Caught in motion: human NTHL1 undergoes interdomain rearrangement necessary for catalysis.

Base excision repair (BER) is the main pathway protecting cells from the continuous damage to DNA inflicted by reactive oxygen species. BER is initiated by DNA glycosylases, each of which repairs a particular class of base damage. NTHL1, a bifunctional DNA glycosylase, possesses both glycolytic and ß-lytic activities with a preference for oxidized pyrimidine substrates. Defects in human NTHL1 drive a class of polyposis colorectal cancer. We report the first X-ray crystal structure of hNTHL1, revealing an open conformation not previously observed in the bacterial orthologs. In this conformation, the six-helical barrel domain comprising the helix-hairpin-helix (HhH) DNA binding motif is tipped away from the iron sulphur cluster-containing domain, requiring a conformational change to assemble a catalytic site upon DNA binding. We found that the flexibility of hNTHL1 and its ability to adopt an open configuration can be attributed to an interdomain linker. Swapping the human linker sequence for that of Escherichia coli yielded a protein chimera that crystallized in a closed conformation and had a reduced activity on lesion-containing DNA. This large scale interdomain rearrangement during catalysis is unprecedented for a HhH superfamily DNA glycosylase and provides important insight into the molecular mechanism of hNTHL1.

Therapeutic metamorphosis: Findings from a grounded theory study of the impact of low-dose prophylaxis in children living with haemophilia in India.

INTRODUCTION: The clinical benefits of administering low-dose prophylaxis in children with haemophilia are well established. Qualitative research describing the impact of prophylaxis on quality of life is comparatively rare in this area. AIM: The aim of this study was to investigate in children the experiences of living and becoming adjusted to haemophilia before prophylaxis, by collecting information directly from children and their parents or guardians. A further goal was to evaluate whether and how the use of low-dose prophylaxis impacts the disease experience. METHODS: A grounded theory design according to Strauss and Corbin was chosen for this study. The study was conducted in the Haemophilia Treatment Centre at Aluva, Kerala, India and involved nineteen participants (children, mothers, father and grandmothers) who were selected by theoretical sampling. Data were collected through audiotaped interviews, which included demographic and semi-structured interview questions. Data were coded and evolved into concepts and categories that lead to the emergence of theory. RESULTS: The study resulted in the construction of 'Theory of Therapeutic Metamorphosis'. It comprised two stages: stage of bondage (enduring hardships), experienced during the absence of prophylaxis or on-demand treatment and stage of freedom (deliverance/reductions, energized life/improvements and behaviour to seek prophylaxis) experienced during low-dose prophylaxis. CONCLUSION: This study illustrates the challenges faced by children with haemophilia and their families and the positive impact of low-dose prophylaxis. Further prospective research studies are required to add to the growing knowledge in this area.

Modeling the impact of school reopening on SARS-CoV-2 transmission using contact structure data from Shanghai.

BACKGROUND: Mathematical modeling studies have suggested that pre-emptive school closures alone have little overall impact on SARS-CoV-2 transmission, but reopening schools in the background of community contact reduction presents a unique scenario that has not been fully assessed. METHODS: We adapted a previously published model using contact information from Shanghai to model school reopening under various conditions. We investigated different strategies by combining the contact patterns observed between different age groups during both baseline and "lockdown" periods. We also tested the robustness of our strategy to the assumption of lower susceptibility to infection in children under age 15 years. RESULTS: We find that reopening schools for all children would maintain a post-intervention R0 < 1 up to a baseline R0 of approximately 3.3 provided that daily contacts among children 10-19 years are reduced to 33% of baseline. This finding was robust to various estimates of susceptibility to infection in children relative to adults (up to 50%) and to estimates of various levels of concomitant reopening in the rest of the community (up to 40%). However, full school reopening without any degree of contact reduction in the school setting returned R0 virtually back to baseline, highlighting the importance of mitigation measures. CONCLUSIONS: These results, based on contact structure data from Shanghai, suggest that schools can reopen with proper precautions during conditions of extreme contact reduction and during conditions of reasonable levels of reopening in the rest of the community.

A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data.

We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease.

VKORC1 and CYP2C9 genotype and patient characteristics explain a large proportion of the variability in warfarin dose requirement among children.

