Chinese Team Integrates Neurointerfaces with MRI for Real‑Time Brain Mapping

Researchers in China have announced a platform that merges neurointerface technology with magnetic resonance imaging (MRI). Until now, MRI served only as an external diagnostic tool while neurointerfaces operated independently. The new approach combines signal acquisition, decoding, targeted stimulation and outcome verification within a single hardware‑software system.

The main obstacle—mutual interference between the MRI’s strong magnetic field and the neurointerface’s electronics—was overcome by built‑in field‑distortion compensation. The system records neuronal activity with millisecond precision and constructs an anatomical brain map with a spatial resolution of less than one millimeter.

As a result, the device functions both as an ultra‑sharp microscope and as a tool for precise, point‑by‑point brain correction. This integration makes human brain neuroplasticity far more quantifiable than before.

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We need to translate Russian news post into natural 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 have a preprint on bioRxiv dated August 24. That's a date. Also numbers: 1,531 healthy human samples, 14 tissue/cell groups, ~90% variance explained, 163 CpG sites, CpG islands entropy 23% lower, blood and colon >90% variance explained around islands, brain islands contribution up to 99%, flanking region ~1000 bp DNA calendar age explains 47-68% local dispersion. Also mention SCARLET model linking age-related DNA methylation marks in blood with hematopoietic stem cell division mode.

We need to select at most 4-5 key facts to bold. Choose: 1,531 samples, 14 tissue groups, ~90% variance explained, 163 CpG sites, 23% lower entropy in CpG islands, >90% variance explained in blood/colon, brain islands up to 99%, calendar age 47-68% in flanking region. That's many; need limit to 4-5. Choose most important: 1,531 samples, ~90% variance explained, 163 CpG sites, CpG islands entropy 23% lower, brain islands contribution up to 99% (or blood/colon >90%). Let's pick: 1,531 samples, ~90% variance explained, 163 CpG sites, CpG islands entropy 23% lower, brain islands contribution up to 99%. That's 5.

Now citations: The preprint on bioRxiv August 24. Also mention SCARLET model (maybe a study). We need to wrap study/journal citations and publication references in single underscores. So we can put bioRxiv, August 24 and maybe SCARLET model? SCARLET is a model name, not a publication. Could treat as SCARLET? Probably better to put the preprint reference: bioRxiv, August 24. Also maybe mention that it's a preprint (not peer-reviewed). Could also include bioRxiv as citation.

We need to preserve all facts, numbers, names, citations exactly. Do not add commentary.

Now produce headline: something like "Stem cell division history explains most epigenetic noise across tissues". Under 90 chars.

Let's count: "Stem cell division history explains most epigenetic noise across tissues" length: let's count characters including spaces. "Stem"(4) + space1=5, "cell"(4)=9, space=10, "division"(8)=18, space=19, "history"(7)=26, space=27, "explains"(8)=35, space=36, "most"(4)=40, space=41, "epigenetic"(10)=51, space=52, "noise"(5)=57, space=58, "across"(6)=64, space=65, "tissues"(7)=72. So 72 chars, okay.

Now blank line.

Then body paragraphs.

We need short paragraphs 2-3 sentences each.

Let's craft maybe 4 paragraphs.

Paragraph 1: Introduce study: On August 24, a preprint on bioRxiv presented analysis of 1,531 healthy human samples from 14 tissue/cell types. Authors accounted for cell-type proportions and found that estimated stem‑cell division history explained about 90% of the epigenetic noise captured by their statistical model.

We need bold numbers: 1,531, 90%. Also date maybe bold? But we limited bold to 5 items; we already used 1,531 and 90% (two). We'll also bold 163 CpG sites, 23% lower entropy, brain islands up to 99% (that's five). So we should not bold date. Good.

Paragraph 2: Describe epigenetic noise measure: They used Shannon entropy of DNA methylation at individual CpG sites; higher entropy indicates a more mixed methylated/unmethylated state. To isolate division effects, they estimated and subtracted cell‑type composition before correlating age with division history.…

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Radial funds OpenADMET grant for blind AI‑model testing in drug discovery

Radial, a life‑sciences division of the Astera Institute, awarded a grant to the OpenADMET project on 24 August. OpenADMET runs open tests of AI models that predict the properties of small molecules used to make drugs. The grant will fund code, rules and public logs for future blind‑challenge evaluations.

