Diagnostic Paradigm Research Articles

COVID-19 Research, Public Health and Biomedical Basis

Abstract: The coronavirus outbreak (COVID-19, SARS-CoV-2) greatly impacts the world. Despite great biomedical efforts, approximately 30-50% global population was at least once infected with COVID. Human life expectancy is reduced by COVID-19 epidemics worldwide. : It is indispensable to prevent and treat COVID-19 as effectively as possible. In order to well prevent and treat the coronavirus infection, clinical diagnostic or therapeutic paradigms should be updated. Since the outside condition of COVID-19 prevention is imbalanced among regions and countries, global prevention and treatment action should be aimed. : Despite global vaccination for COVID, a significant proportion of humans still constantly exposes and infected with viruses. Advanced viral biological knowledge and vaccine techniques can alleviate viral spread and promote therapeutics. Universal and specific preventive and treatment paradigms should be designed, integrated, and introduced. : The origin and hidden nature of COVID-19 biology and pathogenesis are broadly understood now. Viral vaccines, pathogenesis, diagnosis, treatment, and personalized medicine are progressing rapidly. Many viral vaccines and personalized medicine should be especially emphasized. : To speed up global efforts against COVID-19, new knowledge and breakthroughs of viral transmissibility, vaccine technique innovation, diagnostic widening, and therapeutic variability worldwide are discussed. Different strategic platforms and landscapes can reach different outcomes. By facilitating global machinery against different variants of COVID, viral-induced socioeconomic burden and imbalance could be reduced.

Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies.

Thrombophilia, a predisposition to thrombosis, poses significant diagnostic challenges due to its multi-factorial nature, encompassing genetic and acquired factors. Current diagnostic paradigms, primarily relying on a combination of clinical assessment and targeted laboratory tests, often fail to capture the complex interplay of factors contributing to thrombophilia risk. This paper proposes an innovative artificial intelligence (AI)-based methodology aimed to enhance the prediction of thrombophilia risk. The designed multidimensional risk assessment model integrates and elaborates through AI a comprehensive collection of patient data types, including genetic markers, clinical parameters, patient history, and lifestyle factors, in order to obtain advanced and personalized explainable diagnoses.

Transcutaneous auricular vagus nerve stimulation as a novel therapy connecting the central and peripheral systems: a review.

Currently, clinical practice and scientific research mostly revolve around a single disease or system, but the single disease-oriented diagnostic and therapeutic paradigm needs to be revised. This review describes how transcutaneous auricular vagus nerve stimulation (taVNS), a novel non-invasive neuromodulation approach, connects the central and peripheral systems of the body. Through stimulation of the widely distributed vagus nerve from the head to the abdominal cavity, this therapy can improve and treat central system disorders, peripheral system disorders, and central-peripheral comorbidities caused by autonomic dysfunction. In the past, research on taVNS has focused on the treatment of central system disorders by modulating this brain nerve. As the vagus nerve innervates the heart, lungs, liver, pancreas, gastrointestinal tract, spleen and other peripheral organs, taVNS could have an overall modulatory effect on the region of the body where the vagus nerve is widespread. Based on this physiological basis, the authors summarize the existing evidence of the taVNS ability to regulate cardiac function, adiposity, glucose levels, gastrointestinal function, and immune function, among others, to treat peripheral system diseases, and complex diseases with central and peripheral comorbidities. This review shows the successful examples and research progress of taVNS using peripheral neuromodulation mechanisms from more perspectives, demonstrating the expanded scope and value of taVNS to provide new ideas and approaches for holistic therapy from both central and peripheral perspectives.

