Anti-Inflammatory Drug Could Help Some People with Depression

Research led by the University of Bristol suggests treatment with an anti-inflammatory drug, already approved to treat arthritis, could help some people with depression.

The study, published in JAMA Psychiatry,  was small but showed gradual improvement in the severity of depression and physical symptoms like fatigue, as well as a lowering of anxiety in those treated with tocilizumab versus placebo.

“Low-grade systemic inflammation is a putative causal factor in depression, present in approximately 30% of patients,” write co-lead author Golam Khandakar, MD, PhD, a professor and researcher at the University of Bristol, and colleagues.

“Individuals with difficult-to-treat depression have higher cytokine, e.g., interleukin 6 (IL-6) and C-reactive protein (CRP) levels, than treatment-responsive patients and controls.”

Tocilizumab is a humanized monoclonal antibody that blocks the IL‑6 receptor and is used as an immunosuppressive drug in several inflammatory and cytokine‑driven conditions. For example, it is approved by the FDA to treat rheumatoid arthritis and cytokine release syndrome in patients with severe COVID-19.

To test whether IL-6 inhibition could help people with depression, the authors carried out a small randomized controlled trial as a proof-of-concept study. Overall, 14 participants were given a tocilizumab infusion, and 16 participants were randomly assigned to receive a saline placebo infusion.

Adults were eligible to be in the study if they had moderate‑to‑severe, difficult‑to‑treat depression despite antidepressants, showed persistent low‑grade inflammation on repeat CRP tests, and had prominent physical depressive symptoms like fatigue.

After receiving a single infusion, the participants were followed up for four weeks with evaluations at one week, two weeks and four weeks. All were taking anti-depressants when enrolled and continued them during the trial.

The trial was not large enough or long enough to show a statistically significant improvement, but it did show a consistent pattern of greater, clinically sized improvement with tocilizumab by the end of the follow-up period, especially in patients with higher baseline CRP.

No real differences were seen at one or two weeks, but at four weeks people given tocilizumab showed bigger improvements in overall depression scores, fatigue, energy levels, anxiety and quality of life than those in the placebo group. Around 54% of participants in the tocilizumab went into remission at four weeks versus 31% of the placebo group.

“This work represents an important milestone in the development of new treatments for depression especially difficult-to-treat depression, which affects millions of people in the U.K. alone,” said Khandakar in a press statement.

“This is one of the first randomized controlled trials to test immunotherapy for depression, the first to test the IL-6 receptor as the treatment target, and the first to use a targeted approach to select patients most likely to benefit, and to show that it works.”

The researchers now want to carry out a larger randomized trial to assess if the effects they saw are significant in a bigger treatment population.

The post Anti-Inflammatory Drug Could Help Some People with Depression appeared first on Inside Precision Medicine.

<![CDATA[In this CME article, explore a 4-stage framework that detects suicide risk without ideation disclosure, guiding clinicians to spot crisis signs and act fast.]]>
<![CDATA[Synendos launches a phase 2 trial of SYT-510, a first-in-class endocannabinoid modulator, aiming to ease generalized anxiety symptoms with improved safety and adherence.]]>

Carolina Aguilar: Brain-Computer Interfaces that Heal Neural Circuits

Jonathan D. Grinstein, PhD, North American Editor of Inside Precision Medicine, hosts a new series called Behind the Breakthroughs that features the people shaping the future of medicine. With each episode, Jonathan gives listeners access to his guests’ motivational tales and visions for this emerging, game-changing field.

Brain-computer interfaces have captured global attention in recent years, but most public discussion has focused on assistive technologies—systems designed to help patients control computers, prosthetics, or digital devices through thought alone. Carolina Aguilar, co-founder and CEO of INBRAIN Neuroelectronics, believes the larger opportunity lies elsewhere: using advanced neural interfaces not just to decode the brain but to treat disease directly. Built around graphene-based technology, INBRAIN is developing implantable systems capable of reading and writing neural signals with far greater precision than traditional metal-based devices.

Before founding INBRAIN, Aguilar spent 13 years at Medtronic, including a decade leading the company’s global neuromodulation business. During that time, she saw both the extraordinary potential of brain stimulation therapies and the limitations of incremental innovation inside large medical technology companies. Her background in neuroscience research, including early work studying the relationship between pesticides and Parkinson’s disease, shaped a long-term interest in circuit modulation and neurotherapeutics. That experience eventually converged with the emergence of graphene as a promising material for next-generation neural interfaces.