Drug‑development models are often benchmarked against published lab measurements, but if those data were in the training set or contain systematic bias, a high score says little about true predictive power for the next experiment. In a blind test the organizer keeps new laboratory results hidden; teams submit predictions in advance and the system later matches them to the experimental outcomes, revealing how well the model forecasts properties of unseen compounds.

OpenADMET already has an ongoing CYP Challenge, in which participants predict how 750 compounds inhibit the activity of four CYP enzymes involved in drug metabolism. In the current leaderboard predictions are scored on half of the test set; after the deadline of 3 November 2026 the evaluation will cover the full set. For each challenge the organizers must distribute data, collect predictions, verify file formats and compute metrics that compare prediction accuracy.

The grant supports open‑source code for these operations, guidance for organizers and public records of the results. OpenADMET plans to give labs and consortia ready‑to‑use code and procedures for the next blind test, and to store the outcome in a verifiable public record.

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We need to translate Russian news post into natural 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 have a preprint on bioRxiv dated August 24 (presumably 2024? Not given year). The text: "24 августа авторы выложили на bioRxiv препринт об электрической стимуляции коры у 14 бодрствующих мышей." So date: August 24 (year unspecified). We'll keep as "August 24". Wrap date in double asterisks? It's a key date. Could be one of the important facts. Also numbers: 14 mice, 2,725 neurons, 53 brain areas, frequencies 8, 28, 140 Hz, 10 series of 10 seconds each, 5 µA current, 300 ms, percentages: 16%, <1%, 21–25 percentage points, 77%, 30%, <10%, 15%, 0%. Also mention Neuropixels probes.

We need to select at most 4-5 double asterisk facts. Choose maybe: 14 mice, 2,725 neurons, 8 Hz, 28 Hz, 140 Hz, 16%, 21–25 percentage points. That's 5. Ensure not whole sentence.

We need to wrap study/journal citations in single underscores. There's a bioRxiv preprint. So we can put bioRxiv, August 24 (or bioRxiv preprint, August 24). Use single underscores.

We must not add commentary. Just translation.

Now produce headline: maybe "Cortical stimulation frequency dictates neural response scale and mechanism in mice". Count characters: Let's count: "Cortical stimulation frequency dictates neural response scale and mechanism in mice". That's about 78 characters. Good.

Now blank line.

Then body paragraphs.

We need to split into short paragraphs 2-3 sentences each.

Let's craft paragraphs:

Paragraph 1: Introduce experiment: 14 awake mice, two Neuropixels probes recorded 2,725 neurons across 53 areas. Authors posted preprint on bioRxiv August 24 about electrical cortical stimulation.

Paragraph 2: Describe stimulation: sinusoidal current at 8, 28, or 140 Hz, ten 10‑second blocks each, 5 µA amplitude. During rest and running, they measured spike timing relative to wave phase.

Paragraph 3: Effects: at 8 Hz neurons synchronized spikes to a specific wave phase with little change in mean firing rate; at 140 Hz near the electrode some cells shifted firing rate briefly. The frequency determined which combination of responses was observed.

Paragraph 4: Modeling: synchrony strength predicted by electric field magnitude at 8 Hz and by local anatomical connectivity at 140 Hz; adding connectivity data improved the model by 21–25 percentage points. Authors link this to a shift from direct field effects to synaptic spread.

Paragraph 5: Local high‑frequency response: 5 µA bursts increased firing in putative inhibitory cells to 16% at 140 Hz (vs <1% at 8 Hz) within first 300 ms, while excitatory‑like cells decreased firing, interpreted as brief local inhibition.

Paragraph 6: Influence of running: running altered distributed synchrony in 77% of areas at 8 Hz, 30% at 28 Hz, and <10% at 140 Hz; local frequency shift depended on running in 15% of areas at 8 and 28 Hz and 0% at 140 Hz.

Paragraph 7: Conclusion: frequency sets mechanism and scale of response, brain state especially modulates distributed synchrony; authors propose using frequency to select desired response type and accounting for state when measuring synchrony.