Origin and Function of Monocytes in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic disease resulting from the interaction of various factors such as social elements, autoimmunity, genetics, and gut microbiota. Alarmingly, recent epidemiological data points to a surging incidence of IBD, underscoring an urgent imperative: to delineate the intricate mechanisms driving its onset. Such insights are paramount, not only for enhancing our comprehension of IBD pathogenesis but also for refining diagnostic and therapeutic paradigms. Monocytes, significant immune cells derived from the bone marrow, serve as precursors to macrophages (Mφs) and dendritic cells (DCs) in the inflammatory response of IBD. Within the IBD milieu, their role is twofold. On the one hand, monocytes are instrumental in precipitating the disease's progression. On the other hand, their differentiated offsprings, namely moMφs and moDCs, are conspicuously mobilized at inflammatory foci, manifesting either pro-inflammatory or anti-inflammatory actions. The phenotypic spectrum of these effector cells, intriguingly, is modulated by variables such as host genetics and the subtleties of the prevailing inflammatory microenvironment. Notwithstanding their significance, a palpable dearth exists in the literature concerning the roles and mechanisms of monocytes in IBD pathogenesis. This review endeavors to bridge this knowledge gap. It offers an exhaustive exploration of monocytes' origin, their developmental trajectory, and their differentiation dynamics during IBD. Furthermore, it delves into the functional ramifications of monocytes and their differentiated progenies throughout IBD's course. Through this lens, we aspire to furnish novel perspectives into IBD's etiology and potential therapeutic strategies.

The Power Threat Meaning Framework: a qualitative study of depression in adolescents and young adults.

Depression constitutes one of our largest global health concerns and current treatment strategies lack convincing evidence of effectiveness in youth. We suggest that this is partly due to inherent limitations of the present diagnostic paradigm that may group fundamentally different conditions together without sufficient consideration of etiology, developmental aspects, or context. Alternatives that complement the diagnostic system are available yet understudied. The Power Threat and Meaning Framework (PTMF) is one option, developed for explanatory and practical purposes. While based on scientific evidence, empirical research on the framework itself is still lacking. This qualitative study was performed to explore the experiences of adolescents and young adults with depression from the perspective of the PTMF. We conducted semi-structured interviews with 11 Swedish individuals aged 15- 22 years, mainly female, currently enrolled in a clinical trial for major depressive disorder. Interviews were transcribed verbatim and analyzed with framework analysis informed by the PTMF. A complex multitude of adversities preceding the onset of depression was described, with a rich variety of effects, interpretations, and reactions. In total, 17 themes were identified in the four dimensions of the PTMF, highlighting the explanatory power of the framework in this context. Not all participants were able to formulate coherent narratives. The PTMF provides a framework for understanding the complexities, common themes, and lived experiences of young individuals with depression. This may be essential for the development of new interventions with increased precision and effectiveness in the young.

Renal Tuberculosis: A Case History that makes or Breaks the Case, Nothing is more Deceptive

This manuscript presents a compelling case of renal tuberculosis in a 15-year-old male, elucidating the intricate diagnostic hurdles and strategic management approaches encountered. The patient initially presented with nonspecific symptoms, including intermittent low-grade fever, weight loss, fatigue, and diffuse skin rashes, which were initially managed as suspected enteric fever. However, as the patient’s condition deteriorated, a comprehensive diagnostic exploration revealed renal tuberculosis. The report meticulously outlines the clinical presentation, diagnostic evaluation, and therapeutic trajectory, emphasizing the enigmatic nature of symptoms and advocating for a multidimensional diagnostic paradigm integrating clinical, radiological, microbiological, and histopathological assessments. Furthermore, this case report provides a comprehensive review of urogenital tuberculosis, discussing its epidemiological underpinnings, clinical manifestations, diagnostic methodologies, and therapeutic tenets. It underscores the paramount significance of early recognition and prompt initiation of treatment in forestalling complications and optimizing patient outcomes. This case report enriches the medical discourse by shedding light on the diagnostic intricacies and therapeutic imperatives pertinent to renal tuberculosis, especially in the younger demographic. We believe that the findings will contribute significantly to the understanding and management of this disease.

Robust metabolic syndrome risk score based on triangular areal similarity.

One of the limitations of currently-used metabolic syndrome (MetS) risk calculations is that they often depend on sample characteristics. To address this, we introduced a novel sample-independent risk quantification method called 'triangular areal similarity' (TAS) that employs three-axis radar charts constructed from five MetS factors in order to assess the similarity between standard diagnostic thresholds and individual patient measurements. The method was evaluated using large datasets of Korean (n = 72,332) and American (n = 11,286) demographics further segmented by sex, age, and race. The risk score exhibited a strong positive correlation with the number of abnormal factors and was closely aligned with the current diagnostic paradigm. The proposed score demonstrated high diagnostic accuracy and robustness, surpassing previously reported risk scores. This method demonstrated superior performance and stability when tested on cross-national datasets. This novel sample-independent approach has the potential to enhance the precision of MetS risk prediction.

