Search Results (10,099)

Despite decades of clinical validation, glioblastoma (GBM) treatment remains tethered to a dismal 15-to-16-month survival plateau, heavily thwarted by the ys blood–brain barrier (BBB) and profound cellular heterogeneity. While the 2021 World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) fundamentally reoriented diagnosis around definitive molecular signatures, such as isocitrate dehydrogenase (IDH)-wildtype status, epidermal growth factor receptor (EGFR) amplification, and +7/−10 chromosomal alterations, the ultimate obstacle to clinical efficacy is the tumor’s intense non-genetic plasticity. Malignant cells reject rigid hierarchies; single-nucleus insights reveal a fluid transcriptomic continuum spanning neurodevelopmental lineages, novel glia-like or neuronal-like states, and highly resilient proneural-mesenchymal (PM) hybrid populations. This intrinsic dynamism is reinforced by functional neuro-gliomal integration into the host brain via electrochemical TM synapses, an immunologically cold niche dominated by secreted phosphoprotein 1 (SPP1+) myeloid cells, and a self-reinforcing hypoxic-angiogenic loop. Under cytotoxic therapy, these networks execute rapid adaptive remodeling, selecting for hypermutator phenotypes and specialized senescence-associated secretory phenotypes (SASP) that dictate aggressive recurrence. Full article

Molecular pathology-driven oncologic diagnostics—genomic profiling, transcriptomics, proteomics, and, increasingly, artificial intelligence applied to tissue and liquid biopsy—can only be as accurate as the biospecimens on which they are performed. Biobanks are the infrastructures that secure this foundation, linking donors, biological samples, and data to discovery and, through the pathology interface, back to diagnosis. Their scientific and diagnostic value, however, is determined less by the number of specimens stored than by the rigor of the guidelines under which those specimens are collected, processed, annotated, governed, and shared. The normative landscape has changed considerably in the last three years: the ISBER Best Practices reached their fifth edition (2023), the NCI Best Practices were comprehensively revised (2026), the Standard PREanalytical Code (SPREC) was updated to version 4.0 (2024/2025), MIABIS Core reached version 3.0 (2024), the 2024 revision of the Declaration of Helsinki explicitly anchored biobank governance to the Declaration of Taipei, the European Health Data Space Regulation (EU) 2025/327 entered into force, and ISO 20387—the accreditation standard for biobanks—is undergoing its first full revision. This review synthesizes the current (2026) status of international biobanking standards, ethical and legal frameworks, pre-analytical and quality management requirements, data and interoperability standards, and sustainability models, drawing on more than 200 sources with emphasis on the 2023–2026 literature. Standards are presented not as an inventory but as an operational system, organized along the biobanking workflow from donor consent to sample distribution and impact tracking. On this basis, we propose a phased, guideline-anchored roadmap and a start-up checklist to help new teams establish a real-world biobank—particularly hospital-integrated biobanks in settings without a mature national biobanking infrastructure—and we review comprehensively the financial, operational, regulatory, and societal challenges that determine whether a new biobank thrives or stalls. Particular attention is given to the specimen classes and quality controls on which molecular tumor diagnostics depend—FFPE tissue and its sequencing artifacts, fresh-frozen tissue, liquid biopsy analytes, and DV200-gated derivative quality—and to the interface between research biobanking and the accredited diagnostic laboratory, which is set out explicitly because the two are governed by different standards. We further provide explicit, endpoint-specific operational control of warm and cold ischemia, a quality-control framework for advanced patient-derived models (xenografts and organoids) and their interface with dedicated model cores, an explicit statement of the evidence underlying the practical recommendations, and a dedicated limitations section. The review is intended as a reference piece for biobankers, pathologists, clinician-researchers, quality managers, and institutional decision-makers building the biobanks on which the coming decade of precision oncology will depend. Full article