Now we need to wrap important facts in double asterisks. We have selected: 14 mice, 2,725 neurons, 8 Hz, 28 Hz, 140 Hz, 16%, 21–25 percentage points. Also maybe 5 µA, 300 ms, but we limited to 4-5. We'll stick to those five.…

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We need to translate Russian news post into natural 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 have a citation: https://www.nature.com/articles/s42256-026-01297-7 . That's a Nature article. We need to wrap the citation in single underscores. Probably we should put something like Nature, 2026 or similar. The URL is given; we need to preserve the citation exactly? The instruction: "Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026)." So we need to identify the citation: it's a Nature article link. We can represent as Nature, 2026 maybe. But we must preserve facts exactly; the URL is a fact. Should we include the URL inside underscores? Probably we should wrap the citation text, not the URL. The URL is not a citation per se but a link. The instruction says "Wrap study/journal citations and publication references in single underscores". The given text includes a URL; we can treat that as the citation reference. We could put the URL inside underscores: https://www.nature.com/articles/s42256-026-01297-7. That would be a single underscore wrapped URL. That preserves the URL exactly. That seems okay.

Now we need to pick key numbers etc to wrap in double asterisks. The text mentions: "мгновенно", "точечная помощь", "непрерывный расчет биологических моментов суставов - точного вращательного момента и усилий", "сколько ньютон-метров крутящего момента нужно подать на сервоприводы бедер, коленей и лодыжек прямо сейчас". No explicit numbers given. There's mention of "за полчаса" (half an hour) for battery drain. Also "мощный привод не сломал оператору ногу". No percentages. There's mention of "жесткие аппаратные лимиты по скорости и силе". No numbers.

We need at most 4-5 double asterisk facts. We can include: half an hour (or 30 minutes), maybe Newton-metres? But that's a unit not a number. Could include Newton-metres as a term? Probably better to include numeric values: we have none explicit except "полчаса". Could also include "мгновенно" but that's not a number. Could include "миллисекунду" (millisecond). That's a time unit but not a number. Could treat "миллисекунду" as a fact? Might be okay to wrap "миллисекунду" in double asterisks? But it's not a number. The rule says "key numbers, percentages, drug/company/gene names, dates". So we should wrap numbers, percentages, names, dates. We have no explicit numbers except "полчаса". Could treat "полчаса" as "half an hour" which is a time duration. Could wrap half an hour. Also maybe we can include the year from the URL: 2026. The article is from 2026 (s42256-026). So we can wrap 2026 as a date. Also maybe the journal name: Nature. That's a name, could wrap Nature? But it's a journal name; could be considered a name. However we already wrap the citation in single underscores; we might not need to double asterisk the journal name. But we could still include Nature as a key name. However we need to be selective: at most 4-5 double asterisks. Let's pick: half an hour, 2026, maybe Newton-metres (though not a number). Could also include IMU (inertial measurement units) as an acronym? Not a number. Could include EMG (electromyography). Not a number.

Better to stick to numbers/dates: we have "полчаса" (half an hour) and the year 2026 from the URL. Also maybe the article's volume? Not given. Could also include "миллисекунду" but it's not a number.…

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Mol-JEPA predicts drug‑like molecule properties from structure and biology

Mol‑JEPA, a model for forecasting the properties of medicinal molecules, was released as a preprint on 23 August. The authors compiled data for 4.69 million small molecules, encompassing 14 types of information ranging from chemical structure to cellular measurements and ADMET profiles (absorption, distribution, metabolism, excretion, and toxicity).

During training, Mol‑JEPA randomly masks one data type and learns to reconstruct its compressed numerical description from the remaining modalities. This forces the model to link a molecule’s structure with its biological and pharmacological observations without distorting the underlying structure.

The approach addresses cases where a candidate binds strongly to a target protein but is rapidly cleared or toxic, and where minor structural tweaks can dramatically alter ADMET outcomes. By preserving the exact structure and training on multiple observation types, Mol‑JEPA learns a unified representation that captures diverse molecular behaviors.

Mol‑JEPA encodes each data type—atomic bond schemata, calculated chemical features, biological assay results, cellular profiles, and ADMET data—into a short numerical vector of uniform format. A shared module then receives the available vectors and predicts the representation of the masked type, thereby training the model to connect structure with function.

