Advances In Diagnostic Technology Research Articles

Opportunity and Opportunism in Artificial-Intelligence-Powered Data Extraction: A Value-Centered Approach.

Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions. This charge is rapidly evolving due to forces such as artificial intelligence [AI], big data [opportunistic imaging, imaging prognostication], and advanced diagnostic technologies. A new "modernistic" paradigm is emerging whereby radiologists, in conjunction with computer algorithms, will be tasked with extracting as much information from imaging data as possible, often without a specific clinical question being posed and independent of any stated clinical need. In addition, AI algorithms are increasingly able to predict long-term outcomes using data from seemingly normal examinations, enabling AI-assisted prognostication. As these algorithms become a standard component of radiology practice, the sheer amount of information they demand will increase the need for streamlined workflows, communication, and data management techniques. In addition, the provision of such information raises reimbursement, liability, and access issues. Guidelines will be needed to ensure all patients have access to the benefits of this new technology and guarantee mined data do not inadvertently create harm. In this article, we discuss challenges and opportunities relevant to radiologists in this changing landscape, with an emphasis on ensuring that radiologists provide high-value care.

Advances in Diagnostic Modalities for Helicobacter pylori Infection

Helicobacter pylori (H. pylori) infection is a widespread global health issue with a varying prevalence influenced by geography, socioeconomic status, and demographics. In the U.S., the prevalence is lower, though certain groups, such as older adults and immigrants from high-prevalence regions, show higher rates. The decrease in infection rates in developed countries is due to improved sanitation, antibiotics, and healthcare, whereas developing countries continue to experience high rates due to poor living conditions. H. pylori infection can be asymptomatic or cause symptoms like dyspepsia, abdominal pain, bloating, nausea, and loss of appetite. Pathophysiologically, H. pylori contribute to conditions such as gastritis, peptic ulcers, and gastric cancer through mechanisms including urease production and the release of virulence factors, leading to chronic inflammation and an increased cancer risk. Diagnostic methods for H. pylori have progressed significantly. Non-invasive techniques, such as serological assays, stool antigen tests, and urea breath tests, are practical and sensitive. Invasive methods, including endoscopic biopsy and molecular diagnostics, are more definitive but resource intensive. Recent advancements in diagnostic technology, including matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), biosensor technology, and next-generation sequencing (NGS), promise improved speed, accuracy, and accessibility. These innovations are expected to enhance the detection and management of H. pylori, potentially reducing the global disease burden. This review aims to discuss these diagnostic modalities with a focus on further advances under investigation.

Economic Evaluation of a Novel Lung Cancer Diagnostic in a Population of Patients with a Positive Low-Dose Computed Tomography Result

Background: Early detection of lung cancer is crucial for improving patient outcomes. Although advances in diagnostic technologies have significantly enhanced the ability to identify lung cancer in earlier stages, there are still limitations. The alarming rate of false positives has resulted in unnecessary utilization of medical resources and increased risk of adverse events from invasive procedures. Consequently, there is a critical need for advanced diagnostics after an initial low-dose computed tomography (LDCT) scan. Objectives: This study evaluated the potential cost savings for US payers of CyPath® Lung, a novel diagnostic tool utilizing flow cytometry and machine learning for the early detection of lung cancer, in patients with positive LDCT scans with indeterminate pulmonary nodules (IPNs) ranging from 6 to 29 mm. Methods: A cost offset model was developed to evaluate the net expected savings associated with the use of CyPath® Lung relative to the current standard of care for individuals whose IPNs range from 6 to 29 mm. Perspectives from both Medicare and private payers in a US setting are included, with a 1-year time horizon. Cost calculations included procedure expenses, complication costs, and diagnostic assessment costs per patient. Primary outcomes of this analysis include cost savings per cohort and cost savings per patient. Results: Our analysis showed positive cost savings from a private payer’s perspective, with expected savings of $895 202 311 per cohort and $6460 per patient, across all patients. Scenario analysis resulted in cost savings of $890 829 889 per cohort, and $6429 per patient. Similarly, savings of $378 689 020 per cohort or $2733 per patient were yielded for Medicare payers, across all patients. In addition, scenario analysis accounting for false negative patients from a Medicare payer perspective yielded savings of $376 902 203 per cohort and $2720 per patient. Discussion: The results suggest substantial cost savings, primarily due to reductions in follow-up diagnostic assessments and procedures, and highlight the importance of accurate diagnostic tools in reducing unnecessary healthcare expenditures. Conclusion: CyPath® Lung utilization yields savings for private and Medicare payers relative to the current standard of care in a US setting for individuals with 6 to 20 mm IPNs.

