BackgroundMobile gaming is important for children’s social interaction, but its impact on real-life social connectedness depends heavily on family dynamics. How family patterns shape children’s emotional and social experiences around gaming remains underexplored, particularly qualitatively. This study examined how family dynamics influence children’s belonging, emotion regulation, and virtual interactions in mobile gaming.MethodsUsing a phenomenological design, we conducted semi-structured interviews with 20 participants from 10 families across urban, suburban, and rural China. Participants included children (n=10, aged 10-12), parents (n=8, all mothers), and siblings (n=2). All children had played mobile games for ≥6 months. Data were analyzed via thematic analysis (Braun & Clarke). Family patterns were identified based on parental mediation, emotional communication, and children’s sense of connectedness.ResultsThree family patterns emerged. Restrictive-Control Families (4/8) showed high monitoring and low trust, linked to concealment, tension, and social shift to virtual peers. Supportive Co-Play Families (3/8) exhibited shared play, emotional communication, and digital-offline continuity, with children reporting greater resilience and belonging. Sibling-Mediated Families (3 families) featured siblings as companions and emotional buffers, helping manage frustration without direct parental involvement. No severe conflicts or distress were directly caused by gaming in any pattern.ConclusionsChildren’s mobile gaming outcomes are shaped by relational context, not just time. This study identifies three family-level regulatory mechanisms: suppression (restrictive-control), cognitive reappraisal (supportive co-play), and co-regulation (sibling-mediated). These findings extend Social Connectedness Theory, showing how family patterns shape children’s emotion regulation. Supportive co-play and sibling mediation facilitate adaptive regulation and connectedness, while restrictive-control may drive children to virtual spaces. Family-based interventions targeting emotion regulation, not just screen reduction, are recommended. Shifting from “anti-addiction” to “developmental enhancement” offers a safe, practical strategy for integrating gaming into family life.
Additive impulsivity and emotion dysregulation in adolescents with comorbid bipolar and substance use disorder: a cross-sectional factorial study
IntroductionComorbid bipolar disorder (BD) and substance use disorder (SUD) in adolescence is associated with poor clinical outcomes, yet the independent and interactive contributions of impulsivity and emotion dysregulation remain poorly understood.MethodsThis cross-sectional study employed a 2 × 2 factorial design to examine impulsivity, measured with the Barratt Impulsiveness Scale-11, and emotion regulation difficulties, measured with the Difficulties in Emotion Regulation Scale, across four groups of adolescents (N = 128; aged 12–18 years): BD+SUD (n = 32), BD-only (n = 32), SUD-only (n = 32), and healthy controls (n = 32). All clinical participants were assessed during euthymia. Factorial analyses of covariance controlled for age, sex, residence, family structure, and income.ResultsSignificant BD × SUD interactions were found for emotion regulation, F(1,120) = 35.89, p < .001, ηp2 = .230, and impulsivity, F(1,120) = 9.51, p = .002, ηp2 = .073. The BD+SUD group showed the highest scores on both measures, exceeding the SUD-only group by 38.90 points on emotion dysregulation and 26.72 points on impulsivity. In the substance-using subsample (n = 64), impulsivity was the strongest predictor of substance use severity (B = 0.61, p < .001; R2 = .48). The BD+SUD group also displayed earlier illness onset, mixed-feature predominance, greater polydrug use, and exclusive high-lethality suicide attempts. Low income was the strongest exploratory predictor of clinical group membership.DiscussionThese findings support an additive comorbidity model in which BD and SUD jointly amplify impulsivity and emotion dysregulation, and they highlight the need for integrated, impulsivity-focused interventions in adolescents with dual diagnoses.
Are AI chatbots making us lose control of our brains?
This week I’ve been at SXSW London. There’s been music, film, and a lot—and I mean a lot—of talk about AI. I also had the opportunity to sit down with Gloria Mark, a psychologist at the University of California, Irvine, who has spent the last 30 years studying how people interact with digital technologies.
