CyberSec Research

A significant hallmark of hypertrophic cardiomyopathy (HCM) is fiber disarray, which is associated with various cardiac events such as heart failure. Quantifying fiber disarray remains critical for understanding the disease s complex pathophysiology. This study investigates the role of heterogeneous HCM-induced cellular abnormalities in the development of fiber disarray and their subsequent impact on cardiac pumping function. Fiber disarray is predicted using a stress-based law to reorient myofibers and collagen within a multiscale finite element cardiac modeling framework, MyoFE. Specifically, the model is used to quantify the distinct impacts of heterogeneous distributions of hypercontractility, hypocontractility, and fibrosis on fiber disarray development and examines their effect on functional characteristics of the heart. Our results show that heterogenous cell level abnormalities highly disrupt the normal mechanics of myocardium and lead to significant fiber disarray. The pattern of disarray varies depending on the specific perturbation, offering valuable insights into the progression of HCM. Despite the random distribution of perturbed regions within the cardiac muscle, significantly higher fiber disarray is observed near the epicardium compared to the endocardium across all perturbed left ventricle (LV) models. This regional difference in fiber disarray, irrespective of perturbation severity, aligns with previous DT-MRI studies, highlighting the role of regional myocardial mechanics in the development of fiber disarray. Furthermore, cardiac performance declined in the remodeled LVs, particularly in those with fibrosis and hypocontractility. These findings provide important insights into the structural and functional consequences of HCM and offer a framework for future investigations into therapeutic interventions targeting cardiac remodeling.

Recent trials of a neuronal pacemaker have shown that cardiac pumping efficiency increases when respiratory sinus arrhythmia (RSA) is artificially restored in animal models of heart failure. This novel device sheds new light on the functional role of RSA, which has long been debated, by allowing the strength of cardiorespiratory coupling to be artificially varied. Here we show that RSA minimizes the cardiac power dissipated within the cardiovascular network. The cardiorespiratory system is found to exhibit mode-locked synchronized regions within which viscoelastic dissipation is reduced relative to the scenario where cardiorespiratory coupling is absent. We determine the gain in cardiac output as the magnitude of RSA increases. We find that cardiac pumping efficiency improves up and until the cardiac frequency, within each breadth intake, is approximately 1.5 times greater than the cardiac frequency in the expiratory phase, at which point it reaches a plateau. RSA was found to be most effective at low cardiac frequencies, in good agreement with clinical evidence. Simulation of the cardiac power saved under RSA is in good agreement with the 17-20% increase in cardiac output observed in RSA-paced animal models.

Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms, in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling, as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.

This study introduces the first 3D spectral-element method (SEM) simulation of ultrasonic wave propagation in a bottlenose dolphin (Tursiops truncatus) head. Unlike traditional finite-element methods (FEM), which struggle with high-frequency simulations due to costly linear-system inversions and slower convergence, SEM offers exponential convergence and efficient parallel computation. Using Computed Tomography (CT) scan data, we developed a detailed hexahedral mesh capturing complex anatomical features, such as acoustic fats and jaws. Our simulations of plane and spherical waves confirm SEM's effectiveness for ultrasonic time-domain modeling. This approach opens new avenues for marine biology, contributing to research in echolocation, the impacts of anthropogenic marine noise pollution and the biophysics of hearing and click generation in marine mammals. By overcoming FEM's limitations, SEM provides a powerful scalable tool to test hypotheses about dolphin bioacoustics, with significant implications for conservation and understanding marine mammal auditory systems under increasing environmental challenges.

The refractory period and conduction delay of the atrioventricular (AV) node play a crucial role in regulating the heart rate during atrial fibrillation (AF). Beat-to-beat variations in these properties are known to be induced by the autonomic nervous system (ANS) but have previously not been assessable during AF. Assessing these could provide novel information for improved diagnosis, prognosis, and treatment on an individual basis. To estimate AV nodal conduction properties with beat-to-beat resolution, we propose a methodology comprising a network model of the AV node, a particle filter, and a smoothing algorithm. The methodology was evaluated using simulated data and using synchronized electrogram (EGM) and ECG recordings from five patients in the intracardiac atrial fibrillation database. The methodology's ability to quantify ANS-induced changes in AV node conduction properties was evaluated by analyzing ECG data from 21 patients in AF undergoing a tilt test protocol. The estimated refractory period and conduction delay matched the simulated ground truth based on ECG recordings with a mean absolute error ($\pm$ std) of 169$\pm$14 ms for the refractory period in the fast pathway; 131$\pm$13 ms for the conduction delay in the fast pathway; 67$\pm$10 ms for the refractory period in the slow pathway; and 178$\pm$28 ms for the conduction delay in the slow pathway. These errors decreased when using simulated ground truth based on EGM recordings. Moreover, a decrease in conduction delay and refractory period in response to head-up tilt was seen during the tilt test protocol, as expected under sympathetic activation. These results suggest that beat-to-beat estimation of AV nodal conduction properties during AF from ECG is feasible, with different levels of uncertainty, and that the estimated properties agree with expected AV nodal modulation.

Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin tissue blocks were distributed to 66 different labs for routine H&E staining. To isolate staining variation, other factors affecting the tissue appearance were kept constant. Further, we used this tissue image dataset to compare the performance of eight different stain normalization methods, including four traditional methods, namely, histogram matching, Macenko, Vahadane, and Reinhard normalization, and two deep learning-based methods namely CycleGAN and Pixp2pix, both with two variants each. We used both quantitative and qualitative evaluation to assess the performance of these methods. The dataset's inter-laboratory staining variation could also guide strategies to improve model generalizability through varied training data

Background and Aim: Decellularized uterine extracellular matrix (dUECM) offers a promising bioactive scaffold for uterine tissue engineering, but its application in 3D constructs has been limited by fabrication challenges. This study aimed to develop a printable, bioactive hydrogel from dUECM suitable for 3D bioprinting and to evaluate its ability to support human uterine myometrial cell growth in vitro. Materials and Methods: Porcine uterine tissues were decellularized using 1 percent Triton X-100 and 0.1 to 1.5 percent SDS for 48 to 72 hours. The resulting dUECM was assessed via histology, DNA and GAG quantification, scanning electron microscopy, FTIR, Raman spectroscopy, and thermogravimetric analysis. Selected dUECM samples were digested with pepsin and blended with 2 or 3 percent alginate to create bioinks. Constructs were printed using extrusion-based bioprinting and evaluated for swelling, degradation, mechanical properties, and printability. Biocompatibility was tested by seeding hTERT-HM cells onto cast hydrogels and performing MTT and Live/Dead assays over seven days. Results: The optimal protocol (1 percent Triton X-100 and 1 percent SDS for 48 hours) reduced DNA to 51.3 ng per mg, while retaining 54.9 micrograms per mg of GAGs. FTIR and Raman spectroscopy confirmed collagen preservation, while thermal stability was moderately reduced. The 3 percent alginate with 1.5 percent dUECM formulation showed superior printability, swelling stability, degradation resistance, and mechanical strength. Cell proliferation reached 258 percent by day 7, significantly outperforming alginate alone. Conclusion: The optimized dUECM hydrogel supports printability, mechanical performance, and cell viability, offering a robust platform for uterine tissue engineering.

The revelation of the supreme authority of nucleic acids in the cellular landscape has precipitated the recognition of the versatility of RNAs in cells. The subsequent discovery of non-coding RNAs was a major breakthrough that revealed their extensive involvement in virtually all physiological processes within the cell. Beyond the barriers of the cell, the current perception seems to support the idea of their participation in intercellular regulation and cross-kingdom communication. However, the presence of non-coding RNAs in the extracellular environment remains essentially a mystery, and the understanding of the significance and the processes governing this presence faces several constraints. This has led us to forge an original and predictive idea that seems to allow an emancipation from the various constraints posed in the current perception of the cited phenomena. In this paper, we will attempt to explore the extent of the probable existence of cellular organizations specializing in the production and management of non-coding RNAs. We will try, through the development of this hypothesis, to draw a picture explaining the significance and logistics of extracellular non-coding RNAs, with an emphasis on microRNAs. This exercise will be realized while relying on and confronting purely theoretical points of view, as well as relevant experimental results. In this manuscript, we will address the presumed morphology, intracellular organization, selective export, transport, transfer, distribution, reception and intracellular function of non-coding RNAs, in the perspective of a regulation cycle orchestrated by NAcrins under normal or disturbed physiological contexts.

Several studies have linked myelin abnormalities with neuropsychiatric disorders; others have implicated psychedelics as a potential therapeutic for such conditions. One risk factor for these demyelinating disorders is a mutation in the Apolipoprotein E gene known as APOE4. This variant impedes the cholesterol regulation of oligodendrocytes responsible for the myelination, or insulation, of neurons when compared to the wild-type phenotype. In this work, I advance knowledge of cellular pathways involved in the progression of APOE4-related diseases and elucidate the effects of psychedelics on the brain. Myelin sheaths are vital for maintaining neural pathways, and healthy oligodendrocytes serve as a prerequisite for axonal integrity. Further, the Kaufer Lab has observed significant behavioral differences between male and female APOE4 mice following psychedelic treatment with 2,5-Dimethoxy-4-iodoamphetamine, or DOI, a serotonin receptor ligand. The sex-dependent mechanisms influencing symptom differences and treatment outcomes in AD are unclear, and could be key to developing successful therapeutics for myelin-related issues. I hypothesize that administration of DOI will increase the myelination activity of oligodendrocytes in female APOE4 mice compared with their male counterparts or controls. Preliminary results show a significant increase in MBP in the CA1, or short-term, and CA2, or long term, areas in only female APOE4 mice post-introduction of DOI to the system. This aligns with behavioral data indicating fewer anxiety-related behaviors in female APOE4 mice after DOI administration. These findings reveal distinct biological mechanisms in male and female brain degeneration and suggest potential for sex-specific therapeutics.

Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. This study presents a beta-Variational Autoencoder Graph Convolutional Neural Network framework for generating synthetic Abdominal Aorta Aneurysms (AAA). Using a small real-world dataset, our approach extracts key anatomical features and captures complex statistical relationships within a compact disentangled latent space. To address data limitations, low-impact data augmentation based on Procrustes analysis was employed, preserving anatomical integrity. The generation strategies, both deterministic and stochastic, manage to enhance data diversity while ensuring realism. Compared to PCA-based approaches, our model performs more robustly on unseen data by capturing complex, nonlinear anatomical variations. This enables more comprehensive clinical and statistical analyses than the original dataset alone. The resulting synthetic AAA dataset preserves patient privacy while providing a scalable foundation for medical research, device testing, and computational modeling.

Risk stratification is a key tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We present a novel method for unsupervised machine learning that directly optimizes for survival heterogeneity across patient clusters through a differentiable adaptation of the multivariate logrank statistic. Unlike most existing methods that rely on proxy metrics, our approach represents novel methodology for training any neural network architecture on any data modality to identify prognostically distinct patient groups. We thoroughly evaluate the method in simulation experiments and demonstrate its utility in practice by applying it to two distinct cancer types: analyzing laboratory parameters from multiple myeloma patients and computed tomography images from non-small cell lung cancer patients, identifying prognostically distinct patient subgroups with significantly different survival outcomes in both cases. Post-hoc explainability analyses uncover clinically meaningful features determining the group assignments which align well with established risk factors and thus lend strong weight to the methods utility. This pan-cancer, model-agnostic approach represents a valuable advancement in clinical risk stratification, enabling the discovery of novel prognostic signatures across diverse data types while providing interpretable results that promise to complement treatment personalization and clinical decision-making in oncology and beyond.

Healing of Tendon-bone interface(TBI) injuries is slow and is often repaired with scar tissue formation that compromises normal function. Despite the increasing maturity of surgical techniques, re-tearing of the rotator cuff after surgery remains common. The main reason for this issue is that the original structure of the rotator cuff at the TBI area is difficult to fully restore after surgery, and anatomical healing of the rotator cuff TBI is challenging to achieve solely through surgery. With the advancement of tissue engineering technology, more and more basic researchers and clinical surgeons are recognizing the enormous potential of tissue engineering in promoting TBI healing. Growing research evidence indicates that tissue engineering technology not only effectively promotes repairing and remodeling of the TBI but also reduces the formation of fibrous vascular scar tissue, leading to more orderly tissue reconstruction. The core of tissue engineering technology approaches lies in combining the use of various scaffolds, cells and bioactive molecules to simulate the natural environment of TBI healing, achieving optimal therapeutic outcomes. In this review, we will systematically summarize and highlight recent progress in the application of tissue engineering on TBI regeneration, particularly focusing on advancements in novel scaffolds and their role and potential in promoting healing of the TBI of rotator cuff, providing valuable references for clinical application and research.

Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant proportion of cancer cases. Early detection dramatically improves outcomes, with stage I cancers achieving up to 90 percent survival. However, traditional diagnosis based on histopathology has limited accessibility in low-resource settings because it is invasive, resource-intensive, and reliant on expert pathologists. On the other hand, oral cytology of brush biopsy offers a minimally invasive and lower cost alternative, provided that the remaining challenges, inter observer variability and unavailability of expert pathologists can be addressed using artificial intelligence. Development and validation of robust AI solutions requires access to large, labeled, and multi-source datasets to train high capacity models that generalize across domain shifts. We introduce the first large and multicenter oral cytology dataset, comprising annotated slides stained with Papanicolaou(PAP) and May-Grunwald-Giemsa(MGG) protocols, collected from ten tertiary medical centers in India. The dataset is labeled and annotated by expert pathologists for cellular anomaly classification and detection, is designed to advance AI driven diagnostic methods. By filling the gap in publicly available oral cytology datasets, this resource aims to enhance automated detection, reduce diagnostic errors, and improve early OSCC diagnosis in resource-constrained settings, ultimately contributing to reduced mortality and better patient outcomes worldwide.

