Generalized Linear Models Research Articles

Trajectories of suicidal risk among individuals with first-episode psychosis: Relationship to recovery and symptoms

This study examined trajectories of suicide-risk and their relationship to symptoms, recovery, and quality of life over time. Data was obtained from the Recovery after an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) study. 404 individuals with first-episode psychosis (FEP) completed measures of suicide-risk, depression, positive symptoms, recovery, and quality of life at baseline, 6mo, 12mo, 18mo, and 24mo. Latent class analysis was used to identify temporal trajectories of suicide-risk. General linear mixed models for repeated measures were used to examine the relationship between the latent trajectories of suicide-risk and clinical variables. Results identified three latent trajectories of suicide-risk (low-risk, worsening, and improving). The low-risk and improving classes experienced improvements in depression, positive symptoms, quality of life, and recovery over time. The worsening class experienced improvements in positive symptoms and quality of life, but no change in depression or recovery. These results suggest that some individuals with FEP are at risk for persistent depression and worsening suicide-risk during treatment despite experiencing improvements in positive symptoms and quality of life. These findings have important clinical implications, as persistent depression and worsening suicide-risk might be masked by the primary focus on positive symptoms and quality of life in most FEP clinics.

Assessment of biometrics and stress indicators of the adult Village Weaver birds (Ploceus cucullatus) during breeding and post-breeding seasons in Jos, Nigeria

In this era of climate change, some biological conservationists' concerns are based on seasonal studies that highlight how wild birds' physiological fitness are interconnected with the immediate environment to avoid population decline. We investigated how seasonal biometrics correlated to stress parameters of the adult Village Weavers (Ploceus cucullatus) during breeding and post-breeding seasons of the Weaver birds in Amurum Forest Reserve. Specifically, we explored the following objectives: (i) the seasonal number of birds captured; (ii) whether seasonal baseline corticosterone (CORT), packed cell volume (PCV), and heterophil to lymphocytes ratio (H:L) were sex-dependent; (iii) whether H:L ratio varied with baseline (CORT); (iv) whether phenotypic condition (post-breeding moult) and brood patch varied with baseline (CORT) and H:L ratio; and (v) how body biometrics co-varied birds’ seasonal baseline (CORT), (PCV) and (H:L) ratio. Trapping of birds (May–November) coincided with breeding and post-breeding seasons. The birds (n = 53 males, 39 females) were ringed, morphologically assessed (body mass, wing length, moult, brood patch) and blood collected from their brachial vein was used to assess CORT, PCV and H:L ratio. Although our results indicated more male birds trapped during breeding, the multiple analyses of variance (MANOVA) indicated that the seasonal temperature of the trapping sites correlated (P < 0.05) significantly to baseline (CORT). The general linear mixed model analyses (GLMMs) indicated that the baseline (CORT) also correlated significantly to H:L ratio of the male and female birds. However, PCV correlated significantly to body size of the birds (wing length) and not body mass. Haematological parameters such as the baseline CORT and the H:L ratio as indicators of stress in wild birds. Hence, there is the possibility that the Village Weaver birds suffered from seasonally induced stress under the constrained effect of environmental temperature. Hence, future studies should investigate whether the effect observed is also attributable to other passerine species.

Differential Effects of Aging on Regional Corpus Callosum Microstructure and the Modifying Influence of Pulse Pressure.