Early in her career, the biggest concerns were the potential impacts of internet and email use on our brains. We may laugh those concerns off today, but it’s true that as the technologies became more ubiquitous and ingrained in our daily lives, our attention spans began to shrink.
Mark is worried that things are only getting worse. The title of our session was “Have we lost control of our brains?” Unfortunately, Mark told me, the answer is yes.
Around two decades ago, Mark started wondering about how our use of devices might affect our attention spans. She set up what she calls “living laboratories,” using sensors and trackers to monitor adult volunteers’ attention, mood, and behavior when they were using devices.
In 2003, she found that the average user had an attention span of around two and a half minutes. That’s how long people could spend focused on one thing before moving on to something else. “That surprised me at the time,” she told me during our session on Wednesday. “I thought: Wow, this is really short.”
But when she repeated the experiment in 2012, she found that attention spans had shrunk—all the way down to around 75 seconds on average, she said. In research she conducted between 2014 and 2020, attention spans shrank further still—to a mere 47 seconds, on average. Yikes.
And it’s not good for us. Mark told me that she’s found switching our attention so frequently is stressful. “We would have people wear heart rate monitors, and … we would see direct correlation between switching attention fast and stress going up,” she told me.
All this distraction makes it harder for us to get stuff done, too. “It just takes longer to do any single task if you’re switching your attention,” she told me. “It’s not great for performance. It’s not great for our emotional well-being.”
And that’s for adults. What about the effects of digital technologies on children? A few months ago, Meta (which owns Facebook and Instagram) and Google’s YouTube were ordered to pay millions of dollars in damages to a 20-year-old woman who had accused the companies of creating products that led her to develop a childhood addiction.
Just a couple of weeks ago, Meta settled another lawsuit, this one brought by a rural school district in Kentucky. The district had also accused the company of designing addictive products that were harmful to students and had sought more than $60 million to cover the costs of their mental-health needs. Around 1,200 other school districts are taking similar legal action against social media companies.
But social media isn’t all bad, all the time. It can provide opportunities for some people, including those from marginalized groups, to form connections that might otherwise be difficult. A 2024 survey of LGBTQ+ teenagers found that while some described social media as a place of rejection and fear, others described it as a place where they felt a sense of belonging, where they could develop friendships and cultivate their identity.
In truth, we can’t definitively say what effects using social media is having on children across the board, says Mark. “There have been lots and lots of studies, and the evidence is to date inconclusive,” she told me. (Despite what you might read in best-selling books on the subject.)
Mark is hopeful that large, long-term studies might finally start shedding a bit more light on this question. An effort of this nature is underway in Australia, which enacted a social media ban for under-16s at the end of last year.
Given this uncertainty over a 20-year-old technology, I wondered if Mark had any thoughts on the potential impacts of AI—an obviously much newer offering that within the space of a couple of years appears to have become deeply integrated into our digital lives.
She told me she’s worried.
When we put in effort to do something—such as evaluating or summarizing content—we’re doing what’s known as “depth of processing,” she told me. “When you’re actively engaged with information, you’re processing it on a very deep level,” she said. “Then you’re more likely to learn it, to understand it, [and] to retain it.”
That’s not happening when most people use AI bots like ChatGPT, Claude, and Gemini. When we ask these tools to write, summarize, or evaluate for us, we’re no longer doing that depth of processing. “You’re deferring your cognitive work to AI,” she said. “And it’s not good for us.”
The risk is that our cognitive abilities will weaken over time. “If you’re not constantly exercising your muscles, they can atrophy,” Mark said. “And that’s exactly what can happen with our minds.” People with weaker critical thinking skills are more likely to fall prey to misinformation, she added.
Interactions with AI-powered “synthetic companions” can be just as harmful. Relationships between human beings take work—time, effort, and understanding. None of that is needed if you’re forming a relationship with a sycophantic bot. The “muscle” we risk atrophying here is emotional intelligence, which surveys suggest is already on the decline, said Mark.
