Diagnostic Methodology Research Articles

Data-driven approach for the detection of faults in district heating networks

At present, District Heating Networks (DHNs) are required to operate more and more reliably and efficiently in order to further save primary energy and reduce environmental impact. Thus, monitoring and diagnostic approaches are necessary to identify the typical faults that affect this type of systems (e.g., anomalous heat and pressure losses), with the final goal of optimizing DHN operation and management. To this aim, this paper presents a data-driven diagnostic methodology that exploits NARX (nonlinear autoregressive network with exogenous inputs) neural networks to simulate DHN healthy operation and a threshold-based criterion for fault detection and identification. The novel diagnostic methodology is tested for evaluating the health state of the DHN of the campus of the University of Parma (Italy), based on the availability of time series of measurable variables (mass flow rate, pressure, and temperature) for both the power plant and the end-users. Both single and multiple faults of anomalous heat losses and anomalous pressure losses, with different magnitudes and location, were artificially implanted in DHN pipes. The main novel contribution of this paper with respect to state-of-the-art literature relies on the development of a real-time simulation approach aimed to predict DHN future operation and detect abnormal deviations from normal operation. The methodology proves to correctly detect and identify all simulated faults related to heat and pressure losses, by also correctly estimating their magnitude even in the most challenging scenarios.

Development of a fault diagnostics and tolerance system: An application to continuous stirred tank reactor

Fault diagnosis and tolerance are crucial for monitoring system health and ensuring stability in industrial processes. Challenges arise in designing fault diagnostic solutions for real-time industrial processes with inherent nonlinear dynamic behaviors, particularly when dealing with multiple operating regions characterized by varying dynamics. This article addresses this challenge and proposes a fault diagnostic and tolerant control scheme for industrial systems. The proposed approach integrates a fuzzy-based realization technique with a subspace-aided methodology to effectively handle the nonlinear dynamic behavior observed across different operational scenarios. A practical solution is presented, significantly reducing the computational burden associated with online diagnostics, as the parity vectors are computed offline using available input–output data for different operating regions. During online diagnostics, only computed parity spaces are used with fuzzy realizations for residual generation, leading to a significant reduction in online computation. Numerical examples demonstrate the effectiveness of the proposed method, achieving a high precision rate in fault diagnostics. Furthermore, the diagnostic methodology is integrated with fault-tolerant control for practical applications, as demonstrated in the application of a continuous stirred tank reactor. This integration enables the system to effectively tolerate faults and ensure sub-optimal operation of the industrial process.

Historic Building Information Modeling for Conservation and Maintenance: San Niccolo’s Tower Gate, Florence

In the field of conservation and protection of heritage buildings, knowledge plays a fundamental role, emphasized by national and international rules and regulations. This aspect becomes fundamental when conducting the structural assessment of a historical building. This study envisaged a cognitive phase via the application of advanced survey and diagnostic methodologies to define the materials, construction techniques, and state of conservation of the structural system of a specific building forming part of Florence’s heritage. The information complex produced formed the basis for the structural assessment and for the experimentation of the BIM methodology within the creation of databases for the management of cognitive processes of historical buildings. The case study is one of the gates of the last circle of walls of the 14th century and is the only one that has maintained its original height, despite modifications: the gate/tower of San Niccolò. The research conducted, in addition to achieving a structural assessment of the tower, has allowed the creation of a dynamic model for organizing and consulting the information, laying the groundwork for the creation of a conservation and maintenance plan.

Prediction, prevention and management of gastroesophageal reflux after per-oral endoscopic myotomy: An update.

