The relationship between age-related cognitive changes and cortical macrostructural properties [i.e. cortical thickness (CTh)] has been extensively studied. However, less is known about the relationship with cortical microstructural characteristics [i.e. cortical mean diffusivity (cMD)] even though these are sensitive to preclinical phases of Alzheimer's disease. We studied a sample of 964 cognitively healthy adults (age: 40-82 years; 52% females) with available structural and diffusion MRI data. The preclinical Alzheimer's cognitive composite was used as the cognition measure, and plasma concentrations of neurodegenerative-related (i.e. phosphorylated tau 181 and neurofilament light) and inflammatory (i.e. high-sensitivity C-reactive protein) biomarkers were assessed, together with apolipoprotein ɛ4 status. Neuroimaging data was preprocessed using FreeSurfer and FSL, and a homemade surface-based approach was used to obtain cMD maps. A two-class generalized linear model was used as the main statistical analysis. We identified a significant negative association between both cortical measures (cMD and CTh) and age. cMD associations were more extensive at earlier ages (<50 years), while CTh associations were greater at older ages (>50 years). cMD was positively correlated with cognition and with both neurodegenerative-related biomarkers in prefrontal regions, while the association was negative and more widespread for the inflammatory biomarker. CTh was positively correlated with cognition in more restricted areas than cMD and only negatively correlated with neurofilament light. Also, cMD presented lower levels in apolipoprotein ɛ4 carriers compared to non-carriers, while no results were found for CTh. Correlating cMD with CTh resulted in a regional pattern of negative and positive correlations, differencing somatosensory and associative areas, respectively. Altogether, we show that cMD can capture microstructural cortical changes occurring across adulthood into older age before CTh alterations. Indeed, it seems more sensitive to age-related cognitive decline and pathological and inflammatory processes related to risk profiles, showing an opposite trend to CTh in relation to neurodegenerative biomarker levels. Furthermore, our results suggest a pattern relating the two cortical metrics perhaps reflecting a cortical organization pattern.

Intracerebral Hemorrhage (ICH) is a devastating stroke subtype. Accelerating hematoma clearance is a critical therapeutic goal. This study evaluated non-invasive pulsed ultrasound for enhancing hematoma clearance, edema resolution, and recovery post-ICH.

Twenty-nine rats with striatal autologous blood-induced ICH were randomized into control (n = 11), 2 MHz ultrasound (n = 7), and 8 MHz ultrasound (n = 11) groups. Ultrasound treatment (60 min/day) was applied for 7 consecutive days following ICH induction. Hematoma volume and perihematomal edema (PHE) were assessed by T2-weighted imaging (T2WI) and susceptibility-weighted imaging (SWI) at days 1 and 7 post-ICH. Neurological function was assessed by corner turn and cylinder tests at baseline and days 1, 3, and 7.

Non-invasive pulsed ultrasound significantly enhanced hematoma clearance (2 MHz: 47.7%; 8 MHz: 47.8% vs. control: 20.4%, p < 0.01) and PHE resolution (2 MHz: 53.9%; 8 MHz: 71.8% vs. control: 31.1%, p < 0.05). Behavioral tests showed reduced right-turn bias and forelimb asymmetry in ultrasound groups (p < 0.05). No frequency difference was found.

Non-invasive pulsed ultrasound significantly enhances hematoma clearance, reduces edema, and improves functional recovery post-ICH, supporting its translational potential.

To evaluate how UK guidelines for individual health conditions consider coexisting multiple long term conditions and to propose improvements to guideline development processes so that guidelines appropriately account for and consider coexisting multiple long term conditions.

Analysis and recommendations from review of single condition guidelines.

Clinical guidelines developed by the National Institute for Health and Care Excellence (NICE), UK, 1 January 2013 to 31 December 2024.

56 clinical guidelines developed by NICE covering a broad range of long term conditions.

The extent to which guideline recommendations consider multiple long term conditions and coexisting conditions, distinguishing between concordant conditions (those affecting the same organ system as the index condition) and discordant conditions (those affecting different systems).

