Claude Science is Anthropic’s newest flagship product

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access to tools that make it particularly useful for research in computational biology and drug development. Along with launching and previewing Claude Science, which is now available to all paid Claude subscribers, Anthropic also announced that it will be using the product to pursue some of its own research into drugs for rare, neglected diseases.

This is not Anthropic’s first foray into AI for science. In October, the company released plug-ins that help Claude make use of scientific software and databases under the heading “Claude for Life Sciences.” But unlike this earlier release, Claude Science is a full-featured, standalone product. Anthropic’s decision to elevate Claude Science to the same rank as Claude Code and Claude Cowork indicates that the company is taking AI’s scientific applications very seriously—or at least wants to give the impression that it is.

“It represents how important this is to our mission that this is right up there with Claude Code and Claude Cowork as the next really significant product that we’re releasing,” says Eric Kauderer-Abrams, Anthropic’s head of life sciences. “Our mission is to develop AI that serves humanity’s long-term well-being, and we believe that by far the greatest opportunity to do that is in the life sciences.”

For the past decade, one company—Google DeepMind—has been at the vanguard of AI for science. CEO Demis Hassabis and researcher John Jumper won the Nobel Prize in chemistry for their work on the company’s AlphaFold model, and DeepMind has also made major contributions to meteorology, materials science, and a variety of other disciplines. But in the past several months, the fast-advancing frontier of AI progress seems to have left DeepMind in the dust. When it comes to coding, which has become the most lucrative use case for LLMs, DeepMind is stuck playing catch-up.

Anthropic is well positioned to take up DeepMind’s scientific mantle. Like Hassabis, Anthropic CEO Dario Amodei is a PhD scientist—unlike OpenAI CEO Sam Altman, who’s a businessman through and through. Many scientists are already avid users of tools such as Claude Code. These days, a lot of scientific research involves some amount of coding, but not all scientists are expert software engineers, and so tools like Claude Code can make a huge difference for their productivity. And the company has recently earned a major scientific vote of confidence: Earlier this month, Jumper announced that he is leaving DeepMind for Anthropic.

Since agents powered by LLMs, including Anthropic’s Opus model series, became capable of useful, independent work in late 2025, scientists have been seeing just how much they can do. In a blog post published on Anthropic’s website, the Harvard physicist Matthew Schwartz estimated, on the basis of his work with Claude Code and other Anthropic tools, that the company’s Opus 4.5 model is about as capable of executing scientific projects as a second-year graduate student.

According to Kauderer-Abrams, Claude Science isn’t intended to displace Claude Code and Claude Cowork in scientists’ workflows. Instead, it’s designed to build on what scientists already find useful about Anthropic’s products. For instance, it not only writes code but also helps scientists run their code on powerful computer clusters, which many many scientists need for their work but can be difficult to manage. And it prioritizes reproducibility, so that scientists can trace back the source of any figure or result and check it for accuracy and validity.

Though Claude Science could in principle assist with any area of scientific research, it seems designed and marketed as a tool for molecular and cellular biology, and for drug development in particular. It can interface with various tools used in genetics, chemistry, and protein biology, all of which could come in handy for researchers on the hunt for new drugs. During the Tuesday event, Alexander Tarashansky, who led the development of Claude Science, demonstrated how the system could autonomously identify new drug candidates for phenylketonuria, a rare genetic disease.

And Anthropic isn’t leaving all that work to the pharma companies and university labs that were represented at the event. Armed with Claude Science, it will be pursuing its own research into drug candidates for neglected diseases—both to help move science forward and to gain a clearer sense of how Claude Science works in the real world.

There are obvious humanitarian reasons to prioritize drug development when creating a general-purpose scientific research tool, and AI industry leaders often cite curing disease as a major potential upside of the technology. But it’s also notable that pharmaceutical companies have far deeper pockets than academic researchers. Anthropic says it’s set to see its first profitable quarter, and if major new contracts with pharmaceutical companies are forthcoming, they could help ensure it stays profitable as the tokenmaxxing craze dies down—something that’s ever more important as an IPO approaches later this year.

