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Amino acid reprogramming and biofilm-specific tricarboxylate transporters in PET-degrading Piscinibacter sakaiensis.

Plastic-degrading bacteria predominantly colonize polymer surfaces as biofilms, yet it remains unclear whether the biofilm phenotype contributes to metabolism beyond retaining extracellular enzymes. Here, we combine population-level RNA-sequencing across three conditions-biofilm cells on polyethylene terephthalate (PET), planktonic cells incubated with PET, and planktonic cells on maltose-with single-cell Raman spectroscopy to characterize the PET response of Piscinibacter sakaiensis (formerly Ideonella sakaiensis). This integrated approach reveals two metabolically distinct response layers. A carbon-source-driven response shared by all PET-exposed cells is dominated by a broad amino acid reprogramming, led by upregulation of branched-chain amino acid transport genes, enhanced serine biosynthesis, and reduced chemotaxis. A biofilm-specific layer selectively induces tripartite tricarboxylate transporter genes from three distinct genomic loci. This transcriptional feature is accompanied by a single-cell phenotype consistent with a protein-rich and saturated membrane. These results suggest that biofilm formation is not limited to enzyme retention but is associated with selective activation of transport systems, consistent with a putative role in capturing PET-derived intermediates at the polymer interface. This two-layer model separates general metabolic adaptation to PET from biofilm-specific functions and provides a framework for understanding how surface-associated bacterial physiology contributes to plastic degradation.IMPORTANCEPolyethylene terephthalate (PET) degradation in natural and engineered environments is largely mediated by surface-attached microbial communities, yet the physiological role of biofilm state during plastic degradation remains poorly understood. Using the model PET degrader Piscinibacter sakaiensis, we show that biofilm-associated cells are not simply retained near the polymer surface but exhibit a distinct metabolic program characterized by selective induction of tripartite tricarboxylate transporters. In contrast, extensive amino acid reprogramming occurs in both biofilm and planktonic PET-exposed cells, indicating that it is driven by carbon source rather than surface attachment. These findings reveal that PET degradation involves two separable physiological layers: a general metabolic response to PET-derived carbon shared across cell phenotypes, and a biofilm-specific transport response potentially linked to substrate capture at the plastic interface. This work advances our understanding of how microbial physiology is organized during plastic biodegradation and identifies transport processes as previously unrecognized components of PET-degrading biofilms.

PET biodegradation

Context-dependent functional diversity of dorsomedial posterior parietal neurons revealed by single-unit fMRI mapping during naturalistic viewing.

The dorsomedial posterior parietal cortex (dmPPC) plays an important role in episodic processing by integrating sensory, cognitive, and motor information across distributed brain systems. However, how individual dmPPC neurons participate in large-scale functional organization during naturalistic experience remains poorly understood. To address this question, we combined single-unit electrophysiology and awake fMRI in five rhesus macaques of both sexes viewing identical naturalistic video stimuli. Using single-unit fMRI mapping, we generated whole-brain neuron-BOLD functional maps by correlating individual neuronal activity with voxel-wise fMRI signals across the brain. We found that neuron-BOLD functional maps exhibited strong context-dependent organization, with neurons recorded during the same video context showing substantially greater similarity than neurons recorded during different video conditions. Compared with neuronal spiking activity or critical fMRI frames alone, neuron-BOLD functional maps more robustly captured contextual structure. Despite this shared large-scale organization, a substantial subset of neighboring neurons recorded simultaneously from the same electrode displayed markedly distinct whole-brain association patterns, revealing substantial local functional heterogeneity within the dmPPC. This local heterogeneity was not readily explained by waveform-based putative cell class or by opposing neuronal firing dynamics. In addition, distributed cortical and medial temporal regions exhibited highly context-dependent neuron-BOLD association patterns during naturalistic viewing. Together, these findings demonstrate that dmPPC neurons participate in dynamic and heterogeneous large-scale functional organization during naturalistic episodic processing. More broadly, this study establishes single-unit fMRI mapping as a framework for linking single-neuron activity to distributed whole-brain dynamics across contextual conditions.Significance Statement Using single-unit fMRI mapping, this study examined how individual dorsomedial posterior parietal cortex (dmPPC) neurons relate to large-scale brain activity during naturalistic video viewing in macaque monkeys. We found that neuron-BOLD functional maps exhibit strong context-dependent organization and capture contextual structure more robustly than neuronal spiking activity or fMRI frames alone. Despite this shared organization, a substantial subset of neighboring dmPPC neurons displayed markedly distinct whole-brain association patterns, revealing local functional heterogeneity that was not readily explained by waveform-based putative cell class or opposing firing dynamics. These findings provide insight into how local neuronal populations participate in distributed brain-wide functional organization during naturalistic episodic processing.

