Monitoring Disease Progression Research Articles

Drone Technology for Crop Disease Resistance: Innovations and Challenges

Drones have been used for diverse application purposes in precision agriculture and new ways of using them are being explored. Many drone applications have been developed for different purposes such as pest detection, crop yield prediction, crop spraying, yield estimation, water stress detection, land mapping, identifying nutrient deficiency in plants, weed detection, livestock control, protection of agricultural products and soil analysis. Drones can create georeferenced maps that pinpoint the exact location of disease outbreaks within a field. These maps help farmers and agronomists monitor disease progression and plan targeted interventions. Drone operations are highly dependent on weather conditions. High winds, rain, and fog can hinder drone flights and affect the quality of images captured. Addressing technical limitations, regulatory and safety concerns, economic barriers, and data management issues will be crucial for the widespread adoption of drones in agriculture. By overcoming these challenges, drone technology can become a vital tool in sustainable and effective crop disease management.

DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries.

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.

Gold Nanoparticles: An Emerging Novel Technology for Targeted Delivery System for Site-Specific Diseases

Abstract: Gold Nanoparticles (GNPs) have emerged as a novel technology in the field of targeted delivery systems, offering promising solutions for site-specific disease treatment. These nanoparticles possess unique physicochemical properties, such as controlled size, shape, and surface chemistry, which enable precise manipulation for enhanced therapeutic efficacy. The biocompatibility and ease of functionalization of GNPs facilitate the conjugation with various biomolecules, including drugs, peptides, and nucleic acids, thereby improving their targeted delivery capabilities. Recent advancements in nanotechnology have leveraged GNPs for the treatment of a range of diseases, particularly in oncology, cardiology, and neurology. In cancer therapy, GNPs can be engineered to target tumor cells selectively, minimizing damage to healthy tissues and reducing side effects. This is achieved through the conjugation of GNPs with tumor-specific ligands, antibodies, or aptamers, which direct the nanoparticles to malignant cells, allowing for localized drug release and improved therapeutic outcomes. Moreover, GNPs exhibit remarkable potential in diagnostic imaging and photothermal therapy. Their unique optical properties, such as surface plasmon resonance, enable their use as contrast agents in imaging techniques, providing high-resolution and real-time monitoring of disease progression. In photothermal therapy, GNPs convert light energy into heat, effectively destroying targeted cells with minimal invasiveness. The development of GNP-based delivery systems also addresses significant challenges in drug resistance and bioavailability. By overcoming biological barriers and enhancing cellular uptake, GNPs improve the pharmacokinetics and pharmacodynamics of therapeutic agents. However, despite these advancements, the clinical translation of GNPs faces challenges such as potential toxicity, long-term stability, and regulatory hurdles. In conclusion, gold nanoparticles represent a cutting-edge approach in targeted delivery systems, offering significant potential for site-specific disease treatment. Continued research and innovation are essential to overcome existing challenges and fully realize the clinical applications of GNPs, ultimately revolutionizing precision medicine. Gold nanoparticles exhibit unique physicochemical and optical properties. The gold nanoparticles have advanced techniques to cure different chronic diseases. Today, gold nanoparticles are aided by photodynamic therapy and radiation therapy as drug carriers. Due to this, researchers now focus on medical sciences to treat various diseases and therapeutic applications. This review provides all the aspects of gold-based nanoparticles, methods, and their pharmacological benefits in different fields of medical sciences. We also discuss various preparation methods and their advantages in pharmaceutical formulations.

Monitoring mouse papillomavirus-associated cancer development using longitudinal Pap smear screening.

Papanicolaou (Pap) smear has saved millions of women's lives as a valuable early screening tool for detecting human papillomavirus (HPV) cervical precancers and cancer. However, more than 200,000 women in the United States alone remain at risk for cervical cancer due to pre-existing HPV infection-induced precancers, as there are currently no effective treatments for HPV-associated precancers and cancers other than invasive procedures including a loop electrosurgical excision procedure (LEEP) to remove abnormal tissues. In the current study, we validated the use of Pap smears to monitor disease progression in our recently established mouse papillomavirus model. To the best of our knowledge, this is the first study that provides compelling evidence of applying Pap smears from cervicovaginal swabs to monitor disease progression in mice. This HPV-relevant cytology assay will enable us to develop and test novel antiviral and anti-tumor therapies using this model to eliminate HPV-associated diseases and cancers.