In this conversation, Aguilar discusses why INBRAIN chose to focus on therapeutic BCI applications rather than assistive computing, how graphene may enable higher-resolution neural decoding and stimulation, and why Parkinson’s disease became the company’s first major target. She also outlines INBRAIN’s broader vision for personalized neurotechnology, AI-driven therapies, and future applications ranging from epilepsy and memory restoration to bioelectronic treatments for cardiometabolic disease.

This interview has been edited for length and clarity.

 

IPM: You spent more than a decade at Medtronic before founding INBRAIN. What made you realize that incremental innovation was no longer enough, and why did graphene feel like the right breakthrough technology at the right time?

Aguilar: INBRAIN is a graphene-based brain-computer interface (BCI) company developing the most intelligent and adaptive interface between the neural system and AI to solve health for billions.

I started my career in consumer goods, but then I spent 13 years at Medtronic, where, for ten of those 13 years, I was leading the brain stimulation, or neuromodulation, business globally. I was always extremely impressed by what the company vision was and the number of disease areas and patients that we could help. However, I saw that every year we were innovating, but it was a bit incremental versus breakthrough. It’s normal for big companies to prioritize preserving shareholder value and revenue opportunities.

Investing in breakthroughs is harder; they usually just acquire those breakthroughs and then integrate them. And, of course, they revitalized their innovation. So I thought at that point that I could be one of those breakthrough innovators that could take a little bit more risk but then bring much bigger value into the field.

When the right opportunity presented itself to us, after the European Union put €1 billion into bringing graphene to market and after having gone through different speeds as pieces, we realized that it was the time to build INBRAIN and make that breakthrough a step instead of the incremental innovation of the past.

It’s interesting because I actually studied neuroscience at Virginia Tech, and at Virginia Tech, I had to define my thesis to study for my master’s degree. I picked up a combination of pesticides in the study. The study focused on the effects of pesticides and their combinations on brain chemistry in Parkinson’s disease. Right. So it was already quite oriented toward what we are solving today. And I got a grant award for the best vision on circuit modulation. So it was not about targeting the brain. It was about the circuit that was actually managing some of these dynamics of Parkinson’s disease.

At that early stage, I was already very attracted to the problem we are solving today, even though I did not know I would have a company dedicated to that field and to this innovation. However, it was always driving me in that direction. Medtronic was the company that turned that ambition into a real product and broad platform, which I also helped launch globally. So I guess it was always there. It was the seed that had been growing over time.

 

IPM: Medtronic exposed you to commercial, upstream product development, and engineering challenges. What did you learn that prepared you to build INBRAIN, and how did graphene change your engineering perspective?

Aguilar: It also evolves over time. So we start with a problem, such as pesticides in the development of Parkinson’s. Then I think I stepped into Medtronic as a solution to the problem that existed at that time. I was like, great. Now we have a problem. We have a solution. Let’s fix Parkinson’s disease.

At Medtronic, I was exposed to new experiences even while working in a commercial role, as I became a global director. I was exposed to the upstream. At Medtronic, there is a downstream process from which all employees receive their compensation. Then there’s the upstream piece, which is the product development and the planning of the next platforms. I was not in charge of that, but I was exposed to the process, the thinking, what is coming next, how we can make it happen, and where the constraints are. And that intrigued me a lot.

I discovered that I had a little bit of an engineering part inside of me that I never exploited. When the right opportunity came, I received a call from an investor friend of mine, who sometimes asked me to do some small due diligence. And he said, “Hey, there are some guys who have some graphene technology here that you might understand better than me. Do you want to come to the pitch?” I said, “Perfect.”

Then I saw that some of these constraints that we were dealing with in Medtronic in terms of miniaturization, higher-resolution interfaces, charge injection limits, and many of these engineering problems, partially, we could also solve by going graphene and going a different kind of electronics and a different kind of platform. When these consolidated and we created INBRAIN, I had to put a huge amount of effort into understanding semiconductors, microelectronics, mechanical configurations, and data architectures. So it’s been a huge learning journey for me.

 

IPM: INBRAIN appears to be building a platform strategy across multiple applications and devices. What are the product verticals and why does graphene offer capabilities that traditional neural interfaces cannot?

Aguilar: So it’s effectively a platform with three product verticals. When Morgan Stanley released the BCI industry report, it stated that the market was a $400 billion opportunity. We already knew that with one device, you could not capture all that immense market. So we were already working on a stepwise approach to the biggest opportunity. And this is what we have today on the table in the sense that we have a first product. The first product I can show you is a cortical interface. Starting with about 100 contacts, we can grow to a thousand. But in this case, it was not necessary.