Achalasia cardia, the most prevalent primary esophageal motility disorder, is predominantly characterized by symptoms of dysphagia and regurgitation. The principal therapeutic approaches for achalasia encompass pneumatic dilatation (PD), Heller's myotomy, and the more recent per-oral endoscopic myotomy (POEM). POEM has been substantiated as a safe and efficacious modality for the management of achalasia. Although POEM demonstrates superior efficacy compared to PD and an efficacy parallel to Heller's myotomy, the incidence of gastroesophageal reflux disease (GERD) following POEM is notably higher than with the aforementioned techniques. While symptomatic reflux post-POEM is relatively infrequent, the significant occurrence of erosive esophagitis and heightened esophageal acid exposure necessitates vigilant monitoring to preclude long-term GERD-related complications. Contemporary advancements in the field have enhanced our comprehension of the risk factors, diagnostic methodologies, preventative strategies, and therapeutic management of GERD subsequent to POEM. This review focuses on the limitations inherent in the 24-h pH study for evaluating post-POEM reflux, potential modifications in the POEM technique to mitigate GERD risk, and the strategies for managing reflux following POEM.

A Rare Case of Hairy Cell Leukemia with COVID Infection: A Case Report

Abstract Hairy cell leukemia (HCL) is a distinct subset of chronic lymphoid leukemia, presents with unique features, including the characteristic "hairy" projections seen in neoplastic B cells. We reported a case on hairy cell leukemia (HCL), emphasizing the challenges posed by this rare hematological malignancy, especially in the context of concurrent health crises such as the COVID-19 pandemic. This malignancy, though uncommon, necessitates comprehensive diagnostic methodologies, such as peripheral blood studies, flow cytometry, and bone marrow biopsy. Treatment strategies for HCL involve tailored approaches based on symptomatic presentations. Purine analogs, cladribine and pentostatin, stand as first-line interventions, showcasing efficacy in inducing and sustaining remission. However, the prolonged immunosuppression resulting from these treatments warrants vigilant monitoring for potential infectious complications. Emerging therapies, such as the BRAF inhibitor vemurafenib, provide additional options for refractory or progressive cases. The post-treatment phase demands meticulous follow-up, with an emphasis on regular blood work assessments by an oncology nurse and primary care physician. Our case further highlights the need for heightened awareness among healthcare professionals, particularly in the post-COVID-19 phase, where persistent symptoms led to the discovery of an underlying hematological disorder. Keywords: Hairy cell leukemia, Evan syndrome, Covid-19.

Diagnosis, Management, and Prognosis of Cystic Fibrosis-Related Liver Disease in Children.

Cystic fibrosis (CF) is a multifaceted disorder predominantly investigated for its pulmonary manifestations, yet patients with CF also exhibit a spectrum of extrapulmonary manifestations, notably those involving the hepatobiliary system. The latter constitutes the third leading cause of morbidity and mortality in individuals with CF. Cystic fibrosis-related liver disease (CFLD), with an escalating prevalence, manifests diverse clinical presentations ranging from hepatomegaly to cirrhosis and hepatopulmonary syndrome. Consequently, early detection and appropriate management are imperative for sustaining the health and influencing the quality of life of CF patients afflicted with CFLD. This review aims to consolidate existing knowledge by providing a comprehensive overview of hepatobiliary manifestations associated with CF. It delineates the clinical hepatobiliary manifestations, diagnostic methodologies, incorporating minimally invasive markers, and therapeutic approaches, encompassing the impact of novel CFTR modulators on CFLD. Given the exigency of early diagnosis and the intricate management of CFLD, a multidisciplinary team approach is essential to optimize care and enhance the quality of life for this subset of patients. In conclusion, recognizing CF as more than solely a pulmonary ailment, the authors underscore the imperative for further clinical investigations to establish a more robust evidence base for CFLD management within the continuum of this chronic disease.

Bearing fault detection by using graph autoencoder and ensemble learning

The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.

Quantitative detection of cassava common mosaic virus for health certification of cassava (Manihot esculenta Crantz) germplasm using qPCR analysis

