The Download: AI hiring biases, and weather data sabotage

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.

AI is more likely than humans to form biases when hiring

The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. 

We already know that LLMs pick up human biases from their training data. New research suggests they can also develop their own biases from experience—and stereotype job applicants more than humans do.

As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases. 

Read the full story on AI’s alarming potential to stereotype job applicants.

—Michelle Kim

The risk of weather data sabotage is rising

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on weather forecasts. More recently, the forecasts have become relevant for another industry: prediction markets, where people bet money on all kinds of real-world events, including the weather. 

The temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. 

As experts in the field, we can foresee scenarios where the risks snowball into far bigger, more systemic problems. 

Find out why the threats to weather data are growing—and how to stay ahead of them.

—Monique Kuglitsch, Jesper Dramsch, Franz G. Kuglitsch, & Andrea Toreti

The must-reads

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

1 SpaceX is negotiating to sell the Pentagon AI compute
It would provide data center capacity worth billions of dollars. (WSJ $)
+ Deepening ties between Elon Musk’s company and the DoD. (Reuters $)
+ Meanwhile, Anthropic is in talks with Meta to acquire compute. (CNBC)
+ The compute explosion is only just beginning. (MIT Technology Review)

2 Trump Media wants $100,000 a month for early access to Trump’s posts
The premium feed is being pitched to trading firms and banks. (FT $)
+ It aims to monetize Trump’s market-moving social media posts. (Reuters $)
+ Critics described the plan as “brazen corruption.” (Guardian)
 
3 ICE shared Medicaid data it wasn’t supposed to have with Palantir
Court filings show the data reached the contractor before being deleted. (NPR)
+ ICE is using data broker tools to identify “unaccompanied minors.” (Wired $) 
 
4 Apple briefly overtook Nvidia as the world’s most valuable company
The iPhone maker’s earnings durability has impressed investors. (Reuters $)
+ While Nvidia’s rise has stalled amid shifting AI bets. (CNBC)
 
5. Politicians are trying to change what chatbots say about them
A new industry has sprung up to help them edit AI outputs. (NYT $)
+ Chatbots can sway voters better than political ads. (MIT Technology Review)
 
6 Washington is opening the door to armed robots
The Pentagon is accelerating AI weapons development. (WP $)
+ “Humans in the loop” in war is an illusion. (MIT Technology Review)
 
7 China’s Moonshot has paused new subscriptions amid surging uptake
Demand for the headline-grabbing Kimi ​K3 has strained capacity. (SCMP)
+ China’s open-source AI is challenging US models. (MIT Technology Review)

8 Lab-grown teeth could soon replace fillings and implants
Scientists believe regenerative medicine could transform dentistry. (BBC)
+ Humanlike “teeth” have been grown in mini pigs. (MIT Technology Review)
 
9 AI slop on birdwatching forums is putting research at risk
It could contaminate records of species. (Guardian)

10 Heart experts have good news for your coffee habit
Roughly five cups per day is fine—and may even be beneficial. (Gizmodo)

Quote of the day

“The most authoritarian government is producing the most egalitarian models, and what should be the most democratic government is breeding companies that are the most authoritarian.” 

—Rayan Krishnan, CEO of Vals AI, a company that evaluates AI performance, gives the New York Times his take on the competition between Chinese and American models.

One More Thing

RICHARD CHANCE


The curious case of the disappearing Lamborghinis

A new wave of theft is rocking the luxury car industry—mixing high tech with old-school chop-shop techniques to snag vehicles while they’re in transport. 

It’s remained under the radar, even as it’s rocked the industry over the past two years. MIT Technology Review identified more than a dozen cases involving high-end vehicles, obtained court records, and spoke to law enforcement, brokers, drivers, and victims in multiple states to reveal how transport fraud is wreaking havoc across the country.

Find out how a new wave of transport fraud is wreaking havoc across the country. 

—Craig Silverman

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.)

+ Here’s the perfect outfit for a heat wave: the world’s first self-cooling clothes.
+ The “unhinged” cake decorators at a Missoula Walmart have become local legends.
+ This visual guide to chili peppers around the world is a lavishly illustrated ode to the world’s hottest fruit.
+ Take a quirky tour of mid-20th-century cinema in this thread of film posters inadvertently photographed by postwar town planners.

Architecture-data matching for EEG-EMG decoding: compact deep models match classical spectral decoders on the WAY-EEG-GAL grasp-and-lift dataset

