Search Results (705)

Background/Objectives: Schizophrenia is one of the most extensively studied yet conceptually unstable disorders in the history of medicine and brain sciences. Since its formalization at the turn of the twentieth century, the disorder has been repeatedly redefined, reflecting changes in clinical observation, diagnostic philosophy, and neuroscientific models of brain function. The objective of this review is to critically examine the historical evolution of schizophrenia as a medical construct and to analyze how shifts in diagnostic systems have shaped the search for biological and molecular biomarkers. Methods: A narrative-historical review of the literature was conducted, integrating classical psychiatric texts, diagnostic manuals, and contemporary neuroscientific studies. Key milestones in the conceptualization of schizophrenia were analyzed alongside the development of biological hypotheses, including neurochemical, electrophysiological, neuroimaging, genetic, immunological, omics-based, and digital approaches. Emphasis was placed on identifying conceptual continuities, ruptures, and methodological limitations across historical periods. Results: The analysis reveals that the evolution of schizophrenia has been characterized by increasing diagnostic standardization accompanied by growing biological heterogeneity. While successive biological models have provided valuable insights into specific aspects of the disorder, none have yielded single, robust diagnostic biomarkers. Instead, findings consistently reflect partial overlaps between clinical phenotypes and biological signals, strongly influenced by historically derived diagnostic categories. Conclusions: The persistent absence of definitive diagnostic biomarkers for schizophrenia reflects not only technical limitations but also the historical construction of the disorder as a heterogeneous clinical category. Understanding this historical context is essential for interpreting current findings in brain sciences. Future research is likely to benefit from stratification-based, dimensional, and integrative frameworks that move beyond categorical diagnosis while preserving clinical relevance. Full article

Digital biomarkers derived from eye-tracking and facial expression hold significant potential for the non-invasive screening of cognitive decline (CD). However, existing approaches predominantly rely on single-task or feature engineering-based unimodal methods, which struggle to capture complex temporal behavioral patterns. While deep learning (DL) excels at extracting hierarchical features and intricate temporal dynamics from behavioral sequences, its application in this specific multimodal sensing domain remains exploratory. Addressing this gap, this study designed an assessment system integrating five multi-dimensional cognitive paradigms and collected eye-tracking and facial expression data from 20 healthy controls (HC) and 20 individuals with CD. For these multimodal sequences, we propose a deep neural network capable of multi-scale representation learning. By utilizing subspace exploration and multi-scale convolutions, this architecture extracts deep representations directly from data and incorporates a decision fusion mechanism to enhance diagnostic robustness. Experimental results demonstrate that our method achieves a 90% classification accuracy, outperforming machine learning models. Furthermore, statistical analyses conducted in this study validated several features associated with CD and also explored some novel potential behavioral patterns. This study confirms the feasibility of a DL framework based on eye-tracking and facial expression signals for identifying CD, providing a reference for developing objective and efficient digital screening tools. Full article

Background: Early screening of cognitive impairment is essential for timely clinical intervention; however, conventional cognitive tests such as the Mini-Mental State Examination (MMSE) rely on fixed thresholds that may not be optimal in real-world screening settings. Methods: This study developed a threshold-aware multimodal screening framework integrating MMSE item-level scores with wearable-derived sleep and physical activity lifelog data. A dataset of 174 adults was analyzed and categorized into cognitively normal (CN), mild cognitive impairment (MCI), and dementia, with MCI and dementia combined as an impaired group. A CatBoost-based binary classification model was trained using five-fold cross-validation. The optimal decision threshold was determined by maximizing balanced accuracy using out-of-fold predictions. Results: The optimized threshold (0.49) achieved an accuracy of 0.818 and a balanced accuracy of 0.728 on the validation set. The recall values were 0.885 for CN and 0.571 for the impaired group, with an area under the ROC curve of 0.676. Feature importance and stability analyses showed that variability-related sleep and activity features were consistently selected across folds. Conclusions: These findings suggest that threshold optimization combined with multimodal lifelog integration may improve the interpretability of screening decisions. Variability-based lifelog features may provide complementary information alongside MMSE, although their role remains exploratory and requires further validation in larger and longitudinal cohorts. Full article