Cassava (Manihot esculenta Crantz) is a crop of global economic and food safety importance, used for human consumption and in various industrial applications. The genebank of the Genetic Resources Program of the Alliance of Bioversity International and CIAT currently holds the world's largest cassava collection, with 5965 in vitro accessions from 28 countries. Managing this extensive collection involves indexing quarantine pathogens as a phytosanitary certification requirement for safely distributing cassava germplasm. The study therefore aimed to optimize a quantitative diagnostic protocol to detect cassava common mosaic virus (CsCMV) using quantitative PCR (qPCR) as a better alternative to other molecular techniques. This was done through designing primers and a probe in the RdRP region of CsCMV, and optimizing the qPCR conditions of the diagnostic protocol using primer concentration assays, and reaction amplification conditions such as volume and reaction time. We also evaluated the qPCR protocol by comparing the results of 140 cassava accession evaluations using three diagnostic methodologies (DAS-ELISA, end-point PCR, and qPCR) for CsCMV. Our protocol established that qPCR technique analysis is ten-times more sensitive in detecting CsCMV compared to end-point PCR, showing a maximum detection level of 77.97 copies/μL of plasmid, with 76 min of reaction time. The comparison allowed us to verify the level of CsCMV detection through the techniques evaluated, concluding that qPCR was more sensitive and allowed the quantification of viral concentration. The optimized qPCR protocol will be used to accelerate diagnostic screening of cassava germplasm for the presence or absence of CsCMV to ensure safe movement and distribution of disease-free germplasm.

Incidence of Sudden Cardiac Death in Low- and Middle-Income Countries: A Systematic Review of Cohort Studies.

Sudden cardiac death (SCD) is a leading cause of mortality worldwide and, in recent years, has become an urgent public health concern in low- and middle-income countries (LMICs). Data from LMICs, however, remains limited. As such, the aim of this article is to systematically review the current literature on the incidence of SCD in LMICs to inform policymakers and identify potential research gaps. A search of PubMed and Embase was utilized to capture the targeted condition, outcome, and setting. Only peer-reviewed cohort studies in LMICs reporting SCD incidence estimates in the general population of individuals aged ≥1 year were eligible for selection. Papers providing incidence data for specific types of SCD, including sudden coronary death or death from sudden cardiac arrest, were also included. After deduplication, 1941 citations were identified and screened. Seven studies representing four countries-Cameroon, China, India, and Iran-met the criteria for inclusion and were considered in our analysis. The crude incidence rate for SCD ranged from 19.9 to 190 cases per 100,000 person-years, while age-adjusted rates ranged from 33.6 to 230 cases per 100,000 person-years. There was notable variability in methods utilized to ascertain SCD cases. These findings suggest that the incidence of all-cause SCD in LMICs and may exceed that of high-income countries; however, observed disparities may be partly attributable to differences in case ascertainment methods. Additional research is needed to better understand the true incidence of SCD in developing countries. It is crucial that future studies across regions utilize standard diagnostic criteria and methodology for identifying SCD, which would provide a framework by which to compare outcomes between settings.

A Review of AI in Breast Cancer Detection

Cancer stands out as one of the most pressing global health challenges, and over the past decade, significant advancements have been made in diagnostic tests and methodologies. These tests fall into categories such as imaging tests, and endoscopic procedures, generating substantial volumes of data. This data needs expert evaluation to distinguish between benign and malignant tumors. Enter artificial intelligence (AI), which offers improved accuracy in analyzing large quantities of diagnostic imaging, thereby enhancing the efficiency of healthcare systems. The integration of new AI algorithms, technical advances, and enhanced computer hardware enables the training of diagnostic neural networks. This allows machines to learn from a diverse range of scans, leading to a comprehensive understanding of cancer scanning data. AI software has been evaluated against conventional diagnostic tools used by cancer specialists, and the results show significantly increased precision, making it highly effective in early diagnosis and extended forecasting for various types of cancers. In the realm of breast cancer prognosis, AI systems have demonstrated the potential to surpass human specialists, enabling much earlier diagnosis. Similarly, informatics has developed AI algorithms and deep learning techniques capable of predicting individuals' likelihood of developing lung cancer through low-dose CT analysis. The use of convolutional neural networks (CNNs) has been instrumental in diagnosing the invasion depth of gastric cancer based on gastric endoscopy.

Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning

Background: In response to the escalating global concerns surrounding skin cancer, this study aims to address the imperative for precise and efficient diagnostic methodologies. Focusing on the intricate task of eight-class skin cancer classification, the research delves into the limitations of conventional diagnostic approaches, often hindered by subjectivity and resource constraints. The transformative potential of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms is underscored, emphasizing significant improvements in accuracy and accessibility. Methods: Utilizing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis is conducted, employing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering “Naturalize” augmentation technique is introduced. This technique leads to the creation of two indispensable datasets—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The “Naturalize” augmentation technique involves the segmentation of skin cancer images using the Segment Anything Model (SAM) and the systematic addition of segmented cancer images to a background image to generate new composite images. Results: The research showcases the pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer. The proficiency of AI in analyzing vast datasets and discerning subtle patterns significantly augments the diagnostic prowess of dermatologists. Quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations are meticulously evaluated. The culmination of these endeavors resulted in an unprecedented achievement—100% average accuracy, precision, recall, and F1-score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. Conclusion: This groundbreaking exploration highlights the transformative capabilities of AI-driven methodologies in reshaping the landscape of skin cancer diagnosis and patient care. The research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care. This pioneering work paves the way for a new era in dermatological diagnostics, heralding the dawn of unprecedented precision and efficacy in the identification and classification of skin cancers.

THE DIAGNOSTIC MODEL OF THE MOTIVATION OF MILITARY SERVICEMEN WITH AN EMPHASIS ON THE VALUE SPHERE

The article analyzes the motivational sphere of military personnel, on the basis of which a motivation diagnosis model was developed with an emphasis on the value sphere. It was found that the motivational and value aspect in the professional activity of military personnel is a key element of their mental activity, which contributes to the balanced development of personal and professional qualities. Understanding the problems of professional motivation within the framework of the relationship “motivation ‒ personality ‒ activity” allows us to reveal the key importance of this component in the process of forming the personality of each military person. The main components of the model, which include the orientation of the individual, which depends on psychological and social conditions, have been determined. It is proposed to take into account the value orientations of the individual, which are divided into personal, prestige-egoistic and social-social values, which help in determining the level of adaptation and professional growth. The importance of integrating meaningful orientation into professional activity, which allows to evaluate life satisfaction and perception of personal responsibility, is substantiated. In addition, a diagnostic toolkit is proposed for each component, so in order to study the orientation of the personality, it is necessary to use the methodology of diagnostics of the orientation of the personality, which was developed by V. Smekal and M. Kucher; The Connor-Davidson-10 resilience scale and J. Rotter՚s “Diagnosis of the level of subjective control” method. For the purpose of studying value orientations ‒ the value questionnaire of Sh. Schwartz. And for the diagnosis of meaningful life orientations, the “Meaningful Life Orientations” test was proposed by D.A. Leontiev and “Morphological test of vital values” (V. Sopovoy and L. Karpushyna). Conclusions regarding the importance of an integrated approach in the diagnosis of motivational and value aspects, including both theoretical and empirical methods of analysis, are substantiated and formed. Prospects for further research are focused on the expansion and refinement of the model, taking into account the dynamics of military conflicts and psychosocial factors affecting individual motivation.

Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging.

The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

Goldenhar syndrome with limbal neoformation, microtia and skeletal deformities: a case report and literature review

BackgroundTo report a case of a 4-year-old patient with Goldenhar syndrome.Case presentationThe author presents a rare case report involving a 4-year-old boy with multiple malformations. A comprehensive examination showed that the patient primarily had a limbal dermoid. He also has bilateral microtia and ear canal deformities. The skull CT scan and spine X-ray showed Maxillofacial Abnormalities and scoliosis. Whole Exome Sequencing revealed potential gene variations related to microtia. Although certain circumstances prevented us from initiating follow-up treatment for the patient, we have provided a detailed account of the diagnostic methodologies used for this condition.ConclusionsGoldenhar syndrome is a congenital condition, predominantly presenting as sporadic cases. Its diagnosis and management typically necessitate the involvement of multiple disciplines, including otolaryngology and craniofacial surgery. The syndrome encompasses a variety of craniofacial features, which can facilitate early diagnosis and guide subsequent therapeutic interventions.

