Parents of infants hospitalized in neonatal intensive care units (NICUs) are at elevated risk for emotional dysregulation and distress, disrupting early parent-infant interactions and infant self-regulation. This paper describes the development, refinement, and evaluation of the feasibility and acceptability of the NICU Parent Engagement and Readiness Program (N-PREP), a brief, site-of-care intervention designed to promote parental emotional regulation and responsive caregiving.

Through a five-stage iterative process grounded in implementation science, N-PREP was developed and refined. Feasibility and acceptability were evaluated using session delivery metrics and semi-structured qualitative interviews. Outcomes were assessed across three small pilot implementations: a randomized delayed-start pilot trial (n = 12), a prospective single-arm trial (n = 10), and a quality improvement implementation project (n = 9) using standardized measures of parental emotional regulation, parenting self‑efficacy, sensitivity to infant cues, and bonding, along with post‑then‑pre ratings of improvement.

Across two NICUs, intervention delivery demonstrated strong feasibility, with 94% of dyads receiving at least one session and 60% completing all four. Qualitative findings indicated high acceptability, with parents describing the intervention as supportive, appropriately timed, and responsive to emotional and practical needs during hospitalization. Quantitative findings showed within‑subject improvements across parental emotional regulation, self‑efficacy, sensitivity to infant cues, and bonding. Improvements in emotional regulation were associated with reductions in parent mood, anxiety, and trauma symptoms over time.

N-PREP is highly feasible and acceptable, with promising signals of improving emotional regulation and decreasing distress in parents and infants. Findings support further evaluation in a fully powered hybrid effectiveness-implementation trial.

Metabolic syndrome (MetS) represents a clinical disorder characterized by the clustering of conditions including obesity, elevated blood sugar levels, and high lipid levels, which are intricately linked to the onset of cardiovascular diseases (CVDs), such as atherosclerosis, myocardial ischemia-reperfusion injury (MIRI), and heart failure (HF). Salvia miltiorrhiza Bunge (SM), recognized as a blood-regulating herb in traditional Chinese medicine (TCM), is renowned for its effects in activating blood and resolving stasis. By summarizing the pharmacological research progress and clinical efficacy of SM and its active components in the management of MetS and CVDs, this review analyzes its multi-dimensional pharmacological characteristics and multi-target action pathways, and identifies common pharmacological effects and target networks. Through a comprehensive computational integration of network pharmacology, bioinformatics analyses, ADMET profiling, PASS online bioactivity prediction, and molecular docking, candidate targets and shared signaling pathways underlying the therapeutic potential of SM in MetS and CVDs were explored. These findings were then interpreted in the context of an integrated appraisal of clinical evidence for various SM preparations in managing MetS and CVDs, together with a thorough examination of pharmacological data on SM and its major bioactive compounds. The reviewed clinical and experimental evidence indicates that SM-containing formulations and major SM constituents may modulate several pathophysiological mechanisms shared by MetS and CVDs, particularly inflammation, metabolic dysregulation, oxidative stress, and tissue remodeling. The exploratory computational integration further prioritized PIK3CG, IKBKB, JAK2, STAT3, and IL1B as candidate shared targets associated with NF-κB, PI3K/Akt, MAPK, and AGE/RAGE signaling. SM and its active components have shown potential multi-target, multi-pathway, and multi-action effects in MetS and CVDs, providing preliminary evidence for the treatment principle of "same treatment for different diseases."

Difficult ureter (DU) is a challenging condition that prevents successful access to the upper urinary tract during retrograde ureteroscopic lithotripsy (URS). The present study aimed to identify clinical risk factors for DU and to evaluate the predictive performance of machine learning-based models in patients undergoing URS for ureteral stones.

Medical records of patients who underwent retrograde ureteroscopic lithotripsy between January 2023 and January 2024 were retrospectively reviewed. A total of 221 patients were included and classified into difficult ureter (DU) and non-difficult ureter (NDU) groups. Demographic characteristics, comorbidities, laboratory parameters, and perioperative findings were compared. Conventional statistical analyses were performed using univariate and multivariate regression models. In addition, supervised machine learning algorithms, including decision tree, logistic regression, naive Bayes, support vector machine, k-nearest neighbors, bagged trees, and multilayer perceptron artificial neural network (ANN), were developed to predict DU. Model performance was assessed using 3-fold cross-validation.

