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The Download: China’s brain implant ambitions

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.

China has approved the world’s first invasive brain-computer chip—here’s what’s next

Sitting in the courtyard of his house in China’s Henan province last October, Dong Hui decided to try holding a pen. Six years after a car accident left him paralyzed from the neck down, he slowly wrote his name, “Thank you,” and the date.

The breakthrough was made possible by a brain implant called NEO. In March, it became the world’s first invasive brain-computer interface approved for use beyond clinical trials. The approval is expected to accelerate China’s push to become a global leader in brain implants.

Read the full story on how China reached this milestone—and what it means for the future of brain-computer interfaces.

—You Xiaoying

The must-reads

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

1 Nvidia is launching its first AI chip for personal computers
The RTX Spark will power laptops from Dell, HP, Microsoft, and others. (BBC)
+ They’re being designed specifically to run AI agents. (WSJ $)
+ The first devices are set to launch on Windows PCs in the fall. (CNBC)
+ The move marks a challenge to Apple and Intel. (FT $)

2 The US is stopping exports of AI chips to Chinese firms abroad
It’s closed a loophole allowing exports to Chinese subsidiaries. (Reuters $)
+ Which may have enabled unlicensed access to Nvidia chips. (Al Jazeera)
+ Export curbs have led China to redesign its chip industry. (MIT Technology Review)

3 Surgeons have transplanted pig liver and kidneys into a living person
The clinically dead recipient’s organs worked for almost five days. (Nature)
+ Pig organs could ease transplant shortages. (Guardian)
+ Putin says organ transplants could grant immortality. (MIT Technology Review) 

4 The US, Australia, and UK will defend seabed cables with underwater drones
They’re developing the vehicles via the trilateral AUKUS defense ⁠pact. (CNN)
+ Undersea internet cables face growing threats. (BBC)

5 A new study has revealed chatbots’ manipulative ‘dark patterns’ 
It found they prey on emotions to encourage harmful behavior. (404 Media)
+ They can also sway voters better than political ads. (MIT Technology Review)

6 Apple plans to disrupt the traditional glasses market
Its smart glasses target the broader spectacles industry. (Bloomberg $)
+ Smart glasses are also gaining traction in warfare. (MIT Technology Review)

7 AI super PACs are dueling over the midterms
Split between Anthropic and OpenAI, they’re fighting to shape AI regulation. (NYT $)

8 SoftBank has overtaken Toyota as Japan’s most valuable company
The AI boom pushed SoftBank’s market value above $305 billion. (Bloomberg $) 

9 A botnet of more than 17 million devices has been dismantled in Europe
Dutch authorities linked the network to a Russian proxy service. (Ars Technica)

10 Tech leaders are uniting around a transhuman vision for AI
They’re working toward a post-human agenda. (Guardian)

Quote of the day

“It’s just been shoved down their throats in secrecy. And that makes them upset.” 

—Legendary environmental activist Erin Brockovich tells “The Jim Acosta Show” why citizens are angry about data centers expanding into their communities.

 One More Thing

Dr. Nicholas Passalacqua, Forensic Anthropology Facilities Director at Western Carolina University observes a body at the decomp facility.

MIKE BELLEME


What happens when you donate your body to science

Rebecca George doesn’t mind the vultures. At Western Carolina University’s body farm, forensic anthropologists monitor donors—sometimes for years—as they become nothing but bones.

Around 20,000 people donate their cadavers to scientific research and education each year. At anatomy labs and body farms, they help train doctors, advance research, and teach scientists more about the human body long after death.

But what actually happens after a body is donated? Read the full story to find out.

—A.W. Ohlheiser

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.)

+ This map of moments turns the planet into a shared diary.
+ Let editors curate your ideal podcast moments with this app.
+ Architecture lovers will enjoy this encyclopedia of famous buildings.
+ Get in touch with your emotions through this map exploring more than 100 feelings.

