Tag: Academic research

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ProPure™ Endotoxin-Free Proteins for Reliable Cancer Research

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In cancer research and therapy development, even trace levels of endotoxins (LPS) in recombinant proteins can severely distort results. In discovery and preclinical studies, endotoxins are silent disruptors of animal immunization, sensitive biological assays, and toxicity assessments, compromising results and safety evaluations. Endotoxin-free recombinant proteins are therefore essential for generating reliable research data and successful development of next-generation cancer therapeutics.

Invisible interference in cancer therapy and vaccine development

Endotoxin contamination can severely compromise antibody generation in animal models. Even small amounts of endotoxins can alter the host’s immune response, reducing antibody specificity, consistency, and overall quality. Endotoxin-contaminated recombinant proteins can subtly—but significantly—alter cellular behavior through immunostimulatory and cytotoxic effects. Endotoxin-induced systemic inflammation in animals can further disrupt experiments, potentially leading to study suspension or even termination.

In cell-based studies, endotoxin contamination can be a hidden disruptor. Immune cells such as dendritic cells, macrophages, monocytes, and T cells can respond strongly even to trace amounts of endotoxins, leading to cytokine release, altered proliferation, or unexpected activation. These effects can easily produce misleading or non-reproducible results.

The demand for endotoxin-free reagents is even more critical in the development of cancer vaccines. Since these therapies rely on precise modulation of the immune system, endotoxin contamination can trigger unintended immune activation, masking the true efficacy of the vaccine candidate and introducing safety risks. Using endotoxin-free proteins is therefore vital to accurately evaluate immunogenicity and support safe clinical translation.

Sino Biological’s ProPure™ solution to minimize endotoxin risk

While pharmacopeial guidelines such as USP <85> provide general limits for endotoxin, cutting-edge immunology and translational oncology studies often require far stricter control. Sino Biological’s ProPure endotoxin-free recombinant proteins are designed to eliminate this variable at the source, supporting reliable results from early discovery through IND-enabling studies. Produced at the state-of-the-art Center for Bioprocessing (C4B) in Houston, Texas, ProPure reagents are rigorously controlled to achieve levels as low as 0.05 EU/mg, with select products reaching an exceptional 0.01 EU/mg—over ten times lower than typical industry standards.

By incorporating endotoxin-free proteins, researchers in cancer therapy and vaccine development can confidently achieve consistent and accurate results in critical applications, including:

  • Animal immunization for antibody generation—ensuring high-quality antibodies and predictable host immune responses.
  • Preclinical toxicology and pharmacokinetics (PK)—minimizing confounding immune activation in animal models.
  • In vitro cell proliferation and differentiation assays—reducing false positives caused by endotoxin-sensitive cells such as dendritic cells, macrophages, and T cells.
  • Precise detection and quantification of cytokines—supporting reliable immunological readouts and biomarker analyses.

How ProPure achieves ultra-low endotoxin levels

ProPure quality is not achieved by end-stage cleanup alone. C4B employs an integrated Prevention–Isolation–Detection strategy across the entire production lifecycle.

  • Prevention at the source: Endotoxin-free plasmids and buffers, low endotoxin-binding plastics, and stringent clean-in-place (CIP) procedures minimize endotoxin introduction from cloning through purification.
  • Environmental isolation: The facility follows an E. coli-free principle, eliminating a major source of endotoxin introduction in recombinant protein production.
  • Dual detection: Each batch is tested using Limulus Amebocyte Lysate (LAL) and/or recombinant Factor C (rFC) assays for sensitive, redundant detection, and fully traceable batch data.
ProPure triple-control strategy for ultra-low endotoxin illustration
ProPure triple-control strategy for ultra-low endotoxin.

This end-to-end design ensures that ProPure proteins arrive ready for use in the most demanding oncology and immunology applications, without extra purification steps that can damage protein quality or delay timelines.

With advanced technologies and rigorous quality control, Sino Biological delivers endotoxin-free proteins that meet the needs of highly sensitive research and translational applications. ProPure proteins help researchers reduce variability, improve reproducibility, and accelerate the development of next-generation cancer therapies.

