Pregnancies with 'double-positive' multiple marker screening results: a population-based study in Ontario, Canada.

BACKGROUND: Multiple marker screening is offered to pregnant individuals in many jurisdictions to screen for trisomies 21 and 18. On occasion, the result is 'double-positive'-a screening result that is unexpectedly positive for both aneuploidies. Although this occurs rarely, the paucity of available evidence about the outcomes of these pregnancies hinders patient counselling. This study aimed to investigate the association of double-positive results with preterm birth and other adverse perinatal outcomes. METHODS: We conducted a population-based retrospective cohort study of pregnancies with an estimated date of delivery from September 1, 2016, to March 31, 2021, using province-wide perinatal registry data in Ontario, Canada. Pregnancies with double-positive screening results where trisomies 21 and 18 were ruled-out were compared to pregnancies with screen negative results for both aneuploidies. We used modified Poisson regression models with robust variance estimation to examine the association of double positive results with preterm birth and secondary outcomes. RESULTS: From 429 540 pregnancies with multiple marker screening, 863 (0.2%) had a double-positive result; trisomies 21 and 18 were ruled out in 374 pregnancies, 203 of which resulted in a live birth. Among the pregnancies in the double-positive group resulting in a live birth, the risk of preterm birth was increased compared to pregnancies with a screen negative result: adjusted risk ratio (aRR) 2.6 (95%CI 2.0-3.6), adjusted risk difference (aRD) 10.5% (95%CI 5.4-15.7). In a sensitivity analysis excluding all diagnosed chromosomal abnormalities, the risk of preterm birth remained elevated to a similar degree: aRR 2.6 (95%CI 1.9-3.7), aRD 10.0% (95%CI 4.8-15.3). The risk of other adverse perinatal outcomes was also higher, including the risk of chromosomal abnormalities other than trisomies 21 and 18: aRR 81.1 (95%CI 69.4-94.8), aRD 34.0% (95%CI 29.2-38.8). Pregnancies with double-positive results were also less likely to result in a live birth, even when excluding all diagnosed chromosomal abnormalities; and at increased risk of adverse perinatal outcomes for those resulting in a live birth. CONCLUSION: Although rare, double-positive multiple marker screening results are associated with an increased risk of preterm birth and other adverse perinatal outcomes, even when excluding all identified chromosomal abnormalities.

Elevations in plasma glucagon are associated with reduced insulin clearance after ingestion of a mixed-macronutrient meal in people with and without type 2 diabetes.

AIMS/HYPOTHESIS: The temporal suppression of insulin clearance after glucose ingestion is a key determinant of glucose tolerance for people without type 2 diabetes. Whether similar adaptations are observed after the ingestion of a mixed-macronutrient meal is unclear. METHODS: In a secondary analysis of data derived from two randomised, controlled trials, we studied the temporal responses of insulin clearance after the ingestion of a standardised breakfast meal consisting of cereal and milk in lean normoglycaemic individuals (n=12; Lean-NGT), normoglycaemic individuals with central obesity (n=11; Obese-NGT) and in people with type 2 diabetes (n=19). Pre-hepatic insulin secretion rates were determined by the deconvolution of C-peptide, and insulin clearance was calculated using a single-pool model. Insulin sensitivity was measured by an oral minimal model. RESULTS: There were divergent time course changes in insulin clearance between groups. In the Lean-NGT group, there was an immediate post-meal increase in insulin clearance compared with pre-meal values (p<0.05), whereas insulin clearance remained stable at baseline values in Obese-NGT or declined slightly in the type 2 diabetes group (p<0.05). The mean AUC for insulin clearance during the test was ~40% lower in the Obese-NGT (1.3 ± 0.4 l min-1 m-2) and type 2 diabetes (1.4 ± 0.7 l min-1 m-2) groups compared with Lean-NGT (1.9 ± 0.5 l min-1 m-2; p<0.01), with no difference between the Obese-NGT and type 2 diabetes groups. HOMA-IR and glucagon AUC emerged as predictors of insulin clearance AUC, independent of BMI, age or insulin sensitivity (adjusted R2=0.670). Individuals with increased glucagon AUC had a 40% reduction in insulin clearance AUC (~ -0.75 l min-1 m-2; p<0.001). CONCLUSIONS/INTERPRETATION: The ingestion of a mixed-macronutrient meal augments differing temporal profiles in insulin clearance among individuals without type 2 diabetes, which is associated with HOMA-IR and the secretion of glucagon. Further research investigating the role of hepatic glucagon signalling in postprandial insulin kinetics is warranted. TRIAL REGISTRATION: ISRCTN17563146 and ISRCTN95281775.

