Search Results (3,812)

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, driven by marked molecular and cellular heterogeneity that complicates diagnosis and treatment. Despite advances in targeted therapies and immunotherapies, treatment resistance frequently emerges, and clinical benefits remain limited to specific molecular subtypes. To improve early detection and dynamic monitoring, novel diagnostic strategies—including liquid biopsy, low-dose computed tomography scans (CT) with radiomic analysis, and AI-integrated multi-modal platforms—are under active investigation. Non-invasive sampling of exhaled breath, saliva, and sputum, and high-throughput profiling of peripheral T-cell receptors and immune signatures offer promising, patient-friendly biomarker sources. In parallel, multi-omic technologies such as single-cell sequencing, spatial transcriptomics, and proteomics are providing granular insights into tumor evolution and immune interactions. The integration of these data with real-world clinical evidence and machine learning is refining predictive models and enabling more adaptive treatment strategies. Emerging therapeutic modalities—including antibody–drug conjugates, bispecific antibodies, and cancer vaccines—further expand the therapeutic landscape. This review synthesizes recent advances in NSCLC diagnostics and treatment, outlines key challenges, and highlights future directions to improve long-term outcomes. These advancements collectively improve personalized and effective management of NSCLC, offering hope for better-quality survival. Continued research and integration of cutting-edge technologies will be crucial to overcoming current challenges and achieving long-term clinical success. Full article

Background: Neurological disorders are the leading cause of disability, affecting over three billion people worldwide. Amyotrophic lateral sclerosis (ALS) is among the most feared and uniformly fatal neurodegenerative diseases, with no therapy capable of restoring lost function. Methods: We report the first application of therapeutic fever to ALS using Computerized Brain-Guided Intelligent Thermofebrile Therapy (CBIT²). This fully noninvasive treatment, delivered through an FDA-approved computerized platform, digitally reengineers the 1927 Nobel Prize-recognized malarial fever therapy into a modern treatment guided by the Brain–Eyelid Thermoregulatory Tunnel. CBIT² induces therapeutic fever through synchronized hypothalamic feedback, activating heat shock proteins, which are known to restore proteostasis and neuronal function. Case presentation: A 56-year-old woman was diagnosed with progressive ALS at the Mayo Clinic, with electromyography (EMG) demonstrating fibrillation and fasciculation indicative of denervation corroborated by neurological and MRI findings; the patient was informed that she had an expected survival of three to five years. A neurologist from Northwestern University confirmed the diagnosis and thus maintained the patient on FDA-approved ALS drugs (riluzole and edaravone). Her condition rapidly worsened despite pharmacological treatment, and she underwent CBIT², resulting in (i) electrophysiological reversal with complete disappearance of denervation; (ii) biomarker correction, including reductions in neurofilament and homocysteine, IL-10 normalization (previously linked to mortality), and robust HSP70 induction; (iii) restoration of gait, swallowing, respiration, speech, and cognition; (iv) reconstitution of tongue structure; and (v) return to complex motor tasks, including golf, pickleball, and swimming. Discussion: This case provides the first documented evidence that ALS can be reversed through digitally reengineered fever therapy aligned with thermoregulation, which induces heat shock response and upregulates heat shock proteins, resulting in the patient no longer meeting diagnostic criteria for ALS and discontinuation of ALS-specific medications. Beyond ALS, shared protein-misfolding pathology suggests that CBIT² may extend to Alzheimer’s, Parkinson’s, and related disorders. By modernizing this Nobel Prize-recognized therapeutic principle with computerized precision, CBIT² establishes a framework for large-scale clinical trials. A century after fever therapy restored lost brain function and so decisively reversed dementia paralytica such that it earned the 1927 Nobel Prize in Medicine, CBIT² now safely harnesses the therapeutic power of fever through noninvasive, intelligent, brain-guided thermal modulation. Amid a global brain health crisis, fever-based therapies may offer a path to preserve thought, memory, movement, and independence for the more than one-third of humanity currently affected by neurological disorders. Full article