She’s not painting a particularly rosy picture.
“If we continue on this trajectory, attention spans are diminished, loneliness is rising, boredom is rising, emotional intelligence decreasing, and actually our sense of purpose, according to studies, is also decreasing,” she said.
Luckily, she thinks we can course-correct by changing our relationship with these technologies. The key factor is effort.
The more effort we put into something, the deeper the satisfaction we stand to gain, Mark told me. That means making an effort to read a book rather than skimming its summary, and to meet with friends in person when you can. Try not to use GPS in places where you can probably manage without it.
“I love technology; we can’t give it up,” she told me. “[But] we have to learn how to create new life routines.”
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
Experienced Physicians Still Beat AI at Skin Cancer Diagnosis
Artificial intelligence could help support less-experienced clinicians in identifying skin cancer but it still performs more poorly compared with expert dermatologists, research suggests.
The findings, in JAMA Dermatology, suggest that the oft-reported superiority of AI in diagnosing skin cancer may need closer inspection in situations that are more similar to daily clinical practice.
First-generation convolutional neural network (CNN) systems did not maintain their reported advantages when confronted with a broad spectrum of cases, including rare and atypical presentations.
Foundation models were more promising, reproducing a substantial portion of clinical expertise and approaching the diagnostic accuracy of well-trained clinicians while surpassing that of novices.
Nonetheless, these models still fell short of the best experts who had at least a decade of experience.
“This shows that human expertise at the highest level remains indispensable and that experience continues to be the most powerful tool for performance,” reported Luc Thomas, PhD, from Hôpital Lyon Sud in France, and co-workers.
They suggested: “AI tools may be most valuable as decision-support systems for less experienced clinicians, effectively functioning as a virtual mentor.”
The prevailing narrative suggests that AI has matched or surpassed human expertise in medical diagnosis, particularly in the imaging-based specialties.
Yet a substantial gap remains between promising results under controlled experimental conditions and meaningful clinical implementation, which requires integrating factors such as patient demographics, medical history, physical findings, and contextual information.
To get a better comparison in realistic clinical settings, Thomas and team compared the diagnostic performance of 652 physicians with varying dermatological expertise with three AI algorithms: a first-generation CNN model and the PanDerm uni- and multimodal foundation models.
The dataset comprised dermatological images—including clinical and dermoscopic images with associated metadata—from 1117 cases that represented everyday clinical scenarios.
Results showed that expert dermatologists with at least a 10 years’ experience achieved the highest multiclass accuracy, at a mean of 74.2%, outperforming all AI models on this primary endpoint.
The lowest accuracy was for CNN, at 56.7%, while unexpectedly the modern unimodal foundation model outperformed the multimodal version, at a corresponding 72.2% versus 66.3%.
All human readers outperformed the CNN, with the former collectively having an accuracy of 65.9%. However, the unimodal model was better than that of readers with less than a year of experience and those with less than three years of experience, who had accuracies of 59.1% and 68.2%, respectively.
Among the malignant lesions missed by both foundation models, there appeared to be a preponderance of acral localizations.
“The future likely lies in collaboration between humans and machines to optimize diagnostic performance,” the researchers concluded.
“For novice practitioners, AI could serve as a safety net and educational tool. For experts, it could provide an efficient triage modality and a systematic second reading, particularly useful for reducing errors caused by fatigue or inattention.”
The post Experienced Physicians Still Beat AI at Skin Cancer Diagnosis appeared first on Inside Precision Medicine.