Although genetic and environmental factors explain approximately half of the interindividual variability in warfarin dose requirement in adults, there is limited information available in children. In a cross-sectional study of anticoagulated children from 5 tertiary care centers, 120 children with a stable warfarin dose were genotyped for VKORC1 (-1639G > A; rs9923231), CYP2C9 (*2 and *3 alleles; rs1799853 and rs1057910), and CYP4F2 (V433M; rs2108622) polymorphisms. Clinical and demographic features were recorded. Multiple regression analysis of the data showed that, although CYP4F2 made no contribution to the dose model, 72.4% of the variability in warfarin dose requirement is attributed to by patient height, genetic polymorphisms in VKORC1 and CYP2C9, and indication for warfarin. The recently published International Warfarin Pharmacogenetics Consortium pharmacogenetic-based warfarin dosing algorithm (based on data derived from anticoagulated adults) consistently overestimated warfarin dose for our cohort of children. A similar proportion of the interindividual variability in warfarin dose is explained by genetic factors in children compared with adult patients, although height is a greater predictor in children. A pharmacogenomic approach to warfarin dosing has the potential to improve the efficacy and safety of warfarin therapy in children. However, algorithms should be derived from data in children if their potential benefit is to be realized.

Global report on COVID-19 vaccination and reasons not to vaccinate among adults with intellectual disabilities: Results from secondary analyses of Special Olympics' program planning.

The COVID-19 pandemic has disproportionately affected people with intellectual disabilities worldwide. The objective of this study was to identify global rates of COVID-19 vaccination and reasons not to vaccinate among adults with intellectual disabilities (ID) associated with country economic income levels. The Special Olympics COVID-19 online survey was administered in January-February 2022 to adults with ID from 138 countries. Descriptive analyses of survey responses include 95% margins of error. Logistic regression and Pearson Chi-squared tests were calculated to assess associations with predictive variables for vaccination using R 4.1.2 software. Participants (n = 3560) represented 18 low (n = 410), 35 lower-middle (n = 1182), 41 upper-middle (n = 837), and 44 high (n = 1131) income countries. Globally, 76% (74.8-77.6%) received a COVID-19 vaccination while 49.5% (47.9-51.2%) received a COVID-19 booster. Upper-middle (93% (91.2-94.7%)) and high-income country (94% (92.1-95.0%)) participants had the highest rates of vaccination while low-income countries had the lowest rates (38% (33.3-42.7%)). In multivariate regression models, country economic income level (OR = 3.12, 95% CI [2.81, 3.48]), age (OR = 1.04, 95% CI [1.03, 1.05]), and living with family (OR = 0.70, 95% CI [0.53, 0.92]) were associated with vaccination. Among LLMICs, the major reason for not vaccinating was lack of access (41.2% (29.5-52.9%)). Globally, concerns about side effects (42%, (36.5-48.1%)) and parent/guardian not wanting the adult with ID to vaccinate (32% (26.1-37.0%)) were the most common reasons for not vaccinating. Adults with ID from low and low-middle income countries reported fewer COVID-19 vaccinations, suggesting reduced access and availability of resources in these countries. Globally, COVID-19 vaccination levels among adults with ID were higher than the general population. Interventions should address the increased risk of infection for those in congregate living situations and family caregiver apprehension to vaccinate this high-risk population.

Identifying neuroanatomical and behavioral features for autism spectrum disorder diagnosis in children using machine learning.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause significant social, communication, and behavioral challenges. Diagnosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. The present study adopts a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to select features most significant for distinguishing individuals with and without ASD, and is able to accommodate datasets having a small number of samples with a large number of feature measurements. The dataset is unique and comprises both behavioral and neuroimaging measurements from a total of 28 children from 7 to 14 years old. Potential biomarker candidates identified include brain volume, area, cortical thickness, and mean curvature in specific regions around the cingulate cortex, frontal cortex, and temporal-parietal junction, as well as behavioral features associated with theory of mind. A separate machine learning classifier (i.e., k-nearest neighbors algorithm) was used to validate the CCEA feature selection and for ASD prediction. Study findings demonstrate how machine learning tools might help move the needle on improving diagnostic and predictive models of ASD.

Immunotranscriptomic profiling the acute and clearance phases of a human challenge dengue virus serotype 2 infection model.

About 20-25% of dengue virus (DENV) infections become symptomatic ranging from self-limiting fever to shock. Immune gene expression changes during progression to severe dengue have been documented in hospitalized patients; however, baseline or kinetic information is difficult to standardize in natural infection. Here we profile the host immunotranscriptome response in humans before, during, and after infection with a partially attenuated rDEN2Δ30 challenge virus (ClinicalTrials.gov NCT02021968). Inflammatory genes including type I interferon and viral restriction pathways are induced during DENV2 viremia and return to baseline after viral clearance, while others including myeloid, migratory, humoral, and growth factor immune regulation factors pathways are found at non-baseline levels post-viremia. Furthermore, pre-infection baseline gene expression is useful to predict rDEN2Δ30-induced immune responses and the development of rash. Our results suggest a distinct immunological profile for mild rDEN2Δ30 infection and offer new potential biomarkers for characterizing primary DENV infection.

