Ray Kurzweil Joins Subsense as Advisor on Brain‑Nanoparticle Interface

On September 3, Ray Kurzweil became a product and vision advisor to the California startup Subsense. The company says it will introduce two types of nanoparticles through the nose to interface with the brain, using a wearable device to read and stimulate neuronal activity.

According to Subsense, plasmonic particles would scatter near‑infrared light differently in the presence of a local electric field, allowing the wearable to read signals. Magnetoelectric particles would convert an external magnetic field into a local electric effect to stimulate neurons. The system must deliver particles to the target brain area, retain them, read the signal, and induce a local effect.

At the September 3 event, Kurzweil described the ultimate goal: “Ultimately we want to merge smartphone with brain.” A Science Advances 2021 study showed a related step: magnetoelectric particles injected into specific mouse brain areas were exposed to static and alternating magnetic fields. The combination of particles and both fields increased c‑Fos‑positive neurons and altered gait parameters in mice, confirming local stimulation.

Kurzweil will advise Subsense on product development, with the company’s first focus on neurological diseases. Subsense plans pilot clinical trials for 2027–2029.

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Starr Foundation Funds $37M Brown Aging Research Alliance

On September 3, Brown University announced the Starr Healthspan Innovation Alliance, a five‑year program backed by a $37 million grant from the Starr Foundation to link its Center for Biology of Aging with clinical trials run by Brown University Health. The initiative aims to translate laboratory discoveries into human studies by providing the researchers, trial teams, and participants needed for clinical testing.

Funds will cover laboratory work, the hiring of new researchers, and the expansion of the clinical research network. In addition, the grant will create two new endowed professorships in geroscience and support the recruitment of specialist staff.

Brown University Health currently conducts about 500 clinical trials in Rhode Island, with roughly two‑thirds focused on aging and related diseases. These trials provide a substantial platform for testing interventions that emerge from the Center for Biology of Aging.

The alliance will pay for specialists, participant recruitment and support through local communities, and will foster partnerships with scientific groups and industry. It builds on an organizational foundation established in 2

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AI system Astra drafts five NIH R01 grant proposals in under an hour

On September 6, Lokseyl posted on X that he asked Astra to read his published works, identify new directions, and prepare five drafts of R01 grant applications for the US National Institutes of Health. He noted that the task took about an hour and required roughly about $20 of computational resources.

One attached draft page poses a question about how cancer cell metabolism responds to enzyme inhibition when nutrient availability shifts, aiming to pinpoint which enzyme sets the reaction rate under those conditions. The draft builds on prior lab results to formulate a hypothesis that cellular nutrition, chemical state, and energy demand determine which step of glucose processing becomes rate‑limiting.

The first aim proposes varying nutrient conditions and the activity of three enzymes to see which step begins to limit flux. The second aim calls for comparing multiple mechanistic explanations with measurements and constructing a simplified model of the observed changes. The third aim requires pre‑registering predictions for new nutrient combinations and cell cultures, then testing them experimentally.

Lokseyl wrote that Astra “well reproduced this logic,” turning his existing work into questions, hypotheses, and experiments that would seem familiar and justified to an expert review panel. He judged all five drafts to be “quite reasonable,” grounded in his research and resembling a grant he himself might have written.

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Gene activity entropy varies with age and cancer across tissues

Researchers analyzed RNA from over 25 000 human and mouse tissue samples, computing Shannon entropy to measure whether gene activity is concentrated in few genes or spread across many. They first asked whether this entropy changes with age uniformly across the body. After adjusting for available sample characteristics, entropy decreased in brain, stomach, and blood; it increased in salivary gland, heart, skin, fat, and skeletal muscle; and remained stable in other tissues.

The age‑related pattern therefore differs by organ. To separate intracellular changes from shifts in cell‑type composition, the authors estimated cellular makeup mathematically; the entropy rise seen in skin and skeletal muscle persisted after this correction. In single‑cell data from liver cancer and melanoma, tumor cells often showed higher entropy even among cells of the same type.

In matched tumor‑normal pairs, primary tumors of most types had higher entropy than adjacent normal tissue. For melanoma, entropy rose from non‑sun‑exposed skin through primary tumor to late metastases, using skin and tumor data from separate cohorts. In 5 out of 6 paired melanoma samples that acquired therapy resistance, entropy increased.

