SEARCH · Search PubMed

Results for “Visual Fields”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

139 records · Page 5Linked to original sources

PubMed2026

Neuroimaging anxious children and adolescents before and after cognitive behavioral therapy: a systematic review.

OBJECTIVE: This systematic review investigates brain changes in youths with anxiety disorders following cognitive behavioral therapy (CBT) and neural markers that predict CBT responses. METHODS: We conducted a systematic search using the electronic databases PubMed, Web of Science, and ProQuest. The inclusion deadline was set to October 27, 2025. We included fifteen peer-reviewed neuroimaging studies that examined the effects of CBT in youths under 19 years old with a primary clinical diagnosis of an anxiety disorder based on DSM-5 criteria. RESULTS: Although the existing literature is marked by substantial diversity in methods and outcomes, task-related neural response in the anterior cingulate cortex (ACC, 2/8, 25.0%), insula (1/8, 12.5%) increased from pre to post CBT and these changes were further correlated with clinical symptom improvements. Moreover, CBT outcomes were predicted by pre-treatment activity or connectivity in the ACC and amygdala (3/13, 23.0%). A smaller proportion of studies (2/13, 15.3%) found that activity or connectivity in the insula, precuneus/cuneus, postcentral gyrus, and activity or structure in the nucleus accumbens (NAcc) predicted response to CBT. The low consistency of these findings was driven by methodological variability, low reliability of the neural markers, and relatively small sample sizes. CONCLUSIONS: This review highlights promises of neural predictors and outcomes to enhance anxiety disorder treatments in children and adolescents, facilitating future personalized and effective CBT. Beyond this initial promise, the field is hindered by methodological inconsistencies and limited replications. While longitudinal and personalized approaches are important next steps, the central challenge remains: identifying neural markers that are both reliable and robust.

Adolescent↗

PubMed2026

The influence of organizational culture on medication safety practices and associated risk factors in the community setting: A systematic review.

BACKGROUND: Increasing attention has been given to the role of organizational culture in influencing medication safety practices across healthcare settings. The lack of widely accepted standardized instrumentation makes operational measurement of organizational culture and medication safety challenging. The purpose of this systematic review was to examine the impact of organizational culture on medication safety within community healthcare settings. METHODS: MEDLINE, CINAHL, Scopus, and Nursing & Allied Health were searched in August 2025 using keywords, subject terms, field codes, and Boolean operators to identify papers relevant to the review question; bibliographies of included studies were also reviewed. Screening and full-text review were completed independently by two reviewers with a third to adjudicate conflicts. The Critical Appraisal Skills Programme was used for quality assessment. The PRISMA statement guided the development and implementation of the review. RESULTS: Thirteen articles were included representing various community settings. Most studies reported on untoward medication events, but few measured systematically collected safety data before and after an intervention. Organizational culture was seldom defined or operationalized. Most studies were methodologically sound, but the overall level of evidence was weak to moderate. CONCLUSION: Organizational culture influences medication safety through aspects such as communication channels, teamwork, training, and an environment that allows error and near-miss reporting. Few studies explicitly evaluate the causal impact of culture interventions on measurable medication safety outcomes in community healthcare settings. Further research should incorporate standardized measurement tools and intervention-based, pre-post designs to better understand how organizational culture influences medication safety in community healthcare settings.

Organizational Culture↗

PubMed2026

Effectiveness of kinesiologic tape in the management of postoperative trismus, discomfort, and edema in mandibular fractures: a randomized controlled trial.