Knowledge Graphs Based on Meta-Analysis Papers Improve the Quality of Case Formulation: Mixed Methods Design

Background: Case formulation (CF) is a core skill for therapists; however, creating high-quality CFs requires considerable time. Objective: This study aims to demonstrate that providing a knowledge graph based on meta-analytic literature can enhance CF quality. Methods: Five groups were established, including 4 large language model groups and 1 human expert group, each generating 25 CFs based on 25 vignettes. The control group with Claude (Sonnet 3.7; Anthropic) produced 25 CFs. The personalization group served as the control group with additional personalization prompts. The knowledge graph group used a large language model that generated 25 CFs, which was provided with a meta-analysis knowledge graph. Further incorporation of additional personalization prompts then comprised the knowledge graph with personalization group. Finally, the expert group consisted of 25 CFs generated by a human expert. These 125 CFs in total were evaluated for general quality (ie, correctness, completeness, feasibility, and consistency) using a 7-point scale and 18 essential elements with binary scores (0 or 1) by another human expert. The CFs were also qualitatively analyzed. Results: The knowledge graph and knowledge graph with personalization groups scored significantly higher than the control group in terms of correctness, completeness, and feasibility. The expert group scored significantly higher on consistency than the machine-generated groups. Additionally, there was no significant difference in the feasibility scores among the knowledge graph, knowledge graph with personalization, and expert groups. The qualitative evaluation suggested that human CFs narrow the text to content that is easy for the client to read, whereas machine CFs are more likely to include expressions that are unnatural to the client. Conclusions: These results indicate that providing knowledge graphs to novice therapists increases the correctness, completeness, and feasibility of CF. Providing experienced therapists with knowledge graphs is suggested to improve the quality of their CF and mental health services.
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Stakeholder Experiences With the Pneumococcal Conjugate Vaccine Chatbot as a Complementary Capacity-Building Tool for Frontline Health Workers in India: Qualitative Study

<strong>Background:</strong> Pneumonia remains the leading cause of mortality in individuals aged 5 years or younger globally, with India bearing a disproportionately high burden. The introduction of the pneumococcal conjugate vaccine (PCV) in India necessitated innovative approaches to support frontline health workers (FLHWs), particularly in remote settings. To address this, a customizable WhatsApp-based PCV chatbot was developed as a complementary tool to traditional training and reference materials. <strong>Objective:</strong> This study aimed to document the opportunities, challenges, and mitigation measures encountered during the development and rollout of the PCV chatbot, and to explore its use and user experience as a capacity-building and support tool for FLHWs during new vaccine introduction. <strong>Methods:</strong> A qualitative study was conducted across 4 Indian states—Arunachal Pradesh, Delhi, Karnataka, and West Bengal—using purposive sampling at the district and block levels. Data collection involved key informant interviews with immunization officials and chatbot developers, and focus group discussions with auxiliary nurse midwives. A Likert scale–based tool captured quantitative feedback on user satisfaction. <strong>Results:</strong> Stakeholders appreciated the chatbot’s accessibility, familiarity (through WhatsApp), and multilingual functionality. Most auxiliary nurse midwives found it easy to use and rated responses highly for completeness and usefulness. The chatbot enabled immediate access to information, saving time and bridging gaps, especially when traditional training was delayed or unavailable in hard-to-reach areas. Challenges included occasional technical issues, limited content related to dropout and left-out scenarios, and difficulties in typing regional languages. Recommendations included implementing predictive text, expanding scenario coverage, and strengthening user-centered design and field testing. <strong>Conclusions:</strong> The PCV chatbot demonstrated acceptability and perceived value as an on-demand knowledge tool among FLHWs. Continuous user-driven refinement, expanded content, and enhanced usability are essential for its scalability and sustained use in vaccine introduction and capacity-building efforts.

Expedited Transition to Digital Delivery of Recovery Support Services Due to the COVID-19 Pandemic: Mixed Methods Needs Assessment