Journal Article

An individualized nomogram for predicting progression-free survival in systemic anaplastic large cell lymphoma: a multicenter, retrospective, and internally validated study.

OBJECTIVES: To develop an individualized nomogram for predicting disease progression risk in systemic anaplastic large cell lymphoma (sALCL). METHODS: Independent predictors of progression-free survival (PFS) were identified using Cox regression in a multicenter retrospective cohort of 109 sALCL patients (2010-2022). These were incorporated into a three-factor nomogram, evaluated via bootstrapped internal validation (1000 resamples), ROC analysis, C-index, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 PFS events occurred during a median follow-up of 31 months. Multivariable modelling selected serum β2-microglobulin elevation, extranodal disease, and front-line chemotherapy choice (CHOP versus CHOPE or BV+CHP) as autonomous progression drivers. Upon internal bootstrap validation, the nomogram yielded strong prognostic accuracy, achieving AUCs of 0.81, 0.85 and 0.87 for 1-, 3- and 5-year progression-free survival, alongside a corrected C-index of 0.779 (95% CI: 0.699 - 0.861). Calibration plots showed close agreement between predicted and observed outcomes, while DCA confirmed superior net clinical benefit versus conventional IPI or Ann Arbor stratification across multiple decision thresholds. CONCLUSION: This first sALCL-specific nomogram integrates clinical and treatment variables to provide personalized PFS risk estimation. While internally validated, this exploratory, observation-based tool requires external validation and recalibration in prospective cohorts before clinical implementation.

Humans

The Case for Master Protocols for Rare Neurological Diseases.

Master protocol trials allow for simultaneous multiple hypothesis testing within a common framework and might be applicable for rare diseases. In May 2025, the Network for Excellence in Neuroscience Clinical Trials convened a multistakeholder conference to discuss master protocol trials in rare neurological disorders. In this paper, we explore how master protocol trial designs may apply to rare neurological disorders, using the neuronal ceroid lipofuscinoses as an example. Through shared protocol elements and trial infrastructure, master protocols may decrease cost and improve efficiency in testing potential therapeutics in rare disease, accelerating the delivery of urgently needed therapies to patients. ANN NEUROL 2026;100:477-486.

Humans

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

Humans

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products

First insights into the role of evolutionary history in shaping venom composition of Vipera ammodytes.

Understanding intraspecific venom variation requires distinguishing the contributions of neutral population history from natural selection. This study aims to determine whether venom variation in Vipera ammodytes species complex is structured across eight phylogenetic groups. Despite a complex evolutionary history, venom composition did not differ among phylogenetic groups within the analytical framework used, suggesting that shared ancestry alone does not explain venom variation. Whether local adaptation to environmental conditions explains the observed variation remains an open question for future studies.

Animals

Longitudinal Repeated Protein Measurements in a Multiethnic Cohort Identify Novel Diabetes Biomarkers That Reveal Unique Disease Pathways.

There is up to a fourfold increase in diabetes biomarkers identified with longitudinal repeated versus single time point proteomic measurements. The increase in biomarkers identified with longitudinal repeated measurements is supported by a similar proportion being nominated as causal for type 2 diabetes with Mendelian randomization. Proteins unique to the longitudinal repeated analyses highlighted biological pathways (e.g., posttranslational protein modification and cellular structure and cycle regulation) that were distinct from pathways enriched among the shared proteins (e.g., small-molecule metabolic and catabolic processes). Longitudinal protein measurements identify additional novel disease biomarkers and disparate biological pathways compared with single measurement analyses.

Journal Article

Adeno-Associated Virus Gene Therapy Translation: Lessons from Early Regulatory Meetings.