Modern deep learning has broadened the tools available for non-invasive neural decoding, but its advantage over well-engineered classical pipelines remains unclear at clinical neural-engineering sample sizes. We compared four classical decoders, three multilayer perceptron (MLP) variants, and three deep-learning architectures (an EEGNet-style compact convolutional network, a four-layer Transformer encoder trained from scratch, and a graph attention network) on the public WAY-EEG-GAL grasp-and-lift dataset (12 participants, 3,528 trials). Models were evaluated using leave-one-subject-out (LOSO) cross-validation to decode object weight (165, 330, and 660 g) and grasp-surface friction (sandpaper, suede, and silk). After Benjamini–Hochberg false discovery rate (BH-FDR) correction within the primary/robustness family, none of the deep-versus-best-classical comparisons reached significance. The graph attention network led nominally on weight (0.643), and the compact convolutional network (CNN) led nominally on surface (0.565), but neither exceeded the best classical baselines (HGBM = 0.617 for weight; logistic regression = 0.562 for surface). Two-direction bandwidth controls showed that the nominal graph neural network (GNN) advantage on weight reflected access to higher-frequency electromyography (EMG) content rather than a robust architectural gain. The GNN weight signal was concentrated in the first 500 ms of sustained hold (0.639 early vs. 0.540 late, q = 0.007), whereas the compact CNN was comparatively robust to bandwidth and phase. A four-layer Transformer trained from scratch underperformed (q = 0.003 for both tasks), consistent with a parameter–data mismatch at n = 12. Modality ablation showed that both tasks were dominated by peripheral EMG features: electroencephalography (EEG)-only decoding was near chance for weight (0.34–0.37) and surface (0.36–0.38; all q < 0.0015 vs. EMG-only). EMG-only HGBM achieved the highest accuracy in the study (0.712 for weight and 0.584 for surface), and adding EEG channels reduced HGBM weight decoding (q = 0.007) and CNN surface decoding (q = 0.012). In contrast, the graph attention network was robust to EEG-induced fusion dilution (q > 0.5), consistent with attention-based down-weighting of low-information channels. Together, bandwidth, modality, phase, and conditioning controls indicate that, at clinical sample sizes, the critical design choice is not architectural complexity but modality-relevant channel selection and the ability to suppress low-information channels.

Quantification of [11C]CURB PET using an irreversible reference tissue model with a cluster-derived pseudo-reference region

IntroductionQuantification of [11C]CURB, an irreversible positron emission tomography (PET) radiopharmaceutical used to image fatty acid amide hydrolase (FAAH), requires invasive arterial sampling. Developing a non-invasive approach for [11C]CURB is challenging, since traditional reference region models are not directly applicable and the ubiquitous brain expression of FAAH complicates the identification of a ligand-free reference region. This study aimed to introduce and validate the irreversible reference tissue model (IRTM) used in conjunction with a data-driven, clustering-based white matter (WM) pseudo-reference region.MethodsIRTM was implemented using a coupled-fit approach to estimate a common k2′, resulting in two independent parameters per volume-of-interest: R1 and kf. Primary quantification was performed using the macroparameters Ri=Ki/K1′=R1⁢k3/kf (relative net influx) and R⁢k3=λ⁢k3/K1′=R1⁢k3/k2 (relative trapping index). Simulations characterized the sensitivity of IRTM to differences in cerebral blood volume between target and reference tissues. The model was validated against the gold standard arterial input function (AIF)-based quantification using retrospective [11C]CURB PET data from 10 healthy participants. A partition-based clustering algorithm was used to extract the centermost WM time-activity curve as a pseudo-reference region.ResultsIn the IRTM validation study with human data, Ri emerged as a more robust metric than Rk3, with the latter exhibiting high between-subject variability. Although simulations identified systematic biases in IRTM estimates driven by blood volume asymmetries, IRTM-derived Ri estimates showed strong concordance with AIF-based Ri (R2 = 0.96), λk3 (R2 = 0.90), and Ki (R2 = 0.96). Mean errors in Ri ranged from −11.2 ± 9.9% in the frontal lobe to −3.1 ± 10.4% in the amygdala. Voxel-wise parametric maps demonstrated high image quality and signal-to-noise ratio at the individual subject level.ConclusionThis study provides a validated, fully non-invasive framework for [11C]CURB PET quantification. Combined with a partition-based clustering approach to select a data-driven WM pseudo-reference region, IRTM achieved excellent concordance with AIF-based measurements, supporting the feasibility of IRTM for non-invasive FAAH imaging.

Patient journey mapping in an open-door psychiatric inpatient unit

BackgroundOpen-door policies in inpatient psychiatric care have been increasingly adopted to enhance patient autonomy and reduce coercive practices. However, systematic methods for evaluating patient experiences in these settings remain limited. This study applied Patient Journey Mapping in an acute psychiatric unit operating a structured, time-limited (6 hours/day) open-door policy to comprehensively analyze the hospitalization experience.MethodsAn exploratory sequential mixed-methods design was used. Qualitative data were first collected through focus groups with patients (n = 18), following GRAMMS guidelines, to identify critical touchpoints across the hospitalization journey. Quantitative data were then collected from patients (n = 32) using an adapted Likert-type ordinal scale to assess satisfaction with the identified touchpoints. Data were analyzed using content analysis methods.ResultsThe analysis delineated the main phases of hospitalization and identified key experiential touchpoints, including “Moments of Truth,” “Moments of Pain,” and “Wow Moments,” which substantially shaped patients’ perceptions of care quality. Open-door policies were perceived as supporting autonomy and reducing psychological distress. However, several challenges remained, particularly regarding the admission process, privacy during medication administration, and communication about structured activities.ConclusionPatient Journey Mapping provides a useful patient-centered approach for evaluating experiences in open-door psychiatric inpatient settings. The findings highlight opportunities to optimize service delivery, strengthen therapeutic environments, and support recovery-oriented practices in acute psychiatric care.