Background: Cerebral echinococcosis is a rare, potentially serious parasitic disease in children, that can lead to intracranial hypertension, focal neurological deficits, seizures, and severe complications. We conducted a systematic review and meta-analysis on diagnostic, therapeutic approaches, and outcomes in pediatric cerebral echinococcosis. Methods: A systematic search was performed on PubMed, Scopus, and Web of Science, selecting English studies on children (0–18 years). Studies describing clinical, imaging, surgical, pharmacological, and outcome data were eligible. Statistical analyses (Fisher’s exact and chi-square tests) were performed in R. Results: A total of 100 studies with 462 pediatric patients met the inclusion criteria. High-resolution imaging has largely replaced invasive diagnostics; MRI-based diagnosis correlated with better outcomes. Headaches, vomiting, papilledema, seizures, and hemiparesis were common. Surgical cysts’ removal remained the main therapy. Additional treatment with albendazole was associated with a higher probability of good outcome (p < 0.001). A greater number of cyst localizations was significantly associated with a worse prognosis (p < 0.001). Overall mortality was 8.9%, while approximately 2/3 of patients achieved a good outcome. Conclusions: Advances in non-invasive imaging, refinement of surgical technique, and targeted antiparasitic therapy improved outcomes. Nevertheless, heterogeneous reporting and the prevailing paucity of evidence limit definitive recommendations. Prospective multicenter studies are needed to refine treatment and develop pediatric-specific guidelines. Full article

Radiogenomics, which integrates imaging phenotypes with genomic profiles, enhances diagnosis, prognosis, and treatment planning for glioblastomas. This study specifically establishes a correlation between radiomic features and MGMT promoter methylation status, advancing towards a non-invasive, integrated diagnostic paradigm. Conventional genetic analysis requires invasive biopsies, which cause delays in obtaining results and necessitate further surgeries. Our methodology is twofold: First, an enhanced U-Net model segments brain tumor regions with high precision (Dice coefficient: 0.889). Second, a hybrid classifier, leveraging the complementary features of EfficientNetB0 and ResNet50, predicts MGMT promoter methylation status from the segmented volumes. The proposed framework demonstrated superior performance in predicting MGMT promoter methylation status in glioblastoma patients compared to conventional methods, achieving a classification accuracy of 95% and an AUC of 0.96. These results underscore the model’s potential to enhance patient stratification and guide treatment selection. The accurate prediction of MGMT promoter methylation status via non-invasive imaging provides a reliable criterion for anticipating patient responsiveness to alkylating chemotherapy. This capability equips clinicians with a tool to inform personalized treatment strategies, optimizing therapeutic efficacy from the outset. Full article

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive and lethal malignancies, driven by late diagnosis, limited therapeutic options, and high metastatic potential. Beyond their canonical roles in hemostasis, platelets have emerged as active modulators of tumor progression and promising noninvasive biomarkers. Among platelet-associated molecules, α-defensins, particularly Defensin Alpha 1/3 (DEFA1/3), have been implicated in inflammation and immunity; however, their contribution to PDAC pathogenesis remains unclear. We combined bioinformatic analysis of platelet transcriptomes with functional and in vivo zebrafish xenograft validation to investigate the impact of DEFA1/3 on PDAC aggressiveness. DEFA1/3 was significantly upregulated in PDAC-derived platelets. Defensin-enriched platelet-like particles (defensin-rich platelets, DRPs) and recombinant DEFA1/3 enhanced pancreatic cancer cell proliferation, migration, and three-dimensional growth in vitro and promoted tumor dissemination in zebrafish xenografts. Transcriptomic profiling revealed the upregulation of SPARC, KDM6A, and GATA6, whereas clinical data from The Cancer Genome Atlas (TCGA)-PDAC linked high DEFA1/3 expression to poor survival, increased immune infiltration, and activation of epithelial–mesenchymal transition (EMT). Platelet-derived DEFA1/3 acts as a functional modulator of PDAC progression, linking platelet granule content to tumor aggressiveness and highlighting a potential biomarker and therapeutic target within the platelet–tumor axis. Full article