Difficult ureter was identified in 34 patients (15.4%). Absence of hydronephrosis, lack of medical expulsive therapy, elevated C-reactive protein (CRP > 5 mg/L), and gout were significantly associated with DU. Multivariate logistic regression analysis identified elevated CRP levels and diabetes mellitus as independent predictors. Among the machine learning models, the multilayer perceptron ANN demonstrated the best performance, with an accuracy of 86.9% and an area under the receiver operating characteristic curve of 0.816. Feature importance analysis revealed CRP as the most influential predictor, consistent with conventional statistical findings.

Elevated CRP levels, gout, absence of hydronephrosis, and lack of medical expulsive therapy are important risk factors for difficult ureter during ureteroscopic lithotripsy. Machine learning-based models, particularly artificial neural networks, may provide additional predictive value and serve as decision-support tools for preoperative risk stratification in clinical practice.

In the UK there is variation in which tests are used for monitoring Long Term Conditions (LTCs). Guidance that underpins testing practices for LTCs is often based on expert opinion rather than evidence. There is little insight into the views of primary care staff as to why such variation exists or on whether current LTC blood test monitoring is optimal.

To understand the views of Healthcare Professionals on blood test monitoring for three LTCs (type 2 diabetes, hypertension and chronic kidney disease), identify factors that shape monitoring practices for LTCs and inform the development and implementation of an optimised testing intervention for primary care.

Qualitative, in depth, semi-structured interviews with HCPs engaged in monitoring blood tests for LTCs in primary care in the West of England.

Twenty-one HCPs were recruited from four GP practices. Practices represented a range of social deprivation scores, patient list size and demographics. Qualitative interviews were recorded, transcribed verbatim and analysed using a reflexive thematic approach.

We identified variation in views and testing behaviours in blood test monitoring across HCPs and practices. HCPs reported that many of the drivers of testing variation in their experience were systemic, including barriers from IT systems, funding structures and inconsistent testing guidelines. Other drivers included socio-behavioural factors such as patient behaviours and HCP experience.

There is a recognition in primary care that testing for LTCs could be optimised but a lack of consensus on how to approach this. Practices employ different and various approaches to testing. This has important implications for effective implementation of interventions to optimise testing practices.

Photoelectrochemical biosensing has become a powerful technique for disease monitoring and therapeutic tracking. Most existing photoelectrochemical platforms are only capable of detecting a single biomarker, which limits their effectiveness in comprehensive disease management. To address this challenge, we developed a novel biosensor that integrates DNA-based logic gates with an In₂O₃/ZnIn₂S₄/CdS photoelectrode for dual-miRNA detection. The rationally designed ternary heterojunction exhibited significantly enhanced photoelectric conversion performance compared with binary composites, providing a high-quality photoelectric sensing platform. Based on it, a photoelectrochemical biosensor with DNA logic gates and a cascade reaction was designed, in which APE1 enabled target recovery and DNA walker movement. MiRNA-144 and Let-7a are recognized as promising diagnostic biomarkers for type 2 diabetes mellitus, early clinical application is hindered for their low-abundance. Upon miRNA introduction, APE1 cleaved hairpins to release strands, leading to GOD immobilization on the electrode. The subsequent reaction with glucose enhanced the photocurrent, enabling quantitative miRNA detection. Benefiting from the APE1-assisted cascade signal amplification strategy, the optimized biosensor displays a good linear correlation between photocurrent response and the logarithmic concentration of dual miRNAs within the range of 0.001 nM-10 nM, with a low detection limit of 0.43 pM (S/N = 3). Moreover, the carefully designed DNA tracks enable a well-ordered sensing interface, offering broad possibilities for clinical diagnosis and disease monitoring.

High intake of ultra-processed foods (UPFs) is associated with an increased risk of type 2 diabetes. However, evidence from randomised controlled trials evaluating the effect of UPF reduction on postprandial glucose responses and glycaemic variability in young adults is limited. The Ultra-Processed Food Reduction Intervention and Continuous Glucose Monitoring (ULTRA-CGM) trial aims to evaluate the effects of a UPF-reducing dietary intervention on postprandial glucose responses and glycaemic variability in healthy Korean young adults.

