Peptidomic profile of human milk as influenced by fortification with different protein sources: An in vitro dynamic digestion simulation.

Fortification of human milk (HM) is often necessary to meet the nutritional requirements of preterm infants. The present experiment aimed to establish whether the supplementation of HM with either an experimental donkey milk-derived fortifier containing whole donkey milk proteins, or with a commercial bovine milk-derived fortifier containing hydrolyzed bovine whey proteins, affects peptide release differently during digestion. The experiment was conducted using an in vitro dynamic system designed to simulate the preterm infant's digestion followed by digesta analysis by means of LC-MS-MS. The different fortifiers did not appear to influence the cumulative intensity of HM peptides. Fortification had a differential impact on the release of either donkey or bovine bioactive peptides. Donkey milk peptides showed antioxidant/ACE inhibitory activities, while bovine peptides showed opioid, dipeptil- and propyl endo- peptidase inhibitory and antimicrobial activity. A slight delay in peptide release from human lactoferrin and α-lactalbumin was observed when HM was supplemented with donkey milk-derived fortifier.

Real Customization or Just Marketing: Are Customized Versions of Generative AI Useful?

Background: Large Language Models (LLMs), as in the case of OpenAI TM ChatGPT-4 TM Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalised through a fine-tuning process to meet the student demands on every particular subject, like statistics. Recently, OpenAI launched the possibility of fine-tuning their model with a natural language web interface, enabling the creation of customised GPT versions deliberately conditioned to meet the demands of a specific task. Methods: This preliminary research aims to assess the potential of the customised GPTs. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behaviour was evaluated and compared with that of ChatGPT-4 Turbo. Firstly, each professor collected 15-30 genuine student questions from "Statistics and Probability" and "Business Statistics" courses across seven degrees, primarily from second-year courses. These questions, often ambiguous and imprecise, were posed to ChatGPT-4 Turbo and BSVP, with their initial responses recorded without follow-ups. In the third stage, professors blindly evaluated the responses on a 0-10 scale, considering quality, depth, and personalization. Finally, a statistical comparison of the systems' performance was conducted. Results: The results lead to several conclusions. Firstly, a substantial modification in the style of communication was observed. Following the instructions it was trained with, BSVP responded in a more relatable and friendly tone, even incorporating a few minor jokes. Secondly, when explicitly asked for something like, "I would like to practice a programming exercise similar to those in R practice 4," BSVP could provide a far superior response. Lastly, regarding overall performance, quality, depth, and alignment with the specific content of the course, no statistically significant differences were observed in the responses between BSVP and ChatGPT-4 Turbo. Conclusions: It appears that customised assistants trained with prompts present advantages as virtual aids for students, yet they do not constitute a substantial improvement over ChatGPT-4 Turbo.

Overcoming the pitfalls of categorizing continuous variables in ecology, evolution and behaviour.

Many variables in biological research-from body size to life-history timing to environmental characteristics-are measured continuously (e.g. body mass in kilograms) but analysed as categories (e.g. large versus small), which can lower statistical power and change interpretation. We conducted a mini-review of 72 recent publications in six popular ecology, evolution and behaviour journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common variables and realistic examples. We show that categorizing continuous variables is common (31% of publications reviewed). We also underscore that predictor variables can and should be collected and analysed continuously. Finally, we provide recommendations on how to keep variables continuous throughout the entire scientific process. Together, these pieces comprise an actionable guide to increasing statistical power and facilitating large synthesis studies by simply leaving continuous variables alone. Overcoming the pitfalls of categorizing continuous variables will allow ecologists, ethologists and evolutionary biologists to continue making trustworthy conclusions about natural processes, along with predictions about their responses to climate change and other environmental contexts.

Association of early statin initiation during COVID-19 admission with inpatient mortality at an academic health system in Illinois, March 2020 to September 2022: a target trial emulation using observational data.