Treatment outcomes of an integrated treatment model for patients with co-occurring opioid use and schizophrenia spectrum disorders
BackgroundIndividuals with schizophrenia spectrum disorders are at elevated risk for developing opioid use disorder and often experience higher substance use rates and morbidity. However, integrated treatment models for such patients remain rare.ObjectiveThis study addresses a gap in clinical knowledge by evaluating outcomes from a dual-diagnosis program tailored for patients with both disorders.MethodsThis retrospective case series analyzed electronic medical record data from when patients were enrolled in the program through March 1st, 2025 or program discharge.ResultsSeventeen patients attended at least one appointment. All patients had a diagnosis of opioid use disorder. Co-occurring psychotic disorders included schizoaffective disorder (13/17, 76%), schizophrenia (3/17, 18%), and unspecified psychotic disorder (1/17, 6%). On average, patients attended 58.8% of scheduled visits, and 47.1% remained actively enrolled as of 3/1/25. Medication adherence was high, with patients taking prescribed medications for opioid use disorder at 90.5% of visits and antipsychotics at 96.9% of visits. Compared to an equivalent pre-enrollment period, patients showed a significant reduction in emergency department visits (P=0.00051) and inpatient hospitalizations (P=0.00090).ConclusionFindings suggest that the integrated treatment model could offer benefits for patients with co-occurring opioid use disorder/schizophrenia spectrum disorders; patients had significantly reduced emergency department visit frequency and frequency/duration of hospitalizations. Patients demonstrated high adherence to medications for opioid use disorder and antipsychotics; appointment attendance remains an area for improvement. Further research with larger samples and longer follow-up is warranted.
Top ultra-processed food researchers call for sweeping policy change: ‘The system is rigged’
The all-star lineup of ultra-processed food researchers who teamed up on a new special edition of the American Journal of Public Health have an overarching message for policymakers: “Do policy!”
That directive, offered by food politics scholar Marion Nestle during a press call ahead of the issue’s release, is accompanied by new polling that shows broad cross-partisan concerns over the health harms associated with ultra-processed foods.
A survey of 2,000 U.S. adults included in the new issue found that the overwhelming majority of Democrats, Republicans, and independents agreed that ultra-processed foods are addictive and a major cause of obesity, type 2 diabetes, and cardiovascular disease. The survey also found majority support in all parties for government interventions including testing additives for safety before they can be included in food products, banning artificial dyes, requiring warning labels, and ordering companies to reduce the amount of sugar and salt in their foods.
The Download: AI can run your admin department now
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
How small businesses can leverage AI
From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. Large companies can hire experts to handle these tasks, but small businesses don’t always have that luxury.
That’s where AI comes in. Today’s models can already take on a range of basic administrative work, from organizing notes and summarizing meetings to invoicing, goal-setting, and social media planning. Find out how small-business owners can put AI to work.
—Peter Hall
This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox, sign up here.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Anthropic has confidentially filed for IPO ahead of OpenAI
It aims to go public as early as this fall. (CNN)
+ The company did not disclose its target valuation. (Guardian)
+ It’s expected to list shortly after a trillion-dollar IPO by SpaceX. (BBC)
+ Beating OpenAI in the IPO race could have a big impact. (WSJ $)
2 The EU may exclude US cloud giants from critical contracts
The likes of Amazon, Microsoft, and Google could be shut out. (Reuters $)
+ The EU aims to reduce its dependence on US tech. (FT $)
+ Trump supercharged this sovereignty push. (Politico $)
3 Florida has become the first state to sue OpenAI
The lawsuit targets ChatGPT’s alleged child safety risks. (NPR)
+ Florida says OpenAI put profit ahead of safety. (Reuters $)
+ Chatbots are now starting to check user ages. (MIT Technology Review)
4 Hackers stole Instagram accounts just by asking Meta AI for them
They easily broke into a host of celebrity profiles. (404 Media)
+ The exploit shows the risk of offloading support to AI. (TechCrunch)
+ AI is making online crimes easier. (MIT Technology Review)
5 Chinese universities with military ties are seeking Nvidia chips
Two are blacklisted by the US Commerce Department. (Bloomberg $)
+ The Chinese military has sought restricted Nvidia chips for years. (NYT $)
+ US senators have slammed a loophole in chip export rules. (Reuters $)
6 Blue Origin and NASA disagree on a crucial rocket’s next flight
+ Blue Origin says the rocket will fly again this year. (Engadget)+ But NASA is less optimistic. (CNBC)+ The rocket’s failure cast doubt on NASA’s moon plans. (BBC)
7 Moderna has won funding to develop an Ebola mRNA vaccine
The CEPI has pledged over $60 million to the effort. (Ars Technica)
+ To fight an outbreak raging out of control. (MIT Technology Review)
8 China is using AI to predict future political dissent
A company called Geedge Networks is developing the tech. (NYT $)
9 Geoengineering can thicken Arctic ice, but melt results are mixed
Trials show the tech has had a limited impact. (New Scientist $)
10 Top AI labs are expanding research into machine ‘consciousness’
Meta, Anthropic, and DeepMind are increasing their investments. (FT $)
+ A new tool could show how consciousness works. (MIT Technology Review)
Quote of the day
“Sam Altman and ChatGPT have chosen the AI race over the safety and security of our kids. They have chosen profit over public safety, and we’re not going to stand for it in here in Florida.”
