Independent Populations Research Articles

Direct and vicarious exposure to healthcare discrimination and erasure among transgender and gender independent individuals: Testing the indirect effect of mistrust in healthcare on utilization behaviors

RationaleDirect exposure to gender identity-related discrimination and erasure among the transgender and gender independent (TGI) population are associated with healthcare underutilization, which may further exacerbate the health disparities that exist between this population and cisgender individuals in the United States (U.S.). Although the impacts of direct exposure to healthcare discrimination and erasure may have on TGI individuals are known, exposure to such harm vicariously (i.e., through observation or report) is underexplored. ObjectiveThe present study examined the relationships among direct and vicarious gender identity-related healthcare discrimination and erasure exposure and past-year healthcare utilization. MethodGender identity-based mistrust in healthcare was also assessed, as a mechanism through which direct and vicarious gender identity-related healthcare discrimination and erasure predict healthcare utilization behaviors among a sample (N = 385) of TGI adults in the U.S., aged 18 to 71 recruited online. ResultsResults indicated direct lifetime and vicarious healthcare discrimination and erasure exposure significantly predicted past-year healthcare underutilization when participants anticipated encountering gender identity-related healthcare discrimination. Mediational analyses indicated that higher levels of exposure to direct lifetime and vicarious healthcare discrimination and erasure were related to higher levels of mistrust in healthcare, through which past-year underutilization was significantly related. ConclusionsThese findings are vital to informing healthcare practice and policy initiatives aimed at ensuring the barriers that deleteriously influence the accessibility of healthcare among TGI individuals are ameliorated.

A Revised Definition of Left Bundle Branch Block Using Time to Notch in Lead I

Current left bundle branch block (LBBB) criteria are based on animal experiments or mathematical models of cardiac tissue conduction and may misclassify patients. Improved criteria would impact referral decisions and device type for cardiac resynchronization therapy. To develop a simple new criterion for LBBB based on electrophysiological studies of human patients, and then to validate this criterion in an independent population. In this diagnostic study, the derivation cohort was from a single-center, prospective study of patients undergoing electrophysiological study from March 2016 through November 2019. The validation cohort was assembled by retrospectively reviewing medical records for patients from the same center who underwent transcatheter aortic valve replacement (TAVR) from October 2015 through May 2022. Patients were classified as having LBBB or intraventricular conduction delay (IVCD) as assessed by intracardiac recording. Sensitivity and specificity of the electrocardiography (ECG) criteria assessed in patients with LBBB or IVCD. A total of 75 patients (median [IQR] age, 63 [53-70.5] years; 21 [28.0%] female) with baseline LBBB on 12-lead ECG underwent intracardiac recording of the left ventricular septum: 48 demonstrated complete conduction block (CCB) and 27 demonstrated intact Purkinje activation (IPA). Analysis of surface ECGs revealed that late notches in the QRS complexes of lateral leads were associated with CCB (40 of 48 patients [83.3%] with CCB vs 13 of 27 patients [48.1%] with IPA had a notch or slur in lead I; P = .003). Receiver operating characteristic curves for all septal and lateral leads were constructed, and lead I displayed the best performance with a time to notch longer than 75 milliseconds. Used in conjunction with the criteria for LBBB from the American College of Cardiology/American Heart Association/Heart Rhythm Society, this criterion had a sensitivity of 71% (95% CI, 56%-83%) and specificity of 74% (95% CI, 54%-89%) in the derivation population, contrasting with a sensitivity of 96% (95% CI, 86%-99%) and specificity of 33% (95% CI, 17%-54%) for the Strauss criteria. In an independent validation cohort of 46 patients (median [IQR] age, 78.5 [70-84] years; 21 [45.7%] female) undergoing TAVR with interval development of new LBBB, the time-to-notch criterion demonstrated a sensitivity of 87% (95% CI, 74%-95%). In the subset of 10 patients with preprocedural IVCD, the criterion correctly distinguished IVCD from LBBB in all cases. Application of the Strauss criteria performed similarly in the validation cohort. The findings suggest that time to notch longer than 75 milliseconds in lead I is a simple ECG criterion that, when used in conjunction with standard LBBB criteria, may improve specificity for identifying patients with LBBB from conduction block. This may help inform patient selection for cardiac resynchronization or conduction system pacing.

