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PubMed2026

Hemotropic mono- and coinfections in Colombian ruminants: descriptive occurrence and host-related factors associated with coinfection in cattle.

Hemotropic pathogens such as Anaplasma, Babesia, Mycoplasma, and Trypanosoma are endemic to cattle and can cause coinfections, complicating disease dynamics and control. However, the host-related factors influencing these infections under tropical conditions remain poorly understood. This study aimed to investigate the occurrence of hemotropic monoinfections and coinfections in ruminants tested for hemotropic pathogens and to identify host-related factors associated with coinfection in cattle under field conditions in Colombia. A total of 104 animals were included: 91 cattle, 10 buffaloes, and 3 goats. Among the cattle, 34 (37.4%) exhibited monoinfections, 47 (51.6%) had coinfections, and 10 tested negative. In buffaloes, seven (70%) presented monoinfections, and two (20%) presented coinfections; in goats, one had a monoinfection, and one had a coinfection, most frequently involving Mycoplasma spp. The predominant coinfection patterns were Anaplasma + Mycoplasma and Mycoplasma + Trypanosoma, particularly in Bos indicus cattle. Bivariate and multivariable analyses revealed that breed was the strongest predictor of coinfection, with animals of less common breeds showing 93% lower odds (aOR = 0.07; 95% CI: 0.02-0.30; p < 0.001). Bos taurus individuals also tended toward lower odds of coinfection in the multivariable model, although this trend did not reach statistical significance. Our findings demonstrate a high frequency of hemotropic coinfections in cattle, particularly those involving Mycoplasma spp., and highlight the influence of host-related factors on infection dynamics. These results underscore the importance of integrating demographic and genetic information into surveillance and prevention strategies to improve the management of hemotropic infections in tropical livestock systems.

Animals↗

PubMed2026

Time to subsequent therapy (TTST) as an endpoint in clinical studies: development of standardized documentation of subsequent therapy through systematic literature review, expert interviews, and Delphi survey.

BACKGROUND: The endpoint Time to Subsequent Therapy (TTST) is an intermediate endpoint used in research and regulatory assessments. TTST denotes initiation of subsequent therapy and is a clearly definable, clinically relevant event for healthcare professionals. However, it has not been systematically established to which extent TTST is subjectively meaningful to patients. The objective of this study was to define TTST as a patient-relevant intermediate endpoint. METHODS: The study examined five oncological indications (breast cancer, prostate cancer, melanoma, multiple myeloma, and non-small cell lung cancer) using a systematic literature review, analysis of case report forms used in international randomized controlled trials, review of German Federal Joint Committee (G-BA) documents, semi-structured interviews and a two-stage Delphi survey with healthcare professionals, patients, and relatives. RESULTS: A total of 35 individuals participated in qualitative interviews. Most of them rated TTST as particularly significant. The Delphi Survey included 264 interviewees in round one, and 117 in round two. Patient-relevance of TTST was confirmed by 81% of respondents (95% confidence interval 76%, 85%). Nine treatment scenarios that justify TTST were identified. To capture patient-relevance, prospective collection of reasons for and consequences of therapy change are required. A checklist with standardized response formats plus free-text fields was developed: a comprehensive master checklist for flexible, complete documentation and a short version focused on therapy change-specific items. CONCLUSIONS: TTST is an intermediate endpoint whose systematic documentation of characteristics demonstrating patient-relevance can be standardized in research and clinical practice using the developed checklists.

Humans↗

PubMed2026

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↗

PubMed2026

Proprioception Training and Surrogate Outcomes: A Systematic Review of Definitions, Measures, and Effectiveness Claims.