Among cancer types where the link to overall survival was significant, high entropy accompanied worse survival in roughly ~70% of cases. In cellular reprogramming experiments, a less successful chemical protocol yielded higher entropy than a more successful one. The same entropy calculation reflects both cellular state and tissue composition, and its meaning depends on the specific organ and sample makeup. Aging Cell, 4 September

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Body‑Channel Communication Enables High‑Bandwidth Wireless Neural Implants

The main problem with modern wireless neuroimplants is the tight limits on bandwidth, power consumption, and heating: a high‑resolution microelectrode array (MEA) with 1000‑channel MEA generates a data stream exceeding 300 Mbps. Radio‑frequency transmission of this volume produces too much heat, inevitably damaging brain tissue.

The EU‑funded IoN (Intranet of Neurons) project tackled this with a two‑stage wireless architecture that uses body‑channel communication (BCC), treating the body’s tissues as a wire and abandoning

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TMP-316 improves hind‑limb weight bearing in rats after spinal cord injury

A preprint posted on bioRxiv, September 4 describes TMP-316, a compound that simultaneously inhibits four related branches of the AGC‑kinase family.

The authors started from RO48, which had shown spinal‑cord‑injury benefit in mice but also acted on the cardiac hERG channel and had short cerebrospinal‑fluid half‑life. They generated 371 analogues, screened them for neurite outgrowth and activity against S6K1 and ROCK2, and identified TMP-316 as the lead.

In neuronal cultures, inhibition of S6K1 and ROCK2 promoted neurite extension, and adding blockade of PKCγ or PKX further increased growth. All four kinases share an ATP‑binding pocket that modeling predicted TMP-316 could occupy.

In the animal study, male rats were randomly assigned to three groups (vehicle, ~0.3 mg/kg, or ~1 mg/kg TMP-316; n = 12‑13 per group). The compound was injected into the lumbar cerebrospinal‑fluid space immediately before a C5‑C6 contusion, and investigators assessing locomotion were blinded to treatment.

At the ~1 mg/kg dose, a composite gait score began to differ from controls on day 14, with the greatest divergence occurring between days 28 and 42. Motion analysis showed that the injured fore‑paw bore more weight and that bipedal support returned during stance.

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Fruit Fly Connectome Data Used to Simulate 'Bad Apple' Touhou Track

Researchers took recently published connectome data from adult male fruit flies and fed it into a neural simulation. They then translated the simulated neural activity into the melody of the Touhou song 'Bad Apple'.

The motion‑detecting neurons in the simulation directly controlled a virtual fly, making it move to the music. Looking ahead, one could imagine a digital copy of a person being forced to play internet meme tracks—a darkly humorous take on transhumanism.

🔗 Source: @solid_state_humanity

FishNAP screen identifies seven reversible blood‑brain barrier‑opening molecules

On September 4, researchers at Rutgers University released version 2 of a preprint describing the FishNAP assay, a zebrafish‑larvae screen for molecules that transiently increase blood‑brain barrier permeability. The assay first measures behavioral changes caused by loperamide entry into the brain, then quantifies leakage of a fluorescent tracer into brain tissue. FishNAP preprint, Rutgers University, version 2, September 4

They screened 2,320 FDA‑approved small molecules and found 11 that repeatedly altered larval behavior; direct tracer measurements confirmed seven compounds with pronounced passage of the smallest marker into the brain. All seven showed reversible barrier opening, with function restored within 24 hours after compound removal.

The three selected molecules—calcitriol, lovastatin, sunitinib—were tested in adult mice, where each increased tracer and albumin accumulation in brain tissue; calcitriol and lovastatin also allowed IgG entry. In cortical tissue, levels of CLDN5 and MFSD2A decreased while CAV1 increased, indicating loosened tight junctions and enhanced transcellular transport.

The authors link this protein pattern to weakened endothelial tight junctions and heightened vesicular transport, noting that FishNAP selects for small molecules that transiently alter vascular wall permeability. The method’s usefulness depends on the size of the intended cargo and which barrier property the hit compound modifies.