—Florida Attorney General James Uthmeier tells reporters why his state is suing OpenAI, the LA Times reports.
One More Thing
ALESSANDRO GANDOLFI
Why the sci-fi dream of cryonics never died
Cryonics is best known for its appearance in sci-fi films like 2001: A Space Odyssey. But its adherents have held on to a dream that advances in medicine will one day allow for resuscitation and additional years on Earth.
Around 500 people are preserved in liquid nitrogen globally, while another 4,000 are on waiting lists. Despite scant evidence that cryonics can work, believers remain optimistic that future science could eventually revive them.
Discover why the hope of human reanimation refuses to die.
—Laurie Clarke
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Hear Dolly Parton reimagined through this spot-on Dire Straits-style cover of “Jolene”.
+ Find out which birds people search for most in this interactive visualization of bird popularity.
+ Explore thousands of Q&As between students and astronauts on the ISS at this interactive site.
+ Paris’s oldest bridge disappeared beneath a giant inflatable cave in this surreal public art installation.
Pharma’s Trial Problem: Outdated Systems, Broken Data, and the Coming AI Reset
Managing Director
Silicon Foundry, a Kearney Company
Clinical development has become the most resource-intensive stage of drug innovation. Across the industry, clinical trials consume 60–70% of total R&D spending, a proportion that continues to rise as trials grow more complex, more data-heavy, and more operationally demanding. The irony is that while science has advanced dramatically, the underlying model for running trials still reflects assumptions from a pre-digital era. The result is an ecosystem in which timelines stretch, costs multiply, and meaningful efficiency gains remain elusive.
AI has reached a level of maturity capable of reshaping this landscape, but its potential remains constrained by a fundamental issue the industry has been slow to confront. The data used to power these systems was never designed with AI in mind. In fact, the true crisis in clinical development today is structural and deeply rooted in how trial data is organized, contextualized, and interpreted.
Why trial models are failing
Clinical trials were built for physical sites, paper workflows, and slow-moving systems. Modern trials look nothing like that. They are distributed, data-heavy, biomarker-driven, and increasingly adaptive, yet they still run on infrastructure designed for a simpler era.
For years, clinical operations have been organized around sites and checklists rather than continuous insight. Data moves in bursts, workflows remain fragmented, and systems rarely talk to one another. Precision medicine expanded what trials could ask of data, but the way trials actually operate has barely evolved.
The problem isn’t only speed or scale. It’s also the quiet erosion of efficiency in places trial plans rarely account for. Across the industry, leaders describe a growing layer of “invisible waste”: repeated handoffs, duplicative manual work, incompatible data structures, and everyday operational friction that steadily stretches timelines and drives up costs, even though it seldom appears in formal project plans.
AI changes the equation, but only if trial data can support it.
