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Trillions of microorganisms inhabit the human gut and are regarded as potential key factors for health 1 , 2 . Characteristics such as diet, lifestyle, or genetics can shape the composition of the gut microbiota 2 – 6 and are usually shared by individuals from comparable ethnic origin. So far, most studies assessing how ethnicity relates to the intestinal microbiota compared small groups living at separate geographical locations 7 – 10 . Using fecal 16S ribosomal RNA gene sequencing in 2,084 participants of the Healthy Life in an Urban Setting (HELIUS) study 11 , 12 , we show that individuals living in the same city tend to share similar gut microbiota characteristics with others of their ethnic background. Ethnicity contributed to explain the interindividual dissimilarities in gut microbiota composition, with three main poles primarily characterized by operational taxonomic units (OTUs) classified as Prevotella (Moroccans, Turks, Ghanaians), Bacteroides (African Surinamese, South-Asian Surinamese), and Clostridiales (Dutch). The Dutch exhibited the greatest gut microbiota α-diversity and the South-Asian Surinamese the smallest, with corresponding enrichment or depletion in numerous OTUs. Ethnic differences in α-diversity and interindividual dissimilarities were independent of metabolic health and only partly explained by ethnic-related characteristics including sociodemographic, lifestyle, or diet factors. Hence, the ethnic origin of individuals may be an important factor to consider in microbiome research and its potential future applications in ethnic-diverse societies. Stool microbiota composition correlates with the ethnic backgrounds of people living in the same city, suggesting that geographical location and ethnicity have distinct effects on microbiota.

\"The book scrutinizes the scientific and expert practices of Roma classification in a historic perspective focusing on the expert discourses that gave rise to Roma-related policies in the last two decades. Epistemic communities that classify and describe Roma obey the commandments of political regimes in power, to the disciplinary research traditions and to the organizational interests. The resultant of knowledge subordination is a negative Roma public image that creates and reinforce stereotypical views held by the society at large. Case studies and thorough examples in the book show that both the census as an administrative and scientific practice, as well as policy related surveys are crafting Roma identity in an essentializing manner. The census reifies Roma by the use of mutually exclusive categories and by post-codification of data while the surveys do so by unfounded representativeness claims. Roma are relegated by the experts to several types of determinism: to a social category, to a frozen culture and to a biologized entity. The recently reemerged scholarship in Roma-related genetics imported classifications and narrations created in the fields of social sciences and contributed to circulation of bio-historical narratives that singularize, pathologize and exoticize Roma\"--Provided by publisher.

Background Over 50,000 individuals become infected with HIV annually in the U.S., and over a quarter of HIV infected individuals are heterosexuals. Undiagnosed HIV infection, as well as a lack of retention in care among those diagnosed, are both primary factors contributing to ongoing HIV incidence. Further, there are racial/ethnic disparities in undiagnosed HIV and engagement in care, with African Americans/Blacks and Latinos remaining undiagnosed longer and less engaged in care than Whites, signaling the need for culturally targeted intervention approaches to seek and test those with undiagnosed HIV infection, and link them to care with high retention. Methods/Design The study has two components: one to seek out and test heterosexuals at high risk for HIV infection, and another to link those found infected to HIV care with high retention. We will recruit sexually active African American/Black and Latino adults who have opposite sex partners, negative or unknown HIV status, and reside in locations with high poverty and HIV prevalence. The “Seek and Test” component will compare the efficacy and cost effectiveness of two strategies to uncover undiagnosed HIV infection: venue-based sampling and respondent-driven sampling (RDS). Among those recruited by RDS and found to have HIV infection, a “Treat and Retain” component will assess the efficacy of a peer-driven intervention compared to a control arm with respect to time to an HIV care appointment and health indicators using a cluster randomized controlled trial design to minimize contamination. RDS initial seeds will be randomly assigned to the intervention or control arm at a 1:1 ratio and all recruits will be assigned to the same arm as the recruiter. Participants will be followed for 12 months with outcomes assessed using medical records and biomarkers, such as HIV viral load. Discussion Heterosexuals do not test for HIV as frequently as and are diagnosed later than other risk groups. The study has the potential to contribute an efficient, innovative, and sustainable multi-level recruitment approach and intervention to the HIV prevention portfolio. Because the majority of heterosexuals at high risk are African American/Black or Latino, the study has great potential to reduce racial/ethnic disparities in HIV/AIDS. Trial registration ClinicalTrials.gov, NCT01607541 , Registered May 23, 2012.