OBJECTIVE: We assessed the association of early statin initiation with inpatient mortality among hospitalised COVID-19 patients. DESIGN, SETTING AND PARTICIPANTS: This observational study emulated a hypothetical target trial using electronic health records data from Northwestern Medicine Health System, Illinois, 2020-2022. We included patients who were ≥40 years, admitted ≥48 hours for COVID-19 from March 2020 to August 2022 and had no evidence of statin use before admission. INTERVENTIONS: Individuals who initiated any statins within 48 hours of admission were compared with individuals who did not initiate statins during this period. PRIMARY OUTCOME MEASURES: Inpatient mortality at hospital days 7, 14, 21 and 28 were determined using hospital records. Risk differences between exposure groups were calculated using augmented inverse propensity weighting (AIPW) with SuperLearner. RESULTS: A total of 8893 individuals (24.5% early statin initiators) were included. Early initiators tended to be older, male and have higher comorbidity burdens. Unadjusted day 28 mortality was higher in early initiators (6.0% vs 3.6%). Adjusted analysis showed slightly higher inpatient mortality risk at days 7 (RD: 0.5%, 95% CI: 0.2 to 0.8) and 21 (RD: 0.6%, 95% CI: 0.04 to 1.1), but not days 14 (RD: 0.4%, 95% CI: -0.03 to 0.9) and 28 (RD: 0.4%, 95% CI: -0.2 to 1.1). Sensitivity analyses using alternative modelling approaches showed no difference between groups. CONCLUSIONS: Early statin initiation was not associated with lower mortality contrasting with findings of previous observational studies. Trial emulation helped in identifying and addressing sources of bias incompletely addressed by previous work. Statin use may be indicated for other conditions but not COVID-19.

Reliable event rates for disease mapping.

When analyzing spatially referenced event data, the criteria for declaring rates as "reliable" is still a matter of dispute. What these varying criteria have in common, however, is that they are rarely satisfied for crude estimates in small area analysis settings, prompting the use of spatial models to improve reliability. While reasonable, recent work has quantified the extent to which popular models from the spatial statistics literature can overwhelm the information contained in the data, leading to oversmoothing. Here, we begin by providing a definition for a "reliable" estimate for event rates that can be used for crude and model-based estimates and allows for discrete and continuous statements of reliability. We then construct a spatial Bayesian framework that allows users to infuse prior information into their models to improve reliability while also guarding against oversmoothing. We apply our approach to county-level birth data from Pennsylvania, highlighting the effect of oversmoothing in spatial models and how our approach can allow users to better focus their attention to areas where sufficient data exists to drive inferential decisions. We then conclude with a brief discussion of how this definition of reliability can be used in the design of small area studies.

Evaluating the concordance between International Classification of Diseases, Tenth Revision Code and stroke severity as measured by the National Institutes of Health Stroke Scale.

Background: The National Institutes of Health Stroke Scale (NIHSS) scores have been used to evaluate acute ischaemic stroke (AIS) severity in clinical settings. Through the International Classification of Diseases, Tenth Revision Code (ICD-10), documentation of NIHSS scores has been made possible for administrative purposes and has since been increasingly adopted in insurance claims. Per Centres for Medicare & Medicaid Services guidelines, the stroke ICD-10 diagnosis code must be documented by the treating physician. Accuracy of the administratively collected NIHSS compared with expert clinical evaluation as documented in the Paul Coverdell registry is however still uncertain. Methods: Leveraging a linked dataset comprised of the Paul Coverdell National Acute Stroke Program (PCNASP) clinical registry and matched individuals on Medicare Claims data, we sampled patients aged 65 and above admitted for AIS across nine states, from January 2017 to December 2020. We excluded those lacking documentation for either clinical or ICD-10-based NIHSS scores. We then examined score concordance from both databases and measured discordance as the absolute difference between the PCNASP and ICD-10-based NIHSS scores. Results: Among 87 996 matched patients, mean NIHSS scores for PCNASP and Medicare ICD-10 were 7.19 (95% CI 7.14 to 7.24) and 7.32 (95% CI 7.27 to 7.37), respectively. Concordance between the two scores was high as indicated by an intraclass correlation coefficient of 0.93. Conclusion: The high concordance between clinical and ICD-10 NIHSS scores highlights the latter's potential as measure of stroke severity derived from structured claims data.

