Search Results (8,797)

Background and Objectives: Lung cancer in never-smokers (LCINS) has become a major global health concern, ranking as the fifth leading cause of cancer-related mortality. Unlike smoking-related lung cancer, LCINS arises from complex interactions between environmental carcinogens and distinct genomic alterations. This review summarizes current evidence on environmental risks, molecular features, and therapeutic progress shaping lung cancer management. Methods: A narrative review was conducted to examine risk factors for lung cancer in non-smokers. Studies reporting driver mutations in never-smokers and smokers were identified across major lung cancer histological subtypes, including small-cell lung cancer (SCLC), lung adenocarcinoma (LUAD), squamous cell carcinoma (SCC), and large-cell carcinoma (LCC). In addition, PubMed was searched for phase III trials and studies on targeted therapies related to driver mutations published between 2016 and 2025. Results: Environmental factors such as cooking oil fumes, radon, asbestos, arsenic, and fine particulate matter (PM_2.5) are strongly associated with LCINS through oxidative stress, DNA damage, and chronic inflammation. EGFR, PIK3CA, OS9, MET, and STK11 mutations are characteristic of never-smokers, in contrast to TP53 mutations, which are more common in smokers. Recent advances in targeted therapy and immunotherapy have improved survival and quality of life, emphasizing the importance of molecular profiling for treatment selection. Conclusions: LCINS represents a distinct clinical and molecular entity shaped by complex interactions between environmental exposures and genetic susceptibility. Genetic alterations promote tumor immune evasion, facilitating cancer development and progression. Continued advances in air quality control, molecular diagnostics, and precision therapies are essential for prevention, early detection, and reduction of the global disease burden. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide. Despite advances in screening and therapeutic strategies, early detection and individualized treatment remain major challenges. In recent years, an expanding repertoire of biomarkers has emerged, spanning genomic, transcriptomic, proteomic, and metabolomic signatures. Epigenetic features, such as DNA methylation panels, as well as non-coding RNAs and the gut microbiome, hold potential not only for improving early diagnosis but also for refining prognosis and predicting therapeutic responses within the framework of precision oncology. This narrative review provides an updated, integrative overview of CRC diagnostic, prognostic, and predictive biomarkers. We distinguish established markers already in clinical practice, such as RAS and BRAF mutations, HER2 amplification, microsatellite instability/mismatch repair deficiency (MSI/dMMR), and widely investigated molecular alterations including TP53 mutations and immune-checkpoint-related markers, from novel biomarkers with growing translational potential. We also discuss the implementation challenges of these biomarkers in clinical practice, including issues related to validation, standardization, and cost-effectiveness, as well as the multi-modal approach for the development of composite diagnostic panels. Full article

Prostate cancer remains one of the most common malignancies in men worldwide, with incidence and mortality steadily increasing across diverse populations. While early detection and radical prostatectomy can achieve durable control in a subset of patients, approximately 40% of men will ultimately experience biochemical recurrence often in the absence of clinically detectable disease. Conventional imaging approaches—CT, MRI, and bone scintigraphy—have limited sensitivity for early relapses, frequently leading to delayed diagnosis and suboptimal treatment planning. The discovery of prostate-specific membrane antigen (PSMA) in 1987 and its subsequent clinical translation into positron emission tomography (PET) imaging with [⁶⁸Ga]Ga-PSMA-11 in 2012, followed by U.S. FDA approval in 2020, has transformed the landscape of prostate cancer imaging. PSMA PET has demonstrated superior accuracy over conventional imaging, as highlighted in the landmark proPSMA trial and now serves as the foundation for theranostic approaches that integrate diagnostic imaging with targeted radioligand therapy. The clinical approval of [¹⁷⁷Lu]Lu-PSMA-617 (Pluvicto^®: (lutetium Lu 177 vipivotide tetraxetan, Advanced Accelerator Applications USA, Inc., a Novartis company) has established targeted radioligand therapy as a viable option for men with metastatic castration-resistant prostate cancer, extending survival in patients with limited alternatives. Emerging strategies, including next-generation ligands with improved tumor uptake and altered clearance pathways, as well as the integration of artificial intelligence for imaging quantification, are poised to further refine patient selection, dosimetry, and treatment outcomes. This review highlights the evolution of PSMA-based imaging and therapy, discusses current clinical applications and limitations, and outlines future directions for optimizing theranostic strategies in prostate cancer care. Full article

