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PubMed2026

Time-varying hazard rates reveal patterns of progression in HR+/HER2- metastatic breast cancer: Towards risk-adapted monitoring.

BACKGROUND: optimal imaging intervals for patients with hormone receptor-positive/HER2-negative metastatic breast cancer (MBC) remains undefined. Aim of this study was to analyze the temporal patterns of disease progression to identify high risk subgroups that may benefit from intensified monitoring. METHODS: we analyzed 149 hormone receptor-positive/HER2-negative MBC patients prospectively enrolled in the MAGNETIC.1 trial (NCT05814224) and treated with first line endocrine therapy. Hazard rates (HR) for disease progression were determined according to clinico-pathological and liquid biopsy features. RESULTS: in the overall population, two distinct progression-risk peaks emerged at 2-3 months (32.9/1000 person-months) and at 24 months (28.0/1000). Higher risk of progression was observed in lobular carcinoma (61.1) [HR 61.12 per 1000 person month (pm)], progesterone receptor-negative status (HR 39.07), fulvestrant-based treatment (HR 46.88), liver metastases (HR 59.00), and presence of ≥ 3 metastatic sites (HR 40.10). CONCLUSIONS: Hazard distribution in hormone receptor-positive/HER2-negative MBC is biphasic and modulated by readily available clinical variables. High-risk subgroups may benefit from intensified radiologic and liquid-biopsy surveillance during the first three months and around two years after treatment start.

Breast cancer↗

PubMed2026

An Assessment of Reliability Estimation Methods for Binomial Health Care Quality Measures.

We evaluated the performance of commonly used methods for estimating the reliability of binomial health care quality measures using simulated datasets spanning a range of performance score means and variances, numbers of entities, and patient sample sizes. For each simulation, reliability was estimated for all selected methods and compared with the known true reliability derived from the simulation parameters, with methods assessed on their accuracy and precision. Logistic regression with reliability estimated on the outcome scale demonstrated the highest accuracy and precision among all methods evaluated. The widely used Adams beta-binomial method performed poorly, although a modification recommended by Nieser and Harris substantially improved its performance. These approaches are applicable only to binomial measures. Among methods that can be applied to both binomial and continuous measures, permutation resampling of the Spearman rank correlation coefficient was the most accurate and precise, outperforming other commonly used approaches. Overall, for binomial quality measures, logistic regression on the outcome scale is the preferred method for reliability estimation, followed closely by the modified beta-binomial approach, while for non-binomial measures, permutation-based Spearman rank correlation appears to be the most suitable method.

Reproducibility of Results↗

PubMed2026

The future of pediatric vesicoureteral reflux management.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux (VUR) is a common condition in pediatric urology, yet important uncertainties persist regarding risk stratification, imaging strategies, and prevention of long-term renal damage. Emerging technologies may help address these challenges. This review provides a forward-looking overview of recent advances in artificial intelligence (AI) and immunomodulation that may influence future management of pediatric VUR. METHODS: A forward-looking literature review was performed using the PubMed database (January 2000-March 2025), focusing on studies addressing AI, immunomodulation, or vaccination in the context of VUR and urinary tract infections. Criteria of inclusion were the relevance to pediatric VUR, the novelty of the proposed concept, the potential clinical implications and, for the AI literature, the existence of a clinical evaluation of the algorithm on a dataset from patients. KEY FINDINGS AND LIMITATIONS: AI-based models show promising performance in supporting clinical decision-making, including prediction of the need for voiding cystourethrography, automated grading of VUR, estimation of recurrent urinary tract infection risk and prediction of chemoprophylaxis. These tools may facilitate more individualized diagnostic and therapeutic strategies, although current evidence is largely retrospective and requires prospective validation. Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways associated with renal scarring. While early experimental and adult clinical data are encouraging, pediatric-specific evidence remains limited, and clinical applicability in children with VUR is not yet established. CONCLUSION: Artificial intelligence and immunologically targeted strategies represent complementary, emerging approaches that may contribute to more personalized management of pediatric VUR. At present, both should be regarded as exploratory tools whose clinical impact will depend on further validation and appropriately designed pediatric studies.