Background: Recovery support services (RSS) are an evidence-based approach to support recovery from substance use disorders, most often composed of peer-to-peer support, referrals to housing, job training, and other forms of prosocial engagement and activities. During the COVID-19 pandemic, RSS providers quickly converted in-person services to digital delivery to avoid disruption. It is unclear if this rapid conversion impacted the delivery of services or if this delivery model could enhance RSS reach and uptake more generally by extending the reach of RSS providers and offering an alternative delivery method and access point. Objective: The goal of this study was to identify how RSS providers in Texas adapted their services for digital delivery and to what extent, if at all, technology limitations (eg, lack of digital infrastructure) were present. Methods: We conducted an electronic survey of 85 RSS providers, assessing their current capacity and methods for the digital recovery support service (D-RSS), followed by semistructured online interviews with a subset of 20 respondents. Results: Most survey respondents (74/85, 87.1%) used D-RSS, though they used many dated technologies, devices, and platforms for service delivery. Many respondents indicated that they use Zoom (Zoom Video Communications) videoconferencing to communicate with participants; however, providers also indicated that they must use several different technology platforms to accomplish their service delivery goals. Four main themes emerged from the interviews: (1) the impact of the COVID-19 pandemic on RSS, (2) barriers and facilitators to technology-delivered D-RSS, (3) awareness and expectations regarding the use of D-RSS, and (4) training needs to deliver D-RSS. Conclusions: RSS organizations have access to technology for D-RSS; however, the technology is often outdated. Because the pandemic required a rapid and unexpected shift to D-RSS to maintain and potentially expand access during a public health emergency, providers desire guidance for training staff and participants on how to best use technology. A subset of providers endorsed the potential of a unified platform for D-RSS delivery, especially for data capture. Most barriers to D-RSS identified by our respondents may be addressable through the streamlined deployment of technology resources, rigorous training and onboarding programs in best practices for providers and participants, and tailored implementation strategies for varying local contexts.
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Governing Ethical Tensions in Youth Digital Mental Health Research

As mental health research increasingly aims to generate societal impact, researchers operate at the intersection of innovation and ethical responsibility. Drawing on experiences from the cocreated NEON Young Norway Study on youth recovery narratives, this viewpoint identifies four ethical tensions that arise from the existing governance frameworks in youth digital mental health research: (1) balancing safeguarding against harm with youth participation, (2) protecting privacy without undermining authentic storytelling, (3) governing unpredictable outcomes of cocreated research, and (4) meeting ethical and legal standards while ensuring youth-friendly communication. These tensions highlight limitations in mental health research that adopts participatory and digital approaches, as this often struggles to accommodate iterative designs, narrative data, and cross-sector collaboration. We argue that responsible youth mental health research requires ethics to be understood as a dynamic, participatory practice that supports safe and equitable inclusion, rather than having a focus on risk prevention. Ethical governance, therefore, needs to evolve toward proportionate, context-sensitive approaches that can enable innovation while protecting young people’s rights, agency, and voices.
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Impact of an mHealth App on Digital Transformation: Randomized Clinical Trial on Strengthening Digital Skills in Older Women

Background: The rapid growth of digital technologies has transformed daily activities, health management, and social interaction. Older adults, however, continue to face challenges in adopting and using these tools due to limited previous exposure, age-related sensory or cognitive decline, and low digital confidence. In Brazil, internet access among adults aged 60 years or older has increased, yet digital exclusion persists, worsening health disparities. Mobile health (mHealth) apps offer a potential strategy to promote digital inclusion, strengthen digital competencies, and support healthy aging. Nonetheless, studies show that culturally adapted, multidisciplinary interventions for this group remain scarce and are rarely assessed through both quantitative and qualitative methods. Objective: This study aimed to evaluate the impact of a lifestyle mHealth app on improving digital skills, as well as to analyze the level of satisfaction and usability of the app. Methods: In this mixed methods study, a 14-week randomized clinical trial was conducted in Ribeirão Preto, São Paulo, Brazil. A total of 40 older adult women were randomized into an intervention group (n=21), who used the mobile app, and a control group (n=19). Digital competencies were measured before and after the intervention using a semistructured questionnaire based on the (MCDMSênior; Digital Competency Model for M-learning with a focus on older adults) framework, covering 6 domains—basic technology use, internet navigation, mobile app use, online research, digital communication, and usage of digital resources. Additionally, satisfaction with the educational content was evaluated using the suitability assessment of materials, and system usability was assessed using the System Usability Scale. Qualitative data were collected through semistructured, in-person interviews conducted immediately after the intervention with all intervention participants. Interviews explored perceptions of the app’s usability, satisfaction with its content, barriers, and facilitators to engagement, and perceived changes in digital skills. All interviews were audio-recorded, transcribed, and analyzed thematically by 2 independent researchers using an inductive coding approach. Results: Postintervention analyses revealed significant differences in specific digital competencies. The intervention group demonstrated a moderate improvement in internet navigation skills, while gains in basic technology use and digital communication were minimal. Conversely, the control group exhibited moderate improvement in basic technology skills and lower effects in online research and digital communication. Overall, satisfaction with the educational content was low, and usability was rated as average. Qualitative findings indicated that, although participants valued the clarity of navigation and cultural relevance, persistent age-related fears and insecurities in using digital technologies were reported. Participants highlighted the need for more personalized guidance, ongoing motivational support, and technical adjustments to improve usability and engagement. Conclusions: mHealth apps can effectively enhance certain digital competencies in older women, particularly internet navigation, but improvements in content suitability and usability are needed. Refinements in design and tailored support are essential to overcome age-related barriers and foster digital inclusion. Trial Registration: Brazilian Registry of Clinical Trials RBR-6wgkzs8; https://ensaiosclinicos.gov.br/rg/RBR-6wgkzs8
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Emotion Classification in Japanese Cancer Survivor Interview Narratives Using Sentiment Polarity and Plutchik Emotion Frameworks: Model Development and Evaluation Study

