BackgroundThe university stage represents a critical period for the development of individual mental health. Mobile phone addiction is closely linked to depression and anxiety among college students, and both friendship quality and preference for solitude are tightly associated with college students’ mobile phone addiction and emotional health. Therefore, this study aimed to investigate the relationships and internal mechanisms among mobile phone addiction, friendship quality, preference for solitude, depression and anxiety in college students.MethodsA total of 1083 Chinese college students (58.2% female; mean age = 19.87 ± 1.692 years) were included as participants. Data were collected using the Mobile Phone Addiction Index, Friendship Quality Questionnaire, Preference for Solitude Questionnaire, and Depression Anxiety Stress Scale. Data processing and analyses were conducted using SPSS 26.0 and the PROCESS macro.Results(1) Mobile phone addiction was significantly negatively correlated with friendship quality, and significantly positively correlated with both depression and anxiety; friendship quality was significantly negatively correlated with depression and anxiety; preference for solitude was significantly positively correlated with depression and anxiety. (2) Mobile phone addiction not only directly and positively predicted depression and anxiety among college students, but also predicted depression and anxiety through the mediating role of friendship quality. (3) The direct effect of mobile phone addiction on depression and the mediating effect of friendship quality in the relationships between mobile phone addiction and depression/anxiety were both moderated by preference for solitude, whereas the moderating effect of preference for solitude on the association between mobile phone addiction and anxiety was not significant.ConclusionFriendship quality serves as an important mediating pathway between mobile phone addiction and depressive and anxiety symptoms among Chinese college students. Preference for solitude may amplify the associations of mobile phone addiction with poorer friendship quality and elevated depressive symptoms.
Collaborative care treatment for major depressive disorder
IntroductionMajor Depressive Disorder (MDD) is a burdensome behavioral health condition that is costly and difficult to treat, particularly for patients with severe cases. The Collaborative Care Model (CoCM) has been shown to be effective for moderate depression treatment but less is known about its effectiveness for severe depression. This study analyzes the impact of CoCM treatment on outcomes for depression patients across all ranges of MDD severity at Concert Health.Materials and methodsAnalysis was completed utilizing all closed patient treatment episodes (N = 30,162) at Concert Health between 2018 and 2025. Of these patients, 5,693 began treatment with severe depression. We compare effect sizes for change between baseline and final screener scores across severity levels. Additionally, we utilize logistic regression to complete analyses to understand treatment factors and patient characteristics that are associated with treatment response and remission, as measured by changes in PHQ-9 scores.ResultsThe primary analysis showed that patients with severe depression (PHQ-9 > 20) had slightly lower odds of achieving response compared to patients with moderate depression (OR: 0.93). The treatment factors of insurance type, suicide risk, anxiety presence, and touchpoints also had significant effects on the odds of achieving response and remission.DiscussionThe results suggest that CoCM may be effective for patients with severe depression in achieving treatment response. Patients on Medicaid or with more complex conditions such as anxiety presence or elevated risk for suicide may need higher levels of engagement from the care team to achieve response and remission.
Smoking as a correlate of suicidal behavior and self-harm in adolescents with depressive disorders
Suicidal behavior and self-harm are major public health concerns among adolescents, particularly those with depressive disorders. While smoking has been linked to suicidality in general populations, its independent role in both suicidal behavior and self-harm within well-characterized clinical samples of youth with depressive disorders remains understudied. This study consecutively enrolled 2,343 adolescents (aged 12–18 years) diagnosed with unipolar depression, bipolar disorder, or depressive episode according to DSM-5 criteria at Nanhai Public Health Hospital of Foshan City and The Third People’s Hospital of Foshan between January 2025 and December 2025. Suicidal behavior (≥1 suicide attempt) and self-harm (intentional self-injury or poisoning regardless of intent) were assessed via clinical interviews. Multivariable binary logistic regression models were constructed to identify factors independently associated with each outcome, adjusting for age, sex, education, income, parental education, psychiatric diagnosis, family history of mental illness, physical disease, and smoking status. Among 2,343 participants (mean age 14.99 ± 1.65 years; 77.8% female), the prevalence of self-harm was 76.0% and suicidal behavior was 44.2%. In fully adjusted models, smoking status was the only variable significantly associated with suicidal behavior after adjustment for measured covariates: current smokers (OR = 2.74, 95% CI: 1.81–4.22, P<0.001) and past quitters (OR = 2.32, 95% CI: 1.66–3.28, P<0.001) had higher odds compared to never smokers. For self-harm, current smoking was significantly associated with increased risk (OR = 2.31, 95% CI: 1.33–4.37, P = 0.006). Education level showed a borderline association with self-harm, with each additional year of schooling corresponding to a 10% lower odds (OR = 0.90, 95% CI: 0.81–1.00, P = 0.050); however, this finding should be interpreted cautiously given the exploratory nature of the analysis. No other demographic or clinical variables, including age, sex, or psychiatric diagnosis, were independently associated with either outcome. Smoking was strongly associated with both suicidal behavior and self−harm after adjustment for measured demographic and clinical covariates. These findings underscore the importance of assessing tobacco use as a potential clinical marker of vulnerability in youth mental health settings.