BACKGROUND: "Proprioception training" is widely advocated in rehabilitation and sports practice, yet the term encompasses heterogeneous constructs, interventions, and outcomes. Many trials infer proprioceptive benefits from surrogate outcomes (balance, strength, or pain) rather than direct psychophysical indices. OBJECTIVE: We aimed to examine how proprioception is defined and measured, and how improvement is claimed, in randomized controlled trials. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, PubMed, Scopus, and Web of Science were searched to October 2025. Eligible randomized controlled trials explicitly described interventions as "proprioceptive" or "sensorimotor training" and reported at least one proprioceptive outcome, either direct (e.g., joint position reproduction, threshold to detection of passive motion, active movement extent discrimination) or indirect (e.g., sway, balance). Methodological quality was appraised with the Physiotherapy Evidence Database (PEDro) scale and risk of bias using the Cochrane Risk of Bias 2 (RoB 2) tool. RESULTS: Fifty-one randomized controlled trials (n = 2319) were included. Comparative synthesis showed that improvements inferred from surrogate outcomes were more frequent and often larger than improvements observed in direct psychophysical measures. Directly targeted practice, angle specific, attentionally demanding, and aligned with the measured proprioceptive submodality and task construct, produced the most consistent benefits in position-reproduction accuracy/error, movement-detection sensitivity, or discrimination performance, depending on the outcome assessed. In contrast, multimodal regimens (balance, strengthening, taping, manual therapy) commonly improved balance, pain, strength, or function without comparably consistent evidence of enhanced direct psychophysical proprioceptive function. CONCLUSIONS: Specific psychophysical components of proprioceptive function appear modifiable, but only when training explicitly targets the sensory construct measured. The field remains conceptually diffuse, with frequent conflation of sensorimotor performance and proprioception. Progress depends on defining proprioceptive submodalities a priori, privileging validated psychophysical outcomes over surrogate outcomes, and aligning intervention content with measurement to substantiate true perceptual learning rather than generic motor adaptation.

Journal Article↗

PubMed2026

Ecotoxicological responses of aquatic macrophytes to 2,4-D: A global synthesis of species sensitivity and ecological risk.

The widespread use of 2,4-dichlorophenoxyacetic acid (2,4-D) has raised concern about its persistence, mobility, and effects on non-target aquatic vegetation in freshwater ecosystems. Here, we provide a global synthesis of the ecotoxicological responses of aquatic macrophytes to 2,4-D based on a PRISMA-guided systematic review of 86 peer-reviewed studies published between 1947 and 2025. A consistent gradient of species-specific sensitivity was observed across macrophyte growth forms. The submerged species Myriophyllum spicatum showed high susceptibility, with EC₅₀ values of 0.04-0.182 mg/L and marked growth inhibition at low concentrations, whereas floating species such as Lemna minor and Pontederia crassipes were more tolerant, requiring higher concentrations (7.08 to >100 and 8.1 mg/L, respectively) to produce comparable effects. Importantly, this sensitivity ranking was consistent across laboratory and field experimental settings. These interspecific differences likely reflect variation in herbicide uptake, translocation, and detoxification capacity associated with growth form. The overlap between EC₅₀ values for M. spicatum and regulatory thresholds for 2,4-D in surface waters suggests that current limits may be insufficient to protect sensitive submerged macrophyte communities. Regarding remediation, L. minor and Salvinia natans emerged as the most promising candidates for phytoremediation, while P. crassipes showed limited capacity to reduce herbicide concentrations in water. Despite advances, no study directly compared oxidative stress biomarkers between submerged and floating species, representing a critical gap in understanding the biochemical basis of the sensitivity gradient. Overall, this synthesis highlights the need to account for taxon-dependent sensitivity when evaluating the ecological risks of 2,4-D and provides a basis for improving regulatory frameworks and management of herbicide contamination in freshwater ecosystems.

2,4-Dichlorophenoxyacetic Acid↗

PubMed2026

Metabolomic and structural signatures of pigmented and non-pigmented Himalayan rice landraces.

