Regulatory T Cell Insufficiency in Autoimmune Diabetes Is Driven by Selective Loss of Neuropilin-1 on Intraislet Regulatory T Cells.

Approaches to reverse or limit regulatory T cell (Treg) insufficiency are of great interest for development of immunotherapeutic treatments for autoimmune patients, including type 1 diabetes. Treg insufficiency is heavily implicated in the progression of autoimmune diabetes in the NOD mouse model and is characterized by defects in Treg numbers, development, and/or function. Utilizing a Treg-centric screen, we show that intraislet Tregs have a uniquely dysfunctional phenotype, hallmarked by an almost complete lack of neuropilin-1 (Nrp1), a cell surface receptor required to maintain Treg stability. Intraislet Nrp1- Tregs exhibit hallmark features of fragility, including reduced suppressive capacity, decreased CD73 and Helios, and increased Rorγt and Tbet. Intraislet Nrp1- Tregs also exhibit decreased Foxp3 expression on a per cell basis, suggesting that Nrp1 may also be required for long-term Treg stability. Mechanistically, Treg-restricted augmentation of Nrp1 expression limited the onset of autoimmune diabetes in NOD mice suggesting that Nrp1 critically impacts intraislet Treg function. Transcriptional analysis showed that Nrp1 restoration led to an increase in markers and pathways of TCR signaling, survival, and suppression, and when Nrp1 protein expression is examined by cellular indexing of transcriptomes and epitopes by sequencing, significant differences were observed between Nrp1+ and Nrp1- Tregs in all tissues, particularly in markers of Treg fragility. This translated into substantive differences between Nrp1+ and Nrp1- Tregs that afforded the former with a competitive advantage in the islets. Taken together, these data suggest that maintenance of Nrp1 expression and signaling on Tregs limits diabetes onset and may serve as a strategy to combat Treg insufficiency in autoimmune disease.

The RNA binding protein Arid5a drives IL-17-dependent autoantibody-induced glomerulonephritis.

Autoantibody-mediated glomerulonephritis (AGN) arises from dysregulated renal inflammation, with urgent need for improved treatments. IL-17 is implicated in AGN and drives pathology in a kidney-intrinsic manner via renal tubular epithelial cells (RTECs). Nonetheless, downstream signaling mechanisms provoking kidney pathology are poorly understood. A noncanonical RNA binding protein (RBP), Arid5a, was upregulated in human and mouse AGN. Arid5a-/- mice were refractory to AGN, with attenuated myeloid infiltration and impaired expression of IL-17-dependent cytokines and transcription factors (C/EBPß, C/EBPδ). Transcriptome-wide RIP-Seq revealed that Arid5a inducibly interacts with conventional IL-17 target mRNAs, including CEBPB and CEBPD. Unexpectedly, many Arid5a RNA targets corresponded to translational regulation and RNA processing pathways, including rRNAs. Indeed, global protein synthesis was repressed in Arid5a-deficient cells, and C/EBPs were controlled at the level of protein rather than RNA accumulation. IL-17 prompted Arid5a nuclear export and association with 18S rRNA, a 40S ribosome constituent. Accordingly, IL-17-dependent renal autoimmunity is driven by Arid5a at the level of ribosome interactions and translation.

The medical competence of health care providers in sub-Saharan Africa: Evidence from 16 127 providers across 11 countries.

Despite a consensus that quality of care is critically deficient in low-income countries, few nationally representative studies provide comparable measures of quality of care across countries. To address this gap, we used nationally representative data from in-person administrations of clinical vignettes to measure the competence of 16 127 health care providers across 11 sub-Saharan African countries. Rather than large variations across countries, we found that 81% of the variation in competence is within countries and the characteristics of health care providers do not explain most of this variation. Professional qualifications-including cadre and education-are only weakly associated with competence: across our sample, one-third of nurses are more competent than the average doctor in the same country and one-quarter of doctors are less competent than the average nurse. Finally, while younger cohorts do tend to be more competent, perhaps reflecting improvements in medical education, it would take 25 decades of turnover to improve care by 10 percentage points, on average, if we were to rely on such improvements alone. These patterns necessitate a fundamentally different approach to health care human resource management, calling into question typical staffing policies based on qualifications and seniority rather than directly measured quality.

Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.

New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.

Temporal dynamics and genomic programming of plasma cell fates.

Affinity-matured plasma cells (PCs) of varying lifespans are generated through a germinal center (GC) response. The developmental dynamics and genomic programs of antigen-specific PC precursors remain to be elucidated. Here, using a model antigen in mice, we demonstrate biphasic generation of PC precursors, with those generating long-lived bone marrow PCs preferentially produced in the late phase of GC response. Clonal tracing using single-cell RNA sequencing and B cell antigen receptor sequencing in spleen and bone marrow compartments, coupled with adoptive transfer experiments, reveals a new PC transition state that gives rise to functionally competent PC precursors. The latter undergo clonal expansion, dependent on inducible expression of TIGIT. We propose a model for the proliferation and programming of precursors of long-lived PCs, based on extended antigen encounters in the GC.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

Prevalence of long COVID symptoms in Haryana, India: a cross-sectional follow-up study.

Background: Emerging research indicates growing concern over long COVID globally, although there have been limited studies that estimate population burden. We aimed to estimate the burden of long COVID in three districts of Haryana, India, using an opportunity to link a seroprevalence study to follow-up survey of symptoms associated with long COVID. Methods: We used a population-based seroprevalence survey for COVID-19 conducted in September 2021 across Haryana, India. Adults from three purposively selected districts (Rohtak, Gurugram, and Jhajjar) were eligible to participate; 2205 of 3213 consented to participate in a survey on health status. Trained investigators administered a structured questionnaire that included demographic characteristics, self-reported symptoms of illness in the last six months before the survey, mental health, and history of COVID-19. Findings: Unadjusted regression estimates indicated positive correlations between symptomatic complaints and COVID-19 exposure, suggesting lingering effects of COVID-19 in this population. The overall physical morbidity index was higher among those who tested positive for COVID-19, as was the incidence of new cases. However, both morbidity and incidence became statistically insignificant after adjustment for multiple comparisons. Cough emerged as the only statistically significant individual persistent symptom. Sex-stratified analyses indicated significant estimates only for physical morbidity in women. Interpretation: This study is one of the first from India that uses a large population-based sample to examine longer term repercussions of COVID infections. The burden of long COVID should primarily be addressed in clinical settings, where specialised treatment for individual cases continues to evolve. Our analyses also provide insight into the size and nature of studies required to assess the population-level burden of long COVID. Funding: This paper was produced under the auspices of the Lancet COVID 19 Commission India Task Force, which was supported financially by the Reliance Foundation. The Lancet COVID 19 Commission was set up in July 2020 and submitted its final report by October 2022. This report by the India Task Force was prepared during the same period.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.

Care pathways of individuals with tuberculosis before and during the COVID-19 pandemic in Bandung, Indonesia.

The COVID-19 pandemic is thought to have undone years' worth of progress in the fight against tuberculosis (TB). For instance, in Indonesia, a high TB burden country, TB case notifications decreased by 14% and treatment coverage decreased by 47% during COVID-19. We sought to better understand the impact of COVID-19 on TB case detection using two cross-sectional surveys conducted before (2018) and after the onset of the pandemic (2021). These surveys allowed us to quantify the delays that individuals with TB who eventually received treatment at private providers faced while trying to access care for their illness, their journey to obtain a diagnosis, the encounters individuals had with healthcare providers before a TB diagnosis, and the factors associated with patient delay and the total number of provider encounters. We found some worsening of care seeking pathways on multiple dimensions. Median patient delay increased from 28 days (IQR: 10, 31) to 32 days (IQR: 14, 90) and the median number of encounters increased from 5 (IQR: 4, 8) to 7 (IQR: 5, 10), but doctor and treatment delays remained relatively unchanged. Employed individuals experienced shorter delays compared to unemployed individuals (adjusted medians: -20.13, CI -39.14, -1.12) while individuals whose initial consult was in the private hospitals experienced less encounters compared to those visiting public providers, private primary care providers, and informal providers (-4.29 encounters, CI -6.76, -1.81). Patients who visited the healthcare providers >6 times experienced longer total delay compared to those with less than 6 visits (adjusted medians: 59.40, 95% CI: 35.04, 83.77). Our findings suggest the need to ramp up awareness programs to reduce patient delay and strengthen private provide engagement in the country, particularly in the primary care sector.