Carpal tunnel syndrome (CTS) is the most common peripheral nerve entrapment disorder, with a lifetime prevalence estimated at approximately 10%. This narrative review explores the historical evolution, current management strategies, and emerging trends in CTS diagnosis and treatment. Early recognition of CTS led to the development of conservative interventions, including splinting, corticosteroid injections, and physical therapy, aimed at alleviating median nerve compression and associated symptoms. The advent of open carpal tunnel release established surgery as the definitive treatment for moderate-to-severe CTS, with subsequent refinements—such as mini-open and endoscopic techniques—focused on minimizing tissue trauma and expediting recovery. Comparative studies demonstrate similar long-term efficacy between surgical modalities, though endoscopic approaches often provide faster short-term recovery. Advances in diagnostic imaging, including high-resolution ultrasound, have improved early detection and dynamic assessment of median nerve compression. Emerging therapies, such as regenerative biologics, neuromobilization, and minimally invasive surgical innovations, offer promising adjuncts to current care. Despite substantial progress, further research is needed to clarify optimal patient selection, refine minimally invasive techniques, and explore regenerative interventions. This review underscores the importance of individualized, evidence-based, and patient-centered approaches to CTS management, integrating both established and emerging strategies to optimize functional outcomes and quality of life. Full article

Background: Artificial intelligence (AI) integrated with cone-beam computed tomography (CBCT) has rapidly advanced the diagnostic capability of orthodontics, particularly for quantifying external root resorption (ERR). High-risk scenarios such as bilateral maxillary canine impaction require objective tools to guide treatment decisions and prevent irreversible damage. Objectives: To evaluate the diagnostic accuracy and clinical applicability of AI-assisted CBCT for orthodontically induced ERR, and to demonstrate its value in a complex clinical case where decision-making regarding canine traction versus extraction required precise risk quantification and definition of biological limits. Methods: A systematic review following PRISMA 2020 guidelines was conducted in PubMed, ScienceDirect, and Cochrane Library (2015–September 2025). Eligible studies applied AI-enhanced CBCT to assess ERR in orthodontic patients. Additionally, a clinical case with bilaterally impacted maxillary canines was evaluated using CBCT with automated AI segmentation and manual refinement to quantify root volume changes and determine prognostic thresholds for treatment modification. Results: Nine studies met the inclusion criteria. AI-based imaging, predominantly convolutional neural networks, showed high diagnostic accuracy (up to 94%), improving reproducibility and reducing operator dependency. In the clinical case, volumetric monitoring showed rapid progression of ERR in the lateral incisors (LI) associated with a persistent unfavorable 3D spatial relationship between the canines and incisor roots, despite controlled distal traction with skeletal anchorage, leading to a timely change in the treatment plan and extraction of the severely compromised incisors with substitution by the canines. AI-generated data provided objective evidence supporting safer decision-making and prevented further structural deterioration. Conclusions: AI-enhanced CBCT enables early, objective, and quantifiable ERR assessment, strengthening prognosis-based decisions in orthodontics. Findings of this review and the clinical case highlight the translational relevance of AI for managing high-risk cases, such as maxillary canine impaction with extensive LI resorption, supporting future predictive AI models for safer canine traction. Full article

Liquid biopsy is moving beyond mutation-centric assays to multimodal frameworks that integrate cell-free DNA (cfDNA) signals with additional analytes such as circulating tumor cells (CTCs) and extracellular vesicles (EVs). In this review, we summarize emerging technologies across analytes for early cancer detection, emphasizing sequencing and error-suppression strategies and the growing evidence for multi-cancer early detection (MCED), tissue-of-origin (TOO) inference, diagnostic triage, and longitudinal surveillance. At low tumor fractions, fragmentomic and methylation features preserve tissue and chromatin context; when combined with radiomics using deep learning, they support blood-first, high-specificity risk stratification, increase positive predictive value (PPV), reduce unnecessary procedures, and enhance early prediction of treatment response and relapse. Building on these findings, we propose a pathway-aware workflow: initial blood-based risk scoring, followed by organ-directed imaging, and targeted secondary testing when indicated. We further recommend that model reports include not only discrimination metrics but also calibration, decision-curve analysis, PPV/negative predictive value (NPV) at fixed specificity, and TOO accuracy, alongside multi-site external validation and blinded dataset splits to improve generalizability. Overall, liquid biopsy is transitioning from signal discovery to deployable multimodal decision systems; standardized pre-analytical and analytical workflows, robust error suppression, and prospective real-world evaluations will be pivotal for clinical implementation. Full article