Breath analysis is a non-invasive diagnostic method that offers insights into both physiological and pathological conditions. Exhaled breath contains volatile organic compounds, which act as biomarkers for disease detection, allowing for the monitoring of treatments and the tailoring of medicine to individuals. Recent advancements in chemical sensing, mass spectrometry, and spectroscopy have improved the ability to identify these biomarkers; however, traditional statistical approaches often struggle to handle the complexities of breath data. Artificial intelligence (AI) and machine learning (ML) have revolutionized breath analysis by uncovering intricate patterns among volatile breath markers, enhancing diagnostic precision, and facilitating real-time disease identification. Despite significant progress, challenges remain, including issues with data standardization, model interpretability, and the necessity for extensive and varied datasets. This study reviews the applications of ML in analyzing breath volatile organic compounds, highlighting methodological shortcomings and obstacles to clinical validation. A thorough literature review was performed using the PubMed and Scopus databases, which included studies that focused specifically on the role of machine learning in disease diagnosis and incidence prediction via breath analysis. Among the 524 articles reviewed, 97 satisfied the specified inclusion criteria. The selected studies applied ML techniques, fell within the scope of this review, and emphasize the potential of ML models for non-invasive diagnostics. The findings indicate that traditional ML methods dominate, while ensemble methods are on the rise, and deep learning (DL) techniques (especially CNNs and LSTMs) are increasingly used for classifying respiratory diseases. Techniques for feature selection (such as PCA and ML-based methods) were frequently implemented, though challenges related to explainability and data standardization persist. Future studies should focus on enhancing model transparency and developing methods to further integrate AI into the clinical setting to facilitate early disease detection and advance precision medicine. Full article

Speech and language offer a rich, non-invasive window into brain health. Advances in machine learning (ML) have enabled increasingly accurate detection of neurodevelopmental and neurodegenerative disorders through these modalities. This paper envisions the future of ML in the early detection of neurodevelopmental disorders like autism spectrum disorder and attention-deficit/hyperactivity disorder, and neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease, through speech and language biomarkers. We explore the current landscape of ML techniques, including deep learning and multimodal approaches, and review their applications across various conditions, highlighting both successes and inherent limitations. Our core contribution lies in outlining future trends across several critical dimensions. These include the enhancement of data availability and quality, the evolution of models, the development of multilingual and cross-cultural models, the establishment of regulatory and clinical translation frameworks, and the creation of hybrid systems enabling human–artificial intelligence (AI) collaboration. Finally, we conclude with a vision for future directions, emphasizing the potential integration of ML-driven speech diagnostics into public health infrastructure, the development of patient-specific explainable AI, and its synergistic combination with genomics and brain imaging for holistic brain health assessment. Overcoming substantial hurdles in validation, generalization, and clinical adoption, the field is poised to shift toward ubiquitous, accessible, and highly personalized tools for early diagnosis. Full article

Background/Objectives: Late presentations of advanced hepatocellular carcinoma (HCC) indicate a lack of detection of underlying cirrhosis and a need to identify clinical and socioeconomic risk factors contributing to early-stage HCC recognition. This study tested associations between early diagnostics of HCC and demographic, socioeconomic, clinical, and healthcare-related indicators. Methods: A retrospective analysis of clinical data accumulated between February 2018 and February 2024 was completed at a quaternary care centre (South Australia). Results: We identified 388 cases of newly diagnosed HCC during a six-year period. There were 131 (33.7%) patients with early-stage (Barcelona clinic liver cancer (BCLC) stage 0–A) and 257 (66.3%) patients with late-stage (BCLC B–D) HCC. Late-stage HCC was found in 66.3% of patients, with half of the cohort not having a diagnosis of cirrhosis at the time of HCC detection. A retrospectively calculated Fibrosis Index (FIB-4) of >3.25 was present in nearly half of patients with newly diagnosed HCC with no prior diagnosis of cirrhosis. Engagement with healthcare (p < 0.05), a history of liver cirrhosis (p < 0.001), and gastroenterologist-led care with surveillance programmes (p < 0.001) was associated with early-stage presentation and curative treatment. Late-stage HCC was associated with male sex (p = 0.041), failing to attend appointments (p < 0.001), and liver function tests ordered by general physicians (p = 0.002) or non-gastroenterologist specialists (p = 0.023). Logistic regression analysis indicated that engaging in a surveillance programme, assessment by a gastroenterologist, and Model for End-Stage Liver Disease scores were important factors contributing to early detection of HCC; the area under the curve for this model on the ROC analysis was 0.892 (95% CI 0.855–0.929). Conclusions: Better cirrhosis detection is required, given that 60% of patients had a retrospectively calculated FIB-4 > 3.25. Routine use of non-invasive scores by all healthcare providers may increase engagement with surveillance and improve HCC screening. Full article