Prostate cancer (PCa) remains one of the most prevalent urological malignancies worldwide, with early and accurate diagnosis being critical for improving patient outcomes. Traditional screening approaches, such as digital rectal examination and prostate-specific antigen (PSA) testing, have long served as frontline tools; however, their limited specificity and sensitivity contribute to high rates of false positives, unnecessary biopsies, and overtreatment. Recent UK guidelines and international consensus increasingly question the role of PSA-based population screening, advocating for risk-stratified pathways and multiparametric MRI as first-line investigations. In parallel, advances in molecular biology have identified promising cancer-specific biomarkers, such as prostate cancer antigen 3 (PCA3) and transmembrane protease serine 2 (TMPRSS2:ERG), that outperform PSAs in terms of specificity and prognostic value. These developments have catalysed innovation in biosensor technologies, enabling rapid, cost-effective, and non-invasive detection of single and multiplex biomarkers in urine and serum. Electrochemical and optical affinity-based biosensors offer transformative potential for the development of personalised point-of-care platforms and diagnostics, reducing the reliance on invasive procedures and improving clinical decision-making. The latter can be augmented with artificial intelligence (AI) tools. This review critically examines the limitations of PSAs, synthesises evidence on novel biomarkers and imaging-led strategies, and evaluates the design, performance, and translational challenges of biosensor-based assays. Furthermore, it outlines future directions, including standardisation, large-scale clinical validation, and integration of multiplex biosensors with AI for precision diagnostics. By bridging molecular insights with engineering innovations, these approaches promise to redefine PCa screening and enable accurate, patient-centred care. Full article

Cognitive impairment is a prevalent non-motor manifestation of Parkinson’s disease (PD), yet early detection remains limited by the sensitivity of conventional cognitive assessments. Emerging evidence suggests that motor dysfunction, particularly gait and balance abnormalities, reflects underlying cognitive vulnerability. This study examined motor–cognitive associations and evaluated whether motor-derived features can be used to classify low-MoCA status in PD without direct cognitive testing. Data from 102 individuals with PD were analyzed, incorporating clinical assessments, physical function measures, lifestyle factors, and gait-derived biomarkers. Multiple regression identified Unified Parkinson’s Disease Rating Scale Part III, stride length of the more affected side during 360° turning at preferred speed, and maximum ankle jerk on the less affected side during forward walking as independent predictors of Montreal Cognitive Assessment scores, collectively explaining 34.7% of the variance. Network analysis revealed integrative relationships among global motor severity, gait smoothness, and cognitive performance. Using a compact motor-based feature set, logistic regression achieved a mean accuracy of 65.8% and an AUC of 0.737 in classifying low-MoCA status under cross-validation. These findings demonstrate that motor-derived digital biomarkers capture clinically meaningful information about cognitive status in PD and may serve as adjunctive tools for identifying cognitive vulnerability in clinical settings. Full article

Breast cancer remains the most frequently diagnosed malignancy in women worldwide, accounting for approximately 2.3 million new cases and 670,000 deaths annually. The diagnostic landscape has undergone a paradigm shift over the past two decades, evolving from morphology-based classification toward molecularly informed, precision-guided strategies. Early and accurate diagnosis is fundamental to improving outcomes; advances in imaging technology, including digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and abbreviated magnetic resonance imaging (MRI), have improved sensitivity and specificity in diverse patient populations. Simultaneously, the integration of artificial intelligence (AI) and radiomics into screening workflows offers unprecedented potential for risk stratification and a reduction in false-positives. At the pathological level, multi-gene expression profiling assays such as Oncotype DX, MammaPrint, Prosigna, and EndoPredict have refined prognostic classification and guide adjuvant chemotherapy decisions in early-stage hormone receptor-positive disease. The emergence of liquid biopsy, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomal biomarkers provides minimally invasive tools for real-time monitoring of response, residual disease, and the evolution of resistance mechanisms. Precision diagnostics now encompass next-generation sequencing (NGS)-based comprehensive genomic profiling, enabling identification of actionable alterations such as PIK3CA mutations, HER2 amplification, BRCA1/2 pathogenic variants, and NTRK fusions, each linked to approved therapeutic agents. The purpose of this review is to provide a comprehensive synthesis of current and emerging diagnostic modalities in breast cancer—from population-level screening to individualized molecular profiling—and to examine how integrative, multimodal diagnostic platforms are reshaping clinical decision-making in the era of precision medicine. Full article