Chronic low back pain (CLBP) is a leading cause of disability and is characterized by marked clinical heterogeneity. Structural imaging remains fundamental for diagnosis and treatment planning, yet anatomical abnormalities alone do not fully explain differences in pain, disability, physical performance, or treatment response. Increasing evidence implicates paraspinal muscle morphology, composition, neuromuscular behavior, endurance, and physical conditioning as complementary determinants of spinal function. This narrative review synthesizes current evidence within the concept of spinal muscle health, defined here as a multidimensional, region-specific construct reflecting the structural, compositional, neuromuscular, and functional capacity of the muscles contributing to spinal control and load management. The construct is not proposed as a new diagnosis or as a substitute for established concepts such as sarcopenia, myosteatosis, or deconditioning; rather, it provides an integrative framework for considering complementary muscle-related domains that cannot be represented adequately by a single imaging or performance measure. We review muscle-specific and level-specific findings, measurement approaches, sources of heterogeneity, longitudinal and interventional evidence, and the interaction of muscle-related features with systemic and psychosocial factors. Current evidence supports associations between selected paraspinal muscle abnormalities and CLBP, particularly for multifidus composition, but findings vary substantially across muscles, spinal levels, populations, and measurement methods. Most evidence remains cross-sectional, and causal direction is uncertain. Clinical improvement after exercise may occur without consistent normalization of imaging-derived muscle composition. Accordingly, spinal muscle health is best regarded at present as a research and clinical reasoning framework rather than a validated diagnostic phenotype. Standardized measurement, longitudinal studies, and intervention trials linking changes in muscle-related parameters to patient-centered outcomes are required before routine clinical implementation. Full article

Lassa virus (LASV), the most important human pathogen within the Arenaviridae family, causes Lassa fever (LF), an acute hemorrhagic fever disease endemic in West Africa with high case fatality rates, particularly in pregnant women. Currently, there are no licensed vaccines or antivirals approved for clinical use against LASV infection. Despite its significant impact on public health, important knowledge gaps remain regarding LASV disease burden, transmission dynamics, pathogenesis, and virus−host interactions, impeding effective disease control and development of therapeutic interventions. The emergence of new lineages and the remarkable genetic diversity of LASV poses substantial challenges to accurate and prompt diagnosis, epidemiological surveillance, and the development of broad-spectrum medical countermeasures. Here, we synthesize current knowledge on the disease burden of LF, recent outbreaks, the geographic distribution and genetic diversity of LASV, infection and transmission in natural reservoirs and other susceptible hosts, emerging evidence suggesting possible sexual transmission, viral pathogenesis, host immune responses, diagnostic advances and challenges, and ongoing preclinical and clinical efforts to develop effective vaccines and antivirals. Full article

Background/Objectives: Artificial intelligence (AI) is being applied across palliative and end-of-life care, including prognostication, symptom detection, clinical documentation, communication support, decision support and care coordination. General AI reporting guidelines exist, but none captures the value-sensitive requirements specific to palliative care, which is integrated from the diagnosis of a serious illness and spans the whole trajectory, alongside disease-directed treatment, to the end of life and bereavement. We aimed to develop an evidence-informed checklist to guide the design and the transparent reporting of AI tools in this setting. Methods: We conducted a narrative review of the literature on AI in palliative care, prognostic communication, surrogate decision-making, digital-health co-design, algorithmic fairness, explainability, AI governance and existing reporting guidelines (TRIPOD+AI, CONSORT-AI, SPIRIT-AI, DECIDE-AI). During revision, the search was formalised and re-executed in PubMed (25 August 2026), each item was graded for certainty of evidence, and the checklist was piloted on four published studies. Rather than reusing generic requirements, we derived each item from a tension specific to serious illness, named the failure modes those tensions produce, and mapped where the framework diverges from generic guidance. Results: PALLI-AI comprises seven domains—Purpose set by goals of care; Alignment with total suffering and dignity; Lived experience, family and bereavement; Learning data and the equity of access; Interpretability and prognostic communication; Accountability in serious illness; and Implementation and palliative outcomes—operationalised as 22 items, each with a researcher and a developer formulation. We name six failure modes specific to this setting, including the self-fulfilling prognosis, the algorithmic surrogate and abandonment by automation, and we set out, domain by domain, what the framework adds beyond existing guidelines. In the pilot, all items were ratable; safeguards against the self-fulfilling prognosis, decedent data governance and answerability were unreported across studies. Conclusions: PALLI-AI is a pragmatic, non-prescriptive checklist intended to raise the trustworthiness of AI tools for people approaching the end of life. It is a starting point for formal consensus development and empirical validation, not a finished standard. Full article