A Statistical Framework for Assessing the Relationship between Biomarkers and Clinical Endpoints in Alzheimer's Disease.

Changes in biomarker levels of Alzheimer's disease (AD) reflect underlying pathophysiological changes in the brain and can provide evidence of direct and downstream treatment effects linked to disease modification. Recent results from clinical trials of anti-amyloid ß (Aß) treatments have raised the question of how to best characterize the relationship between AD biomarkers and clinical endpoints. Consensus methodology for assessing such relationships is lacking, leading to inconsistent evaluation and reporting. In this review, we provide a statistical framework for reporting treatment effects on early and late accelerating AD biomarkers and assessing their relationship with clinical endpoints at the subject and group levels. Amyloid positron emission tomography (PET), plasma p-tau, and tau PET follow specific trajectories during AD and are used as exemplar cases to contrast biomarkers with early and late progression. Subject-level correlation was assessed using change from baseline in biomarkers versus change from baseline in clinical endpoints, and interpretation of the correlation is dependent on the biomarker and disease stage. Group-level correlation was assessed using the placebo-adjusted treatment effects on biomarkers versus those on clinical endpoints in each trial. This correlation leverages the fundamental advantages of randomized placebo-controlled trials and assesses the predictivity of a treatment effect on a biomarker or clinical benefit. Harmonization in the assessment of treatment effects on biomarkers and their relationship to clinical endpoints will provide a wealth of comparable data across clinical trials and may yield new insights for the treatment of AD.

Latent Archetypes of the Spatial Patterns of Cancer.

The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2-4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached.

Reconsidering Public Attitudes Toward Deceased Organ Donation Registration.

Background: The aim of the current study is to understand Americans' attitudes toward deceased organ donation and to assess their level of acceptance on positions toward organ donation. Methods: In summer and winter of 2023, 2 national samples completed measures of attitudes toward donation, reported their registration status, rated positions related to donation, and provided demographic information. Results: Data from 2 samples indicated registration estimates between 50% and 57% and a significant proportion of those unregistered reported reluctance to register. The believability of national registry estimates and ratings of acceptability of 9 organ donation positions differed by registration status. Conclusion: Mass media and motor vehicles interventions seeking to increase registration rates should take into consideration individual attitudes and support toward donation may not be as strong as once thought.

Exploring the Link Between Parental Sociodemographic Characteristics and Multiple Births: Insights from National Birth Data in Japan, 1995-2020.

This present study investigated the parental characteristics of multiple births using national birth data in Japan. This study included birth data from Vital Statistics: Occupational and Industrial Aspects every five fiscal years from 1995 to 2020. The multiple birth rates were defined as the number of live-birth deliveries with multiple fetuses (e.g., twins, triplets) per total live-birth deliveries. Parental ages, nationalities, occupations and household occupation (occupation of the top earner of the household) were considered as parental characteristics. The multiple birth rates were calculated based on parental characteristics for each year, and a log-binomial regression model was used to assess the association between parental characteristics and multiple births. The multiple birth rate for Japanese mothers consistently exceeded that for non-Japanese mothers over the years, and the rate increased progressively from manual workers to lower non-manual workers and then to upper non-manual workers for both maternal and paternal occupations. The regression results indicated that the risk ratio (RR) for multiple births among non-Japanese mothers was significantly lower than that among Japanese mothers. Moreover, concerning household occupation, the RRs of self-employed individuals, full-time employees at smaller companies, others, and the unemployed were significantly lower than those of full-time employees at larger companies. Furthermore, the RRs of lower non-manual and manual workers were significantly lower than those of upper non-manual workers in maternal and paternal occupations. The results suggested an association between multiple births and parental socioeconomic status in Japan.