Effects of exercise interventions on executive function in autism spectrum disorder: a three-level meta-analytic review

ObjectiveThis study aimed to use a three-level meta-analysis to examine the effects of exercise interventions on executive function (EF) in children and adolescents with autism spectrum disorder (ASD), and to explore the relationship between exercise dose and intervention effects through subgroup analyses.MethodsA systematic search was conducted in Web of Science, PubMed, Embase, and the Cochrane Library from database inception to November 17, 2025. All included studies were randomized controlled trials. A three-level random-effects model was applied to integrate multiple dependent effect sizes. Hedges’ g was used to calculate effect sizes, and negative values indicated improvements in EF following exercise interventions. Study quality was assessed using the PEDro scale, and publication bias was evaluated using Egger’s regression and the trim-and-fill method.ResultsSeventeen studies involving 626 participants were included. Exercise interventions produced a moderate and significant improvement in EF (g = –0.34, p < 0.0001) with low heterogeneity (Q = 50.42, p =0.268). Subgroup analyses showed that only intervention duration significantly moderated the effects, with programs lasting ≥10 weeks yielding the greatest benefits.ConclusionExercise interventions significantly improve EF in children and adolescents with ASD, with the largest effect sizes observed in interventions lasting ≥10 weeks. Further large-scale studies are needed to clarify dose–response relationships and inform optimal exercise prescriptions. This meta-analysis was designed and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251236112

Thyroid-stimulating hormone, fasting blood glucose and suicidal ideation in Chinese adolescents with major depressive disorder: a cross-sectional study

BackgroundThyroid function and glycolipid metabolic alterations are often associated with major depressive disorder (MDD), yet their roles in suicide risk among adolescents with MDD remain unclear. This study aimed to investigate thyroid-stimulating hormone (TSH) levels, glycolipid metabolism parameters, and their associations with suicidal ideation (SI) in adolescents with MDD.MethodsThis cross-sectional study was conducted at one general hospital and one psychiatric hospital in Anhui Province, China. Socio-demographic data and laboratory parameters were collected from participants, and the patients’ depressive symptoms and SI severity were assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24) and the Positive and Negative Suicide Ideation Inventory (PANSI), respectively. TSH levels and glycolipid metabolism parameters were also measured.ResultsA total of 146 adolescents with MDD and 70 healthy controls (HCs) were enrolled in this study. Compared with HCs, patients had lower fasting blood glucose (FBG) levels (P < 0.001). Logistic regression analyses showed that a worse relationship with family, a higher HAMD-24 total score, and higher TSH and FBG levels were independently associated with concurrent SI in adolescents with MDD (all P < 0.05). Furthermore, receiver operating characteristic (ROC) curve analysis showed that the combination of these four factors had a good discriminatory ability for SI, with an area under the curve (AUC) of 0.851.ConclusionIn this cross-sectional study, TSH and FBG levels were associated with SI in adolescents with MDD. Nevertheless, whether these parameters can serve as clinically useful biomarkers requires further validation in larger prospective studies.

AI is more likely than humans to form biases when hiring

The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience—and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases. 

Researchers at Princeton University and the University of Chicago ran LLMs, including ChatGPT, Claude, and Gemini, through a simulated hiring game, adapted from a psychology study that explored how humans can form stereotypes. Each model was told it had been hired as a consultant by the mayor of a fictional city and was then asked to help hire people for 20 jobs, including doctors, lawyers, child-care aides, and janitors. Candidates came from four fictional ethnic groups: Tufa, Aima, Reku, and Weki. 

In each round, there was a new job opening and four candidates, one from each group. After the model hired a candidate, it learned whether they succeeded at their job and moved onto the next round. The model was told to make as many successful hires as possible over 40 rounds. Unbeknownst to the models, all candidates were equally likely to succeed at every job.

The models quickly started segregating candidates from different groups into different jobs on the basis of early observations of hiring outcomes. For example, when a model was told an Aima had failed as a doctor, a job considered to require high levels of warmth and competence, it veered away from hiring all Aimas as doctors. Instead, it started hiring Aimas as janitors, which the model classified as being less warm and competent than doctors. 

The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study’s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI’s reasoning model o3 scoring 1.83, close to the maximum possible.

That’s because LLMs “really are eager to create generalizations from limited data,” says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a paper at ICML in Seoul in July. “That’s literally a lot of what they’re optimized for.”

Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better—a phenomenon psychologists call the “exploration-exploitation dilemma.” It’s like choosing between a new restaurant and your reliable favorite. 

Because LLMs are trained on math, coding, and science problems—tasks that reward generalizing from just a few examples—they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype.

In the experiment, newer models with higher reasoning capabilities, such as OpenAI’s o3 and DeepSeek’s R1, showed even stronger biases. When LLMs rush to generalize in social settings, “that’s when things tend to go wrong,” says Liu. OpenAI and Anthropic did not respond to requests for comment.

The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can “over-index on the same kinds of behaviors it’s experienced before” and form biases, she says.

Simply having chatbots remember less isn’t a fix, though, because users want chatbots to remember what they say. “We still are trying to figure out just the right amount that isn’t too much or too little,” says Wang.

Telling the model to be fair didn’t change its behavior much. “Either it can’t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,” says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that “incorporate desirable social values in order to make the large language model act in socially desirable ways,” says Liu.