Background: Cancer survivors often experience complex and coexisting emotions throughout diagnosis, treatment, and posttreatment life. Emotion classification of patient narratives may help in understanding survivorship experiences; however, evidence remains limited for multidimensional classification using cancer survivor interview narratives. Objective: This study aimed to develop and evaluate natural language processing–based emotion classification models using Japanese cancer survivor interview narratives and to examine whether polarity and multidimensional emotion labels provide complementary perspectives. Methods: We analyzed verbatim transcripts from 15 cancer survivor interviews published by the Cancer Note, Nonprofit Organization. Survivor utterances were extracted, noninformative conversational elements were removed, texts were segmented at Japanese punctuation marks, and 5 consecutive sentences were grouped into 1 chunk. Two annotators labeled 1998 text chunks with 3-class sentiment polarity labels (positive, neutral, or negative) and multilabel Plutchik 8-emotion labels (joy, trust, fear, surprise, sadness, disgust, anger, and anticipation). Japanese BERT (Bidirectional Encoder Representations from Transformers) and LUKE (Language Understanding with Knowledge-based Embeddings) were fine-tuned to build a multiclass polarity classifier and a multilabel 8-emotion classifier. Performance was evaluated using precision, recall, -score, macroaveraged metrics, Micro- for polarity, and Hamming loss for multilabel classification. For comparison, the same architectures were fine-tuned on WRIME (writers’ and readers’ intensities of emotion for their estimation), a Japanese social media emotion dataset, and evaluated on Cancer Note texts as a domain-transfer analysis. The 95% CIs were estimated using bootstrap resampling with 1000 iterations. Results: Neutral was the most frequent polarity label, trust was the most frequent 8-emotion label, and anger was the least frequent emotion label. Label distributions were imbalanced, with most-to-least frequency ratios of 3.47 for polarity and 8.10 for 8-emotion labels. In the 3-class sentiment polarity task, interview-trained models outperformed WRIME-trained transfer models. Interview Text-BERT achieved the highest micro- of 0.696 (95% CI 0.676‐0.716), whereas Interview Text-LUKE achieved the highest macro- of 0.660 (95% CI 0.639‐0.682). In the 8-emotion multilabel task, Interview Text-LUKE achieved the highest macro- of 0.427 (95% CI 0.398‐0.453) and the lowest Hamming loss of 0.078 (95% CI 0.073‐0.082). WRIME-trained transfer models showed lower performance, particularly in the 8-emotion task. Sadness and trust co-occurred most frequently, suggesting that positive and negative emotional elements may coexist in the same narratives. Conclusions: This exploratory study suggests the feasibility of domain-specific emotion classification for Japanese cancer survivor interview narratives. Models fine-tuned on target-domain narratives generally outperformed WRIME-trained transfer models, although the best architecture differed by task and metric. Polarity labels and Plutchik 8-emotion labels provided complementary perspectives on complex and coexisting emotions in survivorship narratives. However, performance for rare emotions remained limited, and the models should be regarded as preliminary research tools rather than clinically actionable systems. Larger, more diverse, prospectively or externally validated datasets, imbalance-aware methods, and user-centered evaluation are needed before clinical translation.
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Institutional Member Updates: Summer 2026

Institutional Members are clinics and programs in the US and around the globe that offer residential and/or intensive treatment for OCD and related disorders, are specialty outpatient clinics with a large staff dedicated to treating OCD, or provide low-cost treatment options through research studies.