An optimized instrument variable selection approach to improve causality estimation in association studies.

Mendelian randomization (MR) is an emerging tool for inferring causality in genetic epidemiology. MR studies suffer bias from weak genetic instrument variables (IVs) and horizontal pleiotropy. We introduce a robust integrative framework strictly adhering with STROBE-MR guidelines to improve causality inference through MR studies. We implemented novel t-statistics-based criteria to improve the reliability of selected IVs followed by various MR methods. Further, we include sensitivity analyses to remove horizontal-pleiotropy bias. For functional validation, we perform enrichment analysis of identified causal SNPs. We demonstrate effectiveness of our proposed approach on 5 different MR datasets selected from diverse populations. Our pipeline outperforms its counterpart MR analyses using default parameters on these datasets. Notably, we found a significant association between total cholesterol and coronary artery disease (P = 1.16 × 10-71) in a single-sample dataset using our pipeline. Contrarily, this same association was deemed ambiguous while using default parameters. Moreover, in a two-sample dataset, we uncover 13 new causal SNPs with enhanced statistical significance (P = 1.06 × 10-11) for liver-iron-content and liver-cell-carcinoma. Likewise, these SNPs remained undetected using the default parameters (P = 7.58 × 10-4). Furthermore, our analysis confirmed previously known pathways, such as hyperlipidemia in heart diseases and gene ME1 in liver cancer. In conclusion, we propose a robust and powerful framework to infer causality across diverse populations and easily adaptable to different diseases.

Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods.

Statistical modeling for massive spatial data sets has generated a substantial literature on scalable spatial processes based upon Vecchia's approximation. Vecchia's approximation for Gaussian process models enables fast evaluation of the likelihood by restricting dependencies at a location to its neighbors. We establish inferential properties of microergodic spatial covariance parameters within the paradigm of fixed-domain asymptotics when they are estimated using Vecchia's approximation. The conditions required to formally establish these properties are explored, theoretically and empirically, and the effectiveness of Vecchia's approximation is further corroborated from the standpoint of fixed-domain asymptotics.

Space-time scanning statistics in the prediction and evaluation of dengue epidemic clusters.

Objectives: To detect clusters of dengue hemorrhagic fever in an urbanized district of Hai Phong City, Vietnam using Poisson space-time retrospective and prospective analysis. Methods: A cross-sectional and retrospective study analyzed dengue surveillance data in the period from January 01, 2018, to December 31, 2022. Spatial-temporal scanning statistics were performed using the free software SatScan v10.1.2. Results: A total of 519 cases were recorded. The cumulative incidence per 100,000 inhabitants was 3.37, 127.36, 10.96, 0, and 296.04 in 2018, 2019, 2020, 2021, and 2022, respectively. By retrospective Poisson model-based analysis, seven clusters were detected. Six of these seven detected outbreaks occurred in November and December 2022. The largest cluster had a relative risk (RR) of 1539.5 (P <0.00001). The smallest cluster has a RR of 316.1 (P = 0.006). Prospective analysis using the Poisson model significantly detected four active case clusters at the time of the study. The largest cluster of cases with RR was 47.7 (P <0.00001) and the smallest cluster with RR was 18.2 (P <0.00001). Conclusions: This study provides a basis for improving the effectiveness of interventions and conducting further investigations into risk factors in the study area, as well as in other urban and suburban areas nationwide.

Exploring the equity of distribution of general medical services funding allocations in Wales: a time-series analysis.