Abstract 2264: Identifying and analyzing independent clonal populations in single Barrett’s esophagus biopsies

Abstract Barrett’s esophagus (BE) is a non-obligate precursor of esophageal adenocarcinoma (EAC), often measuring multiple centimeters in length and characterized by columnar epithelium replacing normal squamous epithelium in the distal esophagus. Early studies in BE suggested that all cells within a patient’s Barrett’s segment are progenies of a single founder cell. However, in recent years this previously accepted model of BE as a monoclonal disease has been retired in favor of a multiclonal model with new evidence suggesting that a patient’s Barrett’s segment can be composed of multiple independent clones with independent founder cells. Nonetheless, single BE samples are still routinely assumed to be monoclonal, leading to independent sets of genomic aberrations erroneously being analyzed as a single set in the case of multiclonal samples. Here, we utilize a previously generated whole-genome sequenced BE dataset and repurpose an established pipeline for detecting subclonal populations to identify BE samples that are comprised of multiple independent clones. Additionally, we present a novel approach for validating multiclonality from sequencing data by phasing pair-end sequencing reads using germline variants. We demonstrate that 20/80 patients have at least one multiclonal sample and that the independent clones contained in these single biopsies can have drastically different genomic profiles. Further, we speculate that these multiclonal samples capture a snapshot of clonal competition and evolution within BE. These findings highlight the importance of accounting for multiclonality when studying BE and provide the first systematic analysis of multiclonal BE samples. Citation Format: Mariya Kazachkova, Phoebe He, Ludmil B. Alexandrov. Identifying and analyzing independent clonal populations in single Barrett’s esophagus biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2264.

Abstract P493: A Multivariate Analysis of Genetic Risk Scores for Heart Failure and Left Ventricular Mass Derived From Multi-Ancestry Populations and Left Ventricular Mass Amongst African Americans: The Jackson Heart Study

Background: Increased left ventricular (LV) mass is an outcome of LV remodeling that can result in development of left ventricular hypertrophy (LVH), a maladaptive disorder that increases the risk of heart failure. Elevated LV mass is a sensitive predictor of adverse cardiovascular events. African Americans experience a disproportionate burden of heart failure risk and development of LVH. The influence of genetics on this health disparity is unclear and a topic of interest. Genome-wide association studies (GWAS) have uncovered many variants associated with numerous adverse cardiovascular outcomes, such as heart failure and LV phenotypes. However, the relevance of these studies for African Americans may be limited as these analyses have been conducted in multi-ancestry populations with limited representation of African heritage. The question of whether these genetic risk variants can be used to predict LV phenotypes in an independent African American population remains unanswered. Hypothesis: Variant loci that have been associated with heart failure and LV mass in multi-ancestry populations will be associated with LV mass amongst African Americans in the Jackson Heart Study (JHS). Methods: Thirty-nine (39) SNPs associated with heart failure and ten (10) SNPs associated with LV mass were identified from two published studies in which participants of African ancestry or Black individuals comprised less than 15% and 2%, respectively. Using the published effect size estimates, genetic risk scores (GRSs) for heart failure and LV mass were computed for 2,175 African American participants (mean age 55, 63% female) in the JHS with complete data and consent for genetic research. We assessed the linear association of heart failure and LV mass GRSs with LV mass indexed to height (LVM h ) and indexed to body surface area (LVM bsa ). A multivariate general linear model was fit to both LVM h and LVM bsa containing both GRS predictors and accounting for age, sex, BMI (mean 31.5), diabetes (22%), and hypertension (52%). No multicollinearity was noted among these explanatory variables using variance inflation factors. Due to non-constant error variance, a log transformation was conducted on both LV mass response variables. Type III MANOVA Pillai tests were run to assess the effects of the GRSs on the log LVM bsa and log LVM h values. Conclusion: When accounting for age, BMI, sex, diabetes, and hypertension, we found that neither the heart failure GRS (p-val = 0.4012) nor the LV mass GRS (p-val = 0.8332) had an effect on both log LVM bsa and log LVM h . There is insufficient evidence to suggest that heart failure and LV mass variants derived from predominantly European multi-ancestry populations are linearly associated with log LV mass amongst African Americans in the Jackson Heart Study. Our work highlights the need for greater recruitment and targeted studies of populations with African ancestry for GWAS of LV phenotypes.

