Medtech OEMs face a rare but closing window of opportunity

This is a manufacturing decision you can’t defer in 2026. Mark Freitas, Alvarez & Marsal Private-equity-backed CDMO platforms are aging into exits. OEMs who know what they want will move first. The 2022-2024 structural reset is over and the financing gap is narrowing. The sector has emerged from a period of value depression and as…

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A Gamified Pain Management Intervention for Adults With Chronic Pain in Mainland China: Single-Arm Pre-Post Pilot Study With Machine Learning Predictive Modeling

Background: The widespread prevalence of chronic pain (CP) significantly impacts daily functioning worldwide. In mainland China, maintaining engagement in biopsychosocial interventions remains challenging. Gamification, designed based on self-determination theory, can enhance motivation, while machine learning (ML) algorithms can assist clinicians in dynamically optimizing pain management. Objective: This study aimed to (1) evaluate the preliminary effectiveness of a gamified pain management (GPM) program on CP and psychological outcomes and (2) identify key factors of significant pain improvements through the application of ML to guide intervention adjustments. Methods: A single-arm, pre-post study was conducted with 16 participants with CP in mainland China, recruited via social media using convenience sampling. Participants engaged in a 10-week web-based GPM intervention consisting of education, physical activities, and gamified elements, including points, avatars, and feedback. Primary outcomes were pain intensity and interference measured by the Brief Pain Inventory. Secondary outcomes included anxiety, depression, and quality of life. Analysis included paired tests, and ML models were trained to predict clinically meaningful pain reductions. Shapley additive explanations, least absolute shrinkage and selection operator regression, association rule mining, and Kaplan-Meier survival analysis were used to identify key predictors and optimal sessions and intervention durations across subgroups. Results: A total of 16 participants were engaged, with a mean age of 27.63 (SD 9.584) years. Results from paired tests reported significant improvements in pain intensity (decreased by 27.3%, 95% CI 1.061 to 3.064; =.001), pain interference (decreased by 27.3%, 95% CI 8.159-17.216; <.001), and psychological distress, including anxiety (=3.538, 95% CI 0.969 to 3.906; =.003) and depression (=4.559, 95% CI 2.230 to 6.145; <.001). The gradient boosting model demonstrated the highest predictive accuracy (area under the curve=0.89 and accuracy=0.82). Least absolute shrinkage and selection operator regression identified session 3 (β=−0.45, 95% CI −0.68 to −0.22; <.001) and session 5 (β=−0.32, 95% CI −0.59 to −0.05; =.02) as most predictive of clinical success, while association rule mining revealed effective session combinations for different patient subgroups. Time-to-event analyses indicated that individuals with low back pain and higher baseline severity required longer intervention durations for improvement (5 wk; =.03). Conclusions: This pilot study presents an innovative method that combines ML with dynamic engagement data from a GPM program during interventions, rather than relying on static baseline data in prior studies. The results show preliminary efficacy and identify specific optimal session combinations and personalized treatment durations for different pain subgroups. These exploratory findings contribute to the field by providing a data-driven method for adaptive, personalized digital health interventions that move beyond one-size-fits-all strategies, potentially enabling clinicians to modify content and dosage to improve engagement and outcomes if validated in larger sample trials. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400094247; https://www.chictr.org.cn/showprojEN.html?proj=245138

Synaptic remodeling and the female depression exposome: a mini-review of neuroendocrine, epigenetic, and social determinants

Depression is a multifactorial, chronic disorder and represents a leading cause of disability, with women exhibiting nearly twice the lifetime prevalence compared to men. Growing evidence indicates that this disparity cannot be explained by hormonal or psychosocial factors, but rather by dynamic interactions between environmental exposures, neuroendocrine signaling, and epigenetic regulation across development. This mini-narrative review aimed to examine how sex-specific exposome components interact with epigenetic mechanisms and synaptic remodeling processes to influence vulnerability to Major Depressive Disorder in women. The reviewed evidence demonstrates that fluctuations in ovarian hormones modulate HPA axis responsivity, neuroinflammatory signaling, and glutamatergic transmission through epigenetic regulation of stress-responsive genes such as NR3C1, SLC6A4, and BDNF, consequently influencing synaptic remodeling within corticolimbic circuits. Environmental and social exposures, particularly early-life adversity and psychosocial stressors, further interact with microglial activation and chromatin remodeling to produce long-lasting alterations in hippocampal and prefrontal plasticity. Collectively, these findings support a model in which sex-dependent neuroendocrine sensitivity amplifies exposome-driven epigenetic programming across the lifespan. Future research directions emerging from this synthesis include longitudinal life-course studies integrating multi-omic biomarkers, quantitative exposome assessment, and neuroimaging approaches to identify modifiable environmental targets and advance precision, sex-informed preventive and therapeutic strategies in depression.

