Tag: Academic research

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AI Model Offers Map of How Genes Work Together in Different Cellular Contexts

Scientists at the Icahn School of Medicine at Mount Sinai have created a new artificial intelligence (AI) model that helps reveal how genes function together inside human cells, offering a powerful new way to understand biology and disease. Their study, headed by Avi Ma’ayan, PhD, professor of pharmacological sciences and director of the Mount Sinai Center for Bioinformatics at the Icahn School of Medicine at Mount Sinai, introduces a gene set foundation model (GSFM) designed to learn patterns in how genes are grouped and function across thousands of biological contexts.

The work draws inspiration from advances in large language models (LLMs) such as ChatGPT, which learn how words gain meaning depending on their context. In a similar way, a GSFM learns how genes behave differently depending on their cellular “context.”

The model provides a new way to understand the structural and functional organization of genes and their products inside human cells. This improved understanding could eventually support the development of better diagnostics, biomarkers, and therapies. By mapping how genes relate to one another across many biological situations, the GSFM creates a reference framework that can help scientists interpret complex multiomics datasets more effectively, say the investigators. “The organization of genes within cells remains one of the major unsolved questions in biology,” Ma’ayan noted. “The GSFM helps address this by learning from millions of gene groupings derived from published research and gene expression datasets.”

Ma’ayan is senior corresponding author of the team’s published paper in Patterns, titled “GSFM: A gene set foundation model pre-trained on a massive collection of diverse gene sets.”

In their paper the authors explained, “Genes are a bit like words, and gene sets are a bit like sentences, because words are reused in different contexts to express unique meanings, and cells reuse genes to carry out different biological functions.”

“Genes rarely act alone,” Ma’ayan further noted. “Instead, they participate in multiple biological processes, forming different molecular groupings depending on where and when they are active in the cell. A single gene can play different roles in different settings, much like a word can have different meanings in different sentences. Just as modern language models learn the meaning of words from context, we asked whether AI could learn the ‘meaning’ of genes in the same way. Our GSFM was designed to do exactly that.”

To build the model, the researchers compiled millions of gene sets from published scientific studies and gene expression datasets. In total, the system learned from hundreds of thousands of independent research efforts.

The AI model was trained in a way similar to solving a puzzle: it was given part of a gene set and asked to predict the missing pieces. Over time, it learned underlying patterns that describe how genes are grouped and interact.

The AI model was then benchmarked against other approaches and demonstrated strong performance, including the ability to identify gene-gene and gene-function relationships before they were confirmed experimentally. To evaluate this, the model was trained using gene sets from publications up to a defined cutoff date, and then tested on whether it could predict discoveries reported in studies published after that cutoff date.

“Unlike previous biological AI models that primarily rely on gene expression data, our GSFM is uniquely trained on gene sets, a different and largely underused type of biological information,” Ma’ayan stated. “This approach allows the model to integrate diverse data from many diseases, experimental methods, and research conditions, creating a unified representation of gene relationships across biology.”

The team’s studies showed that the new model can help identify the function of poorly understood genes without immediate laboratory experiments, highlight genes involved in disease processes, and suggest potential new drug targets and biomarkers. The model offers a reusable knowledge system for many types of biomedical research data analysis tasks—for example, improved gene set enrichment analysis. In essence, the researchers suggested, GSFM offers a new “map” of how genes work together in different contexts. “Unlike prior methods that are mainly based on similarity of all genes to annotated genes, GSFM’s architecture can capture the more complex non-linear and multi-modal relationships between genes and the gene modules these genes constitute,” the investigators wrote. “GSFM’s ability to predict genes held out from known gene sets can be useful for many applications in computational systems biology.”

GSFMs could enhance existing bioinformatics tools and improve the interpretation of data collected with omics technologies. One immediate application is in gene set enrichment analysis, a widely used method in molecular biology research. By improving how scientists interpret gene groupings, the model may help uncover new biological insights from both existing and future datasets.