BACKGROUND: This study investigated the anti-oxidant properties, starch composition, pasting behavior, structural properties, textural properties and non-targeted metabolomic profiles of pigmented and non-pigmented rice landraces as potential next-generation functional food ingredients. RESULTS: Pigmented rice demonstrated 1.34 times more anti-oxidant activity as compared to non-pigmented rice. Pigmented landraces showcased superior nutritional and functional attributes, including higher total dietary fiber and starch content. Fourier-transform infrared (FTIR) analysis revealed distinct molecular signatures with enhanced peak transmittance, while X-ray diffraction (XRD) indicated greater crystallinity ranging from 36-44.3% in pigmented rice compared with 30-40% in non-pigmented rice, suggesting improved digestibility and processing versatility. Pigmented rice recorded less amylose content hence tended to possess increased adhesiveness values whereas non-pigmented rice revealed greater amylose content hence was coupled with greater hardness values. Field-emission scanning electron microscopy (FE-SEM) images revealed that pigmented rice had densely packed and polygonal starch granules whereas non-pigmented rice had loosely packed starch granules with intergranular voids. Untargeted gas chromatography-mass spectrometry (GC-MS) profiling identified 84 metabolites, including unique compounds such as 3,3-dimethylbutanol and ethanoic acid, along with shared metabolites such as sucrose and linoleic acid, highlighting notable biochemical diversity. Multivariate statistical analyses using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping further differentiated the metabolomic landscapes, with variable importance in the projection (VIP) scores identifying key bioactive contributors. CONCLUSION: Pigmented rice landraces exhibited significant functional and nutritional advantages, making them promising candidates for functional food development and nutritional improvement programs. These findings support their potential role in advancing sustainable and health-oriented food systems. © 2026 Society of Chemical Industry.

Oryza↗

PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗

PubMed2026

Development of a core descriptor set for studies assessing interventions for diabetes-related foot ulceration.

AIMS/HYPOTHESIS: Foot ulceration is a common complication of diabetes and is associated with high mortality and costs. The quality of evidence to inform clinical practice is limited, partly because clinical studies do not consistently report baseline participant characteristics. This study aimed to develop a core descriptor set (CDS), a minimum set of descriptors to be measured in all studies evaluating interventions for people with diabetes-related foot ulceration. METHODS: A longlist of descriptors was generated through a systematic review of studies assessing interventions for diabetes-related foot ulcers, pre-registered with PROSPERO (CRD42019128250). The identified descriptors were then ranked based on perceived importance by healthcare professionals from different fields and geographical locations using a nine-point Likert scale in the first round of a Delphi survey. Using standardised criteria, descriptors without consensus were re-ranked in round two. Critical descriptors and those without consensus after the Delphi process were discussed in the consensus meeting to finalise the CDS. RESULTS: The systematic review yielded 95 candidate descriptors. The two Delphi rounds were completed by 102 and 69 healthcare professionals, respectively. The Delphi process identified 34 critically important descriptors and 13 descriptors without consensus, which were discussed in the consensus meeting. The ratified CDS included 28 descriptors across nine domains: demographic variables; individual factors; ulcer characteristics; limb characteristics; ongoing medical interventions; previous surgical interventions; medication history; biochemical measurements; and quality of life/function/symptoms. CONCLUSIONS/INTERPRETATION: This CDS reflects characteristics important to health professionals and researchers when reporting clinical studies on diabetes-related foot ulceration. Its use will aid the reporting of future studies.

Humans↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Mixed Methods Research on Family Caregiving for Stroke Survivors: A Methodological Systematic Review.

AIM: To examine how mixed methods research has been applied in studies of family caregiving for stroke survivors, focusing on key methodological components (rationale, design types, integration strategies, and use of joint displays). DESIGN: Methodological systematic review. METHODS: A systematic search of five databases yielded 17 studies. The extraction focused on mixed methods features (rationale, design, integration, joint displays), and quality was appraised using the Mixed Methods Appraisal Tool. DATA SOURCES: PubMed, CINAHL, Scopus, Web of Science, and PsycINFO were searched for relevant studies published from 2010 to 2025. RESULTS: The included studies addressed topics such as caregiver burden, coping, resilience, and intervention outcomes. Convergent and explanatory sequential designs predominated. Complementarity was the most frequent rationale for mixing methods. Integration occurred mainly through merging, with fewer instances of connecting or building. Three studies included joint displays to integrate the results. CONCLUSION: Mixed methods research is increasingly applied in family caregiving. To advance the field, researchers should strengthen integration during analysis and results and improve transparency in reporting key design features. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Strengthening methodological rigour in mixed methods studies on stroke caregiving will improve the evidence base for nursing practice. Intentional and meaningful integration of qualitative and quantitative evidence can better inform effective interventions and support programs, ultimately enhancing care for stroke survivors and their families. IMPACT: This review evaluates how mixed methods research is applied in family caregiving studies. It identifies significant methodological gaps, including unclear reporting of design and limited use of advanced integration techniques. The recommendations provide practical guidance for researchers to improve reporting and integration, yielding richer evidence to inform interventions and policies that support family caregivers. REPORTING METHOD: The review followed the PRISMA 2021 guidelines for transparent reporting of systematic reviews. PATIENT OR PUBLIC CONTRIBUTION: No patient or public involvement.

