Search Results (333)

Over a decade ago, WHO introduced the ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end-users) criteria to guide diagnostic assay development. Today, lateral flow assays (LFAs) best meet these standards, evolving from simple rapid tests to advanced diagnostics integrating AI and nanotechnology for precise, quantitative results. Notably, nanoparticle-enhanced LFAs have achieved limits of detection (LOD) as low as 0.01 pg/mL (a 100-fold improvement over conventional methods), while AI algorithms have reduced interpretation errors by 40% in low-contrast conditions. The COVID-19 pandemic underscored the societal impact of LFAs, with over 3 billion antigen tests deployed globally, demonstrating 98% specificity in real-world surveillance. Beyond infectious diseases, LFAs are revolutionizing cancer screening through liquid biopsy, achieving a 92% concordance rate with gold-standard assays, food safety and environmental monitoring. Despite these advancements, challenges remain in scalability, reproducibility, sustainable manufacturing, and how to enhance the sensitivities and lower the LOD. However, innovations in biodegradable materials, roll-to-roll printing, CRISPR-integrated multiplexing, and efficient functionalization methods like photochemical immobilization technique offer promising solutions, with projected further cost reductions and scalability. This review highlights the technological evolution, diverse applications, and future trajectories of LFAs, highlighting their critical role in democratizing diagnostics. Full article

Background: Digital Pathology (DP) and Artificial Intelligence (AI) have strongly developed in recent years, especially in pathology, with a high interest in dermatopathology. Accelerated by the COVID-19 pandemic, DP and AI are now integrated in pathology, research and education, bringing value to histopathological diagnoses, telepathology and personalized medicine. This narrative review presents a comprehensive literature review by defining three research directions, using scientometric analysis, of the current state of DP and AI in pathology and dermatopathology. Methods: The research was conducted through the Pubmed and Web of Science databases, within the research period of January 2019–July 2025: a two-phase methodology. Four independent pathologists selected the articles in accordance with the inclusion and exclusion criteria, and the synthesis of the articles was based on three research directions. Results: The research shows that CNN (Convolutional Neural Network), AI powered diagnostic platforms and telepathology strongly contribute to increasing the speed and accuracy of diagnostics, especially on cutaneous malignant skin tumors. There are still several challenges and limitations in terms of validation, interoperability, initial high implementation costs, ethics and transparency in AI and equity in healthcare. Conclusions: DP and AI are essential pillars of modern dermatopathology, with a high necessity of standardization, regulation and a multidisciplinary approach. Full article

Background/Objectives: The rise in urinary tract infections caused by multidrug-resistant (MDR) bacteria presents a serious public health challenge across the Balkans, a region already burdened by aging populations, healthcare resource limitations, and fragmented antimicrobial surveillance systems. Methods: This review explores the epidemiology, risk factors, and consequences of MDR UTIs, particularly in the context of the COVID-19 pandemic, which significantly accelerated antimicrobial resistance (AMR) due to widespread, inappropriate antibiotic use. Results: The paper discusses region-specific data on resistance trends, highlights the gaps in diagnostic infrastructure, and evaluates emerging clinical strategies including antimicrobial stewardship (AMS), rapid diagnostic technologies, novel antibiotics, and non-antibiotic alternatives such as bacteriophage therapy and vaccines. Conclusions: Policy recommendations are provided to strengthen surveillance, promote evidence-based treatment, and ensure equitable access to diagnostic and therapeutic tools. A multidimensional and regionally coordinated response is essential to curb the MDR UTI burden and safeguard public health across the Balkans. Full article

Telemedicine has transformed obstetrics and gynecology (OB/GYN), accelerated by the COVID-19 pandemic. This study aims to synthesize evidence on the adoption, effectiveness, barriers, and technological innovations of telemedicine in OB/GYN across diverse healthcare settings. This scoping review synthesized 63 peer-reviewed studies (2010–2023) using PRISMA-ScR guidelines to map global applications, outcomes, and challenges. Key modalities included synchronous consultations, remote monitoring, AI-assisted triage, tele-supervision, and asynchronous communication. Results demonstrated improved access to routine care and mental health support, with outcomes for low-risk pregnancies comparable to in-person services. Adoption surged >500% during pandemic peaks, stabilizing at 9–12% of services in high-income countries. However, significant disparities persisted: 43% of rural Sub-Saharan clinics lacked stable internet, while socioeconomic, linguistic, and cultural barriers disproportionately affected vulnerable populations (e.g., non-English-speaking, transgender, and refugee patients). Providers reported utility but also screen fatigue (41–68%) and diagnostic uncertainty. Critical barriers included fragmented policies, reimbursement variability, data privacy concerns, and limited evidence from conflict-affected regions. Sustainable integration requires equity-centered design, robust policy frameworks, rigorous longitudinal evaluation, and ethically validated AI to address clinical complexity and systemic gaps. Full article