“Like the way LLMs predict the next word in a sentence, GSFM guesses the next missing gene when presented with a gene set,” the scientists stated. “With this power, GSFM can be used to reliably assign the most likely functions to understudied genes, and make gene set enrichment analysis more precise, ranking the most relevant enriched terms when presented with any query gene set.”

The research team plans to expand the system by combining GSFM with other AI foundation models. One goal is to integrate it with language-based models to generate natural-language explanations of gene functions. Another future direction is combining GSFM with drug-focused AI models, with the long-term aim of predicting how drugs interact with cells and supporting the design of new therapeutics.

“In summary, GSFM’s ability to distil knowledge from large amounts of unlabeled gene sets automatically, and to do so successfully across multiple sources of knowledge, can be translated into many ‘‘low-hanging fruit’’ hypotheses that could be tested in wet lab experiments to rapidly advance knowledge in biomedical research,” the investigators concluded.

The gene pages and the GSFM model are accessible at https://gsfm.maayanlab.cloud and https://github.com/MaayanLab/gsfm.

The post AI Model Offers Map of How Genes Work Together in Different Cellular Contexts appeared first on GEN – Genetic Engineering and Biotechnology News.

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Scaling creativity in the age of AI

Storytelling is core to humanity’s DNA, stemming from our impulse to express ideals, warnings, hopes, and experiences. Technology has always been woven through the medium and the distribution: from early humans’ innovation of natural pigments and charcoals for cave paintings to literal representation by the camera.

The landscape of storytelling continues to shift under our feet. Social and streaming platforms have multiplied, audiences have fragmented, and our demand for fresh, unique media is insatiable. A recent McKinsey podcast cites that we are watching upwards of 12 hours of video content daily, often on multiple devices and multiple platforms.

All this content is expensive to produce: With a baseline budget of $150M, a Hollywood feature runs $1M per minute of finished film; prestige streaming content is in the hundreds of thousands per minute. And since consumers want to engage with authentic, original material, every company is now effectively a media company. That means we all face the same pressure: more content, with the same time and budget constraints.

There is no longer a question whether to use AI for content; the math doesn’t work any other way. What leaders need to focus on now is how to adapt responsibly, protect brand integrity, uplift team creativity, and build customer trust.

A few things worth holding onto as this era accelerates:

  • AI amplifies what’s already there, both good and bad. Weak strategy stays weak.
  • Responsible adoption means knowing what’s in your tools and models. Provenance and transparency are the foundation, not the finish line.
  • Scale without taste is just noise. Investing in your team’s judgment is what makes more content matter.
  • Fundamentals of great storytelling have not changed. Regardless of format or channel, what makes audiences lean in are still characters, arc, ingenuity, and surprise.

The permanent sprint

Creative teams are trapped on the endless hamster wheel of production, and it’s not slowing down. According to Adobe research, content demand will grow 5x over the next two years. Social content shelf life is now measured in hours, not weeks. Keeping fresh work in the pipeline is a permanent sprint, requiring teams to rethink how creative production functions.

The first move is freeing creative teams by having AI absorb the repetitive work so they have space for the strategic creative decisions that require human ingenuity. In a recent study from Adobe, 94% of creatives report that AI helps them produce content faster, saving an average of 17 hours per week. That recovered time is not a productivity metric; it is renewed creative capacity.

As a use case, Nestlé offers a useful blueprint. Its teams operate across 180 countries with a portfolio of iconic brands including Nescafé, KitKat, and Purina. Using Adobe Firefly Custom Models embedded in existing content workflows allows teams to generate assets in a brand-informed style without disrupting creative flow. At Nestlé, workflow cycle times dropped 50%. “With Firefly Custom Models, we can react at the speed of culture. It’s the closest thing we’ve had to magic.” says Wael Jabi, global strategic comms lead for KitKat.

As we move into the agentic era, the possibilities expand further. Adobe’s Creative Agent thinks in systems, not tasks, orchestrating across workflows, apps, and processes to close the gap between idea and execution, and get teams out of the production cycles that consume their productivity.