Stage-specific ERP correlates of audiovisual facial emotion processing across depressive tendencies

Emotional dysregulation can emerge as early as the initial stages of depression. This study aimed to examine event-related potential characteristics during the perception of negative, positive, and neutral facial expressions in healthy individuals across depressive tendencies. Twenty-six healthy participants underwent ERP measurements during emotion recognition using a facial emotion recognition task in visual and audiovisual modalities. The Emotion Regulation Questionnaire (ERQ), the Difficulties in Emotion Regulation Scale (DERS-16), and the Beck Depression Inventory II (BDI-II) assessed cognitive strategy, emotion regulation difficulties, and depression severity, respectively. Facial affect elicited larger amplitudes compared to neutral faces, from the N170 and early posterior negativity (EPN) in the temporo-occipital region to the late positive potential (LPP) in the centroparietal region. Under audiovisual conditions, P1 peak latency to negative stimuli in the temporal region exhibited significant negative correlations with DERS-16 and BDI-II scores. N170 peak latency to positive stimuli also demonstrated a significant negative correlation with BDI-II scores. Under visual conditions, EPN amplitude to negative stimuli in the occipital region exhibited a significant positive correlation with BDI-II scores. P1 and N170 latencies, or neural response speeds, and EPN amplitude, which represents emotional reaction strength, correlate with depressive tendencies in healthy individuals. These early components function as initial neural signals that may serve as electrophysiological markers of abnormal emotional processing within neuropsychological functions prior to clinical depression.

Esketamine ameliorates depression-like behavior in mice via modulation of the NRG1–ErbB4 pathway

BackgroundEsketamine has a significant and rapid antidepressant effect. Although studies have shown that Neuregulin 1 (NRG1) and it’s signaling pathway are associated with depression, the possible regulatory relationship of esketamine on the NRG1-ErbB4 pathway is not yet clear.MethodsTo induce depressive-like behavior in mice, a Chronic Social Defeat Stress (CSDS) model was established. Behavioral indicators were then employed to assess depression in these mice, categorized into control, susceptible, and resilient groups. Following intraperitoneal injection of a subanesthetic dose of esketamine, behavioral tests were conducted at 30 minutes and 24 hours post-injection to observe any improvements in depressive-like behavior. Additionally, changes in immunofluorescence and protein expression levels of NRG1-ErbB4 and GAD67 in the prefrontal cortex were evaluated.ResultsCompared with the control group, the CSDS susceptible group mice showed decreases in social interaction ratio in the contact area, sucrose preference ratio, NRG1 immunofluorescence protein expression in the prefrontal cortex and NRG1 expression in tissue homogenate; showed significant increases in immobility time; the expression of NRG1 decreased;no significant change in GAD67 and ErbB4 expression level. in After 30 minutes of intraperitoneal injection of esketamine, the expression of NRG1 in the prefrontal cortex of susceptible mice increased significantly. no significant change in GAD67 and ErbB4 expression level. After 30 minutes and 24 hours of intraperitoneal injection of esketamine, the social interaction ratio of susceptible group improved compared to the control group, and the duration of forced swimming immobility was significantly shortened.ConclusionThe subanesthetic dose of esketamine may regulate the NRG1-ErbB4 signaling pathway and improve depressive like behavior in mice.

Top 5 Firms Engineering Healthcare in the CNS Space

Central nervous system (CNS) treatments are having a major comeback. These five precision medicine players plan to ride the resurgence.