Association of oxidative stress, metacognition, and psychopathology in patients with schizophrenia: a case-control study
BackgroundMetacognitive deficits are common in schizophrenia (SZ) and may worsen symptoms and impair insight. Oxidative stress (OS) abnormalities have also been reported, but findings are inconsistent, and no study has examined their associations with metacognition and psychopathology.MethodsThis case-control study included 89 SZ patients and 90 healthy controls (HC). OS markers, including superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), and glutathione peroxidase (GPX) were measured. The patient group and healthy control group underwent metacognition was assessed using the abbreviated Metacognitive Assessment Scale (MAS-A) and patients’ symptoms with the Positive and Negative Syndrome Scale (PANSS). Covariates included age, gender, education, BMI, and smoking, illness duration, onset age and medication.ResultsPatients showed significantly lower MAS-A total score and subscale scores (all p < 0.01) versus HC. Patients had lower SOD, CAT and GPX (130.69 vs 152.12 ng/L, 2.46 vs 6.62 ng/L, 158.09vs 197.75μmol/L) and higher MDA (9.22vs 7.34μmol/L) than controls (all p < 0.05). Partial correlation revealed that in patients: SOD was negatively correlated with positive/negative/PANSS total and MAS-A decentration scores; CAT was negatively correlated with general pathological/PANSS total scores, and positively correlated with MAS-A total score and its subscales (self-reflectivity, understanding the other’s mind, decentration, mastery), MDA was negatively correlated with negative symptom score and self-reflectivity score, and positively correlated with general pathological score; GPX was positively correlated with most clinical and metacognitive scores. Linear regression revealed SOD, CAT, and GPX significantly associated with the PANSS total score (β = -0.119, -6.169, -0.226; all p < 0.05), and with MAS-A total score (β = 0.021,2.879 0.049, all p < 0.001).ConclusionSchizophrenia patients exhibit OS abnormalities and metacognitive impairments. Greater OS severity correlates with worse metacognition and more severe psychopathology, suggesting OS as a key factor linking these domains.
Bacterial ‘Docking Domains’ May Open New Paths to Next-Generation HDAC Inhibitors
Researchers have uncovered the molecular mechanism bacteria use to build a family of natural anticancer compounds, a discovery that could help scientists engineer improved versions of histone deacetylase (HDAC) inhibitors for cancer treatment.
The study, published in Nature Communications, identifies the long-elusive biosynthetic pathway for FR-901375, a naturally occurring HDAC inhibitor closely related to the approved lymphoma drug Romidepsin. The findings also explain how bacteria “mix and match” components of these molecules to generate structurally diverse compounds, providing a blueprint for designing new drug candidates.
HDAC inhibitors block histone deacetylases—enzymes that help regulate which genes are switched on or off inside cells. By inhibiting these enzymes, the drugs can reactivate genes that suppress tumor growth or trigger cancer cell death. Romidepsin (Istodax) is already approved to treat certain T-cell lymphomas, but researchers have long been interested in developing additional members of this drug family that are more selective and effective.
Although FR-901375 has been known for decades, scientists had never identified the bacterial genes or molecular machinery responsible for producing it.
The new study fills that gap.
The researchers identified the previously unknown biosynthetic gene cluster for FR-901375 in Pseudomonas chlororaphis subsp. piscium and used genetic, biochemical, and structural approaches—including AlphaFold modeling, mutagenesis, mass spectrometry, and gene deletion experiments—to determine how the compound is assembled.
Like Romidepsin and related compounds, FR-901375 belongs to a family of cyclic molecules known as depsipeptides. These drugs are built inside bacteria by enormous enzyme complexes called PKS-NRPS hybrids, which combine two natural-product assembly systems to construct the finished molecule.