Impact of physical and sexual abuse on risk of hospitalisations for physical and mental illnesses: insights from two large prospective cohort studies

Physical abuse can lead to severe health consequences that extend beyond immediate harm. We explored the associations of physical abuse experienced during childhood and adulthood with a wide range of adult health conditions requiring hospital treatment. We utilised data from a sub-cohort of 157,366 UK Biobank participants (46.4% of the baseline population; age range 45-81; 89,101 women) and repeated analyses in an independent population of 85,929 adults from the Finnish Public Sector (FPS) study (age range 17-78; 68,544 women). Participants in both cohorts reported instances of physical and sexual abuse at study baseline. Follow-up included 77 common health conditions ascertained from linkage data to national hospital and mortality registries. Mean follow-up duration was 4.6 years (SD 0.14) in UK Biobank and 10.6 years (4.3) in FPS. Physical and sexual abuse was associated with 22 mental and physical health conditions. After multivariable adjustments, participants who experienced abuse during both early and later stages of life had a 2.12- (95% confidence interval 1.39-3.23) to 3.37-fold (1.52-7.45) increased risk of mental and behavioural disorders, a 1.46 (1.20-1.79) to 1.83 (1.05-3.20) times increased risk of metabolic, haematologic, and respiratory diseases, and a 1.24 (1.07-1.45) times higher risk of inflammatory diseases compared with non-exposed participants. The absolute risk difference between these groups was greatest for metabolic and haematologic conditions (rate 381 and risk difference 160 per 100,000 person-years). Frailty, comorbidities, and competing risk of death did not modify these associations, but the possibility of bias or residual confounding cannot be excluded. Repeated exposure to physical and sexual abuse amplifies the risk of hospitalisations from mental disorders and physical diseases spanning diverse organ systems. Addressing this issue may necessitate multifaceted strategies, including shifts in societal norms, legal measures, and increased healthcare provision for affected individuals and their families. Wellcome Trust, UK Medical Research Council, U.S. National Institute on Aging, Academy of Finland.

The primary ciliary dyskinesia-related genetic risk score is associated with susceptibility to adult-onset asthma.

Disturbance of mucociliary clearance is an important factor in the pathogenesis of asthma. We hypothesized that common variants in genes responsible for ciliary function may contribute to the development of asthma with certain phenotypes. Three independent adult Japanese populations (including a total of 1,158 patients with asthma and 2,203 non-asthmatic healthy participants) were studied. First, based on the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/), we selected 12 common single-nucleotide polymorphisms (SNPs) with molecular consequences (missense, nonsense, and 3'-untranslated region mutation) in 5 primary ciliary dyskinesia (PCD)-related genes and calculated a PCD-genetic risk score (GRS) as a cumulative effect of these PCD-related genes. Second, we performed a two-step cluster analysis using 3 variables, including PCD-GRS, forced expiratory volume in 1 second (%predicted FEV1), and age of asthma onset. Compared to adult asthma clusters with an average PCD-GRS, clusters with high and low PCD-GRS had similar overall characteristics: adult-onset, female predominance, preserved lung function, and fewer features of type 2 immunity as determined by IgE reactivity and blood eosinophil counts. The allele frequency of rs1530496, a SNP representing an expression quantitative trait locus (eQTL) of DNAH5 in the lung, showed the largest statistically significant difference between the PCD-GRS-High and PCD-GRS-Low asthma clusters (p = 1.4 x 10-15). Genes associated with PCD, particularly the common SNPs associated with abnormal expression of DNAH5, may have a certain influence on the development of adult-onset asthma, perhaps through impaired mucociliary clearance.