Chronic kidney disease is rising worldwide, and kidney transplantation remains the preferred modality of kidney replacement therapy. However, long-term graft survival continues to be limited by chronic alloimmune injury, particularly antibody-mediated rejection (ABMR) and its chronic active form. This narrative review synthesizes contemporary evidence on the immunopathogenesis, epidemiology, diagnosis, and management of kidney allograft rejection, with a deliberate focus on studies from the last five years and on United States and European cohorts. We summarize current concepts of T cell–mediated rejection (TCMR), ABMR, mixed and donor-specific antibody (DSA)–negative phenotypes, and the evolution of the Banff classification, highlighting how chronic active ABMR has emerged as a leading cause of death-censored graft loss. We then critically appraise the conventional diagnostic triad of creatinine/eGFR, DSA, and biopsy and review emerging tools, including donor-derived cell-free DNA, urinary chemokines such as CXCL9 and CXCL10, additional blood- and urine-based biomarkers, and biopsy transcriptomics. We also examine how artificial intelligence and machine learning may support digital pathology, multimodal risk prediction, and data integration, while recognizing the current challenges of biological interpretability, external validation, and clinical implementation. Finally, we propose a rejection-focused precision-medicine framework and outline key research gaps, including multicenter validation, trial-ready endpoints, and governance for AI-enabled pathways. Overall, the field is moving from isolated diagnostic signals toward integrated, biologically informed, and clinically actionable approaches to rejection detection and risk stratification. Full article

Telerehabilitation—the remote delivery of rehabilitation services—is undergoing a paradigm shift with the convergence of immersive virtual reality (VR) and wearable biosensor technologies. This perspective article outlines a vision for home-based motor and cognitive rehabilitation that is engaging, personalized, and data-driven. We describe how immersive VR environments (for example, simulations of home settings or supermarkets) coupled with wearable sensors can address current challenges in rehabilitation by increasing patient motivation, enabling real-time biofeedback, and supporting remote clinician supervision. Gamification mechanisms and rich sensory feedback in VR are highlighted as key strategies to enhance user engagement and adherence to therapy. We discuss conceptual innovations such as multi-sensor data integration, dynamic difficulty adaptation, and AI-driven personalization of exercises, derived from recent research and our development experience, and consider their potential benefits for patients with neuro-cognitive-motor impairments (e.g., stroke, Parkinson’s disease, and multiple sclerosis). Implementation scenarios for home-based therapy are presented, emphasizing scalability, standardized digital metrics for monitoring progress, and seamless involvement of clinicians via telehealth platforms. We also critically examine the current limitations of VR and telehealth rehabilitation and how an integrative model could overcome these barriers. More specifically, this perspective defines the engineering requirements of a closed-loop VR-based telerehabilitation framework, including multimodal data synchronization, calibration, signal-quality management, interpretable adaptive control, digital biomarker validation, and practical strategies to improve accessibility, privacy, and scalability in home-based neurological rehabilitation. Full article