The Platform Vector-Gene Therapy (PaVe-GT) program is a National Institutes of Health (NIH) initiative that aims to develop adeno-associated virus (AAV) gene therapies for four monogenic rare diseases, two organic acidemias and two congenital myasthenic syndromes. PaVe-GT's platform-based approach identifies and diminishes redundancies and applies efficiencies in preclinical, clinical, and regulatory activities. The program's hypothesis is that implementing these efficiencies can accelerate clinical trial initiation. Based on its platform-centric experience and public-serving mission, the PaVe-GT program actively shares its scientific and regulatory learnings with the public to benefit the development of similar gene therapy products for rare diseases. PaVe-GT's first investigational AAV gene therapy candidate is AAV serotype 9 human propionyl-CoA carboxylase alpha subunit (AAV9-hPCCA) for propionic acidemia caused by PCCA deficiency, which received initial feedback from the Food and Drug Administration (FDA) in an INitial Targeted Engagement for Regulatory Advice on CBER/Center for Drug Evaluation and Research (CDER) ProducTs (INTERACT) meeting. Upon further product development that took into consideration the FDA's initial advice, the program obtained the Agency's feedback in pre-investigational new drug (IND) (Type B) and Type C meetings. Here, we share our experience from these meetings, including strategy, preparation, pre- and post-meeting feedback from the FDA, and lessons learned during the AAV9-hPCCA regulatory process, which the program plans to apply across the PaVe-GT platform. Topics discussed in the regulatory meetings included animal model and efficacy studies, toxicology study plans, manufacturing of the investigational AAV product, and clinical trial design. The main lessons learned from the pre-IND and Type C meetings for AAV9-hPCCA are: (1) Pharmacology/Toxicology studies in a single rodent species are sufficient for filing an initial IND; (2) FDA feedback guides product quality improvements and early development of a quantitative potency assay; (3) use of biomarkers as potential surrogate endpoints in a future efficacy trial benefits from collection of data in the natural history study and the first-in-human Phase 1/2 study; and (4) evidence from the Phase 1/2 clinical trial could be leveraged to support a license application. Lightly redacted regulatory documents and comprehensive templates developed by the PaVe-GT team are available on the PaVe-GT website.

Dependovirus

Robotic-assisted transbronchial biopsy versus computed tomography-guided transthoracic needle biopsy for peripheral pulmonary lesions: a systematic review and meta-analysis of direct comparative studies.

Robotic-assisted bronchoscopy (RAB) and computed tomography-guided transthoracic biopsy (CTTB) are competing strategies for sampling peripheral pulmonary lesions (PPLs). Whether they differ in yield or safety is uncertain. To our knowledge, this is the first systematic review restricted to direct comparisons. We searched MEDLINE, Europe PMC, Scopus, Web of Science and ClinicalTrials.gov from inception to 7 July 2026 for studies directly comparing RAB with CTTB in adults with PPLs. The primary outcome was strict 2024 American Thoracic Society/American College of Chest Physicians diagnostic yield. Risk of bias was assessed with ROBINS-I and certainty with GRADE. A cohort-genealogy step identified, per outcome, the largest set of cohorts sharing no patients; only that set was pooled, with Hartung-Knapp and Mantel-Haenszel sensitivity analyses. Five retrospective studies from one US health system were eligible. Four share patients; at most three cohorts are mutually independent. Across those three, diagnostic yield was comparable (risk ratio [RR] 0.99, 95% confidence interval [CI] 0.93-1.06; I²=24%; Hartung-Knapp 0.87-1.13), with an identical relative effect under strict and intermediate definitions although absolute yields fell from 88% to 74-84% under strict criteria. Pneumothorax requiring a chest tube and/or admission was about three-quarters less frequent with RAB across all three cohorts (RR 0.25, 95% CI 0.14-0.46; I²=0%; Hartung-Knapp 0.07-0.96). Strict yield (RR 0.99) and any pneumothorax (RR 0.06) were reported by two cohorts each and neither survives the few-studies correction. RAB took about 50 min longer than CTTB where same-session staging endobronchial ultrasound was counted in the robotic time, but only about 8 min longer than CTTB where it was not. Only one cohort reported yield by lesion size category and none reported yield by bronchus sign or lung zone, so lesion-level subgroups could not be pooled. Certainty was low for pleural complications and very low elsewhere. Low-certainty evidence indicates that RAB is associated with fewer pleural complications, with no statistically detectable difference in diagnostic yield; equivalence was not formally established. Because all evidence is retrospective, confined to one health system, and almost never stratified by lesion size or accessibility, these findings are hypothesis-generating and require a multicenter randomized trial.

Humans

Epithelial regeneration in the gastrointestinal tract.

The gastrointestinal tract possesses a remarkable regenerative capacity to maintain tissue homeostasis against various injuries. However, the intestine and stomach exhibit distinct regenerative strategies. In the intestine, damage to Lgr5-positive (Lgr5+) stem cells induces cellular plasticity and the emergence of transient Revival stem cells (RevSCs), a process critically dependent on YAP/TAZ signaling. Conversely, the stomach utilizes paligenosis, where quiescent p57-positive (p57+) mature chief cells act as reserve stem cells, dedifferentiating to restore damaged tissue. Although the cellular origins differ, both organs appear to share some common regenerative features, including transient activation of pro-proliferative programs such as YAP/TAZ signaling. In contrast, whether Retinoic Acid (RA) signaling also serves as a conserved mechanism for regenerative resolution in the stomach remains to be determined. In this review, we discuss the cellular and molecular mechanisms governing regeneration in these two organs. This comparative analysis provides a framework for future research.

