor necrosis factor in macrophages and type 2 conventional dendritic cells partially through the neuropeptide calcitonin gene-related peptide. Macrophages exposed to MrgprA3-derived secretions or bearing cell-intrinsic IL-33 deletion showed increased chromatin accessibility at multiple inflammatory cytokine loci, promoting IL-17/IL-23-dependent changes to the epidermis and anti-helminth resistance. This study reveals a previously unrecognized intercellular communication mechanism wherein itch-inducing MrgprA3 neurons initiate host immunity against skin-invasive parasites by directing cytokine expression patterns in myeloid antigen-presenting cell subsets.

Building a framework for fake news detection in the health domain.

Disinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.

Holistic exploration of reading comprehension skills, technology and socioeconomic factors in Spanish teenagers.

The intricate relationship between teenagers' literacy and technology underscores the need for a comprehensive understanding, particularly in the Spanish context. This study employs explainable artificial intelligence (AI) to delve into this complex interplay, focusing on the pivotal role of reading comprehension skills in the personal and career development of Spanish teenagers. With a sample of 22,400 15-year-olds from the PISA dataset, we investigate the impact of socioeconomic factors, technology habits, parental education, residential location, and school type on reading comprehension skills. Utilizing machine learning techniques, our analysis reveals a nuanced connection between autonomy, technological proficiency, and academic performance. Notably, family oversight of technology use emerges as a crucial factor in managing the impact of digital technology and the Internet on reading comprehension skills. The study emphasizes the necessity for a balanced and supervised introduction to technology from an early age. Contrary to current trends, our findings indicate that online gaming may not contribute positively to reading comprehension skills, while moderate daily Internet use (1-4 h) proves beneficial. Furthermore, the study underscores the ongoing nature of acquiring reading comprehension and technological skills, emphasizing the need for continuous attention and guidance from childhood. Parental education levels are identified as partial predictors of children's performance, emphasizing the importance of a holistic educational approach that considers autonomy and technological literacy. This study advocates for addressing socio-economic and gender inequalities in education and highlights the crucial role of cooperation between schools and families, particularly those with lower educational levels.

Incidence of Thrombosis in COVID-19 Patients Compared to Non-COVID-19 Sepsis Patients in the Intensive Care Unit.

Background/Objectives: The hypercoagulable state associated with COVID-19 infection is associated with adverse outcomes and mortality. Studies have also demonstrated high rates of venous thromboembolism (VTE) events among patients with sepsis. We aimed to evaluate how the increase in thrombotic events in critically ill patients with COVID-19 infection compares to that of critically ill patients with non-COVID-19 sepsis. Methods: A chart review was performed of patients 18 years or older admitted to the intensive care unit (ICU) at Tampa General Hospital between 1 January 2020 and 31 December 2020 diagnosed with COVID-19 or sepsis secondary to other pathogens. Non-COVID-19 sepsis patients and COVID-19 patients were propensity-matched 3:1 on the Charlson Comorbidity Index. Multivariate analyses adjusting for confounding were conducted to report odds ratio (OR) and 95% confidence intervals (95% CIs) of predictors for thrombotic events and overall mortality. Results: After propensity score matching, 492 sepsis patients and 164 COVID-19 patients were included in the analysis. COVID-19 patients were significantly older (p = 0.021) and showed higher BMI (p < 0.001) than sepsis patients. COVID-19 patients did not show significantly higher odds of thrombosis after adjustment for confounders (OR 0.85, 95% CI 0.42-1.72), but had significantly lower odds of mortality than sepsis patients (OR 0.33, 95% CI 0.16-0.66). Conclusions: Our results suggest that further study is required to lower the rate of VTE in COVID-19 and non-COVID-19 sepsis patients admitted to the ICU; it is also reasonable to consider similar thromboembolism practices between these two patient groups.

Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence.

Accurate assessment of Attention Deficit Hyperactivity Disorder (ADHD) is crucial for the effective treatment of affected individuals. Traditionally, psychometric tests such as the WISC-IV have been utilized to gather evidence and identify patterns or factors contributing to ADHD diagnosis. However, in recent years, the use of machine learning (ML) models in conjunction with post-hoc eXplainable Artificial Intelligence (XAI) techniques has improved our ability to make precise predictions and provide transparent explanations. The objective of this study is twofold: firstly, to predict the likelihood of an individual receiving an ADHD diagnosis using ML algorithms, and secondly, to offer interpretable insights into the decision-making process of the ML model. The dataset under scrutiny comprises 694 cases collected over the past decade in Spain, including information on age, gender, and WISC-IV test scores. The outcome variable is the professional diagnosis. Diverse ML algorithms representing various learning styles were rigorously evaluated through a stratified 10-fold cross-validation, with performance assessed using key metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Models were compared using both the full set of initial features and a well-suited wrapper-type feature selection algorithm (Boruta). Following the identification of the most suitable model, Shapley additive values were computed to assign weights to each predictor based on their additive contribution to the outcome and to elucidate the predictions. Strikingly, a reduced set of 8 out of the initial 20 variables produced results comparable to using the full feature set. Among the ML models tested, the Random Forest algorithm outperformed others on most metrics (ACC = 0.90, AUC = 0.94, Sensitivity = 0.91, Specificity = 0.92). Notably, the principal predictors, ranked by importance, included GAI - CPI, WMI, CPI, PSI, VCI, WMI - PSI, PRI, and LN. Individual case examples exhibit variations in predictions depending on unique characteristics, including instances of false positives and negatives. Our ML model adeptly predicted ADHD diagnoses in 90% of cases, with potential for further enhancement by expanding our database. Furthermore, the use of XAI techniques enables the elucidation of salient factors in individual cases, thereby aiding inexperienced professionals in the diagnostic process and facilitating comparison with expert assessments. It is important to note that this tool is designed to support the ADHD diagnostic process, where the medical professional always has the final say in decision-making.

