Axillary Microbiota Compositions from Men and Women in a Tertiary Institution-South East Nigeria: Effects of Deodorants/Antiperspirants on Bacterial Communities
Bibliographic record
Abstract
ABSTRACT The axillary skin microbiota compositions of African populations that live in warm climate is not well studied with modern next-generation sequencing methods. To assess the microbiota compositions of the axillary region of healthy male and female students, we used 16S rRNA metagenomics method and clustered the microbial communities between those students that reported regular use of deodorants/antiperspirants and those that do not. Axillary skin swab was self-collected by 38 male and 35 females following uBiome sample collection instructions. Amplification of the V4 region of the 16S rRNA genes was performed and sequencing done in a pair-end set-up on the Illumina NextSeq 500 platform rendering 2 × 150 base pair. Microbial taxonomy to species level was generated using the Illumina Greengenes database. 26 phyla were identified in males with Actinobacteria as the most abundant (60%), followed by Firmicutes (31.53%), Proteobacteria (5.03%), Bacteroidetes (2.86%) and others. Similarly, 25 phyla were identified in females and Actinobacteria was the most abundant (59.28%), followed by Firmicutes (34.28%), Proteobacteria (5.91%), Bacteroidetes (0.45%) and others. A total of 747 genera were identified, out of which 556 (74.4%) were common to both males and females and 163 (21.8%) were exclusive to males while 28 (3.8%) were exclusive to females. Corynebacterium (53.89% vs 50.17%) was the most relative abundant genera in both male and female subjects, followed by Staphylococcus (19.66% vs 20.90%), Anaerococcus (4.91% vs 7.51%), Propionibacterium (1.21% vs 1.84%). There was a significant difference ( P =0.0075) between those males that reported regular use of antiperspirant/deodorants and those that reported non-use of antiperspirants/deodorants in the relative abundance of Corynebacterium (68.06% vs 42.40%). Higher proportion of Corynebacterium was observed in male subjects than females, while more relative abundance of Staphylococcus was found in females than males. This study detected Lactobacilli in the axilla of over 82% of female and over 81% of male subjects, though in low relative abundance which suggests that Lactobacillus taxa might be considered as part of the normal axillary bacterial community. The study also revealed that the relative abundance of Corynebacterium (68.06% vs 42.40%) was higher in those that reported regular use of deodorants/antiperspirants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".