A nomogram based on radiomics and clinical information to predict prognosis in percutaneous balloon compression for the treatment of trigeminal neuralgia.

To develop a clinical-radiomics nomogram based on clinical information and radiomics features to predict the prognosis of percutaneous balloon compression (PBC) for the treatment of trigeminal neuralgia (TN). The retrospective study involved clinical data from 149 TN patients undergoing PBC at Zhongnan Hospital, Wuhan University from January 2018 to January 2022. The free open-source software 3D Slicer was used to extract all radiomic features from the intraoperative X-ray balloon region. The relationship between clinical information and TN prognosis was analyzed by univariate logistic analysis and multivariate logistic analysis. Using R software, the optimal radiomics features were selected using the least absolute shrinkage and selection operator (Lasso) algorithm. A prediction model was constructed based on the clinical information and radiomic features, and a nomogram was visualized. The performance of the clinical radiomics nomogram in predicting the prognosis of PBC in TN treatment was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). A total of 149 patients were eventually included. The clinical factors influencing the prognosis of TN in univariate analysis were compression severity score and TN type. The lasso algorithm Max-Relevance and Min-Redundancy(mRMR) was used to select two predictors from 13 morphology-related radiomics features, including elongation and surface-volume ratio. A total of 4 predictors were used to construct a prediction model and nomogram. The AUC was 0.886(95% confidence interval (CI), 0.75 to 0.96), indicating that the model's good predictive ability. DCA demonstrated the nomogram's high clinical applicability. Clinical-radiomics nomogram constructed by combining clinical information and morphology-related radiomics features have good potential in predicting the prognosis of TN for PBC treatment. However, this needs to be further studied and validated in several independent external patient populations.

Analysis of the Effect of Population on Unemployment in West Lombok Regency 2012-2021

This research is entitled "Analysis of the Effect of Population on Unemployment in West Lombok Regency 2012-2021". In this study, researchers wanted to see how the population compared to unemployment in West Lombok Regency from 2012-2021. This type of research is quantitative research with an associative approach. The analytical tool used in this research is simple linear regression analysis. The results of this research are an R-square value of 0.545 with a significance value (α) or alpha of 0.05 (5%). The R-square value in this study has meaning, namely that the influence of the population variable is able to explain the unemployment variable in West Lombok Regency by 54.5%. That is, partially the influence of the independent variable population size on unemployment in West Lombok Regency is not significant. This is indicated by the calculated T value being greater than the T table value. T calculated < T table (T calculated 0.737 > T table 2.306).

Sex-specific and polygenic effects underlying resting heart rate and associated risk of cardiovascular disease.