Update on extraesophageal manifestations of gastroesophageal reflux.

Symptoms/complications related to extraesophageal reflux (EER) are increasingly prevalent presentations and pose significant challenges for clinicians. We summarize and discuss clinical advances and developments in pathophysiology, testing and treatment algorithms of upper/lower airway manifestations of EER. Growing evidence supports likely multifactorial causes of laryngeal symptoms, including EER, oropharyngeal pathologies, allergic conditions, and cognitive-affective processes (brain-larynx interaction). Diagnostic paradigm for laryngopharyngeal reflux (LPR) is shifting towards a personalized approach with noninvasive strategies/prediction tools to risk-stratify patients for upfront reflux testing over empiric acid suppression trials. Management should be multipronged to include antireflux therapies and treatments targeting other causes. Lower airway complications of EER may result in lung dysfunction and poor transplant outcomes. Esophageal symptoms are often absent and routine esophageal/reflux testing to guide timely antireflux therapies may lead to improved outcomes. Modalities that leverage impedance technology may be important, given the potential role of nonacidic reflux. Novel impedance-based metrics such as mean nocturnal baseline impedance and postreflux swallow-induced peristaltic wave index may provide adjunctive diagnostic values. Standardized approach to diagnosis/management of EER should include multidisciplinary care teams and consider different phenotypes, nonreflux contributors, and the complex gut-airway relationships. Prompt antireflux therapies after careful candidate selection may improve outcomes of these airway complications.

Malignant Infantile Osteopetrosis Masquerading as Isolated Fetal Femoral Fracture: First Indian Case Report and Review of Literature

AbstractMalignant infantile osteopetrosis is a rare case of isolated fetal femoral fracture, a scarcely documented phenomenon in medical literature. Through detailed examination and genetic testing, it unveils malignant infantile osteopetrosis as an unexpected etiology, challenging existing diagnostic paradigms. This groundbreaking case emphasizes the critical role of genetic testing in unraveling complex fetal anomalies and underscores the necessity for comprehensive approaches in prenatal diagnostics, offering new insights into prenatal manifestations of skeletal dysplasias.

Traumatic Brain Injury and Conversion Disorder in a Veteran’s Atypical Presentation

IntroductionTraumatic brain injury (TBI) induces cognitive and behavioral changes due to environmental impacts on brain tissue.ObjectivesHighlighting the atypical TBI presentation challenging conventional diagnostics and obscured by conversion disorders.MethodsA 36-year-old male veteran, injured by a sniper rifle in 2011, presented with right ear tinnitus and monthly, unresponsive right hemicranial headaches. Seizures occurred every two weeks with no reported loss of consciousness or sensation. The gunshot wound to the neck in 2011 prompted emergency intervention, with entry and exit wounds located in the posterior lateral neck. Post-injury symptoms comprised hearing loss, tinnitus, restricted neck movement, and weakness in the right arm. Seizures persisted, accompanied by numbness and neck movement. Management included physical therapy, hyperbaric oxygen therapy (improving weakness but not tinnitus), and administration of piracetam (2400 mg/day), sertraline (100 mg/day), and ginkgo biloba (2400 mg/day). Psychiatric consultation suggested a diagnosis of “conversion disorder.”ResultsNeuropsychological Evaluation: Raven Standard Progressive Matrices Test showed borderline impairment. Psychiatric Evaluation noted monotonous mimics, occasional depersonalization, reduced emotional involvement, and slowed psychomotor activity. Elevated trait anxiety was observed per the State-Trait Anxiety Inventory. Neurological Examination identified left arm weakness and impaired resting balance. Imaging Findings: F18-FDG PET/CT Scan at 1 year post-GSW showed hypermetabolism in the right frontal lobe, and at 3.5 years post-GSW demonstrated decreased glucose metabolism in the bilateral cerebellar cortex, temporal lobe, and bilateral parietal lobe.ConclusionsA high-kinetic-energy bullet passed through the right lateral base of the neck without causing apparent brain damage. Proposed is the generation of upward pressure waves in neck tissues through the transmission of kinetic energy, compressing and displacing soft tissues toward the skull. Gunshot injuries create cavities, forming high-pressure waves capable of damaging distant brain regions, leading to TBI such as crush injury, edema, and myelin and axonal damage (Courtney & Courtney, 2007). Microscopic brain damage, undetectable by current imaging, may only surface during autopsy (Yilmaz & Pekdemir, 2007). Rat studies after primary blast injuries reveal brain alterations, highlighting that high-pressure pulses can cause neuronal damage, potentially yielding related symptoms (Cernak et al., 2001). The patient’s atypical symptoms, combined with the initial conversion disorder hypothesis, underscore the need for a diagnostic paradigm shift to differentiate traumatic brain injury from other potential misnomers.Disclosure of InterestNone Declared