 

SinoBiological June 2026 QR Code

Learn more about ProPure™ endotoxin-free proteins at sinobiological.com/category/endotoxin-free-proteins.

The post ProPure™ Endotoxin-Free Proteins for Reliable Cancer Research appeared first on Inside Precision Medicine.

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Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation

Background: Pediatric heart disease (PHD), including congenital heart defects, is often incompletely captured in electronic health records, particularly when clinical significance must be inferred from unstructured echocardiogram reports. Automated methods capable of extracting clinically meaningful PHD from narrative reports could improve clinical decision support and research applications. Objective: The aim of the study is to evaluate the feasibility of using supervised fine-tuning of large language models (LLMs), with and without chain-of-thought (CoT) reasoning, to characterize patients with clinically significant or historical PHD from unstructured echocardiogram reports. Methods: We developed a PHD detection algorithm using fine-tuned open-source LLMs, including LLaMA (Meta) and Qwen (Alibaba), to analyze 9749 echocardiogram reports. A subset of 712 reports was adjudicated by 2 pediatric cardiac anesthesiologists, classifying 506 (71.1%) as clinically significant PHD and 206 (28.9%) as not significant. While DeepSeek R1 has shown improved performance with CoT reasoning, its application in medical contexts is underexplored. We incorporated R1-generated CoT into model prompts and fine-tuned backbone LLMs. Results: The fine-tuned Qwen-7B-10k-overthink-CoT achieved the highest accuracy (92.4%), outperforming Qwen-7B-without-CoT (90%), LLaMA-3B-without-CoT (87.9%), Qwen-3B-without-CoT (85.6%), Qwen-3B-10k-overthink-CoT (68.5%), and LLaMA-3B-10k-overthink-CoT (46.2%). In a second dataset, an external validation was performed (n=113; 64 positive, 49 negative), Qwen-7B-10k-overthink-CoT sustained a strong, balanced performance (82.7%), followed by Qwen-7B-without-CoT (88.4%), LLaMA-3B-without-CoT (86.8%), Qwen-3B-without-CoT (84.5%), Qwen-3B-10k-overthink-CoT (58.9%), and LLaMA-3B-10k-overthink-CoT (46.2%). The fine-tuned Qwen-7B model with overthinking CoT (10,000 tokens) achieved the highest internal accuracy (92.4%), with balanced sensitivity and specificity. Across repeated runs, CoT-enhanced models demonstrated improved classification consistency compared to non-CoT models (Qwen-7B-without-CoT: 90%, LLaMA-3B-without-CoT: 87.9%, Qwen-3B-without-CoT: 85.6%). In external validation (n=113), non-CoT variants achieved higher accuracy (up to 88.4%), whereas the Qwen-7B CoT model demonstrated more balanced class performance (accuracy=82.7%). Conclusions: Supervised fine-tuning of LLMs with CoT offers an effective approach for automated PHD detection within unstructured data in the electronic medical record. While CoT-enhanced models demonstrated improved internal performance and more balanced classification, they did not consistently achieve higher accuracy in external validation, highlighting trade-offs between accuracy and class balance. These findings highlight the promise of LLM-based approaches for clinical text phenotyping while underscoring the need for larger, multicenter validation and careful calibration for real-world deployment. Continued validation and integration into the electronic medical record are essential for real-world, artificial intelligence–driven clinical decision support.
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Comparing the Accuracy of ChatGPT-4o, DeepSeek-V3, and Gemini 2.5 Flash in Answering Frequently Asked Questions About Systemic Lupus Erythematosus: Quantitative Study