Resting heart rate (RHR) is associated with cardiovascular disease (CVD) and mortality. This study aimed to identify genetic loci associated with RHR, develop a genome-wide polygenic risk score (PRS) for RHR, and assess associations between the RHR PRS and CVD outcomes, to better understand the biological mechanisms linking RHR to disease. Sex-specific analyses were conducted to potentially elucidate different pathways between the sexes. We performed a genome-wide meta-analysis of RHR (n = 550 467) using two independent study populations, The Trøndelag Health Study (HUNT) and the UK Biobank (UKB), comprising 69 155 and 481 312 participants, respectively. We also developed a genome-wide PRS for RHR using UKB and tested for association between the PRS and 13 disease outcomes in HUNT. We identified 403, 253, and 167 independent single nucleotide polymorphisms (SNPs) significantly associated with RHR in the total population, women, and men, respectively. The sex-specified analyses indicated differences in the genetic contribution to RHR and revealed loci significantly associated with RHR in only one of the sexes. The SNPs were mapped to genes enriched in heart tissue and cardiac conduction pathways, as well as disease-pathways, including dilated cardiomyopathy. The PRS for RHR was associated with increased risk of hypertension and dilated cardiomyopathy, and decreased risk of atrial fibrillation. Our findings provide insight into the pleiotropic effects of the RHR variants, contributing towards an improved understanding of mechanisms linking RHR and disease. In addition, the sex-specific results might contribute to a more refined understanding of RHR as a risk factor for the different diseases.

Validation of Population Pharmacokinetic Models for Clozapine Dosage Prediction.

Clozapine is unique in its capacity to ameliorate severe schizophrenia but at high risk of toxicity. A relationship between blood concentration and clinical response and evidence for concentration-response relationships to some adverse effects justify therapeutic drug monitoring of clozapine. However, the relationship between drug dose and blood concentration is quite variable. This variability is, in part, due to inductive and inhibitory interactions varying the activity of cytochrome P450 1A2 (CYP1A2), the principal pathway for clozapine elimination. Several population pharmacokinetic models have been presented to facilitate dose selection and to identify poor adherence in individual patients. These models have faced little testing for validity in independent populations or even for persisting validity in the source population. Therefore, we collected a large population of clozapine-treated patients (127 patients, 1048 timed plasma concentrations) in whom dosing and covariate information could be obtained with high certainty. A population pharmacokinetic model was constructed with data collected in the first 6 weeks from study enrolment (448 plasma concentrations), to estimate covariate influences and to allow alignment with previously published models. The model was tested for its performance in predicting the concentrations observed at later time intervals up to 5 years. The predictive performances of 6 published clozapine population models were then assessed in the entire population. The population pharmacokinetic model based on the first 6 weeks identified significant influences of sex, smoking, and cotreatment with fluvoxamine on clozapine clearance. The model built from the first 6 weeks had acceptable predictive performance in the same patient population up to the first 26 weeks using individual parameters, with a median predictive error (PE) of -0.1% to -15.9% and median absolute PE of 22.9%-27.1%. Predictive performance fell progressively with time after 26 weeks. Bayesian addition of plasma concentration observations within each prediction period improved individual predictions. Three additional observations extended acceptable predictive performance into the second 6 months of therapy. When the published models were tested with the entire data set, median PE ranged from -8% to +35% with a median absolute PE of >39% in all models. Thus, none of the tested models was successful in external validation. Bayesian addition of single patient observations improved individual predictions from all models but still without achieving acceptable performances. We conclude that the relationship between covariates and blood clozapine concentrations differs between populations and that relationships are not stable over time within a population. Current population models for clozapine are not capturing influential covariates.

Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data.

The wealth of data generated by continuous glucose monitoring (CGM) provides new opportunities for revealing heterogeneities in patients with type 2 diabetes mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM subtypes and investigate their relationship with clinical phenotypes and microvascular complications. The data from 3119 patients with T2DM who wore blinded CGM at an academic medical centre was collected, and a glucose symbolic pattern (GSP) metric was created that combined knowledge-based temporal abstraction with numerical vectorization. The k-means clustering was applied to GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the presence of diabetic retinopathy and albuminuria were compared among the subgroups. The findings were validated in an independent population comprising 773 patients with T2DM. By using GSP, four subgroups were identified with distinct features in CGM profiles and parameters. Moreover, the clustered subgroups differed significantly in clinical phenotypes, including indices of pancreatic β-cell function and insulin resistance (all p < .001). After adjusting for confounders, group C (the most insulin resistant) had a significantly higher risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03-1.39) relative to group D, which had the best glucose control. These findings were confirmed in the validation set. Subtyping patients with T2DM using CGM data may help identify high-risk patients for microvascular complications and provide insights into the underlying pathophysiology. This method may help refine clinically meaningful stratification of patients with T2DM and inform personalized diabetes care.