The models also became less biased when they were told more personal information about individuals. In another experiment in the same study, the researchers asked the models to resettle members of different ethnic groups in cities across Canada. When the models were told personal information relevant to the ability to adapt to a new city, such as age and education, they were less likely to segregate people by their ethnicity. But when they were given irrelevant information, such as hair color and tattoo shape, the models largely fell back to sorting people by their ethnicity again. 

To what extent AI systems will stereotype job applicants in the real world is still an open question. While the models in the experiment immediately learned whether they’d made successful hires, a model screening résumés in the real world doesn’t get an instant report card. Companies can take a long time to find out whether a new hire is any good. 

But when feedback does trickle in, a model could still read too much into those results when making future hires. As companies increasingly deploy LLMs to screen résumés and even conduct interviews, the finding that models can form biases from their hiring experience “is a really serious implication that they should grapple with,” says Wang. 

As LLMs learn from experience to make decisions about who gets hired, who gets a loan, or who gets parole, the biases we should worry about may include ones no human ever taught them. “These novel biases—they’re sort of ever present,” says Liu.

StockWatch: Lilly’s Up-to-$3.8B Deal for AtaiBeckley a Good Trip for Psychedelic Drugs, Analysts and Investors Agree

It wasn’t too long ago that biopharma giants stayed away from developing psychedelic drugs—but positive clinical data plus a friendlier regulatory climate in Washington have prompted the largest drug developers to embrace the field.

The latest and most telling example of pharma embracing psych drugs came when Eli Lilly (NYSE: LLY) announced that it agreed to acquire AtaiBeckley (Nasdaq: ATAI) for up to $3.8 billion—of which Lilly will pay $2.8 billion upfront. The deal, set to close in the third quarter, expands Lilly’s neuroscience portfolio by adding the pipeline of AtaiBeckley led by BPL-003 (mebufotenin benzoate), a Phase III candidate for treatment-resistant depression (TRD) that is a synthetic form of 5-MeO-DMT administered intranasally. BPL-003 has been granted the FDA’s Breakthrough Therapy designation.

BPL-003 wowed analysts and others back in April after AtaiBeckley published positive data from a Phase IIa trial (NCT05660642) showing that a single intranasal dose of BPL-003 led to rapid and sustained reductions in Montgomery-Åsberg Depression Rating Scale (MADRS) scores from baseline in 12 TRD patients who remained on stable SSRI therapy throughout the study. Both the six patients dosed at 10 mg and six at 12 mg showed a 66.7% antidepressant response rate (defined as ≥50% reduction from baseline MADRS score) at Day 2, with five of six participants in the 10 mg cohort (83%) and four of six in the 12 mg cohort (66.7%) maintaining their response at Week 12.

“Especially with progress on BPL-003, we see the company as positioning itself well to becoming a significant player in the mental health therapeutics space,” Sumant Kulkarni, a senior analyst covering biotechnology with Canaccord Genuity, wrote on news of the positive data, adding: “We also still see this space as large enough to accommodate multiple approaches/competitors.”

$3.7B in projected peak sales

Kulkarni also raised Canaccord Genuity’s peak unadjusted U.S. sales forecast for BPL-003 to $3.7 billion by 2036 from $2 billion, after revising the firm’s model by raising the list price from $20,000 to $30,000 per annual treatment course (not accounting for insurance coverage), about the same price as Spravato® (esketamine), also a nasal spray marketed by Johnson & Johnson (NYSE: JNJ) for TRD plus some depressive symptoms in adults with major depressive disorder (MDD).

Spravato, a noncompetitive N-methyl D-aspartate (NMDA) receptor antagonist, crossed the $1 billion sales threshold during the second quarter, as it generated $584 million, up 25% quarter-over-quarter from $464 million in Q1—and up 43% from $734 million in the first half of 2025.

“Sales are tracking to reach annual sales guidance of $3-3.5B+ by 2027–28,” Jefferies equity analyst Andrew Tsai wrote in a research note focused on J&J’s second-quarter results. “Spravato’s trajectory supports the notion psychedelics can be commercially viable in hard-to-treat mental health disorders, by leveraging JNJ’s infrastructure.”

Given the data for BPL-003, Lilly got a bargain, Tsai wrote in a separate note on the Lilly-AtaiBeckley acquisition.

“We think the deal heavily favors LLY, as ATAI’s lead asset BPL-003 (intranasal 5-MeO-DMT) should have multibillion dollar peak sales potential,” Tsai wrote, rather than the $1 billion-plus that he thinks was implied by the deal price.

Tsai and Jefferies had previously forecast peak sales of between $1 billion and $2 billion—a range he said was “arguably conservative” since BPL-003 could, if it aces its Phase III trial, show superiority to Spravato, which is on track to reach up to $5 billion-plus in peak sales.

Positive implications

“At the same time, we appreciate LLY has significantly more resources to maximize the long-term value of ATAI’s psychedelic assets. In any case, the deal has (+) [positive] implications for the entire psychedelic space,” Tsai added.