A rapid molecular detection tool for toxigenic M1UK Streptococcus pyogenes.

BACKGROUND: The gradual replacement of the Streptococcus pyogenes M1global genotype by a newly emergent M1UK variant is a global public health threat warranting increased surveillance. M1UK differs from progenitor M1global genotype by 27 single nucleotide polymorphisms (SNPs) and is characterised by increased speA superantigen expression in vitro. METHODS: An allele-specific real-time PCR assay was developed for the rapid detection of M1UK strains. The assay was used in combination with whole-genome sequencing to determine emm (sub)type distribution for 51 invasive (n = 9) and non-invasive (n = 42) S. pyogenes clinical isolates. RESULTS: Emm1 was the most prevalent S. pyogenes emm serotype (n = 11) in this set of clinical isolates, with M1UK being the dominant emm1 genotype (4/5 invasive, 3/6 non-invasive isolates). The assay accurately detected M1UK strains. Whole genome sequencing revealed continued presence of Australian M1UK sub-lineages associated with epidemic scarlet fever-causing S. pyogenes in Asia. CONCLUSIONS: Our study establishes a suitable target for detection of the toxigenic M1UK, and confirms the maintenance of M1UK strains in Queensland, Australia. This assay can be deployed in laboratories and provides a valuable, cost-effective tool to enhance surveillance of the expanding M1UK clone.

Association between prenatal air pollutant exposure and autism spectrum disorders in young children: A matched case-control study in Canada.

The direction and magnitude of association between maternal exposure to ambient air pollutants across gestational windows and offspring risk of autism spectrum disorders (ASD) remains unclear. We sought to evaluate the time-varying effects of prenatal air pollutant exposure on ASD. We conducted a matched case-control study of singleton term children born in Ontario, Canada from 1-Apr-2012 to 31-Dec-2016. Provincial birth registry data were linked with applied behavioural analysis services and ambient air pollutant datasets to ascertain prenatal exposure to nitrogen dioxide (NO2), ground-level ozone (O3), fine particulate matter (PM2.5), and ASD diagnoses. Covariate balance between cases and controls was established using coarsened exact matching. Conditional logistic regression was used to assess the association between prenatal air pollutant exposure and ASD. Distributed lag non-linear models (DLNM) were used to examine the effects of single-pollutant exposure by prenatal week. Sensitivity analyses were conducted to assess the impact of exposure period on the observed findings. The final sample included 1589 ASD cases and 7563 controls. Compared to controls, cases were more likely to be born to mothers living in urban areas, delivered by Caesarean section, and assigned male sex at birth. NO2 was a consistent and significant contributor to ASD risk after accounting for co-exposure to O3, PM2.5 and covariates. The odds ratio per interquartile range increase was 2.1 (95%CI 1.8-2.3) pre-conception, 2.2 (2.0-2.5) for the 1st trimester, 2.2 (1.9-2.5) for the 2nd trimester, and 2.1 (1.9-2.4) for the 3rd trimester. In contrast, findings for O3 and PM2.5 with ASD were inconsistent. Findings from DLNM and sensitivity analyses were similar. Exposure to NO2 before and during pregnancy was significantly associated with ASD in offspring. The relationship between prenatal O3 and PM2.5 exposure and ASD remains unclear. Further investigation into the combined effects of multi-pollutant exposure on child neurodevelopment is warranted.

A retrospective analysis of treatment patterns, overall survival, and real-world disease-free survival in early-stage non-small cell lung cancer following complete resection.