This is a parallel, two-arm randomised controlled trial enrolling 50 participants (aged 20-39 years) with UPF intake ≥25% of daily energy. Following a 10-day baseline monitoring period using CGM and lifestyle diaries, participants are randomised 1:1 to a 40-minute one-on-one UPF-reducing dietary intervention or a 40-minute one-on-one standard dietary counselling based on national dietary guidelines (control). The intervention focuses on reducing UPF consumption, with participants counselled to maintain their usual total energy intake and lifestyle behaviours. After the intervention, participants undergo a 10-day CGM monitoring period and lifestyle diaries. The primary outcome is the change in postprandial glucose responses, quantified as 2-hour incremental area under the curve following meals, from baseline to postintervention. Secondary outcomes include changes in glycaemic variability metrics (coefficient of variation, SD, mean amplitude of glycaemic excursions, glucose spike frequency), time-in-range metrics, body weight, waist circumference, body composition, blood pressure and handgrip strength. For the analyses, between-group differences will be evaluated using analysis of covariance adjusting for baseline values, with daily CGM data analysed using linear mixed-effects models. This trial will provide evidence on whether targeted reduction of UPFs improves postprandial glucose control and glycaemic variability in healthy young adults.

This study is approved by the Korea University Institutional Review Board (KUIRB-2025-0600-05). It will be conducted in accordance with the principles of the Declaration of Helsinki and the principles of the International Conference on Harmonisation Good Clinical Practice guidelines. Results will be disseminated through publications, academic conferences, PhD dissertation and social and lay media.

NCT07175701.

Digital health literacy (DHL) may be associated with health-related quality of life (HRQoL) in older adults with type 2 diabetes, but the behavioural and contextual correlates of this association remain unclear. This study examined that association, the indirect association through proactive health behaviours (PHB), and potential effect modification by the digital healthcare environment.

A cross-sectional survey was conducted from July to August 2025 among older adults with type 2 diabetes recruited by convenience sampling from primary healthcare chronic disease management registries in Huai'an, China. DHL, PHB and four single-item digital healthcare environment indicators were assessed using structured instruments, and HRQoL using the EuroQol five-dimensional three-level questionnaire (EQ-5D-3L) and EuroQol Visual Analogue Scale (EQ-VAS). Adjusted indirect-association (5000 bias-corrected bootstrap resamples) and effect-modification analyses used heteroscedasticity-consistent type 3 (HC3) standard errors.

Among 1053 participants, the mean DHL score was 21.24±10.43, the mean PHB score 76.75±9.88 and the mean EQ-5D-3L utility score 0.968±0.069. The adjusted total association between DHL and utility was significant (B=0.000569, 95% CI 0.000228 to 0.000910), whereas the direct association was not (B=0.000152; p=0.338). The indirect association through PHB was significant (B=0.000418, bias-corrected 95% CI 0.000260 to 0.000619) and accounted for 73.5% of the total. There was no robust evidence of effect modification for EQ-5D-3L utility: interactions with remote service use and online health management use reached significance only under HC3 inference and were not reproduced in sensitivity analyses. An exploratory positive PHB-by-remote-service-use interaction was observed for the supplementary EQ-VAS outcome.

Higher digital health literacy was associated with better HRQoL among older adults with type 2 diabetes, with a significant cross-sectional indirect association observed through proactive health behaviours. No robust effect modification was identified for the primary EQ-5D-3L outcome. These findings support further longitudinal and intervention research on digital health capabilities and proactive health behaviours in this population.

Incisional hernia repair is challenging because of its high recurrence rate. However, the impact of sarcopenia on the outcomes of incisional hernia remains controversial. This study aimed to investigate the clinical effects of computed tomography (CT)-derived muscle quality in patients who underwent incisional hernia repair.

Clinical data and images of patients who underwent ventral incisional hernia repair between 2016 and 2018 at a university-based tertiary referral center were retrospectively collected. Muscle quality was assessed via CT using the hounsfield unit average calculation (HUAC), total psoas index (TPI), and indexed psoas muscle area (PMAi). The primary outcome was the association between hernia recurrence and CT-derived muscle quality, while the secondary outcome compared the different muscle quality assessment methods in detecting incisional hernia recurrence.