The corpus callosum is composed of several subregions, distinct in cellular and functional organization. This organization scheme may render these subregions differentially vulnerable to the aging process. Callosal integrity may be further compromised by cardiovascular risk factors, which negatively influence white matter health. Here, we test for heterochronicity of aging, hypothesizing an anteroposterior gradient of vulnerability to aging that may be altered by the effects of cardiovascular health. In 174 healthy adults across the adult lifespan (mean age = 53.56 ± 18.90; range, 20-94 years old, 58.62% women), pulse pressure (calculated as participant's systolic minus diastolic blood pressure) was assessed to determine cardiovascular risk. A deterministic tractography approach via diffusion-weighted imaging was utilized to extract fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) from each of five callosal subregions, serving as estimates of microstructural health. General linear models tested the effects of age, hypertension, and pulse pressure on these cross-sectional metrics. We observed no significant effect of hypertensive diagnosis on callosal microstructure. We found a significant main effect of age and an age-pulse pressure interaction whereby older age and elevated pulse pressure were associated with poorer FA, AD, and RD. Age effects revealed nonlinear components and occurred along an anteroposterior gradient of severity in the callosum. This gradient disappeared when pulse pressure was considered. These results indicate that age-related deterioration across the callosum is regionally variable and that pulse pressure, a proxy of arterial stiffness, exacerbates this aging pattern in a large lifespan cohort.

Tacklers' shoulder abduction and flexion at contact alter when engaging in different front-on, one-on-one tackle instructions from an expert coach

ObjectivesTo ascertain how the three-dimensional shoulder kinematics of tacklers alter when performing four legal types of front-on, one-on-one, rugby-style torso tackles. DesignControlled laboratory study. MethodsThree-dimensional motion capture measured 15 male amateur-level rugby code players (24.3 ± 6.1 years) who were instructed by an expert coach to perform four sets of 10 front-on, one-on-one tackles. Four sets comprised two smother and two dominant tackles: two based on the Australian National Rugby League coaching manual (Dominant NRL, Smother NRL); and two modifications via increasing the contact height from the lower- to mid-torso (Dominant, Torso Stick) or from the upper- to mid-upper torso with a vertical ‘pop action’ that changes the way the tackler contacted the ball carrier’s upper torso (Smother, Pop, Lock). Mixed general linear models were applied. ResultsGreater shoulder abduction, flexion and internal rotation were displayed by the DNRL tackle technique than in any other technique (p < 0.001). At contact, the Smother and Dominant NRL (p < 0.03) showed greater head-uptrunk contralateral rotation away from the tackle than the Smother, Pop, Lock. ConclusionsTacklers modified the way they positioned their shoulder joint when engaging in legal front-on, one-on-one tackles with different tackle instructions. More prominent positions of shoulder abduction and flexion, and head-uptrunk contralateral rotation were observed when executing the traditional tackle techniques (Smother NRL, Dominant NRL) as opposed to two variants of these (Smother, Pop, Lock, Dominant, Torso Stick). Inclusion of tackle specific coaching instructions provides emerging scientific evidence to support revised coaching tackling technique interventions that might enhance player safety.

Nurses' Personality Traits and Perceived Work Environments During Public Health Emergencies: Implications for Nursing Workforce Planning.

The nursing workforce faces substantial challenges, particularly in the aftermath of the COVID-19 era. Developing an effective strategy for workforce maintenance and the strategic deployment of nurses is crucial. This study aimed to explore and categorize nurses' personality traits, with a focus on analyzing differences in their perceptions of the nursing work environment. Between January 2023 and February 2023, a multi-center cross-sectional study was carried out involving nurses from 12 tertiary hospitals actively engaged in frontline COVID-19 response duties. Through cluster sampling, surveys were distributed among eligible nursing staff, comprising a general information questionnaire, the Chinese Big Five Personality Questionnaire-Short Form, subjective evaluations of emergency nursing management, and the Chinese Nursing Work Environment Scale for Public Health Emergencies. Various statistical analyses, such as descriptive analysis, cluster analysis, non-parametric tests, and general linear model analysis, were employed to investigate the correlation between personality types and the perception of nursing work environments. The analysis encompassed 1059 valid questionnaires, reflecting the experiences of frontline nurses. The majority of these nurses possessed 1-5 years of experience, held junior professional titles, volunteered for their roles, and served as attending nurses. Categorization based on personality traits revealed three groups: resilient (35.60%), ordinary (16.15%), and distressed (48.25%) types. Significantly distinct perceptions of nursing work environments emerged among these categories, with resilient and ordinary types expressing notably higher satisfaction compared to the distressed group (H value = 256.487, p < 0.001). This study illustrates the connection between nurses' perceived working environment and their personality traits. Nursing managers should factor in nurses' personality traits when choosing and deploying frontline responders during public health emergencies. Prioritizing resilient-type nurses and crafting a supportive work environment that aligns with nurses' characteristics is indispensable for an effective emergency response.