All but one of the NICE guidelines (n=55, 98%) included some advice on managing the index condition in the presence of coexisting conditions, and 50 (89%) guidelines offered general guidance on tailoring care. Only 11 (20%) guidelines, however, explicitly referred to multiple long term conditions, and none included a dedicated section on multiple long term conditions or how care should be adapted in this context. 19 (34%) guidelines featured sections looking at specific coexisting conditions or coexisting conditions generally. Coverage of coexisting conditions varied widely across categories of conditions, with mental health guidelines dealing with the most coexisting conditions (median 10, interquartile range (IQR) 4.5-14.75) in contrast with guidelines on cancer and eye disease covering the fewest conditions (median 3, 1-4.5; median 3, 2.25-2.75, respectively). Of the 397 possible concordant pairings, 120 (30%) were referenced, whereas of the 3859 possible discordant pairings, 259 (7%) were referenced, indicating greater coverage of same system combinations. Data on the composition of guideline committees showed wide variation in size, disciplinary diversity, inclusion of generalist clinicians (eg, general practitioners, general physicians, or others with no single specialty focus), and public contributors, although lived experience of multiple long term conditions was rarely specified.

Despite widespread acknowledgement of coexisting or multiple long term conditions, NICE guidelines are predominantly condition specific and offer limited tailored support for the care of multiple long term conditions. Recommendations rarely considered common condition clusters or the cumulative effect of multiple long term conditions. Structured improvements, such as clearer guidance on adapting care, broader cross condition referencing, and more transparent inclusion of lived experience could enhance the relevance and usability of guidelines for clinicians managing patients with multiple long term conditions.

Chronic low back pain has a high incidence rate and poses a threat to human physical and mental health. As the disease progresses over time, patients with chronic low back pain may exhibit corresponding clinical symptoms not only in localized back pain and functional limitations but also in movement patterns, psychological states, and cognitive aspects. As research continues to advance, maladaptive changes in the brains of patients with chronic low back pain have drawn the attention of scholars. Under sustained pathological stimulation, structural and functional alterations may occur in brain regions such as the sensorimotor cortex, prefrontal cortex, and cerebellum. Recently, non-invasive brain stimulation techniques have gained widespread clinical application and hold significant therapeutic value in the treatment of chronic low back pain. This paper outlines the targets and mechanisms of non-invasive brain stimulation techniques for treating chronic low back pain. It summarizes the current clinical applications of transcranial magnetic stimulation and transcranial direct current stimulation in patients with chronic low back pain, and explores the prospects of non-invasive brain stimulation for managing this condition. The aim is to introduce novel approaches to rehabilitation therapy for chronic low back pain and provide a solid foundation for future research directions.

Artificial intelligence (AI) is increasingly integrated into modern healthcare, with rapidly expanding applications in medical diagnostics, laboratory medicine, rehabilitation, and patient-centered digital health solutions. The aim of this narrative review is to provide a critically curated overview of current clinical applications of AI across the healthcare continuum, from diagnosis to rehabilitation, while highlighting their clinical benefits, limitations, and implementation challenges. A targeted narrative literature search was conducted using major biomedical databases, including PubMed/MEDLINE, Scopus, Web of Science, and Embase, with emphasis on recent and influential studies published primarily over the past decade. Evidence was qualitatively synthesized across key clinical domains, including diagnostic imaging, laboratory diagnostics, rehabilitation technologies, and conversational agents. The reviewed literature indicates that AI systems can achieve diagnostic performance comparable to healthcare professionals in selected, well-defined tasks, particularly within imaging-based specialties such as radiology, mammography, ophthalmology, dermatology, and digital pathology, predominantly under retrospective or controlled study conditions. In laboratory medicine, AI-based tools support workflow optimization, result interpretation, and clinical decision support, while in rehabilitation, AI-enabled systems - including robotics, motion analysis platforms, and large language models - facilitate personalized therapy and functional recovery, albeit with heterogeneous evidence and limited prospective validation. AI-based chatbots demonstrate potential to support patient education, mental health interventions, and communication workflows, particularly as adjuncts to clinician-led care. Despite these advances, challenges related to generalizability, algorithmic bias, ethical implementation, and regulatory oversight persist. Overall, this review underscores that AI should be regarded as a supportive clinical decision-support technology rather than a replacement for healthcare professionals, with future research prioritizing prospective validation, real-world effectiveness, and responsible integration into routine clinical practice.