In property‑prediction tasks on small subsets, Mol‑JEPA achieved lower mean absolute error than competing methods, and its advantage widened as test molecules became more structurally distinct from the training set. On public temporal splits of the TabICLv2 benchmark (a tabular‑data model), Mol‑JEPA was sometimes outperformed in both MAE and head‑to‑head comparisons. Random splits can place molecules sharing a core scaffold in both training and test, leading to overly optimistic estimates; the authors note that evaluating on structurally distant compounds provides a stricter test of transferability.

An ablation study showed that adding more information improves accuracy: a version trained only on molecular graphs and ECFP4 fingerprints was outperformed by the full model using all data types, which reduced MAE by 14 % on a simple task and 13 % on a more complex one.

For drug development, linking molecular structure to accumulated behavior data is essential. Mol‑JEPA consolidates this information into a single numerical descriptor that can be used to predict the properties of new drug candidates.

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Magnetite nanodiscs trigger astrocyte‑specific calcium response in mouse hippocampus

On 19 August the authors posted a preprint describing an experiment in which mice received magnetite nanodiscs coated with an antibody that binds the astrocyte surface protein GLAST, injected into the dentate gyrus of the hippocampus.

Applying alternating magnetic fields of 25–28–50 mT produced a statistically larger intracellular calcium response in astrocytes than hematite particles of the same shape; the calcium signal was monitored by fiber photometry of a genetically encoded calcium‑sensitive reporter.

Between the second and fifth week after a single injection, magnetite discs elicited a stronger astrocyte response than the hematite control, whereas mice expressing the reporter in neurons showed no difference between the two materials.

The method builds on earlier work from 2022 that used antibody‑functionalized magnetic particles moved by strong field gradients and a 2025 open coil for weak alternating fields developed by Po‑Han Chang’s group; three Science studies have linked astrocyte calcium signals to neural network state switching, allowing causal tests.

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We need to translate Russian news post into natural English, format per rules.

First line: short headline under 90 chars, no markdown. Then blank line, then body paragraphs short (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 ....

We need to preserve all facts, numbers, names, citations exactly. No added commentary.

We need to identify important facts: DARPA preparing Firefox program for 100-channel neurointerface without surgery, date: September 1 2026 meeting. Also: goal: system reads cortex signals with light through intact skull. Noninvasive coherent optical system already developed records neural signals at cortical depth in humans. Firefox to expand to multi-channel interface. Agency set path to 100 channels, spatial resolution 50 micrometers, temporal 100 microseconds. Program 36 months, three yearly phases; results checked by independent partner. Firefox divided into two related engineering tasks: dynamic scanning to steer light to several brain areas; other task: process large measurement stream: seeking architectures beyond ordinary digital data processing. Same physical difficulty as completed program N3: light, sound, electromagnetic signals scatter and attenuate passing through skin, skull, brain tissue. N3 set benchmark: work with 16 independent channels in volume of neural tissue 16 mm³ and temporal accuracy up to 50 milliseconds. Firefox builds on this problem and sets new measurement parameters.

We need to wrap key numbers/dates etc in . Choose maybe: September 1 2026, 100 channels, 50 micrometers, 100 microseconds, 36 months, three yearly phases, 16 channels, 16 mm³, 50 milliseconds. That's many; we need at most 4-5. So we need to be selective: maybe highlight date, 100 channels, spatial resolution 50 µm, temporal resolution 100 µs, program duration 36 months. That's 5. Or we could include N3 benchmark as well but limit.

We need to wrap study/journal citations in single underscores. There are none explicit citations like journal names. The text mentions "уведомлении о Firefox DARPA пишет" but no journal. So maybe none. We can leave none.

We must not wrap whole sentence. So we need to embed double asterisks around the selected facts within sentences.

We need to ensure not to exceed 4-5 double asterisk occurrences. Each occurrence is a pair .... So we need max 5 pairs.

Let's pick: September 1, 2026, 100 channels, 50 µm spatial resolution, 100 µs temporal resolution, 36‑month program. That's 5.

Make sure to keep numbers exactly as in source: "100 каналов", "50 микрометров", "100 микросекунд", "36 месяцев". Use appropriate formatting.