Event-Triggered Federated Learning for Fault Diagnosis of Offshore Wind Turbines With Decentralized Data

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

CariesXrays: Enhancing Caries Detection in Hospital-Scale Panoramic Dental X-rays via Feature Pyramid Contrastive Learning

Dental caries has been widely recognized as one of the most prevalent chronic diseases in the field of public health. Despite advancements in automated diagnosis across various medical domains, it remains a substantial challenge for dental caries detection due to its inherent variability and intricacies. To bridge this gap, we release a hospital-scale panoramic dental X-ray benchmark, namely “CariesXrays”, to facilitate the advancements in high-precision computer-aided diagnosis for dental caries. It comprises 6,000 panoramic dental X-ray images, with a total of 13,783 instances of dental caries, all meticulously annotated by dental professionals. In this paper, we propose a novel Feature Pyramid Contrastive Learning (FPCL) framework, that jointly incorporates feature pyramid learning and contrastive learning within a unified diagnostic paradigm for automated dental caries detection. Specifically, a robust dual-directional feature pyramid network (D2D-FPN) is designed to adaptively capture rich and informative contextual information from multi-level feature maps, thus enhancing the generalization ability of caries detection across different scales. Furthermore, our model is augmented with an effective proposals-prototype contrastive regularization learning (P2P-CRL) mechanism, which can flexibly bridge the semantic gaps among diverse dental caries with varying appearances, resulting in high-quality dental caries proposals. Extensive experiments on our newly-established CariesXrays benchmark demonstrate the potential of FPCL to make a significant social impact on caries diagnosis.

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition.&#x0D; OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment.&#x0D; METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4].&#x0D; RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. .&#x0D; CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

The Genetics of Tuberous Sclerosis Complex and Related mTORopathies: Current Understanding and Future Directions.

The mechanistic target of rapamycin (mTOR) pathway serves as a master regulator of cell growth, proliferation, and survival. Upregulation of the mTOR pathway has been shown to cause malformations of cortical development, medically refractory epilepsies, and neurodevelopmental disorders, collectively described as mTORopathies. Tuberous sclerosis complex (TSC) serves as the prototypical mTORopathy. Characterized by the development of benign tumors in multiple organs, pathogenic variants in TSC1 or TSC2 disrupt the TSC protein complex, a negative regulator of the mTOR pathway. Variants in critical domains of the TSC complex, especially in the catalytic TSC2 subunit, correlate with increased disease severity. Variants in less crucial exons and non-coding regions, as well as those undetectable with conventional testing, may lead to milder phenotypes. Despite the assumption of complete penetrance, expressivity varies within families, and certain variants delay disease onset with milder neurological effects. Understanding these genotype-phenotype correlations is crucial for effective clinical management. Notably, 15% of patients have no mutation identified by conventional genetic testing, with the majority of cases postulated to be caused by somatic TSC1/TSC2 variants which present complex diagnostic challenges. Advancements in genetic testing, prenatal screening, and precision medicine hold promise for changing the diagnostic and treatment paradigm for TSC and related mTORopathies. Herein, we explore the genetic and molecular mechanisms of TSC and other mTORopathies, emphasizing contemporary genetic methods in understanding and diagnosing the condition.

Chronic Abdominal Discomfort Syndrome (CADS): Defining and Discussing a Novel Diagnosis.