Mechanical rheological model on the assessment of elasticity and viscosity in tissue inflammation: A systematic review.

Understanding the extent of inflammation is crucial for early disease detection, monitoring disease progression, and evaluating treatment responses. Over the past decade, researchers have demonstrated the need to understand the extent of inflammation through qualitative or quantitative characterization of tissue viscoelasticity using different techniques. In this scientific review, an examination of research on the association between elasticity and Viscosity in diseases, particularly as tissue inflammation progresses, is conducted. A review of utilizing mechanical rheological models to characterize quantitative viscoelastic parameters of normal and inflamed tissues is also undertaken. Based on inclusion and exclusion criteria, we identified 14 full-text studies suitable for review out of 290 articles published from January 2000 to January 2024. We used PRISMA guidelines for the systematic review. In the review, three studies demonstrated the criterion used by the researchers in identifying the best rheological model. Eleven studies showed the clinical application of the rheological model in quantifying the viscoelastic properties of normal and pathological tissue. The review quantified viscoelastic parameters for normal and pathological tissue across various soft tissues. It evaluated the effectiveness of each viscoelastic property in distinguishing between normal and pathological tissue stiffness. Furthermore, the review outlined additional viscoelastic-related parameters for researchers to consider in future stiffness classification studies.

Parotid saliva and blood biomarkers in juvenile idiopathic arthritis in relation to temporomandibular joint magnetic resonance imaging findings.

Juvenile idiopathic arthritis (JIA) often affects the temporomandibular joint (TMJ) caused by an abnormal immune system that includes overactive inflammatory processes. Salivary biomarkers may be a powerful tool that can help establishing diagnosis, prognosis and monitor disease progress. The objective was to investigate biomarkers in parotid saliva and blood plasma in relation to temporomandibular joint (TMJ) magnetic resonance imaging (MRI) findings in patients with JIA and healthy individuals. Forty-five children aged 6 to 16 years with JIA and 16 healthy age- and sex-matched controls were included. Unstimulated parotid saliva samples and venous blood were collected. Biochemical analyses were performed for the cytokine biomarkers. The participants underwent MR imaging of the TMJs, where changes in the inflammatory and the damage domains were assessed. In the JIA patients, lower concentrations of IL-6R and gp130 were found in parotid saliva than in plasma. Higher concentrations of IL-6 were found in parotid saliva than in plasma. IL-6, IL-6R and gp130 in parotid saliva explained the presence of bone marrow oedema and effusion in the JIA patients. This study suggests that the IL-6 family in parotid saliva is associated with TMJ bone marrow oedema and effusion in patients with JIA, suggesting that IL-6 has promising properties as a parotid saliva biomarker for TMJ inflammatory activity.

Associations between serum metabolites and female cancers: A bidirectional two-sample mendelian randomization study

Female cancers, especially breast, ovarian, cervical, and endometrial cancers, constitute a major threat to women's health worldwide. In view of the complex genetic background of cancers cannot be fully explained with current genetic information, we used a bidirectional two-sample mendelian randomization approach to explore the causal associations between serum metabolites and four major female cancers—breast, ovarian, cervical, and endometrial cancers. We analyzed the metabolites dataset from the Canadian Longitudinal Study of Aging and cancer datasets from the 10th round of the Finngen project. Replication analyses was performed with Cancer Association Consortium and Leo’s studies. Instrumental variables were analyzed using methods including the Wald ratio, inverse-variance weighted, MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane’s Q, Egger’s intercept, MR-PRESSO, and leave-one-out methods. After meticulous analysis, we obtained levels of 3-hydroxyoleoylcarnitine, hexadecanedioate, tetradecanedioate, and carnitine C14 with robust causal associations with breast cancer, levels of 5alpha-androstan-3alpha,17beta-diol monosulfate (1), androstenediol (3beta,17beta) monosulfate (1), androsterone sulfate, and 5alpha-androstan-3beta,17beta-diol disulfate causal associations with endometrial cancer. The reverse analysis showed that breast, ovarian, and endometrial cancer and survival of breast and ovarian cancer were found to have causal relationships with 8, 5, 2, 6, and 3 metabolites, respectively. These insights underscore the potential roles of specific metabolites in the etiology of female cancers, providing new biomarkers for early detection, risk stratification, and disease progression monitoring. Further research could elucidate how these metabolites influence specific pathways in cancer development, offering theoretical foundations for prevention and treatment strategies.

Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging.

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson's disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.

Empowered chaotic local search-based differential evolution algorithm with entropy-based hybrid objective function for brain tumor segmentation

In neuro-oncology, the precise segmentation of brain tumors from Magnetic Resonance Images is crucial for diagnosis, treatment planning, and monitoring disease progression. Accurate segmentation helps determine the tumor’s size, location, and growth potential, which is essential for formulating effective treatment strategies. In response to this challenge, we developed a novel approach using Chaotic Local Search-Enhanced Differential Evolution (CJADE). CJADE, particularly its variant CJADE-M, which employs chaotic maps selected through a probability-based approach, has proven effective in optimizing brain tumor segmentation. Our study shows that CJADE-M outperforms traditional metaheuristic algorithms on various evaluation metrics. We further enhanced CJADE-M with an entropy-based hybrid objective function, which improved accuracy and reduced computational time in tumor segmentation compared to conventional methods like Minimum Cross-Entropy and Kapur. This makes our method suitable for real-time medical imaging analysis. Our findings indicate that CJADE-M, equipped with the hybrid objective function, achieves superior segmentation performance for both benign lobulated and malignant irregular tumors across metrics such as PSNR, FSIM, QILV, and HPSI. By providing a more accurate and efficient tool, our approach can significantly enhance the outcomes of brain tumor diagnosis and treatment, improving patient care in neuro-oncology.

Value of Mac-2 Binding Protein Glycosylation Isomer (M2BPGi) in Assessing Liver Fibrosis in Metabolic Dysfunction-Associated Liver Disease: A Comprehensive Review of its Serum Biomarker Role.

Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) is a broad condition characterized by lipid accumulation in the liver tissue, which can progress to fibrosis and cirrhosis if left untreated. Traditionally, liver biopsy is the gold standard for evaluating fibrosis. However, non-invasive biomarkers of liver fibrosis are developed to assess the fibrosis without the risk of biopsy complications. Novel serum biomarkers have emerged as a promising tool for non-invasive assessment of liver fibrosis in MAFLD patients. Several studies have shown that elevated levels of Mac-2 binding protein glycosylation isomer (M2BPGi) are associated with increased liver fibrosis severity in MAFLD patients. This suggests that M2BPGi could serve as a reliable marker for identifying individuals at higher risk of disease progression. Furthermore, the use of M2BPGi offers a non-invasive alternative to liver biopsy, which is invasive and prone to sampling errors. Overall, the usage of M2BPGi in assessing liver fibrosis in MAFLD holds great promise for improving risk stratification and monitoring disease progression in affected individuals. Further research is needed to validate its utility in clinical practice and establish standardized protocols for its implementation.

Exploring explainable AI features in the vocal biomarkers of lung disease

This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.

The predictive role of the platelet-to-lymphocyte ratio for the risk of non-alcoholic fatty liver disease and cirrhosis: a nationwide cross-sectional study.