A total of 105 patients were included, with a recurrence rate of 30.5% (32/105). Adjusting for diabetes mellitus, surgery method, mesh position, hernia width, and all three methods for determined muscle quality, Multivariate analysis identified HUAC L3 without contrast (cutoff ≤ 20.1) as an independent predictor of recurrence (p = 0.035). Kaplan-Meier analysis revealed significantly different recurrence-free survival based on the HUAC cutoff (p = 0.017), whereas TPI and PMAi showed no significant association with incisional hernia recurrence (p > 0.05).

CT-derived muscle quality (HUAC ≤ 20.1 on non-contrast CT at the L3 level) was potentially associated with incisional hernia recurrence. HUAC was superior to TPI and PMAi as a surrogate for muscle quality.

Individuals with diabetes are prioritised for pancreatic cancer screening. Tailored risk prediction models can improve stratification and screening efficiency. However, most existing predictive tools focus on new-onset diabetes, overlooking individuals with long-standing type 2 diabetes.

To develop and validate a short-term pancreatic cancer risk prediction model applicable to both new-onset and long-standing type 2 diabetes.

This retrospective longitudinal cohort study used two independent Chinese cohorts: the Tianjin Diabetes and Health Cohort (n=330 767, registration ID: NCT06913153) for derivation and internal validation, and the Xiamen Regional Electronic Health Record Database (n=105 537) for external validation. The Short-Term risk predictiOn of Pancreatic cancer in Diabetes Mellitus (STOP-DM) model was developed using a random forest algorithm and 11 routinely available clinical predictors to estimate pancreatic cancer risk at 6, 12, 24 and 36 months. Model performance was further evaluated in subgroups stratified by diabetes duration.

The STOP-DM model exhibited the highest predictive performance at a 6-month time window, with the area under the receiver operating characteristic curve of 0.88 (95% CI 0.84 to 0.92) in the derivation cohort, 0.89 (0.83 to 0.94) in internal validation and 0.86 (0.83 to 0.89) in external validation. The top 5% high-risk group identified 59.37% and 48.03% of incident cases in the two validation cohorts and had 11.00-fold and 14.37-fold higher risk than the lowest-risk group. The model performed well in both new-onset and long-standing diabetes, and outperformed existing models for new-onset diabetes in sensitivity and positive predictive value.

The STOP-DM model provides a practical approach to stratify pancreatic cancer risk and deliver targeted screening for individuals with type 2 diabetes, particularly those living with long-standing diabetes. Prospective validation and real-world implementation studies are warranted.

To evaluate whether artificial intelligence (AI)-derived retinal vascular parameters provide incremental predictive value for incident type 2 diabetes (T2DM) beyond the American Diabetes Association Risk Test (ADART), and to assess external performance in an independent Chinese community-based cohort.

We analysed the UK Biobank (n = 63,968; 70:30 derivation/internal validation split) and the BRAVE cohort (n = 1159; external validation). Multivariable Cox proportional hazards models with/without AI-derived retinal parameters were constructed to predict the risk of incident T2DM. Model performance was assessed using Harrell's C-index with bootstrap confidence intervals, calibration, continuous net reclassification improvement (NRI) with event and non-event components, integrated discrimination improvement (IDI), and decision curve analysis.

Retinal arterial fractal dimension (FDa) and arterial tortuosity (atts) were independently associated with incident T2DM after adjustment for ADART. In the UK Biobank internal validation set, adding FDa and atts increased the C-index from 0.746 to 0.751 (delta = 0.005, 95 % CI 0.003 to 0.008); continuous NRI was 0.258, with event NRI 0.158 and non-event NRI 0.100. In BRAVE, the C-index increased from 0.618 to 0.631 (delta = 0.013, 95 % CI 0.009 to 0.016), supporting external validation of discrimination.

AI-derived retinal vascular parameters, particularly FDa and atts, provided measurable incremental predictive information beyond ADART for incident T2DM. These findings support the use of retinal AI biomarkers as complementary risk markers, although prospective validation with large sample sizes, recalibration in target populations, and cost-effectiveness assessment are required before clinical use.