Build for your brand, not every brand

A company’s brand is how the world recognizes and connects with them. And it’s more than a collection of assets—it is dynamic, subjective, and expressed in thousands of micro-decisions made every day by the people who know it best. As production scales, keeping everything tuned to the brand gets more challenging. Off-the-shelf AI cannot replicate the level of nuance creative teams bring to content, and there’s a real cost to getting it wrong; diluting a brand in market with almost-right output is not an acceptable option. Customer trust is fragile.

Starting with a bespoke AI model built with Adobe Firefly Foundry addresses this directly. Firefly Foundry starts with a commercially safe base model and trains further on a company’s IP, making it possible to produce content that genuinely reflects the team’s vision.

And to ensure that Firefly Foundry models truly represent the creatives at the helm, Adobe has partnered with film studios like Wonder Studios, Promise.ai, and B5 Studios, and the “big three” talent agencies CAA, UTA, and WME to deeply understand what it means (and what it takes) to build an IP-immersive model that keeps creatives at the center as these film studios and talent agencies scale their visions. These brand ecosystems can accelerate nearly every phase of the production process, from ideation and storyboarding to production and promotion, all while preserving artistry and authorship. And to power the next generation of creativity and content, Adobe has recently announced a strategic partnership with NVIDIA, delivering best-in-class creative control along with enterprise-grade, commercially safe content at scale.

Generic AI gives teams a starting point. But a model trained on a brand’s own IP gets them to the finish line, while still leaving room for the creative calls that matter most.

When agents become the audience

AI is not only reshaping how we create; it is reshaping how customers find and engage with brands entirely. According to Adobe Digital Insights, AI-powered shopping has surged 4,700%. Agentic web traffic is up 7,851% year over year. Yet, most businesses still have significant gaps in AI-led brand visibility. If content is invisible to AI agents, then a brand is invisible to customers.

Major League Baseball is ahead of this curve. Using Adobe LLM Optimizer, the league monitors how its content surfaces across AI interfaces and makes real-time adjustments to maintain visibility. As fans search for tickets, stats, or game-day experiences, the league ensures its brand shows up wherever that search is happening. And with Adobe’s recent acquisition of Semrush, brand visibility goes even further.

The agentic web created an entirely new content surface that did not exist two years ago, and this exponential proliferation of content illustrates precisely why scaled, on-brand content production has become a strategic imperative. A well-built agentic foundation offers full visibility into (and control over) every piece of content, from production to performance.

How to prepare for AI integration

Here are a few steps to get started:

Audit before automation. Content supply chains usually include duplicated processes, unclear ownership, and assets living in many different places. Before AI can accelerate anything, develop a clear map of how content moves through the organization today: who creates it, who approves it, where it lives, and where it breaks down. AI applied to a broken process just breaks it faster.

Walk through workflows. Resist the urge to overhaul everything at once. Start with production tasks that are high-volume, low-stakes, and well-defined: asset resizing, localization, and background generation. Use those wins to build internal confidence before expanding into more complex creative territory.

Build responsible governance from the start. Governance added as an afterthought becomes a bottleneck. Building it in from the beginning creates a competitive advantage that lets teams move fast with confidence. And this means clear policies on model training, content provenance, human review thresholds, and communicating AI use to customers. The brands that earn lasting trust will treat transparency as a feature, not a footnote.

This content was produced by Adobe. It was not written by MIT Technology Review’s editorial staff.

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Guarding biotech from China and big bets in longevity

On this week’s episode of “The Readout LOUD,” the hosts discuss STAT’s Breakthrough Summit West, where powerful leaders from health care and science rubbed shoulders. They share some of the juicy conversations and insights, including BridgeBio CEO Neil Kumar’s comments that the company is publishing  “not the right structures” in their research publications.

Listeners also will hear Allison’s interview with Joe Betts-LaCroix, CEO of the Sam Altman-backed longevity company Retro Biosciences. Retro is preparing for its first clinical data readout and examining just how much impact AI can have in drug development.

Read the rest…

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Next-Generation Cardiac AI System Outperforms Existing Models

A next-generation artificial intelligence system can analyze complex heart scans better than existing models without the need for laboriously, manually labeled training data.