Background: Coronavirus disease 2019 (COVID-19) has led to the expansion of the spectrum of invasive fungal infections beyond traditional immunocompromised populations. Although COVID-19-associated pulmonary aspergillosis is increasingly being recognised, COVID-19-associated mucormycosis remains rare, particularly in non-endemic regions. Concurrent COVID-19-associated invasive tracheobronchial aspergillosis and pulmonary mucormycosis with histopathological confirmation is exceedingly uncommon and poses significant diagnostic and therapeutic challenges. Case presentation: We report the case of a 57-year-old female with myelodysplastic syndrome who underwent haploidentical allogeneic haematopoietic stem cell transplantation. During post-transplant recovery, she developed COVID-19 pneumonia, complicated by respiratory deterioration and radiological findings, including a reverse halo sign. Bronchoscopy revealed multiple whitish plaques in the right main bronchus. Despite negative serum and bronchoalveolar lavage fluid galactomannan assay results, cytopathological examination revealed septate hyphae and Aspergillus fumigatus was subsequently identified. Given the patient’s risk factors and clinical features, liposomal amphotericin B therapy was initiated. Subsequent surgical resection and histopathological analysis confirmed the presence of Rhizopus microsporus. Following antifungal therapy and surgical intervention, the patient recovered and was discharged in stable condition. Conclusions: This case highlights the critical need for heightened clinical suspicion of combined invasive fungal infections in severely immunocompromised patients with COVID-19, even in non-endemic regions for mucormycosis. Early tissue-based diagnostic interventions and prompt initiation of optimal antifungal therapy are essential for obtaining ideal outcomes when co-infection is suspected. Full article

Lower respiratory tract infections remain a leading cause of morbidity and mortality among Intensive Care Unit patients, with severe cases often progressing to acute respiratory distress syndrome (ARDS). This life-threatening syndrome results from alveolar–capillary membrane injury, causing refractory hypoxemia and respiratory failure. Early detection and management are critical to treat the underlying cause, provide protective lung ventilation, and, eventually, improve patient outcomes. The 2012 Berlin definition standardized ARDS diagnosis but excluded patients on non-invasive ventilation (NIV) or high-flow nasal cannula (HFNC) modalities, which are increasingly used, especially after the COVID-19 pandemic. By excluding these patients, diagnostic delays can occur, risking the progression of lung injury despite ongoing support. Indeed, sustained, vigorous respiratory efforts under non-invasive modalities carry significant potential for patient self-inflicted lung injury (P-SILI), underscoring the need to broaden diagnostic criteria to encompass these increasingly common therapies. Recent proposals expand ARDS criteria to include NIV and HFNCs, lung ultrasound, and the SpO₂/FiO₂ ratio adaptations designed to improve diagnosis in resource-limited settings lacking arterial blood gases or advanced imaging. However, broader criteria risk overdiagnosis and create challenges in distinguishing ARDS from other causes of acute hypoxemic failure. Furthermore, inter-observer variability in imaging interpretation and inconsistencies in oxygenation assessment, particularly when relying on non-invasive measurements, may compromise diagnostic reliability. To overcome these limitations, a more nuanced diagnostic framework is needed—one that incorporates individualized therapeutic strategies, emphasizes lung-protective ventilation, and integrates advanced physiological or biomarker-based indicators like IL-6, IL-8, and IFN-γ, which are associated with worse outcomes. Such an approach has the potential to improve patient stratification, enable more targeted interventions, and ultimately support the design and conduct of more effective interventional studies. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

