The human gut microbiota refers to a diverse community of microorganisms that symbiotically exist in the human intestinal system. Altered microbial communities have been linked to many human pathologies. However, there is a lack of rapid and efficient methods to assess gut microbiota signatures in practice. To address this, we established an appraisal system containing 45 quantitative real-time polymerase chain reaction (qPCR) assays targeting gut core microbes with high prevalence and/or abundance in the population. Through comparative genomic analysis, we selected novel species-specific genetic markers and primers for 31 of the 45 core microbes with no previously reported specific primers or whose primers needed improvement in specificity. We comprehensively evaluated the performance of the qPCR assays and demonstrated that they showed good sensitivity, selectivity, and quantitative linearity for each target. The limit of detection ranged from 0.1 to 1.0 pg/µL for the genomic DNA of these targets. We also demonstrated the high consistency (Pearson\'s r = 0.8688, P < 0.0001) between the qPCR method and metagenomics next-generation sequencing (mNGS) method in analyzing the abundance of selected bacteria in 22 human fecal samples. Moreover, we quantified the dynamic changes (over 8 weeks) of these core microbes in 14 individuals using qPCR, and considerable stability was demonstrated in most participants, albeit with significant individual differences. Overall, this study enables the simple and rapid quantification of 45 core microbes in the human gut, providing a promising tool to understand the role of gut core microbiota in human health and disease. KEY POINTS: • A panel of original qPCR assays was developed to quantify human gut core microbes. • The qPCR assays were evaluated and compared with mNGS using real fecal samples. • This method was used to dynamically profile the gut core microbiota in individuals.

Introduction: The emigration of Venezuelans has seen a significant increase in recent years. The aim of this study was to identify factors associated with access to contraceptives in migrant women from Venezuela residing in Peru. Methods: An exploratory cross-sectional study was conducted using data from the Second Survey of the Venezuelan Population Residing in Peru. Access to contraceptives (yes/no) was established as the dependent variable. A Poisson regression model was performed for complex samples, and crude (PRc) and adjusted (PRa) prevalence ratios were reported. Results: A total of 3617 migrant women were analyzed, with 50.12% reporting access to contraceptives. Factors associated with greater access to contraceptives included being between 20 to 29 years old, having a university education, the entry period after the declaration of COVID-19 quarantine was associated with lower access to contraceptives. Conclusions: Access to contraceptives for Venezuelan migrants should be provided to all, as it is a free service in Peru. Additionally, education and family planning should be provided from the basic or middle school level to prevent unintended pregnancies in the future. Complete coverage should be provided regardless of the time of entry into Peru.

OBJECTIVE: To explore: a) students\' perceived stress, social support and satisfaction with their initial clinical placement; and b) identify the relationship between perceived stress, social support and student\'s satisfaction with their initial clinical placement.
BACKGROUND: Clinical placements are a central element in shaping future nursing professionals. Students\' satisfaction with clinical placements influences their learning outcomes and intention to continue their studies. Students experience stress from various sources during their clinical placements and social support has been found to moderate their stress. However, there is a paucity of studies addressing the associations between perceived stress, social support and satisfaction with the initial clinical placement.
METHODS: Quantitative cross-sectional study.
METHODS: Participants included 165s-year nursing students enrolled in the bachelor\'s program at a central university in Israel during their initial clinical placement. Data were collected between December 2022 and February 2023 using an online questionnaire that contained four parts: personal details, perceived social support, perceived stress and satisfaction with clinical placement.
RESULTS: Students\' perceived stress was found to be negatively correlated to satisfaction with their clinical instructor (r = -0.47, p < 0.001) and with their clinical placement (r = -0.47, p < 0.001). Positive associations were found between satisfaction with the clinical placement and satisfaction with the clinical instructor (r = 0.67, p < 0.001). Significant differences were found in perceived stress and social support according to sociodemographic characteristics. Students perceived stress, social support from family and friends and satisfaction with their clinical instructor explained 54% of their satisfaction with their initial clinical placements.
CONCLUSIONS: Clinical instructors and nurse educators are central to students\' initial clinical experience. It is recommended that they assess students\' perceived stress in the clinical environment, integrate stress reduction strategies into the curriculum and encourage students to turn to various sources for social support. These can promote students\' satisfaction and success in their studies.

OBJECTIVE: Examining loneliness and social isolation during population-wide historical events may shed light on important theoretical questions about age differences, including whether these differences hold across different regions and the timecourse of the unfolding event. We used a systematic, preregistered approach of coordinated data analysis (CDA) of four studies (total N = 1,307; total observations = 18,492) that varied in design (intensive repeated-measures and cross-sectional), region, timing, and timescale during the first year of the COVID-19 pandemic.
METHODS: We harmonized our datasets to a common period within 2020-2021 and created a common set of variables. We used a combination of ordinary least squares regression and multilevel modeling to address the extent to which there was within- and between-person variation in the associations between social isolation and loneliness, and whether these associations varied as a function of age.
RESULTS: Within- and between-person effects of social interactions were negatively associated with loneliness in one study; in follow-up sensitivity analyses, these patterns held across early and later pandemic periods. Across all datasets, there was no evidence of age differences in the within-person or between-person associations of social interactions and loneliness.
CONCLUSIONS: Applying the CDA methodological framework allowed us to detect common and divergent patterns of social interactions and loneliness across samples, ages, regions, periods, and study designs.