Collective terms BAME and BME should be abandoned

Ethnicity recognition has become increasingly important for a wide range of applications, highlighting the need for accurate and robust predictive models. Despite advances in machine learning, ethnicity classification remains a challenging research problem due to variations in facial features, class imbalance, and generalization issues. This study provides a concise synthesis of prior work to motivate the problem and then introduces a novel experimental framework for ethnicity recognition rather than a survey review. It proposes an improved approach that leverages transfer learning to enhance classification performance. The inclusion of various imaging sensors in the proposed methodology allows for an examination of how these imaging sensors impact the performance of facial recognition systems when a variety of images are captured under a number of real-world conditions, using professional and consumer-grade devices to create a range of conditions; from this dataset, the UTKFace dataset will be used to train and validate our method; an additional balanced dataset of Test Celebrities Faces was also created, representing five different ethnic groups (Black, Asian, White, Indian, and Other); the “Other” classification was specifically excluded for final evaluations to eliminate ambiguity and enhance stability. Rigorous preprocessing of both datasets was performed for optimal extraction of features from the sensors’ acquired images; the performance of several pre-trained CNN (Convolutional Neural Network) models (VGG16, DenseNet169, VGG19, ResNet50, MobileNetV2, InceptionV3 and EfficientNetB4) was used to identify an Ideal Hyperparameter Configuration for Optimal Performance. The resulting experimental results indicate that the VGG19 model achieved an 87% validation accuracy and a Maximum test accuracy of 75% on the Primary Dataset of Celebrity Faces; subsequently, the VGG19 model demonstrated a Range of Per-Class Accuracies, in addition to an overall accuracy of 87% across all five ethnic groups (51–90%+). This work demonstrates that leveraging transfer learning on imaging-sensor-captured images enables robust ethnicity classification with high accuracy and improved training efficiency relative to full model retraining. Furthermore, systematic hyperparameter optimization enhances model generalization and mitigates overfitting. Comparative experiments with recent state-of-the-art methods (2023–2025) further confirm that our optimized VGG19 model achieves competitive performance, reinforcing the effectiveness of the proposed reproducible and fairness-aware evaluation framework.

Few cross-sectional studies report iron deficiency (ID) prevalence in women of different race/ethnicity and ages in US or Canada. We evaluated screening observations on women who participated between 2001-2003 in a cross-sectional, primary care-based sample of adults ages ≥25 y whose observations were complete: race/ethnicity; age; transferrin saturation; serum ferritin; and HFE p.C282Y and p.H63D alleles. We defined ID using a stringent criterion: combined transferrin saturation <10% and serum ferritin <33.7 pmol/L (<15 μg/L). We compared ID prevalence in women of different race/ethnicity subgrouped by age and determined associations of p.C282Y and p.H63D to ID overall, and to ID in women ages 25-44 y with or without self-reported pregnancy. These 62,685 women included 27,079 whites, 17,272 blacks, 8,566 Hispanics, 7,615 Asians, 449 Pacific Islanders, 441 Native Americans, and 1,263 participants of other race/ethnicity. Proportions of women with ID were higher in Hispanics and blacks than whites and Asians. Prevalence of ID was significantly greater in women ages 25-54 y of all race/ethnicity groups than women ages ≥55 y of corresponding race/ethnicity. In women ages ≥55 y, ID prevalence did not differ significantly across race/ethnicity. p.C282Y and p.H63D prevalence did not differ significantly in women with or without ID, regardless of race/ethnicity, age subgroup, or pregnancy. ID prevalence was greater in Hispanic and black than white and Asian women ages 25-54 y. p.C282Y and p.H63D prevalence did not differ significantly in women with or without ID, regardless of race/ethnicity, age subgroup, or pregnancy.

Background Skin colour is a prominent human trait historically used to define ethnicity, yet its validity as a classification tool remains questionable. Materials and Methods We quantitatively analyse over 14 000 skin reflectance measurements from eight ethnically diverse groups in the International Skin Spectra Archive (ISSA), using a standard colour space designed to model human visual perception. We assess intragroup variation and intergroup overlap through two complementary approaches: individual‐level perceptual differences and group‐level shared gamut volumes. Results Results show that within‐group variability in chromaticity and lightness frequently exceeds between‐group differences. At the individual level, 89.4% (95% CI: 81.5%–91.9%) of samples have perceptually indistinguishable counterparts across ethnicities. At the group level, the median shared gamut overlap is 60.5% (95% CI: 54.5%–63.6%), indicating substantial overlap in skin colour distributions. The two methods correlate strongly (r = 0.83, p < 0.001), confirming robust intergroup overlap. Conclusion Skin colour exhibits high within‐group dispersion and extensive between‐group overlap. These findings challenge the use of skin colour as a reliable indicator of ethnicity and underscore the need for objective, data‐driven classification frameworks. They also highlight the complex, continuous nature of human skin variation, beyond simplistic ethnic categories.

Uncovering the world’s ethnic inequalities is hampered by a lack of ethnicity-annotated datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people’s ethnicities from their names. However, since the latest generation of NECs rely on machine learning and artificial intelligence (AI), they may suffer from the same racist and sexist biases found in many AIs. Therefore, this paper offers an algorithmic fairness audit of three NECs. It finds that the UK-Census-trained EthnicityEstimator displays large accuracy biases with regards to ethnicity, but relatively less among gender and age groups. In contrast, the Twitter-trained NamePrism and the Wikipedia-trained Ethnicolr are more balanced among ethnicity, but less among gender and age. We relate these biases to global power structures manifested in naming conventions and NECs’ input distribution of names. To improve on the uncovered biases, we program a novel NEC, N2E , using fairness-aware AI techniques. We make N2E freely available at www.name-to-ethnicity.com .