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

We need to identify citations: The text mentions "статья с промежуточными данными регистра MASTRR: в десяти центрах США устройства Myriad применили у 411 пациентов с 474 сложными дефектами мягких тканей." No explicit journal name. Might be a study article but not given. We may need to wrap any citation like "статья" but we don't have journal name. Possibly we can treat "MASTRR" as registry name, not citation. No explicit journal citation given. So maybe we don't need any underscores. However we could still wrap the registry name? The rule: "Wrap study/journal citations and publication references in single underscores". If none present, we skip.

We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post.

Important facts: 411 patients, 474 defects, 10 US centers, Myriad device, made from sheep rumen extracellular matrix, median follow-up 27.1 weeks, 50 patients (12.2%) had 59 adverse events, superficial infection 2.9%, deep infection 0.7%, skin reaction (likely related) led to removal of 40% of scaffold, antihistamines, split-thickness skin graft, registry ongoing, planned size 800 participants, primary endpoint proportion with adverse events over three years, article published September 3 (maybe date). Also "почти 60% дефектов сохранялись больше месяца" (almost 60% defects persisted >1 month). Also participants often had type 2 diabetes and vascular diseases.

We need to select at most 4-5 double asterisk wrapped items. Choose the most important: number of patients (411), number of defects (474), median follow-up (27.1 weeks), percentage with adverse events (12.2%), planned registry size (800). Also maybe date Sept 3. But we have limit 4-5. Let's pick: 411 patients, 474 defects, 27.1 weeks, 12.2%, 800 participants. That's 5.

We need to ensure we don't wrap whole sentence. Just wrap those numbers/percentages.

Also need to wrap drug/company/gene names: Myriad is a device name; could be considered a company/device name; we could wrap it but that would increase count. We already have 5 items; maybe we replace one with Myriad? But we need to keep numbers. The rule says "Wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. Be selective: at most 4-5 per post". So we can include drug/company names as part of the limited count. We need to decide which to include. Probably include Myriad as it's important. But we also need numbers. Let's see if we can fit 5 items: Myriad, 411 patients, 27.1 weeks, 12.2%, 800 participants. That's 5. Could also include 474 defects but we would exceed. Maybe we can combine "411 patients with 474 defects" as one phrase? But that would be a whole sentence? Not exactly; we could wrap "411 patients" and "474 defects" separately but that's two. Could we wrap "411 patients, 474 defects" as one? That includes two numbers but still one wrapped segment. The rule says wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates. It doesn't forbid wrapping a phrase containing multiple numbers. So we could wrap "411 patients with 474 defects" as one item. That counts as one. Then we have Myriad, 27.1 weeks, 12.2%, 800 participants = total 5. Good.…

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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). There's no citation in the text except maybe Post Bulletin? It's a news source, not a study/journal. So we may not need underscores unless we treat Post Bulletin as a publication reference? The rule says wrap study/journal citations and publication references in single underscores. Post Bulletin is a publication, so we can wrap it: Post Bulletin. But it's a news source; still a publication reference. We'll do that.

We need to preserve all facts, numbers, names, citations exactly.

Important facts: United Therapeutics, completed facility in Stuartville on September 1, cost $110 million, facility designed for 150 genetically edited pigs, organs for human transplantation, xenotransplantation definition, facility size 65,000 sq ft, about 25 employees, labs, veterinary clinic, two surgical blocks, Rodney Lamb (technical operations head of xenoprogram) compared clean zone to "building within a building", internal clean zone separated, staff work with animals in protective suits, equipment and materials sterilized with hydrogen peroxide before entry, these procedures maintain conditions for pigs to become donors for patients, adjacent to completed center company purchased 207 acres for future swine organ production complexes, first center now ready to accept animals.

We need to bold at most 4-5 key facts: maybe the date, cost, number of pigs, facility size, acres purchased. That's 5.

We need to bold them with double asterisks. Ensure not bold whole sentence.

We'll embed bold around the numbers/dates etc.