Background: Major Depressive Disorder (MDD) is increasingly recognized as involving psychomotor slowing and impaired cortical timing. Objective vibrotactile assessments can quantify sensory and cognitive integration, potentially identifying mechanistic biomarkers of depression. Objective: To determine whether tactile performance metrics from the Brain Gauge system differentiate individuals with depression from healthy controls and to identify the most predictive domains using cross-validated modeling. Methods: Eighty-two adults (43 with depression, 39 controls) completed the Brain Gauge battery assessing reaction time (RT), RT variability, amplitude and duration discrimination, temporal order judgment, accuracy, and cortical plasticity. Results: After FDR correction, participants with depression showed significantly slower and more variable tactile responses (FDR-adjusted p < 0.05). Speed and RT variability remained independent predictors (OR = 4.14; OR = 0.015), yielding an AUC = 0.86 (sensitivity = 0.87; specificity = 0.77). These findings suggest reduced cortical stability and efficiency in depression. Conclusions: Tactile timing measures—particularly Speed and RT variability—objectively capture psychomotor and temporal instability in MDD. Cross-validated logistic modeling supports their potential as non-invasive digital biomarkers for depression phenotyping and monitoring. These findings suggest tactile timing instability as a clinically relevant neurofunctional dimension of major depressive disorder, with potential applications in psychiatric phenotyping, objective symptom monitoring, and future precision-guided treatment strategies. Full article

Histopathological evaluation of tissue sections remains the gold standard for the diagnosis, classification, and grading of breast cancer (BC). The widespread adoption of whole-slide imaging (WSI) has enabled the digitization of histological slides and facilitated the development of artificial intelligence (AI) approaches for computational pathology. In recent years, machine learning and deep learning (DL) algorithms have been increasingly investigated for the analysis of hematoxylin and eosin (H&E)-stained images, with potential applications in tumor detection, histological classification, prognostic stratification, and prediction of treatment response. This narrative review summarizes recent developments in AI-driven models applied to BC histopathology and discusses their potential role in supporting diagnostic and prognostic assessment. Several studies have demonstrated the promising performance of DL algorithms in tasks such as the detection of lymph node metastases, assessment of residual tumor after neoadjuvant therapy, and prediction of clinical outcomes from histopathological images. Emerging research has also explored the possibility of inferring molecular and biomarker information from histology images, although these approaches currently identify statistical associations rather than direct molecular measurements. Despite the rapid expansion of this research field, significant barriers remain before routine clinical implementation can be achieved. Key challenges include dataset bias, variability in staining and image acquisition, limited external validation across institutions, and the need for transparent and reproducible model development. In addition, the translation of AI-based systems into clinical practice requires compliance with regulatory frameworks governing software used for medical purposes, such as those established by the U.S. Food and Drug Administration. Overall, AI represents a promising research direction in computational pathology and may contribute to decision-support tools capable of assisting pathologists in the analysis of digital slides. Continued efforts toward methodological rigor, large multicenter datasets, and prospective validation studies will be essential to determine the future role of AI in BC histopathology. Full article

Cigarette smoke contains a high concentration of carcinogenic substances to which smokers are regularly exposed. Passive smoking is seriously harmful to the health of non-smoking humans and animals. Domestic cats are particularly vulnerable because of their constant grooming activity, which can promote oral ingestion of smoke-derived residues. Cotinine, a nicotine metabolite, is a reliable biomarker for tobacco exposure. Considering these observations, our study aimed to (1) characterize cytological alterations in oral mucosal epithelial cells by conventional morphology and automated digital cytomorphometry; (2) quantify urinary cotinine concentration and investigate its possible correlation with oral epithelial cytological alterations. To this aim, oral smears were collected from 30 cats divided into two groups (20 exposed; 10 non-exposed). Smears were stained with May–Grünwald–Giemsa and Papanicolaou to assess inflammation and dysplasia; digital cytomorphometric analysis was used to quantify the nucleus-to-cytoplasm (N/C) ratio. Urinary cotinine was measured by ELISA. Our results showed that exposed cats had significantly higher urinary cotinine levels and higher N/C ratios (p < 0.01) than non-exposed controls, along with mild-to-severe inflammation and dysplastic-like epithelial alterations. These findings support urinary cotinine as a valid biomarker of household tobacco smoke exposure in domestic cats and suggest that such exposure may be correlated with early cytological and cytomorphometric changes in the oral mucosa. Further studies are needed to better investigate the relationship between exposure duration and cytological, cytomorphometric, and molecular alterations. Full article