The system, outlined in Nature Communications, sets the scene for multimodal learning approaches to be further integrated into medical imaging, with the potential to improve diagnoses and patient outcomes.

The vision-language self-supervised learning framework for cardiac magnetic resonance (CMR) imaging uses contrastive language image pretraining (CLIP) and treats scans as videos of the beating heart.

The novel CMR-CLIP system outperformed existing models by 35% and was better at identifying common pathologies such as myocardial fibrosis and left ventricular hypertrophy.

It also beat other models at common computational tasks such as the retrieval of CMR studies or radiology reports and downstream disease classification tasks.

“Systems like CMR-CLIP have the potential to support clinicians through automated screening, and interpretation support, particularly in settings where expert readers are limited,” explained researcher David Chen, PhD, of Cleveland Clinic.

“Such reader assistant tools are critical to improving patient access to this powerful diagnostic technology.”

Cardiac magnetic resonance imaging is the definitive way to diagnose several cardiac diseases including valvular pathologies, cardiomyopathies, pericardial and aortic diseases.

However, interpreting and documenting each exam takes a great deal of time due to the amount of information collected in each CMR exam—often more than 40 minutes per study.

Vision-language models trained using self-supervised learning are therefore crucial to reduce dependency on large volumes of labeled data.

However, conventional self-supervised approaches that rely on precise image-text pairing are not always feasible for CMR, given that it is able to visualize cardiac anatomy, physiology, and microstructure in a single exam.

Unlike either generalists and other biomedical domain-specific models, which are trained using individual images or limited views, CMR-CLIP incorporates a wide variety of standard cardiac views and image types that represent of morphology, function, and myocardial viability.

The vision language model connects images and associated reports, treating the various views of the heart and image types as a sequence of images in video format.

The model was trained on over a million images from over 10,000 unique studies at a single institution and performed well on evaluation in both on internal and external datasets.

The researchers said it achieved “remarkable performance” at real-world clinical tasks, reaching accuracies of 88.5% for non-ischemic cardiomyopathy, 88.0% for ischemic cardiomyopathy, 96.2% for cardiac amyloidosis, and 98.6% for hypertrophic cardiomyopathy.

“This work demonstrates that domain-specific foundation models can significantly outperform general-purpose AI systems in specialized clinical applications,” said researcher Ding Zhao, PhD, Carnegie Mellon University.

“By designing models that reflect the structure and complexity of cardiac MRI data, rather than adapting generic image models, we can unlock new levels of performance and clinical utility.”

The post Next-Generation Cardiac AI System Outperforms Existing Models appeared first on Inside Precision Medicine.

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Open-Source Algorithm Advances Precision Menstrual Health Beyond Fertility

Scientists at SRI International have developed an algorithm that analyzes menstrual cycle data to uncover hidden connections to overall health and aging, moving beyond the fertility focus of most previous research. In a study published in Science Advances, the tool revealed how aging influences key changes during the menstrual cycle and identified markers of individual variability that could be leveraged for the development of personalized approaches to menstrual health. 

“Across the reproductive life stage, a woman living in the United States would have, on average, 450 menstrual cycles, out of which [approximately] 3.2 cycles result in pregnancy. Yet, most of the focus on menstrual health—including research, medical training, customers apps, and patents—are centered solely on the reproductive aspect, and fail to leverage these 99% non-conceptive menstrual cycles as health indicators,” writes Marie Gombert-Labedens, PhD, postdoctoral researcher at SRI International and lead author of the study. 

The menstrual cycle is a complex process that is tightly connected to an individual’s health, influencing many physiological processes including metabolic and immune functions. Gombert-Labedens and colleagues believe that tracking the rhythms of the menstrual cycle can be a valuable yet underexplored diagnostic tool—similar to how cardiac rhythms are routinely monitored to diagnose a wide range of cardiovascular conditions or how circadian rhythms can indicate metabolic disorders. 