Let's craft headline: "United Therapeutics Completes $110 Million Pig‑Organ Facility in Stuartville". Count characters: Let's count: United(6) + space=1 =>7, Therapeutics(12)=>19, space=20, Completes(9)=>29, space=30, $110(4)=>34, space=35, Million(7)=>42, space=43, Pig‑Organ(9 inc hyphen?) Let's count: Pig‑Organ (P i g ‑ O r g a n) that's 9 inc hyphen? Actually "Pig‑Organ": 3+1+6=10? Let's just approximate. We'll be under 90.

Now blank line.

Paragraph 1: mention completion date Sept 1, cost $110M, facility for 150 genetically edited pigs, purpose xenotransplantation. We'll bold date, cost, number of pigs.

Paragraph 2: definition of xenotransplantation, mention facility size 65,000 sq ft, about 25 employees, labs, vet clinic, two surgical blocks. Bold facility size maybe.

Paragraph 3: quote Rodney Lamb, description of clean zone as "building within a building", internal clean zone separated, staff wear protective suits, equipment sterilized with hydrogen peroxide. Possibly bold nothing else.

Paragraph 4: mention adjacent purchase of 207 acres for future complexes, first center now ready to accept animals. Bold acres.

We need to ensure at most 4-5 bolded items total. Let's choose: September 1, $110 million, 150 genetically edited pigs, 65,000 square feet, 207 acres. That's 5.

We need to wrap them in double asterisks exactly.

Now ensure we don't bold whole sentence; just those phrases.

Now need to wrap study/journal citations and publication references in single underscores. We have Post Bulletin as a publication reference. We'll wrap that.

Now produce final output.

Check formatting: first line headline, then blank line, then paragraphs separated by blank lines.

Paragraphs: 2-3 sentences each.

Let's craft.…

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We need to translate Russian news post into natural fluent 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: dates: "4 сентября авторы препринта описали SniffCell." => September 4 (year not given). Could be 2024? Not given. We'll keep date as September 4.

Numbers: average accuracy 80.38%; 16 physically sorted fractions; 83 of 92 DNA sites; 172 donors; median proportion 29.2% for neuronal events and 66.7% for oligodendrocytic events; atlas of methylation 2023 gathered 39 cell groups from 205 healthy samples; SniffCell expanded atlas for eight tissues.

Also key names: SniffCell method; T- and B-cell receptors; tandem repeats; GAA triplet; frontal cortex; neurons and oligodendrocytes; somatic variant.

We need to select at most 4-5 double asterisk facts. Choose maybe: September 4, 80.38%, 172 donors, 29.2%, 66.7%. That's 5.

But also maybe include "83 of 92 sites". Could replace one. Let's decide: we need at most 4-5. Choose 5: September 4, 80.38%, 172 donors, 29.2%, 66.7%.

Wrap each in double asterisks.

Now citations: The text mentions "атлас метилирования 2023 года , который собрал 39 групп клеток из 205 здоровых образцов." That's a reference; we need to wrap study/journal citations and publication references in single underscores. This is not a journal citation but a dataset/reference. We'll treat as methylation atlas 2023 maybe. The instruction: wrap study/journal citations and publication references in single underscores. So we can put methylation atlas 2023 (or 2023 methylation atlas). Also maybe "препринт SniffCell" is a preprint; not a journal citation. We'll just underscore the atlas reference.

Also maybe "обзор о геномной мозаике" is a review; we could underscore that as genomic mosaic review. But we need to be selective; maybe just one underscore.

We'll include methylation atlas 2023 as the citation.

Now produce translation.