Humans↗

PubMed2026

What do we know about medical invalidation and related concepts? - A scoping review and thematic analysis about the definitions, measurements, causes, consequences and potential solutions for medical invalidation.

BACKGROUND: Medical invalidation, medical gaslighting, and related constructs have gained visibility in public discourse but remain inconsistently defined in scientific literature. Despite growing research-often focused on specific diseases- to date, no single review has comprehensively synthesized their definitions, causes, consequences, or methods of measurement. This scoping review addresses this gap by examining medical invalidation and related constructs. METHODS: Using a preregistered protocol, we systematically searched PubMed, CINAHL, Web of Science, Google Scholar, and ProQuest (dissertations) without year restrictions. Eligible sources included peer-reviewed empirical, theoretical, and conceptual work in English addressing invalidation, gaslighting, or closely related notions within healthcare. A total of 158 studies were identified through database searches and citation tracking. Data extraction followed a standardized schema, and findings were synthesized descriptively and through thematic analysis to clarify terminology, map determinants and outcomes, and identify existing measurement approaches. RESULTS: The results showed substantial inconsistency in how "invalidation," "not being taken seriously," and "gaslighting" were defined. Medical invalidation emerged as a multifactorial phenomenon driven by diagnostic challenges, structural and societal factors, provider and patient characteristics, stigma, misattribution, interactional dynamics, academic knowledge gaps, and disease-related complexity. Invalidation was associated with wide-ranging behavioural, emotional, cognitive, physical, relational, and systemic harms, while validation had consistently beneficial effects. Proposed solutions in the summarized studies included communication improvements, clinician training, patient support, targeted research, and structural and systemic changes. DISCUSSIONS: Medical invalidation represents a complex, systemic issue with significant implications for patient safety. The discussion highlights its multifactorial origins, its potential to cause both psychological and physical harm, and the need for clearer conceptualisation within the field. Advancing research requires validated instruments and longitudinal designs to examine underlying mechanisms and consequences. Addressing medical invalidation will demand multi-level interventions to improve communication, reduce structural barriers, and promote equitable, patient-centred care. OSF PREREGISTRATION: https://doi.org/10.17605/OSF.IO/MPE6U.

Humans↗

PubMed2026

An automated geometric modeling framework in GATE for the design and optimization of high-sensitivity converging-beam SPECT collimators.

Objective.The trade-off between detection sensitivity and spatial resolution is a fundamental challenge in designing organ-dedicated Single-photon emission computed tomography (SPECT) collimators. While converging-hole geometries offer a solution, their optimization is often hindered by the lack of flexible computational tools capable of modeling large-scale, non-parallel hole arrays. This study aims to develop an automated geometric modeling framework to facilitate the design and evaluation of complex converging- and diverging-hole collimators within standard Monte Carlo environments.Approach.We developed a specialized modeling framework by implementing custom C++ classes and a vector-based alignment algorithm within GATE. This platform enables automated, orientation-consistent construction of large-scale converging arrays not natively supported by standard implementations. A high-sensitivity pure cone-beam collimator (CBC) was designed using this framework. The evaluation used hot-rod, disc, and Jaszczak phantoms for physical characterization, while XCAT and dedicated brain models were employed for clinical tasks, including cardiac, brain perfusion, and DaTscan SPECT simulations.Main results.The CBC achieved a nearly fourfold sensitivity increase compared to a conventional low-energy high-resolution parallel-hole collimator at a 20 cm radius of rotation, while maintaining comparable spatial resolution. Despite a 52.3% field of view reduction, the CBC yielded a 2.2-fold noise reduction (CV: 11.7% vs 25.9%) and mitigated partial volume effects via geometric magnification. XCAT and brain phantom simulations confirmed enhanced anatomical definition and contrast recovery in cardiac, perfusion, and DaTscan tasks.Significance.This work provides an efficient computational tool for rapid design space exploration of advanced collimator geometries. The results demonstrate that the proposed CBC design offers a significant sensitivity advantage, making it highly suitable for high-performance, small-volume clinical applications such as brain and cardiac molecular imaging.

