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Heterogeneous medial prefrontal cortex ensembles exhibit reproducible internal temporal dynamics during choice behavior
Decision-making requires the coordinated integration of sensory information, internal states, and learned rules across time. While the medial prefrontal cortex (mPFC) is known to contribute to this process, how population-level neural activity in the mPFC is temporally organized during decision-making remains unclear. Here, we investigated neuronal population activity in the mPFC of male mice using in vivo calcium imaging while animals performed a touchscreen-based visual cue-dependent location-choice task. We found that mPFC population activity exhibited a stable, low-dimensional, and continuous temporal structure that emerged reproducibly across trials. Using circular phase decoding, we found that these internal population dynamics tracked trial progression across pre-stimulus, stimulus, and post-stimulus epochs. At the single-neuron level, a large proportion of mPFC neurons exhibited reliable, time-locked activity changes around task events. Hierarchical clustering summarized these heterogeneous responses into temporally organized response profiles spanning different phases of the task. Choice-related decoding was observed across these mPFC response profiles, whereas decoding performance in the primary somatosensory cortex (SSp) remained near chance. Together, these results suggest that mPFC population activity exhibits reproducible temporal organization during visual cue-dependent choice behavior, linking single-neuron heterogeneity with population-level progression dynamics. Significance statement Understanding how the medial prefrontal cortex (mPFC) contributes to decision-making is a central question in neuroscience. Using calcium imaging in male mice performing a touchscreen-based cue–location task, we found that mPFC neurons exhibited heterogeneous but reproducible temporal activity patterns across trials. Population-level analyses revealed low-dimensional temporal dynamics that tracked progression through the task, whereas hierarchical clustering provided a descriptive summary of temporally organized response profiles. Choice-related decoding was observed across these response profiles. These findings suggest that mPFC activity during choice behavior is organized at both single-neuron and population levels, providing insight into how diverse neuronal responses are coordinated over time during goal-directed behavior.
The promise of deep urine proteomics for diagnosis of cancer, neurologic, and metabolic diseases
Introduction Urine offers a noninvasive and low-cost source of disease biomarkers, yet most proteomic studies have targeted single conditions. Using deep proteomic profiling and machine learning, we evaluated whether urinary protein signatures distinguish early-stage cancer, neurologic, and metabolic diseases from healthy controls. Methods This case–control diagnostic accuracy study analyzed urine samples from the Ukraine Association of Biobank (UAB), collected during routine medical check-ups. The study included 22 patients each with kidney, bladder, melanoma, prostate, ovarian, endometrial, and cervical cancers; 22 with multiple sclerosis (MS); 22 with metabolic dysfunction–associated steatohepatitis (MASH); and 66 healthy controls, yielding 264 samples analyzed in September 2023. Proteomic assays were performed using the Olink Explore 3072 platform, with laboratory personnel blinded to disease status. Urine proteomes were profiled to identify disease-specific protein signatures. The primary outcome was diagnostic accuracy of multiprotein urine panels for each disease compared with healthy controls, expressed as the area under the receiver operating characteristic curve (AUC), along with sensitivity and specificity calculated at a prespecified threshold. Results Expected sex‑specific differences (KLK3, MSMB higher in males; KLK8, KLK13 higher in females) supported assay validity. Three expression patterns were observed: (1) few strong, symmetric signals in melanoma and endometrial cancer; (2) asymmetry with many up‑regulated proteins in cervical, ovarian, and prostate cancers and in MS; and (3) broad up‑regulation in kidney and bladder cancers and in MASH. Multiprotein models outperformed single proteins, plateauing at five to seven. Maximum AUCs ranged from 0.88 (MS) to 0.98 0.97 (ovarian cancer), with AUCs ≥ 0.95 for seven of nine diseases. Some proteins (e.g., C9orf40, PPY) showed cross‑disease importance. Conclusions Urine proteomics identified disease‑related signals across cancer and metabolic conditions and may enable accurate, noninvasive classification using multiprotein panels, but given the exploratory design and its limitations, the reported accuracies should be regarded as upper-bound estimates requiring prospective validation.