Background: Systemic lupus erythematosus (SLE) is a complex, fluctuating disease, creating a continuous need for reliable patient information. A prior study concluded that patients with SLE often turn to the internet, including artificial intelligence (AI) chatbots, for information regarding SLE. The rise of AI chatbots as a primary information source presents a critical challenge regarding the accuracy of the information they provide. Objective: This study aimed to evaluate the performance of the latest generation of AI chatbots (ChatGPT-4o, DeepSeek-V3, and Gemini 2.5 Flash) in answering frequently asked questions about SLE. Methods: Twenty-two frequently asked questions about SLE in Bahasa Indonesia (the Indonesian language) were posed to each chatbot. Responses were independently and blindly evaluated for accuracy by 5 clinical immunologists using a 4-point Likert scale. Readability was assessed using the Flesch reading ease score formula. Statistical comparisons for accuracy and readability were performed using repeated-measures ANOVA or the Friedman test, followed by the Bonferroni test for pairwise comparisons. The Spearman ρ was used to evaluate correlations among accuracy, readability, and word count. Results: Gemini 2.5 Flash demonstrated the highest accuracy, with a mean score of 1.25 (SD 0.53), significantly outperforming ChatGPT-4o (mean 1.71, SD 0.61; <.001). Gemini 2.5 Flash significantly outperformed ChatGPT-4o in 2 evaluated domains. The interreliability analysis revealed a statistically significant level of agreement among the 5 evaluators across all responses (Kendall =0.389; <.001). Readability for all 3 chatbots was low (median Flesch reading ease score 42.22‐46.66). Gemini 2.5 Flash produced the longest responses (8509 total words), followed by DeepSeek-V3 (5410 words) and ChatGPT-4o (3632 words). A significant negative correlation was found between word count and lower accuracy (ρ=−0.401; =.001). Conclusions: Our study found that ChatGPT-4o, DeepSeek-V3, and Gemini 2.5 Flash provided overall satisfactory responses to SLE-related questions. The highest accuracy was demonstrated by Gemini 2.5 Flash; however, the absolute differences in scores among the 3 AI chatbots were relatively small. All 3 AI chatbots demonstrated low readability, which may limit accessibility for patient use. This finding highlights a critical “blind spot” in which clinical accuracy, as rated by experts, does not equate to patient accessibility. Thus, further research is required to develop more comprehensive evaluation frameworks incorporating safety, factuality, and calibration of AI chatbots across different medical fields and topics.
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TARGA Imager Enables High-Resolution Imaging of Neurodevelopmental Models

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Neurodevelopment in schizophrenia poses major challenges for experimental study due at least in part to the brain’s genetic complexity, cellular diversity, and limitations in accessing living human tissue. To overcome such barriers, researchers often use complementary human stem cell–derived models: adherent cortical organoids and Neurogenin-2 (NGN2) induced neurons. Adherent cortical organoids form three-dimensional cultures containing diverse cortical neuron types, enabling analysis of network development and long-term maturation over months.1 In contrast, NGN2 neurons generate rapid, two-dimensional, homogeneous populations of excitatory neurons that display robust activity within weeks, making them well suited for scalable, functional assays, and high-throughput screening.2

With the added insight that stem cell models offer into the neural development of the schizophrenic brain, the quantification of patient-derived neurons’ collective function is a priority.3 At Columbia University’s Mortimer B. Zuckerman Mind Brain and Behavior Institute, researchers use NGN2 neurons, yielding reproducible populations of excitatory cortical neurons that scale reliably across experiments.

Calcium imaging provides a powerful functional readout in these NGN2 neuron networks. When a neuron fires an action potential, voltage-gated calcium channels open and intracellular calcium rises sharply.4 Fluorescence calcium indicators convert transient, ionic changes into fluorescence emission that can be quantitatively detected via light microscopy across thousands of cells simultaneously. Coupling calcium-sensitive reporters with high-speed optical microscopy enables noninvasive, population-level measurement of neural activity, synchrony, and network dynamics. Interpreting these rich image sequences requires sophisticated theoretical and numerical approaches.5

Building on the ability to measure neural activity with calcium imaging at scale, Lumencor’s TARGA Imager represents a transformative step in the development of optical imaging hardware for the study of neurodevelopmental conditions such as schizophrenia. It is well suited to workflows where NGN2-neuron cultures are studied across multiple conditions in parallel (Figure 1). In these contexts, TARGA delivers calcium fluorescence images over millimeter-scale fields of view within standard 96-well plates, entire well areas, while maintaining high-speed, faster-than video rate imaging with precision resolution.