Background/Objectives: In late 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a pandemic called the ‘coronavirus disease 2019’ (COVID-19). After the acute SARS-CoV-2 infection, many individuals (up to 33%) complained of unexplained symptoms involving multiple organ systems and were diagnosed as having Long COVID-19 (LC-19). Currently, LC-19 is inadequately defined, requiring the formation of consistent diagnostic parameters to provide a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy. LC-19 represents a significant burden on multiple levels. The reduced ability of workers to return to work or compromised work efficiency has led to consequences at national, economic, and societal levels by increasing dependence on community services. On a personal scale, the isolation and helplessness caused by the disease and its subsequent impact on the patient’s mental health and quality of life are incalculable. Methods: In this paper, we used Walker and Avants’ eight-step approach to perform a concept analysis of the term “Long COVID-19” and define its impact across these parameters. Results: Using this methodology, we provide an improved definition of LC-19 by connecting the clinical symptomology with previously under-addressed factors, such as mental, psychological, economic, and social effects. This definition of LC-19 features can help improve diagnostic procedures and help plan relevant healthcare services. Conclusions: LC-19 represents a complex and pressing public health challenge with diverse symptomology, an unpredictable timeline, and complex pathophysiology. This concept analysis serves as a tool for improving LC-19 definition, but it remains a dynamic disease with evolving diagnostic and therapeutic approaches, requiring deeper investigation and understanding of its long-term effects. Full article

Infectious diseases poses a growing public health challenge. The COVID-19 pandemic has further emphasized the urgent need for rapid, accessible diagnostics. This study presents the development of an integrated, flexible point-of-care (POC) diagnostic system for the rapid detection of Corynebacterium diphtheriae, the pathogen responsible for diphtheria. The system comprises a microfluidic polymerase chain reaction (micro-PCR) device and an electrochemical DNA biosensor, both fabricated on flexible substrates. The micro-PCR platform offers rapid DNA amplification overcoming the time limitations of conventional thermocyclers. The biosensor utilizes specific molecular recognition and an electrochemical transducer to detect the amplified DNA fragment, providing a clear and direct indication of the pathogen’s presence. The combined system demonstrates the effective amplification and detection of a gene fragment from a toxic strain of C. diphtheriae, chosen due to its increasing incidence. The design leverages lab-on-a-chip (LOC) and microfluidic technologies to minimize reagent use, reduce cost, and support portability. Key challenges in microsystem design—such as flow control, material selection, and reagent compatibility—were addressed through optimized fabrication techniques and system integration. This work highlights the feasibility of using flexible, integrated microfluidic and biosensor platforms for the rapid, on-site detection of infectious agents. The modular and scalable nature of the system suggests potential for adaptation to a wide range of pathogens, supporting broader applications in global health diagnostics. The approach provides a promising foundation for next-generation POC diagnostic tools. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by integrating smart diagnostic devices with cloud computing and real-time data analytics. The emergence of infectious diseases, including COVID-19, underscores the need for rapid and decentralized diagnostics to facilitate early intervention. Traditional centralized laboratory testing introduces delays, limiting timely medical responses. While point-of-care molecular diagnostic (POC-MD) systems offer an alternative, challenges remain in cost, accessibility, and network inefficiencies. This study proposes an IoMT-based architecture for fully automated POC-MD devices, leveraging WebSockets for optimized communication, enhancing microfluidic cartridge efficiency, and integrating a hardware-based emulator for real-time validation. The system incorporates DNA extraction and real-time polymerase chain reaction functionalities into modular, networked components, improving flexibility and scalability. Although the system itself has not yet undergone clinical validation, it builds upon the core cartridge and detection architecture of a previously validated cartridge-based platform for Chlamydia trachomatis and Neisseria gonorrhoeae (CT/NG). These pathogens were selected due to their global prevalence, high asymptomatic transmission rates, and clinical importance in reproductive health. In a previous clinical study involving 510 patient specimens, the system demonstrated high concordance with a commercial assay with limits of detection below 10 copies/μL, supporting the feasibility of this architecture for point-of-care molecular diagnostics. By addressing existing limitations, this system establishes a new standard for next-generation diagnostics, ensuring rapid, reliable, and accessible disease detection. Full article

Background/Objectives: Tuberculous meningitis is the most severe form of tuberculosis in children, with a high mortality and morbidity rate if it is not diagnosed and treated in a timely manner. The aim of this study is to highlight the challenges associated with establishing a diagnosis of tuberculous meningitis in a child with immunosuppression, given the presence of nonspecific clinical manifestations. Methods: We present the case of a 15-year-old adolescent with systemic lupus erythematosus, on immunosuppressive therapy, who is diagnosed with tuberculous meningoencephalitis presenting the clinical, diagnostic and imaging characteristics, as well as the diagnostic traps and limitations associated with this condition. Antituberculosis therapy was started empirically, because there was no improvement in the clinical status with conventional antibiotic therapy; the diagnosis was established 7 days after the start of the antituberculosis treatment, with the help of an acid-fast bacilli culture from the cerebrospinal fluid. Results: The course of the tuberculous meningoencephalitis was slowly favorable, despite the superimposed COVID-19 infection. Delay in administering immunosuppressive therapy led to the onset of renal and joint manifestations. Conclusions: Tuberculous meningitis is a highly lethal, often underdiagnosed disease with nonspecific clinical and imaging manifestations, which can have a favorable outcome if the diagnosis is established early on and treatment is started promptly. Full article