In this article, we propose a new diagnostic paradigm known as Chronic Abdominal Discomfort Syndrome (CADS). Patient's presentation centers around chronic abdominal pain not explained by acute pathology with or without accompanying dyspepsia, bloating, nausea and vomiting among other symptoms. The pathophysiology is noted to be neurogenic, possibly stemming from visceral sympathetic nerves or abdominal wall afferent nerves. Diagnosis is supported by signs or symptoms traversing clinical, diagnostic and functional criteria. Included is a tool which can assist clinicians in diagnosing patients with CADS per those domains. We hope to facilitate primary care physicians' and gastroenterologists' utilization of our criteria to provide guidance for selecting which patients may benefit from further interventions or evaluation by a pain physician. The pain physician may then offer interventions to provide the patient with relief.

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.

Wireless Battery-free and Fully Implantable Organ Interfaces.

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.

New advances in the treatment and diagnosis of hypertension in primary care: a literature review

Background: Despite the availability of safe and effective antihypertensive drugs, hypertension continues to be a major risk factor for cardiovascular death and morbidity worldwide. This review explores the contemporary landscape of advances in hypertension (HTN) diagnosis and treatment, examining the discordant diagnostic thresholds proposed by American and European guidelines. Methods: An integrative approach was employed, analyzing pertinent literature from sources such as ScienceDirect, Google Scholar, and PubMed. Inclusion criteria encompassed studies on new advances in HTN diagnosis and treatment in primary care, published in English within the last five years (2018-2023). Results: Diagnostic innovations focus on the preference for automated sphygmomanometers and technological advancements, enhancing precision and reliability. Treatment advancements span neurovascular dimensions, including neuromodulation, and innovations in the renin-angiotensin-aldosterone system, showcasing promising drugs like finerenone and esaxerenone. Sodium-glucose cotransporter-2 inhibitors demonstrate efficacy beyond antidiabetic effects. The complexities of renal denervation and its challenges are scrutinized, emphasizing the need for further research. Clinical decision-making underscores risk stratification, lifestyle modifications, and the emerging use of single-pill combinations and individualized treatment plans. Conclusion: The review highlights the multidimensional approach required for optimal HTN management in primary care, encompassing diagnostic innovations, treatment advancements, and individualized strategies. Ongoing research is pivotal for refining diagnostic and therapeutic paradigms in hypertension management.

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.

Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.

Filamin A Is a Prognostic Serum Biomarker for Differentiating Benign Prostatic Hyperplasia from Prostate Cancer in Caucasian and African American Men.

Prostate cancer represents a significant health risk to aging men, in which diagnostic challenges to the identification of aggressive cancers remain unmet. Prostate cancer screening is driven by the prostate-specific antigen (PSA); however, in men with benign prostatic hyperplasia (BPH) due to an enlarged prostate and elevated PSA, PSA's screening utility is diminished, resulting in many unnecessary biopsies. To address this issue, we previously identified a cleaved fragment of Filamin A (FLNA) protein (as measured with IP-MRM mass spectrometry assessment as a prognostic biomarker for stratifying BPH from prostate cancer and subsequently evaluated its expanded utility in Caucasian (CA) and African American (AA) men. All men had a negative digital rectal examination (DRE) and PSA between 4 and 10 ng/mL and underwent prostate biopsy. In AA men, FLNA serum levels exhibited diagnostic utility for stratifying BPH from patients with aggressive prostate cancer (0.71 AUC and 12.2 OR in 48 men with BPH and 60 men with PCa) and outperformed PSA (0.50 AUC, 2.2 OR). In CA men, FLNA serum levels also exhibited diagnostic utility for stratifying BPH from patients with aggressive prostate cancer (0.74 AUC and 19.4 OR in 191 men with BPH and 109 men with PCa) and outperformed PSA (0.46 AUC, 0.32 OR). Herein, we established FLNA alone as a serum biomarker for stratifying men with BPH vs. those with high Gleason (7-10) prostate cancers compared to the current diagnostic paradigm of using PSA. This approach demonstrates clinical actionability of FLNA alone without the requirement of prostate volume measurement as a test with utility in AA and CA men and represents a significant opportunity to decrease the number of unnecessary biopsies in aggressive prostate cancer diagnoses.