Impact of the COVID-19 pandemic on quality of tuberculosis care in private facilities in Bandung, Indonesia: a repeated cross-sectional standardized patients study.

BACKGROUND: Indonesia has the second highest incidence of tuberculosis in the world. While 74% of people with tuberculosis in Indonesia first accessed the private health sector when seeking care for their symptoms, only 18% of tuberculosis notifications originate in the private sector. Little is known about the impact of the COVID-19 pandemic on the private sector. Using unannounced standardized patient visits to private providers, we aimed to measure quality of tuberculosis care during the COVID-19 pandemic. METHODS: A cross-sectional study was conducted using standardized patients in Bandung City, West Java, Indonesia. Ten standardized patients completed 292 visits with private providers between 9 July 2021 and 21 January 2022, wherein standardized patients presented a presumptive tuberculosis case. Results were compared to standardized patients surveys conducted in the same geographical area before the onset of COVID-19. RESULTS: Overall, 35% (95% confidence interval (CI): 29.2-40.4%) of visits were managed correctly according to national tuberculosis guidelines. There were no significant differences in the clinical management of presumptive tuberculosis patients before and during the COVID-19 pandemic, apart from an increase in temperature checks (adjusted odds ratio (aOR): 8.05, 95% CI: 2.96-21.9, p < 0.001) and a decrease in throat examinations (aOR 0.16, 95% CI: 0.06-0.41, p = 0.002) conducted during the pandemic. CONCLUSIONS: Results indicate that providers successfully identify tuberculosis in their patients yet do not manage them according to national guidelines. There were no major changes found in quality of tuberculosis care due to the COVID-19 pandemic. As tuberculosis notifications have declined in Indonesia due to the COVID-19 pandemic, there remains an urgent need to increase private provider engagement in Indonesia and improve quality of care.

SARS-CoV-2 mRNA Vaccines Induce Greater Complement Activation and Decreased Viremia and Nef Antibodies in Men With HIV-1.

BACKGROUND: Immune dysregulation in people with human immunodeficiency virus-1 (PWH) persists despite potent antiretroviral therapy and, consequently, PWH tend to have lower immune responses to licensed vaccines. However, limited information is available about the impact of mRNA vaccines in PWH. This study details the immunologic responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines in PWH and their impact on HIV-1. METHODS: We quantified anti-S immunoglobulin G (IgG) binding and neutralization of 3 SARS-CoV-2 variants of concern and complement activation in blood from virally suppressed men with HIV-1 (MWH) and men without HIV-1 (MWOH), and the characteristics that may impact the vaccine immune responses. We also studied antibody levels against HIV-1 proteins and HIV-1 plasma RNA. RESULTS: MWH had lower anti-S IgG binding and neutralizing antibodies against the 3 variants compared to MWOH. MWH also produced anti-S1 antibodies with a 10-fold greater ability to activate complement and exhibited higher C3a blood levels than MWOH. MWH had decreased residual HIV-1 plasma viremia and anti-Nef IgG approximately 100 days after immunization. CONCLUSIONS: MWH respond to SARS-CoV-2 mRNA vaccines with lower antibody titers and with greater activation of complement, while exhibiting a decrease in HIV-1 viremia and anti-Nef antibodies. These results suggest an important role of complement activation mediating protection in MWH.