However, more work needs to be done in menstrual health research to identify the most relevant metrics, their relationship with health conditions, and the extent to which individual variations may require a personalized approach. To aid the research community in this endeavor, the team developed the WAVES algorithm, which stands for ‘women’s health assessment through variability in endocrine-related signals.’

Using the WAVES algorithm, the researchers analyzed data from 5,674 menstrual cycles from 753 participants between 18 and 42 years old, including daily temperature measurements, vaginal secretions, age, reproductive history and sexual activity. Results showed that aging was associated with measurable changes in the menstrual cycle, including higher average temperatures, shorter cycles, and a decrease in regularity across multiple metrics. 

Gombert-Labedens and colleagues also looked at individual variability across all metrics, as menstrual cycles are known to vary widely from person to person both in terms of length and regularity. “Although the menstrual cycle is typically described as 28 days long, research based on large datasets indicate that this is more the exception than the rule, as only 12.4% of individuals present 28-day cycles,” she stated. 

Their analysis revealed that each participant showed individual patterns concerning mean body temperature across cycles, minimum and maximum temperature measurements, and the duration of both the full cycle and its phases. “These findings suggest that, across cycles from the same individual, each person has their own temperature baseline measurement around which the menstrual fluctuations are organized and are highly stable,” noted Gombert-Labedens. 

As an open-source platform, the WAVES algorithm is now available to researchers worldwide studying menstrual cycle patterns, helping them parse through large amounts of data to identify relevant biomarkers associated with health, disease, and treatment response. 

“The menstrual cycle is a rich yet underused source of physiological information,” Gombert-Labedens concludes. “This work suggests that the WAVES algorithm can be used for advancing digital biomarker discovery, and highlights the relevance of a personalized approach in the development of next-generation tools for women’s health.” 

The post Open-Source Algorithm Advances Precision Menstrual Health Beyond Fertility appeared first on Inside Precision Medicine.

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Oncology’s Next AI Battleground: Instant Clinical and Commercial Insight

Across the oncology pharmaceutical industry, the bar for precision is constantly being raised. Cancer drug development has become increasingly biomarker-driven, trial populations are narrowing, and the cost of identifying eligible patients for studies continues to rise. At the same time, life sciences organizations are under growing pressure to generate real-world evidence (RWE) faster for commercialization strategies as well as regulators and payers.

The race to operationalize artificial intelligence (AI) across oncology research has entered a new phase. After years of building massive catalogs of real-world data (RWD) from electronic health records (EHRs), molecular testing, and longitudinal patient outcomes, healthcare technology companies are now competing to transform those datasets into interactive intelligence systems capable of answering complex clinical and commercial questions in real time.

That convergence has fueled a wave of oncology AI platform development from companies including SOPHiA GENETICS, Ontada, COTA Healthcare, and now Flatiron Health. “As oncology becomes more complex, the ability to quickly identify the right patients and answer critical research questions is no longer a nice-to-have, it’s essential,” Kate Estep, chief product officer at Flatiron Health, told Inside Precision Medicine.

The move by Flatiron Health supports the continuing trend of data companies in oncology and across healthcare positioning themselves beyond simply aggregating clinical datasets toward creating AI-native research environments where clinicians, commercial strategists, and researchers can interact directly with data using natural language.

The need for speed

Historically, RWE generation has been labor-intensive. Pharmaceutical teams often rely on analysts or biostatistics groups to construct cohorts, validate inclusion criteria, and generate feasibility assessments—a process that can take days or weeks before a research question even begins to take shape. That workflow is increasingly incompatible with modern oncology development, where therapies are often targeted toward highly specific molecular subpopulations.

Cancer research may be uniquely suited for AI-native evidence generation systems. Compared with many therapeutic areas, oncology already produces unusually data-dense patient journeys involving pathology reports, genomic sequencing, imaging, biomarker testing, treatment lines, progression tracking, and survival endpoints. Oncology drug development is also increasingly dependent on identifying narrow molecular populations quickly and accurately. That complexity creates ideal conditions for conversational AI systems capable of navigating structured and unstructured clinical data simultaneously.