Among pharma giants joining J&J in embracing psychedelic drug development in recent years:

  • AbbVie (NYSE: ABBV), which last year acquired the lead pipeline program of privately held Gilgamesh Pharmaceuticals, the moderate-to-severe MDD candidate bretisilocin (GM-2505), for up to $1.2 billion.
  • Otsuka Holdings (Tokyo Stock Exchange: 4578), which in 2023 acquired Mindset Pharma, a Canadian psych drug developer focused on psychiatric and neurological disorders, for C$80 million ($56 million).

With its deal for AtaiBeckley, Lilly becomes the latest pharma giant to perceive the positive implications Tsai cited.

“Treatment-resistant depression persists even after multiple treatments have failed. Millions of people are still searching for relief and desperately need a therapy that works,” Carole Ho, executive vice president and president, Lilly Neuroscience, said in a statement. “Advancing AtaiBeckley’s investigational therapies gives us a real chance to change that.”

Investors agreed with Lilly, giving the pharma a 1% increase Thursday, the day the acquisition was announced, from $1,156.63 to $1,169.17—no small feat since buyers typically stay flat or see their shares slide after announcing an acquisition. And not surprisingly, AtaiBeckley investors were enthusiastic about the deal, as its stock leaped 33% from $5.36 to $7.15. On Friday, Lilly inched up 0.8% to $1,178.58 while AtaiBeckley rose 1% to $7.22.

The AtaiBeckley buyout is Lilly’s eighth announced acquisition of a smaller biopharma this year.

Lilly is acquiring three infectious diseases vaccine developers—Vaccine Company for up to $1.55 billion, Curevo for up to $1.5 billion, and LimmaTech Biologics for up to $780 million—as well as in vivo chimeric antigen receptor T-cell (CAR T) developer Kelonia Therapeutics for up to $7 billion); JAK inhibitor developer Ajax Therapeutics for up to $2.3 billion; next-generation dual-payload antibody-drug conjugate (ADC) developer CrossBridge Bio for up to $300 million; and nonviral DNA delivery-focused drug developer Engage Biologics for up to $202 million cash.

The deal spree reflects Lilly’s desire to capitalize on the billions of dollars it is generating from sales of its obesity and diabetes drugs based on glucagon-like peptide 1 (GLP-1) receptor agonists alone or in tandem with a glucose-dependent insulinotropic polypeptide (GIP).

“If we see great ideas that we think we can use to help people that need them, of course we’ll do deals,” Daniel M. Skovronsky, MD, PhD, Lilly’s chief scientific and product officer and president of Lilly Research Laboratories, said on CNBC.

“Positive development”

David Risinger, a senior managing director and senior research analyst covering diversified biopharmaceuticals at Leerink Partners, said his firm viewed Lilly’s buyout of AtaiBeckley “as a positive development because it enhances LLY’s pipeline of potential neuroscience blockbuster candidates.”

That pipeline is led by five Phase III programs involving four drugs, none of them a psychedelic. Two of the programs belong to brenipatide, a dual agonist of both the GIP and GLP-1 receptors. Brenipatide is being developed for both MDD and alcohol use disorder.

Also in Lilly’s late-stage neuroscience pipeline are:

  • Donanemab, which binds to deposited amyloid plaque in the brain and is being studied for the treatment of cognitively unimpaired Alzheimer’s disease.
  • Ixoberogene Soroparvovec (Ixo-Vec), an intravitreal gene therapy being studied as a single one-time treatment for vision loss associated with neovascular (wet) age-related macular degeneration (AMD).
  • Remternetug (LY3372993), which also binds to deposited amyloid plaque in the brain and is under study as a treatment of cognitively unimpaired/mild cognitive impairment due to Alzheimer’s disease, with potential for subcutaneous delivery.

In addition, AtaiBeckley “would provide ​differentiated exposure in psychiatry and reinforce [Lilly’s] ​broader effort to diversify beyond ​its cornerstone cardiometabolic franchise,” observed Evan David Seigerman, a managing director and head of healthcare research at BMO Capital Markets, as reported by Reuters.

AtaiBeckley was formed last November by the merger of atai Life Sciences and Beckley Psytech. The company’s stock has nearly doubled, soaring 98% over the past six months from $3.64 on January 16.

“Going mainstream”

“Psychedelic Medicine is going mainstream,” declared Steve Jurvetson, co-founder of Future Ventures, in a post on X. Jurvetson and Future were among early investors, along with Peter Thiel in atai Life Sciences.

AtaiBeckley is one of numerous psychedelic drug developers to show significant six-month gains since January: As of Friday’s closing bell, Compass Pathways (Nasdaq: CMPS) shares jumped 68% to $12.35, GH Research ballooned 69% to $28.71, while Definium Therapeutics (Nasdaq: DFTX) nearly tripled, zooming 194% to $44.29.

Interestingly, those three companies did not get a solid bounce from AtaiBeckley’s acquisition by Lilly. Since the deal was announced Thursday, Compass fell 7% from $13.31 pre-announcement, Definium dipped 3% from $45.66. GH rose 8% Thursday from $26.92 to $29.13, before sliding 1.4% the following day.