Development of a Web-based Tomato Plant Disease Detection and Diagnosis System using Transfer Learning Techniques

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

A fault diagnosis method based on an improved diffusion model under limited sample conditions.

As a critical component in mechanical systems, the operational status of rolling bearings plays a pivotal role in ensuring the stability and safety of the entire system. However, in practical applications, the fault diagnosis of rolling bearings often encounters limitations due to the constraint of sample size, leading to suboptimal diagnostic accuracy. This article proposes a rolling bearing fault diagnosis method based on an improved denoising diffusion probability model (DDPM) to address this issue. The practical value of this research lies in its ability to address the limitation of small sample sizes in rolling bearing fault diagnosis. By leveraging DDPM to generate one-dimensional vibration data, the proposed method significantly enriches the datasets and consequently enhances the generalization capability of the diagnostic model. During the model training process, we innovatively introduce the feature differences between the original vibration data and the predicted vibration data generated based on prediction noise into the loss function, making the generated data more directional and targeted. In addition, this article adopts a one-dimensional convolutional neural network (1D-CNN) to construct a fault diagnosis model to more accurately extract and focus on key feature information related to faults. The experimental results show that this method can effectively improve the accuracy and reliability of rolling bearing fault diagnosis, providing new ideas and methods for fault detection and prevention in industrial applications. This advancement in diagnostic technology has the potential to significantly reduce the risk of system failures, enhance operational efficiency, and lower maintenance costs, thus contributing significantly to the safety and efficiency of mechanical systems.

Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications

The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential diagnostic applications. Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. We explore the fabrication and operational mechanisms of these transistors, the role of machine learning in metabolite analysis, and their potential clinical applications by analyzing practical human blood samples for hypernatremia syndrome. This synergy not only improves diagnostic precision but also holds potential for the development of personalized diagnostics, tailoring treatments for individual metabolic profiles.

Bridging the divide: addressing discrepancies between clinical guidelines, policy guidelines, and biomarker utilization.

This paper aims to identify and address gaps in cancer treatment and diagnosis within European health services, focusing specifically on discrepancies between clinical guidelines and policy guidelines. It seeks to highlight how the underutilization of advanced diagnostic techniques recommended by medical societies contributes to missed opportunities for improving patient outcomes. A comprehensive analysis was conducted across multiple European countries to assess the compliance and integration of clinical guidelines with the availability of advanced diagnostic technologies. Secondary data related to clinical and policy guidelines in cancer care were collected and analyzed. Key indicators of adoption and utilization of next-generation sequencing and liquid biopsy were examined to evaluate their impact on health service efficiency and patient care. The analysis revealed significant discrepancies between the recommendations of medical societies regarding advanced diagnostic techniques and their adoption in health policy decisions across Europe. Country-specific assessments indicated varying levels of alignment between clinical guidelines and the availability of advanced diagnostics. These findings underscored missed opportunities for optimizing patient care and health service efficiency through better alignment and integration of clinical guidelines with policy decisions. This study concludes that there is a critical need for health policy decision-makers to prioritize the adoption of clinical guidelines in resource allocation and health service organization. Greater attention to the recommendations of medical societies regarding advanced diagnostic techniques could significantly enhance diagnostic accuracy, treatment efficacy, and overall patient outcomes in cancer care. The paper advocates for policy reforms that acknowledge and leverage the potential benefits of advanced diagnostics in improving health service performance and patient-centered care across Europe.