N-glycosylation of immunoglobulin A in children and adults with type 1 diabetes mellitus

AimsTo identify N-glycan structures on immunoglobulin A related to type 1 diabetes mellitus among children at the disease onset and adults with type 1 diabetes mellitus. MethodsHuman polyclonal IgA N-glycans were profiled using hydrophilic interaction ultra performance liquid chromatography in two cohorts. The first cohort consisted of 62 children at the onset of type 1 diabetes mellitus and 86 of their healthy siblings. The second cohort contained 84 adults with the disease and 84 controls. Associations between N-glycans and type 1 diabetes mellitus were tested using linear mixed model for the paediatric cohort, or general linear model for the adult cohort. False discovery rate was controlled by Benjamini-Hochberg method modified by Li and Ji. ResultsIn children, an increase in a single oligomannose N-glycan was associated with type 1 diabetes mellitus (B = 0.529, p = 0.0067). N-glycome of the adults displayed increased branching (B = 0.466, p = 0.0052), trigalactosylation (B = 0.466, p = 0.0052), trisialylation (B = 0.629, p < 0.001), and mannosylation (B = 0.604, p < 0.001). The strongest association with the disease was a decrease in immunoglobulin A core fucosylation (B = −0.900, p < 0.001). ConclusionsChanges in immunoglobulin N-glycosylation patterns in type 1 diabetes point to disruptions in immunoglobulin A catabolism and dysregulated inflammatory capabilities of the antibody, potentially impacting immune responses and inflammation.

The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression.

Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.

Longitudinal albuminuria patterns predict coronary artery calcification progression: Findings from the Coronary Artery Risk Development in Young Adults study

Background and aimsAlbuminuria is an established risk factor for adverse cardiovascular outcomes. However, few studies have characterized longitudinal albuminuria patterns based on long-term measurement of urine albumin-to-creatinine ratio (UACR) levels. We aimed to evaluate the association between longitudinal albuminuria patterns in midlife adults and subsequent CAC progression. MethodsWe included 1919 participants with CAC assessment by computed tomography from CARDIA (Coronary Artery Risk Development in Young Adults) study. CAC progression was determined for each individual as the difference of logarithmic CAC scores at follow-up and baseline. Albuminuria patterns across a 10-year span were estimated by longitudinal UACR levels. Multivariable general linear models were used to evaluate the association of long-term albuminuria patterns with CAC progression. ResultsOf the 1919 included participants, 583 (30.4 %) participants experienced CAC progression, and the mean (SD) age was 50.4 (3.5) years at year 25. A total of four dynamic albuminuria patterns were identified. After multivariable adjustment, there were significant differences in CAC progression by albuminuria patterns as demonstrated as percent change in CAC with 36.0 % (SE, 1.5) progression for mid- and late-life normoalbuminuria group, 46.0 % (SE, 7.6) for midlife normoalbuminuria and late-life high albuminuria group, 52.4 % (SE, 7.1) for midlife high albuminuria and late-life normoalbuminuria group, and 54.5 % (SE, 8.7) for mid- and late-life high albuminuria group (p = 0.019). Similar findings were also observed in CAC volume score changes. ConclusionsLongitudinal albuminuria patterns across a 10-year span were associated with worse CAC progression independent of baseline CAC level and albuminuria changes, suggesting that it may provide early identification of high-risk individuals with dynamic rises in albuminuria who may benefit from aggressive risk factor modification.