Now produce headline: maybe "DARPA Launches Firefox Program for 100‑Channel Non‑Invasive Neurointerface". Count chars: Let's count: DARPA (5) + space (1) =6, Launches (8) =>14, space=15, Firefox (7)=>22, space=23, Program (6)=>29, space=30, for (3)=>33, space=34, 100‑Channel (12 inc hyphen?) Let's count: "100‑Channel" characters: 100 (3) + ‑ (maybe hyphen) + Channel (7) = 11? Actually "100‑Channel": 3 + 1 + 7 =11. So total 34+11=45, space=46, Non‑Invasive (13? Non‑ (4) + Invasive (8) + hyphen? Actually "Non‑Invasive": Non‑ (4 inc hyphen?) Let's count: N o n ‑ I n v a s i v e = 4 letters + hyphen + 8 =13. So 46+13=59, space=60, Neurointerface (13?) Neurointerface: N e u r o i n t e r f a c e =12? Actually "Neurointerface": Neuro (5) + interface (8) =13. So 60+13=73. Under 90. Good.

Now blank line, then body paragraphs.

We need short paragraphs 2-3 sentences each.

Let's craft maybe 3 paragraphs.

Paragraph 1: mention meeting date and goal.

Paragraph 2: mention existing system, target specs.

Paragraph 3: program duration, phases, tasks, relation to N3.…

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We need to translate Russian news into English, format with headline line under 90 chars, then blank line, then body paragraphs short (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. Wrap study/journal citations and publication references in single underscores.

We need to preserve all facts, numbers, names, citations exactly. No added commentary. No source link.

We need to identify important facts: $40 млн, date 25 августа (August 25), Highland Europe fund, doubling team in Lausanne, opening office with lab in London Q4 2026, Claude campaign 1,320 mini-proteins for 15 targets, Anthropic, Twist Bioscience, first data ~21 days, quote.

We need to wrap key numbers etc in . Choose maybe: $40 million, August 25, Highland Europe, Q4 2026, 1,320 mini-proteins, 15 targets, 21 days. That's 7, but limit 4-5. Need to be selective. Perhaps choose: $40 million, August 25, Q4 2026, 1,320 mini-proteins, 21 days. That's 5. Also need company names? They are important but maybe not wrap. Could wrap Highland Europe? But we already have 5. Could replace one. Let's decide: important facts: funding amount, date, fund name, expansion timeline, Claude campaign size, first data time. That's 6. Need max 5. Could combine fund name with amount? Not. Choose: $40 million, August 25, Highland Europe, Q4 2026, 21 days. Then we omit 1,320 mini-proteins and 15 targets? But they are facts. Could wrap them in double asterisks as well but limit. Maybe we can wrap 1,320 mini-proteins and 15 targets together as one fact? Eg "1,320 mini-proteins across 15 targets". That counts as one wrapped fact. Then we have: $40 million, August 25, Highland Europe, Q4 2026, 1,320 mini-proteins across 15 targets, 21 days => 6. Need 5 max. Could drop fund name? But fund is important. Could drop date? Date is important. Could drop Q4 2026? Maybe less important than others? But expansion timeline is important. Could combine date and fund? Eg "Highland Europe led a $40 million round on August 25". That's one wrapped fact containing fund, amount, date. Then we have: Highland Europe led a $40 million round on August 25, Q4 2026, 1,320 mini-proteins across 15 targets, 21 days. That's 4. Good.

Now we need to wrap study/journal citations in single underscores. There are none explicit besides maybe "Claude campaign" not a journal. No citations. So none.

Now produce headline under 90 chars. Something like: "Adaptive Bio Secures $40 Million to Expand AI‑Driven Protein Lab". Count characters: Let's count: Adaptive(8) + space=1 =>9, Bio=3 =>12, space=13, Secures=7 =>20, space=21, $40=3 =>24, space=25, Million=7 =>32, space=33, to=2 =>35, space=36, Expand=6 =>42, space=43, AI‑Driven=?? "AI‑Driven" includes hyphen and maybe special char. Count characters: A(1) I(2) ‑ (maybe en dash) counts as 1? We'll treat as one. D(4) r(5) i(6) v(7) e(8) n(9) => total 10? Actually "AI‑Driven" length 10 inc hyphen. So add 10 =>53, space=54, Protein=6 =>60, space=61, Lab=3 =>64. So under 90.

Now body paragraphs: need short paragraphs 2-3 sentences each, separated by blank lines.

We'll produce maybe 4 paragraphs.

Paragraph 1: Adaptive Bio announced a $40 million investment round led by Highland Europe on August 25. The funds will double the team in Lausanne and open a London office with a laboratory in Q4 2026.…

🔗 Read original →

We need to translate Russian news post into natural 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.