Humans↗

PubMed2026

Genomic science and the nurse educator's role: Promoting integration from curriculum to clinical practice.

BACKGROUND: Registered nurses and nurse educators play a critical role in preparing future clinicians to translate genomic discoveries into practice. However, emerging evidence suggests that both groups may lack sufficient knowledge and confidence in genomics, potentially limiting their ability to teach, mentor, and apply genomics in real-world settings. This gap is especially concerning in Aotearoa New Zealand, where the genomic literacy of nurse educators and clinicians remains underexplored. OBJECTIVE: This study aims to: (1) assess nurse educators' genomic literacy and confidence in teaching genomics; and (2) evaluate registered nurses' knowledge and confidence in applying and teaching genomics in clinical practice. DESIGN: Exploratory descriptive qualitative. SETTING: This study was conducted in the greater Auckland area. PARTICIPANTS: A total of 17 participants were recruited using purposive sampling to ensure a diverse range of perspectives across varying levels of teaching experience, disciplinary backgrounds, and exposure to genomic content. METHODS: Data were collected using semi-structured focus group interviews, a method well-suited for generating in-depth discussion and facilitating interaction among participants with shared professional interests. The collected data were analysed using thematic analysis methods. RESULTS: The findings offer insight into the preparedness of New Zealand's nursing workforce to engage with genomic-informed healthcare and inform strategies for integrating genomics into nursing curricula and continuing professional development. Given the interdisciplinary nature of genomic healthcare, these insights may also be relevant to other health professionals-including midwives, pharmacists, and allied health practitioners-who increasingly encounter genomic information in clinical practice and require foundational competencies to support patient care. CONCLUSION: Addressing this educational gap is critical to ensuring that nurses-key facilitators of patient care and public health-are equipped to deliver safe, equitable, and evidence-based genomic healthcare.

Humans↗

PubMed2026

Meniscal preservation in the age of biologics: toward a quantitative decision algorithm for personalized repair.

BACKGROUND: Despite advances in arthroscopic repair and biologic augmentation, surgical indication for meniscal tears remains heterogeneous. No standardized framework currently integrates biomechanical, clinical, and biological determinants to guide repair versus resection. PURPOSE: To develop a quantitative decision model-the Meniscal Preservation Score (MPS)-that unifies biomechanical and biological evidence to stratify reparability potential and standardize treatment selection in meniscal surgery. METHODS: A systematic evidence synthesis conducted in accordance with PRISMA 2020 reporting standards of studies published from 2000 to 2025 in PubMed, Embase, and Scopus identified key determinants of meniscal healing. Five consistent predictors-patient age, vascularity, tear morphology, associated pathology, and activity profile-were weighted through a two-round modified Delphi consensus among ten experienced knee surgeons. The resulting 0-9-point MPS was incorporated into a stepwise decision tree linking lesion morphology, biological context, and surgical strategy. Conceptual validation used 50 simulated cases and a retrospective cohort of 45 patients to test agreement between algorithm recommendations and expert surgical decisions. RESULTS: The MPS achieved 86% concordance with expert judgment in simulation and 84% agreement in clinical validation. In this retrospective exploratory cohort, cases in which surgical management was concordant with MPS recommendations demonstrated higher mean IKDC scores at 24 months and lower observed reoperation rates. These findings should be interpreted as associative rather than causal, as treatment allocation was not controlled and discordant cases may have represented inherently more complex pathology. CONCLUSION: The MPS represents an evidence-informed decision-support framework designed to systematize reparability assessment. While exploratory analyses suggest structural coherence with expert reasoning, prospective implementation and external validation are required before clinical adoption as a predictive tool. LEVEL OF EVIDENCE: conceptual model with exploratory validation.

Humans↗

PubMed2026

Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study.