Below are quarterly updates from our Institutional Members organized alphabetically. Click the (+) to open each menu and read updates and find contact information for clinics near you:

Do you work at a residential program, intensive outpatient program (ITP), or specialty outpatient clinic and looking to advertise your services? Learn more about becoming an Institutional Member and having your program updates included below!

The post Institutional Member Updates: Summer 2026 appeared first on International OCD Foundation.

What Are Intrusive Thoughts?

When a child confesses a frightening thought that seemed to come out of nowhere — “What if I hurt someone with this knife?” “What if mom dies in a car accident?” “What if germs get into this paper cut and I die of an infection?”  — you can both find it confusing and disturbing.  But in most cases these intrusive thoughts are not evidence of a problem.

Intrusive thoughts are unwanted ideas, images, or urges that pop into the mind seemingly out of nowhere. They might feel embarrassing, violent, sexual, or just plain strange — and they feel completely out of character, which is exactly why they’re so upsetting. “An intrusive thought is a lot like your brain sending junk mail,” says Theresa Welles, PhD, a clinical psychologist and director of the Bubrick Center for Pediatric OCD at the Child Mind Institute. “Just because it shows up doesn’t mean it’s important or true or something you even want.”

It also doesn’t necessarily mean that a child has OCD or another mental health disorder. Though intrusive thoughts are associated with OCD — in which unwanted thoughts (called obsessions) drive children to perform rituals (called compulsions) to alleviate them — for many children they are just fleeting thoughts. It’s only when kids become unable to let them go that they are concerning. Another way to think about it, says Dr. Welles, is that “the brain’s job is to generate thoughts, the same way an apple tree’s job is to produce apples. Not every apple is perfect — some are misshapen or wormy. And not every thought is meaningful or worth paying attention to. Having a thought is not the same as wanting it or intending to act on it.”

Who has intrusive thoughts

“Everyone who has a brain has them,” says Caitlyn Downie, LCSW, director of trauma and resilience at the Child Mind Institute. “It’s part of the human existence.” A child might suddenly imagine something terrible happening to a parent, or a teenager might have a violent or sexual thought that feels shocking or shameful. Most of the time, these pass quickly — unpleasant, but easy enough to brush aside.

That’s the key distinction: not the thought itself, but what happens after it. The concern isn’t that the thought appeared — it’s how the child responds, how often it returns, and whether it starts getting in the way of daily life.

For some children — particularly those who are anxious, perfectionistic, or who have OCD — intrusive thoughts become “sticky.” Instead of passing through, the thought snags. The child starts paying attention to it, trying to figure it out or make it go away, which only makes it feel more powerful. “Young people lack the experience to recognize that thoughts aren’t the same as intentions, desires, or actions,” Dr. Welles says. “The thoughts feel alarming. So the child pays more attention, and the more attention they give it, the more often it returns.” That loop of fear and self-doubt is what parents and clinicians need to be alert to.

When should parents be concerned?

Many children are too ashamed or frightened to describe what’s actually going on, so parents may never hear about the thought itself. Instead, changes in behavior are often the first clue. Look for signs like:

  • Increased distress, irritability, or moodiness
  • Avoidance of something that wasn’t previously a problem
  • Trouble concentrating or sleeping
  • Excessive guilt or repeated reassurance seeking
  • Rituals like checking, counting, washing hands, or going through routines in a specific way

It’s worth seeking professional support when intrusive thoughts are frequent and intense, hard to shake, causing real distress, or getting in the way of school, friendships, or daily routines.

Why intrusive thoughts feel so frightening

When an intrusive thought appears, it can set off the body’s alarm system — the same ancient survival mechanism that helped people run from danger or fight it off. In anxiety and OCD, that alarm bell rings when there’s no real emergency. The child has a thought, the body reacts with panic, and the child assumes the thought must be important because it feels big and important.

Children may also fall into what clinicians call thought-action fusion. “That’s the mistaken belief that having a thought makes it more likely to happen,” explains Dr. Welles, “or that it reveals something terrible about who they are.” A child who thinks, “What if I hurt my baby brother?” may become convinced the thought means they secretly want to — but intrusive thoughts are often the precise opposite of what a child would ever want. Paradoxically, Dr. Welles says, “for most people with anxiety disorders and OCD, these thoughts are the actual opposite of what they would ever do.”

How parents can help

The first thing to do is stay calm — harder than it sounds if the thought is violent, sexual, or taboo. Children look to their parents to gauge whether something is truly dangerous, so if you look horrified, your child takes that as confirmation the thought is something to fear.