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Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI

IntroductionThree-dimensional assessment of fetal cortical morphology from MRI is essential for understanding early brain neurodevelopment. However, measurement can be affected by fetal imaging quality, number and selection of available stacks, and reconstruction methods.MethodsWe evaluated the within-session reliability of an automated cortical morphometry pipeline in 30 typically developing fetuses [22–36 weeks gestational age (GA)]. For each subject, two disjoint subsets of 2D T2-weighted stacks (no shared stacks) were independently reconstructed into 3D volumes using the Neural Slice-to-Volume Reconstruction (NeSVoR) and the Slice-to-Volume Reconstruction Toolkit (SVRTK). Cortical plate volume, surface area, mean sulcal depth, and absolute mean curvature were extracted, and measurement reliability was assessed using absolute percent difference (APD) and intraclass correlation coefficients (ICC). Multiple linear regression evaluated the effects of mean stack quality, quality difference between subsets, stack count, and GA on measurement reliability.ResultsNeSVoR-derived metrics showed high reliability for all measures (mean APD < 3%, ICC > 0.99). SVRTK-derived metrics were also robust (mean APD < 5%, ICC > 0.97). Reliability increased with greater stack count and older GA in NeSVoR, and with higher mean stack quality in SVRTK.DiscussionThese results demonstrate that automated cortical morphometry from fetal MRI yields highly consistent measurements of volumetric and surface metrics within the proposed within-session design, once minimum levels of image quality and stack count are met. This study proposes a within-session benchmark for automated fetal cortical measurements and underscores that systematic reliability assessment is essential for confident application of automated pipelines in fetal neuroimaging.
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Domain-aware domain–class adaptation network for motor execution to motor imagery EEG classification

IntroductionMotor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have been increasingly adopted to further improve MI-EEG decoding performance, thereby facilitating the practical deployment of BCIs. In transfer learning, the similarity between the source and target domains is a critical factor influencing its effectiveness. Given the analogous cortical activation patterns observed in MI and motor execution (ME) tasks, cross-task transfer learning from ME to MI presents a promising yet underexplored direction.MethodsTo tackle the underexplored problem of cross-task transfer learning from ME to MI, we propose a domain-aware domain–class adaptation network (DDCA Net), which consists of a domain-shared feature extractor, two classifiers, and two domain-specific feature re-weighting blocks. Domain-level alignment is achieved by minimizing the maximum mean discrepancy between source and target feature distributions, while domain-specific feature re-weighting preserves discriminative characteristics unique to each task. In addition, a bi-classifier adversarial learning framework is employed to encourage consistency of decision boundaries across domains, thereby enabling implicit class-level alignment.ResultsExtensive experiments were conducted on a public dataset with over 100 subjects under varying proportions of target-domain training samples. When 80% of target-domain samples are used for training, the proposed DDCA Net significantly outperforms the within-task baseline, achieving a 7.71% improvement in classification accuracy and converting approximately 80% of previously BCI-illiterate subjects into BCI-literate users.DiscussionTo the best of our knowledge, this is the first work to verify the feasibility of applying domain adaptation for cross-task transfer learning in MI-EEG classification. The findings of this study provide new insights for integrating ME and MI in advanced BCIs.
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GLOBE: an explainable machine learning platform for preoperative prediction of thromboembolism and neurological deterioration in patients with glioma

BackgroundPatients with glioma are at high risk of postoperative venous thromboembolism (VTE) and postoperative neurological deterioration (PND). Conventional clinical scoring systems have limited accuracy in predicting these perioperative risks. This study aimed to develop and validate machine-learning models for individualized preoperative prediction of postoperative VTE and PND in patients with glioma.MethodsA retrospective cohort of 427 patients with glioma was included. Patients were randomly divided into training and test sets at an 8:2 ratio using stratified random sampling. Multiple machine-learning algorithms were trained and evaluated. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis. An online prediction platform was developed to facilitate individualized risk assessment.ResultsAmong 427 patients, postoperative VTE and PND occurred in 34 and 35%, respectively. For VTE prediction, the final Top-10 random forest model outperformed the Caprini score alone and achieved an AUC of 0.815 (95% CI, 0.720–0.910) in the held-out test set. Performance remained strong in the clinically significant VTE sensitivity analysis (AUC, 0.923; 95% CI, 0.847–0.998). SHAP analysis indicated that older age, elevated D-dimer and fibrin degradation products (FDP), as well as lower hemoglobin levels, were associated with increased predicted VTE risk. For PND prediction, the final Top-10 logistic regression model achieved an AUC of 0.741 (95% CI, 0.627–0.854). Older age, recurrent glioma, higher Caprini score, higher neutrophil percentage, and hypertension history tended to increase predicted PND risk. Models were deployed in the GLOBE web platform (https://gliomas.shinyapps.io/GLOBE/) for real-time preoperative risk prediction.ConclusionWe developed accurate, interpretable, and clinically meaningful preoperative prediction models for postoperative VTE and PND in patients with glioma. The GLOBE online prediction system translates these models into a practical tool for individualized perioperative risk stratification.
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Efficacy of repetitive transcranial magnetic stimulation for insomnia disorder: a systematic review and meta-analysis of randomized controlled trials