OBJECTIVE: This study compared kinesiologic taping (KT) with conventional elastic adhesive bandaging in managing postoperative morbidity following open reduction and internal fixation. STUDY DESIGN: In this prospective, randomized controlled trial conducted at KLE Dr Prabhakar Kore Hospital, 26 patients with unilateral mandibular fractures were allocated into two groups: Group 1 received an elastic adhesive bandage (n = 13) and Group 2 used KT (n = 13). Pain (Visual Analog Scale), facial swelling (standardized linear measurements), and maximum interincisal distance were recorded at baseline and on postoperative Days 2 and 5. Data were analyzed using repeated measures ANOVA and independent t tests (p < .05). RESULTS: The KT group showed significantly lower pain scores and reduced facial edema at Days 2 and 5 compared with controls (P < .05). Trismus improved in both groups without significant intergroup differences. No adverse effects were observed. CONCLUSIONS: KT is a safe and effective adjunct for reducing early postoperative pain and edema after mandibular fracture fixation.

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

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

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗

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

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

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↗

PubMed2026

K-wire versus screw fixation in Scarf-Akin osteotomy for hallux valgus: A retrospective cohort study.

BACKGROUND: Retention of metal implants after Scarf-Akin osteotomy (SAO) may cause irritation and psychological discomfort, often necessitating a hardware removal procedure. This study aimed to introduce K-wire fixation, allowing for outpatient removal, and to compare it with screw fixation. METHODS: This retrospective study included 64 patients with hallux valgus, comprising 32 in the K-wire fixation group and 32 in the screw fixation group. Clinical outcomes were assessed using the American Orthopaedic Foot and Ankle Society (AOFAS) score, visual analogue scale (VAS), and patient satisfaction. Radiographic parameters included hallux valgus angle(HVA), intermetatarsal angle(IMA), and distal metatarsal articular angle(DMAA). RESULTS: Both groups showed significant clinical and radiographic improvement (P < 0.01). No significant between-group differences were observed in the other clinical or radiographic outcomes (P > 0.05). Treatment costs were significantly lower in the K-wire group (P < 0.001). CONCLUSIONS: K-wire fixation provides clinical and radiographic outcomes comparable to screw fixation, while avoiding the need for an additional procedure to remove the implant. LEVEL OF EVIDENCE: Level III.

Humans↗

PubMed2026

Orbital involvement in sickle cell disease: A systematic review.

Orbital involvement in sickle cell disease (SCD) is rare but potentially vision-threatening and is often misdiagnosed due to overlap with infectious orbital disease. We conducted a systematic review of case reports and series describing orbital complications in patients with confirmed SCD, following PRISMA and MOOSE guidelines. Across 53 studies, 76 cases were identified. Patients were predominantly male (77.6%), with an average age of 13.2 years. Orbital disease was the initial SCD manifestation in 6.6%. Presentations included periorbital edema in all, proptosis in 64.1%, restricted ocular motility in 56.5%, reduced visual acuity in 28.1%, and bilateral involvement in 38.2%. Laboratory findings commonly included leukocytosis (73%) and raised inflammatory markers (86.7%). Radiologically, orbital subperiosteal hematoma were observed in 70%, combined orbital bone infarction and hematoma in 38.2%, and orbital bone infarction alone in 19.7%. Magnetic resonance imaging is critical for accurate diagnosis. Intracranial hemorrhage was present in 9.2%. Less frequent manifestations included orbital apex syndrome, lacrimal gland disease, and nonspecific soft tissue swelling. Management was primarily conservative (82.9%), and surgery was reserved for vision-threatening or intracranial complications. Complete recovery was achieved in 93.1% of cases. While severe vision-threatening complications are uncommon, early recognition remains critical to optimising outcomes in sickle cell orbitopathy.

Humans↗

PubMed2026

The Effect of Slow Deep Breathing Relaxation Exercise on Pain and Anxiety Levels During and Post-Chest Tube Removal After CABG.