The occurrence of cysts and tumors in pediatric patients varies across different age groups. Follicular and dentigerous cysts are among the most common lesions. However, typical odontogenic tumors in juvenile patients are not frequently observed. Early stages of cyst and odontogenic tumor development might exhibit some similar characteristics due to the presence of unerupted teeth or their relationship with various stages of tooth formation and eruption. Many small lesions are discovered accidentally on routine orthopantomography (OPG), while the bigger ones manifest themselves as bone swelling, cortical perforation, or displacement and mobility of teeth. Each odontogenic tumor has characteristic clinical and radiological features. Biopsy of larger lesions, or incisional biopsy of smaller lesions, allows detailed histopathological evaluation to determine tumor type and growth behavior and guide appropriate treatment planning. In some cases, atypical signs on OPGs, like asymmetry in dental follicles, occurrence of round or oval bone lesions near impacted or retained teeth, and visibility of irregular radiolucent, radiopaque, or mixed jawbone lesions, might suggest the occurrence of some possible odontogenic tumor in juvenile patients. Each case should be handled individually. In this case, we demonstrate how atypical appearances of dental follicles on panoramic radiographs may not correspond with cone-beam computed tomography findings and may indicate the early stages of odontogenic myxoma in a juvenile patient. Full article

Sarcopenia and physical frailty are interconnected geriatric syndromes that frequently coexist in older adults, sharing common pathophysiological pathways. However, their early detection in community settings is limited by resource constraints and by the lack of simplified, scalable diagnostic tools. This cross-sectional study aimed to estimate the prevalence and overlap of sarcopenia and frailty in a real-world public health screening programme and to evaluate the diagnostic performance of a pragmatic two-step algorithm. In September 2025, a total of 256 consecutive community-dwelling adults aged ≥65 years underwent standardized assessment using the SARC-F questionnaire, handgrip strength dynamometry, and selective bioelectrical impedance analysis (BIA). Sarcopenia was defined according to 2019 EWGSOP2 criteria, and frailty according to the Fried phenotype. Confirmed sarcopenia was identified in 37 participants (14.5%, 95% CI 10.7–19.1%) and frailty in 31 (12.1%, 95% CI 8.6–16.7%), with substantial overlap (77.4% of frail individuals also had sarcopenia; Cohen’s κ = 0.62). The two-step algorithm (Step 1: SARC-F ≥ 4; Step 2: handgrip strength and BIA only in screen-positive participants) demonstrated excellent accuracy for confirmed sarcopenia (AUC 0.913, 95% CI 0.871–0.955), with sensitivity 91.9%, specificity 81.3%, and a 53.9% reduction in BIA use. Factors independently associated with confirmed sarcopenia included older age, BMI < 22 kg/m², physical inactivity, and higher SARC-F score. A simple, function-centered two-step approach enables efficient and scalable identification of sarcopenia and frailty in community settings, supporting early preventive strategies to preserve physical function. Full article

Background and Objectives: Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Early recognition is crucial to improve outcomes, but conventional biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT) show limited diagnostic accuracy. Materials and Methods: We performed a narrative review of the literature on sepsis biomarkers, with a focus on their biological role, diagnostic performance, clinical applicability, and limitations. Particular attention was given to presepsin (P-SEP) and monocyte distribution width (MDW), which have recently gained relevance. Results: Several novel biomarkers—including lipopolysaccharide-binding protein (LBP), soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), mid-regional pro-adrenomedullin (MR-proADM), neutrophil gelatinase-associated lipocalin (NGAL), Proenkephalin (PENK), and circulating microRNAs—have been studied, though most remain investigational. Among them, P-SEP shows rapid kinetics and correlation with disease severity, while MDW, derived from routine complete blood count, offers encouraging sensitivity and cost-effectiveness in emergency settings. Both biomarkers appear practical and potentially valuable for early sepsis detection. Conclusions: P-SEP and MDW emerge as the most promising biomarkers for timely sepsis recognition and risk stratification. Further validation and standardization are required to include them into routine clinical practice. Full article