BACKGROUND: Good access to quality primary care in high-income countries can improve population health. Access to primary care is however often not equal among socioeconomic groups; our analysis sought to explore whether funding, a determinant of service supply, is equitably distributed among GP practices in Wales. AIM: We sought to explore the relationship between funding and deprivation among GP practices in Wales, to understand the equity of current funding policies. DESIGN & SETTING: We obtained funding data for general practices in Wales between 2014 and 2022 and explored the equity of distribution using the percentage of practice patients living in the 20% most deprived small areas in Wales. We generated a linear regression model exploring the relationship between practice funding and deprivation, with an interaction term with time in years. RESULTS: Practice funding rose for all practices between 2014 and 2022. Practice deprivation and time in years were both associated with practice funding, with increases in practice deprivation associated with reduced funding allocations, and time being associated with a small increase in funding over the study period. Over the period of analysis of 2004-2022, for every 10% increase in patients living in the most deprived LSOAs, funding per patient decreases on average by 1%. CONCLUSION: Primary cares in Wales in more deprived areas receive discernibly less funding per patient than those in less deprived areas. Given the potential and likelihood primary care can affect population health outcomes, this underinvestment may be contributing to existing health inequalities and requires urgent further analysis and action.

How to evaluate local fit (residuals) in large structural equation models.

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntax and output files are freely available to readers.

Genomic signatures of positive selection in Awarik dromedary camels from southwestern of Saudi Arabia.

Introduction: The Awarik camel population in southwestern Saudi Arabia exhibits unique genetic and phenotypic traits compared to other domestic camel populations. This study aims to explore the genomic signatures of positive selection in Awarik camels to understand their evolutionary history and identify genetic adaptations potentially shared with East African camel populations. Methods: Whole genome sequencing data from nine Awarik camels were analyzed using two robust intra-population haplotype-based statistical methods: integrated haplotype score (iHS) and number of segregating sites by length (nSL). These analyses were conducted to identify candidate regions under positive selection within the Awarik camel genome. Results and discussion: These analyses identified 66 and 53 candidate selection regions, encompassing 185 and 123 genes, respectively. The iHS analysis revealed significant selection signals on chromosomes 15 and 16, including a robust overlap on chromosome 15 (10 regions) involving the TRNAI-AAU gene, suggesting its critical role in adaptive processes. Additionally, chromosome 3 exhibited the highest number of candidate regions totaling 10. The nSL analysis highlighted statistically significant regions on chromosomes 2 and 7, as well as a high concentration of candidate regions on chromosome 14, totaling five regions. Notably, large candidate regions were also identified on chromosome 11 (200 kb: 51.750-51.950 kb) and chromosome 9 (325 kb: 45.825-46.150 kb). Functional annotation of these genes revealed involvement in diverse biological processes including olfactory activity, immune regulation, metabolism, insulin secretion, reproductive performance, kidney function, and cellular signaling, with specific genes like BAG5, septin 7, SLC13A1, PCED1B, BMPR1B, ZAR1, JAKMIP2, and NOTCH2 highlighted. These findings contribute to our understanding of the adaptive mechanisms of Awarik camels and have important implications for breeding and conservation strategies. Further research on these genetic adaptations, particularly those affecting immune response, is crucial to mitigate the impacts of climate change on camel populations.

Altered Brain Structure in Hemifacial Spasm Patients: A Multimodal Brain Structure Study.

Objective: Hemifacial spasm (HFS) is a clinical neurosurgical disease, which brain structural alterations caused by HFS remain a topic of debate. We evaluated changes in brain microstructure associated with HFS and observed their relevance to clinical characteristics. Methods: We enrolled 72 participants. T1-weighted structural and diffusion tensor images were collected from all participants using 3.0T magnetic resonance equipment. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to identify changes in gray matter volume (GMV) and disruptions in white matter (WM) integrity. The severity of the spasms was graded using the Cohn scale. Results: VBM analysis revealed that the GMV was significantly reduced in the left Thalamus and increased GMV in the right Cerebellum IV-V of the HFS group. TBSS analysis showed that FA in the left superior longitudinal fasciculus (SLF) of the HFS group was significantly increased. GMV in the thalamus showed a negative correlation with disease duration and Cohn grade, while FA in the left SLF had a positive correlation with both the disease duration and Cohn grade. Conclusion: We identified regions with altered GMV in HFS patients. Additionally, we determined that FA in the left SLF might serve as a significant neural indicator of HFS.

Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy.

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form a typical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of a beamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.