Identify important facts: "26 августа" date, "Рамез Наам", "Дарио Амодеи", "мощный ИИ может дать биологии следующие 100 лет прогресса за 5–10 лет", "четырёх признаках", maybe numbers: 100 years, 5–10 years, four criteria. Also maybe "Go" result immediate, etc. But we need at most 4-5 double asterisks. Choose key numbers: date (26 August), 100 years, 5–10 years, four criteria. That's four.

We need to wrap them in . Ensure not whole sentence.

Citations: There's mention of essay "Machines of Loving Grace" by Amodei. Also maybe mention of review of AI agents for bio labs? Not a journal citation. The essay is a reference; we can wrap in single underscores: Machines of Loving Grace. Also maybe mention of "review of AI agents for bio labs" but not a journal. We'll just underscore the essay title.

We need to preserve all facts exactly; translate.

Let's draft.

Headline: something like "Ramez Naam Links AI Speed in Biology to Organism Response". Under 90 chars.

Now body paragraphs.

Paragraph 1: On August 26, Ramez Naam discussed Dario Amodei's forecast: powerful AI could deliver biology’s next 100 years of progress in just 5–10 years. Naam says the pace depends on how quickly an experiment returns a clear result tied to a real goal.

Paragraph 2: He proposes evaluating a task by four criteria: can success be distinguished from error without mistake; is there enough data; does feedback arrive at computer speed; does the measured outcome match the true objective. In Go the result is immediate; in formal mathematics verification is similar; in programming a test shows code behavior in a given scenario, though human intent may be broader.

Paragraph 3: Fast feedback lets the next attempt be corrected right away. In aging research, cell‑based experiments are only the first step; next we must see if the effect repeats in animals and humans. Naam puts the gap as: “What we get in cells is not the same as what we find in the organism.” A cell living longer in a dish tells us only about cells; to know if an intervention extends human life we must test it on the whole organism.

Paragraph 4: Moving from cells to animals and people changes the measured outcome: a system may improve an early marker while the real question requires a whole‑organism result. In his essay Machines of Loving Grace, Amodei describes AI as a virtual biologist that plans and runs experiments, suggests how to measure outcomes, and directs lab robots or people. He also notes the unavoidable delay of experiments and equipment design.

Paragraph 5: Amodei calculates that smart experiment selection and massive parallelism can hunt for solutions while individual experiments run their course. Naam adds another question: how fast can we go from a cellular result to an answer about humans? If that transition needs long observation of an organism, it sets the learning tempo. In a survey of AI agents for bio labs the cycle looked like: model gathers literature, proposes a hypothesis, sends it to filters and experiment. Naam adds that for this cycle to be fast the experiment must return a signal linked to the desired human outcome.

Now we need to apply formatting: double asterisks around key numbers/dates etc.

Identify key facts to bold:…

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Remote-Controlled Neuroimplant Delivers Light and Drugs Via Internet

Researchers in Chicago demonstrated a neuroimplant that can be operated from anywhere via the internet. They remotely controlled a device implanted in the brain of a laboratory rat located in Daejeon, South Korea. The implant functions in real time, turning on stimulation or drug delivery with a simple web command.

The platform combines optogenetics and microfluidics, allowing neurons to be activated by light or inhibited while drugs are precisely injected. By eliminating bulky tethers and external hardware, it removes the observer effect and reduces stress on the animals.

Inside the chip sits an electrochemical micropump: applying voltage triggers electrolysis, generating gas that pushes the drug from its reservoir; cutting the current lets the gas recombine, resetting the system. Dosing and the frequency of light pulses from a micro-LED are adjusted through a web interface, and the drug cartridge attaches magnetically for easy replacement or refilling without additional surgery.

Stability and durability were validated in a four-week experiment on rats. In addiction studies, scientists remotely delivered cocaine to the Nucleus Accumbens while simultaneously suppressing neural activity with light, successfully curbing addictive behavior. Looking ahead, Korean developers aim to link such IoT implants with AI to close the loop—letting algorithms read brain states and automatically administer the appropriate drug or light pulse for conditions such as depression, neurodegenerative disorders, or epilepsy.