Identification of non-tuberculous Mycobacteria clinical isolates to species level using four different advanced diagnostic technologies

Identification of non-tuberculous Mycobacteria clinical isolates to species level using four different advanced diagnostic technologies

Development of CNN-based sub-sonic speed vehicle body frame micro damage assessment technology using guided wave spectrogram images

Due to advancements in diagnostic technology, sub-sonic speed trains are subjected to significant fatigue stress on their bodies. This phenomenon can be considered a menace, leading to significant accidents on railways due to the tiny size of defects on the train bodies. In this research, a deep learning approach is employed to identify the initial damage status. The goal is to develop an evaluation technique for verifying an initial wall-thinning crack using frequency spectrogram images. The wall-thinning effects of 6.3 mm thick aluminum AL-2024 T6 plates, primarily used in frames and bodies, are confirmed through the frequency responses of guided waves. To verify the effects of wall thinning, both higher-order harmonic frequencies of guided waves and nonlinear ultrasonic techniques are simultaneously used. To identify the tendency of frequency response, frequency spectrogram images, illustrated by short-time Fourier transforming analysis, are chosen. For training the detection system using the Region-based Convolutional Neural Network (R-CNN), spectrogram images are generated for wall-thinning steps with an increment of 0.2 mm (3% of the total thickness). The ResNet-50 is employed in the convolutional layer of the R-CNN engine to address the issue of gradient vanishing. The results of this research indicate that the depth of wall thinning in the plates can be identified. Based on the R-CNN training set, the initial wall-thinning failure can be verified with reliable accuracy.

Integrating Radiological Diagnostic Tools in Shalya Tantra: Advancements and Clinical Applications

The integration of advanced diagnostic imaging technologies into Ayurvedic surgery marks a significant advancement, blending ancient principles from texts like Sushruta Samhita with modern innovations. This review examines the evolution and clinical applications of diagnostic imaging in Shalya Tantra, emphasizing the synergy between traditional diagnostic methods and contemporary modalities such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), artificial intelligence (AI), and augmented reality (AR). These technologies enhance diagnostic accuracy, surgical planning, and therapeutic outcomes across diverse medical specialties within Ayurveda. Case studies illustrate their pivotal role in diagnosing abdominal, pelvic, musculoskeletal, and obstetric conditions, guiding accurate interventions and improving patient care. Future directions explore the potential of AI-driven analytics, virtual simulations, and portable ultrasound devices to further optimize diagnostic capabilities and expand access to advanced imaging in Ayurvedic practice.