ObjectiveInsomnia Disorder (ID) is associated with significant health burdens. First-line treatments are limited by accessibility or side effects, necessitating alternative approaches. rTMS, a noninvasive neuromodulation technique, has shown promise in treating various neuropsychiatric disorders by modulating cortical excitability. This comprehensive meta-analysis explores the effect of rTMS on ID and identifies possible factors that influence it.MethodsA comprehensive search of the Cochrane Library, Embase, Web of Science, PubMed, CNKI, and Wanfang databases identified RCTs evaluating the effects of rTMS on insomnia disorder. Data synthesis and subgroup analysis were performed via SMD, WMD, relative risk (RR), and 95% CI to evaluate the effects of rTMS and its influencing factors. The review protocol was prospectively registered in PROSPERO (CRD42024626833).ResultsNineteen studies contributed 23 trials involving 1,690 adult participants. The rTMS group demonstrated markedly improved sleep quality compared with sham rTMS recipients in individuals with insomnia disorder. (PSQI total scores; ISI; p < 0.001); (PSG (SE); p = 0.003). Combined rTMS and medication were more effective than medication alone. (PSQI total scores; p = 0.003). In the subgroup analysis, after excluding a study with high heterogeneity, the rTMS cohort showed greater improvement in sleep quality than the other treatment groups. (PSQI total scores; p = 0.03).ConclusionIndependent rTMS and rTMS-medication combinations significantly improve sleep patterns and rest quality in patients with Insomnia Disorder. The safety and efficacy of LF-rTMS are also significant. The duration of the disease, treatment duration, and stimulation site may influence the sleep quality of patients with ID.
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The effects of unilateral deprivation amblyopia on fixation stability

IntroductionDeprivation amblyopia is a neurodevelopmental disorder caused by obstruction of the visual pathway due to congenital cataracts, ptosis or corneal opacities that occur during early visual development. Visual deficits persist into adulthood even though the obstruction (e.g. cataracts) have been removed early in life. The effects of deprivation amblyopia on oculomotor control have not been studied. The present study evaluates the effects of unilateral deprivation amblyopia resulting from congenital cataracts on fixation stability.MethodSeven adults with unilateral deprivation amblyopia and 18 adults with normal vision were tested during binocular and monocular viewing. A video-based eye tracker was used to record eye position of the viewing eye(s) (closed-loop condition with visual feedback) and the covered eye (open-loop condition with no visual feedback).ResultsFindings for the control group were consistent with previous studies. Fixation stability (eye position stability), evaluated using bivariate contour ellipse area (BCEA), microsaccade rate, amplitude and slow drift velocity, was best during binocular viewing, and significantly worse during open-loop monocular viewing. In comparison to the control group, patients had similar fellow eye fixation stability under binocular viewing, but fixation (eye position stability) was poorer under monocular closed-loop and open-loop viewing. Fixation stability was worst in the amblyopic eye in all viewing conditions.DiscussionOur findings demonstrate fixation stability deficits in adults with unilateral deprivation amblyopia, underscoring the lasting impact of early visual deprivation on oculomotor function.
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Exercise interventions for depressive symptoms in adults with lung and digestive cancer: a meta-analysis of randomized controlled trials