Forecasting of the Dental Workforce with Machine Learning Models

The aim of this study is to determine the factors affecting the dental workforce in Turkey to estimate the dentists employed with machine learning models. The predicted results were obtained by applying machine learning methods; namely, generalized linear model (GLM), deep learning (DL), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM) were compared. The RF model, which has a high correlation value (R2=0.998) with the lowest error rate (RMSE=656.6, AE=393.1, RE=0.025, SE=496115.7), provided the best estimation result. The SVM model provided the worst estimate data based on the values of the performance measurement criteria. This study is the most comprehensive in terms of the dental workforce, which is among the healthcare resources. Finally, we present an example of future applications for machine learning models that will significantly impact dental healthcare management.

Deciphering cuproptosis-related signatures in pediatric allergic asthma using integrated scRNA-seq and bulk RNA-seq analysis

Objective Allergic asthma (AA) is common in children. Excess copper is observed in AA patients. It is currently unclear whether copper imbalance can cause cuproptosis in pediatric AA. Methods The datasets about pediatric AA (GSE40732 and GSE40888) were obtained from Gene Expression Omnibus (GEO) database. The expression of cuproptosis-related genes (CRGs) and immune cell infiltration in pediatric AA samples were analyzed. Single-cell RNA sequencing (scRNA-seq) data (GSE193816) were used to evaluate the expression patterns of CRGs in AA. The identification of differentially expressed genes within clusters was conducted using weighted gene co-expression network analysis. Subsequently, disease progression and cuproptosis-related models were screened using random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and general linear model (GLM) algorithms. Results Four CRGs were notably increased in pediatric AA samples. CD4+ T cells, macrophages and mast cells exhibited a lower cuproptosis score in AA samples, indicating that these immune cells may be closely associated with cuproptosis in AA development. Co-expression network of CRGs in AA was constructed. AA samples were divided into two cuprotosis clusters. Following construction of four machine-learning models, SVM model exhibited the highest efficacy of prediction in the testing set (AUC = 0.952). SVM model containing five important variables can be used for prediction of AA. Conclusion This work provided a machine learning model containing five important variables, which may have good diagnostic efficiency for pediatric AA.

The Influence of Learning Models and Student Learning Activities on Collaboration Skills and Student Learning Outcomes

This research was conducted to determine whether or not there are differences in the value of collaboration skills and student learning outcomes taught using various learning models and student learning activities with the help of Macromedia Flash media on chemical bonding material, as well as to see the interaction between learning models and student learning activities on collaboration skills and student learning outcomes. Experimental class I (X-6) applied the Discovery Learning model and experimental class II (X-10) applied the Guided Inquiry model. The instrument used is a test instrument, a non-test instrument. Hypothesis testing was carried out using two-way ANOVA with multivariate General Linear Model (GLM). The research results obtained a price of sig. 0.000 for collaboration skill value and sig price. 0.001 for learning outcomes. Price sig. &lt; 𝛼 (0.05) so there is a significant difference in the average value of collaboration skills and student learning outcomes with various learning models. Obtained sig value. 0.000 on collaboration skills and learning outcomes. Sig price value. &lt; 𝛼 (0.05) so there is a significant difference in the average value of collaboration skills and student learning outcomes with various learning activities. The interaction between the learning model and learning activities obtained a sig value. 0.045 for collaboration skills and sig. 0.014 for learning outcomes. From the two sig values. &lt; 𝛼 (0.05) so that Ha is accepted and Ho is rejected, there is an interaction between the learning model and student learning activities on the value of collaboration skills and student learning outcomes in the Chemical Bond material.