BACKGROUND: The predictD intervention, delivered by family physicians (FPs), has demonstrated effectiveness and cost-efficiency in preventing depression and anxiety. The e-predictD study aims to design, develop, and evaluate a novel personalized intervention for depression prevention by integrating information and communication technologies (ICTs), risk prediction algorithms, and decision support systems (DSS) for both patients and FPs. OBJECTIVE: To evaluate the satisfaction, usability, and acceptability, of a beta version of the e-predictD intervention in primary care settings. METHODS: The e-predictD intervention follows a biopsychosocial approach, including an initial patient-FP interview, specific FP training, and an app. A β-version was tested in a pilot study without a control group over three months. The app integrates a validated depression risk prediction algorithm, decision algorithms, and a monitoring system supporting the DSS. The DSS generates a personalized prevention plan (PPP) from eight intervention modules: physical exercise, social relationships, problem-solving, communication skills, decision-making, assertiveness, sleep improvement, and cognitive restructuring. Patients and FPs discussed the PPP in a 15-minute baseline interview, selecting modules for implementation over three months. Semi-structured interviews gathered feedback. Assessments included depression (PHQ-9), anxiety (GAD-7), quality of life (SF-12), and major depression risk (predictD algorithm). RESULTS: Six FPs from six Spanish cities enrolled 56 non-depressed patients at moderate-to-high risk of depression; 47 (84%) completed follow-up. The app was used for a median of six days (interquartile range: 1-30). Both FPs and patients expressed satisfaction, leading to incorporated improvements. After three months, significant reductions in major depression risk and anxiety symptoms were observed, alongside improved mental quality of life. However, no significant changes were found in depressive symptoms or physical quality of life. CONCLUSION: This pilot study supports the feasibility and acceptability of the e-predictD β-version, despite lower-than-expected app usability. Health improvements were observed, warranting confirmation in a randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT03990792.

Adult↗

PubMed2026

Targeting SIRT6: the design and therapeutic implications of activators and inhibitors.

Sirtuin 6 (SIRT6) is an NAD+-dependent deacylase that maintains genomic stability, regulates metabolism, and influences aging, making it an attractive but challenging therapeutic target. Pharmacological modulation of SIRT6 holds promise for cancer and metabolic disorders, yet its context-dependent functions demand precise intervention strategies. Potent, selective, and drug-like chemical probes are therefore essential to dissect SIRT6 biology and to validate its therapeutic potential. This review critically evaluates recent medicinal chemistry advances in SIRT6 modulation. We focus on structure-guided design strategies and structure-activity relationships (SAR) that have transformed initial hits into optimized leads for both activators and inhibitors, highlighting the remaining challenges in achieving isoform selectivity and drug-like properties.

Sirtuins↗

PubMed2026

Food-derived extracellular vesicles as delivery platforms for medicine-food homology components in metabolic syndrome.

Diet-induced obesity and associated metabolic syndromes have become major global public health challenge, highlighting the urgent need for safe and effective strategies. Recently, food-derived extracellular vesicles (FDEVs) have garnered increasing attention as natural nanocarriers due to their excellent biocompatibility and specific targeted delivery capabilities. FDEVs can efficiently deliver medicine-food homology components (MFHCs) to precisely regulate lipid metabolism, inflammatory responses, and insulin sensitivity, thereby improving obesity and its metabolic abnormalities. This systematic review summarizes recent advances in the use of FDEVs as delivery vehicles for MFHCs to suppress diet-induced obesity and metabolic syndrome, with a particular focus on the underlying molecular mechanisms, including signaling pathway regulation and cellular metabolic remodeling. In addition, the clinical translational potential and industrial application prospects of FDEVs are evaluated, and key challenges related to preparation techniques, safety assessment, and large-scale production are discussed. By integrating current evidence, this review aims to provide theoretical framework and future perspectives for the development of FDEVs as a novel targeted delivery platform and treatment of metabolic diseases.

Extracellular Vesicles↗

PubMed2026

Pelvic lymph node dissection in prostate cancer: current evidence, controversies, and future directions.