METB-11. HOW METHYLATION PROFILING HELPS IN DIAGNOSING AND TREATING PEDIATRIC BRAIN TUMORS IN INDONESIA

Abstract BACKGROUNDS Pediatric brain tumors (PBT) exhibit extensive heterogeneity, impacting treatment outcomes. While traditionally classified based on histopathological features, recent WHO classifications have integrated DNA methylation array profiling amongst other molecular characteristics, which improves diagnostic accuracy and thus treatment decisions. However, access to this technology is limited in low- and middle-income countries (LMICs) like Indonesia. As part of the MNP Outreach Consortium, we aimed to introduce methylation profiling in Indonesia to aid in diagnosing and stratifying PBT patients. METHODS In 2023, we conducted a case series involving seven challenging PBT cases from Cipto Mangunkusumo National General Hospital, re-assessing them with methylation profiling. RESULTS Through collaboration with the Institute of Neuropathology Heidelberg and the DKFZ, our centre adopted a discerning approach to re-evaluate histopathological samples from complex cases. This endeavour yielded notable results, reaffirming four initial diagnoses and altering three others Amongst the findings, one case initially diagnosed as anaplastic ependymoma, CNS WHO grade 3, was reclassified as a supratentorial ependymoma with YAP1 fusion gene, based on methylation class. Noteworthy within this latter group, one case initially categorized as low-grade pilocytic astrocytoma was reclassified as high-grade diffuse midline glioma, H3 K27 altered. Moreover identifying the copy number plot could indicate a KIAA1549::BRAF fusion to reclassify a case from diffuse pediatric glioma WHO grade 2 to pilocytic astrocytoma WHO grade 1. The recurrent pigmented neuroectodermal tumor of infancy (MNTI) was reclassified as Medulloblastoma, non-WNT/non-SHH, group 3 subtype II on methylation profiling, highlighting molecular similarities and potential treatment insights. These findings offer insights into genetic mechanisms and guide treatment strategies. CONCLUSION Accurate diagnosis, facilitated by advanced molecular profiling, enhances prognostication and enables personalized treatment plans, even in LMICs. This underscores the importance of expanding access to advanced diagnostic technologies in resource-limited settings.

Predicting Lung Cancer Survival to the Future: Population-Based Cancer Survival Modeling Study.

Lung cancer remains the leading cause of cancer-related mortality globally, with late diagnoses often resulting in poor prognosis. In response, the Lung Ambition Alliance aims to double the 5-year survival rate by 2025. Using the Taiwan Cancer Registry, this study uses the survivorship-period-cohort model to assess the feasibility of achieving this goal by predicting future survival rates of patients with lung cancer in Taiwan. This retrospective study analyzed data from 205,104 patients with lung cancer registered between 1997 and 2018. Survival rates were calculated using the survivorship-period-cohort model, focusing on 1-year interval survival rates and extrapolating to predict 5-year outcomes for diagnoses up to 2020, as viewed from 2025. Model validation involved comparing predicted rates with actual data using symmetric mean absolute percentage error. The study identified notable improvements in survival rates beginning in 2004, with the predicted 5-year survival rate for 2020 reaching 38.7%, marking a considerable increase from the most recent available data of 23.8% for patients diagnosed in 2013. Subgroup analysis revealed varied survival improvements across different demographics and histological types. Predictions based on current trends indicate that achieving the Lung Ambition Alliance's goal could be within reach. The analysis demonstrates notable improvements in lung cancer survival rates in Taiwan, driven by the adoption of low-dose computed tomography screening, alongside advances in diagnostic technologies and treatment strategies. While the ambitious target set by the Lung Ambition Alliance appears achievable, ongoing advancements in medical technology and health policies will be crucial. The study underscores the potential impact of continued enhancements in lung cancer management and the importance of strategic health interventions to further improve survival outcomes.

Current genetic models for studying congenital heart diseases: Advantages and disadvantages.

Congenital heart disease (CHD) encompasses a diverse range of structural and functional anomalies that affect the heart and the major blood vessels. Epidemiological studies have documented a global increase in CHD prevalence, which can be attributed to advancements in diagnostic technologies. Extensive research has identified a plethora of CHD-related genes, providing insights into the biochemical pathways and molecular mechanisms underlying this pathological state. In this review, we discuss the advantages and challenges of various In vitro and in vivo CHD models, including primates, canines, Xenopus frogs, rabbits, chicks, mice, Drosophila, zebrafish, and induced pluripotent stem cells (iPSCs). Primates are closely related to humans but are rare and expensive. Canine models are costly but structurally comparable to humans. Xenopus frogs are advantageous because of their generation of many embryos, ease of genetic modification, and cardiac similarity. Rabbits mimic human physiology but are challenging to genetically control. Chicks are inexpensive and simple to handle; however, cardiac events can vary among humans. Mice differ physiologically, while being evolutionarily close and well-resourced. Drosophila has genes similar to those of humans but different heart structures. Zebrafish have several advantages, including high gene conservation in humans and physiological cardiac similarities but limitations in cross-reactivity with mammalian antibodies, gene duplication, and limited embryonic stem cells for reverse genetic methods. iPSCs have the potential for gene editing, but face challenges in terms of 2D structure and genomic stability. CRISPR-Cas9 allows for genetic correction but requires high technical skills and resources. These models have provided valuable knowledge regarding cardiac development, disease simulation, and the verification of genetic factors. This review highlights the distinct features of various models with respect to their biological characteristics, vulnerability to developing specific heart diseases, approaches employed to induce particular conditions, and the comparability of these species to humans. Therefore, the selection of appropriate models is based on research objectives, ultimately leading to an enhanced comprehension of disease pathology and therapy.