First line headline: something like "SniffCell method links DNA methylation to cell types and finds repeat expansions in neurons". Count characters: Let's count: "SniffCell method links DNA methylation to cell types and finds repeat expansions in neurons". Count characters including spaces: Let's count: S(1)n2i3f4f5C6e7l8l9 (space10)m11e12t13h14o15d16 (space17)l18i19n20k21s22 (space23)D24N25A26 (space27)m28e29t30h31y32l33a34t35i36o37n38 (space39)t40o41 (space42)c43e44l45l46 (space47)t48y49p50e51s52 (space53)a54n55d56 (space57)f58i59n60d61s62 (space63)r64e65p66e67a68t69 (space70)e71x72p73a74n75s76i77o78n79s80 (space81)i82n83 (space84)n85e86u87r88o89n90s91. That's 91 characters, okay under 90? Actually 91 > 90. Need under 90. Let's shorten: "SniffCell links DNA methylation to cell types, finds neuronal repeat expansions". Count: S1n2i3f4f5C6e7l8l9 (space10)m11e12t13h14o15d16 (space17)l18i19n20k21s22 (space23)D24N25A26 (space27)m28e29t30h31y32l33a34t35i36o37n38 (space39)t40o41 (space42)c43e44l45l46 (space47)t48y49p50e51s52, (space53)f54i55n56d57s58 (space59)n60e61u62r63o64n65a66l67 (space68)r69e70p71e72a73t74 (space75)e76x77p78a79n80s81i82o83n84s85. That's 85 characters. Good.

Now blank line.

Now body paragraphs.

We need to preserve facts. Let's craft paragraphs 2-3 sentences each.…

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VisionECG builds 3D left‑ventricle model from ECG

On September 4, researchers posted a preprint on medRxiv preprint, September 4 describing VisionECG, a model trained on 71,132 ECG‑MRI pairs from the UK Biobank. The model learns to generate a sequence of 50 successive 3‑D surfaces of the left ventricle from a standard 12‑lead ECG and eight basic subject characteristics.

For 5,000 patients with both ECG and echocardiography, the model‑derived ejection fraction was compared to ultrasound measurements. From the reconstructed ventricular motion the model can compute volume, mass, wall thickness, ejection fraction and myocardial strain.

On an external test set of 2,000 participants the median relative error of volume over the cardiac cycle was 8.34%, and wall‑thickness error was 9.54%. When the individual ECG was replaced by the cohort average, the volume error in dilated cardiomyopathy rose from 12.59% to 20.29%, showing the model relies on person‑specific electrical activity.

Ejection fraction — the fraction of blood expelled by the left ventricle each beat — was able to distinguish reduced function at thresholds of 50%, 45% and 35% with AUC values ranging from 0.79–0.81. AUC measures how well the model separates groups.

In a follow‑up cohort of 17,566 subjects, the same features yielded a five‑year heart‑failure forecast with a C‑index of 0.76, compared with 0.63 for a forecast based on conventional ECG parameters. A single predicted ventricular‑motion sequence thus provides both anatomical measurements and risk prediction.

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Arcadia’s Attempt to Turn GFP Orange Yields Only Green Variant

Arcadia Science published a study on 4 September about generating fluorescent proteins with a set colour and brightness. The team tried to shift the green fluorescent protein EGFP toward orange by altering amino acids around the chromophore.

An algorithm directed amino‑acid substitutions toward the target spectrum by comparing similarity to natural fluorescent‑protein variants and the closeness of predicted excitation/emission peaks. For five orange candidates the researchers added a brightness check, using a classifier that labelled variants as bright or dim based on their

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Insilico Reports 83% Accuracy for TargetPro+ in Predicting Clinical Drug Targets

On September 4, Insilico Medicine

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SIRT6 RNA stability varies with age and Alzheimer's disease

A preprint released on 7 September describes SIRT6.db, an open database of experiments on six species that links SIRT6 changes to the activity of other genes. The resource lets researchers test whether SIRT6 RNA levels reflect the cellular and tissue aging state. SIRT6 is a nuclear protein that regulates chromatin — the packaging of DNA.

In a study

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

We need a 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).

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

We need to identify important facts: Thomas Kaiser, date 8 сентября (September 8), column BioSpace, physician, medicinal chemist, ML specialist. He discusses conditions where foundation models can help drug development. Foundation model learns on broad open data then fine-tuned for specific task. He proposes checking quality of scientific results it was trained on and preservation of link between molecular structure and its property in new chemical series. Engineer can configure one version of model so that for same query it responds identically. This repeatability speaks to computation tuning. Scientific publication may report a link that further research cannot reproduce, and model will stably repeat it. Kaiser formulates risk: "Reproducible link based on non-reproducible science, alas, does not become progress." Repeatability of answer and verification and checking scientific link are two different tasks. Second risk Kaiser shows on small molecules — comparatively small organic compounds. Chemical series — family of molecules with similar structure. One and the same small structural edit can differently change property of molecule in two series. At Avicenna Biosciences Kaiser describes such problem: a rule useful for prediction in one series may throw model off in another, because for this edit the causal link with molecular property changes. Therefore Kaiser proposes setting foundation model a narrow scientific question, in which chemical context already given. Among potential tasks he names searching biological networks — linked genes and proteins that could become drug targets — and analyzing clinical data for patient selection. Model can also search link between measured treatment outcomes, patient selection, and preclinical models reproducing disease signs. Experimental verification of these links and careful data assessment Kaiser leaves to researcher.