After a decade of stagnation, the CNS space is seeing a revival in sales and R&D spending as the market was last year projected to surpass $80 billion for the first time since 2013 and hit around $127 billion.

Recent landmark approvals have brought attention back to the CNS, including the U.S. Food and Drug Administration (FDA)’s greenlight of Eisai/Biogen’s lecanemab (Leqembi) for the treatment of Alzheimer’s disease in 2023, and the FDA approval of Bristol-Myers Squibb’s schizophrenia treatment xanomeline/trospium chloride (Cobenfy) in 2024.

At the same time, Johnson & Johnson’s depression treatment, esketamine (Spravato), is on its way to blockbuster status, showcasing the growth potential of the CNS market.

These successes accompany an emerging shift in psychiatry clinical trials from subjective rating scales to more objective endpoints, including digital and physiological measures, with the potential to better tailor treatments to a patient’s biological makeup.

Startups and scaleups are attracting increasing investor attention for their potential to change the way we treat CNS conditions. Check out our list of the most exciting companies that have netted the biggest investor dollars.

 

1. Aerska

Founded: 2025 | Headquarters: Dublin, Ireland

Aerska logo

Aerska’s name is derived from an Irish proverb stating that people survive in each other’s shelter, emphasising the strength of its team.

This team includes co-founder Jack O’Meara, previously co-founder of the liver-focused RNA interference (RNAi) biotech Ochre Bio, who is driven by the experience of loved ones suffering from Alzheimer’s disease.

Aerska is developing RNAi therapies for neurodegenerative conditions, including Parkinson’s and Alzheimer’s disease.

While there are already FDA-approved RNAi therapies on the market, such as Alnylam’s patisiran (Onpattro), these are typically focused on liver and cardiometabolic conditions rather than the CNS.

Aerska’s technology consists of antibody “brain shuttles” that bind to proteins on the blood-brain barrier (BBB). They then carry a payload RNA into the brain.

The payload, which is designed based on data-driven patient stratification and disease biomarkers, then silences specific genes driving the disease.

Aerska has already raised $60 million since its launch, including a $21 million seed round in October 2025 and a $39 million Series A round in February 2026, co-led by EQT Life Sciences and age1.

The company, which has research operations in the U.K., is using the latest funding to drive its pipeline programs toward clinical testing.

 

2. Beacon Biosignals

Founded: 2019 | Headquarters: Boston, Massachusetts, U.S.

Beacon Biosignals logo

Beacon Biosignals was co-founded by a team including its CEO—MIT neuroscientist Jacob Donoghue, MD, PhD—and its CTO, the machine learning researcher Jarrett Revels.

Boasting more than 100 employees, the company’s goal is to provide objective biomarkers in drug development that neurology and psychiatry have traditionally lacked compared with other areas of precision medicine.

Its FDA-cleared Waveband device measures the brain’s activity, known as electroencephalography (EEG), while patients sleep at home. The EEG data is then stored, quality-controlled, and fed into AI models that can guide the design of clinical trials.

For example, Beacon’s EEG data can identify patients with Alzheimer’s disease who have worse outcomes and might need a more targeted treatment or a different clinical trial than other patients.

Beacon raised $27 million in a Series A round in 2021 and an oversubscribed Series B round worth $86 million in November 2025.

The B round, which included investors such as Innoviva, Google Ventures, and Nexus NeuroTech, will help the startup to accelerate the discovery of neurobiomarkers and broaden clinical adoption of the technology.

Beacon acquired the French sleep monitoring company Dreem in 2023 to access its monitoring data and headband technology. Beacon then acquired the Ohio-based CleveMed in April 2025 to harness technology measuring breathing, oxygen, and other signals.

 

3. Brainomix

Founded: 2010 | Headquarters: Oxford, U.K.

Brainomix logo

Brainomix was founded by a team including CEO Michalis Papadakis, PhD, who was scientific director of the preclinical stroke lab at the University of Oxford.

Brainomix is dedicated to speeding up patient care in cases of stroke, where speedy treatment is key.

Brainomix’s flagship product, Brainomix 360 Stroke, is designed to harness AI to interpret brain scans and detect blood clots in patients with stroke, speeding up clinical decision-making.