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Enhancement.bio replaces T1–T10 scale with S–E evidence letter for gene

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Alex Colvill: Longevity Interest Went Mainstream Before Human Therapies Arrived

On 26 August, in a new episode of the Core Memory podcast, Ashley Vance spoke with Alex Colvill, co‑founder of venture fund age1. Colvill said that interest in longevity had already become widespread even before ready‑to‑use therapies for people are available.

He believes the number of interventions that can be tested in people

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Self‑Training Bioengineered Muscle Implant Improves Aging in Mice

Scientists took mouse muscle stem cells, expanded them, formed tissue, and implanted it under the skin of aged animals. The resulting bio‑transplants (myografts) autonomously built a vascular network and began contracting spontaneously 24 hours a day, 7 days a week, without any nervous‑system or brain input.

The contracting muscle acts as a continuous biological factory, releasing myokines and signaling molecules into the bloodstream. Old mice receiving these subcutaneous “patches” showed increased lean body mass, stronger grip, better treadmill endurance, higher bone density, and reduced inflammation markers.

In the mice brains, the number of degrading neurons in the hippocampus fell, BDNF levels rose, and spatial memory improved. (Although the brain‑test sample was tiny – only 3 individuals per group.)

The myograft is not just a gym mimic but a removable biological reactor. Researchers genetically engineered the implanted cells to secrete parathyroid hormone (PTH) and growth hormone, giving a stable blood protein level without the spikes and drops seen with injections.

The experiments used Matrigel matrix, which is unsuitable for humans, so clinical translation will require a different scaffold and scaling of autologous cell production. Nevertheless, the concept of a subcutaneous muscle‑doser has already proven viable.

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Actin cytoskeleton disruption shortens worm lifespan, while mild stabilization extends it

On August 24, a bibliographic record of an iScience article appeared in Crossref. In the full text, authors altered actin cytoskeleton function in the roundworm Caenorhabditis elegans: network disruption shortened life, while mild chemical stabilization extended it; a high dose produced the opposite effect.

The actin cytoskeleton helps muscles contract, maintains intestinal cell junctions, and participates in intracellular transport. In prior work from the same research line, increased production of the BET‑1 protein preserved actin in old worms and extended their lifespan.

The new study examines the converse: what happens when the network loses order. Authors sequentially weakened actin and three proteins that direct its assembly, disassembly, and anchoring. In muscle, intestine, and cuticle, filaments lost normal organization earlier, and mobility declined with age. In some worms, gene activity resembled that of older animals.

Because the age of intervention matters — in another C. elegans experiment, transient NuA4 suppression in early development extended lifespan, while later intervention shortened it — part of the new experiments began on the first day of adult life. Lifespan reduction persisted upon suppression of arx-2, a component of the Arp2/3 complex that builds branched actin networks.

Separately, adult worms were given two substances. Latrunculin A breaks actin filaments and, with increasing dose, shortened life. Jasplakinolide helps filaments assemble and stabilize: at low concentrations it extended life, at high concentrations it shortened. Authors link the harm of high doses to overly stabilized filaments being less able to remodel.

Actin disruption simultaneously altered mitochondrial shape and function, protein homeostasis, cellular component recycling, and intestinal barrier integrity. In two independent human cohorts, authors also correlated ACTB gene variants with the pace of age‑related walking slowdown.

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Gordian’s Mosaic Screening Yields 1,327 Measurements Across Six Diseases

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We need to translate Russian news post into natural English, format per rules.

First line: short headline under 90 chars, no markdown. 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). There's no explicit citation in the text; but maybe we can treat "BixBench3" as a benchmark? Not a citation. The text mentions "26 августа компания Edison Scientific представила BixBench3". That's a date. So we can wrap the date in double asterisks? It's a date, yes. Also numbers: 20 tasks, 13 models, best average result 48%, 138 checkable results, 4-14 results per task, best model GPT 5.6 Sol average 48%, tasks with data >100 GB average 10%, 50-100 GB average 34%, <50 GB average 37%, when result depended on three or more sequential steps average 24%. That's many numbers; we need to select at most 4-5 important facts to bold. Choose: date (August 26), 20 tasks, 13 models, best average 48%, >100 GB 10%, 50-100 GB 34%, <50 GB 37%, three+ steps 24%. That's many. We need max 4-5. Let's pick: date, number of tasks (20), best model average (48%), performance on >100 GB (10%), and performance on three+ sequential steps (24%). That's 5.