Novel Evolutionary Algorithm Identifies Interactions Driving Infestation of Triatoma dimidiata, a Chagas Disease Vector.

Chagas disease is a lethal, neglected tropical disease. Unfortunately, aggressive insecticide-spraying campaigns have not been able to eliminate domestic infestation of Triatoma dimidiata, the native vector in Guatemala. To target interventions toward houses most at risk of infestation, comprehensive socioeconomic and entomologic surveys were conducted in two towns in Jutiapa, Guatemala. Given the exhaustively large search space associated with combinations of risk factors, traditional statistics are limited in their ability to discover risk factor interactions. Two recently developed statistical evolutionary algorithms, specifically designed to accommodate risk factor interactions and heterogeneity, were applied to this large combinatorial search space and used in tandem to identify sets of risk factor combinations associated with infestation. The optimal model includes 10 risk factors in what is known as a third-order disjunctive normal form (i.e., infested households have chicken coops AND deteriorated bedroom walls OR an accumulation of objects AND dirt floors AND total number of occupants ≥ 5 AND years of electricity ≥ 5 OR poor hygienic condition ratings AND adobe walls AND deteriorated walls AND dogs). Houses with dirt floors and deteriorated walls have been reported previously as risk factors and align well with factors currently targeted by Ecohealth interventions to minimize infestation. However, the tandem evolutionary algorithms also identified two new socioeconomic risk factors (i.e., households having many occupants and years of electricity ≥ 5). Identifying key risk factors may help with the development of new Ecohealth interventions and/or reduce the survey time needed to identify houses most at risk.

Residual survival and local dispersal drive reinfestation by Triatoma dimidiata following insecticide application in Guatemala.

Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and transmitted by triatomine insect vectors. In Guatemala, insecticide spraying is an integral part of management of the main vector, Triatoma dimidiata. Spraying typically has low efficacy, which may be due to incomplete elimination from infested houses, within-village dispersal, or influx from other villages or sylvan environments. To evaluate how these mechanisms contribute to reinfestation, we conducted a time-course analysis of T. dimidiata infestation, abundance and household genetic structure in two nearby villages in Jutiapa, Guatemala; houses in the first village were surveyed, treated with insecticide if infested and then re-surveyed at eight and 22 months following spraying, while the second village served as an untreated control to quantify changes associated with seasonal dispersal. Insects were genotyped at 2-3000 SNP loci for kinship and population genetic analyses. Insecticide application reduced overall infestation and abundance, while the untreated village was stable over time. Nevertheless, within two years 35.5% of treated houses were reinfested and genetic diversity had largely recovered. Insects collected from reinfested houses post-spraying were most closely related to pre-spray collections from the same house, suggesting that infestations had not been fully eliminated. Immigration by unrelated insects was also detected within a year of spraying; when it occurred, dispersal was primarily local from neighboring houses. Similar dispersal patterns were observed following the annual dispersal season in the untreated village, with high-infestation houses serving as sources for neighboring homes. Our findings suggest that the efficacy of pyrethroid application is rapidly diminished by both within-house breeding by survivors and annual cycles of among-house movement. Given these patterns, we conclude that house structural improvements, an integral part of the Ecohealth approach that makes houses refractory to vector colonization and persistence, are critical for long-term reduction of T. dimidiata infestation.

Uncovering vector, parasite, blood meal and microbiome patterns from mixed-DNA specimens of the Chagas disease vector Triatoma dimidiata.

Chagas disease, considered a neglected disease by the World Health Organization, is caused by the protozoan parasite Trypanosoma cruzi, and transmitted by >140 triatomine species across the Americas. In Central America, the main vector is Triatoma dimidiata, an opportunistic blood meal feeder inhabiting both domestic and sylvatic ecotopes. Given the diversity of interacting biological agents involved in the epidemiology of Chagas disease, having simultaneous information on the dynamics of the parasite, vector, the gut microbiome of the vector, and the blood meal source would facilitate identifying key biotic factors associated with the risk of T. cruzi transmission. In this study, we developed a RADseq-based analysis pipeline to study mixed-species DNA extracted from T. dimidiata abdomens. To evaluate the efficacy of the method across spatial scales, we used a nested spatial sampling design that spanned from individual villages within Guatemala to major biogeographic regions of Central America. Information from each biotic source was distinguished with bioinformatics tools and used to evaluate the prevalence of T. cruzi infection and predominant Discrete Typing Units (DTUs) in the region, the population genetic structure of T. dimidiata, gut microbial diversity, and the blood meal history. An average of 3.25 million reads per specimen were obtained, with approximately 1% assigned to the parasite, 20% to the vector, 11% to bacteria, and 4% to putative blood meals. Using a total of 6,405 T. cruzi SNPs, we detected nine infected vectors harboring two distinct DTUs: TcI and a second unidentified strain, possibly TcIV. Vector specimens were sufficiently variable for population genomic analyses, with a total of 25,710 T. dimidiata SNPs across all samples that were sufficient to detect geographic genetic structure at both local and regional scales. We observed a diverse microbiotic community, with significantly higher bacterial species richness in infected T. dimidiata abdomens than those that were not infected. Unifrac analysis suggests a common assemblage of bacteria associated with infection, which co-occurs with the typical gut microbial community derived from the local environment. We identified vertebrate blood meals from five T. dimidiata abdomens, including chicken, dog, duck and human; however, additional detection methods would be necessary to confidently identify blood meal sources from most specimens. Overall, our study shows this method is effective for simultaneously generating genetic data on vectors and their associated parasites, along with ecological information on feeding patterns and microbial interactions that may be followed up with complementary approaches such as PCR-based parasite detection, 18S eukaryotic and 16S bacterial barcoding.