Chest tube removal after coronary artery bypass graft is frequently reported by patients as stressful and painful, highlighting the need for effective nonpharmacological interventions. Slow deep breathing relaxation exercises (SDBREs) may serve as a simple nursing strategy to reduce discomfort. In this study, we aimed to evaluate the effect of SDBRE on pain and anxiety during and after chest tube removal following coronary artery bypass grafting in Nablus hospitals. An experimental design was used with 80 patients recruited from 2 hospitals. Participants were randomly assigned to either an intervention group (n = 40) that practiced SDBRE or a control group (n = 40) that received standard care. Data were collected through a self-administered questionnaire, the Numeric Pain Scale, and the Visual Anxiety Scale. Data collection occurred from March to October 2024. The intervention group reported significantly lower pain scores during removal (M: 5.325 vs 7.125, P < .001) and after removal (P < .001). Anxiety scores were significantly lower both during and after removal (P < .001). Pain correlated with duration, with more complex operations and prolonged chest tube insertion linked to higher scores. SDBRE significantly reduced pain and anxiety during and after chest tube removal, supporting its integration into routine postoperative nursing care.

Humans↗

PubMed2026

Real-time intraoperative perfusion assessment using indocianine green in pediatric extrinsic ureteropelvic junction obstruction with crossing vessel.

INTRODUCTION: In vascular hitch (VH) particular attention must be paid to preserving lower pole perfusion. Hypoperfusion is normally excluded by macroscopic visual assessment of parenchyma appearance. Our aim is to explore the possible role of indocyanine green (ICG) in highlighting focal hypoperfusion. MATERIALS AND METHODS: This prospective study included pediatric patients with UPJO caused by crossing vessels, treated with robot-assisted VH. Intraoperative evaluation assessed UPJ appearance, reduction of hydronephrosis after vessel mobilization, and the adequacy of pelvic drainage during diuretic testing. ICG was used to assess renal perfusion via NIRF imaging. A 25 mg ICG solution was prepared in 10 mL and administered in 1 mL doses. Fluorescence distribution, operative time, and complications were recorded. Follow-up at 3, 6, and 12 months included clinical evaluations, blood pressure measurements, and Doppler ultrasound. RESULTS: Eight patients (median age 8years) were enrolled between October 2023 and February 2025. ICG assessed renal perfusion post-procedure; one case of focal hypoperfusion due to vessel tension was resolved with intraoperative revision. At a median follow-up of 18 months, no hypertension, pain, or UTIs were observed. Ultrasound demonstrated improved hydronephrosis and normal Doppler flow. CONCLUSION: ICG angiography is a safe and effective tool for the real-time assessment of renal perfusion during pediatric VH procedures.

Humans↗

PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP + AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗

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

Imaging techniques for assessing the hand in systemic sclerosis: a systematic review.

BACKGROUND: Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease frequently associated with hand involvement, leading to significant functional impairment. Imaging techniques provide unique opportunities to visualize and quantify structural and functional abnormalities of the hand, supporting diagnosis, monitoring, and treatment evaluation. This systematic review summarizes the imaging techniques used in SSc. METHODS: A systematic search of PubMed and Embase was conducted. Eligible studies included original research articles in English that applied or evaluated imaging techniques of the hands in SSc, published after 2000. Ultrasound and nailfold capillaroscopy were excluded, given their established use. Screening was performed independently by two authors. Findings were synthesized by clinical manifestations, study quality was assessed using the QUADAS-2 tool. RESULTS: Sixty-one studies met the inclusion criteria. In total, 25 distinct imaging techniques were identified, enabling assessment of various hand structures, including vascular involvement, inflammation, fibrosis, calcifications, erosions, and bone marrow edema. Vascular imaging was most extensively studied, particularly in the context of Raynaud's phenomenon and digital ischemia, with multiple techniques demonstrating impaired perfusion and altered thermoregulatory responses. MRI consistently detected subclinical inflammatory and erosive changes of joints and soft tissues,. CT-based techniques provided detailed assessment of calcinosis cutis, while optical and photoacoustic methods showed promise for quantifying skin fibrosis. CONCLUSION: Imaging techniques provide valuable, complementary insights into hand involvement in SSc, often revealing subclinical disease. Despite promising results, limited standardization and longitudinal validation currently restrict clinical implementation. Future studies should focus on harmonizing protocols and validating against clinically meaningful outcomes.

Humans↗