BackgroundDepressive symptoms are common among patients with cancer and can substantially impair quality of life, treatment adherence and overall well-being. Although exercise has been increasingly recognised as a promising non-pharmacological strategy for alleviating depression in oncology settings, existing evidence has focused predominantly on breast cancer, with limited attention to lung and digestive cancers. This meta-analysis aimed to evaluate the effects of exercise interventions on depressive symptoms in adults with lung and digestive cancer.MethodsA systematic search was conducted in PubMed, Web of Science, Embase, Cochrane Library and Scopus from database inception to March 2026. Randomized controlled trials investigating the effects of exercise interventions on depressive symptoms in adults with lung or digestive cancer were included. The primary outcome was depressive symptoms measured using validated instruments. Subgroup analyses were performed according to intervention format, exercise type and training frequency. Risk of bias was assessed using the Cochrane Risk of Bias tool version 1.ResultsEight randomized controlled trials were included in the meta-analysis. Baseline analysis showed no significant difference in depressive symptoms between the exercise and control groups before intervention. Post-intervention meta-analysis demonstrated that exercise significantly reduced depressive symptoms compared with control conditions (SMD = -0.45, P = 0.02), Although substantial heterogeneity was observed. Individually delivered programmes, walking-based exercise and moderate-frequency training (3–5 times per week) showed numerically larger effect estimates.ConclusionsExercise interventions may reduce depressive symptoms in adults with lung and digestive cancer and represent a promising adjunctive strategy for psychological care in these populations. Although subgroup differences were not statistically significant, certain intervention characteristics may be associated with greater benefit. Further large-scale, high-quality randomized trials are needed to confirm these findings and to establish the optimal exercise prescription for reducing depressive symptoms in adults with lung and digestive cancer.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD420261336578.
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Predictive value of antioxidant and thyroid function indicators for non-suicidal self-injury in adolescents with major depressive disorder

BackgroundNon-suicidal self-injury (NSSI) is highly prevalent in adolescents with major depressive disorder (MDD); however, its underlying pathophysiological mechanisms remain incompletely elucidated. Emerging evidence suggests a potential association between antioxidant markers, thyroid function parameters, and the occurrence of NSSI, although research in this domain remains limited. Accordingly, this study aimed to investigate the predictive efficacy of combining antioxidant and thyroid biomarkers with clinical symptoms for NSSI in adolescents with MDD.MethodsThis study recruited 162 adolescents with MDD between September 2022 and January 2026. Participants were stratified into groups based on the presence or absence of NSSI, in accordance with DSM-5 diagnostic criteria. Multidimensional scales were employed to assess the severity of depression, anxiety, perceived stress, and internet addiction (IA). Concurrently, blood samples were collected to measure bilirubin levels and thyroid function parameters. Stepwise logistic regression analysis was subsequently performed to identify independent risk factors associated with NSSI. Finally, receiver operating characteristic (ROC) curves were constructed to quantify the predictive performance of these identified independent factors.ResultsThe prevalence of NSSI in adolescents with MDD was 57.4%. Multivariate logistic regression analysis identified females (OR = 2.246, 95% CI = 1.032-4.888, P = 0.041), HAMD-17 score (OR = 1.183, 95% CI = 1.088-1.286, P < 0.001), indirect bilirubin (OR = 0.890, 95% CI = 0.797-0.995, P = 0.040), and TSH (OR = 2.060, 95% CI = 1.254-3.385, P = 0.004) as independent predictors of NSSI. Furthermore, ROC curve analysis further demonstrated that the four-item combination of sex, HAMD-17 score, indirect bilirubin, and TSH (AUC = 0.776, 95% CI = 0.701-0.850, P < 0.001) had a better ability to identify NSSI.ConclusionAdolescents with MDD, particularly females, represent a high-risk population for NSSI. Reduced levels of indirect bilirubin coupled with elevated TSH levels may constitute the underlying pathophysiological basis of NSSI and demonstrate significant clinical predictive value. In the future, targeted intervention strategies focusing on the antioxidant defense system and thyroid function may offer novel therapeutic avenues for the management of NSSI.
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Two years of COVID-19: persistently reduced well-being and increases in global psychopathology during the pandemic in a representative Austrian population-sample within the COH-FIT study