Alzheimer’s disease (AD) is the most common form of dementia marked by cognitive decline and memory loss. Early detection is essential for timely intervention; however, traditional biomarkers, including cerebrospinal fluid (CSF) assays, neuroimaging, and cognitive assessments, are limited by cost, invasiveness, and accessibility. Digital biomarkers, obtained from wearable sensors, smartphone applications, speech analysis, and other passive monitoring technologies, represent a promising alternative for scalable, non-invasive, and continuous assessment of early cognitive decline. This paper provides a comprehensive review of the current landscape of digital biomarkers for AD diagnosis, emphasizing their potential application in the preclinical and prodromal stages of the disease. In addition, a bibliometric analysis demonstrates the rapid expansion of digital biomarker research, highlighting key trends in publication volume, influential authors, institutions, and interdisciplinary collaborations. Despite the significant promise of digital biomarkers, challenges remain regarding validation, regulatory approval, data privacy, and integration into clinical practice. The results indicate that future research should prioritize standardization, multimodal biomarker integration, and large-scale longitudinal studies to fully realize the potential of digital technologies in AD detection. Full article

Background and Objectives: Immunohistochemistry (IHC) is a keystone in gastric cancer (GC) management, allowing treatment customization, including for advanced or metastatic diseases. This review aims to evaluate the critical role of IHC markers, analyzing their efficiency in molecular subclassification and prediction of response to gastric cancer-targeted therapies, while also describing state-of-the-art IHC techniques and perspectives. Results: The major challenges for the GC management were structured in two main sections, as follows: (i) the current paradigm of gastric neoplasia diagnosis, which includes subsections related to the methodological and morphological foundations, the epidemiological dynamics, and risk factors, as well as differential diagnosis of poorly differentiated tumors; and (ii) the progress in 3,3′-diaminobenzidine (DAB) application and advanced reagents in gastric cancer immunohistochemistry. Discussion: Considering the role of IHC and DAB, the following topics were successively addressed in seven sections: GC key biomarkers, such as human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1), and DNA replication mismatch repair (MMR) system, allow direct correlation between tissue morphology and protein expression; intestinal and gastrointestinal differentiation markers; emerging and aggressive histological subtypes; epithelial–mesenchymal transition, E-cadherin, and the process of tumor budding; implementation of innovative procedures in gastric cancer immunohistochemistry; and automation, quality control, and sustainability in the pathology laboratory. Perspectives: The main directions were focused on the integration of artificial intelligence (AI) algorithms for digital quantification of the IHC signal and also on the expansion of panels to new targets, such as Claudin 18.2 (CLDN 18.2), which redefines treatment approaches in advanced stages. Conclusions: Although faced with technical and biological limitations, immunohistochemistry remains indispensable in modern gastric oncology. The evolution towards digital pathology and the refinement of scoring criteria will transform IHC from a complementary test into a visual tool that is essential for personalizing oncological treatment. Full article

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a high-burden, under-researched condition characterized by heterogeneous and fluctuating symptoms, including cognitive dysfunction commonly described by patients as “brain fog”. Despite growing interest in digital health technologies for symptom monitoring and personalized care, their application to the assessment and management of cognitive dysfunction in ME/CFS remains unclear. This descriptive review aimed to examine the current scientific evidence on digital approaches related to brain fog in ME/CFS. A structured literature search following PRISMA guidance was conducted to identify relevant studies. The available literature remains limited in scope and methodological maturity. During synthesizing across studies, three main functional domains of digital application become apparent: (1) digital tools for cognitive assessment, which have the strongest evidence base; (2) digital platforms for longitudinal monitoring; and (3) digitally mediated interventions or rehabilitation approaches, both of which are less well studied. Simultaneously, the findings suggest that patient-reported brain fog may represent a visible component of the broader ME/CFS disease spectrum and could serve as an early clinical indicator guiding diagnosis and management. Interpreting these symptoms within a biopsychosocial framework may facilitate understanding of the complex nature of the disease and optimize the use of digital technologies for monitoring cognitive dysfunction and supporting patient-centered care in ME/CFS. Full article