Flatiron Telescope attempts to address that bottleneck by giving users a conversational interface layered on top of Flatiron’s oncology-specific datasets. Researchers can describe inclusion and exclusion criteria in natural language, dynamically refine cohorts, and immediately view patient counts, attrition curves, treatment patterns, and survival analyses without writing code. “We were chatting with one of our early access partners last week, and this person was remarking, ‘I could answer my question in 30 minutes, and that would have taken me two days before waiting for my data team to come back to me,’” Estep said during a media briefing ahead of launch.

That acceleration may ultimately become the defining metric in the AI healthcare infrastructure market: not simply the size of a company’s dataset, but how quickly actionable insight can be extracted from it.

From data vendors to research platforms

But the challenge is not merely access to information. Trust and scientific validity remain central concerns. “One of the things our head of data science was sharing is that off-the-shelf models are roughly 60% accurate,” Estep said. “When built and trained with the clinical and scientific best practices that we have applied to model context because we have been asking cohort questions of our data for 15 years, that’s 90% plus accuracy.”

Those comments point toward an increasingly important divide in healthcare AI: the distinction between general-purpose AI models and clinically fine-tuned systems trained on domain-specific workflows. For companies like Flatiron, the competitive moat may ultimately come less from the underlying language models themselves and more from proprietary clinical context, curation methodologies, and validated evidence-generation pipelines.

The emergence of platforms like Telescope also reflects a broader transformation occurring across healthcare AI. The first generation of healthcare data companies focused primarily on aggregation, assembling electronic health record (EHR) data, claims data, genomic profiles, and imaging repositories into structured datasets. The second generation is now focused on orchestration: enabling users to interrogate those datasets continuously through AI-driven systems.

Flatiron is betting that domain specificity will matter more than generic AI capability. “Most people in the space either give you data, they give you analytics, or they give you a platform,” Estep said. “Very few cut across all three buckets.”

That positioning distinguishes Flatiron somewhat from competitors. Tempus AI has built a broad precision medicine tech ecosystem for both providers and life sciences companies. SOPHiA GENETICS has emphasized multimodal analytics and genomic interpretation. Ontada, backed by McKesson, combines oncology data assets with point-of-care tools and network analytics.

Flatiron, by contrast, is leaning heavily into its reputation for longitudinal oncology RWE and EHR-derived clinical depth. The company says Telescope is powered by more than 15 years of oncology-specific data infrastructure spanning over 4,700 providers and 1,600 clinical sites in the United States, representing approximately 40% of U.S. community oncology practices. Globally, the company now manages data from more than five million patient journeys across the U.S., U.K., Germany, and Japan.

That scale matters because oncology AI systems depend heavily on context-rich longitudinal data. Large language models alone are insufficient if the underlying clinical infrastructure lacks standardized outcomes, biomarker histories, treatment sequences, or progression events. “Flatiron has spent the last decade and a half building high-quality, oncology-specific real-world datasets,” Estep said. “Telescope really sits at the epicenter of that.”

Global oncology intelligence

Another major shift underway in oncology AI involves international interoperability. Historically, most RWE systems were fragmented geographically, with datasets built independently for different markets. But as pharmaceutical companies globalize clinical development programs, pressure is increasing to harmonize datasets across countries.

Flatiron says it is now building globally interoperable oncology datasets across the U.S., U.K., Germany, and Japan, beginning with prostate cancer data expected later this year. “We are ensuring that our datasets are interoperable from a global perspective,” Estep said. “Conclusions drawn on definitions of variables and data models in one market can easily be applied or explored in another.”

The long-term implications could be substantial. Globally harmonized oncology datasets would allow researchers to study treatment variation, biomarker prevalence, and outcomes across healthcare systems at a scale previously difficult to achieve. It may also help address longstanding concerns around representativeness in RWE generation. “Representativeness of a RWD is probably one of the single biggest requirements for us as we think about whether this dataset is considered reliable,” Estep said.