Introduction: A group of approximately 15 Candida species are frequently found to be responsible for human invasive candidiasis, an infection that appears in patients with prolonged hospitalization, particularly in Intensive Care Units, and in immunosuppressed individuals. Given the considerable burden if not rapidly treated, clinicians face diagnostic challenges in distinguishing infection. The objective of this narrative review is to summarize the clinically applicable biomarkers used for invasive candidiasis and to evaluate their performance and create a diagnostic algorithm for clinical practice. Methods: This narrative review was conducted by searching PubMed and Scopus for studies published between 1990 and 2025, using keywords related to invasive candidiasis and non-culture diagnostic biomarkers. Clinical guidelines and consensus documents from major infectious diseases societies were additionally reviewed to supplement. Results: Blood cultures, which are considered the “gold standard” for diagnosis, face important fallouts caused by the limited sensitivity of 50%. Polymerase Chain Reaction assays can identify Candida species at an early stage when compared to blood cultures, demonstrating high specificity that ranges between 91% and 98, due to their high cost, and the limitations regarding only the identification of certain species, their widespread use remains limited. Non-culture serological tests such as mannan, anti-mannan and 1-3-β-D-glucan can detect fungal cell wall components or antibodies directed towards them. These tests have the advantage of being performed directly from blood samples. Reported sensitivity and specificity are 83% and 86% for mannan/anti-mannan, and 73% and 80% for 1-3-β-D-glucan, respectively. They are used for early detection of candidemia in high-risk patients, including immunocompromised individuals. Conclusions: Our report suggests that the traditional “gold standard” for diagnosing invasive candidiasis can be improved by integrating and combining novel biomarkers in the diagnostic pathways, and, thus, potentially reducing the time spent for diagnosing and facilitating early treatment access. Full article

Background/Objectives: Breast cancer is the most common malignancy among women, and early detection is critical for improving outcomes. The Breast Imaging Reporting and Data System (BI-RADS) standardizes reporting, but the BI-RADS 4 category presents a major challenge, with malignancy risk ranging from 2% to 95%. Consequently, most women in this category undergo biopsies that ultimately prove unnecessary. This study evaluated whether exhaled breath analysis could distinguish malignant from benign findings in BI-RADS 4 patients. Methods: Participants referred to the McGill University Health Centre Breast Center with BI-RADS 3–5 findings provided multiple breath specimens. Breathprints were captured using an electronic nose (eNose) powered breathalyzer, and diagnoses were confirmed by imaging and pathology. An autoencoder-based model fused the breath data with BI-RADS scores to predict malignancy. Model performance was assessed using repeated cross-validation with ensemble voting, prioritizing sensitivity to minimize false negatives. Results: The breath specimens of eighty-five participants, including sixty-eight patients with biopsy-confirmed benign lesions and seventeen patients with biopsy-confirmed breast cancer within the BI-RADS 4 cohort were analyzed. The model achieved a mean sensitivity of 88%, specificity of 75%, and a negative predictive value (NPV) of 97%. Results were consistent across BI-RADS 4 subcategories, with particularly strong sensitivity in higher-risk groups. Conclusions: This proof-of-concept study shows that exhaled breath analysis can reliably differentiate malignant from benign findings in BI-RADS 4 patients. With its high negative predictive value, this approach may serve as a non-invasive rule-out tool to reduce unnecessary biopsies, lessen patient burden, and improve diagnostic decision-making. Larger, multi-center studies are warranted. Full article