BACKGROUND: Real-world data regarding patient characteristics, adjuvant treatment patterns, and long-term survival outcomes are needed to better understand unmet needs among patients with completely resected early-stage non-small cell lung cancer (NSCLC). METHODS: Electronic medical records from the U.S.-based ConcertAI Patient360™ database were analyzed in patients with stage IB-IIIA NSCLC who underwent complete resection prior to March 1, 2016. Patients were followed until death or July 1, 2021. This study evaluated adjuvant chemotherapy use, and overall survival (OS) and real-world disease-free survival (rwDFS) outcomes using the Kaplan-Meier method. The correlation between OS and rwDFS was assessed using the Kendall rank test. Among patients who did not recur 5 years following surgery, landmark analyses of OS and rwDFS were conducted to understand the subsequent survival impact of remaining disease-free for at least 5 years. RESULTS: Data from 441 patients with completely resected stage IB-IIIA NSCLC were included. About 35% of patients received adjuvant chemotherapy post-resection. Median OS and rwDFS from resection were 83.1 months and 42.4 months, respectively. The 5-year OS and rwDFS rates were 65.7% and 42.1%, respectively. OS and rwDFS were positively correlated (Kendall rank correlation coefficient = 0.67; p < 0.0001). Among patients without recurrence within 5 years after resection, the subsequent 5-year OS and rwDFS survival rates were 52.9% and 36.6%, respectively. CONCLUSIONS: Use of adjuvant chemotherapy was low, and the overall 5-year OS rate remained low despite all patients having undergone complete resection. Patients who remained non-recurrent over time had favorable subsequent long-term survival.

Interventions for intimate partner violence during the perinatal period: A scoping review: A systematic review.

Background: Intimate partner violence (IPV) is a prevalent global health problem. IPV that occurs before pregnancy often continues during the perinatal period, resulting in ongoing violence and many adverse maternal, obstetrical, and neonatal outcomes. Objectives: This scoping review is designed to broadly capture all potential interventions for perinatal IPV and describe their core components and measured outcomes. Search Methods: We conducted a search for empirical studies describing IPV interventions in the perinatal population in June 2022. The search was conducted in MEDLINE, EMBASE, PsycInfo, CINAHL, Cochrane Central Register of Controlled Trials, Web of Science, Applied Social Sciences Index & Abstracts, ClinicalTrials.gov and MedRxiv. Hand searching of references from select articles was also performed. Selection Criteria: Included studies described an intervention for those experiencing IPV during the perinatal period, including 12 months before pregnancy, while pregnant or in the 12 months post-partum. The search encompassed January 2000 to June 2022 and only peer-reviewed studies written in either English or French were included. Included interventions focused on the survivor exposed to IPV, rather than healthcare professionals administering the intervention. Interventions designed to reduce IPV revictimization or any adverse maternal, obstetrical, or neonatal health outcomes as well as social outcomes related to IPV victimization were included. Data Collections and Analysis: We used standard methodological procedures expected by The Campbell Collaboration. Main Results: In total, 10,079 titles and abstracts were screened and 226 proceeded to full text screening. A total of 67 studies included perinatal IPV interventions and were included in the final sample. These studies included a total of 27,327 participants. Included studies originated from 19 countries, and the majority were randomized controlled trials (n = 43). Most studies were of moderate or low quality. Interventions included home visitation, educational modules, counseling, and cash transfer programs and occurred primarily in community obstetrician and gynecologist clinics, hospitals, or in participants' homes. Most interventions focused on reducing revictimization of IPV (n = 38), improving survivor knowledge or acceptance of violence, knowledge of community resources, and actions to reduce violence (n = 28), and improving maternal mental health outcomes (n = 26). Few studies evaluated the effect of perinatal IPV interventions on obstetrical, neonatal or child health outcomes. Authors' Conclusions: The majority of intervention studies for perinatal IPV focus on reducing revictimization and improving mental health outcomes, very few included obstetrical, neonatal, and other physical health outcomes. Future interventions should place a larger emphasis on targeting maternal and neonatal outcomes to have the largest possible impact on the lives and families of IPV survivors and their infants.

Temporal Trends in Gestational Diabetes Mellitus and Associated Risk Factors in Ontario, Canada, 2012-2020: A Population-Based Cohort Study.