"Cancer Perspectives And Recent Advances In Anti-Cancer Drug Research: A Review In The Lens Of Siddha Medicine"

Introduction: Cancer is a global health challenge, demanding innovative approaches for prevention, diagnosis, and treatment. Despite ongoing research efforts, finding a definitive cure for cancer remains a formidable challenge. In response to these challenges, the concept of integrative oncology has gained prominence, aiming to combine conventional treatments with evidence-based complementary therapies. Objective: This review explores the integration of Siddha medicine, an ancient South Indian system, with modern oncology to offer holistic cancer care. Siddha perspectives on cancer, its classifications, etiopathogenesis, diagnostic methodologies, and treatment modalities are thoroughly analysed in comparison with modern approach. Recent research on Siddha medicines demonstrating anti-cancer potential is also being reviewed through a systematic literature analysis. Methodology: Analyses of various Siddha texts is done to know the terminologies and categories of cancer. Siddha's etiopathogenesis, diagnostic methodologies and Siddha treatment modalities are discussed, emphasizing the balance of humours. Recent research on Siddha medicines for cancer treatment is compiled through a systematic literature review. Discussion: The manuscript decodes Siddha's conceptual framework underlying cancer hallmarks, aligning Vatham, Pitham, and Kabam with the eight hallmark capabilities. Siddha diagnostic methodologies, including pulse reading and urine examination, are compared with modern techniques. Siddha treatment modalities, rooted in herbal formulations, are presented. A comprehensive compilation of Siddha drugs with anti-cancer potential is provided, supported by in-vitro, in-vivo, and clinical studies. Conclusion: The multifaceted nature of cancer necessitates a comprehensive approach, and Siddha medicine provides unique insights. While Siddha may not directly align with modern diagnostics, its holistic principles contribute valuably to cancer discussions. Integrating Siddha into mainstream oncology requires scientific rigor, with further research and clinical trials to validate safety and efficacy. The fusion of Siddha wisdom with modern oncology holds promise for a more holistic and patient-centric cancer care approach.

Navigating Lung Cancer: Exploring Progress and Obstacles - A Comprehensive Review

Lung cancer persists as a significant global health dilemma, characterized by elevated incidence and mortality rates worldwide. Despite recent strides in diagnostic methodologies, screening approaches, and therapeutic modalities, lung cancer retains its status as the foremost cause of cancer-related fatalities internationally. The disease is predominantly categorized into two primary forms: small-cell carcinomas and the notably more prevalent non-small-cell carcinomas, constituting approximately 85% of cases. While smoking stands as the primary risk factor for lung cancer, accounting for 80-90% of related deaths, other factors such as dietary habits, air pollution, genetic predisposition, and prior lung conditions play substantial roles. Diagnostic procedures encompass a range of techniques, including radiography, sputum cytology, bronchoscopy, biomarker analysis, among others. The imperative for progress and refinement in diagnostic tools, especially for early detection and monitoring in high-risk demographics, underscores the urgency. Continual research and enhancement of surgical, systemic, and localized treatments are indispensable in the battle against lung cancer. Despite advancements, challenges persist in achieving timely diagnoses, particularly in high-risk groups, and augmenting overall survival rates. This comprehensive review delves into current research advancements in lung cancer, with specific focus on classification, patient well-being, risk factors, diagnostic methodologies, and treatment options.

Comprehensive analysis of antigenic variations and genomic properties of hepatitis B virus in clinical samples in the mid-north east region of Bangladesh

This investigation delineates an exhaustive analysis of the clinical, immunological, and genomic landscapes of hepatitis B virus (HBV) infection across a cohort of 22 verified patients. The demographic analysis unveiled a pronounced male bias (77.27%), with patient ages spanning 20 to 85 years and durations of illness ranging from 10 days to 4 years. Predominant clinical manifestations included fever, fatigue, anorexia, abdominal discomfort, and arthralgia, alongside observed co-morbidities such as chronic renal disorders and hepatocellular carcinoma. Antigenic profiling of the HBV envelope proteins elucidated significant heterogeneity among the infected subjects, particularly highlighted by discordances in the detection capabilities of small and large HBsAg assays, suggesting antigenic diversity. Quantitative assessment of viral loads unveiled a broad spectrum, accompanied by atypical HBeAg reactivity patterns, challenging the reliability of existing serological markers. Correlative studies between viral burden and antigenicity of the envelope proteins unearthed phenomena indicative of diagnostic evasion. Notably, samples demonstrating robust viral replication were paradoxically undetectable by the large HBsAg ELISA kit, advocating for more sophisticated diagnostic methodologies. Genotypic examination of three HBV isolates classified them as genotype D (D2), with phylogenetic alignment to strains from various global origins. Mutational profiling identified pivotal mutations within the basic core promoter and preS2/S1 regions, associated with an augmented risk of hepatocellular carcinoma. Further, mutations discerned in the small HBsAg and RT/overlap regions were recognized as contributors to vaccine and/or diagnostic escape mechanisms. In summation, this scholarly discourse elucidates the intricate interplay of clinical presentations, antigenic diversity, and genomic attributes in HBV infection, accentuating the imperative for ongoing investigative endeavors to refine diagnostic and therapeutic modalities.