We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. Important facts: date September 8 (8 сентября). Company names: BioSpace (maybe a column, but it's a publication), Avicenna Biosciences. Possibly also "Thomas Kaiser". Also maybe "small molecules". But we need at most 4-5 double asterisks. Choose: September 8, BioSpace, Avicenna Biosciences, maybe Thomas Kaiser? But name may be considered important. However we need to be selective. Let's pick date, column, company, maybe "small molecules"? But small molecules is a class not a specific name. Could also include "foundation model". But that's generic. Let's choose: September 8, BioSpace, Avicenna Biosciences, Thomas Kaiser (maybe 4). Ensure not whole sentence.…

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We need to translate Russian news into 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.

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

We need to identify important facts: FDA authorized eGenesis to test genetically prepared pig kidney on 33 people on September 3. eGenesis reported: in expanded access program, genetically prepared pig kidney EGEN-2784 given to five patients with kidney failure. Three had kidney working without dialysis >8 months; company expects to start trial in 2027 on 33 people from human kidney waiting list. Severe kidney failure requires dialysis or waiting for donor kidney. In September 3 release, eGenesis says from March 2024 they transplanted kidney to five patients via expanded access. Three kidney worked without dialysis >8 months, one >9 months; two then received human donor kidneys. Xenotransplantation is transplant of animal organ to human. One transition from pig kidney to human described in article in medical journal The Lancet. Tim Andrews received pig kidney Jan 25 2025; 271 days he was dialysis-free. Damage to small vessels caused graft failure, removed. After 82 days Andrews received human kidney, which started immediately. In EGEN-2784 cells, three pig antigens removed — molecules by which immune system recognizes foreign tissue. According to eGenesis, seven added human genes should regulate immune response, inflammation, and blood clotting. eGenesis says FDA authorized trial RESTORE for 33 patients aged 50-70 already on human kidney waiting list. Company expects to start study Q1 2027.

We need to select at most 4-5 double-asterisk facts. Likely: September 3, 33 patients, EGEN-2784, five patients, >8 months, >9 months, March 2024, Tim Andrews, 271 days, January 25 2025, first quarter 2027, RESTORE, 50-70 years. But we need only 4-5. Choose most salient: FDA authorized test on 33 patients on September 3; five patients received EGEN-2784 via expanded access since March 2024; three kidneys functioned without dialysis >8 months (one >9 months); FDA authorized RESTORE trial for 33 patients aged 50‑70 to start Q1 2027. That's 5 double-asterisk facts.

We need to ensure we don't wrap whole sentences. So we embed the markers around those specific phrases.

Citations: The Lancet article. Wrap in single underscores: The Lancet.

Also maybe mention "eGenesis" but that's a company name; not required to be underscored.

Now produce translation.

Headline: something like "FDA Clears eGenesis to Test Pig Kidney EGEN-2784 in 33 Patients". Under 90 chars.

Let's count: "FDA Clears eGenesis to Test Pig Kidney EGEN-2784 in 33 Patients" length: count characters: F(1) D2 A3 space4 C5 l6 e7 a8 r9 s10 space11 e12 G13 e14 n15 e16 s17 i18 s19 space20 t21 o22 space23 T24 e25 s26 t27 space28 P29 i30 g31 space32 K33 i34 d35 n36 e37 y38 space39 E40 G41 E42 N43 -44 2 45 7 46 8 47 4 48 space49 i50 n51 space52 3 53 3 54 space55 P56 a57 t58 i59 e60 n61 t62 s63. That's about 63 chars. Good.

Now blank line.

Now body paragraphs.…

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