It’s made of graphene. So it’s not about the number of contacts but the quality of what these contacts can decode. What is the precision of anatomy and the resolution of disease-related biomarkers that we can decode at high resolution in a way that that resolution is superior to a standard medical technology?

In some cases, standard platinum and iridium are used; in some cases, iridium oxide. We decided to create this first product for use for less than 30 days once it’s approved. It’s not yet approved, but we are getting closer, and it is the one that we actually took first-in-human. The results of those three reports are being generated using this device to demonstrate both the safety of graphene and its decoding superiority compared to metal technology. And, of course, within that first-in-human study, we also decoded speech at the phoneme level.

It was about creating the first step toward making graphene and consolidating advanced materials in clinical practice. So from there, the second product is actually an implantable platform that uses some of that configuration. So we use a cortical and subcortical interface to actually decode a circuit.

In our case, the microcircuit that is involved in Parkinson’s disease. And that is the second one. And the third product is actually the same platform, where instead of these two cortical and subcortical interfaces, we put in the central nervous system. We are truly connected to a vagus nerve interface that decodes the fibers within the vagus nerve, which go into the different modulations of the different organs of the body.

We go from reading the brain to reading the body. This sequence of product verticals opens up the immense possibility of a $400 billion opportunity. But even more important is the number of patients we could actually touch and improve therapeutically with such a platform.

 

IPM: Many BCI companies focus on assistive applications like computer and prosthetic control. INBRAIN appears to be therapeutic. Why did you choose direct disease treatment, and what makes it harder?

Aguilar: People say “invasive” or “noninvasive.” I call them implantable and non-implantable. And within the implantable, there might be different levels of invasiveness, but we were looking at the problem to solve, and especially coming from the field we come from and having experience for ten years, the use cases we saw are assistive BCI, meaning I transfer thought to action in the computer as solving a very important problem, but actually in a small population because, at the end, paraplegia and ALS. They are very important disease areas, but compared to other areas, there are smaller populations.

There were many people and companies, great companies, already aiming to solve that. So Neuralink and Synchron and Paradromics were always positioned there. And we thought, well, we have a similar approach, but maybe with a material that is much better suited to actually read and write bidirectionally, therapeutically. In assistive BCI, you do a ton of decoding. So, you read it and then transfer it to the computer. On the therapeutic side, where we are doing it, you have to read, write, and do that computing already within the implanted system.

It’s a much more complex architecture and a harder problem to solve. Now the benefit is solving the disease for as long as the system is active. And Parkinson’s was only the beginning. We are currently examining the validation of this program. We’re looking at memory restoration. We are looking at a set of disease areas on the cardiometabolic side that I cannot disclose without an NDA that we are developing with Merck KGaA. Those deals were unexplored or suboptimally explored by some of the low-resolution companies in the field.

The post Carolina Aguilar: Brain-Computer Interfaces that Heal Neural Circuits appeared first on Inside Precision Medicine.

Systemic inflammation response index as an independent predictor of unfavorable prognosis and its application in risk stratification in patients with aneurysmal subarachnoid hemorrhage

BackgroundAneurysmal subarachnoid hemorrhage (aSAH) is a devastating cerebrovascular disease associated with high rates of mortality and long-term disability. Early risk stratification is essential to guide personalized management. Systemic inflammation plays a key role in secondary brain injury after aSAH. The systemic inflammation response index (SIRI), a novel inflammatory marker combining neutrophil, monocyte, and lymphocyte counts, has shown prognostic value in multiple disorders, but its long-term prognostic role in aSAH remains unclear.ObjectivesThis study aimed to investigate the association between admission SIRI and 12-month unfavorable functional outcomes (modified Rankin Scale [mRS] ≥ 3) in patients with aSAH, verify its independent prognostic value, and construct a clinically useful prediction nomogram.MethodsA retrospective cohort study was performed including 258 patients with aSAH admitted between January 2021 and December 2024. Patients were divided into a favorable prognosis group (mRS 0–2, n = 158) and an unfavorable prognosis group (mRS ≥ 3, n = 100). Baseline characteristics, imaging indices including modified Fisher scale, laboratory parameters, and treatment data were collected. Multivariate logistic regression with forced entry was used to identify independent prognostic factors. Restricted cubic spline (RCS) analysis was applied to explore the non-linear relationship between SIRI and prognosis. A prediction nomogram was constructed and validated using temporal validation (training cohort n = 170; validation cohort n = 88). Model performance was evaluated using discrimination, calibration, and decision curve analysis.ResultsSIRI was significantly higher in the unfavorable prognosis group (p < 0.001). Multivariate analysis confirmed that SIRI (OR = 1.20, 95% CI: 1.08–1.34, p = 0.001), age, hypertension, GCS score ≤ 8, modified Fisher scale, and treatment modality were independent prognostic factors. RCS analysis demonstrated a non-linear relationship (P for nonlinearity = 0.020), with a clear threshold at SIRI = 4.36; the risk of unfavorable outcomes rose steeply above this cutoff. The nomogram showed excellent discrimination (AUC = 0.881 in training; 0.919 in validation) and satisfactory calibration. Decision curve analysis confirmed favorable clinical utility.ConclusionAdmission SIRI is an independent predictor of 12-month unfavorable functional outcomes in patients with aSAH. A threshold value of 4.36 can effectively identify high-risk patients. The SIRI-integrated nomogram provides accurate and individualized prognosis prediction across both training and temporal validation cohorts. This validated tool provides robust evidence to support clinical risk stratification and personalized decision-making.