We'll bold those: August 26, 20 tasks, 48%, 10%, 24%. Ensure not whole sentence bolded.

Now we need to wrap study/journal citations in single underscores. There's none; maybe we can treat "BixBench3" as a benchmark but not a citation. Could we add a placeholder? No, we must not add invented detail. So we leave none.

Now produce English translation.

Let's craft headline: "Edison Scientific launches BixBench3 to test AI agents on reproducing biological analysis". Count characters: Let's count: "Edison Scientific launches BixBench3 to test AI agents on reproducing biological analysis". Count: Edison(6) + space=1 =>7, Scientific(11)=>18, space=19, launches(8)=>27, space=28, BixBench3(9)=>37, space=38, to(2)=>40, space=41, test(4)=>45, space=46, AI(2)=>48, space=49, agents(6)=>55, space=56, on(2)=>58, space=59, reproducing(11)=>70, space=71, biological(10)=>81, space=82, analysis(8)=>90. Exactly 90 characters? Let's recount precisely: "Edison Scientific launches BixBench3 to test AI agents on reproducing biological analysis"

Characters:
E(1)
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space37
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.90

So 90 characters including period. Under 90 chars? It says under 90 chars. This is exactly 90, maybe need less than 90. Remove period. Then 89 characters. So headline without period: "Edison Scientific launches BixBench3 to test AI agents on reproducing biological analysis". Count without period: 89. Good.

Now body: need short paragraphs 2-3 sentences each.

We'll translate content.

Paragraph 1: Introduce BixBench3 release date, number of tasks, description.

Paragraph 2: Explain process: agent gets goal, methods, raw data, produce intermediate results, compare to original work, early error propagates.

Paragraph 3: Details of benchmark: 20 papers, 138 checkable results, each task yields 4-14 results, scoring based on preserving biological interpretation.…

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We Will Cure Releases Longevity Biotech Clinical Trials Map Through 2027

On August 25, the editorial project We Will Cure published a map of longevity biotech clinical trials planned through 2027. It aligns programs that have already begun human studies, obtained initial data, or are preparing the next stage. By the end of 2027 they will answer whether humans tolerate the intervention, whether it affects the targeted process, and whether there is grounds to expand the trial.

Companies test aging‑biology ideas through concrete diseases and quantifiable

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Cryoprotectant penetrates fixed human brain over nine months

On 24 August PLOS One published a protocol for storing whole human brains at −20 °C after chemically fixing their cellular structure. The goal was to assess how a cryoprotectant solution diffuses through the tissue and whether fine structure survives cooling, storage, and rewarming.

Brains were first fixed with a cross‑linking agent to lock in tissue architecture after death.

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AI-designed short proteins improve CAR-T receptor function via surface charge tuning

On 20 August, researchers from the University of Bonn and Bonn University Hospital published work on AI-designed short proteins for CAR‑T therapy. CAR‑T adds an artificial receptor to T cells; its external part recognizes a tumor protein and its internal part triggers

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Neurosurgeon Proposes Brain Preservation to Curb Risky AI Rush

On August 27 neuroscientist Ariel Zeleznikov‑Johnston published an essay in the official mailing of the Brain Preservation Foundation about biostasis – preserving a dying person’s brain with chemicals and cold. He argues that this procedure could give people time to wait for future medicine and reduce their personal willingness to take risks for rapid AI development.

He begins with the dilemma: a superintelligent AI could accelerate drug discovery, but loss of control over such a system might be catastrophic. Nick Bostrom’s calculation of the cost of a pause makes acceleration attractive for those who would need future medicine in their lifetime, while David Wood had proposed slowing the race and strengthening biology.

In the new essay Zeleznikov‑Johnston shifts the calculation to the individual. Biostasis stabilizes the brain of a dying person with chemicals and cold; he links long‑term memory and personality traits to the brain’s physical structure. Preserving that structure, he writes, offers a chance to await future treatments or even reading of the mind. “Make death less likely – and big risks stop seeming so reasonable. If you knew you’d still be here in fifty years regardless of whether superintelligence appears and when, reasons to bet on dangerous AI acceleration would be far fewer.”

In his model biostasis provides another way to await future medicine, thereby diminishing the benefit of risky AI

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