Hookworms dynamically respond to loss of Type 2 immune pressure.

The impact of the host immune environment on parasite transcription and fitness is currently unknown. It is widely held that hookworm infections have an immunomodulatory impact on the host, but whether the converse is true remains unclear. Immunity against adult-stage hookworms is largely mediated by Type 2 immune responses driven by the transcription factor Signal Transducer and Activator of Transcription 6 (STAT6). This study investigated whether serial passage of the rodent hookworm Nippostrongylus brasiliensis in STAT6-deficient mice (STAT6 KO) caused changes in parasites over time. After adaptation to STAT6 KO hosts, N. brasiliensis increased their reproductive output, feeding capacity, energy content, and body size. Using an improved N. brasiliensis genome, we found that these physiological changes corresponded with a dramatic shift in the transcriptional landscape, including increased expression of gene pathways associated with egg production, but a decrease in genes encoding neuropeptides, proteases, SCP/TAPS proteins, and transthyretin-like proteins; the latter three categories have been repeatedly observed in hookworm excreted/secreted proteins (ESPs) implicated in immunosuppression. Although transcriptional changes started to appear in the first generation of passage in STAT6 KO hosts for both immature and mature adult stages, downregulation of the genes putatively involved in immunosuppression was only observed after multiple generations in this immunodeficient environment. When STAT6 KO-adapted N. brasiliensis were reintroduced to a naive WT host after up to 26 generations, this progressive change in host-adaptation corresponded to increased production of inflammatory cytokines by the WT host. Surprisingly, however, this single exposure of STAT6 KO-adapted N. brasiliensis to WT hosts resulted in worms that were morphologically and transcriptionally indistinguishable from WT-adapted parasites. This work uncovers remarkable plasticity in the ability of hookworms to adapt to their hosts, which may present a general feature of parasitic nematodes.

"MrgprA3 neurons selectively control myeloid-derived cytokines for IL-17 dependent cutaneous immunity".

Skin employs interdependent cellular networks to facilitate barrier integrity and host immunity through ill-defined mechanisms. This study demonstrates that manipulation of itch-sensing neurons bearing the Mas-related G protein-coupled receptor A3 (MrgprA3) drives IL-17+ γδ T cell expansion, epidermal thickening, and resistance to the human pathogen Schistosoma mansoni through mechanisms that require myeloid antigen presenting cells (APC). Activated MrgprA3 neurons instruct myeloid APCs to downregulate interleukin 33 (IL-33) and up-regulate TNFα partially through the neuropeptide calcitonin gene related peptide (CGRP). Strikingly, cell-intrinsic deletion of IL-33 in myeloid APC basally alters chromatin accessibility at inflammatory cytokine loci and promotes IL-17/23-dependent epidermal thickening, keratinocyte hyperplasia, and resistance to helminth infection. Our findings reveal a previously undescribed mechanism of intercellular cross-talk wherein "itch" neuron activation reshapes myeloid cytokine expression patterns to alter skin composition for cutaneous immunity against invasive pathogens.

Harnessing the Immune System: An Effective Way to Manage Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a heterogenous hematological disorder with malignant potential controlled by immunological characteristics of the tumor microenvironment. Rapid breakthrough in the molecular pathways has made immunological approaches the main anchor in the management of DLBCL, with or without chemotherapeutic agents. Rituximab was the first monoclonal antibody approved for the treatment of DLBCL. Following rituximab that transformed the therapeutic landscape, other novel immunological agents including chimeric antigen T-cell therapy have reshaped the management of relapsed/refractory DLBCL. However, resistance and refractory state remain a challenge in the management of DLBCL. For this literature review, we screened articles from Medline, Embase, Cochrane databases and the European/North American guidelines from March 2010 through October 2022 for DLBCL. Here we discuss immunological agents that will significantly affect future treatment of this aggressive type of lymphoma.

[WITHDRAWN] Neuron-dependent tuft cell expansion initiates sinonasal allergic Type 2 inflammation.

The authors have withdrawn this manuscript owing to inaccuracies in the calculation of tuft cell numbers and errors in the selection of immunofluorescence images used to support our claims. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Insights into the Impact of Hesitancy on Cancer Care and COVID-19.

World Health Organization findings indicate that the COVID-19 pandemic adversely affected cancer diagnosis and management. The COVID-19 pandemic disrupted the optimal management of outpatient appointments, scheduled treatments, and hospitalizations for cancer patients because of hesitancy among patients and health-care providers. Travel restrictions and other factors likely affected medical, surgical, and radiation treatments during the COVID-19 pandemic. Cancer patients were more likely to be affected by severe illness and complications if they contracted COVID-19. A compromised immune system and comorbidities in cancer patients may have contributed to this increased risk. Hesitancy or reluctance to receive appropriate therapy or vaccination advice might have played a major role for cancer patients, resulting in health-care deficits. The purpose of this review is to evaluate the impact of COVID-19 on screening, entry into clinical trials, and hesitancy among patients and health-care professionals, limiting adjuvant and metastatic cancer treatment.