IntroductionThe COVID-19 pandemic worsened well-being and mental health worldwide, but effects have diminished over time. However, prospective national data within representative samples remain scarce. We aimed to examine the change in well-being and psychopathology from pre-pandemic to intra-pandemic times in an Austrian representative general population sample, to identify vulnerable subgroups, and explore most effective coping strategies to mitigate the impact of COVID-19.MethodsData were collected in Austria as part of the Collaborative Outcomes Study on Health and Functioning During Infection Times (COH-FIT) survey, an international, multilingual, anonymous online survey assessing mental health indicators during COVID-19. Adults ≥18 years old participated through nationally representative sampling across three waves from 05/2020-04/2022. Outcomes included the WHO well-being index (WHO-5) and a global psychopathology score (‘P-score’), alongside 12 predefined risk factors and 16 coping strategies.ResultsAcross 4,148 adults, the mean WHO-5 well-being score decreased by 7.5 ± 17.7 points from the pre-pandemic baseline (73.2 ± 19.7) to the intra-pandemic average (65.7 ± 24.1) (p<.001). Participants with female sex, pre-existing mental or physical health conditions, and unemployment experienced greater declines. The proportion of individuals scoring <50, indicating depression, increased from 12.6% pre-pandemic baseline to 25.1% intra-pandemic, and the proportion scoring <29, indicating major depression, increased from 3.3% to 9.7% (both p<.001). The ‘P-score’ increased by 9.6 ± 15.0 points from 24.1 ± 19.5 pre-pandemic baseline to 33.7 ± 22.4 intra-pandemic (p<.001) with the same risk groups (except female sex). Although the greatest deterioration in both outcomes occurred during the mid-pandemic period (04/2021), neither well-being nor ‘P-score’ levels returned to pre-pandemic baseline values by 04/2022, nor to values from 05/2020 (Wave 1). Greater deterioration in WHO-5 and the P-score were associated with female sex, unemployment, pre-existing mental or physical disorders, and COVID-19 infection. The most commonly reported helpful coping strategies included internet use, physical activity, media consumption, social media and remote interaction, and meaningful hobbies.DiscussionCOVID-19 had a persistent negative impact on well-being and mental health in Austria. Vulnerable subgroups – including those with prior health conditions and unemployment – were particularly affected. The findings underscore the importance of implementing public health measures together with targeted interventions, preventive measures, and long-term psychosocial support, especially for risk populations.
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The efficacy of acupuncture for depression-associated chronic pain: a systematic review and meta-analysis

ObjectiveThe comorbidity of pain and depression is prevalent, adding difficulty to the treatment of depression. This systematic review with meta-analysis aims to determine the efficacy and safety of acupuncture in treating depression-associated chronic pain (DACP).MethodsA comprehensive search was conducted across four international databases, namely PubMed, Embase, Web of Science, and the Cochrane Library, along with four regional databases, including Wanfang Data, CNKI, VIP database, and SinoMed, from inception to March 2025. The Cochrane Risk of Bias 2 tool was utilized to assess risk of bias in the included research articles, and the Grading of Recommendations Assessment, Development, and Evaluations system was employed to evaluate the certainty of evidence. Meta-regression analysis was performed to explore the influence of patient age and treatment duration on the study results, and sensitivity analysis was used to verify the stability of the results. The publication bias was evaluated when the number of included studies exceeded ten. All data analyses were completed using Stata15.1.ResultsTen randomized controlled trials involving 761 participants were included. Acupuncture combined with conventional medications was more effective than medication alone in improving depressive symptoms (standardized mean difference (SMD): -0.72; 95% confidence interval (CI): -0.91 to -0.53; P < 0.01) and reducing pain (SMD: -0.85; 95% CI: -1.36 to -0.34; P < 0.01). Head-to-head comparisons revealed that acupuncture is similar to medication in improving the Hamilton Depression Rating Scale scores (SMD: -0.05; 95% CI: -0.61 to 0.51; P > 0.05) and the Visual Analogue Scale scores (SMD: -0.33; 95% CI: -0.94 to 0.29; P > 0.05), suggesting no statistically significant difference between the two treatments. In contrast, acupuncture was associated with a better safety profile (relative risk: 0.40; 95% CI: 0.27 to 0.60). Further subgroup analysis found the advantage of a 4-week acupuncture treatment in improving depressive symptoms, while longer-term treatment tended to be more effective in relieving pain.ConclusionsAcupuncture appears to have comparable antidepressant and analgesic effects to conventional oral medications. When applied as an adjuvant therapy, acupuncture may enhance the clinical efficacy of monotherapy for DACP. Regarding treatment duration, a 4-week acupuncture intervention may be superior to a longer cycle (> 4 weeks) in alleviating depressive symptoms, while long-term acupuncture treatment may provide greater benefits in analgesia.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD420251026454.