The piston pin operates under severe mechanical and thermal conditions, making accurate lubrication prediction essential for engine durability. This study presents a comprehensive digital engineering framework for piston pin bearings, built upon a fully coupled thermo-elasto-hydrodynamic (TEHD) formulation. The framework integrates: (1) a Reynolds-equation hydrodynamic solver with temperature-/pressure-dependent viscosity and cavitation; (2) elastic deformation obtained from FEA (finite element analysis)-based compliance matrices; (3) a break-in module that iteratively adjusts surface profiles before steady-state simulation; (4) a three-body heat transfer model resolving heat conduction, convection, and solid–liquid interfacial heat exchange. Applied to a heavy-duty diesel engine, the framework reproduces experimentally observed behaviors, including bottom-edge rounding at the small end and the slow unidirectional drift of the floating pin. By integrating multi-physics modeling with design-level flexibility, this work aims to provide a robust digital twin for the piston-pin system, enabling virtual diagnostics, early-stage failure prediction, and data-driven design optimization for engine development. Full article

Background and Clinical Significance: Cerebral candidiasis (Candida albicans meningoencephalitis) is a rare but severe central nervous system (CNS) infection, usually associated with neurosurgical procedures or indwelling devices. Diagnosis is challenging due to frequent negativity of cerebrospinal fluid (CSF) cultures, and mortality remains high despite antifungal therapy. Case Presentation: We describe a 64-year-old woman who underwent retrosigmoid resection of a left vestibular schwannoma. The early postoperative course was complicated by fever, neurological deterioration, and hydrocephalus requiring external CSF drainage. Multiple lumbar punctures revealed inflammatory CSF profiles but persistently negative cultures. One month post-surgery, intraoperative samples from mastoid repair material grew Candida albicans, prompting antifungal therapy. Despite treatment, the patient experienced fluctuating neurological status and required multiple external ventricular drains. Three months after surgery, she clinically deteriorated and died. Autopsy showed diffuse meningeal thickening and purulent exudates at the brain base and posterior fossa. Histopathology confirmed chronic lympho-histiocytic meningitis with necrotizing foci containing Candida albicans. Conclusions: This case underscores the diagnostic and therapeutic challenges of post-neurosurgical Candida CNS infections. Repeatedly negative CSF cultures delayed diagnosis, emphasizing the value of ancillary tests such as β-d-glucan and molecular assays. Even with antifungal therapy, prognosis is poor. Autopsy remains essential for uncovering fatal healthcare-associated fungal infections and informing clinical vigilance and medico-legal assessment. Full article

Greywater reuse presents a promising strategy for reducing potable water demand and supporting the irrigation of urban green infrastructure, yet its implementation in early planning phases remains limited by fragmented regulations, data gaps, and the absence of practical decision support tools. This study develops a comprehensive evaluation matrix based on Multi-Criteria Decision Analysis (MCDA) to assess the feasibility of greywater reuse in residential district development. The framework integrates eight domains (legal, technical, infrastructural, ecological, economic, and social factors) and is complemented by automated supporting worksheets for water balance, ecological indicators, and economic parameters. Application of the matrix to two contrasting residential case studies demonstrated its diagnostic value: the new-build district in Dortmund showed a high reuse potential, strongly influenced by favourable infrastructure conditions and ecological indicators, whereas the existing building in Weimar yielded a moderate potential due to infrastructural constraints and lower greywater availability. Sensitivity analyses further revealed that local water tariffs, intended-use scenarios, and stakeholder weightings substantially affect outcomes. Overall, the results show that the matrix supports transparent early-stage decision-making, identifies critical bottlenecks, and strengthens governance-oriented integration of greywater reuse in sustainable urban development. Full article

Artificial intelligence (AI) is rapidly transforming medical diagnostics by allowing for early, accurate, and data-driven clinical decision-making. This review provides an overview of how machine learning (ML), deep learning, and emerging multimodal foundation models have been used in diagnostic procedures across imaging, pathology, molecular analysis, physiological monitoring, and electronic health record (EHR)-integrated decision-support systems. We have discussed the basic computational foundations of supervised, unsupervised, and reinforcement learning and have also shown the importance of data curation, validation metrics, interpretability methods, and feature engineering. The use of AI in many different applications has shown that it can find abnormalities and integrate some features from multi-omics and imaging, which has shown improvements in prognostic modeling. However, concerns about data heterogeneity, model drift, bias, and strict regulatory guidelines still remain and are yet to be addressed in this field. Looking forward, future advancements in federated learning, generative AI, and low-resource diagnostics will pave the way for adaptable and globally accessible AI-assisted diagnostics. Full article