BACKGROUND: Pelvic lymph node dissection (PLND) remains controversial in the management of prostate cancer. Although it provides the most accurate pathological staging, its therapeutic value beyond staging has long been debated due to conflicting evidence and concerns regarding procedure-related morbidity. OBJECTIVE: To critically evaluate the contemporary role of PLND, particularly extended pelvic lymph node dissection (ePLND), in prostate cancer management in the context of modern imaging, risk stratification tools, and evolving oncologic endpoints. EVIDENCE ACQUISITION: A narrative review of recent literature was conducted, focusing on high-level evidence including randomized trials, observational studies, and contemporary guideline recommendations addressing the indications, extent, oncologic outcomes, and complications of PLND. EVIDENCE SYNTHESIS: Recent randomized and observational studies suggest that ePLND improves nodal staging accuracy and may be associated with modest improvements in metastasis-free survival (MFS) in selected patients with intermediate- and high-risk prostate cancer, although the absolute benefit remains limited and causality is not definitively established. Advances in molecular imaging, particularly prostate-specific membrane antigen (PSMA) PET/CT, together with multiparametric MRI, validated nomograms, and emerging genomic classifiers, now allow more precise identification of patients most likely to benefit from ePLND. The integration of these tools supports a more individualized surgical strategy, including image-guided and sentinel lymph node approaches designed to maximize staging accuracy while minimizing unnecessary dissection. CONCLUSIONS: In the contemporary PSMA imaging era, ePLND continues to play an important role in nodal staging and may contribute to improved oncologic outcomes in carefully selected patients.

Humans↗

PubMed2026

The gut microbiota-obesity axis in the pathogenesis and prognosis of breast cancer.

BACKGROUND: Breast cancer (BC) remains a major global health concern, accounting for 11.7% of all cancer cases and ranking as the second leading cause of female cancer-related deaths worldwide. Increasing evidence highlights the interplay between gut microbiota (GM) dysbiosis and obesity-associated metabolic dysfunction in BC progression. This review aims to elucidate the role of GM in obese patients with BC. METHODS: A systematic literature search was conducted in PubMed and Web of Science databases for publications from July 2015 to January 2025. Search terms combined BC, GM, obesity, dysbiosis, immunity, and microbiome. Article selection prioritized studies investigating microbial alterations in BC patients, mechanistic links between obesity and cancer progression, and GM-targeted interventions. Both original studies and authoritative reviews were included, supplemented by manual reference screening. DISCUSSION: Obesity may trigger systemic inflammation, altered adipokine secretion, and disrupted steroid hormone metabolism via gut-derived β-glucuronidase activity, thereby exacerbating BC occurrence and recurrence. GM dysbiosis-driven metabolites such as branched-chain amino acids (BCAAs) and short-chain fatty acids (SCFAs) can activate oncogenic signaling pathways and immunosuppressive myeloid-derived suppressor cells (MDSCs), fostering tumor immune evasion. Conversely, dietary interventions, probiotics, and fecal microbiota transplantation (FMT) can alleviate dysbiosis, strengthen gut barriers, and restore anti-tumor immunity, improving chemotherapy response and reducing recurrence. However, challenges persist in deciphering BC subtype-related microbial signatures and optimizing microbiota-targeted therapies. CONCLUSION: Future longitudinal studies are needed to clarify causal relationships, validate microbial biomarkers, and translate preclinical findings into clinical applications. Addressing the gut-breast axis may offer transformative potential for precision oncology in obesity-driven BC.

Humans↗

PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

Assay-dependent variability in peptide biomarker quantification: experimental evidence from renalase in chronic kidney disease.