Adolescent depression is a growing concern in Thailand, yet school-based services remain fragmented. This study developed and evaluated "TeenTalk Mender," a digital platform guided by the WHO Six Building Blocks framework to support school health services. A 12-week quasi-experimental pilot was conducted with 55 secondary students, stratified into "general" and "at-risk" groups based on patient health questionnaire for adolescents scores. Feasibility, user satisfaction (technology acceptance model), and depression scores were assessed. The platform achieved a 100% engagement rate. Satisfaction scores were high across both groups with no significant difference (p > 0.05), indicating successful universal design. Although depression scores in the at-risk group decreased, the change was not statistically significant (p = 0.113). The platform demonstrates high feasibility and acceptability, serving as an effective digital tool to connect adolescents with school nurses. Future research should involve larger-scale trials to assess clinical efficacy.

Adolescent depression with nonsuicidal self-injury (NSSI) is a serious public health issue. NSSI involves intentional self-harm without suicidal intent and is common amongst depressed teens, leading to considerable psychological and physical risks. Early detection and intervention are essential to reduce these risks. To explore the influencing factors of adolescent depression with NSSI behaviour and construct a nomogram prediction model and verify its clinical application value.

From January 2023 to April 2025, 136 cases of adolescent depression admitted to our hospital were selected. Patients were randomly divided into training (n = 95) and verification (n = 41) sets in a 7:3 ratio. Multivariate logistic regression was used to analyse the risk factors of NSSI behaviour in the training set, and a nomogram prediction model was constructed. Receiver operating characteristic (ROC) and calibration curves were drawn to evaluate the prediction efficiency of the nomogram model, and verification was conducted on the basis of the verification set. Decision curve analysis was applied to assess the clinical application value of the nomogram model for the prediction of NSSI behaviour.

The training and verification sets included 38 (40.00%) and 15 (36.59%) of cases of NSSI behaviour, respectively. No statistically significant differences in the incidence and clinical characteristics of NSSI behaviour were found between the training and verification sets (p > 0.05). Multivariate logistic regression analysis on the training set indicated that tense parental relationship, long depression duration, co-occurring physical diseases, high depression severity, high anxiety levels, childhood trauma, Electroencephalogram (EEG) frontal α power, functional Magnetic Resonance Imaging (fMRI) dorsolateral prefrontal cortex activation and negative life events were factors associated with NSSI behaviour (p < 0.05). The nomogram model showed good calibration and fit between prediction and reality on the training and verification sets with C-index values of 0.936 and 0.923, respectively. average absolute errors between predicted and actual values of 0.092 and 0.105, respectively. and Hosmer-Lemeshow test p values of 0.452 and 0.523, respectively. ROC curves indicated that the areas under the curve of the nomogram model for predicting the NSSI behaviour of patients with adolescent depression in the training and verification sets were 0.941 (95% CI: 0.887-0.995) and 0.928 (95% CI: 0.834-1.000), respectively, with the sensitivity of 0.929 and 0.846, respectively, and specificity of 1.000 and 0.667, respectively.

The nomogram prediction model based on risk factors for depression with NSSI behaviour is beneficial for the early prediction of such behaviour in adolescents with depression, guiding appropriate clinical decisions and minimising the risk of NSSI behaviour, thereby safeguarding adolescent mental health.

A resilience scale tailored for individuals with eating disorders (EDs) could significantly enhance our understanding and treatment of EDs. Therefore, we developed and psychometrically evaluated a new Resilience in Eating Disorders scale (RED) following COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines.

Informed by prior qualitative interviews, the new RED scale underwent an initial pilot test among patients with EDs (n = 52) and field tests among patients with EDs (n = 169), ED-recovered individuals (n = 61), and a normative sample of the general population (n = 349), all aged between 27.9 and 29.8 years and residing in Spain. In this study, the participants completed the RED scale, Resilience Scale-25 (RS-25), Eating Attitudes Test-26 (EAT-26), World Health Organisation Quality of Life Scale - Brief Version (WHOQOL-BREF), and Hospital Anxiety and Depression Scale (HADS). Data were collected at baseline and after 1 year. Alongside machine learning techniques, exploratory and confirmatory analyses were employed to evaluate the reliability, construct validity, convergent validity, known-groups validity, predictive validity and responsiveness of the RED scale.