OBJECTIVES: The prevalence of gestational diabetes mellitus (GDM) has been increasing globally over recent decades; however, underlying reasons for the increase remain unclear. We analyzed trends in GDM rates and evaluated risk factors associated with the observed trends in Ontario, Canada. METHODS: We conducted a retrospective population-based cohort study using the Better Outcomes Registry and Network Ontario, linked with the Canadian Institute for Health Information Discharge Abstract Database. All pregnant individuals who had a singleton hospital delivery from 1 April 2012 to 31 March 2020 were included. We calculated rates and 95% CIs for GDM by year of delivery and contrasted fiscal year 2019/20 with 2012/13. Temporal trends in GDM were quantified using crude and adjusted risk ratios by modified Poisson regression. We further quantified the temporal increase attributable to changes in maternal characteristics by decomposition analysis. RESULTS: Among 1 044 258 pregnant individuals, 82 896 (7.9%) were diagnosed with GDM over the 8 years. GDM rate rose from 6.1 to 10.4 per 100 deliveries between fiscal years 2012/13 and 2019/20. The risk of GDM in 2019/20 was 1.53 times (95% CI 1.50-1.56) higher compared with 2012/13. 27% of the increase in GDM was due to changes in maternal age, 8 BMI, and Asian ethnicity. CONCLUSIONS: The GDM rate has been consistently increasing in Ontario, Canada. The contribution of increasing maternal age, pre-pregnancy obesity, and Asian ethnicity to the recent increase in GDM is notable. Further investigation is required to better understand the contributors to increasing GDM.

CAManim: Animating end-to-end network activation maps.

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions. Examples and source code can be found at: https://omni-ml.github.io/pytorch-grad-cam-anim/.

Changes associated with the COVID-19 pandemic on postpartum screening results in Ontario, Canada: The healthy babies healthy children screening tool.

BACKGROUND: Research on the impact of the COVID-19 pandemic on mothers/childbearing parents has mainly been cross-sectional and focused on psychological symptoms. This study examined the impact on function using ongoing, systematic screening of a representative Ontario sample. METHODS: An interrupted time series analysis of repeated cross-sectional data from a province-wide screening program using the Healthy Babies Healthy Children (HBHC) tool assessed changes associated with the pandemic at the time of postpartum discharge from hospital. Postal codes were used to link to neighborhood-level data. The ability to parent or care for the baby/child and other psychosocial and behavioral outcomes were assessed. RESULTS: The co-primary outcomes of inability to parent or care for the baby/child were infrequently observed in the pre-pandemic (March 9, 2019-March 15, 2020) and initial pandemic periods (March 16, 2020-March 23, 2021) (parent 209/63,006 (0.33%)-177/56,117 (0.32%), care 537/62,955 (0.85%)-324/56,086 (0.58%)). Changes after pandemic onset were not observed for either outcome although a significant (p = 0.02) increase in slope was observed for inability to parent (with questionable clinical significance). For secondary outcomes, worsening was only seen for reported complications during labor/delivery. Significant improvements were observed in the likelihood of being unable to identify a support person to assist with care, need of newcomer support, and concerns about money over time. CONCLUSIONS: There were no substantive changes in concerns about ability to parent or care for children. Adverse impacts of the pandemic may have been mitigated by accommodations for remote work and social safety net policies.

Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review.

Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.

Comparing prospectively assigned trial and real-world lung cancer patients.

Aim: To evaluate the comparability of a probable clinical trial (CT) cohort derived from electronic medical records (EMR) data with a real-world cohort treated with the same therapy and identified using the same inclusion and exclusion criteria to emulate an external control. Methods: We utilized de-identified patient-level structured data sourced from EMRs. We then compared patterns of overall survival (OS) between probable CT patients with those drawn from non-contemporaneous real-world data (RWD) using a two-sided log-rank test, hazard ratios (HRs) using a Cox proportional-hazards model and Kaplan-Meier (KM) survival curves. Each regression estimate was calculated with a corresponding 95% confidence interval. We additionally conducted multiple matching methods to assess their relative performance. Results: Median (standard deviation) OS was 10.2 (0.7) months for the RWD arm and 11.3 (1.3) for the probable CT arm with a Log rank p-value equal to 0.4771. OS in both cohorts is longer than the reported CT median OS of 9.2 (0.6). The HRs generated under all five assessed matching methods (including without adjustment) were not statistically significant at the 95% confidence level. Conclusion: Our results suggest, with caveats noted, that survival patterns between real-world and CT cohorts in this NSCLC setting are not statistically significantly different.

A harmonized public resource of deeply sequenced diverse human genomes.

Underrepresented populations are often excluded from genomic studies owing in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high-quality set of 4094 whole genomes from 80 populations in the HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also show substantial added value from this data set compared with the prior versions of the component resources, typically combined via liftOver and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared with previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality-control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.

Rare germline structural variants increase risk for pediatric solid tumors.