Lumencor NGN2-neurons calcium imaging workflow
Figure 1. TARGA implementation for NGN2-neurons calcium imaging workflow

These capabilities allow researchers to observe chemical communications across large neuronal networks rather than isolated cells in real time. Images can be acquired at frequencies up to 100 Hz, enabling capture of fast calcium transients of collective neuronal dynamics. Rapid switching of multicolor excitation light supports multiplexed fluorescence dyes, linking functional activity with cellular structure and organization.

Overall, TARGA achieves high spatial, temporal, and spectral resolution simultaneously with precise, automated opto-mechanical architecture. These data are well matched to modern image analysis and AI algorithms, generating robust fluorescence traces from complex neuronal populations (Figure 2). In combination, these features make the TARGA Imager a revelatory neuroscience tool, uniquely enabling visualization of emergent collective behavior at millimeter scale with exceptional resolution. Such integrated performance accelerates discovery by bridging cellular mechanisms and systems-level phenotypes relevant to schizophrenia pathophysiology and therapeutic screening. By uniting scale, speed, and precision in a single optical platform, TARGA empowers researchers to probe experimental neuroscience and strengthens translational studies of complex psychiatric disease at population scale.

NGN2-neurons calcium imaging workflow
Figure 1. TARGA implementation for NGN2-neurons calcium imaging workflow

 

References

  1. Van der Kroeg et al. Human adherent cortical organoids in a multiwell format. eLife. 2024. 13:e98340.
  2. Shan et al. Fully defined NGN2 neuron protocol reveals diverse signatures of neuronal maturation. Cell Reports Methods. 2024. 4:100858.
  3. Rao et al. Aberrant pace of cortical neuron development in brain organoids from patients with 22q11.2 deletion syndrome‑associated schizophrenia. Nature Communications. 2025. 16:6986.
  4. Zhang et al. Fast and sensitive GCaMP calcium indicators for imaging neural populations. Nature. 2023. 615:884–891.
  5. Pasarkar et al. maskNMF: A denoise‑sparsen‑detect approach for extracting neural signals from dense imaging data. bioRxiv. 2023. 2023.09.14.557777.

 

Lumencor_June 2026 sponsored content QRCode

 

To learn more, visit: lumencor.com or contact our team at: info@lumencor.com.

The post TARGA Imager Enables High-Resolution Imaging of Neurodevelopmental Models appeared first on Inside Precision Medicine.

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360° Video-Based Virtual Reality for Preparing Medical Students for Body Donor Dissection: Randomized Controlled Trial