Why AI stumbles in pharma
There is no shortage of AI talent, tools, or ambition in the life sciences sector. What is scarce is data that AI can meaningfully learn from. Most early AI-for-clinical-trials initiatives failed not because the models were immature, but because the data they were fed was not curated with clinical intent.
Two challenges define this crisis:
1. General-purpose models cannot interpret clinical nuance.
Models trained on large public corpora can identify patterns, but they lack clinical judgment. If the data is unstructured, inconsistently labeled, or lacks contextual metadata, the model will draw the wrong conclusions with absolute confidence. The well-known “ruler problem”—in which an AI system learned to detect malignant skin lesions based on the presence of a ruler beside the lesion—illustrates how easily models latch onto irrelevant signals.
2. Pharma’s internal data is both rich and unusable.
Organizations hold decades of trial data, but these assets are rarely AI-ready. Different study teams, CROs, and geographies used different standards. Biomarker and imaging data are often stored in systems that cannot communicate with EDC or safety platforms. And clinical notes, PDFs, and unstructured documents require interpretation that models cannot perform without curated training sets.
AI amplifies the quality of the data it is given. If the input is clinically inconsistent, overgeneralized, or disconnected from the trial context, the outputs will be clinically meaningless.
Recognizing this, many pharmas are now investing heavily in curated internal datasets, governance frameworks, and senior AI leadership, often in the form of newly created chief AI officer roles. These leaders are tasked with not just deploying tools, but rebuilding the data infrastructure from which future AI insights will emerge.
The new AI toolkit for clinical trials
Once the data foundation is strong, AI becomes a force multiplier across the entire trial lifecycle. Several categories show particularly high near-term impact potential.
Clinical-grade language models: Purpose-built models that ingest curated internal datasets can help draft protocols, refine eligibility criteria, flag operational risks, and interpret historical trial performance. Unlike general-purpose systems, these models are tuned to reason the way experienced clinical scientists do.
Multimodal AI for patient stratification and endpoint optimization: Integrating imaging, labs, digital biomarkers, and historical trial outcomes enables more precise cohort selection and improves the likelihood of detecting true therapeutic effect. These tools help convert today’s complex data streams into actionable insights.
Synthetic and hybrid control arms: While still emerging, these approaches reduce dependence on large traditional control cohorts by incorporating real-world evidence and model-generated comparators when appropriate. The result is faster recruitment and more efficient statistical design.
AI agents for operations: Operational agents can triage site queries, assist with eligibility adjudication, coordinate scheduling, and draft routine documentation. They are particularly helpful in reducing the administrative burden that slows trial execution.
The most underestimated category, and the one with the most long-term potential, is clinical-driven AI, where the model is trained to interpret clinical data the way a researcher with a PhD or a clinician would. This approach addresses the core issue of context, which is essential for decision-making in regulated environments.
From site-centric to data-centric trials
Trials are gradually evolving away from rigid site-based infrastructure and toward data-centric execution. AI accelerates this shift by enabling continuous monitoring, adaptive decision-making, and greater representation across diverse populations. The next phase of this transition requires progress in several areas:
- Reliable digital biomarkers collected via wearables and sensors that feed directly into the trial data ecosystem.
- Real-world evidence integration that allows trial designs to incorporate external data while maintaining regulatory rigor.
- Improved cohort diversity, supported by AI-driven recruitment models that identify and engage underrepresented populations.
- Always-on trial oversight, where adaptive protocols adjust based on real-time data rather than periodic interim reviews.
As these elements mature, trials will resemble dynamic learning systems rather than static sequences of predefined events.
Pharma cannot do this alone
The clinical-trial innovation ecosystem is now incredibly fragmented. A myriad of startups, many founded within the last five years, are attempting to solve different slices of the trial process. Some focus on recruitment; others on protocol simulation, operational automation, predictive enrollment, or digital biomarker analysis.