Tomography, Emission-Computed, Single-Photon↗

PubMed2026

Artificial intelligence in genitourinary oncology: publication trends and systematic review.

OBJECTIVE: To conduct an analysis of publication trends and a systematic review of randomized controlled trials (RCTs) to characterize the current state of artificial intelligence (AI) use in genitourinary (GU) oncology, as AI has emerged as a transformative tool in healthcare with potential applications in diagnostics, treatment planning, and prognostication. METHODS: We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE; Ovid), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) Ultimate for studies related to AI and GU oncology, excluding non-English papers, non-human studies, review articles, and articles using AI solely for manuscript writing. Publication trends were analysed from 2013 to 2023 and categorized by study design and cancer type. RCTs were evaluated through systematic review using Covidence (Veritas Health Innovation Ltd, Melbourne, Victoria, Australia) for screening and data extraction. Two reviewers independently assessed all studies, with risk of bias (RoB) evaluated using the Cochrane RoB 2.0 tool. RESULTS: Of 2409 articles identified, 1220 met inclusion criteria. These included 962 retrospective articles, 175 prospective studies, 79 studies with combined retrospective/prospective methods, and four RCTs. Studies most commonly addressed prostate (n = 923), renal (n = 274), and urothelial (n = 194) cancers. Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration in 2019. Four RCTs were identified - one in urothelial cancer and three in prostate cancer. Two RCTs evaluated AI-based diagnostics, demonstrating improved performance over conventional methods; the remaining two RCTs evaluated AI in prognostication and treatment planning, showing improved gains in imaging interpretation and operational efficiency. RoB varied across studies, primarily related to randomisation and deviations from intended interventions. CONCLUSIONS: Artificial intelligence research in GU oncology has grown, although high-level evidence from RCTs remains limited. Existing trials underscore AI's promise in diagnostics, prognostication, and treatment planning, and the rapidly evolving nature of this field warrants continued prospective investigation.

Humans↗

PubMed2026

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗

PubMed2026

The Interrelationship between Cadmium, Smoking, and Migraine in the ELSA-Brasil Study.

This study explored the relationship between serum cadmium (Cd), smoking exposure, and migraine in the ELSA-Brasil cohort (2008-2010). The analysis included 2,750 participants whose serum Cd levels were measured using inductively coupled plasma mass spectrometry. Smoking exposure was assessed using self-report data of active smoking and second-hand smoke. Migraine, including definite and probable migraine, was diagnosed according to the International Classification of Headache Disorders, 3rd edition (ICHD-3). Logistic regression was used to estimate the odds of migraine across serum Cd quintiles, using the third quintile as the reference category, while linear regression examined the relationship between smoking exposure and Cd levels. Polynomial contrasts tested linear trends. Participants had a mean (SD) age of 53.2 (9.0) years and 53.1% were women. Migraine prevalence was 29.1% (including probable migraine). Individuals with migraine had slightly higher median serum Cd levels than controls [0.050 µg/L (IQR 0.035-0.077) vs. 0.048 µg/L (0.035-0.066); p = 0.021], although smoking exposure scores did not differ between groups. Smoking exposure showed a strong positive association with serum Cd concentrations (p-trend < 0.001). Participants in the highest Cd quintile had greater odds of migraine [aOR 1.51 (95% CI 1.09-2.08), p = 0.011] after adjustment for smoking exposure and sociodemographic and clinical confounders. Sex-stratified analysis yielded even stronger associations among males [aOR 1.84 (95% CI 1.07-3.16), p = 0.027]. However, in the sensitivity analysis including definite migraine cases only, this association was no longer significant [aOR: 1.14 (0.71, 1.83), p = 0.579]. Main findings suggest that Cd exposure from smoking may contribute to migraine occurrence, particularly in males. However, other sources of Cd that could influence migraine should not be disregarded, and future investigation in this field is warranted.