Background: Dengue fever remains a major global health problem, and early diagnosis is challenging where confirmatory testing is limited. Machine-learning studies using routine hematological data have focused mainly on discrimination, whereas calibration, decision-analytic performance, interpretability, and robust validation have received less attention. This study aimed to develop and compare tree-based machine-learning models for dengue classification, benchmark them against L2-penalized logistic regression (LR), and evaluate discrimination, calibration, potential decision-analytic benefit, and interpretability. Methods: This retrospective secondary analysis used an open-access dataset from Bangladesh comprising 1523 patients, 18 demographic and hematological predictors, and a binary dengue test outcome. Data were divided into stratified training (80%) and test (20%) sets. The Synthetic Minority Over-sampling Technique was applied only within the training workflow. Random Forest (RF), XGBoost, and LightGBM were optimized using Optuna with stratified five-fold cross-validation. L2-penalized LR was evaluated using the same predictors and training–test partition. Held-out test-set performance was assessed using AUROC, AUPRC, accuracy, sensitivity, specificity, predictive values, F1-score, and Brier score. Calibration, decision curve analysis, SHAP values, and permutation importance were also examined. Results: LightGBM, RF, and XGBoost yielded AUROCs of 0.709, 0.704, and 0.702, respectively, indicating closely similar discrimination. The primary SMOTE-trained LR yielded a numerically lower AUROC of 0.608 (95% CI: 0.536–0.677) and a higher Brier score of 0.310 than the tree-based models (0.174–0.176); however, in sensitivity analysis without SMOTE, the LR AUROC increased numerically to 0.655 and the Brier score decreased to 0.194. At the training-derived threshold of 0.558, LightGBM achieved a sensitivity of 0.914 and a specificity of 0.458, reflecting a high-sensitivity, low-specificity profile. The LightGBM calibration curve suggested closer agreement in the low-to-moderate predicted-probability range, with greater deviation at higher probabilities. Decision curve analysis suggested potential net benefit across a range of threshold probabilities but did not establish clinical utility. Platelet count, monocyte percentage, and neutrophil percentage were consistently among the leading predictors across the tree-based models. Conclusions: Tree-based models showed moderate discrimination, with high sensitivity but limited specificity, and yielded numerically higher AUROCs and lower Brier scores than the primary SMOTE-trained LR within this internal-validation framework. They should not replace etiological testing or be used as standalone diagnostic tools; their observed operating characteristics are more compatible with a potential adjunctive screening or triage-support role. External and prospective validation across independent populations and settings is required before clinical use or superiority over simpler statistical models can be established. Full article