Perhaps the most important industry trend underlying Telescope’s launch is the democratization of advanced analytics. Historically, sophisticated oncology data analysis required teams of data scientists, epidemiologists, or biostatisticians. AI interfaces are beginning to collapse those barriers, enabling clinical operations leaders, medical affairs teams, and commercial strategists to interact directly with research-grade datasets. “There’s no coding required,” Estep said. “Any team member can use it, not just your data analysts.”

That shift could fundamentally change how oncology organizations make decisions, reducing delays between hypothesis generation and evidence generation while broadening access to sophisticated analytical capabilities across enterprise teams.

Whether Telescope ultimately becomes a dominant platform remains to be seen. But its launch reflects a broader reality now reshaping healthcare: in oncology, the future competitive advantage may belong not simply to companies with the most data but to those capable of turning clinical complexity into usable intelligence fastest.

The post Oncology’s Next AI Battleground: Instant Clinical and Commercial Insight appeared first on Inside Precision Medicine.

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Association between frailty and postoperative delirium after transcatheter aortic valve replacement: a meta-analysis

BackgroundPostoperative delirium (POD) is a common complication following transcatheter aortic valve replacement (TAVR) and is associated with adverse outcomes in older patients. Frailty, a multidimensional geriatric syndrome, has been increasingly recognized as a potential risk factor for POD. However, existing evidence remains inconsistent. This meta-analysis aimed to evaluate the association between frailty and POD after TAVR.MethodsA systematic search of PubMed, Embase, and Web of Science was conducted from inception to January 22, 2026. Cohort studies evaluating the association between preprocedural frailty and POD after TAVR were included. Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using a random-effects model accounting for the influence of potential heterogeneity.ResultsTen cohort studies involving 7,702 patients were included. Frailty was present in 2,062 (26.8%) patients, and 786 (10.2%) developed POD. Pooled analysis showed that frailty was significantly associated with an increased risk of POD after TAVR (OR: 2.17, 95% CI: 1.60–2.95; I2 = 55%). The association was stronger in studies with sample size ≥ 500 compared with < 500 (OR: 2.74 vs. 1.38; p for subgroup difference < 0.001). The effect estimates were consistent across subgroups stratified by study design, age, sex, frailty assessment methods, follow-up duration, analytic models, and study quality (all p for subgroup difference > 0.05). Notably, studies using CAM-ICU to diagnose POD showed a stronger association than those using DSM criteria or other methods (OR: 3.60 vs. 1.56 and 2.53; p = 0.006). Meta-regression identified sample size as a significant source of heterogeneity (p = 0.02).ConclusionsFrailty is associated with an increased risk of POD after TAVR. These findings highlight the importance of frailty assessment for perioperative risk stratification and support targeted strategies to prevent delirium in high-risk patients undergoing TAVR.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD420261352173.
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Associations between serum tumor necrosis factor-alpha, hippocampal-prefrontal-ventricular volumes, and clinical profiles in schizophrenia: a biomarker and neuroimaging study in North Sumatera, Indonesia

IntroductionSchizophrenia is a neurodevelopmental disorder with systemic inflammation that is thought to play a role in its pathophysiology. The pro-inflammatory cytokine tumor necrosis factor-alpha (TNF-α) has been reported to be dysregulated in individuals with schizophrenia, but its relationship with brain volume changes remains inconsistent. This study aimed to analyze differences in TNF-α levels and brain volume (hippocampus, prefrontal cortex, and lateral ventricles) and their correlations in schizophrenia patients compared to healthy controls.MethodsThis analytical cross-sectional investigation was conducted on 50 schizophrenia patients and 50 healthy Batak controls at the Prof. M. Ildrem Provincial Mental Hospital, Medan. Serum TNF-α levels were measured by ELISA, while brain structure volume was measured by 1.5 T MRI and analyzed using AnalyzePro software.ResultsTNF-α levels in the schizophrenia group were significantly lower (3.35 pg/dl) than in the control group (16.90 pg/dl). No significant differences in hippocampal and prefrontal cortex volume were found between the groups. However, lateral ventricle volume was significantly larger in schizophrenia. Correlation analysis showed a weak negative relationship between TNF-α and prefrontal cortex volume only in the schizophrenia group.DiscussionThe finding of low TNF-α in schizophrenia supports the complexity of immune dysfunction in schizophrenia, which may be influenced by medication or disease phase. Consistent ventricular enlargement reinforces previous neuroanatomical findings. The association of TNF-α with the prefrontal cortex only within the schizophrenia cohort indicates a specific interaction between inflammation and brain morphology in this patient population.ConclusionsThe findings support TNF-α dysregulation in schizophrenia, albeit at lower levels. Lateral ventricular enlargement was consistently found, while changes in hippocampal and prefrontal cortex volume may be more subtle in this population. The negative correlation between TNF-α and the prefrontal cortex in schizophrenia suggests a potential role for inflammation in the pathology of this brain region.
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Baseline working memory was associated with improvement in psychological quality of life in patients with persistent depressive symptoms: a prospective observational study