Transcriptomic and Proteomic Characterization of the Immune Response to Elective Spinal Reconstructive Surgery: Impact of Aging and Comparison with Traumatic Injury Response.

BACKGROUND: Major surgery triggers trauma-like stress responses linked to age, surgery duration, and blood loss, resembling polytrauma. This similarity suggests elective surgery as a surrogate model for studying polytrauma immune responses. We investigated stress responses across age groups and compared them with those of polytrauma patients. STUDY DESIGN: Patients undergoing major spinal reconstruction surgery were divided into older (age >65 years, n = 5) and young (age 18 to 39 years, n = 6) groups. A comparison group consisted of matched trauma patients (n = 8). Blood samples were collected before, during, and after surgery. Bone marrow mononuclear cells and peripheral blood mononuclear cells were analyzed using cellular indexing of transcriptomes and epitopes sequencing or single-cell RNA sequencing. Plasma was subjected to dual-platform proteomic analysis (SomaLogic and O-link). RESULTS: Response to polytrauma was highest within 4 hours. By comparison, the response to surgery was highest at 24 hours. Both insults triggered significant changes in cluster of differentiation 14 monocytes, with increased inflammation and lower major histocompatibility complex-class 2 expression. Older patient's cluster of differentiation 14 monocytes displayed higher inflammation and less major histocompatibility complex-class 2 suppression; a trend was also seen in bone marrow mononuclear cells. Although natural killer cells were markedly activated after polytrauma, they were suppressed after surgery, especially in older patients. In plasma, innate immunity proteins dominated at 24 hours, shifting to adaptive immunity proteins by 6 weeks with heightened inflammation in older patients. Senescence-associated secretory phenotype proteins were higher in older patients at baseline and further elevated during and after surgery. CONCLUSIONS: Although both major surgery and polytrauma initiate immune and stress responses, substantial differences exist in timing and cellular profiles, suggesting major elective surgery is not a suitable surrogate for the polytrauma response. Nonetheless, distinct responses in young vs older patients highlight the utility of elective spinal in studying patient-specific factors affecting outcomes after major elective surgery.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches.

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition.

Multiple cancers exhibit aberrant protein arginine methylation by both type I arginine methyltransferases, predominately protein arginine methyltransferase 1 (PRMT1) and to a lesser extent PRMT4, and by type II PRMTs, predominately PRMT5. Here, we perform targeted proteomics following inhibition of PRMT1, PRMT4, and PRMT5 across 12 cancer cell lines. We find that inhibition of type I and II PRMTs suppresses phosphorylated and total ATR in cancer cells. Loss of ATR from PRMT inhibition results in defective DNA replication stress response activation, including from PARP inhibitors. Inhibition of type I and II PRMTs is synergistic with PARP inhibition regardless of homologous recombination function, but type I PRMT inhibition is more toxic to non-malignant cells. Finally, we demonstrate that the combination of PARP and PRMT5 inhibition improves survival in both BRCA-mutant and wild-type patient-derived xenografts without toxicity. Taken together, these results demonstrate that PRMT5 inhibition may be a well-tolerated approach to sensitize tumors to PARP inhibition.

The family doctor: health, kin testing and primary care in Patna, India.