Background and Objectives: Periodontitis is a chronic multifactorial inflammatory disease characterized by progressive destruction of the tooth-supporting tissues. Although periodontal diagnosis is primarily based on clinical and radiographic parameters, salivary biomarkers may provide additional biological information on inflammatory activity and host-response regulation. Matrix metalloproteinase-9 (MMP-9) is involved in extracellular matrix degradation and periodontal tissue destruction, whereas melatonin is a circadian-related molecule with antioxidant, anti-inflammatory, and immunomodulatory properties. However, evidence regarding the simultaneous assessment of salivary MMP-9 and melatonin using standardized pre-sleep saliva collection remains limited. This pilot study aimed to evaluate salivary melatonin and MMP-9 concentrations in periodontal health and stage III periodontitis, to assess their interrelationship, and to explore their preliminary in-sample ability to distinguish periodontal health from stage III periodontitis. Materials and Methods: This cross-sectional pilot study included 18 systemically healthy non-smoking adults: 9 periodontally healthy participants and 9 patients with stage III periodontitis. Pre-sleep unstimulated saliva was collected between 23:00 and 24:00 h or immediately before sleep, following a standardized protocol. Salivary MMP-9 and melatonin concentrations were determined using enzyme-linked immunosorbent assay. Periodontal diagnosis was established according to the 2017 World Workshop classification. Group comparisons, correlation analyses, and receiver operating characteristic curve analyses were performed as exploratory analyses. Results: Salivary melatonin concentrations were significantly lower in patients with stage III periodontitis than in periodontally healthy participants (p = 0.019), with a large effect size (Hedges’ g = 1.261). Salivary MMP-9 concentrations were higher in the periodontitis group, but the difference was not statistically significant (p = 0.465). No significant correlation was found between salivary melatonin and MMP-9 levels. Receiver operating characteristic analysis suggested preliminary in-sample discriminatory ability for melatonin (AUC = 0.827; p = 0.019), whereas MMP-9 showed limited and non-significant discriminatory ability (AUC = 0.593; p = 0.508). Conclusions: In this pilot sample, lower pre-sleep salivary melatonin levels were associated with stage III periodontitis, whereas MMP-9 showed limited standalone discriminatory ability. These findings should be interpreted as preliminary and hypothesis-generating. The present study does not establish a clinically applicable diagnostic cut-off or validated diagnostic utility for salivary melatonin. Larger, independently validated studies are needed to confirm these observations. Full article

Background: Scabies is a World Health Organization–designated neglected tropical disease of rising incidence with increasingly reported treatment failure, making objective verification of cure important. Dermoscopy is well established for diagnosis, but its role in assessing treatment response has never been mapped. Objectives: To map how dermoscopy assesses treatment response in scabies—which markers, timepoints and cure definitions are applied, and how it relates to clinical and microscopic standards. Methods: A scoping review following PRISMA-ScR searched PubMed/MEDLINE, Web of Science, EBSCO and Scopus (2016–2026, English) using a two-concept strategy (scabies × dermoscopy); eligibility required baseline dermoscopy plus at least one further, separately reported dermoscopic assessment during or after therapy. Two reviewers screened independently, supplemented by citation searching. Results: Ten studies were included. The same mite structure appeared under at least four names; assessment timepoints (day 2 to beyond day 28) and cure definitions were inconsistent. Dermoscopy was the genuine object of investigation in few studies; only one used ultraviolet-induced fluorescence to track response. Conclusions: Despite the disease’s recognised importance, dermoscopic monitoring of scabies remains a declared rather than a designed research aim. No standardised marker, timepoint or cure definition exists; consensus standards analogous to the 2020 IACS diagnostic criteria are needed. Full article

Bearing fault diagnosis increasingly relies on multiple sensors, since compound or multi-location faults can produce signatures that are only partially captured by a single sensor. The decision-level fusion (DLF) of independently trained models offers a practical way to combine such complementary information, yet systematic comparisons across DLF techniques remain scarce, particularly for measurements from different sensor locations. This paper benchmarks six DLF strategies, i.e, Max, Average, Majority Voting, Weighted Sum, Dempster–Shafer, and Stacking, on a dual-branch one-dimensional convolutional neural network (1D-CNN) with each branch trained end-to-end on vibration signals from a distinct bearing location. On a two-sensor test bench covering seven health conditions, all methods exceed 99.7% accuracy on clean signals, while Dempster–Shafer fusion proves markedly more robust under noise, retaining up to 84% accuracy at a 5 dB signal-to-noise ratio (SNR). A conflict-coefficient analysis further provides an interpretable account of when fusion succeeds, linking performance to the confidence complementarity between branches. Full article