This mixed-methods study used an online cross-sectional survey to explore perspectives of 533 adult Australian women living with endometriosis pain, and their relationship with biopsychosocial factors. Four themes were constructed: The primary theme, \'Stigma and change\' reflected women\'s experience of dismissal, and the wish to reverse the narrative of pain as normal. Some women emphasised self-education and self-advocacy to affect change, reflecting the theme \'self-empowerment\'. Participants described the \'debilitating impact\' of endometriosis and the enduring difficulty of \'inadequate healthcare\', reflecting themes three and four. Analysis indicated type of social support may impact perceived outcomes for endometriosis. Hierarchical regression analyses indicated too few significant relationships between biopsychosocial factors and themes to indicate meaningful patterns without risk of common method variance. Future research should explore the influence of social support and interventions which develop participant autonomy and practitioner competence and knowledge, using disease-specific measures over time.

Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata. Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses.

Anthocyanins, a subclass of flavonoids known for their vibrant colors and health-promoting properties, are pivotal in the nutritional science and food industry. This review article delves into the analytical methodologies for anthocyanin detection and quantification in food matrices, comparing quantitative and topical techniques. Quantitative methods, including High-performance Liquid Chromatography (HPLC) and Mass Spectrometry (MS), offer precise quantification and profiling of individual anthocyanins but require sample destruction, limiting their use in continuous quality control. Topical approaches, such as Near-infrared Spectroscopy (NIR) and hyperspectral imaging, provide rapid, in situ analysis without compromising sample integrity, ideal for on-site food quality assessment. The review highlights the advancements in chromatographic techniques, particularly Ultra-high-performance Liquid Chromatography (UHPLC) coupled with modern detectors, enhancing resolution and speed in anthocyanin analysis. It also emphasizes the growing importance of topical techniques in the food industry for their efficiency and minimal sample preparation. By examining the strengths and limitations of both analytical realms, this article aims to shed light on current challenges and prospective advancements, providing insights into future research directions for improving anthocyanin analysis in foods.

BACKGROUND: Data cleaning is the series of procedures performed before a formal statistical analysis, with the aim of reducing the number of error values in a dataset and improving the overall quality of subsequent analyses. Several study-reporting guidelines recommend the inclusion of data-cleaning procedures; however, little practical guidance exists for how to conduct these procedures.
OBJECTIVE: This paper aimed to provide practical guidance for how to perform and report rigorous data-cleaning procedures.
METHODS: A previously proposed data-quality framework was identified and used to facilitate the description and explanation of data-cleaning procedures. The broader data-cleaning process was broken down into discrete tasks to create a data-cleaning checklist. Examples of the how the various tasks had been undertaken for a previous study using data from the Australia and New Zealand Intensive Care Society Adult Patient Database were also provided.
RESULTS: Data-cleaning tasks were described and grouped according to four data-quality domains described in the framework: data integrity, consistency, completeness, and accuracy. Tasks described include creation of a data dictionary, checking consistency of values across multiple variables, quantifying and managing missing data, and the identification and management of outlier values. The data-cleaning task checklist provides a practical summary of the various aspects of the data-cleaning process and will assist clinician researchers in performing this process in the future.
CONCLUSIONS: Data cleaning is an integral part of any statistical analysis and helps ensure that study results are valid and reproducible. Use of the data-cleaning task checklist will facilitate the conduct of rigorous data-cleaning processes, with the aim of improving the quality of future research.

UNASSIGNED: Engagement in work has an important association with cognitive health in later life, yet little is known about this association among Native Hawaiian and other Pacific Islander (NHPI) older adults. This study assesses the associations between various work characteristics and memory problems among this population.
UNASSIGNED: Using data from the 2014 Native Hawaiian and Pacific Islander National Health Interview Survey (NHPI NHIS), the research question was explored among those who were aged 50+.
UNASSIGNED: Engagement in work, certain occupation types (e.g., clerical or professional occupations compared to blue-collar jobs), and the current/most recent job that is also the longest job held were associated with lower odds of having memory problems.
UNASSIGNED: The study\'s results suggest that work characteristics and opportunities to engage in work are important considerations in preventing memory problems in later life. As the NHPI population experiences cognitive health disparities earlier than other groups, timely interventions that focus on work engagement and a culturally relevant environment require further investigation.

The technology acceptance model (TAM) has not been applied beyond the technological context, despite its widespread applications. This study extends the TAM to consumer goods and validates the conceptualised consumer goods acceptance model (CGAM) using tea-to-go as a proxy for consumer goods. An exploratory research approach based on a quota sample is used to adjust and test the CGAM, showing that the adjusted CGAM and its measures are reliable and valid, thus supporting the hypothesis that extending the TAM to a non-technological context is possible. The results are discussed in light of the data collection, which is based on a cross-sectional setting, assuming consumers\' knowledge of the good and forcing answers based on assumptions. The results show that despite major changes to the exogenous variables, the difference in variance levels between perceived usefulness (PU) and perceived ease of use (PEOU) is in accordance with TAM 3. Thus, PU is the strongest predictor of behavioural intention (BI), supporting the choice of constructs influencing PU. This also indicates that usefulness outweighs ease of use. The findings demonstrate that each variable category has a favourable influence on PU and PEOU. Specifically, cognitive instrumental processes (CIP) and value-related aspects (VRA) are significant predictors of PU, whereas CIP is the most critical driver, highlighting that consumer actions are driven by factors correlated with lifestyle, trends, and price. VRA_1 had the strongest impact on PU, accentuating the high potential of functional ingredients, such as vitamins. Furthermore, the results show that the anchor variables have a stronger impact on PEOU than the adjustment variables, emphasising the importance of enjoyment and indulgence in consumer goods, especially tea-to-go. These findings show that the empirically tested and adjusted CGAM provides a solid basis for measuring the acceptance of consumer goods and identifying key drivers.