Regeneration

Community-tailored One Health educational intervention to enhance knowledge and practices for zoonotic disease prevention in rural Thailand: A protocol for a prospective cluster randomised controlled Trial in Chanthaburi, Thailand (Saan Suk trial).

BACKGROUND: Zoonotic infectious disease risk arises at human-animal-environment interfaces where pathogen spillover can occur. Rural communities living in biodiverse settings may experience frequent contact with wildlife and shared environments through livelihoods, food practices, and economic activities. Reducing spillover risk and strengthening pandemic prevention requires both structural and individual-level change. Community-based interventions that promote awareness, risk perception, self-efficacy, pro-environmental behaviour, and safe coexistence with wildlife may support prevention by shifting behavioural determinants of zoonotic disease risk. The Saan Suk intervention was co-developed with rural communities in Thailand using a Human-Centred Design approach and is grounded in the Health Belief Model and One Health principles. The intervention is intended to be feasible, acceptable, and deliverable through Thailand's established Village Health Volunteer (VHV) system. METHODS: This protocol describes a parallel-arm, cluster-randomised controlled superiority trial that will be conducted during July - October 2026, in Chanthaburi Province, Thailand. 24 villages will be equally randomised to the Saan Suk intervention or the current practice (control). In intervention villages, trained VHVs will deliver, once a week over four weeks, a multimodal One Health educational intervention designed to improve knowledge of zoonotic spillover, promote protective behaviours, reduce risky wildlife-related contacts, and support respectful coexistence with wildlife. Trained outcome assessment teams will conduct structured interviews with 42 adult participants per village, yielding a total sample size of 1,008 participants. The sample size was calculated for the primary outcome, accounting for clustering, with 90% power to detect a medium effect size (6 points on the 0-100 knowledge scale) at a significance level of 0.05, accounting for a design effect with an ICC of 0.028. The primary outcome is knowledge of zoonotic spillover, transmission pathways, risk factors, protective and risky behaviours, and safe coexistence with wildlife. Secondary outcomes include attitudes, self-efficacy, preventive and risky behaviours, and reported contacts with major local reservoir hosts. A structured questionnaire was developed, expert-reviewed, and piloted for the outcome assessment. Outcomes will be analysed using mixed-effects regression models with random effects for village and adjustment for relevant pre-specified confounders. Primary analyses will follow the intention-to-treat principle. DISCUSSION: This trial will evaluate whether a co-designed, VHV-delivered One Health educational programme can improve knowledge of zoonotic disease prevention and behavioural determinants in rural communities living in close contact with wildlife and shared ecosystems. If effective and feasible, Saan Suk could inform integration into routine VHV training and community-based zoonotic disease and pandemic prevention strategies. TRIAL REGISTRATION: The Saan Suk trial is registered with the German Clinical Trials Register (DRKS). Registration ID: DRKS00038582; date of registration: 11 May 2026.

Zoonoses

Could the preoperative urethral curve be used to predict immediate urinary continence following Retzius-sparing robot-assisted radical prostatectomy? A retrospective multi-center study.

PURPOSE: Immediate urinary continence (UC) recovery following Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) remains highly variable, highlighting the need for reliable preoperative prediction. We aimed to develop and validate models to identify patients likely to achieve immediate UC recovery following RS-RARP. MATERIALS AND METHODS: A total of 580 prostate cancer patients who underwent RS-RARP from four medical centers were assigned to a training set (n=348), an internal validation set (n=103) and an external validation set (n=129). Independent predictors were identified through univariate analysis and LASSO regression. A nomogram was constructed using multivariate logistic regression. Its performance was evaluated with receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. RESULTS: Immediate UC recovery was observed in 84.5% (294/348) of patients in the training cohort, 80.6% (83/103) in the internal validation cohort, and 81.4% (105/129) in the external validation cohort, respectively. Multivariate analysis identified membranous urethral length (MUL) (OR=1.23, P=0.029) and urethral curvature (OR=2.84, P<0.001) as independent predictors, while prostate volume (PV) (OR=0.84, P <0.001) as a protective factor. The nomogram integrating MUL, PV, and urethral curvature demonstrated superior predictive accuracy, with an AUC of 0.87 (95% CI, 0.83-0.91) in the training cohort. The bootstrap-corrected calibration slope was 0.96, and the Brier score was 0.08.&#xa0;Calibration curves and decision curve analysis confirmed the predictive accuracy and clinical utility of the nomogram. CONCLUSIONS: Our study introduces a novel quantitative method for assessing urethral curvature. The mpMRI-based model, integrating urethral curvature and prostate spatial configuration, offers enhanced predictive accuracy for postoperative immediate UC recovery.