Expression characteristics and clinical significance of PSCA in pancreatic cancer
Explaining the reasons for the divorce of young couples in Western Iran: A qualitative Study from the Beneficiary's point of view
Divorce is an important social issue that has destructive consequences for both the individual and the family. It is a multi-dimensional and complex phenomenon that occurs under the influence of various factors, so this research was conducted to explain the reasons for divorce among young couples in the West of Iran with a qualitative approach. This research was carried out with a qualitative approach and conventional content analysis. Purposeful and snowball sampling were used to identify the participants. Semi-structured interviews were used to collect data and continued until theoretical saturation was reached. In this research, 16 key informants and 32 main participants were present. For data analysis, Graneheim and Lundman method was used. Also, to increase the quality of the research, Guba and Lincoln's four criteria were observed. 212 primary codes, 24 subcategories, and 3 categories came from the analysis of the interviews. Categories and sub-categories include 1- individual factors (sexual problems, mental-psychological disorders, spouse's behavioral characteristics, individualism, physical appearance problems, Formation of a positive understanding of divorce consequences, having defective relationships before marriage, drug use), 2- socio-cultural factors (de-tabooing of divorce, women-oriented social changes, media and social networks, lack of a suitable support system, inappropriate spouse choosing, economic challenges, modeling of divorce), 3- family factors (job's impact on family life, improper communication with spouse's family, accumulation of negative feelings in marital life, issues related to children, cheating on spouse, inefficient relationships, heterogeneity of lifestyle, violence, inability to manage challenges). The results indicate that divorce is a complex, multi-dimensional phenomenon influenced by individual, social, cultural, and family factors. Prevention requires multi-level interventions, including educating couples on sexual issues and communication skills, promoting responsible marital roles, preventing superficial marriages, reducing stigma around seeking psychological help, managing personality and lifestyle differences, guiding families on conflict resolution, and providing access to counseling and mental health services before and after marriage.
Nanomagnetic cyanuric chloride supported Cu as an efficient and reusable nanocatalyst for the synthesis of ortho aminocarbonitriles
Depressive symptoms among older adults in Turkey: Evidence from a nationally representative ageing survey
Background Late-life depression is a growing public health issue in ageing societies, influenced by health, functional, socioeconomic, and psychosocial factors. Evidence integrating health, functional, psychosocial, and healthcare-access determinants using nationally representative data in Turkey remains limited. This study examined determinants of depressive symptoms among adults aged ≥65 years. Methods This study analyzed data from 10,348 participants aged ≥65 years from the nationally representative Turkey Older Persons Profile Survey (TYPA 2023). All analyses incorporated survey sampling weights. Depressive symptoms were defined using the Geriatric Depression Scale-30 (GDS-30) (cut-off ≥10). Hierarchical logistic regression models were constructed in sequential blocks (sociodemographic; health and functional status; psychosocial and healthcare-related factors) to identify independent correlates. Results The weighted mean age was 72.8 years; 55.1% were women, and 46.2% screened positive for depressive symptoms according to the applied screening threshold. In the fully adjusted model, depressive symptoms were associated with female sex, lower education, unmarried status, poorer self-rated health, sensory and functional limitations, perceived age-related restriction, perceived exclusion, and difficulties accessing or communicating with healthcare services. Conclusions Depressive symptoms among older adults in Turkey reflect the combined influence of health decline, functional limitations, negative psychosocial perceptions of ageing, and barriers to healthcare access. Interventions addressing functional decline, financial strain, ageism, and access barriers may support better mental health in later life and inform public health and ageing-related policies.
MRI-derived 3D lower limb muscle shape as a biomarker for disease severity in Duchenne muscular dystrophy
Abstract Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration leading to loss of ambulation. Identification of predictive biomarkers of loss of ambulation is crucial, yet analysis of muscle shape remains underexplored. Using MRI data from 17 children with DMD (10 followed by loss of ambulation) and 10 healthy controls, we analyzed the muscle shapes and volumes of the lower limbs. Correlations with functional metrics (gait tests, dynamometric forces) were evaluated, and exploratory predictive modeling tasks (classification and regression) were performed via random forests with cross-validation. Compared with controls, key muscle groups, particularly the triceps surae, in individuals with DMD presented distinct shape patterns, including increased thickness and reduced extensibility, without consistent differences in absolute volume. In cross-validated analyses and within the limits of this small cohort, models based on shape descriptors showed high accuracy for distinguishing controls from DMD and yielded a mean absolute error of approximately 110 days for time-to-ambulation-loss prediction. These findings suggest that geometric descriptors may provide complementary, acquisition-agnostic information to MRI fat-related measures, but require validation in larger, independent cohorts before any clinical use is considered.