Private primary care providers are usually the first site where afflictions come under institutional view. In the context of poverty, the relationship between illness and care is more complex than a simple division of responsibilities between various actors-with care given by kin, and diagnosis and treatment being the purview of providers. Since patients would often visit the provider with family members, providers are attuned to the patients' web of kinship. Providers would take patients' kinship arrangements into account when prescribing diagnostic tests and treatments. This paper terms this aspect of the clinical encounter as 'kin testing' to refer to situations/clinical encounters when providers take into consideration that care provided by kin was conditional. 'Kin testing' allowed providers to manage the episode of illness that had brought the patient to the clinic by relying on clinical judgment rather than confirmed laboratory tests. Furthermore, since complaints of poor health also were an idiom to communicate kin neglect, providers had to also discern how to negotiate diagnoses and treatments. Kinship determined whether the afflicted bodies brought to the clinics were diagnosed, whether medicines reached the body, and adherence maintained. The providers' actions make visible the difference that kinship made in how health is imagined in the clinic and in standardized protocols. Focusing on primary care clinics in Patna, India, we contribute to research that shows that kinship determines care and management of illnesses at home by showing that relatedness of patients gets folded in the clinic by providers as well.

Temporal dynamics and genomic programming of plasma cell fates.

Affinity-matured plasma cells (PCs) of varying lifespans are generated through a germinal center (GC) response. The developmental dynamics and genomic programs of antigen-specific PC precursors remain to be elucidated. Using a model antigen, we demonstrate biphasic generation of PC precursors, with those generating long-lived bone marrow PCs preferentially produced in the late phase of GC response. Clonal tracing using scRNA-seq+BCR-seq in spleen and bone marrow compartments, coupled with adoptive transfer experiments, reveal a novel PC transition state that gives rise to functionally competent PC precursors. The latter undergo clonal expansion, dependent on inducible expression of TIGIT. We propose a model for the proliferation and programming of precursors of long-lived PCs, based on extended antigen encounters followed by reduced antigen availability.

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.

BACKGROUND: e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients. OBJECTIVE: The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic. METHODS: The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients. RESULTS: Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model). CONCLUSIONS: Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

The gut protist Tritrichomonas arnold restrains virus-mediated loss of oral tolerance by modulating dietary antigen-presenting dendritic cells.

Loss of oral tolerance (LOT) to gluten, driven by dendritic cell (DC) priming of gluten-specific T helper 1 (Th1) cell immune responses, is a hallmark of celiac disease (CeD) and can be triggered by enteric viral infections. Whether certain commensals can moderate virus-mediated LOT remains elusive. Here, using a mouse model of virus-mediated LOT, we discovered that the gut-colonizing protist Tritrichomonas (T.) arnold promotes oral tolerance and protects against reovirus- and murine norovirus-mediated LOT, independent of the microbiota. Protection was not attributable to antiviral host responses or T. arnold-mediated innate type 2 immunity. Mechanistically, T. arnold directly restrained the proinflammatory program in dietary antigen-presenting DCs, subsequently limiting Th1 and promoting regulatory T cell responses. Finally, analysis of fecal microbiomes showed that T. arnold-related Parabasalid strains are underrepresented in human CeD patients. Altogether, these findings will motivate further exploration of oral-tolerance-promoting protists in CeD and other immune-mediated food sensitivities.

Stability and heterogeneity in the antimicrobiota reactivity of human milk-derived immunoglobulin A.

Immunoglobulin A (IgA) is secreted into breast milk and is critical for both protecting against enteric pathogens and shaping the infant intestinal microbiota. The efficacy of breast milk-derived maternal IgA (BrmIgA) is dependent upon its specificity; however, heterogeneity in BrmIgA binding ability to the infant microbiota is not known. Using a flow cytometric array, we analyzed the reactivity of BrmIgA against bacteria common to the infant microbiota and discovered substantial heterogeneity between all donors, independent of preterm or term delivery. Surprisingly, we also observed intradonor variability in the BrmIgA response to closely related bacterial isolates. Conversely, longitudinal analysis showed that the antibacterial BrmIgA reactivity was relatively stable through time, even between sequential infants, indicating that mammary gland IgA responses are durable. Together, our study demonstrates that the antibacterial BrmIgA reactivity displays interindividual heterogeneity but intraindividual stability. These findings have important implications for how breast milk shapes the development of the preterm infant microbiota and protects against necrotizing enterocolitis.