This fragmentation creates noise but also opportunity. The organizations that succeed will be those that adopt a hybrid strategy, in which internal data expertise is paired with carefully selected external partners. Evaluating early-stage companies requires disciplined technical assessment and an understanding of which partners can meet enterprise requirements in a regulated environment.
Pharma organizations also face a structural talent challenge. The best AI engineers often gravitate toward startups rather than large enterprises. This dynamic reinforces the need for partnership models that combine internal governance with external innovation rather than relying exclusively on one or the other.
What AI can (and cannot) fix
While AI can dramatically shorten timelines and improve decision-making, it is not a cure-all. It will not rescue a flawed trial design, replace human oversight, or eliminate the need for regulatory rigor. What it can do is accelerate the work around those elements, optimizing how protocols are developed, how patients are selected, how data is interpreted, and how milestones are achieved. The organizations that reap the greatest benefit will be those with disciplined data stewardship and a willingness to rethink long-held operational assumptions.
Erik Terjesen is the managing director at Silicon Foundry, a Kearney Company
The post Pharma’s Trial Problem: Outdated Systems, Broken Data, and the Coming AI Reset appeared first on GEN – Genetic Engineering and Biotechnology News.
Predictive value of antioxidant and thyroid function indicators for non-suicidal self-injury in adolescents with major depressive disorder
BackgroundNon-suicidal self-injury (NSSI) is highly prevalent in adolescents with major depressive disorder (MDD); however, its underlying pathophysiological mechanisms remain incompletely elucidated. Emerging evidence suggests a potential association between antioxidant markers, thyroid function parameters, and the occurrence of NSSI, although research in this domain remains limited. Accordingly, this study aimed to investigate the predictive efficacy of combining antioxidant and thyroid biomarkers with clinical symptoms for NSSI in adolescents with MDD.MethodsThis study recruited 162 adolescents with MDD between September 2022 and January 2026. Participants were stratified into groups based on the presence or absence of NSSI, in accordance with DSM-5 diagnostic criteria. Multidimensional scales were employed to assess the severity of depression, anxiety, perceived stress, and internet addiction (IA). Concurrently, blood samples were collected to measure bilirubin levels and thyroid function parameters. Stepwise logistic regression analysis was subsequently performed to identify independent risk factors associated with NSSI. Finally, receiver operating characteristic (ROC) curves were constructed to quantify the predictive performance of these identified independent factors.ResultsThe prevalence of NSSI in adolescents with MDD was 57.4%. Multivariate logistic regression analysis identified females (OR = 2.246, 95% CI = 1.032-4.888, P = 0.041), HAMD-17 score (OR = 1.183, 95% CI = 1.088-1.286, P < 0.001), indirect bilirubin (OR = 0.890, 95% CI = 0.797-0.995, P = 0.040), and TSH (OR = 2.060, 95% CI = 1.254-3.385, P = 0.004) as independent predictors of NSSI. Furthermore, ROC curve analysis further demonstrated that the four-item combination of sex, HAMD-17 score, indirect bilirubin, and TSH (AUC = 0.776, 95% CI = 0.701-0.850, P < 0.001) had a better ability to identify NSSI.ConclusionAdolescents with MDD, particularly females, represent a high-risk population for NSSI. Reduced levels of indirect bilirubin coupled with elevated TSH levels may constitute the underlying pathophysiological basis of NSSI and demonstrate significant clinical predictive value. In the future, targeted intervention strategies focusing on the antioxidant defense system and thyroid function may offer novel therapeutic avenues for the management of NSSI.
Opinion: Sen. Dick Durbin: Trump is letting Big Tobacco target children
When I was 14 years old, I lost my father to lung cancer. He was 53 and smoked two packs of Camels a day. I have made it a priority during my time in Congress to champion policies that help spare others from this tragedy.
Smoking rates have hit record lows. In 1988, I passed legislation that banned smoking on domestic flights, marking the start of cigarettes disappearing from public spaces.