Category: Nevin Manimala

Mol Oncol. 2023 Jan 1. doi: 10.1002/1878-0261.13371. Online ahead of print.

ABSTRACT

The identification of the high-efficiency and non-invasive biomarkers for hepatocellular carcinoma (HCC) detection is urgently needed. This study aims to screen out potential autoantibodies to tumor-associated antigens (TAAbs) and to assess their diagnostic value for HCC. Fifteen potential TAAbs were screened out from the Human Proteome Microarray by 30 HCC sera and 22 normal control sera, of which 8 passed multiple-stage validations by ELISA with a total of 1,625 human serum samples from normal controls (NCs) and patients with HCC, liver cirrhosis, chronic hepatitis B, gastric cancer, esophageal cancer and colorectal cancer. Finally, an immunodiagnostic model including 6 TAAbs (RAD23A, CAST, RUNX1T1, PAIP1, SARS, PRKCZ) was constructed by logistic regression, and yielded the area under curve (AUC) of 0.835 and 0.788 in training and validation sets, respectively. The serial serum samples from HCC model mice were tested to explore the change in TAAbs during HCC formation, and an increasing level of autoantibodies was observed. In conclusion, the panel of 6 TAAbs can provide potential value for HCC detection, and the strategy to identify novel serological biomarkers can also provide new clues in understanding immunodiagnostic biomarkers.

PMID:36587394 | DOI:10.1002/1878-0261.13371

Int Ophthalmol. 2023 Jan 1. doi: 10.1007/s10792-022-02625-7. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the effectiveness of incobotulinumtoxinA (Xeomin^®) in treating upper eyelid retraction in patients with Graves orbitopathy (GO) initially scheduled for surgery via two different application sites.

METHODS: This is a comparative, prospective study, conducted at the Department of Ophthalmology, Medical School, University Hospital Centre Zagreb, EUGOGO site (EUropean Group On Graves’ Orbitopathy) in Croatia from January 2020 till January of 2021 in accordance with national health headquarter recommendations. All patients were classified as inactive with marked eyelid retraction and randomly divided into groups according to application sites. Group A underwent transconjunctival application (18 eyes) and group B transcutaneous application (20 eyes) of incobotulinumtoxinA. The primary end point of this study was lowering the eyelid, to alleviate anterior eye segment symptoms and achieve acceptable aesthetic appearance until surgery becomes available.

RESULTS: There were no nonresponders and we found no statistically significant difference in the degree of lowering the eyelid between the two application sites. Following rules for avoiding spread of SARS-CoV-19, none of the patients included in this study were infected. Moreover, participants reported diminishing of anterior eye segment irritation and improved aesthetics.

CONCLUSION: Treatment of inactive GO patients with incobotulinumtoxinA for upper eyelid retraction is efficient and safe and can be used as an adjuvant treatment while patients wait for surgery, by alleviating symptoms and improving the level of aesthetic satisfaction without causing a threat to anterior eye segment and visual function. The study showed that effect of treatment was the same, whether we applied the toxin transconjunctivaly or transcutaneously.

PMID:36587368 | DOI:10.1007/s10792-022-02625-7

Chaos. 2022 Dec;32(12):123103. doi: 10.1063/5.0106922.

ABSTRACT

Quantitatively assessing the level of confidence on a test score can be a challenging problem, especially when the available information is based on multiple criteria. A concrete example beyond the usual grading of tests occurs with recommendation letters, where a recommender assigns a score to a candidate, but the reliability of the recommender must be assessed as well. Here, we present a statistical procedure, based on Bayesian inference and Jaynes’ maximum entropy principle, that can be used to estimate the most probable and expected score given the available information in the form of a credible interval. Our results may provide insights on how to properly state and analyze problems related to the uncertain evaluation of performance in learning applied to several contexts, beyond the case study of the recommendation letters presented here.

PMID:36587360 | DOI:10.1063/5.0106922

Chaos. 2022 Dec;32(12):123118. doi: 10.1063/5.0118717.

