👤 Yoshiyasu Takefuji

Bodin et al. (2025) provide valuable insights into neurodevelopmental vulnerability by examining radiofrequency electromagnetic fields (RF‑EMF) exposure during early life. Their integrative design, combining whole-body exposure with endpoints such as neonatal brain proteomics, BDNF expression, synaptogenesis, and oxidative stress, offers a comprehensive framework for developmental neurotoxicology. However, interpretation of proteomic clustering relies heavily on principal component analysis (PCA), a linear technique ill-suited for high-dimensional datasets dominated by non-linear dependencies and strong inter-feature correlations. PCA plots (Figure 3) illustrate group separation, yet variance explained (55%) and clustering stability remain underreported, raising concerns about robustness and biological interpretability, particularly given only ten differentially expressed proteins. To enhance inference, future studies should adopt biologically meaningful feature selection and advanced frameworks such as Feature Agglomeration and Highly Variable Feature Selection, alongside non-parametric correlation measures such as Spearman's rho and Kendall's tau. These strategies will improve reproducibility, uncover mechanistic patterns, and strengthen translational relevance for neurodevelopmental research. Show less

Aortic aneurysms are age-linked aortic dilations that progress silently and carry high mortality rates following rupture. Immune cells are recognized drivers of aneurysm pathogenesis. Clonal hematopoiesis is an age-related expansion of somatically mutated hematopoietic stem cells that reshapes immune function and contributes to diverse age-associated diseases. However, its contribution to aneurysm pathogenesis remains unclear. In this study, targeted ultradeep sequencing of patient specimens revealed a high prevalence of clonal hematopoiesis-associated mutations that correlated with faster aneurysm expansion. Thus, we modeled clonal hematopoiesis by competitively transplanting ten-eleven translocation 2-deficient (Tet2-deficient) bone marrow into apoliprotein E-KO (Apoe-KO) mice and induced aneurysms with angiotensin II. Mice with Tet2 clonal hematopoiesis developed significantly greater aortic dilation than did controls. Interestingly, Tet2-deficient macrophages adopted an acid phosphatase 5, tartrate resistant (ACP5+), osteoclast-like state and produced more matrix metalloproteinase 9 (MMP9). Both genetic and pharmacological inhibition of osteoclast-like differentiation suppressed the Tet2-mediated aneurysmal growth in vivo. Thus, Tet2-driven clonal hematopoiesis accelerated aortic aneurysm progression through MMP9-producing, osteoclast-like macrophages and therefore represents a tractable therapeutic axis. Show less

Liu et al. (2025) analyzed UK Biobank data, using Principal Component Analysis (PCA) to identify lipid patterns associated with depression and bipolar disorder. Their work reported that the first principal component (PC1), reflecting Apolipoprotein B (ApoB), cholesterol, and low-density lipoprotein cholesterol (LDL-C), showed a protective effect against depression. However, their methodological approach warrants discussion. PCA is a linear dimensionality reduction technique. The authors noted nonlinear relationships between lipid profiles and mood disorder risk, contradicting PCA's inherent linearity assumption. Applying linear methods like PCA to nonlinear data can lead to significant distortions, systematic bias, and underfitting, failing to capture true data complexity. PC1 may have obscured genuine associations by forcing distinct biological features into a single linear equation, potentially diluting crucial signals. For future research, complementing PCA with unsupervised learning techniques like Feature Agglomeration (FA) and Highly Variable Gene Selection (HVGS) could offer a more robust approach. Additionally, using nonlinear nonparametric statistical methods such as Spearman's rho or Kendall's tau would be beneficial. These methods detect monotonic relationships without linearity assumptions, precisely capturing potentially nonlinear associations and enhancing interpretability in translational biomarker research. Show less