The associations between platelet-to-lymphocyte ratio (PLR) and non-alcoholic fatty liver disease (NAFLD) and cirrhosis are unclear, andthere are still no effective means for diagnosing or monitoring disease progression. Data from the National Health and Nutrition Examination Surveys were collected for analysis. Logistic regression and restricted cubic splines were used to evaluate the associations between PLR and NAFLD and cirrhosis in different populations. The Area Under Curve Receiver Operating Characteristic (AUCROC) was used to distinguish the models. Threshold analysis was performed by constructing a two-piecewise linear regression. Correlation analysis was performed separately on either side of the inflection point. A total of 5724 adults were included. Logistic regression analysis revealed that the PLR was associated with NAFLD and cirrhosis (AUCROC of NAFLD: 0.803; AUCROC of cirrhosis: 0.851). The AUCROC of the PLR for predicting NAFLD incidence was 0.762 in the diabetic population and 0.804 in the nondiabetic population. High PLR predicted cirrhosis in the diabetic population, with an AUCROC of 0.824, whereas a high PLR was not associated with cirrhosis in the nondiabetic population. The restricted cubic spline revealed a negative linear correlation between the PLR and NAFLD incidence. The inflection point of the PLR for NAFLD was 180.74.A PLR ≤180.74 was statistically significant (odds ratio=0.997, 95% confidence interval=0.995-0.999). In the NAFLD population, the PLR was negatively correlated with cirrhosis at a PLR ≤130.5 (odds ratio=0.987, 95% confidence interval=0.977-0.996) and positively correlated with cirrhosis at a PLR > 130.5 (odds ratio=1.006, 95% confidence interval=1.001-1.012). The PLR and NAFLD were negatively correlated in the U.S. population. The PLR had a U-shaped relationship with cirrhosis in the NAFLD population. The PLR has potential value in monitoring NAFLD patient progression to cirrhosis.

Clinical significance of circulating tumor cells in colorectal cancer with peritoneal metastases: a prospective cohort study using a novel method for monitoring treatment response, and assessing minimal residual disease.

The treatment for patients with colorectal cancer with metastases to the peritoneum is complex and may involve both surgery and chemotherapy. Circulating tumor cells (CTCs) have been poorly investigated in peritoneal metastatic colorectal cancer. The aim of the study is to examine the role of circulating tumor cells (CTCs) as a biomarker for monitoring disease progression, treatment response and residual disease using CellMate® - a new promising in vitro diagnostic platform technology. We prospectively followed clinical outcomes of 46 patients treated with cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) for colorectal cancer with peritoneal metastases and examined whether CTCs were present the week of surgery. The CTC measurements were made with the CellMate® technology, which is a platform technology to detect CTCs based on the difference in biomechanical properties compared to blood resident cells. The study was registered online (ClinicalTrials.gov). CTCs were detected in 17 (37%) patients. The presence of CTCs was associated with shorter recurrence-free survival and overall survival after CRS and HIPEC. Both recurrence free survival (HR 4.00, 95%CI 1.15-13.9; P=0.029) and overall survival (HR 5.91; 95% CI 1.18-29.7; P=0.03) were significantly worse if CTCs were detected after neoadjuvant treatment. In the subgroup of patients with CTCs detected, adjuvant therapy tended to improve the prognosis while in CTC negative patients it did not. Pending a prospective multi-center trial to validate these findings, CTCs may in the future be used as a dynamic personalized biomarker for prognostication, predicting response to therapy, and for monitoring disease progression in colorectal cancer with metastases to the peritoneum.

Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors.

The Unified Parkinson's Disease Rating Scale (UPDRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity. The rating is crucial for disease progression monitoring and treatment adjustment. This study aims to advance the capabilities of PD management by developing an innovative framework that integrates deep learning with wearable sensor technology to enhance the precision of UPDRS assessments. We introduce a series of deep learning models to estimate UPDRS Part III scores, utilizing motion data from wearable sensors. Our approach leverages a novel Multi-shared-task Self-supervised Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework that processes raw gyroscope signals and their spectrogram representations. This technique aims to refine the estimation accuracy of PD severity during naturalistic human activities. Utilizing 526 min of data from 24 PD patients engaged in everyday activities, our methodology demonstrates a strong correlation of 0.89 between estimated and clinically assessed UPDRS-III scores. This model outperforms the benchmark set by single and multichannel CNN, LSTM, and CNN-LSTM models and establishes a new standard in UPDRS-III score estimation for free-body movements compared to recent state-of-the-art methods. These results signify a substantial step forward in bioengineering applications for PD monitoring, providing a robust framework for reliable and continuous assessment of PD symptoms in daily living settings.

