Gene therapy is transforming hereditary hematological disorders. This review summarizes approved gene addition, editing, and silencing strategies for sickle cell disease, thalassemia, and hemophilia, highlights curative potential, and discusses remaining challenges such as immune responses, cost, and accessibility

Yuan and colleagues demonstrate that inhaled biodegradable nanosilica activates an alveolar macrophage–NK axis, triggering an IL-12/15/18 triad that programs memory-like NK cells. This non-fibrotic, cell-free strategy suppresses melanoma growth, prevents postsurgical recurrence, and synergizes with anti-PD-1, establishing a robust framework for in vivo NK cell immunotherapy.

arXiv:2609.10305v1 Announce Type: cross Abstract: Language models under one million parameters matter for edge deployment, domain adaptation, and reproducible research, yet a two-layer LSTM or Transformer at embedding width d = 128 still spends roughly one third of its capacity on the output matrix W_out in R^(d x |V|). We propose Riemannian Language Models (RiLM), which remove that layer entirely: context unfolds as a trajectory on a Riemannian manifold, and next-token probabilities arise from squared geodesic distance between the current state and vocabulary embeddings. The same embedding map serves input and output -- decoding is geometry. We instantiate the framework on flat R^d (Flat RiLM) and the Poincare ball H^d (HypRiLM) with a shared MLP composition map phi (~290k parameters, d = 128, |V| = 2000). Across five seeds on WikiText-2, HypRiLM reaches 54.2 +/- 0.2 validation perplexity versus 87.6 +/- 0.6 for Flat RiLM; tied and matched LSTM, Transformer, and SSM controls remain at 113-147 PPL on WT-2 -- HypRiLM leads by roughly 2x over the strongest tied recurrent baseline (SSM, 113.0 +/- 3.8). Penn Treebank and a 10k-vocabulary stress test confirm that geodesic decoding transfers across corpora and larger |V|, while hyperbolic curvature helps selectively. We also characterize boundary collapse in naive hyperbolic recurrence and show how Mobius stabilization restores trainability. Claims are scoped to controlled small-model comparisons, not full-vocabulary state of the art.