When a child shares an intrusive thought, Downie suggests responding with warmth and curiosity: “Say something like, ‘I appreciate you telling me — it sounds like that was really scary.’ It also helps to normalize it: ‘A lot of people have thoughts they don’t particularly like.’” Some other responses that can help:

  • “That sounds really upsetting — I’m glad you told me.”
  • “Having a thought doesn’t mean you want it or that it’ll ever happen.”
  • “You don’t have to figure this out right now.”

The goal is to help your child feel less alone and less ashamed, without treating the thought like a five-alarm emergency. And do your best to avoid reassurance. Reassuring the child about the contents of a specific thought (for example, responding to a child who asks, “Are you sure I’m a good person?” with “Yes, you’re a good person”) can actually make things worse, especially in kids with OCD. They feel very temporary relief but then the thought creeps back and they need more reassurance. It becomes a cycle. Instead try: “I know this feels awful. And I know you can handle it.”

It also helps to redirect the child to something concrete: getting dressed, eating breakfast, watching a show, texting a friend. With younger kids, you might guide them in doing slow breaths or suggest they move to another room so they distract themselves from the thought. With teens, you might mean teach them to resist the urge to Google their fears or thoughts, confess, or ask the same question over and over again. “The idea,” Downie says, “is to validate the feeling without validating the fear. You’re saying: ‘I hear you, this is hard, and you can get through it.’”

What can cause intrusive thoughts?

Intrusive thoughts aren’t a diagnosis on their own — they’re a symptom that can show up across a range of conditions, or in children who have no diagnosis at all. Disorders they may be associated with include:

  • OCD: The most closely associated condition. Common themes include harm, contamination, sexual thoughts, and religious or moral fears.
  • Generalized anxiety: Tends to involve repetitive “what if” worries about everyday concerns — school, safety, family, the future.
  • Social anxiety: Brings intrusive thoughts about embarrassment, rejection, or being judged by peers.
  • PTSD: Can involve intrusive memories, images, or sensations tied to a traumatic event. “A child who has experienced trauma may worry about being harmed again or even about harming someone else,” Downie notes, “but that doesn’t mean every child with trauma will have intrusive thoughts.”
  • Depression: Often involves intrusive thoughts that fit a negative self-image: I’m worthless. I’m a burden. I’m a bad person.
  • Autism spectrum disorder: Repetitive thoughts often center on a special interest and aren’t typically unwanted or distressing the way OCD thoughts are — though they can look similar from the outside.
  • Psychotic disorders: Young people with psychosis tend to experience intrusive thoughts as fixed and real, without the self-awareness that typically accompanies anxiety-driven ones. Psychotic disorders such as schizophrenia are rare in children, though early signs can appear in the teenage years.

How intrusive thoughts are treated

Treatment depends on what’s driving the thoughts and how much they’re disrupting the child’s life:

  • For OCD, the gold-standard treatment is exposure and response prevention (ERP), a specialized form of cognitive behavioral therapy (CBT) where children practice sitting with intrusive thoughts without doing compulsions. Over time, they learn to tolerate uncertainty and discover that the thought, however uncomfortable, isn’t actually dangerous.
  • For anxiety, the same treatments are helpful. CBT helps children understand the connection between thoughts, feelings, and behaviors, and ERP helps kids learn to tolerate the anxiety these thoughts generate, and it gradually diminishes.
  • For trauma, treatment may include trauma-focused CBT. Mindfulness, DBT skills, and breathing exercises can also help regulate the nervous system.
  • Family involvement matters a great deal. “Parents often need help learning how to respond without accidentally feeding the anxiety cycle,” Dr. Welles says. SPACE (Supportive Parenting for Anxious Childhood Emotions) is an evidence-based approach that helps parents reduce accommodation and support their child’s brave behavior instead.
  • For moderate-to-severe OCD or anxiety, medication — typically an SSRI — may also be worth discussing with a psychiatrist or pediatrician.

Helping your child trust their own mind

One of the hardest things about intrusive thoughts is that they can make children afraid of their own minds — convinced that every thought needs to be examined or explained away before they can relax. But no one gets to have only pleasant, well-behaved thoughts.  