Bucking the trend was Cybin, d/b/a Helus Pharma (Nasdaq: HELP), which has climbed 11% since the Lilly-AtaiBeckley announcement, from $6.51 to $7.25. Its shares have slumped 6% since January—but soared 58% over the past month on positive news, such as the 88%+ enrollment rate of patients in Helus’ Phase III APPROACH pivotal trial (NCT06564818) of HLP003 in MDD, on track for topline data readout in Q4 2026.

“We see the potential for 150–200% upside [jump in stock price] if Phase III data in 4Q26 are positive,” Kulkarni wrote, making it the largest potential jump among psychedelic drug developers.

In addition to favorable data, the stock surges also reflect actions by President Donald J. Trump’s administration to encourage psychedelic drug development. In April, President Trump signed Executive Order 14401, directing the FDA and other federal agencies to accelerate research and improve access to psychedelic drugs, citing their potential as promising treatments for serious mental illnesses.

And on July 13, the FDA published “Psychedelic Drugs: Considerations for Clinical Investigations,” a final guidance designed to provide general considerations for developers of psych drugs, with recommendations for how to conduct clinical trials for the treatments.

“Rather than providing specific recommendations on study design, this guidance will present foundational constructs that all sponsors studying the therapeutic potential of psychedelic drugs, including sponsors without commercial drug development as primary interest (e.g., academic researchers), should consider,” the FDA wrote in the final guidance. “Sponsors are encouraged to request meetings with FDA for advice on a specific drug development program.”

Leaders and laggards

  • Q32 Bio (Nasdaq: QTTB) shares nearly doubled, leaping 91% from $11.21 to $21.38 July 13 after the autoimmune and inflammatory disease drug developer announced positive 36-week topline results from Part B of the Phase IIa SIGNAL-AA trial (NCT06018428) assessing bempikibart in patients with severe or very severe alopecia areata. Q32 said it saw clinically meaningful efficacy data on the primary endpoint of mean percent change from baseline in SALT score, with a reduction from baseline of 35.3% in the prespecified modified intent to treat (mITT) analysis. The company also reported that 40.0% of patients (10/25) achieved SALT-20 response at Week 36 in the mITT analysis, while 30.3% of patients (10/33) achieved SALT-20 response at Week 36 in the ITT analysis of all enrolled patients.
  • Veradermics (NYSE: MANE) shares yo-yoed this past week, climbing 12% from $110.17 to $123.70 Wednesday after the pattern hair loss drug developer announced positive topline results from its open-label Phase II Study 207 trial (NCT06527365) assessing VDPHL01, an extended-release oral minoxidil formulation, in women with mild-to-moderate pattern hair loss. Veradermics said most study participants reported improved hair coverage at Month 2, with approximately 88.9% of patients dosed once daily and 90.0% dosed twice daily reporting “improved” or “much improved” outcomes at Month 6. Participants dosed once daily showed a mean increase in non-vellus target area hair count (TAHC) of 22.7 hairs/cm² at Month 6, an average that rose to 23.3 hairs/cm² in twice daily dosed patients. The mini surge was short-lived, however, as investors more than gave back the gain, selling off shares to send them tumbling 14% to $105.83 Thursday amid possible investor questions about whether the good clinical news was already reflected in the stock price.

The post StockWatch: Lilly’s Up-to-$3.8B Deal for AtaiBeckley a Good Trip for Psychedelic Drugs, Analysts and Investors Agree appeared first on GEN – Genetic Engineering and Biotechnology News.

Organ Aging Linked to Breakdown in Immune Cell Interaction and Senescent Neutrophil Clearance

We may age at different rates, but none of us escapes aging. A study in mice and in human cells by Stanford Medicine researchers has linked organ aging to the increased inability—with advancing age—of tissue resident macrophage (TRM) immune cells to clear aged neutrophils, another type of immune cell.

The study found that these TRMs appear to be central coordinators of age-related organ decline. Blocking a single receptor, EP2, on these cells preserved the youthfulness of multiple organs in mice, including the brain, heart, skeletal and heart muscle, liver, spleen, bone marrow, kidney, and colon. The receptor binds specifically to a hormone, prostaglandin E2, which is known to cause inflammation and pain in humans as well as in mice.

The researchers found that in mice, selectively disabling this receptor exclusively on tissue-resident macrophages genetically, or using an experimental selective EP2 antagonist drug, prevented chronic-inflammation-driven disorders of age—including frailty, excessive fat accumulation, and heart trouble—and also substantially slowed cognitive decline.

Research lead Katrin Andreasson, MD, the Edward F. and Irene Thiel Pimley Professor in Neurology and Neurological Sciences, said, “We’ve shown that when tissue-resident macrophages don’t have EP2 on their surfaces anymore or when that receptor is plugged up by a drug, this decline doesn’t happen … We’ve been trying to figure out why we age. Now we know at least one big reason for it.”

The discoveries help to clarify systemic inflammation’s significant contribution to aging and the debilities that accompany it. The findings also point to a pharmaceutical approach that could restrain our organs’ unavoidable march toward senescence and so extend overall health span.

Senior author Andreasson, together with first author Jessy Tan, PhD, an instructor in neurology, and colleagues reported on their findings in Science, in a paper titled “Restored clearance of senescent neutrophils by tissue-resident macrophages limits organ aging.” In their research article summary, the team stated, “This work identifies EP2 signaling in TRMs as a central regulator of organ-wide aging through its control of senescent neutrophil clearance, reframing aging as a failure of active cellular clearance rather than passive degeneration.”