Background: Body donor dissection is fundamental to medical education but often induces anxiety and emotional distress in students, potentially impacting learning outcomes and well-being. Traditional preparation methods emphasize technical and procedural elements while inadequately addressing students’ emotional challenges. Recent advances in educational technology, particularly 360° video-based virtual reality (VR), may enhance students’ emotional readiness by providing immersive previews of dissection environments. However, the application of this technology specifically for emotional preparation for body donor dissection remains largely unexplored. Objective: This study aimed to develop and evaluate a 360° video-based VR application designed to enhance medical students’ emotional preparedness for their first body donor dissection experience. Methods: A randomized controlled longitudinal study was conducted with 43 first-year medical students (26/43, 60.5% female, mean age 20.9, SD 0.57 years) at Weill Cornell Medicine-Qatar in Fall 2025. Participants completed a baseline survey including the 40-item State-Trait Anxiety Inventory and were randomly assigned to intervention (n=22) or control (n=21) groups using computer-generated permuted block randomization. Before their first dissection session, the intervention group viewed a custom-designed 360° video-based VR experience that featured a virtual tour of the anatomy laboratory and a simulated first encounter with a body donor. The control group received no intervention. State-Trait Anxiety Inventory surveys were administered at baseline (survey 1, all participants), post-VR intervention (survey 2, intervention group only), and postfirst dissection (survey 3, all participants). A follow-up perception survey (survey 4) was administered to the intervention group 1 week into the dissection course. Data were analyzed using 2-tailed paired-samples and independent-samples tests, with qualitative responses analyzed using artificial intelligence–assisted thematic analysis. Results: The intervention group demonstrated a statistically significant reduction in trait anxiety (TA) immediately following the VR experience (mean difference 2.32, SD 4.95; =2.20; =.04), while the reduction in state anxiety (SA) was not significant (mean difference 2.41, SD 8.55; =1.32; =.20). No significant differences in SA or TA were found between intervention and control groups immediately before the first dissection session (SA: =0.03; =.98 and TA: =0.70; =.49) or in anxiety trajectories from baseline to postdissection (SA: =0.85; =.41 and TA: =0.46; =.65). Female students reported higher baseline TA compared to normative college populations (45.42 vs 40.40; mean difference 5.02, SD 7.72; =3.32; =.003). Qualitative analysis revealed positive perceptions, with 91% (10/11) reporting clear content and 82% (9/11) recommending it to future cohorts. Key perceived benefits included environmental familiarization, procedural understanding, and psychological preparation. Conclusions: The 360° video-based VR intervention significantly reduced TA and was perceived as valuable for emotional and procedural preparation. The intervention shows promise as a preparatory tool for enhancing emotional and procedural readiness; however, its impact on objective educational outcomes was not assessed and warrants further investigation. Trial Registration: ClinicalTrials.gov NCT07521033; https://clinicaltrials.gov/study/NCT07521033
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Social Partner Effects on Type 2 Diabetes Prevention, Management, and Spillover Health Outcomes: Single-Arm Pre-Post Pilot Intervention

Background: South Asian Americans are at high risk of prediabetes and type 2 diabetes mellitus (T2DM). South Asian populations are typically close-knit communities, with support networks that could be leveraged in lifestyle interventions. Objective: This study was a single-arm, pre-post pilot study to evaluate the feasibility and efficacy of a culturally tailored telehealth intervention for South Asian adults with prediabetes or T2DM and their social partners (trusted household members) who agreed to complete preintervention and postintervention surveys. Methods: Participants attended 5-hour-long health education sessions delivered in English and Bengali. Participant outcomes included pre-post changes in hemoglobin A1c (HbA), BMI, blood pressure (BP), self-reported minutes of physical activity, and dietary choices at baseline and at the 6-month follow-up. For social partners, outcomes included pre-post survey changes in physical activity and dietary choices. We used Pearson chi-square tests and paired 2-tailed tests to compare baseline measures with postintervention outcomes. Results: This pilot study included 54 participants and 106 social partners in Atlanta, Georgia, between March 2021 and November 2023. All participants were Bangladeshi and spoke native Bengali. Social partners were most commonly participants’ children (39/106, 36.8%) or spouses (34/106, 32.1%). The participant baseline HbA level was 7.5% (SD 1.48%), which decreased by −0.83% (95% CI 0.42%-1.30%; <.001). Participants also improved systolic BP by −5.8 mm Hg (95% CI 0.196-11.37; =.04) with no change in diastolic BP (−0.451 mm Hg, 95% CI −1.49 to 2.39; =.60) or BMI (−0.642 kg/m, 95% CI −1.87 to 0.59; =.17). Compared with baseline, 39% more participants exercised at least 150 minutes weekly (<.001), but there was no difference in self-reported fruit and vegetable intake. However, the social partners increased fruit and vegetable intake (=.02), decreased soda intake (<.001), and increased daily moderate exercise (=.003). Conclusions: Including social partners in T2DM prevention and management is feasible and potentially beneficial, but comparative studies are needed to determine the incremental effects of social partners’ participation vs individual-focused lifestyle interventions. Trial Registration: ClinicalTrials.gov NCT05275231; https://clinicaltrials.gov/study/NCT05275231
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<![CDATA[Lawsuits, research, and clinicians clash over youth screen addiction—learn more about how problematic social platform use evades diagnosis and how we can curb harm.]]>
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STAT+: Pharmalittle: We’re reading about Lilly and Pfizer obesity drug data, Roche and J&J deals, and more