A key finding was the discovery of how small protein regions known as docking domains allow different sections of this assembly line to communicate. These molecular connectors enable the portion of the machinery that builds a conserved zinc-binding pharmacophore—the business end of the drug that inhibits HDAC enzymes—to link with a second set of enzymes that constructs a variable peptide “cap.” Differences in this cap influence how individual drugs interact with different HDAC enzymes.
“The βHD domain employs a mechanism to facilitate productive engagement of PKS and NRPS subunits, involving direct binding to a conserved epitope on the SLiM-bearing ACP domain,” the authors write.
The researchers found that one docking element, known as the β-hairpin docking (βHD) domain, plays the central role in joining the two biosynthetic systems. Surprisingly, another docking element previously thought to be equally important contributed relatively little to the interaction and was not essential for FR-901375 production inside bacterial cells.
Structural modeling and laboratory experiments showed that the βHD domain directly binds a conserved region of the acyl carrier protein, allowing the growing molecule to be transferred efficiently from one enzyme complex to the next. The same interaction was conserved across several related HDAC inhibitor pathways, suggesting bacteria use a common strategy to generate multiple drug variants.
“The observation that ACP-SLiM and βHD-C di-domains from noncognate depsipeptide HDAC inhibitor assembly lines engage productively supports the view that the interaction epitope between βHD and ACP domains… plays a key role in the biosynthesis of all members of this clinically important family of anticancer agents,” the authors write.
The team also reconstructed how the FR-901375 pathway likely evolved through gene transfer, duplication, and recombination events that modified the peptide-building portion of the biosynthetic machinery while preserving its ability to connect with the conserved pharmacophore assembly system.
According to the researchers, understanding this evolutionary process could help scientists design entirely new HDAC inhibitors using the same modular strategy that bacteria have refined over millions of years.
“Our work delivers deep insight into evolutionary mechanisms underpinning the combinatorial biosynthesis of depsipeptide HDAC inhibitors,” the authors conclude. “Moreover, it provides a rational basis for developing approaches to the creation of analogues of depsipeptide HDAC inhibitors and other hybrid polyketide-nonribosomal peptides via evolution-guided biosynthetic engineering.”
The findings could ultimately accelerate efforts to develop next-generation HDAC inhibitors with improved potency, greater selectivity, and fewer side effects for treating cancer.
The post Bacterial ‘Docking Domains’ May Open New Paths to Next-Generation HDAC Inhibitors appeared first on Inside Precision Medicine.
A Phase 3 Bridging Study of Viloxazine ER Capsules in Korean Children and Adolescents With ADHD
Conditions: Attention Deficit Hyperactivity Disorder; Attention-Deficit / Hyperactivity Disorder; ADHD
Interventions: Drug: AK-D101; Drug: Placebo
Sponsors: Alvogen Korea
Not yet recruiting
Interventions: Drug: AK-D101; Drug: Placebo
Sponsors: Alvogen Korea
Not yet recruiting
Agriculture is ready for AI, but its data isn’t
Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork.
The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%.
However, what AI vendors usually won’t tell you is that these solutions are only effective if you have a clean, solid data foundation. However, at Reltio, we have experience in this area, including leading technology strategy at a major agricultural distributor and building a data platform used by enterprises worldwide–we’ve seen it first hand.
What AI vendors won’t tell you
Vendor conversations in agriculture tend to follow a familiar pattern. The pitch leads with grand promises around using AI to monitor crop health in real time, optimize irrigation, and squeeze more yield from every acre.
The promise is compelling, but what rarely comes up is the question of whether the data foundation underneath those promises is accurate and complete. If not, there is a real and significant risk that AI will generate misleading outputs that seem authoritative but inspire action that is, at best, counterproductive.
For instance, a yield prediction model fed inconsistent historical data will generate imprecise forecasts. Similarly, a precision irrigation system drawing on fragmented sensor data will make watering decisions that waste resources instead of saving them.
In each case, the AI is failing because the data it was trained on was not sufficient to produce trustworthy outputs. In agriculture, every AI hallucination is a liability, and the likelihood of error is high.
Why agriculture is a uniquely challenging test case
The data landscape across a modern agricultural operation or a large distributor serving thousands of growers is extraordinarily complex.
Modern farming environments make extensive use of IoT devices and machinery. Irrigation systems are automated, tractors navigate fields autonomously, and drones capture field imagery at scale.