Pediatric solid tumors are rare malignancies that represent a leading cause of death by disease among children in developed countries. The early age-of-onset of these tumors suggests that germline genetic factors are involved, yet conventional germline testing for short coding variants in established predisposition genes only identifies pathogenic events in 10-15% of patients. Here, we examined the role of germline structural variants (SVs)-an underexplored form of germline variation-in pediatric extracranial solid tumors using germline genome sequencing of 1,766 affected children, their 943 unaffected relatives, and 6,665 adult controls. We discovered a sex-biased association between very large (>1 megabase) germline chromosomal abnormalities and a four-fold increased risk of solid tumors in male children. The overall impact of germline SVs was greatest in neuroblastoma, where we revealed burdens of ultra-rare SVs that cause loss-of-function of highly expressed, mutationally intolerant, neurodevelopmental genes, as well as noncoding SVs predicted to disrupt three-dimensional chromatin domains in neural crest-derived tissues. Collectively, our results implicate rare germline SVs as a predisposing factor to pediatric solid tumors that may guide future studies and clinical practice.

Sustaining e-health innovations in a complex hospital environment: learning through evidence.

Implementing and sustaining technological innovations in healthcare is a complex process. Commonly, innovations are abandoned due to unsuccessful attempts to sustain and scale-up post implementation. Limited information is available on what characterizes successful e-health innovations and the enabling factors that can lead to their sustainability in complex hospital environments. We present a successful implementation, sustainability and scale-up of a virtual care program consisting of three e-health applications (telemedicine, telehome monitoring, and interactive voice response) in a major cardiac care hospital in Canada. We describe their evolution and adaptation over time, present the innovative approach for their "business case" and funding that supported their implementation, and identify key factors that enabled their sustainability and success, which may inform future research and serve as a benchmark for other health care organizations. Despite resource constraints, e-health innovations can be deployed and successfully sustained in complex healthcare settings contingent key considerations: simplifying technology to make it intuitive for patients; providing significant value proposition that is research supported to influence policy changes; involving early supporters of adoption from administrative and clinical staff; engaging patients throughout the innovation cycle; and partnering with industry/technology providers.

The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.

CONTEXT: The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. OBJECTIVE: We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. METHODS: We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. RESULTS: Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. CONCLUSION: These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D.

Baseline phenotypes with preserved ß-cell function and high insulin concentrations have the best improvements in glucose tolerance after weight loss: results from the prospective DEXLIFE and EGIR-RISC studies.

BACKGROUND: Weight loss and lifestyle intervention improve glucose tolerance delaying the onset of type 2 diabetes (T2D), but individual responses are highly variable. Determining the predictive factors linked to the beneficial effects of weight loss on glucose tolerance could provide tools for individualized prevention plans. Thus, the aim was to investigate the relationship between pre-intervention values of insulin sensitivity and secretion and the improvement in glucose metabolism after weight loss. METHODS: In the DEXLIFE cohort (373 individuals at high risk of T2D, assigned 3:1 to a 12-week lifestyle intervention or a control arm, Trial Registration: ISRCTN66987085), K-means clustering and logistic regression analysis were performed based on pre-intervention indices of insulin sensitivity, insulin secretion (AUC-I), and glucose-stimulated insulin response (ratio of incremental areas of insulin and glucose, iAUC I/G). The response to the intervention was evaluated in terms of reduction of OGTT-glucose concentration. Clusters' validation was done in the prospective EGIR-RISC cohort (n = 1538). RESULTS: Four replicable clusters with different glycemic and metabolomic profiles were identified. Individuals had similar weight loss, but improvement in glycemic profile and ß-cell function was different among clusters, highly depending on pre-intervention insulin response to OGTT. Pre-intervention high insulin response was associated with the best improvement in AUC-G, while clusters with low AUC-I and iAUC I/G showed no beneficial effect of weight loss on glucose control, as also confirmed by the logistic regression model. CONCLUSIONS: Individuals with preserved ß-cell function and high insulin concentrations at baseline have the best improvement in glucose tolerance after weight loss.

Associations of neighborhood greenspace, and active living environments with autism spectrum disorders: A matched case-control study in Ontario, Canada.