Geospatial and Temporal Analysis of Thyroid Cancer Incidence in a Rural Population.

BACKGROUND: The increasing incidence of thyroid cancer has resulted in the rate tripling over the past 30 years. Reasons for this increase have not been established. Geostatistics and geographic information system (GIS) tools have emerged as powerful geospatial technologies to identify disease clusters, map patterns and trends, and assess the impact of ecological and socioeconomic factors (SES) on the spatial distribution of diseases. In this study, these tools were used to analyze thyroid cancer incidence in a rural population. METHODS: Thyroid cancer incidence and socio-demographic factors in Vermont (VT), United States, between 1994 and 2007 were analyzed by logistic regression and geospatial and temporal analyses. RESULTS: The thyroid cancer age-adjusted incidence in Vermont (8.0 per 100,000) was comparable to the national level (8.4 per 100,000), as were the ratio of the incidence of females to males (3.1:1) and the mortality rate (0.5 per 100,000). However, the estimated annual percentage change was higher (8.3 VT; 5.7 U.S.). Incidence among females peaked at 30-59 years of age, reflecting a significant rise from 1994 to 2007, while incidence trends for males did not vary significantly by age. For both females and males, the distribution of tumors by size did not vary over time; ≤1.0 cm, 1.1-2.0 cm, and >2.0 cm represented 38%, 22%, and 40%, respectively. In females, papillary thyroid cancer (PTC) accounted for 89% of cases, follicular (FTC) 8%, medullary (MTC) 2%, and anaplastic (ATC) 0.6%, while in males PTC accounted for 77% of cases, FTC 15%, MTC 1%, and ATC 3%. Geospatial analysis revealed locations and spatial patterns that, when combined with multivariate incidence analyses, indicated that factors other than increased surveillance and access to healthcare (physician density or insurance) contributed to the increased thyroid cancer incidence. Nine thyroid cancer incidence hot spots, areas with very high normalized incidence, were identified based on zip code data. Those locations did not correlate with urban areas or healthcare centers. CONCLUSIONS: These data provide evidence of increased thyroid cancer incidence in a rural population likely due to environmental drivers and SES. Geospatial modeling can provide an important framework for evaluation of additional associative risk factors.

Prolactinoma presenting as chronic anaemia with osteoporosis: a case report.

INTRODUCTION: Unexplained anaemia is a rare mode of presentation for prolactinoma. We describe a case of a man, with chronic anaemia ascribed to old age. Six years later, he was evaluated and diagnosed with a prolactinoma and resultant osteoporosis. Prolactinoma in old people may present insidiously with chronic anaemia and osteoporosis with or without sexual dysfunction. CASE PRESENTATION: We describe the case of a 70-year-old Caucasian man who presented with mild anaemia and tiredness. His anaemia was investigated and ascribed to senescence. Endocrine causes were not considered or tested for. Six years later, he was again referred. Reassessment and direct questioning revealed long-standing sexual dysfunction. It was also discovered that our patient had fractured his radius twice, with minor trauma, during the preceding year. His serum prolactin was massively increased and a magnetic resonance imaging (MRI) scan of the head demonstrated a pituitary mass consistent with a prolactinoma. Dual X-ray absorptiometry revealed osteoporosis. Treatment of the prolactinoma led to a reduction in his serum prolactin with a rise in his haemoglobin to normal levels. This suggested that the prolactinoma was present during the initial presentation and was the cause of his anaemia. CONCLUSION: This case highlights the importance of fully evaluating and investigating unexplained anaemia in older people and that endocrine causes should be considered. Osteoporosis also requires evaluation with secondary causes considered.