Nuclear imaging techniques for cardiac amyloidosis.

Cardiac amyloidosis is a condition marked by the misfolding of precursor proteins into insoluble amyloid fibrils, leading to restrictive cardiomyopathy and heart failure symptoms. This review discusses advancements in nuclear imaging techniques that enhance the diagnosis and guide the management of cardiac amyloidosis, addressing the critical need for early and accurate detection in clinical practice. Recent studies and guidelines emphasizes the pivotal role of nuclear imaging techniques in diagnosing cardiac amyloidosis. Cardiac scintigraphy, using bone-avid tracers like 99mTc-PYP, 99mTc-DPD, and 99mTc-HMDP, is instrumental in distinguishing between transthyretin amyloidosis and light chain amyloidosis. PET, with tracers such as 11C-Pittsburgh Compound B (11C-PiB) and 18F-Florbetapir, offers significant potential in measuring amyloid burden and monitoring disease progression, providing detailed insights into the myocardial involvement. The advancements in nuclear imaging techniques significantly impact the management of cardiac amyloidosis. These methods allow for a more accurate diagnosis, detailed assessment of disease extent, and better differentiation between amyloidosis types, which are crucial for tailoring treatment approaches. The integration of these techniques into clinical practice is essential for improving patient outcomes and advancing research in cardiac amyloidosis.

Tumor-Derived Extracellular Vesicles as Liquid Biopsy for Diagnosis and Prognosis of Solid Tumors: Their Clinical Utility and Reliability as Tumor Biomarkers.

Early cancer detection and accurate monitoring are crucial to ensure increased patient survival. Recent research has focused on developing non-invasive biomarkers to diagnose cancer early and monitor disease progression at low cost and risk. Extracellular vesicles (EVs), nanosized particles secreted into extracellular spaces by most cell types, are gaining immense popularity as novel biomarker candidates for liquid cancer biopsy, as they can transport bioactive cargo to distant sites and facilitate intercellular communications. A literature search was conducted to discuss the current approaches for EV isolation and the advances in using EV-associated proteins, miRNA, mRNA, DNA, and lipids as liquid biopsies. We discussed the advantages and challenges of using these vesicles in clinical applications. Moreover, recent advancements in machine learning as a novel tool for tumor marker discovery are also highlighted.

Imaging the optic nerve with optical coherence tomography.

Optical coherence tomography (OCT) is a non-invasive imaging technology, which may be used to generate in vivo quantitative and qualitative measures of retinal structure. In terms of quantitative metrics, peripapillary retinal nerve fiber layer (pRNFL) thickness provides an indirect evaluation of axonal integrity within the optic nerve. Ganglion layer measures derived from macular scans indirectly reflect retinal ganglion cell status. Notably, ganglion layer indices are platform dependent and may include macular ganglion cell inner plexiform layer (mGCIPL), ganglion cell layer (GCL), and ganglion cell complex (GCC) analyses of thickness or volume. Interpreted together, pRNFL thickness and ganglion layer values can be used to diagnose optic neuropathies, monitor disease progression, and gauge response to therapeutic interventions for neuro-ophthalmic conditions. Qualitative assessments of the optic nerve head, using cross-sectional transverse axial, en face, and circular OCT imaging, may help distinguish papilledema from pseudopapilloedema, and identify outer retinal pathology. Innovations in OCT protocols and approaches including enhanced depth imaging (EDI), swept source (SS) techniques, and angiography (OCTA) may offer future insights regarding the potential pathogenesis of different optic neuropathies. Finally, recent developments in artificial intelligence (AI) utilizing OCT images may overcome longstanding challenges, which have plagued non-vision specialists who often struggle to perform reliable ophthalmoscopy. In this review, we aim to discuss the benefits and pitfalls of OCT, consider the practical applications of this technology in the assessment of optic neuropathies, and highlight scientific discoveries in the realm of optic nerve imaging that will ultimately change how neuro-ophthalmologists care for patients.