BackgroundCognitive dysfunction is prevalent in patients with depressive symptoms and contributes to impaired quality of life (QOL) and functional outcomes. However, the prognostic significance of specific cognitive domains for long-term outcomes remains unclear in patients with persistent depressive symptoms.MethodsIn this prospective observational study, 119 patients completed the detailed examination program in routine clinical care. Of these, 84 patients with persistent depressive symptoms provided consent for study participation and met the eligibility criteria for inclusion in the present study, including completion of the baseline assessment with the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV). The diagnostically heterogeneous cohort included patients with major depressive disorder/dysthymia as well as bipolar and related disorders. The World Health Organization Quality of Life Instrument, Short Version (WHO-QOL-26) and the World Health Organization Disability Assessment Schedule 2.0 were assessed at baseline, three, and six months; 60 participants provided 3-month follow-up data and 50 provided 6-month follow-up data. Primary hierarchical regression analyses were exploratory and conducted using complete-case data.ResultsAt baseline, participants exhibited relatively higher verbal ability and lower processing speed compared to normative data. While no significant group-level improvement in QOL was observed over six months and functioning did not improve overall, the WHODAS 2.0 standardized total score was temporarily higher at 3 months than at baseline. In exploratory analyses of complete cases, higher baseline working memory was significantly associated with greater improvement in the psychological domain of the WHO-QOL-26 at six months (β = 0.40; ΔR² = 0.14; p < 0.01). No other cognitive domains showed such associations.ConclusionsWorking memory was associated with subsequent improvement in psychological well-being and may represent a candidate prognostic marker in patients with persistent depressive symptoms. Given the exploratory nature and modest sample size, these findings require replication in larger, diverse populations.
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Precision diagnosis model for treatment-resistant depression integrating serum metabolomics and clinical risk factors

ObjectiveThis study aimed to construct a high-efficiency dual-modal diagnostic model for treatment-resistant depression (TRD) by integrating serum metabolomics and clinical risk factors, and explore its metabolic pathological mechanisms.MethodsA total of 93 major depressive disorder (MDD) patients (53 TRD, 40 non-TRD) were enrolled for a single-center retrospective study. Serum untargeted metabolomics and clinical baseline data were collected, with differential metabolites and clinical risk factors screened by statistical analysis and multi-step machine learning to identify core features. Five machine learning algorithms were compared to build unimodal and random forest-based dual-modal diagnostic models, and KEGG pathway enrichment analysis was performed.Results3 core clinical risk factors (medical history, HDL, FBG) and 8 core metabolic biomarkers were identified. The dual-modal model achieved AUC 0.996 (training) and 0.911 (validation), outperforming unimodal models. Differential metabolites were mainly enriched in lipid (44.8%) and amino acid (23.9%) metabolism. Fibrinopeptide A516, 12-HETE and the three clinical factors were core driving features.ConclusionThe dual-modal model has high diagnostic efficiency for TRD. TRD is associated with endocannabinoid system hypofunction and metabolic imbalance, which provides an objective diagnostic tool and new insights for TRD mechanism research and therapy development.