Advances in Cancer Research: Current and Future Diagnostic and Therapeutic Strategies.

Cancers of unknown primary (CUP) exhibit significant cellular heterogeneity and malignancy, which poses significant challenges for diagnosis and treatment. Recent years have seen deeper insights into the imaging, pathology, and genetic characteristics of CUP, driven by interdisciplinary collaboration and the evolution of diagnostic and therapeutic strategies. However, due to their insidious onset, lack of evidence-based medicine, and limited clinical understanding, diagnosing and treating CUP remain a significant challenge. To inspire more creative and fantastic research, herein, we report and highlight recent advances in the diagnosis and therapeutic strategies of CUP. Specifically, we discuss advanced diagnostic technologies, including 12-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET/CT) or 68Ga-FAPI (fibroblast activation protein inhibitor) PET/CT, liquid biopsy, molecular diagnostics, self-assembling nanotechnology, and artificial intelligence (AI). In particular, the discussion will extend to the effective treatment techniques currently available, such as targeted therapies, immunotherapies, and bio-nanotechnology-based therapeutics. Finally, a novel perspective on the challenges and directions for future CUP diagnostic and therapeutic strategies is discussed.

Cross-institutional provision of medical care to social emergency casualties

Introduction. Current advances in war-waging strategies have imposed new demands on public medical care and civil defense forces amid social emergency settings. Elimination of medical and sanitary consequences of emergencies compels to explore new horizons in order to develop advanced approaches and strategies of medical care provided to victims at all stages of medical evacuation among affected population, emergency rescue personnel of various departmental affiliations, including military personnel of military rescue formations.The objective is to share the experience of cross-departmental medical forces providing medical care to victims amid disrupted and non-functional healthcare services due to biological and social emergency of regional scale.Results and analysis. The paper describes experience of medical aid efforts in the newly incorporated regions of Russia, suggesting a cross-institutional strategy of dedicated medical care provision to the population and military rescue personnel amid social emergency settings.Conclusion. Deployment of cross-institutional medical forces allows to reinforce the capacities of regular medical organizations in emergency areas, whereas advanced diagnostic, treatment and medical evacuation technologies allow to stabilize the condition of victims to carry out evacuation “as intended” within most optimal timeframes.

Programmable Photoswitchable Microcapsules Enable Precise and Tailored Drug Delivery from Microfluidics.

The development of precision personalized medicine poses a significant need for the next generation of advanced diagnostic and therapeutic technologies, and one of the key challenges is the development of highly time-, space-, and dose-controllable drug delivery systems that respond to the complex physiopathology of patient populations. In response to this challenge, an increasing number of stimuli-responsive smart materials are integrated into biomaterial systems for precise targeted drug delivery. Among them, responsive microcapsules prepared by droplet microfluidics have received much attention. In this study, we present a UV-visible light cycling mediated photoswitchable microcapsule (PMC) with dynamic permeability-switching capability for precise and tailored drug release. The PMCs were fabricated using a programmable pulsed aerodynamic printing (PPAP) technique, encapsulating an aqueous core containing magnetic nanoparticles and the drug doxorubicin (DOX) within a poly(lactic-co-glycolic acid) (PLGA) composite shell modified by PEG-b-PSPA. Selective irradiation of PMCs with ultraviolet (UV) or visible light (Vis) allows for high-precision time-, space-, and dose-controlled release of the therapeutic agent. An experimentally validated theoretical model was developed to describe the drug release pattern, holding promise for future customized programmable drug release applications. The therapeutic efficacy and value of patternable cancer cell treatment activated by UV radiation is demonstrated by our experimental results. After in vitro transcatheter arterial chemoembolization (TACE), PMCs can be removed by external magnetic fields to mitigate potential side effects. Our findings demonstrate that PMCs have the potential to integrate embolization, on-demand drug delivery, magnetic actuation, and imaging properties, highlighting their immense potential for tailored drug delivery and embolic therapy.