Comparative chemical genomics in Babesia species identifies the alkaline phosphatase PhoD as a determinant of antiparasitic resistance

Babesiosis is an emerging zoonosis and widely distributed veterinary infection caused by 100+ species of Babesia parasites. The diversity of Babesia parasites and the lack of specific drugs necessitate the discovery of broadly effective antibabesials. Here, we describe a comparative chemogenomics (CCG) pipeline for the identification of conserved targets. CCG relies on parallel in vitro evolution of resistance in independent populations of Babesia spp. (B. bovis and B. divergens). We identified a potent antibabesial, MMV019266, from the Malaria Box, and selected for resistance in two species of Babesia. After sequencing of multiple independently derived lines in the two species, we identified mutations in a membrane-bound metallodependent phosphatase (phoD). In both species, the mutations were found in the phoD-like phosphatase domain. Using reverse genetics, we validated that mutations in bdphoD confer resistance to MMV019266 in B. divergens. We have also demonstrated that BdPhoD localizes to the endomembrane system and partially with the apicoplast. Finally, conditional knockdown and constitutive overexpression of BdPhoD alter the sensitivity to MMV019266 in the parasite. Overexpression of BdPhoD results in increased sensitivity to the compound, while knockdown increases resistance, suggesting BdPhoD is a pro-susceptibility factor. Together, we have generated a robust pipeline for identification of resistance loci and identified BdPhoD as a resistance mechanism in Babesia species.

Are the tests overpowered or underpowered? A unified solution to correctly specify type I errors in design of clinical trials for two sample proportions.

As one of the most commonly used data types, methods in testing or designing a trial for binary endpoints from two independent populations are still being developed until recently. However, the power and the minimum required sample size comparisons between different tests may not be valid if their type I errors are not controlled at the same level. In this article, we unify all related testing procedures into a decision framework, including both frequentist and Bayesian methods. Sufficient conditions of the type I error attained at the boundary of hypotheses are derived, which help reduce the magnitude of the exact calculations and lay out a foundation for developing computational algorithms to correctly specify the actual type I error. The efficient algorithms are thus proposed to calculate the cutoff value in a deterministic decision rule and the probability value in a randomized decision rule, such that the actual type I error is under but closest to, or equal to, the intended level, respectively. The algorithm may also be used to calculate the sample size to achieve the prespecified type I error and power. The usefulness of the proposed methodology is further demonstrated in the power calculation for designing superiority and noninferiority trials.

Fair concordance between Google Trends and Danish ornithologists in the assessment of temporal trends in Danish bird populations highlights the informational value of big data

The ongoing depletion of natural systems and associated biodiversity decline is of growing international concern. Climate change is expected to exacerbate anthropogenic impacts on wild populations. The scale of impact on ecosystems and ecosystem services will be determined by the impact on a multitude of species and functional groups, which due to their biology and numbers are difficult to monitor. The IPCC has argued that surveillance or monitoring is critical and proposed that monitoring systems should be developed, which not only track developments but also function as “early warning systems.” Human populations are already generating large continuous datasets on multiple taxonomic groups through internet searches. These time series could in principle add substantially to current monitoring if they reflect true changes in the natural world. We here examined whether information on internet search frequencies delivered by the Danish population and captured by Google Trends (GT) appropriately informs on population trends in 106 common Danish bird species. We compared the internet search activity with independent equivalent population trend assessments from the Danish Ornithological Society (BirdLife Denmark/DOF). We find a fair concordance between the GT trends and the assessments by DOF. A substantial agreement can be obtained by omitting species without clear temporal trends. Our findings suggest that population trend proxies from internet search frequencies can be used to supplement existing wildlife population monitoring and to ask questions about an array of ecological phenomena, which potentially can be integrated into an early warning system for biodiversity under climate change.