Cutaneous adnexal carcinomas (CACs) comprise a diverse group of malignant tumors that show morphological differentiation toward one of the four main adnexal structures in normal skin: hair follicles, sebaceous glands, sweat-apocrine glands, and sweat-eccrine glands. These tumors can arise sporadically or may be associated with rare genetic syndromes. A total of 276 CACs cases underwent hybrid capture-based comprehensive genomic profiling (CGP) to assess all classes of genomic alterations (GA). Sequencing data were used to determine microsatellite instability (MSI) status, tumor mutational burden (TMB), genomic loss of heterozygosity (gLOH), genomic ancestry, and COSMIC mutational signatures. PD-L1 expression was evaluated by immunohistochemistry (TPS; Dako 22C3). Statistical analyses were performed using Fisher’s exact test, with false discovery rate correction via the Benjamini–Hochberg method. Sequencing was performed on primary cutaneous tumors in 131 cases (47.4%) and on local recurrence or metastatic site biopsies in 145 cases (52.5%). Across all groups, there was a male predominance (64–81%) and similar mean ages (59–63 years), with apocrine (APO) tumors occurring in older patients than eccrine (ECC) tumors (72 vs. 62 years; p = 0.001). Histologically, 173 tumors (62.7%) were sweat gland-derived (SWT), 55 (19.9%) sebaceous gland-derived (SEB), 14 (5.1%) hair follicle-derived (HRF), and 34 (12.3%) unclassified (UNK). Among SWT tumors, 150 (86.7%) were eccrine and 23 (13.3%) apocrine. SWT tumors included digital papillary adenocarcinomas (DPA, 6.9%), mucinous carcinomas (MC, 6.3%), porocarcinomas (POR, 11.0%), spiradenocarcinomas (SPR, 8.1%), syringoadenocarcinomas (SRNG, 5.8%), and 77 (44.5%) unclassified cases. The number of GA per tumor was highest in SEB compared with SWT tumors (7.9 vs. 4.9; p = 0.005) and lowest in DPA (2.1 vs. 5.0 in non-DPA; p = 0.03). No differences in ancestry distribution were observed. Compared with SWT tumors, SEB tumors exhibited higher frequencies of RB1 (38.2% vs. 8.1%; p < 0.0001) and TP53 alterations (76.4% vs. 43.4%; p = 0.0002), suggesting potential neuroendocrine differentiation. MC tumors showed significantly higher PTCH1 alterations than non-MC tumors (36.4% vs. 1.8%; p = 0.044). MSI-high status was most frequent in SEB tumors compared with all other groups (15.7% vs. 1.2%; p = 0.005), and gLOH > 16% was also more common in SEB than SWT tumors (19.6% vs. 7.2%; p = 0.081). The MMR signature occurred more frequently in SEB than SWT tumors (32.0% vs. 2.1%; p = 0.005). Mean TMB was elevated across most CACs types, ranging from 10.4 mutations/Mb in HRF to 38.8 mutations/Mb in MC, with the exceptions of APO (2.7 mut/Mb; p = 0.001) and DPA (1.4 mut/Mb; p = 0.003). PD-L1 expression was generally low and did not differ significantly between SWT and SEB tumors (37.0% vs. 33.3%; NS). Given the limited data on CAC treatment, this study provides a catalog of commonly observed GA. SEB tumors exhibited the highest frequency of genomic alterations. Prospective clinical trials are needed to determine the prognostic and predictive value of CAC-specific biomarkers for immune checkpoint inhibitor (ICI) response, which is essential for integrating novel therapies into the evolving treatment landscape. Full article