The original 52-item version of the RED scale was refined to 44 items following the pilot phase, and ultimately reduced to a 5-item version (RED-5) after field testing and psychometric evaluation. The RED-5 demonstrated strong psychometric properties, with excellent model fit indices (Root Mean Square Error of Approximation (RMSEA) = 0.03, and Comparative Fit Index (CFI) = 0.99) and acceptable internal consistency (Cronbach's alpha = 0.71). Additionally, the RED-5 scale effectively predicted quality of life, anxiety, depression, and ED symptomatology over a 1-year period.

The RED-5 is a concise, psychometrically robust scale specifically developed to assess resilience in patients with EDs. It significantly predicts ED symptoms and quality-of-life outcomes, making it a valuable tool for both clinical practice and research. The RED-5 allows for quick administration and easy scoring. It provides mental health professionals with a tool to guide resilience-informed assessment and more personalized treatment planning.

To investigate the relationship between postpartum depression (PPD) symptoms in primiparous women and uterine recovery, sleep quality and postpartum stress.

This retrospective study enrolled 194 postpartum women who underwent 42-day postpartum examinations in our hospital from February 2024 to February 2025. General demographic information, uterine recovery (including uterine fundal height decline, lochia volume and pelvic floor muscle recovery), sleep quality (determined using the Pittsburgh Sleep Quality Index [PSQI]) and postnatal stress levels (measured using the Maternal Postpartum Stress Scale [MPSS]) were collected through the electronic medical record system. PPD was assessed using the Edinburgh Postnatal Depression Scale (EPDS). Mothers with an EPDS score ≥10 were classified into the PPD group, and those with <10 were classified into the non-PPD group. Univariate and multivariate logistic regression analyses were performed to assess the factors influencing PPD.

A total of 194 primiparous women were included, 56 in the PPD group and 138 in the non-PPD group. The PPD group showed significantly higher percentage of high school education and below, discordant relationship with husband, lack of regular maternity check-ups, illness during pregnancy and poor health of newborn than the non-PPD group (p < 0.05). PSQI (p = 0.001) and MPSS (p < 0.001) scores were significantly higher in the PPD group than in the non-PPD group. In terms of uterine recovery, the PPD group had a significantly lower lochia volume at 24 and 48 h postpartum than the non-PPD group (p < 0.001) and poorer recovery of pelvic floor muscle strength (p < 0.05). Logistic regression analysis showed that high school education or less, illness during pregnancy, PSQI and MPSS scores were independent risk factors for PPD, whereas higher lochia volume at 24 and 48 h postpartum and pelvic floor muscle strength ≥grade III were protective factors.

This study identified several specific independent risk factors (lower education level, illness during pregnancy, poor sleep quality and high postpartum stress) and protective factors (adequate lochia volume and good pelvic floor muscle recovery) for PPD. Clinical practice should strengthen the identification and intervention of high-risk groups and pay attention to the integrated management of postpartum physiological recovery and mental health.

High comorbidity among mental disorders challenges the utility of categorical classifications. The transdiagnostic perspective focuses on common psychological processes, potentially overcoming these limitations. The aim of this study was to explore transdiagnostic factors related to post-traumatic stress and psychopathology in a clinical sample.

Sixty-one patients (73.8% women; aged 20-66 years, M = 43.79, standard deviation (SD) = 12.75) from the Adult Mental Health Unit of the Hospital Clínico Universitario of Valencia were assessed on symptomatology (post-traumatic stress and psychopathology) and psychological variables (emotional dysregulation, resilience, personality, social support, and stressful life events).

Our results indicated elevated post-traumatic stress scores (M = 70.49, SD = 20.33), suggesting clinically significant distress, together with low exposure to stressful life events (Median (Mdn) = 2.00; interquartile range (IQR) = 2.00). Post-traumatic stress and psychopathology were positively correlated with emotional dysregulation and negatively with resilience, social support, extraversion, and conscientiousness. Higher post-traumatic stress and psychopathology were linked to low social support, low extraversion, and high emotional dysregulation. Emotional dysregulation emerged as a key moderating variable, potentially diminishing the protective effects of resilience on post-traumatic stress.

Overall, findings support the transdiagnostic approach, highlighting that factors like emotion regulation contribute not only to symptom development but also critically influence how risk and protective factors affect mental health outcomes, emphasizing the importance of targeting these processes in clinical intervention and prevention efforts.