Choice of DNA extraction method affects stool microbiome recovery and subsequent phenotypic association analyses

The lack of standardization in the methods of DNA extraction from fecal samples represents the major source of experimental variation in the microbiome research field. In this study, we aimed to compare the metagenomic profiles and microbiome–phenotype associations obtained by applying two commercially available DNA extraction kits: the AllPrep DNA/RNA Mini Kit (APK) and the QIAamp Fast DNA Stool Mini Kit (FSK). Using metagenomic sequencing data available from 745 paired fecal samples from two independent population cohorts, Lifelines-DEEP (LLD, n = 292) and the 500 Functional Genomics project (500FG, n = 453), we confirmed significant differences in DNA yield and the recovered microbial communities between protocols, with the APK method resulting in a higher DNA concentration and microbial diversity. Further, we observed a massive difference in bacterial relative abundances at species-level between the APK and the FSK protocols, with > 75% of species differentially abundant between protocols in both cohorts. Specifically, comparison with a standard mock community revealed that the APK method provided higher accuracy in the recovery of microbial relative abundances, with the absence of a bead-beating step in the FSK protocol causing an underrepresentation of gram-positive bacteria. This heterogeneity in the recovered microbial composition led to remarkable differences in the association with anthropometric and lifestyle phenotypes. The results of this study further reinforce that the choice of DNA extraction method impacts the metagenomic profile of human gut microbiota and highlight the importance of harmonizing protocols in microbiome studies.

Empirical comparison of deep learning models for fNIRS pain decoding.

Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain. In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features. The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models. Overall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios.

Validation of an Anti-Müllerian Hormone Cutoff for Polycystic Ovarian Morphology in the Diagnosis of Polycystic Ovary Syndrome in the HARMONIA Study: Protocol for a Prospective, Noninterventional Study.

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women and is diagnosed using the Rotterdam criteria, including diagnosis of polycystic ovarian morphology (PCOM) by transvaginal ultrasound (TVUS). Due to high cost, availability, and the impact of the operator and ultrasound equipment on the reliability of the antral follicle count (AFC) by TVUS, an unmet need exists for a diagnostic test to determine PCOM without TVUS. A strong positive correlation between elevated anti-Müllerian hormone (AMH) levels and AFCs has been demonstrated in women with PCOS. In addition, recent updates to the international evidence-based PCOS guidelines state that serum AMH can be used as an alternative to TVUS-determined AFC, in the diagnosis of PCOM. The retrospective APHRODITE study derived and validated an AMH cutoff of 3.2 ng/mL for the Elecsys AMH Plus or Elecsys AMH assays (Roche) to diagnose PCOM in patients with PCOS. This study aims to further validate, in an independent prospective cohort, the AMH cutoff (3.2 ng/mL) for PCOM determination, which was previously derived and validated in the APHRODITE study. This large, prospective, multicenter, population-based, noninterventional study will evaluate the previously established AMH cutoff for the determination of PCOM during the diagnosis of PCOS using the Elecsys AMH Plus immunoassay in an independent population. Participants were women born between July 1985 and December 1987 in Northern Finland; the study partially links to the Northern Finland Birth Cohort 1986. We assessed the enrolled women, determined with the 2023 PCOS Guidelines, for current PCOS status and divided them by phenotype if positive. Each participant had 1 study visit to collect serum samples, record clinical data, and undergo a gynecological examination including TVUS. All data were collected by highly trained midwives or trained gynecologists. Sensitivity, specificity, and agreement measures were used to validate the previously determined cutoff in the whole population and in subpopulations based on phenotype and relevant demographic or clinical factors. The minimum target sample size was approximately 1800 women, including approximately 10% with PCOS. At the time of manuscript submission, participant recruitment had concluded, and 1803 women were enrolled into the study. Data collection is complete and biostatistical analysis is planned for 2023. To limit variability, there were few TVUS operators and only 2 TVUS machines of the same type. Additionally, all women who were taking oral contraceptives were excluded from the primary analysis population. Selection bias was limited as this was a population-based study and participants were not seeking treatment for PCOS symptoms. Validating the AMH cutoff in a large, population-based study will provide further evidence on the utility of the Elecsys AMH Plus or Elecsys AMH assays in PCOM diagnosis as an alternative to TVUS. Measuring AMH for PCOM diagnosis could reduce delayed or missed diagnoses due to operator-dependent TVUS examinations. ClinicalTrials.gov NCT05527353; http://tinyurl.com/2f3ffbdz. DERR1-10.2196/48854.