Clinical pathway in Chinese county for lung cancer diagnosis and treatment (2023 edition)

Lung cancer (LC) is the leading cause of death among patients with cancer both in worldwide and China. China accounts for 11.4% of the total number of cancer cases and 18.0% of the total number of cancer deaths in the world. Standardizing the diagnosis and treatment of LC is a key measure to improve the survival rate of LC patients and reduce the mortality rate. However, county hospitals generally face the problem of inaccessibility to advanced diagnostic and treatment technologies. Therefore, when developing quality control standards and clinical diagnosis and treatment specifications, it is necessary to combine the actual situation of county hospitals and formulate specific recommendations. The recommendations of treatment measures also need to consider the approval status of indications and whether it is included in the National Reimbursement Drug List (NRDL), to ensure the access to medicines. In order to solve the above problems, based on existing guidelines at home and abroad and the clinical work characteristics of county hospitals, the first clinical pathway in Chinese county for LC diagnosis and treatment (2023 edition) was compiled. This pathway elaborated on the imaging diagnosis, pathological diagnosis, molecular testing, and precision medicine based on histological-pathological types, tumor-node-metastasis (TNM) classification, and molecular classification, developed different diagnosis and treatment processes for different types of LC patients. Simultaneously, according to the actual work situation of county hospitals, the diagnosis and treatment recommendations in clinical scenarios are divided into basic strategies and optional strategies for elaboration. The basic strategies are the standards that county hospitals must meet, while the optional strategies provide more choices for hospitals, which are convenient for county doctors to put into clinical practice. All the recommended diagnostic and treatment plans strictly refer to existing guidelines and consensus, ensuring the scientificity.

Are We Losing the Final Fight against Cancer?

Despite our increasing understanding of the biology and evolution of the cancer process, it is indisputable that the natural process of cancer creation has become increasingly difficult to cure, as more mutations are found with age. It is significantly more difficult to challenge the curative method when there is heterogeneity within the tumor, as it hampers clinical and genetic categorization. With advances in diagnostic technologies and screening leading to progressive tumor shrinkage, it becomes more difficult over time to evaluate the effects of treatment on overall survival. New treatments are often authorized based on early evidence, such as tumor response; disease-free, progression-free, meta-static-free, and event-free survival; and, less frequently, based on clinical endpoints, such as overall survival or quality of life, when standard guidelines are not available to approve pharmaceuticals. These clearances usually happen quite rapidly. Although approval takes longer, relative survival demonstrates the genuine worth of a novel medication. Pressure is being applied by pharmaceutical companies and patient groups to approve "new" treatments based on one of the above-listed measures, with results that are frequently insignificantly beneficial and frequently have no impact on quality of life.

Recent advances and challenges in plant viral diagnostics.