Aging is accompanied by parallel functional decline across organs, but the cellular drivers remain unclear, the authors wrote. “Although molecular hallmarks of aging have been identified, the cellular events that initiate and propel tissue decline remain poorly defined.”

The most abundant white blood cells in our immune system are neutrophils, which act as the body’s main first responders. Produced in bone marrow, new neutrophils are transferred to the bloodstream, where they circulate and attack bacterial, viral, or fungal pathogens that they encounter. Neutrophils are also extremely short-lived, surviving just 12–24 hours.

Some 90% of circulating neutrophils end up in the liver, spleen, and bone marrow, awaiting execution clearance by another type of immune cell. “Neutrophils are among the shortest-lived immune cells, aging within hours in the circulation and requiring continuous clearance,” the team noted.

This neutrophil clearance is critical. In aged animals, the vast bulk of neutrophils that never see combat undergo a fast transition to senescence, a zombie-like state in which they may injure, age, and inflame neighboring cells. And as we age, the neutrophil count rises, with senescent neutrophils constituting an ever higher percentage. “Senescent neutrophils are killing our tissues,” Andreasson said. “Clearance of these cells is essential for preventing chronic inflammation.”

That’s a job for macrophages. These cells comb the tissues for pathogens, signal other cells to lend a hand in the fight, and pump out growth factors that help repair damaged tissue. But first and foremost, Andreasson said, “They’re the body’s garbage collection crew. A lot of that garbage is defunct cells.” And a lot of those cells are neutrophils—to the tune of 100 billion a day.

Macrophages come in several subtypes. Tissue-resident macrophages are long-lived and ubiquitous. They take up residence in each of the body’s organs during fetal development and remain for their lifetimes in whatever organ they’ve inhabited, adapting their roles to fit that organ.

One of tissue-resident macrophages’ prime responsibilities is to swallow senescent cells. “A core TRM function is efferocytosis, the clearance of apoptotic, senescent, and damaged cells that is essential for preventing chronic inflammation,” they explained. Especially important targets for this operation, the study showed, are the potentially 100 billion neutrophils produced daily, which start showing signs of senescence within 8 to 12 hours after entering the bloodstream. (Neutrophils that haven’t arrived at senescence yet but have lived long enough and seen enough to put out “kill me now” flags of surrender on their cell surfaces are fair game.)

“Among primary TRM targets are neutrophils, the most abundantly produced immune cell, with more than 10 billion and 100 billion generated daily in mice and humans, respectively,” the investigator noted. “Uncleared aged neutrophils release proteases and extracellular traps that damage tissues, propagate inflammation, and promote aging, and are normally removed efficiently by TRMs in the liver, spleen, and bone marrow.”

But tissue-resident macrophages also grow old. As Andreasson and associates showed in a prior study, over the advancing years these long-lived cells become ever more prone to succumb to aging-associated inflammation and to propagate it. In their newly reported paper, they noted, “TRMs comprise 60–90% of macrophages in the brain, liver, lungs, heart, and kidneys, and their long lifespan makes them particularly vulnerable to aging, as they accumulate metabolic, oxidative, and inflammatory injury over years to decades.”

Immune cells produce hormones called prostaglandins. One of the five varieties of prostaglandin, called PGE2, can exert diverse effects on a cell, depending on which type of surface receptor is expressed on that cell’s surface. Of the various subtypes of receptors for PGE2, the EP2 receptor is highly pro-inflammatory. Tissue-resident macrophages are loaded with EP2.

Infection, injury, and toxic chemicals, including those produced by our aging bodies, increase PGE2 output. As the team’s prior work showed, that output grows substantially as we grow older. So does the concentration of EP2 on tissue-resident macrophages. “TRMs express the prostaglandin E2 (PGE2) receptor EP2, which suppresses macrophage metabolism and phagocytosis in aging,” the investigators noted.

This effectively creates a one-two punch. PGE2’s pro-inflammatory influence increases with age. The resulting unrelenting inflammatory PGE2 stimulation on tissue-resident macrophages, the new study showed, downshifts these cells’ ability to clear neutrophils. Senescent neutrophils then accumulate in tissues and blood.

Andreasson and her colleagues had previously shown that with aging, tissue-resident macrophages undergo a slow decay in their energy metabolism. “Once that starts, there’s a steady decline in a macrophage’s performance,” she said.

For their newly reported study, Andreasson’s lab bioengineered a mouse in which, at a time of the scientists’ choosing, the EP2 gene gets deleted—but only in tissue-resident macrophages. The results of their experiments showed that disappearance of EP2 from these cells reinvigorated the neutrophil-clearance process that PGE2 undermines.

For their experiments, the Stanford Medicine researchers studied younger normal mice, aged 6–8 months, which corresponds to late adolescence or early adulthood in humans, and they also studied older normal mice, at 23 to 25 months of age, whose human counterparts would be in their 60s or 70s. They also looked at older mice whose EP2-encoding gene had been deleted at 4 to 6 months of age (equivalent to their “teenage” years).