Good morning, everyone, and welcome to another working week. We hope the weekend respite was relaxing and invigorating because that oh-too-familiar routine of meetings, deadlines, and the like has returned with a vengeance. You knew this would happen, yes? To cope, we are relying, as always, on cups of stimulation. Our choice today is laced with chocolate raspberry. Feel free to join us. Remember, no prescription is required. Meanwhile, here are some tidbits to help you along. Best of luck accomplishing your goals today, and of course, do keep in touch. …

Revolution Medicines’ experimental pancreatic cancer drug has been the star of the oncology field in recent weeks, with new data showing the medicine produced unprecedented outcomes for patients, but its next act — this time as a co-lead — was just revealed, STAT points out. Tango Therapeutics said that in an early-stage clinical trial, a combination of its drug vopimetostat and Revolution’s daraxonrasib led to durable responses in the large majority of pancreatic cancer patients who received both medicines. Tango’s strategy of testing the two targeted drugs is notable because combination approaches in pancreatic cancer often include chemotherapy. The recent successful Revolution trial that has upended the specialty tested daraxonrasib versus chemotherapy as a second-line treatment. 

Eli Lilly has already established that its next-generation obesity drug can lead to rapid weight loss, but researchers disclosed new data that provide more details on the safety and tolerability of the closely watched therapy, STAT writes. Lilly previously said that in one late-stage study, retatrutide helped people with diabetes lower blood sugar and lose a significant amount of weight, which is notable since those who have diabetes tend to lose less weight on treatment than those who do not. New data showed seven out of the 403 participants who received retatrutide experienced arrhythmias, and three treated participants experienced major cardiovascular complications, compared with none in the placebo group.

Continue to STAT+ to read the full story…

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The Download: how the World Cup ball will fly and OpenAI’s “super app”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Why this year’s World Cup ball may not fly as far

Much is new about this month’s FIFA World Cup tournament. It hosts more teams than ever before. It’s the first to occur in three different host countries. 

And, like every World Cup for over half a century, it will employ a football with a brand-new design.

Through wind-tunnel experiments, researchers found that long-distance kicks with Adidas’s new Trionda ball might not travel as far as they did in the past. The payoff is a more predictable flight path, something players have not always enjoyed from World Cup balls.

Find out how a few grooves and seams can change the way the game is played.

—Jenna Ahart

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI plans to turn ChatGPT into a ‘super app’ before its IPO
The revamp would combine coding tools and AI agents. (Financial Times $)
+ The super app ambitions first emerged last year. (Fast Company)
+ OpenAI is also building a fully automated researcher. (MIT Technology Review)

2 Trump wants the US government to take a stake in AI companies
He will meet AI leaders to discuss the plan. (BBC)
+ Which would create “a partnership with the American public.” (Reuters $)
+ He wants a slice of the AI boom. (Axios)

3 Google has agreed to pay SpaceX $30 billion for AI computing power
The $920 million-a-month contract runs through June 2029. (NYT $)
+ Google will use about 110,000 Nvidia GPUs owned by SpaceX. (CNBC)
+ It comes days after Anthropic struck a SpaceX data center deal. (WSJ $)

4 AI is set to make everyday life more expensive
Its insatiable thirst for resources is likely to push up inflation. (WP $)
+ We did the math on AI’s energy footprint. (MIT Technology Review)

5 Europe is accelerating its withdrawal from US Big Tech
New analysis reveals dozens of moves to alternative providers. (Wired $) + Last week, the EU launched a “made in Europe” drive. (Reuters $)

6 ICE plans to give local police a new facial recognition app
It would allow them to verify a person’s immigration status. (404 Media)
+ Is the Pentagon allowed to surveil Americans with AI? (MIT Technology Review)

7 Silicon Valley’s lure is fading for India’s tech talent
Due to Trump’s immigration policies and AI-driven layoffs. (Rest of World) 

