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Category: biotech/medical – Page 19

A new study published in Nature reveals how olfactory sensory neurons (OSNs) achieve extraordinary precision in selecting which genes to express.

The mechanism is surprising in that it involves solid-like molecular condensates that last for days, helping to solve a long-standing puzzle in genome organization.

The research, led by Prof. Stavros Lomvardas from Columbia University, addresses one of biology’s most intriguing questions: How do in the nose manage to express only one (OR) gene out of approximately 1,000 available options?

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Laser interstitial thermal therapy (LITT) has emerged as a minimally invasive treatment for primary CNS tumors. While LITT offers advantages over traditional approaches, perilesional intracranial heatsinks can lead to asymmetrical ablation, impacting patient outcomes. Understanding heatsink effects is crucial for optimizing LITT efficacy.

The authors retrospectively analyzed primary CNS tumors treated with LITT at a single tertiary care center. Ablation outcomes were quantified using the Heatsink Effect Index (HEI), measured on a scale of 0–1 (0 = total symmetry, 1 = complete asymmetry), and extent of ablation (EOA). The heatsink types evaluated were sulci, meninges, vasculature, and CSF spaces, inclusive of ventricles, resection cavities, and CSF cisterns. Statistical analyses were performed to assess the relationship between heatsink proximity and type and ablation outcomes.

A total of 99 patients satisfied all selection criteria. The cohort was 53% female, with a mean age of 61 years. Glioblastoma was the most predominant tumor type (78%), followed by low-grade glioma (15%) and meningioma (4%). Heatsink proximity significantly correlated with ablation asymmetry (HEI) (p < 0.001), particularly at the midpoint of the catheter trajectory. The correlation between closest heatsink distance and HEI varied across the different heatsink types, with distance to vasculature and CSF spaces correlating the strongest with ablation asymmetry. When assessing the relationship between EOA and medial HEI during suboptimal ablations (EOA < 100%), a negative correlation was demonstrated, showing improved EOA as HEI was reduced. Optimal cutoff catheter-heatsink distances for predicting ablation asymmetry ranged from 6.6 to 13.0 mm, emphasizing the impact of heatsink proximity on LITT efficacy.

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This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven by Large Language Models (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation. Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements. In contrast, Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. Through a sequential evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both paradigms. Application domains such as customer support, scheduling, and data summarization are contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure and propose targeted solutions such as ReAct loops, RAG, orchestration layers, and causal modeling. This work aims to provide a definitive roadmap for developing robust, scalable, and explainable AI agent and Agentic AI-driven systems. >AI Agents, Agent-driven, Vision-Language-Models, Agentic AI Decision Support System, Agentic-AI Applications

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Artificial intelligence isn’t always a reliable source of information: large language models (LLMs) like Llama and ChatGPT can be prone to “hallucinating” and inventing bogus facts. But what if AI could be used to detect mistaken or distorted claims, and help people find their way more confidently through a sea of potential distortions online and elsewhere?

As presented at a workshop at the annual conference of the Association for the Advancement of Artificial Intelligence, researchers at Stevens Institute of Technology present an AI architecture designed to do just that, using open-source LLMs and free versions of commercial LLMs to identify potential misleading narratives in reports on .

“Inaccurate information is a big deal, especially when it comes to scientific content—we hear all the time from doctors who worry about their patients reading things online that aren’t accurate, for instance,” said K.P. Subbalakshmi, the paper’s co-author and a professor in the Department of Electrical and Computer Engineering at Stevens.

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In two new studies led by bacteriologist Brandon L. Jutras, Northwestern scientists have identified an antibiotic that cures Lyme disease at a fraction of the dosage of the current “gold standard” treatment and discovered what may cause a treated infection to mimic chronic illness in patients. The studies were published in the journal Science Translational Medicine.

New studies offer insight into disease’s treatment, lingering symptoms.

Northwestern scientists have identified an antibiotic that cures Lyme disease at a fraction of the dosage of the current “gold standard” treatment and discovered what may cause a treated infection to mimic chronic illness in patients.

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The thymus is widely recognized as an immunological niche where autoimmunity against the acetylcholine receptor (AChR) develops in myasthenia gravis (MG) patients, who mostly present thymic hyperplasia and thymoma. Thymoma-associated MG is frequently characterized by autoantibodies to the muscular ryanodine receptor 1 (RYR1) and titin (TTN), along with anti-AChR antibodies. By real-time PCR, we analyzed muscle—CHRNA1, RYR1, and TTN—and muscle-like—NEFM, RYR3 and HSP60—autoantigen gene expression in MG thymuses with hyperplasia and thymoma, normal thymuses and non-MG thymomas, to check for molecular changes potentially leading to an altered antigen presentation and autoreactivity.

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Blood vessels are like big-city highways; full of curves, branches, merges, and congestion. Yet for years, lab models replicated vessels like straight, simple roads.

To better capture the complex architecture of real human , researchers in the Department of Biomedical Engineering at Texas A&M University have developed a customizable vessel-chip method, enabling more accurate vascular disease research and a drug discovery platform.

Vessel-chips are engineered microfluidic devices that mimic human vasculature on a microscopic scale. These chips can be patient-specific and provide a non-animal method for pharmaceutical testing and studying . Jennifer Lee, a biomedical engineering master’s student, joined Dr. Abhishek Jain’s lab and designed an advanced vessel-chip that could replicate real variations in vascular structure.

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