Association between lower extremity arterial calcification and coronary arterial calcification in a population at increased risk of cardiovascular disease

IntroductionThere is conflicting evidence whether lower extremity arterial calcification coincides with coronary arterial calcification (CAC). The aims of this study were to investigate the associations between (1) femoral and crural...

Constraints on the proton fraction of cosmic rays at the highest energies and the consequences for cosmogenic neutrinos and photons

Over the last decade, observations have shown that the mean mass of ultra-high-energy cosmic rays (UHECRs) increases progressively toward the highest energies. However, the precise composition is still unknown and several theoretical studies hint at the existence of a subdominant proton component up to the highest energies. Motivated by the exciting prospect of performing charged-particle astronomy with ultra-high-energy (UHE) protons we quantify the level of UHE-proton flux that is compatible with present multimessenger observations and the associated fluxes of neutral messengers produced in the interactions of the protons. We study this scenario with numerical simulations of two independent populations of extragalactic sources and perform a fit to the combined UHECR energy spectrum and composition observables, constrained by diffuse gamma-ray and neutrino observations. We find that up to of order 10% of the cosmic rays at the highest energies can be UHE protons, although the result depends critically on the selected hadronic interaction model for the air showers. Depending on the maximum proton energy (E max p) and the redshift evolution of sources, the associated flux of cosmogenic neutrinos and UHE gamma rays can significantly exceed the multimessenger signal of the mixed-mass cosmic rays. Moreover, if E max p is above the GZK limit, we predict a large flux of UHE neutrinos above EeV energies that is absent in alternate scenarios for the origin of UHECRs. We present the implications and opportunities afforded by these UHE proton, neutrino and photon fluxes for future multimessenger observations.

Association between cytomegalovirus seropositivity and all-cause mortality: An original cohort study.

To examine the association between cytomegalovirus (CMV) seropositivity and all-cause mortality in a nationwide cohort of US adults. We obtained data from the National Health and Nutrition Examination Survey III (1988-1994), including 16,547 participants aged 18-90 years old with CMV serology assessments. Mortality status was ascertained until December 2019 using the National Death Index linkage data. The Cox proportional hazard model was applied to estimate the association between CMV seropositivity and mortality. During a median follow-up of 26.3 years, 6,930 deaths were recorded. CMV seropositivity was associated with a higher hazard of all-cause mortality after adjusting for attained age, sex, and ethnicity (HR: 1.22, 95% CI: 1.10, 1.36, p < 0.001). The magnitude of the association attenuated slightly after adjusting further for body mass index, family income, smoking status, diabetes, and self-reported cancer history (HR = 1.11, 95% CI: 1.00, 1.23, p = 0.04). While the association was observed for both men and women, it was only statistically significant among non-Hispanic white people (HR: 1.16, 95% CI: 1.06, 1.26, p = 0.001) but not among other ethnic populations. CMV seropositivity might be an independent risk factor for all-cause mortality among US adults. If the findings are validated in an independent population, further research is needed to unveil the biological mechanisms driving the increased mortality with CMV seropositivity.