Advanced machine learning-based modeling of interfacial tension in the crude oil-brine-diethyl ether system: Insights into the effects of temperature and salinity

Solvent injection, a well-established method for enhanced oil recovery (EOR), has demonstrated significant improvements in oil recovery when compared with conventional water flooding techniques. The interfacial tension (IFT) is pivotal in determining the displacement efficiency and overall performance of innovative techniques like dimethyl ether-enhanced waterflooding (DEW), which has gained substantial attention in recent years. In this study, following laboratory measurements of IFT, six advanced machine learning (ML) techniques were employed: Generalized Linear Model (GLM), Generalized Additive Model (GAM), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Boosted Regression Tree (BRT) to model the IFT in both oil-brine and oil-brine-diethyl ether (DEE) systems. The analysis is based on an extensive dataset comprising 7,017 data points for oil-brine and 6,949 data points for oil-brine-DEE systems obtained from experimental studies. The findings indicate that the developed RF model excels in predicting IFT, boasting a remarkable coefficient of determination (R2 = 0.99) along with the lowest root mean squared error (RMSE = 0.2), mean squared error (MSE = 0.04), and mean absolute error (MAE = 0.13). The study underscores the significance of optimizing salinity levels to achieve the most substantial reduction in IFT. This reduction is attributed to the enhanced migration of polar components, such as asphaltene molecules, to the interface of the oil-brine system. Moreover, the research highlights a synergistic decrease in IFT when both DEE and soluble ions are present, resulting in the lowest IFT at around 2 mN/m in 40,000 ppm salinity (S2) at 70 °C (T3). This indicates that the adsorption of DEE at the water–oil interface forms a layer capable of adsorbing ions, thereby enhancing the layer's thickness. As a result, the oil–solvent-ion layer becomes thicker compared to the oil-ion layer, leading to the maximum decrease in IFT. Additionally, with increasing temperature up to 70 °C, the IFT of both systems demonstrated a downward trend, as evidenced by all experiments. The outcomes of this study have the potential to enhance our comprehension of the underlying mechanisms involved in water-soluble solvent EOR techniques.

Comparison of Machinability of Al-4.5%Cu/TiB2/3p MMC for Multi-Layer Coated Insert: Validated FEM and Statistical Approaches

Aluminum-based Metal Matrix Composites (MMC) are commonly used in metal-cutting applications due to their better mechanical and physical properties, such as high strength, hardness, and low weight. Also, modern coating applications, especially multi-layer coated tools, have the cutting-edge potential for relieving the difficulties of machining MMCs to improve insert performances. Therefore, this study aimed to reveal the turning Al-4.5%Cu/TiB2/3p performance of the multi-layer coated cemented carbide insert with verified FEM and statistical approaches. Different coating materials, two and three of which were soft and hard, were appointed at different thicknesses and sequences in the design of experimentally calibrated simulations. The Grey Relation Analysis (GRA) was set to investigate the multi-layer coated insert performance for turning the MMC concerning the resultant cutting forces (FR) and maximum insert temperature (Tmax). The optimal multi-layered coating was found at levels 4-2-4-3-2 for the factors of coating materials: tungsten disulfide (WS2), molybdenum disulfide (MoS2), titanium nitride (TiN), aluminum oxide (Al2O3), and titanium carbo-nitride (TiCN), respectively. The contribution rates of each factor were significant concerning General Linear Model (GLM) at 47.13% and 24.43% for WS2 and Al2O3 coatings materials, respectively. In the future, multi-layered coatings can be a valuable solution for the difficulties of machining the MMCs.

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia.

Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia. To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV. Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure. Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.

CORRELATION ANALYSIS AND MODELLING OF HEAVY METAL CONCENTRATIONS AND PHYSICOCHEMICAL PARAMETERS FROM SOIL SAMPLES OF FIVE SELECTED REFUSE DUMPSITES IN LAFIA LOCAL GOVERNMENT AREA OF NASARAWA STATE, NIGERIA