Accurate and timely diagnosis of plant viral infections plays a key role in effective disease control and maintaining agricultural productivity. Recent advances in the diagnosis of plant viruses have significantly expanded our ability to detect and monitor viral pathogens in agricultural crops. This review discusses the latest advances in diagnostic technologies, including both traditional methods and the latest innovations. Conventional methods such as enzyme-linked immunosorbent assay and DNA amplification-based assays remain widely used due to their reliability and accuracy. However, diagnostics such as next-generation sequencing and CRISPR-based detection offer faster, more sensitive and specific virus detection. The review highlights the main advantages and limitations of detection systems used in plant viral diagnostics including conventional methods, biosensor technologies and advanced sequence-based techniques. In addition, it also discusses the effectiveness of commercially available diagnostic tools and challenges facing modern diagnostic techniques as well as future directions for improving informed disease management strategies. Understanding the main features of available diagnostic methodologies would enable stakeholders to choose optimal management strategies against viral threats and ensure global food security.

Cytomorphological Patterns of AFB Positive Tubercular Lymphadenitis: A Cross-sectional Study

Introduction: Tuberculosis (TB) remains a global threat despite advancements in diagnostic technologies and treatment options. Tubercular lymphadenitis is the most common presentation of Extra Pulmonary Tuberculosis (EPTB) in developing countries, accounting for approximately 30-52% of cases. Fine Needle Aspiration Cytology (FNAC) plays a crucial role in the cost- effective and efficient outpatient diagnosis of tubercular lymphadenitis. Understanding the different cytomorphological patterns and Acid Fast Bacilli (AFB) density in tubercular lymphadenitis aids in early diagnosis, management, and reducing morbidity and mortality. Aim: To analyse the various cytomorphological patterns observed in tubercular lymphadenitis, along with AFB density. Materials and Methods: The present cross-sectional cytomorphological study study was conducted between September and October 2021. Data from September 2020 to August 2021 were collected from the records of Shanthabai Devarao Shivaram(SDS) Tuberculosis Research Centre and Rajiv Gandhi Institute of Chest Diseases, a tertiary care hospital in Bengaluru, Karnataka, India. Out of a total of 128 patients with lymphadenopathy who underwent FNAC, four cases were excluded due to failed aspiration. The remaining 124 cases were available for analysis. Smears that exhibited features suggestive of tuberculosis were selected and categorised based on cytomorphological patterns into the following groups: Pattern-1 (Exudative), Pattern-2 (Epithelioid granuloma without necrosis), Pattern-3 (Epithelioid granuloma with necrosis), and Pattern-4 (Predominantly Necrosis without epithelioid granuloma). Ziehl-Neelsen (ZN) stained AFB positive smears were further categorised into four groups based on mycobacterial load. The categories included scant (1-9 AFB in 100 fields), 1+ (10-99 AFB in 100 fields), 2+ (1-10 AFB per field), and 3+ (>10 AFB per field). Higher numbers indicated greater infectivity. Statistical analysis was performed using the Chi-square test. A probability level of less than 0.05 (p<0.05) was considered statistically significant. Results: Out of the 124 cases studied, 81 (65.32%) were females and 43 (34.68%) were males, with a female preponderance (1.9:1). The overall prevalence of tubercular lymphadenopathy was 89 cases (71.77%), with Pattern 4 (Acellular necrosis without granuloma) being the most common cytomorphological pattern 25 cases (28.09%) followed closely by Pattern-2 (Epithelioid cell granuloma without necrosis) 23 cases (25.84%). AFB positivity was observed in 57 cases (64.04%). Pattern-1 (Exudative pattern) showed 100% AFB positivity (21 out of 21 cases), while Pattern 4 (Predominantly necrosis without epithelioid granuloma) demonstrated 92% AFB positivity (23 out of 25 cases). Conclusion: Understanding the various cytomorphological patterns in tubercular lymphadenitis is crucial for its diagnosis. Additionally, identifying AFB positivity, acid-fast bacilli distribution, and Bacterial Index (BI) in various patterns aids in planning early treatment and ensuring a favourable prognosis. FNAC serves as an easy, inexpensive, and quick procedure that can help avoid unnecessary surgical intervention or biopsy in the diagnosis of tubercular lymphadenitis.