The team’s analyses identified 71 proteins, found in blood, whose levels were significantly altered in older normal mice. Of those proteins, 59 stayed at youthful levels in older mice whose tissue-resident macrophages lacked EP2. Many of these proteins originated in the liver. “The liver is one of the body’s most tissue-resident-macrophage-enriched organs and a major contributor to aging-related changes in blood chemistry,” Andreasson said. “It’s the central organ determining the body’s metabolic rate.”

The study showed that in normal old mice, smoldering senescent neutrophils accumulated in the liver, spleen, and bone marrow and, to a lesser extent, in many other organs the researchers looked at.

But the organs of older mice lacking EP2 on their tissue-resident macrophages retained the lower neutrophil numbers of youth. These mice looked younger, leaner, and more physically fit compared with control littermates. They evidenced less visceral fat and greater muscle mass. Their performance on tests of multiple organs’ function equaled that of young mice.

EP2 deletion in addition reduced inflammation in the blood, liver, colon, heart, kidney, and hippocampus (a brain region tightly tied to memory and navigation ability) in the older mice. Their speed, balance, and forelimb grip strength resembled that of young animals.

Reducing EP2 activity in older mice also preserved their memory capabilities. These animals could thread their way through a maze or recall previously encountered objects almost as well as younger mice—and far better than similarly old mice with tissue-resident macrophages expressing functional EP2. “Reducing TRM EP2 signaling in aged mice preserved youthful mitochondrial fitness and prevented cognitive decline, frailty, sarcopenia, adiposity, cardiac impairment, and systemic inflammation,” they wrote in summary.

There are, today, no approved drugs that selectively shut down EP2 activity, although there are several that target PGE2. Non-steroidal anti-inflammatory painkillers work by blocking PGE2 production, Andreasson said. That’s how aspirin and similar drugs reduce pain, fever, swelling, and redness. But to greater or lesser degrees these drugs all block other vital prostaglandins. Even PGE2 has beneficial properties when it binds to receptors other than EP2, rather than the detrimental inflammatory one examined in this study.

As part of their study, the investigators treated otherwise normal 22-month-old mice for two months with an EP2-inhibiting experimental drug. The results showed that the treatment reduced total and senescent neutrophil counts in old mice toward youthful levels. In culture dishes, old age diminished—but the EP2-blocking drug likewise significantly restored—the mice’s tissue-resident macrophages’ ability to engulf and digest burnt-out neutrophils. “Together, these results demonstrate that pharmacologic EP2 inhibition partially reverses age-associated TRM dysfunction and senescent neutrophil accumulation, with strongest rescue in the liver,” they stated.

Finally, the team turned to a large human database characterizing different cell types in young, old, and diseased human livers. This database revealed the same age-related neutrophil buildup, increased neutrophil senescence, tissue-resident-macrophage decline, and heightened EP2 activity in older—and even more so, diseased—livers that the Stanford Medicine researchers had seen in mice. This was a first-time observation in human cells, according to Andreasson. “These human findings, while correlative, position the TRM EP2-efferocytosis axis as a candidate mechanism in human aging that warrants further functional testing,” they noted. “Specifically, future studies should assess whether the impaired clearance of senescent neutrophils also occurs in human TRMs and whether pharmacological EP2 blockade can restore this defect.”

Andreasson suggested that targeting neutrophil clearance may yield big therapeutic benefits. “We need to develop a safe drug that incapacitates EP2 without disrupting upstream events such as PGE2 production.”

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Development and validation of the digital well-being scale using university students

IntroductionDigital technologies are central to university students’ academic, social, and personal lives, offering opportunities for learning and connection while also introducing challenges such as distraction, emotional strain, and difficulties with self-regulation. Despite increasing interest in digital well-being, there is a lack of comprehensive, psychometrically sound instruments specifically designed to assess digital well-being among university students. This study developed and validated the Digital Well-Being Scale (DWS) for use in higher education.MethodsA cross-sectional survey was conducted among 1,533 undergraduate and postgraduate students from a public university in Ghana. Scale development involved literature review, expert evaluation, pilot testing, and psychometric validation. Exploratory factor analysis (EFA) was used to identify the underlying factor structure, followed by confirmatory factor analysis (CFA) to evaluate construct validity. Reliability, convergent validity, discriminant validity, and measurement invariance across gender were also assessed.ResultsEFA supported a six-factor solution comprising Task Interference, Digital Safety and Responsible Use, Perceived Control and Satisfaction, Digital Life Balance, Emotional Regulation, and Digital Dependence and Frustration. Following CFA-based refinement, a 41-item scale was retained. The six-factor first-order model demonstrated excellent fit to the data and outperformed a higher-order model. The DWS showed satisfactory internal consistency, convergent validity, and discriminant validity across all dimensions. Measurement invariance analyses supported configural and metric invariance across gender, although scalar invariance was only partially supported.DiscussionThe DWS provides a theoretically grounded and psychometrically robust multidimensional measure of digital well-being among university students. The findings suggest that digital well-being encompasses behavioural, emotional, cognitive, self-regulatory, and responsible dimensions of digital engagement rather than representing a single, unidimensional construct. The scale offers a valuable tool for research, screening, and intervention aimed at understanding and promoting digital well-being and digital mental health in higher education settings.