Cadmium↗

PubMed2026

Serotypic and Genomic Diversity of Vibrio anguillarum in Rainbow Trout Farms in Turkey: Implications for Vibriosis Control and Vaccine Candidate Selection.

Outbreaks of vibriosis caused by Vibrio anguillarum are a persistent constraint on rainbow trout (Oncorhynchus mykiss) aquaculture. However, information on the population structure of field strains in Turkey has been lacking. Here, we report the first systematic serotypic, proteomic, and genomic characterization of 23 V. anguillarum isolates collected over 10 years from rainbow trout farms located in six major aquaculture regions of Turkey. Serological analyses based on microagglutination, supported by ELISA characterization of hyperimmune sera, identified a clear predominance of serotype O1, whereas isolate V12 exhibited a non-agglutinating, atypical O-antigen profile. Protein profiling (SDS-PAGE) and immunoblotting showed largely conserved whole-cell protein patterns among the isolates, but distinct immunogenic bands at 14, 18, and 40 kDa were detected in isolates V18 and V21. Long-read whole-genome sequencing revealed that most Turkish isolates grouped within the global O1 clade, while V12, V25, and V28 isolates occupied more distant branches. Comparative genomics demonstrated a conserved core virulence gene set (RTX toxins, siderophore and iron-uptake systems, motility and adhesion factors, Type VI secretion system), with strain-dependent variation in accessory loci such as anguibactin and T6SS-I. Experimental infections of rainbow trout demonstrated significant differences in virulence among isolates (p < 0.05), with the V18 isolate showing high, the V15 intermediate, and the V12 low-mortality rates. By elucidating the relationship among the serotype, immunogenic protein profiles, virulence gene repertoires, and in vivo pathogenicity, this study provides a comprehensive overview of the antigenic and genomic diversity of Vibrio anguillarum isolates from Turkey. Notably, the identification of V18 and V21 as promising candidate strains for further vaccine evaluation, characterized by high virulence and unique immunogenic features, provides a scientific foundation for the development of serotype-specific vaccination strategies to mitigate vibriosis-associated losses in aquaculture.

Animals↗

PubMed2026

Long-term outcomes after a randomized phase II trial of nerve growth factor eye drops for optic pathway gliomas: four-year follow-up findings in children.

PURPOSE: A previous double-blind, randomized, placebo-controlled, phase II clinical trial reported beneficial effects of a short-term treatment (10 days) with murine nerve growth factor (mNGF) eye drops on visual function in children with optic pathway gliomas (OPG). The present study aimed to evaluate long-term changes in clinical and neuroradiological parameters in the cohort of OPG patients who had previously participated in the phase II mNGF trial. METHODS: Fifteen of the 18 patients originally enrolled in the phase II mNGF trial agreed to undergo clinical and neuroradiological monitoring over a 48-month follow-up period. Of these, 9 had originally been randomized to mNGF and 6 to placebo; no additional treatment (mNGF, chemotherapy, or radiotherapy) was administered during the extended follow-up. Every 6 months, patients underwent general clinical and neuro-ophthalmological examination, visual evoked potentials (VEP), and photopic negative response of the electroretinogram (PhNR). Brain MRI was performed every 12 months. RESULTS: Comparison of initial and final follow-up median values revealed no statistically significant changes in visual acuity, VEP amplitude, PhNR amplitude, or visual field radius. No significant differences were observed in any parameter relative to baseline values of the mNGF trial. Brain MRI demonstrated stable disease in all patients throughout the observation period. CONCLUSION: These findings, obtained in an observational extension of the original randomized cohort, indicate favorable long-term safety and tolerability of a short-term course of topical mNGF in children with OPG, with sustained visual and neuroradiological stability over four years, rather than evidence of persistent treatment efficacy. Further prospective, adequately powered and randomized clinical studies are needed to confirm both the short- and long-term clinical efficacy of NGF treatment in preventing OPG-induced visual loss.

Humans↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