Infant traumatic brain injury with a biphasic clinical course and late diffusion restriction: a case report

Traumatic brain injury (TBI) in young children can rarely exhibit a biphasic clinical course with delayed neurological deterioration. We report a 2-year-old boy who fell from 50 cm and briefly lost consciousness with vomiting, initially found to have a right frontotemporoparietal acute subdural hematoma (SDH) with midline shift but no brain contusions. After transient stabilization, he developed new left-sided limb weakness and status epilepticus on day 3 post-injury. Follow-up diffusion-weighted magnetic resonance imaging (DWI) revealed a characteristic “bright tree” pattern of bilateral subcortical white matter diffusion restriction with corresponding decreased apparent diffusion coefficient (ADC) values. Electroencephalography showed generalized slowing with interictal focal epileptiform discharges. The patient was managed with antiepileptic therapy and supportive care. He demonstrated steady improvement and achieved near-complete neurological recovery by 9-month follow-up. This biphasic presentation—early trauma and late-onset seizures with diffusion restriction—is consistent with Traumatic brain injury with a biphasic clinical course and late reduced diffusion (TBIRD). Early recognition of TBIRD is crucial, as it resembles acute encephalopathy with biphasic seizures and late diffusion changes, and likely stems from secondary excitotoxic injury. Timely intervention in our case was associated with a favorable outcome, underscoring the importance of vigilant monitoring for delayed neurologic sequelae in pediatric TBI.

Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers

Background and hypothesisAssessing schizophrenia risk factors is crucial for developing early preventive interventions. We hypothesized that unaffected siblings, who share high genetic risk, exhibit neuroanatomical signatures similar to affected patients, potentially reflecting early pathogenic processes.Study designTo overcome single-center limitations, we analyzed 1,018 participants from five independent, public databases. Brain MRIs were standardized via voxel-based morphometry, and covariate-adjusted z-scores were calculated for regional volumes. An ensemble support vector machine (SVM) approach, incorporating multiple models to ensure robustness, was employed to extract a multidimensional brain signature, from which a schizophrenia-like score (SPS) was derived.ResultsThe ensemble SVM achieved high classification performance (AUC = 0.99861). Across all databases, patients exhibited consistent volume reductions in frontal, temporal, insular, and thalamic regions, alongside globus pallidus enlargement. Notably, unaffected siblings were 3.8 times more likely to show brain morphological similarities to patients than were healthy controls. Furthermore, we identified a novel imaging phenotype in siblings: increased ventral striatal volume, which positively correlated with the SPS. This feature, absent in established schizophrenia, suggests a potential compensatory mechanism or a transient developmental marker of risk.ConclusionApplying machine learning to large-scale, multi-site neuroimaging data effectively identifies structural endophenotypes. Our findings highlight unique structural characteristics, specifically the enlarged ventral striatum, as a critical biological metric for identifying high-risk individuals before clinical onset.