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was evaluated on a SARS-CoV-2 CT scan dataset, demonstrating superior performance across all metrics compared to baseline models. The model achieved an accuracy of 95.98%, precision of 96.07%, recall of 96.01%, F1-score of 95.98%, and AUC of 98.69%, outperforming existing approaches. These results indicate that ViTGNN is an effective diagnostic tool that can be applied beyond COVID-19 detection to other medical imaging tasks. Full article

Background: Effective early risk stratification in COVID-19 remains a critical challenge in emergency care, particularly due to the limitations of RT-PCR testing, including delayed processing and false negatives. There is an unmet need for imaging tools that are fast, reliable, and safe for repeated use in acute clinical settings. Methods: In this prospective, multicenter study, over 1000 patients hospitalized with suspected or confirmed COVID-19 were initially screened. A total of 555 patients with PCR-confirmed infection were ultimately included for analysis. All participants underwent low-dose chest CT (LDCT) at admission. Pulmonary involvement was assessed using the chest CT severity score (CTSS) based on a unified protocol. CTSS values were analyzed in relation to ICU admission, in-hospital mortality, demographic data, oxygen saturation, dyspnea scores, and laboratory markers (CRP, LDH, lymphocyte, and neutrophil counts). Imaging was interpreted by board-certified radiologists under harmonized reporting standards. Results: CTSS values ≥13 and ≥15 were significantly associated with ICU admission and in-hospital mortality, respectively (p < 0.01). Strong correlations were observed between the CTSS and CRP, LDH, and dyspnea scores, with negative correlations to oxygen saturation and lymphocyte count. The standardized LDCT protocol ensured consistent image quality and minimized radiation exposure. Conclusions: LDCT combined with the CTSS provides a robust, reproducible, and radiation-sparing method for emergency risk stratification in COVID-19. Its high clinical utility supports deployment in frontline triage systems and future AI-enhanced diagnostic workflows. Full article

Pancreatic ductal adenocarcinoma (PDAC) presents significant challenges in diagnosis, prevention, and treatment. Predictive biomarkers offer the potential to revolutionize clinical management, particularly in the preoperative setting, but their implementation requires careful consideration of ethical implications. This scoping review analyzes the ethical landscape of using immunohistochemistry (IHC) for molecular subtyping in PDAC, focusing on its utility, accessibility, and potential impact on patient care. We conducted a systematic literature search in the PubMed, Scopus and Google Scholar databases (2015–2025) using COVIDENCE, which identified 130 references. Of these, 79 were reviewed in a full-text format, and 9 ultimately met the inclusion criteria for our analysis. IHC offers several advantages as a companion diagnostic tool. It is relatively inexpensive, widely available in most pathology laboratories, and can be readily integrated into existing clinical workflows. This contrasts with more complex molecular subtyping methods, such as gene expression profiling, which can be costly, require specialized equipment and expertise, and may not be readily accessible in all clinical settings. Furthermore, accurate analysis of gene expression requires the localized targeting of individual cells; therefore, digesting the sample for bulk analysis would be less informative than using spatial localization techniques such as IHC. Because biomarker regulation can occur at the level of transcription or translation, protein-level assessment via IHC is often more accurate than mRNA analysis. Standardized IHC protocols for biomarker assessment are therefore essential for translating the molecular subtyping of PDAC into clinically actionable treatment strategies, especially for aggressive subtypes like basal-like tumors. This readily deployable IHC-based approach can optimize therapy selection, maximizing patient benefits and minimizing exposure to ineffective and potentially toxic treatments. This review critically analyzes the ethical dimensions of this method, grounded in the principles of autonomy, beneficence, non-maleficence, and justice. The review urges the medical community to fully utilize the potential of IHC-driven molecular subtyping to improve outcomes in PDAC, while ensuring equitable and responsible access to the benefits of precision oncology for all patients. Full article