Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

We need to translate Russian news post into English, format per rules. First line: short headline under 90 chars, no markdown, no '#'. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence. Wrap study/journal citations and publication references in single underscores _ (e.g. Nature Aging, July 2026). We need to preserve all facts, numbers, names, citations exactly. No added commentary. We need to identify important facts: Malva, RNA sequence search, data of 74 million single cells, August 26, journal Nature published article about Malva, index of raw RNA reads from single cells. It links short sequence fragments with barcode — label of specific cell, so query finds cells with needed mutation, RNA junction, pathogen trace and shows their tissue, age, disease, original study. Single-cell sequencing reads RNA of each cell separately. Usually from these reads they make a table: how many RNA of known genes per cell. Such table helps compare cells, but question about specific mutation, viral RNA or novel RNA junction requires returning to huge array of raw reads. Malva stores these reads in an index. It splits each read into fragments of 24 nucleotides — letters of genetic sequence — and records cell barcode next to it. Query by sequence returns cells where its fragments met, together with info about them. Indexes of individual samples can be combined, therefore database is supplemented with new data. In version described in article, index covered about 74 million cells from thousands of experiments. Search for a transcript — RNA copy of a gene — length 1000 nucleotides took 0.9 seconds on a single CPU core. Search result — pseudocounter: number of matched reads instead of RNA molecule quantity. Authors compared such pseudocounters with ordinary RNA counts, then looked for sequence variants, RNA junctions, transcript ends. In a small lung cancer sample Malva found mutations in EGFR gene; separate check covered 280 samples of 16 tumor types. These tests show that fast search finds biological signals in cellular data. Researcher can search for sequence linked to hypothesis in already accumulated public data and see in which tissues, ages and disease states it occurs.… 🔗 Read original →