Walking the tightrope of justifiable decision‑making: An exploratory qualitative study identifying barriers and solutions to efficient safety reporting
Background Safety reporting is integral to clinical trial conduct, aiming to protect the rights and safety of trial participants and future patients. Over time, reporting safety events has become increasingly complex, leading to inefficiencies which place extra burden on trial staff and potentially impact patient safety. Attempts have been made to streamline these processes. However, effecting change in practice is challenging. Changes to UK trial regulations introduced in 2025 may reduce inefficiencies. To incorporate these changes effectively, we need to understand what barriers exist for their implementation. This study aimed to identify barriers and solutions to efficient safety reporting processes from the perspective of staff working at registered academic trials units in the UK, with a view to developing recommendations to enable the delivery of more efficient safety reporting practices. Method This was an exploratory, observational, qualitative focus group study of trials unit staff across the UK. Verbatim transcripts of the focus group discussions were analysed using Reflexive Thematic Analysis. Results Twenty-three CTU staff participated in four focus groups. One over-arching theme was generated from the analysis, “ Walking on a tightrope: Making justifiable decisions”. Participants felt that they were performing a balancing act between efficient, risk-proportionate safety reporting and risk-aversion. Possible solutions included clarification of expectations and improved transparency, and improved training and knowledge building. Discussion Despite efforts to streamline safety reporting processes, problems persist with the implementation of risk-proportionate approaches. Concerns that decisions will be deemed inadequate and impact patient safety and well-being make decision-making a difficult balancing-act. Confident decision-making by trial practitioners can be facilitated through access to resources and training provided by regulators, supported by tools and case studies. Trials unit level actions, such as mentoring schemes or platforms to share knowledge and learning, can support knowledge building and confidence development.
Correction: On the Hydrogen Bond Strength and Vibrational Spectroscopy of Liquid Water
Educational strategies to improve health literacy for people with type 2 diabetes in low socio-economic communities: A realist review protocol
Introduction Type 2 diabetes (T2D) has emerged among the top ten causes of disability and mortality worldwide. Health literacy is crucial for effective self-management to reduce the burden associated with T2D. Studies have reported the effectiveness of educational strategies for improving health literacy to promote good health and well-being. However, contextual factors influence the effectiveness of these strategies. Therefore, this paper intends to explain how and why context shapes the mechanisms through which educational strategies work to improve health literacy for adults with type 2 diabetes in low socioeconomic communities. Methods and analysis Theory-driven realist review methods will explain how and why contexts activate different mechanisms through which educational strategies work to produce intended or unintended outcomes in low socioeconomic communities. The following five steps of realist review, which are non-linear and iterative, will be undertaken: (i) Define the scope of the review and locate existing theories on educational strategies to improve health literacy, (ii) Develop the initial programme theories, (iii) Search for evidence, (iv) Select papers and appraise, and (v) Extract data and synthesis. The following databases will be searched, but not limited to, PubMed, Education Resource Information Centre (ERIC), and PsycINFO. Papers will be selected based on relevance, richness, and rigour. Data extraction will follow both inductive and deductive approaches. The Intervention-Context-Actor-Mechanism Outcome (ICAMO) configurations will be utilised to analyse and synthesise data using retroductive reasoning. Finally, the revised programme theories, which explain how the educational strategies are expected to work across different contexts in low socioeconomic communities, will be prepared and disseminated. Conclusion The findings may inform practice, influence policy, and contribute to the design of health literacy programmes in similar settings. The realist review is registered with the Open Science Framework: ( https://osf.io/9w867 )
Nonlinear speaker–listener age interactions shape how listeners’ perceived emotions and voice impressions mediate purchase intention in advertising speech