8 ‘Recursive self-improvement’ has sparked fears of AI escaping control
Nobody is sure about the consequences of RSI. (The Economist $)
+ Here are five ways that AI is learning to improve itself. (MIT Technology Review)

9 Gene-edited embryos are getting closer, but a key safety gap remains
Current techniques still fail to edit every cell. (New Scientist $)
+ “Base-edited baby” is one of our 10 Breakthrough Technologies for 2026. (MIT Technology Review)

10 NASA astronauts will wear high-tech Prada underwear on their moon trips
Ventilation tubes are knitted into the garments. (The Verge)

Quote of the day

“Chat is dead.” 

—A senior OpenAI employee tells the Financial Times why the company is shifting focus from chatbots to AI agents.

One More Thing

BETH HOECKEL


How AI is helping historians better understand our past

The digitization of historical records is making it possible to study the past in new ways. Historians are now using machine learning—particularly deep neural networks—to analyze everything from centuries-old astronomy textbooks to ancient Greek inscriptions.

The technology is helping researchers uncover new patterns in the historical record. But it also introduces risks, including the possibility that machine learning will slip bias or outright falsifications into our understanding of the past.

Read the full story on how AI is transforming the study of history.

—Moira Donovan

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Take a tour of extinct everyday objects to travel back to pre-smartphone life.
+ This a cappella cover of “I Want To Know What Love Is” nails the power-ballad drama.
+ Korea’s ingenious “one-a-day” banana packs are designed so each one ripens sequentially.
+ Casino dialogue has been synced over Looney Tunes footage in this unexpectedly perfect mashup.

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Gut microbiome dynamics in autism: a prospective nested case–control study demonstrates microbial-clinical associations following rehabilitation interventions

BackgroundChildren with autism spectrum disorder (ASD) commonly exhibit gut microbiota dysbiosis and metabolic abnormalities, yet the mechanisms linking these changes to clinical symptoms remain unclear.ObjectiveThis study employed a nested case–control design and multi-omics approaches to evaluate the effects of rehabilitation intervention on clinical symptoms and gut microbiota in children with ASD, identify distinct microbial-metabolic signatures, and explore their mechanistic links with sleep disorders and developmental abilities.MethodsWithin a prospectively established pediatric cohort (n = 45), we implemented a nested case–control design including 26 ASD children (18 males, 8 females; mean age 61.79 ± 11.15 months) and 19 age- and sex-matched healthy controls. All ASD participants received standardized rehabilitation therapy (2 h/day, 5 days/week for 6 months) comprising occupational therapy and cognitive-linguistic training. Primary outcomes included comprehensive clinical assessments [Griffiths Development Scales-Chinese (GDS-C), Children’s Sleep Habits Questionnaire (CSHQ), Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS)] and longitudinal multi-omics analysis (metagenomic sequencing and LC–MS-based metabolomics). Association analyses were performed with FDR correction (q < 0.05).ResultsFollowing the 6-month rehabilitation intervention, significant clinical improvements were observed in sleep quality (CSHQ total and subscores) and developmental performance (GDS-C). Multi-omics profiling revealed distinct biological signatures in ASD children compared to healthy controls, characterized by elevated Intestinibacter_bartlettii and reduced levels of ornithine and siderophore nonribosomal peptide biosynthesis. Crucially, correlation analysis demonstrated that, after FDR correction, ornithine levels were significantly positively correlated with multiple GDS-C developmental domains, while tyrosine was associated with parasomnias. These findings establish a potential mechanistic link where amino acid metabolism connects gut microbial shifts to clinical phenotypes.ConclusionThis study demonstrates that rehabilitation intervention synchronously ameliorates clinical symptoms and modulates the gut-metabolic profile in ASD. The identified associations between specific metabolites (ornithine and tyrosine) and clinical outcomes suggest a metabolic mechanism underlying the gut-brain axis, highlighting the potential of these metabolites as biomarkers for therapeutic monitoring. Further large-scale studies are needed to validate these findings.