Oral squamous cell carcinoma (OSCC) remains one of the most prevalent and aggressive malignancies worldwide, with late diagnosis contributing to poor survival rates. Recent evidence suggests that periodontitis may act as a co-factor in development of OSCC through persistent inflammation, microbial dysbiosis, and subsequent tissue remodeling. Identifying molecular signatures that link periodontitis with early oral cancerization is therefore of paramount importance for risk assessment, prevention, and timely intervention. This narrative review aims to provide an integrative overview of the current knowledge on molecular, genetic, and epigenetic biomarkers associated with oral cancer risk in patients with periodontitis. Specifically, periodontal pathogens such as Porphyromonas gingivalis and Fusobacterium nucleatum promote oral cancerization by modulating molecular, genetic, and epigenetic pathways, including p53, Cyclin D1, Ki-67, p16^INK4A, DNA methylation, histone modifications, and microRNA regulation. Therefore, this review provides a discussion about the role of inflammatory mediators, oxidative stress-related molecules, microbial-derived products, genetic markers and epigenetic mechanisms as early molecular signals of malignant transformation. The study of these salivary biomarkers (salivaomics) has emerged as a promising non-invasive diagnostic tool, although variability in sampling, biomarker stability, and confounding factors such as coexisting periodontal disease remain significant limitations. By synthesizing the available evidence, this review summarizes recent evidence linking periodontitis to oral cancerization, highlights potential salivary, proteomic, and inflammatory biomarkers, and considers the role of periodontal therapy in improving inflammatory profiles and modulating tumor-related biomarkers. Finally, it explores future perspectives, including the integration of Artificial Intelligence (AI) to enhance biomarker-based diagnosis and risk stratification in OSCC patients. Full article

Introduction: It is unclear which patients with testicular cancer (TC) experience a higher incidence of bleomycin-induced pulmonary toxicity. Objective: The aim of this study was to analyze the prognostic significance of lymphocyte-associated inflammation markers that may predict bleomycin-related pulmonary toxicity in TC. Results: Clinical and laboratory data were recorded for 118 patients diagnosed with TC who received bleomycin, with a median age at diagnosis of 32.19 ± 9.62. Symptomatic pulmonary toxicity was present in 19.49% (n = 23) of patients. Of these, 66.67% had a DLCO decrease of more than 10%. When comparing patients with and without pulmonary toxicity, there were no differences in terms of age at diagnosis, performance status, histopathological subgroup, tumor size, lymphovascular invasion, diagnostic symptom, stage, number of adjuvant treatment cycles, and tumor marker levels. Patients with pulmonary toxicity were more likely to be active smokers than those without pulmonary toxicity, and NLR > 1.64, PLR > 93.92, CLR > 0.49, SII > 444.25, and SIRI > 0.66 were found to be statistically significant. Lymphocyte-related inflammation markers (NLR, PLR, LMR, CLR, SII, and SIRI) were found to be prognostic for pulmonary toxicity. There was 5.2 times more pulmonary toxicity in smokers than in non-smokers. The prognostic inflammation markers that enable us to predict pulmonary toxicity are TC. Conclusions: The employment of lymphocyte-related inflammation biomarkers at the commencement of treatment offers a means of predicting bleomycin-related pulmonary toxicity in TC. Full article