BACKGROUND: Renalase is a promising biomarker for kidney disease, but published levels vary widely between studies. We hypothesised that variability in commercial enzyme-linked immunosorbent assays (ELISAs) kits and matrix effects (serum vs plasma) drive these inconsistencies. METHODS: Paired serum and plasma samples from 56 participants (28 chronic kidney disease (CKD) stages 2-5, 28 healthy controls) were tested using three commercial renalase ELISAs (BTLAB, Cloud-Clone, EIAab). We assessed intra-assay precision, inter-assay agreement (Spearman's rank correlation and Bland-Altman analysis on log10-transformed values), matrix effects, and associations with estimated glomerular filtration rate (eGFR). Diagnostic performance was evaluated by Receiver operating characteristic (ROC) analysis. RESULTS: Inter-assay renalase concentrations differed markedly (up to orders of magnitude), with weak inter-assay correlations (r ≤ 0.25). Bland-Altman analyses revealed large, systematic biases between kits. Only the BTLAB assay showed consistent serum/plasma agreement, a significant correlation with eGFR (ρ ≈ 0.32-0.42, p < 0.05), and moderate discriminatory performance for CKD in serum (AUC = 0.70) and plasma (AUC = 0.68). Cloud-Clone and EIAab produced divergent results and strong matrix-dependent biases. CONCLUSIONS: Observed variability among commercial ELISA platforms may compromise comparability between studies. Harmonisation, standardised reference materials, and cross-validation are necessary before renalase assays can be used reliably in clinical practice.

Humans↗

PubMed2026

Breastfeeding Outcomes After Radiofrequency Ablation and Surgical Excision of Fibroadenomas Compared to Natural History: A Retrospective Cohort Study.

BACKGROUND: Fibroadenomas, common benign breast tumors in women of reproductive age, are increasingly managed with minimally invasive radiofrequency ablation (RFA) or surgical excision. However, their impact on breastfeeding outcomes remains underexplored. We aimed to assess breastfeeding ability and breast tissue changes in women treated with RFA or surgery compared to those with untreated fibroadenomas. METHODS: In this retrospective cohort study, we evaluated 153 women with biopsy-confirmed fibroadenomas across five groups: RFA with prior breastfeeding (RFA-PreBF; n = 26), RFA with posttreatment breastfeeding (RFA-PostBF; n = 22), surgical excision with prior breastfeeding (Surgical-PreBF; n = 30), surgical excision with posttreatment breastfeeding (Surgical-PostBF; n = 20), and noninterventional with breastfeeding (Observation-BF; n = 55). Breastfeeding ability was assessed using a 5-point scale, and breast tissue changes were evaluated via ultrasound at 18 months, and all breastfeeding attempts occurred at a minimum of 12 months postprocedure. Fisher's exact test compared complete lactation failure rates, and a power analysis validated the study design (Fig. 1). FINDINGS: The combined RFA group (n = 48) had a higher complete lactation failure rate (14.6%) than the Observation-BF group (3.6%; Fisher's exact test, p = 0.045). Similarly, the combined surgical group (n = 50) had a higher complete lactation failure rate (16.0%) than the Observation-BF group (p = 0.034). When pooled, the combined intervention group (n = 98) showed a complete lactation failure rate of 15.3% versus 3.6% in the Observation-BF group. Residual tissue correlated with lactation impairment in the RFA group (Spearman's ρ, p < 0.05). Ultrasound showed no tumor recurrence, with cystic changes in some cases not linked to breastfeeding impairment. Power analysis confirmed 80% power to detect a medium effect size (f = 0.25, Fig. 1), supporting the study's robustness.7 RFA-PreBF (n = 26), RFA-PostBF (n = 22), Surgical-PreBF (n = 30), Surgical-PostBF (n = 20), and Observation-BF (n = 55). Breastfeeding ability was assessed using a 5-point scale, and breast tissue changes were evaluated via ultrasound at 18 months. Fisher's exact test compared complete lactation failure rates, and a power analysis validated the study design (Fig. 1).[Figure: see text]Interpretation:RFA and surgical excision are effective for fibroadenoma management but increase the risk of complete lactation failure compared to untreated fibroadenomas. Careful patient selection, precise procedural techniques, and vigilant posttreatment monitoring are essential to optimize breastfeeding outcomes, particularly for women planning future pregnancies.