Thousands of tons of solid wastes are generated and disposed daily in open dumps of most Nigerian cities due to rapid population explosion and commercial activities. In this study, concentrations of heavy metals and physicochemical parameters from 20 soil samples each from selected refuse dumpsites in wet and dry seasons were determined using Atomic Absorption Spectrophotometer and Association of Official Analytical Chemists (AOAC) standard methods. The results showed that during the wet and dry seasons, the concentration of heavy metals, Hg, Cr, Mn were above that of World Health Organisation (WHO), and National Environmental Standards and Regulations Enforcement Agency (NESREA), while that of Pb, As, Fe, Zn, Cu and Ni were below the tolerable limits. The results for physicochemical parameters in wet and dry seasons for nitrates, phosphates and organic matter were above WHO standards. Statistical test at 0.05 probability level (p &lt; 0.05) was used to determine the degree of association between pairs of the variables. The principal component analysis (PCA) was performed to measure the direct and inverse relationship between these parameters and heavy metal availability. Based on the different variables obtained at the study areas, a multiple linear regression analysis was determined from which a general linear model was formulated. The study recommends appropriate government agencies should as a matter urgency integrate the regular monitoring of waste disposal into the state developmental plan and framework in order to prevent excessive build-up of these metals in humans through the food chain.

High variability and multiple trade‐offs in reproduction and growth of the invasive grass Cortaderia selloana after cutting

AbstractThe ability to balance the allocation of resources between growth and reproduction as a response to stress factors, can be an advantage for plants in disturbed environments. Invasive alien plants (IAPs) often show high levels of phenotypic variability in resource allocation, a key trait that plays a crucial role in their success to invade new areas. Control management for IAPs must consider this capacity in the development of effective strategies. In this study, we performed continuous measures of leaf growth and reproductive traits of Cortaderia selloana, an IAP of global concern, and applied generalised linear models (GLMs) to evaluate trade‐offs between vegetative growth, leaf composition and reproductive success at different cutting moments. Cutting moment, but not flowering, affected the length of the vegetative growth period (VGP) and average growth rate (AGR), and the interaction with flowering affected AGR and final leaf length (vegetative growth total, VGT). Specific leaf area (SLA), leaf nitrogen (N) content and the isotopic value of δ13C were affected by cutting, and N was also affected by flowering and the interaction with cutting time. Silica also showed a negative correlation with leaf carbon (C) depicting a trade‐off between both structural components. Cortaderia selloana successfully adapted its leaf growth and composition to cutting moment, but this was also modulated by flowering. Moreover, the species is dioecious, and its response may differ between female and hermaphroditic plants. This suggests flexible trade‐offs in resource allocation, therefore the time for cutting must be precisely scheduled to suppress flowering.

Association of objective sleep duration with cognition and brain aging biomarkers in older adults.

The neuropathological mechanisms underlying the association between sleep duration and mild cognitive impairment remain poorly understood. This population-based study included 2032 dementia-free people (age ≥ 60 years; 55.1% women) derived from participants in the Multimodal Interventions to Delay Dementia and Disability in Rural China; of these, data were available in 841 participants for Alzheimer's plasma biomarkers (e.g. amyloid-β, total tau and neurofilament light chain), 1044 for serum microvascular biomarkers (e.g. soluble adhesion molecules) and 834 for brain MRI biomarkers (e.g. whiter matter, grey matter, hippocampus, lacunes, enlarged perivascular spaces and white matter hyperintensity WMH). We used electrocardiogram-based cardiopulmonary coupling analysis to measure sleep duration, a neuropsychological test battery to assess cognitive function and the Petersen's criteria to define mild cognitive impairment. Data were analysed with multivariable logistic and general linear models. In the total sample (n = 2032), 510 participants were defined with mild cognitive impairment, including 438 with amnestic mild cognitive impairment and 72 with non-amnestic mild cognitive impairment. Long sleep duration (>8 versus 6-8 h) was significantly associated with increased likelihoods of mild cognitive impairment and non-amnestic mild cognitive impairment and lower scores in global cognition, verbal fluency, attention and executive function (Bonferroni-corrected P < 0.05). In the subsamples, long sleep duration was associated with higher plasma amyloid-β40 and total tau, a lower amyloid-β42/amyloid-β40 ratio and smaller grey matter volume (Bonferroni-corrected P < 0.05). Sleep duration was not significantly associated with serum-soluble adhesion molecules, white matter hyperintensity volume, global enlarged perivascular spaces and lacunes (P > 0.05). Alzheimer's and neurodegenerative pathologies may represent common pathways linking long sleep duration with mild cognitive impairment and low cognition in older adults.