Background/Objectives: To evaluate the prevalence, age at diagnosis, non-invasive ventilation pressures used in management, and clinical predictors for sleep disordered breathing (SDB) in pediatric patients with muscular dystrophies (MDs). Methods: A retrospective analysis of 195 polysomnography (PSG) studies conducted over 20 years for 98 children with different MDs was performed. Diagnosis of SDB was established if a child met the diagnostic criteria for one or more of the following conditions: obstructive sleep apnea (OSA), central apnea, nocturnal hypoxemia, or nocturnal hypoventilation. Outcomes were assessed and compared between MDs. Positive and negative predictive values (PPV, NPV), sensitivity, and specificity for detecting SDB were calculated for certain clinical parameters. Results: SDB was diagnosed in 73.6% of children with MDs, including OSA in 67%, followed by nocturnal hypoxemia (15.3%), nocturnal hypoventilation (7.7%), and central apnea (6.6%). The age at diagnosis and BiPAP pressures used varied between MDs. Patients with Congenital MD had the lowest mean age and required higher pressures (p < 0.05). PPV was high for maximum inspiratory or expiratory pressures (MIP, MEP) < 40% or <60%, forced vital capacity < 50% or <80%, total lung capacity < 60%, left ventricular ejection fraction < 50%, non-ambulation, and body mass index ≥ 95% for the presence of SDB. However, NPV, sensitivity, and specificity varied. Conclusions: SDB is common in pediatric patients with MDs, with OSA being the most prevalent disorder. The age at diagnosis and required BiPAP pressures for management differ among MD groups. Certain clinical measures may help identify some patients with the disease given the high PPV. Full article