However, machine data is disparate by nature. Add in external sources, including weather feeds, U.S. Department of Agriculture data, and third-party market information, and the question of how you bring all of it together into something coherent becomes a significant undertaking.
Agricultural AI also needs to understand more than just customer attributes; it needs to understand the land: GPS coordinates, farm boundaries, field blocks, and soil variation across a single property. Where do you apply fertilizer, and at what rate, and in which specific area of the farm? Not all parts of a field are the same, and an AI system that treats them as if they are will produce recommendations that are at best imprecise and at worst damaging.
There is also a compliance dimension due to the chemicals and the responsibility involved. Operational AI in agriculture needs significantly more checks and governance than it might in a lower-stakes environment. When a flawed recommendation gets acted upon in the field, the consequences can be severe.
What data readiness means in practice
Data readiness is the difference between AI delivering on its promise vs. a “garbage in, garbage out” scenario. Fundamentally, being ready for AI means having a data model that accurately reflects how the business operates.
For a company like Wilbur-Ellis, a 104-year-old, family-owned agricultural distributor, that means understanding who your customers are, which fields they farm, which inputs they need, which suppliers those inputs come from, what they paid last season, and how all of that connects to margin. That information needs to be current, consistent, and accessible across the organization, rather than locked in separate systems that were never designed to talk to each other.
Similarly, for farming operations themselves, data readiness means having a reliable, connected picture of what is happening across every field: soil health records, input application histories, yield data from previous seasons, equipment performance, and real-time sensor readings from irrigation systems.
Governance matters just as much as structure. Prices change, relationships evolve, and suppliers come and go. An AI system drawing on data that was accurate six months ago but has not been maintained will make recommendations based on a version of the business that no longer exists.
Building the foundation that makes AI trustworthy
The good news is that the path to data readiness is feasible. It starts with a strong data model: a single, governed source of truth that connects customers, suppliers, products, pricing, orders, and margins in a way that reflects how the organization operates.
From there, it requires data pipelines fast enough to deliver insights when decisions need to be made, governance frameworks that keep that data trustworthy over time, and security controls that ensure sensitive commercial information is accessible to the right people under the right conditions.
This is precisely the challenge that Reltio, an SAP company, was built to solve. Reltio enables companies to unify their fragmented data so AI agents and systems can operate from a complete picture of the business. Reltio builds a trusted system of context, known as the context intelligence layer, that brings all entities, relationships, rules together under one roof and makes business data easy to access and interpret.
For Wilbur-Ellis, building that trustworthy data foundation has meant being able to ask more complex questions and trust the answers, which is the precondition for any AI system to be genuinely useful.
How agriculture can drive real value from AI
The question worth asking before the next AI conversation is not whether the use case is promising. It almost certainly is. The question is whether the underlying data foundation is strong enough to make the output trustworthy.
Agriculture has always required its leaders to make high-stakes decisions under uncertainty, and AI offers the genuine prospect of making those decisions faster and better informed. That prospect is only achievable for organizations that have done the foundational work first, and the businesses that will get the most from AI are the ones investing in that foundation now.
This content was produced by Reltio. It was not written by MIT Technology Review’s editorial staff.
The burden of care, parenting stress, and navigating welfare services: parents’ everyday experiences of young children with autism spectrum disorder
BackgroundParenting a child with autism spectrum disorder (ASD) is demanding and affects all aspects of life, yet parents’ experiences during the child’s early years remain underexplored, especially from Scandinavian countries. This study examined parents’ experiences in a Scandinavian context characterized by strong parental involvement of both parents, extensive preschool coverage, and comprehensive welfare systems. Our aim was to explore how parents of preschool children experience everyday parenting and how these experiences shape parenting stress and family life.Materials and MethodsThirteen individual semi-structured interviews were conducted with mothers and fathers of children with ASD aged three to five. This study is part of the “Enabling Parents of Children with Autism Spectrum Disorders – A Randomized Controlled Study on Parenting Programs”, registered at Clinical-Trials.gov (ID: NCT05750095). Data were analyzed using Systematic Text Condensation, a descriptive and exploratory cross-case thematic approach.ResultsThree main categories were identified: “Everyday family life”, “Family and social networks”, and “Meeting the system in daily life”. Parents described continuous adaptation to their child’s needs; everyday life required continuous follow-up while managing concerns of siblings and the child’s safety. Experiences of participation and isolation coexisted, and parents frequently fostered understanding and acceptance of ASD while seeking practical and emotional support in everyday life. Preschool services and support were important. In their interactions with welfare services, parents often encounter bureaucratic complexity when seeking competence, stability, and flexibility.ConclusionParenting a young child with ASD is a dynamic process involving ongoing tasks, adaptation, and learning, strongly shaped by both the child’s needs and the coherence of the surrounding support systems. When services are fragmented, insufficient, or uncoordinated, the parental burden and stress increases, whereas moments of mastery and support foster resilience, underscoring the need for competent, flexible, and family−adapted services.