Three factor delay learning rules for spiking neural networks

Spiking neural networks (SNNs) are hybrid dynamical systems that operate on spatiotemporal data, yet their learnable parameters are often limited to synaptic weights, contributing little to temporal pattern recognition. Learnable parameters that delay spike times can improve classification performance in temporal tasks, but existing methods rely on large networks and offline learning, making them unsuitable for real-time operation in resource-constrained environments. In this paper, we introduce synaptic and axonal delays to leaky integrate and fire (LIF)-based feedforward and recurrent SNNs, and propose three-factor learning rules to simultaneously learn weights and delays online. We employ a smooth Gaussian surrogate to approximate spike derivatives exclusively for the eligibility trace calculation, and together with a top-down error signal determine parameter updates. Our experiments show that incorporating delays improves accuracy by up to 18% over a weights-only baseline, and for networks with similar parameter counts, jointly learning weights and delays yields up to 14% higher accuracy. On the SHD speech recognition dataset, our method achieves similar accuracy to offline backpropagation-based approaches. Compared to state-of-the-art methods, it reduces model size by 6.6× and inference latency by 50%, with only a 2.5% drop in classification accuracy. Our findings would be beneficial for the design of power and area-constrained neuromorphic processors by enabling on-device learning and lowering memory requirements.

CDKL5 deficiency results in atypical subregion-specific expression of perineuronal nets during mouse visual critical period

Perineuronal nets (PNNs) in the primary visual cortex (V1) are specialized extracellular matrix structures that form predominantly on parvalbumin+ GABAergic neurons, marking the closure of visual critical period plasticity. More recently, PNNs are used to characterize deficits in critical period plasticity in mouse models for neurodevelopmental disorders such as Rett syndrome, Fragile X syndrome, and CDKL5 deficiency disorder. Within the mouse V1, studies typically focus on the expression and function of PNNs within the binocular zone, though PNNs are expressed in other subregions of the V1. The expression and role of these PNNs in other subregions are unknown. Here, we performed a systematic whole V1 characterization of PNN expression using Wisteria floribunda agglutinin (WFA) staining, with hemisphere-, subregion-, and anatomical axes- specificity, using a null male mouse model for CDKL5 deficiency disorder during the visual critical period. Patients with CDKL5 deficiency disorder often exhibit cerebral cortical visual impairment, though the underlying mechanisms are unclear. Compared to wild-type controls, Cdkl5-null males show regional-specific changes in WFA expression; specifically, decreased all-PNNs in V1M and increased high-intensity PNNs in V1B at P30, and increased WFA pixel intensities in all three V1 subregions at P15, suggesting precocious altered PNN expression in the Cdkl5-null V1. In both genotypes, the binocular zone has significantly higher density of PNNs at both ages, compared to the monocular zone and the rostral V1. These results lay the groundwork to probe the roles for PNNs beyond the binocular zone and cumulatively suggest that, during visual critical period, subregion-specific variations in PNN expression may lead to functional consequences within the Cdkl5-null cortex.

Perceived stress and mental health in perimenopausal women: a serial mediation study of psychological distress and social support

BackgroundThe perimenopausal phase is associated with a significantly higher prevalence of mental health disorders in women, with stress perception emerging as a pivotal risk factor. However, the psychological and social mechanisms through which stress perception influences women’s mental health during this period remain to be fully elucidated. This study aims to use a stress process model to examine how social support mediates the link between stress perception and psychological symptom severity during perimenopause.MethodsA cross-sectional survey design was used, and 549 Chinese perimenopausal women were surveyed through face-to-face questionnaires. The survey employed the Chinese Perceived Stress Scale, Kessler Psychological Distress Scale, Perceived Social Support Scale, and Psychological symptom severity (BSRS-5) to evaluate participants’ psychological symptom severity. The researchers used SPSS 26.0 for related analyses, PROCESS macro software for regression analyses, and applied the Bootstrap method to assess mediating effects.ResultsThe findings of the study indicate that perceived stress, psychological distress, and psychological symptom severity (BSRS-5) are significantly and positively correlated, and perceived social support is significantly and negatively correlated with these variables (P < 0.01). The study reveals that perceived stress significantly increases psychological symptom severity scores(BSRS-5) (effect size=0.493, 59.60%) after adjusting for confounding variables. Additionally, psychological distress and perceived social support independently mediate this relationship (effect sizes=0.204, 24.67% and 0.101, 12.21%, respectively). Additionally, perceived stress indirectly affects psychological symptom severity(BSRS-5) through the chain-mediated mediating pathway of “psychological distress → perceived social support” (effect size = 0.030, percentage = 3.62%).ConclusionStress can directly increase psychological symptom severity in perimenopausal women and indirect effects can be observed through mediating factors such as psychological distress, perceived social support, and the chain-mediated relationship between these two elements. Thus, reducing symptom severity is essential for improving mental health. The study indicates that enhancing the mental health of this group requires a multifaceted approach. This approach should focus on the alleviation of psychological distress and the promotion of social support systems. This will effectively disrupt the cycle of stress and psychological distress.