The war in Ukraine, beginning in February 2022, disrupted continuous medical care for Ukrainian childhood and adolescent cancer patients (UCC), many of whom were relocated to pediatric cancer centers worldwide, including Switzerland. In this Brief Report, we describe their health-related quality of life (HRQoL) after arrival. In a multicenter, cross-sectional survey across five Swiss pediatric oncology centers, we included patients ≤18 years at diagnosis who arrived after 24 February 2022 and were undergoing active treatment. HRQoL was assessed by the PedsQL™ 4.0 Generic Core Scales (self- and parent-reports). Fourteen of 23 eligible families (61%) participated. HRQoL declined with age, particularly in physical functioning, while psychosocial functioning remained relatively stable but lower among adolescents; parent scores closely matched self-reports. Scores were referenced against a published cohort of healthy Ukrainian children and previously reported pediatric cancer populations. Given the small sample and lack of a matched control group, the findings cannot separate the effects of displacement, cancer, and treatment and are hypothesis-generating. They suggest a potential role for age-tailored supportive care in displaced children with cancer and call for larger, controlled studies. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and functional deterioration. With the rapid growth of the aging population, AD has become a major global health challenge, imposing substantial burdens on patients, families, and healthcare systems. Despite extensive research, early and accurate diagnosis of AD remains challenging due to disease heterogeneity, overlapping clinical manifestations, and the lack of easily accessible, highly sensitive, and specific diagnostic markers. Recent advances in biomedical technologies, including neuroimaging, multi-omics profiling, electronic health records, and digital health tools, have generated large-scale and heterogeneous datasets, providing new opportunities for improving AD diagnosis. However, extracting clinically meaningful information from these complex data sources remains difficult using conventional statistical approaches. Artificial intelligence (AI) has progressively transformed AD diagnosis by evolving from traditional machine learning (ML) approaches based on handcrafted feature engineering to deep learning (DL) models capable of automated representation learning and multimodal information integration. More recently, large language models (LLMs) have further expanded the scope of AI-driven AD diagnosis by enabling contextual understanding of unstructured clinical information, knowledge-guided reasoning, and integration of multimodal biomedical evidence. This transition reflects a shift from feature-based prediction toward more flexible and intelligent diagnostic frameworks. This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs. Particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and enabling multimodal frameworks for AD assessment. Full article

Reliable automated monitoring of photovoltaic modules is essential for improving energy efficiency, operational safety, and predictive maintenance. Infrared thermography is one of the most effective solutions for identifying localised thermal anomalies, such as hotspots, micro-cracks, connection faults, shading effects and other conditions of degradation that can compromise the performance of the photovoltaic system. The interpretation of thermographic images is still frequently reliant on the operator’s experience or on automated procedures based exclusively on image processing techniques or artificial intelligence models often regarded as black-box models, thereby limiting their reliability, robustness and interpretability. This study presents an integrated diagnostic framework combining infrared thermography, computer vision, and artificial intelligence for the automated diagnosis and health monitoring of photovoltaic modules operating under real-world conditions. The proposed methodology extends beyond hotspot detection by integrating thermal image preprocessing, anomaly detection and segmentation, extraction of thermal and geometric descriptors, and intelligent fault classification. The resulting diagnostic information enables automated fault-type classification and quantitative severity assessment, providing interpretable condition indicators for photovoltaic module monitoring. The methodology was validated using a database comprising 1560 thermographic images acquired from photovoltaic modules under representative operating conditions. The experimental evaluation demonstrated an overall classification accuracy of 97.6%, an F1-score of 97.0%, and an area under the ROC curve (AUC) of 0.991 for the fault-type classification task. The proposed framework therefore provides an interpretable and computationally efficient decision-support methodology for photovoltaic condition assessment, while its integration into longitudinal predictive-maintenance systems remains a subject for future investigation. Full article