The product involves a group of tools that automatically analyze images, including results from computed tomography (CT), CT angiography, magnetic resonance imaging (MRI), and CT perfusion.

Brainomix’s technology doubled the rate of thrombectomy treatment in patients with stroke and reduced hospital triage and transfer delays, according to a 2025 study.

The University of Oxford spinout is at a commercial stage, with operations in more than 20 countries, and is expanding into the U.S.

Brainomix raised a $21.2 million Series B round in 2021 and extended its Series C round from $6.5 million in March 2025 to $25.4 million in February 2026, with leading investors including Parkwalk Advisors and Hostplus. The proceeds will fuel the company’s expansion into the U.S. market.

Brainomix has also partnered with heavyweights, including Nvidia, Boehringer Ingelheim, Medtronic, and GE Healthcare.

Brainomix also has a product dedicated to disease monitoring in pulmonary fibrosis.

 

4. Circular Genomics

Founded: 2021 | Headquarters: San Diego, California, U.S.

Circular Genomics Logo

Circular Genomics was spun out of the University of New Mexico, with its founders including CSO Nikolaos Mellios, PhD, and Alexander Hafez, PhD.

The company later moved its headquarters from Albuquerque to San Diego in March 2025 to access scientific and operational know-how from Eli Lilly at Lilly Gateway Labs.

Circular Genomics aims to equip medical professionals with a blood test to detect CNS conditions early, in addition to stratifying and guiding the treatment of patients.

Its technology involves using a polymerase chain reaction (PCR) test of a patient’s blood sample to screen for specific circular RNA molecules produced in the brain that can cross into the blood and be measured as a biomarker of disease in the CNS.

Commercially launched in 2024, Circular Genomics’ MindLight SSRI Antidepressant Response Test predicts whether a patient will benefit from common antidepressants called SSRIs with around 77% accuracy. This is designed to predict a patient’s most suitable antidepressants without needing months of trial-and-error approaches.

The company is applying its technology in Alzheimer’s disease, where the approvals of disease-modifying therapies such as Leqembi have led to demand for tests that can detect the disease at earlier stages than traditional tests.

Circular Genomics raised $15 million in a Mountain Group Partners-led Series A round in December 2025 to finance the development of its technology and expansion of its technology in Alzheimer’s disease.

The company also has its sights on other CNS conditions, including multiple sclerosis and Parkinson’s disease.

 

5. Omniscient Neurotechnology

Founded: 2019 | Headquarters: Sydney, Australia

o8t logo

Omniscient (o8t)’s founders include CMO Michael Sughrue, MD, a neurosurgeon aiming to improve anatomy maps for other surgeons, and machine learning expert Stephane Doyen, PhD.

o8t’s FDA-approved product Quicktome involves using a patient’s MRI brain scans and AI models to map out a patient’s brain circuitry. These maps, accessible from an electronic tablet, can guide surgery to minimize the risk of brain damage compared to using a generalized anatomical diagram.

Quicktome is already in use at major hospitals around the world, including major centers in the U.S. Its partners include U.S. surgical support firm META Dynamic and the U.S. medical device innovation center, The Jacobs Institute.

o8t has raised more than $60 million, and bagged $14 million (AUD 20 million) in January 2026 as part of a Series D round targeted to reach $25 million (AUD 36 million). The round was led by Australia’s National Reconstruction Fund (NRFC) and OIF Ventures, with the aim of keeping the company based in Australia.

The funding is earmarked to fuel the development and commercialization of Quicktome, and grow o8t’s Australian workforce by more than 40. The company also has operations in Atlanta, Georgia, U.S.

o8t also plans to expand the technology into high-growth markets, including brain computer interface targeting, stroke and traumatic brain injury.

 

Jonathan Smith, PhD, is a freelance science journalist based in the U.K. and Spain. He previously worked in Berlin as a reporter and news editor at Labiotech, a website covering the biotech industry. Prior to this, he completed a PhD in behavioral neurobiology at the University of Leicester and freelanced for the U.K. organizations Research Media and Society of Experimental Biology. He has also written for medwireNews, Biopharma Reporter, and Outsourcing Pharma.

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