BACKGROUND: Increasing evidence links early life residential exposure to natural urban environmental attributes and positive health outcomes in children. However, few studies have focused on their protective effects on the risk of autism spectrum disorder (ASD). The aim of this study was to investigate the associations of neighborhood greenspace, and active living environments during pregnancy with ASD in young children (≤6 years). METHODS: We conducted a population-based matched case-control study of singleton term births in Ontario, Canada for 2012-2016. The ASD and environmental data was generated using the Ontario Autism Spectrum Profile, the Better Outcomes Registry & Network Ontario, and Canadian Urban Environmental Health Research Consortium. We employed conditional logistic regressions to estimate the odds ratio (OR) between ASD and environmental factors characterizing selected greenspace metrics and neighborhoods conducive to active living (i.e., green view index (GVI), normalized difference vegetation index (NDVI), tree canopy, park proximity and active living environments index (ALE)). RESULTS: We linked 8643 mother-child pairs, including 1554 cases (18%). NDVI (OR 1.034, 0.944-1.024, per Inter Quartile Range [IQR] = 0.08), GVI (OR 1.025, 95% CI 0.953-1.087, per IQR = 9.45%), tree canopy (OR 0.992, 95% CI 0.903-1.089, per IQR = 6.24%) and the different categories of ALE were not associated with ASD in adjusted models for air pollution. In contrast, living closer to a park was protective (OR 0.888, 0.833-0.948, per 0.06 increase in park proximity index), when adjusted for air pollution. CONCLUSIONS: This study reported mixed findings showing both null and beneficial effects of green spaces and active living environments on ASD. Further investigations are warranted to elucidate the role of exposure to greenspaces and active living environments on the development of ASD.

Infant Infections Following Cesarean Delivery on Maternal Request: A Population-Based Cohort Study.

OBJECTIVES: Investigations about cesarean delivery (CD) on maternal request (CDMR) and infant infection risk frequently rely on administrative data with poorly defined indications for CD. We sought to determine the association between CDMR and infant infection using an intent-to-treat approach. METHODS: This was a population-based cohort study of low-risk singleton pregnancies with a term live birth in Ontario, Canada between April 2012 and March 2018. Subjects with prior CD were excluded. Outcomes included upper and lower respiratory tract infections, gastrointestinal infections, otitis media, and a composite of these 4. Relative risk and 95% CI were calculated for component and composite outcomes up to 1 year following planned CDMR versus planned vaginal deliveries (VDs). Subgroup and sensitivity analyses included age at infection (≤28 vs. >28 days), type of care (ambulatory vs. hospitalisation), restricting the cohort to nulliparous pregnancies, and including individuals with previous CD. Last, we re-examined outcome risk on an as-treated basis (actual CD vs. actual VD). RESULTS: Of 422 134 pregnancies, 0.4% (1827) resulted in a planned CDMR. After adjusting for covariates, planned CDMR was not associated with a risk of composite infant infections (adjusted relative risk 1.02; 95% CI 0.92-1.11). Findings for component infection outcomes, subgroup, and sensitivity analyses were similar. However, the as-treated analysis of the role of delivery mode on infant risk for infection demonstrated that actual CD (planned and unplanned) was associated with an increased risk for infant infections compared to actual VD. CONCLUSIONS: Planned CDMR is not associated with increased risk for neonatal or infant infections compared with planned VD. Study design must be carefully considered when investigating the impact of CDMR on infant infection outcomes.

Efficacy of Alum-Adjuvanted Peptide and Carbohydrate Conjugate Vaccine Candidates against Group A Streptococcus Pharyngeal Infection in a Non-Human Primate Model.

Vaccine development against group A Streptococcus (GAS) has gained traction in the last decade, fuelled by recognition of the significant worldwide burden of the disease. Several vaccine candidates are currently being evaluated in preclinical and early clinical studies. Here, we investigate two conjugate vaccine candidates that have shown promise in mouse models of infection. Two antigens, the J8 peptide from the conserved C-terminal end of the M protein, and the group A carbohydrate lacking N-acetylglucosamine side chain (ΔGAC) were each conjugated to arginine deiminase (ADI), an anchorless surface protein from GAS. Both conjugate vaccine candidates combined with alum adjuvant were tested in a non-human primate (NHP) model of pharyngeal infection. High antibody titres were detected against J8 and ADI antigens, while high background antibody titres in NHP sera hindered accurate quantification of ΔGAC-specific antibodies. The severity of pharyngitis and tonsillitis signs, as well as the level of GAS colonisation, showed no significant differences in NHPs immunised with either conjugate vaccine candidate compared to NHPs in the negative control group.

Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.