This article focuses on current and emerging therapeutics for CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). CADASIL is an inherited vascular disease that impairs blood flow in the small cerebral vessels of the brain, leading to strokes and other neurological deficits. The disease is caused by a mutation in the NOTCH3 gene located on chromosome 19. NOTCH3 encodes a transmembrane receptor expressed on vascular smooth muscle cells. In CADASIL, mutations in the NOTCH3 gene lead to the accumulation and deposition of the receptor, affecting the number of cysteine residues in its extracellular domain. These mutations result in the loss or gain of a cysteine residue within the epidermal growth factor-like repeat (EGFr) domains of the NOTCH protein. Beyond traditional symptomatic treatments for stroke, this work highlights advances in disease modifying approaches including gene editing, cell therapies, and immune-based interventions aimed at altering the course of CADASIL. It also examines ongoing clinical trials and recent patents related to these novel strategies. In addition to summarizing diagnostic methods and molecular mechanisms, the article emphasizes the translational potential of current research and the experimental models driving therapeutic development. The goal is to offer a comprehensive overview of CADASIL and emerging interventions that hold promise for improving long-term outcomes.

The main goal from this study is to discuss the main features of Artificial intelligence (AI) as well as their applicability for early cardiovascular Disease (CVDs) Detection, Material and Method : Systematic review approach Results : It was seen that integrating AI algorithm the diagnosis of CVDs become more accurate and lee time consuming. Conclusion: Now the concept of using AI technologies in cardiovascular health care holds the potential to transform disease management .

Understanding the interactions between cells and the extracellular matrix (ECM) during collective cell invasion is crucial for advancements in tissue engineering, cancer therapies, and regenerative medicine. This study focuses on the roles of contact guidance and ECM remodelling in directing cell behaviour, with a particular emphasis on exploring how differences in cell phenotype impact collective cell invasion. We present a computationally tractable two-dimensional hybrid model of collective cell migration within the ECM, where cells are modelled as individual entities and collagen fibres as a continuous tensorial field. Our model incorporates random motility, contact guidance, cell-cell adhesion, volume filling, and the dynamic remodelling of collagen fibres through cellular secretion and degradation. Through a comprehensive parameter sweep, we provide valuable insights into how differences in the cell phenotype, in terms of the ability of the cell to migrate, secrete, degrade, and respond to contact guidance cues from the ECM, impacts the characteristics of collective cell invasion.

Computational inverse problems for biomedical simulators suffer from limited data and relatively high parameter dimensionality. This often requires sensitivity analysis, where parameters of the model are ranked based on their influence on the specific quantities of interest. This is especially important for simulators used to build medical digital twins, as the amount of data is typically limited. For expensive models, such as blood flow models, emulation is employed to expedite the simulation time. Parameter ranking and fixing using sensitivity analysis are often heuristic, though, and vary with the specific application or simulator used. The present study provides an innovative solution to this problem by leveraging polynomial chaos expansions (PCEs) for both multioutput global sensitivity analysis and formal parameter identifiability. For the former, we use dimension reduction to efficiently quantify time-series sensitivity of a one-dimensional pulmonary hemodynamics model. We consider both Windkessel and structured tree boundary conditions. We then use PCEs to construct profile-likelihood confidence intervals to formally assess parameter identifiability, and show how changes in experimental design improve identifiability. Our work presents a novel approach to determining parameter identifiability and leverages a common emulation strategy for enabling profile-likelihood analysis in problems governed by partial differential equations.

The collective chemotaxis of multicellular clusters is an important phenomenon in various physiological contexts, ranging from embryonic development to cancer metastasis. Such clusters often display interesting shape dynamics and instabilities, but their physical origin, functional benefits, and role in overall chemotactic migration remain unclear. Here, we combine computational modeling and experimental observations of malignant lymphocyte cluster migration in vitro to understand how these dynamics arise from an interplay of chemotactic response and inter-cellular interactions. Our cell-based computational model incorporates active propulsion of cells, contact inhibition of locomotion, chemoattractant response, as well as alignment, adhesive, and exclusion interactions between cells. We find that clusters remain fluid and maintain cohesive forward migration in low chemoattractant gradients. However, above a threshold gradient, clusters display an instability driven by local cluster-shape dependent velocity differentials that causes them to elongate perpendicular to the gradient and eventually break apart. Comparison with our in vitro data shows the predicted transition to the cluster instability regime with increased gradient, as well as quantitative agreement with key features such as cluster aspect ratio, orientation, and breaking frequency. This instability naturally limits the size of multicellular aggregates, and, in addition, clusters in the instability regime display optimal forward migration speeds, suggesting functional implications in vivo. Our work provides valuable insights into generic instabilities of chemotactic clusters, elucidates physical factors that could contribute to metastatic spreading, and can be extended to other living or synthetic systems of active clusters.