Background/Objectives: Extramammary Paget’s disease (EMPD) is a rare cutaneous malignancy arising in areas rich in apocrine glands that poses diagnostic and therapeutic difficulties. Although surgery remains the standard of care, achieving clear margins is challenging and recurrence rates are high. This review explores the contribution of non-invasive imaging for diagnosis and monitoring, and evaluates conservative, non-surgical therapies as alternatives to radical surgery. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a systematic review was conducted: eligible studies included interventional and observational research, as well as case series and reports, assessing non-invasive diagnostic methods or non-surgical treatments for EMPD. Data extraction and risk-of-bias evaluation were performed independently by multiple reviewers, and a narrative synthesis summarized therapeutic outcomes and diagnostic performance. Results: Of 808 identified records, 82 met the inclusion criteria: 66 focused on non-surgical therapies, 15 on diagnostic techniques, and one on both. Reflectance confocal microscopy (RCM) and photodynamic diagnosis (PDD) showed high concordance with histopathology, aiding both diagnosis and margin delineation. Among therapies, topical imiquimod and photodynamic therapy (PDT) demonstrated encouraging response rates, while radiotherapy, laser ablation, and systemic chemotherapy were less consistently reported. Evidence quality was limited by small cohorts, heterogeneous regimens, and variable follow-up. Conclusions: Non-invasive imaging enhances diagnostic accuracy and surgical planning, while non-surgical treatments—particularly imiquimod and PDT—offer viable alternatives in selected cases. Larger prospective studies are needed to establish standardized protocols and clarify long-term outcomes. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers, largely due to late diagnosis and the lack of reliable non-invasive biomarkers. Altered trace element homeostasis has been implicated in tumor biology and systemic inflammation, but comprehensive metallomic profiling in PDAC is still limited. Methods: Using inductively coupled plasma mass spectrometry (ICP-MS), we quantified 20 serum and 15 urinary metals in 71 PDAC patients and 69 matched controls. Statistical analyses included univariate Wilcoxon testing, correlation with systemic inflammatory indices (NLR, MLR, SIRI, AISI, HGB/RDW, PCT), and multivariate chemometric modeling (PCA-LDA). K-means clustering was applied to identify patient subgroups with distinct biochemical signatures. Results: PDAC patients showed significantly elevated urinary antimony, chromium, cadmium, and vanadium, whereas controls exhibited higher serum selenium, zinc, barium, vanadium, and cobalt (all p < 10⁻⁵). The PCA-LDA model achieved 99% classification accuracy (Monte Carlo cross-validation, 1000 iterations), highlighting complementary diagnostic contributions of serum and urinary profiles. Serum selenium was inversely associated with SIRI and NLR, while urinary cobalt correlated positively with NLR. Clustering revealed three PDAC subgroups with different inflammatory and metallomic patterns, suggesting underlying biological heterogeneity. Conclusions: PDAC is characterized by opposite serum-urine metal signatures, indicating altered absorption-excretion dynamics. Selenium depletion may represent a protective biomarker, whereas urinary cobalt excretion reflects systemic inflammation. This integrative ICP-MS–chemometric approach provides a promising diagnostic tool for improving early detection and patient stratification in clinical practice. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is becoming more common among adolescents, but non-invasive biomarkers for early detection are still limited. Liver-expressed antimicrobial peptide-2 (LEAP-2), a ghrelin receptor antagonist, has been connected to obesity and liver fat buildup in adults, but pediatric data are limited. This study investigates the hypothesis that higher levels of LEAP-2 are associated with hepatic steatosis and the role of LEAP-2 serum levels in the earlier and easier diagnosis of MASLD in children. Methods: In this cross-sectional study, 51 adolescents aged 12–18 were divided into three groups: one with MASLD and obesity (MASLD-Ob) (confirmed hepatosteatosis by imaging studies such as magnetic resonance or ultrasound, along with at least one cardiometabolic criterion and a body mass index (BMI) > 2 SD) (n = 19), another with obesity without any liver pathology or MASLD (BMI > 2 SD) (n = 14), and healthy controls (n = 18). The controlled attenuation parameter (CAP) was measured using FibroScan^® Mini + 430 (Echosens SA, Créteil, France), and serum ghrelin and LEAP-2 levels were determined via ELISA. Correlations between LEAP-2, ghrelin, CAP, BMI z-score, and metabolic parameters were analyzed. Results: LEAP-2 and ghrelin levels among the three groups were similar (p = 0.148, p = 0.515). A positive correlation was observed between LEAP-2 levels and CAP values in the obese group (both the MASLD-Ob and obesity groups) (r = 0.379, p = 0.030). When a cutoff of 240 dB/m was used, the median LEAP-2 level in cases above this value was 2.20 ng/mL, compared to 1.37 ng/mL in cases below it (p = 0.021), which was significantly different. When analyzing the obese group (both the MASLD-Ob and obese groups) a statistically significant correlation was found between serum LEAP-2 levels and CAP, AST, GGT, and total bilirubin values (r = 0.379, p = 0.030; r = 0.369, p = 0.035; r = 0.369, p = 0.035; r = 0.357, p = 0.049, respectively). Conclusions: Interventional imaging methods and biomarkers for diagnosing and monitoring hepatosteatosis have become well-established in the literature. However, since these tests are not available at all centers and can be costly, there is an increasing search for other easily accessible diagnostic and follow-up parameters. LEAP-2 could be a promising non-invasive biomarker for pediatric MASLD, especially when used alongside CAP measurements. The application of this biomarker in pediatric MASLD provides valuable data to help identify and monitor the condition in adolescents. We believe our study offers strong evidence to support further research and the development of drug treatments for MASLD that aim to reduce plasma LEAP-2. Full article

Exhaled breath analysis is emerging as one of the most promising non-invasive strategies for the early detection of life-threatening diseases, especially lung cancer, where rapid and reliable diagnosis remains a major clinical challenge. In this study, we designed and optimized an electronic nose (e-nose) platform composed of quantum resistive vapor sensors (vQRSs) engineered by polymer-carbon nanotube nanocomposites via spray layer-by-layer assembly. Each sensor was tailored through specific polymer functionalization to tune selectivity and enhance sensitivity toward volatile organic compounds (VOCs) of medical relevance. The sensor array, combined with linear discriminant analysis (LDA), demonstrated the ability to accurately discriminate between cancer-related biomarkers in synthetic blends, even when present at trace concentrations within complex volatile backgrounds. Beyond artificial mixtures, the system successfully distinguished real exhaled breath samples collected under challenging conditions, including before and after smoking and alcohol consumption. These results not only validate the robustness and reproducibility of the vQRS-based array but also highlight its potential as a versatile diagnostic tool. Overall, this work underscores the relevance of nanocomposite chemo-resistive arrays for breathomics and paves the way for their integration into future portable e-nose devices dedicated to telemedicine, continuous monitoring, and early-stage disease diagnosis. Full article