Breast milk Δ9-tetrahydrocannabinol in cannabis users during the postpartum period: correlation between breast milk, maternal urine and saliva samples during early lactation
IntroductionCannabis use during pregnancy and the postpartum period has increased in recent years, raising clinical concerns regarding maternal and infant health, particularly during lactation. However, evidence regarding Δ9-THC concentrations in breast milk during the early postpartum period and their relationship with other biological matrices remains limited.ObjectiveThis study aimed to assess Δ9-THC concentrations in breast milk and saliva, and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH) concentrations in urine, among postpartum women with cannabis use at the time of delivery. A secondary objective was to explore correlations between these biological matrices during early lactation.MethodsA longitudinal observational study was conducted at Vall d’Hebron University Hospital (Barcelona, Spain) between April 2022 and December 2023. Thirteen postpartum women aged over 18 years with a positive urine test for cannabis at delivery and intent to breastfeed were included. Saliva, urine, and breast milk samples were collected at 24 hours, 48 hours, and one week after birth. Δ9-THC concentrations in breast milk and saliva and THC-COOH concentrations in urine were analyzed using liquid chromatography–tandem mass spectrometry (LC-MS/MS).ResultsAmong participants who remained abstinent during the first postpartum week, urinary THC-COOH concentrations progressively decreased but remained quantifiable across all study stages. In contrast, Δ9-THC concentrations in breast milk decreased over time and were below the limit of quantification (LOQ) one week postpartum. Salivary Δ9-THC concentrations were generally low and frequently below the LOQ. Breast milk Δ9-THC concentrations at the first sampling stage were significantly correlated with salivary Δ9-THC and urinary THC-COOH concentrations, whereas no significant correlations were observed at later stages.ConclusionsThis preliminary study suggests that Δ9-THC concentrations in breast milk may decline rapidly after postpartum cannabis cessation, becoming non-quantifiable within the first postpartum week among participants who discontinued use after delivery. In contrast, urinary THC-COOH remained quantifiable for a longer period. Salivary Δ9-THC showed limited concordance with breast milk Δ9-THC and should therefore be interpreted cautiously as a potential surrogate marker. Larger prospective studies are needed to confirm these findings and to support evidence-based breastfeeding counseling for women with recent cannabis use.
Stigma and quality of life in hospitalized schizophrenia patient-family caregiver dyads in Northern China: an actor-partner interdependence model analysis
BackgroundSchizophrenia is a chronic and relapsing mental disorder that is consistently associated with a severely diminished quality of life (QoL) for patients. Existing research has predominantly focused on how the stigma experienced by patients with schizophrenia relates to their own QoL. However, stigma among family caregivers has received considerably less attention, and its potential association with patients’ QoL, in particular, remains underexplored. Therefore, this study aims to systematically analyze the dyadic associations of stigma—as experienced by both patients with schizophrenia and their family caregivers—with QoL, utilizing an actor-partner interdependence model (APIM). Through this framework, this study seeks to explore the interdependence of stigma between patients and their family caregivers and its correlational links to their quality of life.MethodsTwo hundred and sixty-four pairs of schizophrenic patients and their family caregivers were included, and the subjects’ stigma was measured using the Internalized Stigma of Mental Illness Scale and the Conjunctive Stigma Scale, respectively, and the quality of life was measured using the World Health Organization Quality of Life Measurement Short Form. The actor-partner effect of stigma on quality of life was explored by constructing an actor-partner reciprocity model.ResultsThe actor effect of stigma on quality of life was significant for people with schizophrenia and their family caregivers (β=-0.472, p < 0.001, β=-0.779, p < 0.001), and the partner effect of stigma on quality of life was significant for people with schizophrenia and their family caregivers (β=-0.128, p = 0.033, β=-0.419, p < 0.001).ConclusionIn future research and interventions aimed at improving the quality of life for people with schizophrenia and their caregivers, it is important to consider not only the individual’s own stigma, but also how the other person’s stigma is associated with one’s quality of life.