Background: Postpartum depression (PPD) is a prevalent and debilitating disorder, with increasing evidence implicating the gut microbiota–brain axis. However, integrated alterations in gut microbiota and circulating metabolites in PPD remain insufficiently characterized. Methods: Fecal and serum samples were collected from patients with PPD and healthy controls (HC). Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HAMD-17). Gut microbiota was analyzed by 16S rRNA sequencing, and serum metabolites were profiled using untargeted LC–MS-based metabolomics. Spearman correlation and receiver operating characteristic (ROC) analyses were performed. Results: A total of 63 participants (42 PPD, 21 HC) were included. Significant alterations in gut microbial composition were observed in PPD, including decreased Faecalibacterium and Akkermansia and increased Ralstonia and Fusobacterium. Candidate differential serum metabolic features, including LPE-related and energy-metabolism-related features, were identified. Exploratory correlation analyses suggested distinct microbiota–metabolite association patterns, and several microbial taxa and serum metabolic features were associated with HAMD-17 scores. ROC analysis showed that several taxa and metabolic features exhibited preliminary discriminative performance with this cohort, although further validation is required. Conclusions: PPD was associated with alterations in gut microbiota composition and exploratory circulating metabolite profiles, potentially involving lipid dysregulation, neuroinflammation, steroid-related metabolism, and neurotoxicity-related pathways. The identified taxa and putatively annotated metabolites should be regarded as exploratory PPD-associated candidates rather than validated biomarkers or mechanistic mediators. Further validation in larger, independent cohorts is warranted. These findings may also inform future microbiota- and nutrition-oriented strategies for postpartum mental health management.
Shengxuan Li, Min Pi, Zhuoxin Yang et al.· Nutrients· 0 citations
Background Highly variable gene (HVG) selection begins almost every single-cell RNA-seq analysis. While ranking formulas have been compared extensively, the integer gene budget at which any ranking must be truncated is typically left to the user and habitually fixed near 2,000. Relying on such a convention carries hidden costs—lists that are too short erase subtle structure, whereas lists that are too long add noise and computational overhead. Moreover, because global rankings measure variance across all cells, markers for rare populations often lose the “variance vote count” to dominant bulk variation, leading to an unfair feature allocation at the hard cutoff. Whether this convention is defensible, and whether the budget and tail can be set from data without disturbing the ranking, has not been examined systematically. Results Under a frozen seurat_v3 ranking, k-sweeps across 18 labeled datasets show that n = 2,000 is ARI-optimal on 1 of 18 datasets and that the best available budget is worth a mean ARI gain of +0.033 over it, establishing cardinality as a real and largely unexploited design axis. We present scFair, a Scanpy-compatible HVG layer that automates list length alone: geometry-aware auto_n sets a base size k from multi-seed density and stability features of an intermediate embedding (trading a modest, intentional compute increase for a safer data-driven default), and a same-rank append step acts as a conservative safeguard against cutoff unfairness by adding a short near-miss tail. The ranking is never recomputed or reweighted. On the 18-dataset panel, the default path improved Leiden–label agreement over HVG@2000 (median ΔARI = +0.016; 13/5; Wilcoxon P = 0.0077) and outperformed the neighborhood-based selector triku at author defaults on 15/18 datasets (median +0.024; P = 0.004), while triku did not improve on HVG@2000. Controls locate the effect: cell-number-only rules do not beat HVG@2000, an FDR-chosen length imposed on the frozen ranking is flat, and a fixed HVG@2200 default is not a general substitute because it cannot produce the short lists that compact matrices call for. Conclusions A fixed budget near 2,000 HVGs is frequently suboptimal, and list cardinality is a separable design axis that can be automated without changing the ranking formula. Effect sizes are modest, the short-list branch rests on four datasets, and rule thresholds were developed with partial overlap to the evaluation panel.
Zhao Li, Aaron W. James, Shengxuan Li· bioRxiv· 0 citations
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