Diet and Meal Pattern Determinants of Glucose Levels and Variability in Adults with and without Prediabetes or Early-Onset Type 2 Diabetes: A Pilot Study.

This observational pilot study examined the association between diet, meal pattern and glucose over a 2-week period under free-living conditions in 26 adults with dysglycemia (D-GLYC) and 14 with normoglycemia (N-GLYC). We hypothesized that a prolonged eating window and late eating occasions (EOs), along with a higher dietary carbohydrate intake, would result in higher glucose levels and glucose variability (GV). General linear models were run with meal timing with time-stamped photographs in real time, and diet composition by dietary recalls, and their variability (SD), as predictors and glucose variables (mean glucose, mean amplitude of glucose excursions [MAGE], largest amplitude of glucose excursions [LAGE] and GV) as dependent variables. After adjusting for calories and nutrients, a later eating midpoint predicted a lower GV (β = -2.3, SE = 1.0, p = 0.03) in D-GLYC, while a later last EO predicted a higher GV (β = 1.5, SE = 0.6, p = 0.04) in N-GLYC. A higher carbohydrate intake predicted a higher MAGE (β = 0.9, SE = 0.4, p = 0.02) and GV (β = 0.4, SE = 0.2, p = 0.04) in N-GLYC, but not D-GLYC. In summary, our data suggest that meal patterns interact with dietary composition and should be evaluated as potential modifiable determinants of glucose in adults with and without dysglycemia. Future research should evaluate causality with controlled diets.

The safeguarding capability of adults in Catholic Church ministries: A global perspective

BackgroundIn the wake of historical sexual abuse across the Catholic Church globally, the Church continues to develop policies and processes to prevent and respond to child sexual abuse, including supporting the skills, knowledge, and confidence of members of the Church. ObjectiveWe investigated the safeguarding capabilities of a range of people with different roles within Catholic Church ministries in various countries. Participants and settingOur 184 participants included lay people, religious men and women, school staff, safeguarding officers and tertiary students associated with the Catholic Church. Data were collected across seven different countries. MethodsWe measured the awareness, confidence, attitudes, and knowledge of participants and examined differences between participants in different roles within the Church and different countries through General Linear Models. ResultsWe found varying levels of awareness, confidence, attitudes, and knowledge regarding sexual abuse prevention and safeguarding. We pinpointed the significant differences in three of these domains (confidence, attitudes, and knowledge) both between people with different roles in the church worldwide, but also between the countries from which participants came from. ConclusionsWe found that people in various countries and roles within the Church are at different stages of their safeguarding journey. Some are still understanding their roles (attitudes), some are still learning about how it is operationalised (awareness), and others are acquiring skills that will prepare them for enacting safeguarding policies and practices (confidence).

Attrition from Jail Reentry Program Increases Recidivism

AbstractReentry programs represent an increasingly popular method to reduce recidivism for individuals exiting prison and jail systems throughout the United States. Most evaluations tend to focus on recidivism as the primary outcome of interest. Attrition, however, can function an important supplementary measure that complements recidivism outcomes. To demonstrate, we analyze a jail reentry program built around peer navigators serving as staff members that refer participants to necessary support services while also serving as a mentor to participants exiting jail. We use a combination of general linear models (GLMs), Mahalanobis distance matching (MDM), and panel regression to both predict attrition and compare recidivism outcomes between three attrition groups: program completers, program quitters, and matched controls. Participants that successfully completed the program did not avoid new convictions or reincarceration significantly more or less than matched controls. Participants that quit the program, however, saw significantly higher conviction and reincarceration rates compared to matched controls. The nuance added to our program evaluation by adding attrition as a differential factor is worth consideration by other reentry programs who may not be realizing the full picture of their results by presenting recidivism outcomes alone.