Development of a scale for measuring the perception of artificial intelligence among mental health consumers
Background Artificial Intelligence (AI) has emerged as a transformative force revolutionizing various sectors, including healthcare, particularly the mental health field. However, the acceptance and integration of AI technologies in different healthcare systems can be influenced by various factors, including cultural, social, and individual aspects. Nevertheless, there is a need for a valid and reliable tool for assessing AI’s perception among healthcare consumers. Aim To develop and validate a tool for the perception of AI among healthcare consumers and apply the tool to assess AI’s perception among mental health consumers in the Jordanian healthcare system. Method A cross‐sectional descriptive correlational design was utilized in the study. Data was collected from a convenience sample of 431 mental health consumers visiting mental health clinics of the International Medical Corps and university hospitals in Jordan. Structured interviews were conducted using an AI Perception (AIP) questionnaire developed by the authors. The questionnaire’s content validity was assessed by an expert panel. Using Principal Component Analysis (PCA), the construct validity of the tool was evaluated, and its internal consistency was examined using Cronbach’s alpha. Descriptive statistics were used to assess the levels of AI perception among participants. Results The final AIP tool consisted of 20 items across 4 domains and has demonstrated strong internal consistency across its four domains: AI acceptance and readiness (α = 0.92), AI perceived importance (α = 0.92), AI perceived risk (α = 0.9), and AI perceived challenges (α = 0.85). The construct validity of the four-domain structure of the tool was supported by PCA. Additionally, the mean scores for each domain indicated the average level of agreement with AI perception items among participants. Specifically, the mean score for AI acceptance and readiness was ( 2 . 7 ± 0 . 96 ). AI perceived importance was (2.18 ± 0.83), AI perceived risk was (2.58 ± 0.92), and AI perceived challenge was (2.78 ± 0.87). Conclusion The findings of this study resulted in developing a valid and reliable 20-item tool to assess AI’s perception among mental health consumers. The tool can be used to assess the predictors of AI’s readiness among mental health consumers. Therefore, aiding policymakers and other stakeholders in understanding the AI adoption barriers from the perspective of end-users. In addition, this study developed the AIP tool that can be validated and used among other populations in future research.
Comprehensive genetic analysis defines the genetic architecture and phenotypic spectrum of Korean patients with congenital hypogonadotropic hypogonadism
Silicon nanoparticle morphology and aggregation state modulate plant antioxidant responses under abiotic stress: a meta-analysis
Abstract Drought, salinity, and heavy metal toxicity collectively reduce global crop yields by more than 50% and share a common biochemical outcome: excessive reactive oxygen species (ROS) accumulation. Silicon dioxide nanoparticles (SiO 2 NPs) have attracted growing interest as a means of bolstering plant antioxidant defences against these stresses, yet the factors governing their efficacy remain poorly defined. Following PRISMA 2020 guidelines, we synthesised data from 31 studies encompassing 313 effect sizes across 11 plant species and three stress categories. Random-effects meta-analysis (restricted maximum likelihood, REML; Paule-Mandel I 2 correction) revealed significant enhancements of catalase (CAT; + 49%), glutathione reductase (GR; + 53%), ascorbate peroxidase (APX; + 29%), and superoxide dismutase (SOD; + 39%), with concomitant reductions in malondialdehyde (MDA; − 32.3%) and hydrogen peroxide (H 2 O 2 ; − 32.2%). Effects were strongest under drought (CAT + 77%) and heavy metal stress (GR + 70%). High heterogeneity (I 2 † ≥ 89% for all variables) was substantially explained by nanoparticle morphology: hollow and porous SiO 2 NPs release up to 14-fold more silicic acid than solid particles, associated with correspondingly larger antioxidant responses. The DLS/TEM hydrodynamic ratio served as a significant negative moderator of efficacy (β = − 0.087; p = 0.042), suggesting that aggregation in suspension reduces nanoparticle bioavailability. Critically, 76% of included studies did not report dynamic light scattering characterization, leaving aggregation state unknown for the majority of the literature. P-curve analysis confirmed genuine biological effects rather than selective-reporting artefacts. What emerges from this synthesis is that NP architecture and dispersibility, rather than dose or species identity, are the variables most worth engineering in next-generation nano-Si crop protection formulations.