High sensitivity saliva-based biosensor in detection of breast cancer biomarkers: HER2 and CA15-3.

The prevalence of breast cancer in women underscores the urgent need for innovative and efficient detection methods. This study addresses this imperative by harnessing salivary biomarkers, offering a noninvasive and accessible means of identifying breast cancer. In this study, commercially available disposable based strips similar to the commonly used glucose detection strips were utilized and functionalized to detect breast cancer with biomarkers of HER2 and CA15-3. The results demonstrated limits of detection for these two biomarkers reached as low as 1 fg/ml much lower than those of conventional enzyme-linked immunosorbent assay in the range of 1∼4 ng/ml. By employing a synchronized double-pulse method to apply 10 of 1.2 ms voltage pulses to the electrode of sensing strip and drain electrode of the transistor for amplifying the detected signal, and the detected signal was the average of 10 digital output readings corresponding to those 10 voltage pulses. The sensor sensitivities were achieved approximately 70/dec and 30/dec for HER2 and CA15-3, respectively. Moreover, the efficiency of this novel technique is underscored by its swift testing time of less than 15 ms and its minimal sample requirement of only 3 μl of saliva. The simplicity of operation and the potential for widespread public use in the future position this approach as a transformative tool in the early detection of breast cancer. This research not only provides a crucial advancement in diagnostic methodologies but also holds the promise of revolutionizing public health practices.

A Preliminary Diagnostic Model for Forward Head Posture among Adolescents Using Forward Neck Tilt Angle and Radiographic Sagittal Alignment Parameters.

Despite numerous attempts to correct forward head posture (FHP), definitive evidence-based screening and diagnostic methods remain elusive. This study proposes a preliminary diagnostic methodology for FHP, utilizing a noninvasive body angle measurement system as a screening test for FHP and incorporating radiological parameters for sagittal alignment. We enrolled 145 adolescents for FHP screening. The forward neck tilt angle (FNTA), defined as the angle between the vertical line and the line connecting the participant's acromion and tragus, was measured using the POM-Checker (a noninvasive depth sensor-based body angle measurement system). A whole-spine standing lateral radiograph was obtained, and eight sagittal alignment parameters were measured. Statistical analyses of the association between the FNTA and eight sagittal alignment parameters were conducted. We used 70% of the participant data to establish a preliminary diagnostic model for FHP based on FNTA and each sagittal alignment parameter. The accuracy of the model was evaluated using the remaining 30% of the participant data. All radiological parameters of sagittal alignment showed weak statistical significance with respect to FNTA (best case: r = 0.16, p = 0.0500; cranial tilt). The proposed preliminary diagnostic model for FHP demonstrated 95.35% agreement. Notably, the model using FNTA without radiological parameters accurately identified (100%) participants who required radiographic scanning for FHP diagnosis. Owing to the weak statistical significance of the association between radiological parameters and external body angle, both factors must be considered for accurate FHP diagnosis. When a clear and severe angle variation is observed in an external body angle check, medical professionals should perform radiographic scanning for an accurate FHP diagnosis. In conclusion, FNTA assessment of FNTA through the proposed preliminary diagnostic model is a significant screening factor for selecting participants who must undergo radiographic scanning so that a diagnosis of FHP can be obtained.