Validating a Curvature-Based Marker of Cervical Carotid Tortuosity for Risk Assessment in Heritable Aortopathies.

Cervical arterial tortuosity is associated with adverse outcomes in Loeys-Dietz syndrome and other heritable aortopathies. A method to assess tortuosity based on curvature of the vessel centerline in 3-dimensional space was developed. We measured cervical carotid tortuosity in 65 patients with Loeys-Dietz syndrome from baseline computed tomography angiogram/magnetic resonance angiogram and all serial images during follow-up. Relations between baseline carotid tortuosity, age, aortic root diameter, and its change over time were compared. Patients with unoperated aortic roots were assessed for clinical end point (type A aortic dissection or aortic root surgery during 4 years of follow-up). Logistic regression was performed to assess the likelihood of clinical end point according to baseline carotid tortuosity. Total absolute curvature at baseline was 11.13±5.76 and was relatively unchanged at 8 to 10 years (fold change: 0.026±0.298, P=1.00), whereas tortuosity index at baseline was 0.262±0.131, with greater variability at 8 to 10 years (fold change: 0.302±0.656, P=0.818). Baseline total absolute curvature correlated with aortic root diameter (r=0.456, P=0.004) and was independently associated with aortic events during the 4-year follow-up (adjusted odds ratio [OR], 2.64 [95% CI, 1.02-6.85]). Baseline tortuosity index correlated with age (r=0.532, P<0.001) and was not associated with events (adjusted OR, 1.88 [95% CI, 0.79-4.51]). Finally, baseline total absolute curvature had good discrimination of 4-year outcomes (area under the curve=0.724, P=0.014), which may be prognostic or predictive. Here we introduce cervical carotid tortuosity as a promising quantitative biomarker with validated, standardized characteristics. Specifically, we recommend the adoption of a curvature-based measure, total absolute curvature, for early detection or monitoring of disease progression in Loeys-Dietz syndrome.

Individualized survival predictions using state space model with longitudinal and survival data.

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

Computational modeling for the quantitative assessment of cardiac autonomic response to orthostatic stress

Objective. The autonomic nervous system (ANS) plays a critical role in regulating not only cardiac functions but also various other physiological processes, such as respiratory rate, digestion, and metabolic activities. The ANS is divided into the sympathetic and parasympathetic nervous systems, each of which has distinct but complementary roles in maintaining homeostasis across multiple organ systems in response to internal and external stimuli. Early detection of ANS dysfunctions, such as imbalances between the sympathetic and parasympathetic branches or impairments in the autonomic regulation of bodily functions, is crucial for preventing or slowing the progression of cardiovascular diseases. These dysfunctions can manifest as irregularities in heart rate, blood pressure regulation, and other autonomic responses essential for maintaining cardiovascular health. Traditional methods for analyzing ANS activity, such as heart rate variability (HRV) analysis and muscle sympathetic nerve activity recording, have been in use for several decades. Despite their long history, these techniques face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit their effectiveness in personalized health assessments. Approach. This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems’ input–output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input–output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model’s capacity for personalized health assessments. Main results. The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males, age: 42 ± 18) and diabetic subjects (8 females and 6 males, age: 47 ± 14). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p < 0.05). Additionally, the data suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p < 0.05), characterized by increased responsiveness and intensity of the sympathetic nervous system to stimuli, i.e. fluctuations in blood pressure, leading to more pronounced changes in heart rate, these phenomena can be directly reflected by gain parameters and time response parameters of the model. Significance. The MMARCS model represents an innovative computational approach for quantifying ANS functionality. This model guarantees the accuracy of physiological modeling while reducing mathematical complexity, offering an easy-to-implement and widely applicable tool for clinical measurements of cardiovascular health, disease progression monitoring, and home health monitoring through wearable technology.