This study assessed the geographic, demographic, and temporal factors underlying the evolution of the HIV/AIDS epidemic across the health macroregions of Minas Gerais, Brazil, comparing the periods before (2009–2014) and after (2015–2022) the implementation of universal HIV notification and immediate antiretroviral therapy (ART). An ecological study was conducted using statewide surveillance and clinical monitoring databases. Epidemiological indicators, including HIV detection, AIDS incidence, AIDS-related mortality, ART coverage, and sustained viral suppression (SVS), were analyzed according to sex and health macroregion using generalized linear models with negative binomial regression and spatial analysis. HIV detection increased markedly following the expansion of mandatory notification, while ART coverage and SVS also increased significantly. In contrast, AIDS incidence and AIDS-related mortality declined during the later period. Substantial heterogeneity was observed across macroregions, with important differences in epidemiological indicators and HIV care cascade performance. These findings demonstrate that the benefits of expanded HIV detection and universal ART have not been distributed uniformly across the state. Regional disparities in access to diagnosis, treatment, and long-term care remain important barriers to HIV control, highlighting the need for targeted, region-specific public health strategies to reduce territorial inequalities and strengthen the HIV care cascade. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are increasingly prescribed for type 2 diabetes mellitus (T2DM). However, its mental health safety profile in routine care, particularly among Medicaid beneficiaries, remains uncertain. We aim to examine the association between glucagon-like peptide-1 receptor agonist (GLP-1 RA) use and newly documented depression diagnosis among adults with type 2 diabetes mellitus (T2DM) enrolled in Medicaid. Methods: We conducted a retrospective longitudinal cohort study using the 2022–2023 Merative™ MarketScan^® Multi-State Medicaid Database. Adults aged 18–64 years with T2DM, continuous Medicaid enrollment, and no documented depression during the 2022 baseline period were included. Baseline GLP-1 RA use was assessed in 2022, and newly diagnosed depression was identified in 2023. Associations were estimated using multivariable logistic regression, inverse probability weighting (IPW), and a doubly robust estimator. Results: Among 74,760 eligible adults, 14,953 (20.0%) were GLP-1 RA users. Incident depression occurred in 12.9% of users and 11.1% of non-users (absolute difference, 1.8 percentage points; p < 0.001). GLP-1 RA use was associated with higher odds of newly documented depression diagnosis in multivariable analysis (adjusted OR 1.10, 95% CI 1.04–1.16), IPW analysis (OR 1.09, 95% CI 1.03–1.15), and doubly robust analysis (OR 1.09, 95% CI 1.03–1.15). Conclusions: Baseline GLP-1 RA use was associated with modestly higher odds of a newly documented depression diagnosis during the subsequent year. Prospective studies are needed to determine whether this association is causal. Full article

Background/Objectives: A potential association between individualized multimodal immunotherapy (IMI) within a multiphase combined treatment strategy and prolonged survival was explored through retrospective real-world data analysis. Methods: The patient selection criteria were as follows: treatment after 27 May 2015, adults between ages 18 and 70, GB diagnosis and IDH1wt status documented, known MGMT promoter methylation status (methylated versus unmethylated), known status of OS, and no second malignancy. IMI is currently only accessible to patients with a >60 KPI and sufficient financial resources. Results: A total of 104 patients were identified, distributed as per MGMT methylation versus unmethylation status, respectively, as 18% female/25% male versus 23% f/38% m; the median age at intake was 54 y versus 50 y; the resection extent was 12 R0, 28 < R0 and three not documented versus 25, 28 and eight; and the median KPI at intake was 70 overall. Patients received a median of 37 versus 31 modulated electro-hyperthermia sessions, 37 versus 32 NDV injections, and two versus one dendritic cell vaccines. The median OS and percentage 2 y OS were 33.1 months and 69.7% versus 19.0 months and 35.4%. There were no major adverse reactions (ARs), but the AR burden increased with checkpoint inhibitor use. A 33-patient subset was analyzed on health-related quality of life (HRQoL) throughout IMI treatment, based on at least five EQ-5D-5L questionnaires available from intake. Mobility and self-care affected HRQoL less than usual activity, pain/discomfort and anxiety/depression. Throughout IMI, HRQoL remained stable. Before progressive disease, the median health utility index was 0.86, resulting in a median quality-adjusted life-months of 4.6 versus a median of 5.4 calendar months. Conclusions: Real-world observation indicates IMI may prolong GB patient OS while maintaining HRQoL. Full article