Humans↗

PubMed2026

Effects of erector spinae plane block on postoperative pain in patients undergoing implant-based breast reconstruction for breast cancer: a randomized controlled trial.

BACKGROUND: Implant-based breast reconstruction after mastectomy causes acute pain. OBJECTIVE: To determine whether a single-shot T5 erector spinae plane block (ESPB) reduces postoperative pain. DESIGN: Single-center, RCT with allocation concealment; blinded assessors and statisticians. SETTING: Tertiary cancer center in China. PATIENTS: 100 adults scheduled for radical mastectomy with implant reconstruction were randomized (1:1); follow-up complete. INTERVENTION: Before induction, ESPB was given under ultrasound guidance at T5 with 30 mL of 0.375% ropivacaine plus dexmedetomidine 1 μg/kg; controls received no block. Standardized general anesthesia and postoperative PCA for both groups. MAIN OUTCOME MEASURES: Resting NRS at 6 h (MCID=1). Secondary outcomes were opioid consumption, quality of recovery, and PONV. RESULTS: ESPB did not significantly reduce resting pain at 6 h at the median (τ =0.50; adjusted difference -0.9; p = 0.08). At the upper tail, pain intensity was lower (τ = 0.75; -1.8; p <0.01). Repeated measures provided additional time-point information, improving estimation precision and test sensitivity. ESPB get lower pain scores at 6, 12, and 24 hours (all p <0.01). But, the 95% CI includes the MCID, the clinical benefit remains uncertain. Opioid use decreased at 24 h (-13.5 mg; p <0.01) and 48 h (-6.6 mg; p <0.01). Quality of recovery improved at 24 h (difference 5 points; p <0.01), but not later. No differences were observed in intraoperative hemodynamics or PONV. CONCLUSIONS: Single-shot T5 ESPB with perineural dexmedetomidine may reduce postoperative pain and opioid requirements and improve early recovery. Further large trials are warranted. Clinical relevance remains to be confirmed. TRIAL REGISTRATION: ClinicalTrials.gov NCT06143020.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Bipolar circumcision with mucocutaneous plane separation: A safe, precise, blood-sparing, dressing-free approach.

Conventional circumcision techniques may result in bleeding, limited control of the mucosal collar, and the need for dressings. We describe a modified bipolar technique enabling near-bloodless dissection with precise mucosal trimming. Following bipolar division of the preputial skin, the inner mucosa is separated using two forceps and retracted proximally, creating a protective plane for safe skin adjustment. The mucosal collar is trimmed with scissors. The wound is closed with sutures and 2-octyl cyanoacrylate, avoiding dressings. This technique has been performed in 55 consecutive cases (median age: 2 years and 2 months; range: 0 days-17 years and 5 months) with no reported complications over a median follow-up of 2 years and 7 months (range: 3 months-4 years and 1 month).

Humans↗

PubMed2026

Interfacial engineering of cobalt tungstate-halloysite nanotube nanocomposite for electrochemical detection of synthetic vanillin in food matrices.

In processed foods and medicine, synthetic vanillin is widely used, although excessive intake poses toxicological risks. Due to the rising usage of synthetic vanillin in food products and associated health hazards, quick, sensitive, and reliable analytical methods are needed to precisely measure vanillin in complex food matrices. This work introduces a CoWO4@F-HNT/GCE nanocomposite as an efficient electrocatalytic modifier for glassy carbon electrodes aimed at trace-level synthetic vanillin detection. Structural and microscopic analyses confirmed phase-pure monoclinic CoWO4, preservation of the tubular aluminosilicate framework, and homogeneous nanoparticle anchoring on F-HNT. Differential pulse voltammetry provided a broad linear range from 0.01 to 372.14 μM and a low detection limit of 4.3 nM, together with excellent selectivity against common interferents, good cycling stability, and high inter-electrode reproducibility. These characteristics position the CoWO4@F-HNT-modified electrode as a cost-effective and reliable platform for on-site quality control of synthetic vanillin in complex food matrices.

Benzaldehydes↗