What children can learn is that a thought can be upsetting without being meaningful, loud without being true, and it can pass through without becoming a verdict on who they are. As parents, the most powerful thing you can offer is a calm, steady presence — taking it seriously without treating it as a catastrophe. When your child sees you aren’t panicked, they get to borrow some of that calm for themselves.

Frequently Asked Questions

What are intrusive thoughts?

Intrusive thoughts are unwanted ideas, images, or urges that pop into your mind unexpectedly. They often feel upsetting or out of character, but they’re essentially “junk mail” from the brain — not meaningful or important.

Are intrusive thoughts normal?

Yes, everyone can have them. Most children (and adults) experience intrusive thoughts at times, and in many cases they pass quickly without causing problems.

What causes intrusive thoughts?

They’re a normal byproduct of how the brain works, but they can become more frequent or “sticky” in kids who are anxious, perfectionistic, or dealing with conditions like OCD or trauma. Paying extra attention to the thought can also make it return more often.

Do intrusive thoughts mean I want to act on them?

No. Having an intrusive thought doesn’t mean you want to act on it or that it reflects who you are. In fact, these thoughts are often the opposite of what someone would ever want or do.

The post What Are Intrusive Thoughts? appeared first on Child Mind Institute.

Claude Science is Here, Antibiotics Designed by Text Prompt Among Applications

Anthropic has released Claude Science, an AI workbench for scientists that consolidates fragmented research tools, including over 60 scientific databases and connectors pre-configured for genomics, proteomics, structural biology, and more, into a single reasoning layer. The platform joins an increasingly crowded ecosystem of tech platforms specialized for biology and aims to accelerate scientific discovery by making domain expertise more accessible.

Anthropic’s life science partners are delivering applications. Basecamp Research is targeting global public health, where drug-resistant infections play a role in nearly five million deaths per year. The London-based team has announced that its antibiotic design and vaccine target prediction EDEN models will now be available through Claude Science.

A metagenomic foundation model, EDEN demonstrated a 97% success rate when designing functional peptides with high potency against World Health Organization (WHO) critical-priority and multidrug-resistant pathogens. The work was done in collaboration with César de la Fuente, PhD, presidential associate professor at the University of Pennsylvania.

In a Claude Science demo, Oliver Vince, PhD, co-founder at Basecamp, uploaded a sample patient microbiology report. When given a simple natural language prompt, the platform designed peptides, predicted their efficacy, and provided a shortlist of candidates most likely to succeed in experiments in minutes.

While generating human-ready antibiotics at the click of a button is still a step away, Vince said democratizing these tools is a powerful first step, particularly for researchers in regions where accelerated computing infrastructure is not readily accessible.

“Most models require you to be a computational scientist,” Vince told GEN Edge. “Now, potentially any clinician in the world can chat with Claude and design an antibiotic that may work.”

“From a strategic perspective, you want the people with the most agency to solve the problem,” added Phil Lorenz, PhD, CTO at Basecamp. “Not the model builders who are two or three steps removed.”

Full stack

Founded in 2019, Basecamp has spent its initial years building a full computational stack spanning data, models, and therapeutic assets.

In addition to antibiotics and vaccines, the company’s U.S. office, based in Cambridge and led by Jonathan Finn, PhD, Basecamp CSO and former CSO of Tome Biosciences, has fine-tuned EDEN for programmable gene insertion. The approach places large therapeutic DNA sequences at precise locations in the human genome, expanding upon CRISPR-based approaches that use small edits to address a limited number of indications.

EDEN’s generalizability is enabled by training on BaseData, the company’s proprietary dataset composed of 9.8 billion protein sequences collected over 200 diverse and extreme locations, including thermal springs, polar ice, and high-altitude plateaus, across more than 30 countries. The database provides a 10-fold expansion of known protein diversity when compared to all public databases combined.

In March, the team published the compounding advantages of BaseData on model performance in a technical report on scaling laws for metagenomics. Basecamp is steadily pushing forward that data diversity through the Trillion Gene Atlas, a partnership with Anthropic, NVIDIA, PacBio, and Ultima Genomics that aims to scale BaseData 100-fold over the next two years.

Vince emphasizes that model deployment and integration into real-world workflows will be critical for these models to reach their full potential. Basecamp anticipates releasing more applications over the next year.

“I think it will surprise people what these models can do,” he said.

The post Claude Science is Here, Antibiotics Designed by Text Prompt Among Applications appeared first on GEN – Genetic Engineering and Biotechnology News.