Humans

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&#xa0;=&#xa0;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&#xa0;=&#xa0;35), colorectal cancer (n&#xa0;=&#xa0;21), and pancreatic cancer (n&#xa0;=&#xa0;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

Voluntary Knowledge Brokering to Promote Evidence-Based Nursing Practice: A Qualitative Study.

Knowledge brokering is a process of connecting knowledge producers with users to facilitate evidence-based practice through relationship building and information sharing. This descriptive qualitative study aimed to clarify knowledge brokering by nurses in Japanese hospitals. Twelve registered nurses in Japanese hospitals participated. They had over 5&#x2009;years' clinical experience, including experience in conducting research, particularly staff research, and education. Data were collected through semi-structured individual interviews and analyzed using qualitative content analysis. The analysis revealed a central theme: continuous efforts to foster empathy among colleagues and spontaneously promote evidence-based practice: multifaceted brokering activities by clinical nurses. Findings identified 10 categories categorized into four interconnected gears: establishing the foundational ground, assessing clinical needs and staff readiness, tailoring and diffusing evidence, and sustaining and evolving evidence-based practice. Even nurses without formal titles voluntarily bridged the research-practice gap, providing new insights into informal brokering. Brokers communicated considerately, balanced evidence with clinical context, negotiated practical compromises, and fostered staff research competency.

Humans

Standardized visual overlays enhance laparoscopic instruction: A mixed-methods evaluation.

Effective communication during laparoscopic procedures is frequently undermined by spatial disorientation and inconsistent terminology between instructors and trainees. This study examined whether standardized visual overlays on endoscopic monitors could enhance communication and learning. We conducted a three-phase mixed-methods study: qualitative observation of 20 laparoscopic teaching cases; a randomized trial of 63 second-year medical students assigned to control, clock, or alphanumeric grid (AG) overlays during three trials of a standardized transfer task; and intraoperative implementation in 44 cases (30 AG, 14 clock) with post-case surveys and qualitative feedback. In simulation, the clock overlay produced the fastest completion times, whereas the AG yielded the lowest error scores, and both overlays outperformed the control. Intraoperatively, the AG was rated higher than the clock for communication clarity, spatial orientation, perceived operative efficiency, and trainee confidence. Standardized visual overlays, particularly the AG, appear to support intraoperative teaching by providing a shared spatial frame of reference.

Laparoscopy

Probiotic-derived extracellular vesicles as food-based nanocarriers: Mechanisms, functional applications, and future perspectives in food systems.

Probiotic-derived extracellular vesicles (PDEVs) are a promising type of postbiotic nanoparticle derived by fermentation of probiotics, and have gained growing interest as a potential application in food science and nutrition. These are lipid bilayer vesicles of nanoscale, which are naturally released by probiotic cells and contain a wide variety of bioactive molecules, such as proteins, nucleic acids, and metabolites. Moreover, PDEVs are highly stable, biocompatible, and can be easily engineered to have surfaces with high functionality, which makes them good candidates in functional engineering. In contrast to traditional live probiotics, PDEVs overcome the difficulties of preserving microbial viability during processing and storage, thus providing superior safety, stability, and predictable biological performance. This is a systematic review of the various functions of PDEVs in food systems. We conclude on the processes through which PDEVs control intestinal barrier integrity, alter gut microbiota composition, and alter host immune responses, and their potential to enhance gut health when added to functional foods. In addition to their health-promoting effects, PDEVs have shown significant potential as natural antimicrobial agents to preserve food and as effective nanocarriers of hydrophobic bioactive compounds, including fucoxanthin, to improve their stability, bioavailability, and targeted delivery. Moreover, PDEVs can be used as new regulators of microbial fermentation. However, it should be noted that a lot of the evidence that is available is still preliminary and the effectiveness of these applications in real food-processing and storage conditions has not been fully proven. Although they have potential, there are a number of challenges that still hinder the widespread use of PDEVs in the food industry. These involve the creation of scalable and cost-effective production processes, batch-to-batch consistency, vesicle stability in a variety of food matrices, and regulatory and safety considerations. Other emerging engineering approaches, such as surface functionalization and cargo loading, are also discussed in this review and could further increase the specificity, functionality, and application versatility of PDEVs in food systems. Moving forward, the incorporation of PDEVs into the next generation functional foods, novel food preservation methods, and customized nutrition plans should be prioritized in future studies. Further developments in these fields can make PDEVs useful platforms at the interface of food microbiology, nanotechnology, and human health.

Probiotics