ABSTRACT

The aim of this study is to formulate a new methodology based upon informational tools to detect patients with cardiac arrhythmias. As it is known, sudden death is the consequence of a final arrhythmia, and here lies the relevance of the efforts aimed at the early detection of arrhythmias. The information content in the time series from an electrocardiogram (ECG) signal is conveyed in the form of a probability distribution function, to compute the permutation entropy proposed by Bandt and Pompe. This selection was made seeking its remarkable conceptual simplicity, computational speed, and robustness to noise. In this work, two well-known databases were used, one containing normal sinus rhythms and another one containing arrhythmias, both from the MIT medical databank. For different values of embedding time delay τ, normalized permutation entropy and statistical complexity measure are computed to finally represent them on the horizontal and vertical axes, respectively, which define the causal plane H×C. To improve the results obtained in previous works, a feature set composed by these two magnitudes is built to train the following supervised machine learning algorithms: random forest (RF), support vector machine (SVM), and k nearest neighbors (kNN). To evaluate the performance of each classification technique, a 10-fold cross-validation scheme repeated 10 times was implemented. Finally, to select the best model, three quality parameters were computed, namely, accuracy, the area under the receiver operative characteristic (ROC) curve (AUC), and the F1-score. The results obtained show that the best classification model to detect the ECG coming from arrhythmic patients is RF. The values of the quality parameters were at the same levels reported in the available literature using a larger data set, thus supporting this proposal that uses a very small-sized feature space to train the model later used to classify. Summarizing, the attained results show the possibility to discriminate both groups of patients, with normal sinus rhythm or arrhythmic ECG, showing a promising efficiency in the definition of new markers for the detection of cardiovascular pathologies.

PMID:36587353 | DOI:10.1063/5.0118717

Chaos. 2022 Dec;32(12):121103. doi: 10.1063/5.0128743.

ABSTRACT

The role of topological heterogeneity in the origin of extreme events in a network is investigated here. The dynamics of the oscillators associated with the nodes are assumed to be identical and influenced by mean-field repulsive interactions. An interplay of topological heterogeneity and the repulsive interaction between the dynamical units of the network triggers extreme events in the nodes when each node succumbs to such events for discretely different ranges of repulsive coupling. A high degree node is vulnerable to weaker repulsive interactions, while a low degree node is susceptible to stronger interactions. As a result, the formation of extreme events changes position with increasing strength of repulsive interaction from high to low degree nodes. Extreme events at any node are identified with the appearance of occasional large-amplitude events (amplitude of the temporal dynamics) that are larger than a threshold height and rare in occurrence, which we confirm by estimating the probability distribution of all events. Extreme events appear at any oscillator near the boundary of transition from rotation to libration at a critical value of the repulsive coupling strength. To explore the phenomenon, a paradigmatic second-order phase model is used to represent the dynamics of the oscillator associated with each node. We make an annealed network approximation to reduce our original model and, thereby, confirm the dual role of the repulsive interaction and the degree of a node in the origin of extreme events in any oscillator associated with a node.

PMID:36587354 | DOI:10.1063/5.0128743

Chaos. 2022 Dec;32(12):122101. doi: 10.1063/5.0124274.

ABSTRACT

Sharp changes in state, such as transitions from survival to extinction, are hallmarks of evolutionary dynamics in biological systems. These transitions can be explored using the techniques of statistical physics and the physics of nonlinear and complex systems. For example, a survival-to-extinction transition can be characterized as a non-equilibrium phase transition to an absorbing state. Here, we review the literature on phase transitions in evolutionary dynamics. We discuss directed percolation transitions in cellular automata and evolutionary models, and models that diverge from the directed percolation universality class. We explore in detail an example of an absorbing phase transition in an agent-based model of evolutionary dynamics, including previously unpublished data demonstrating similarity to, but also divergence from, directed percolation, as well as evidence for phase transition behavior at multiple levels of the model system’s evolutionary structure. We discuss phase transition models of the error catastrophe in RNA virus dynamics and phase transition models for transition from chemistry to biochemistry, i.e., the origin of life. We conclude with a review of phase transition dynamics in models of natural selection, discuss the possible role of phase transitions in unraveling fundamental unresolved questions regarding multilevel selection and the major evolutionary transitions, and assess the future outlook for phase transitions in the investigation of evolutionary dynamics.

PMID:36587338 | DOI:10.1063/5.0124274

Chaos. 2022 Dec;32(12):123131. doi: 10.1063/5.0129066.

ABSTRACT

Noakes and Stoyanov [Mathematics 9, 2434 (2021)] introduced a method of recovering strictly convex planar obstacles from their set of traveling times. We provide an extension of this construction for obstacles on Riemannian surfaces under some general curvature conditions. It is required that no smooth geodesic intersects more than two obstacles.

PMID:36587331 | DOI:10.1063/5.0129066

Chaos. 2022 Dec;32(12):123136. doi: 10.1063/5.0097187.

ABSTRACT

White noise is a fundamental and fairly well understood stochastic process that conforms to the conceptual basis for many other processes, as well as for the modeling of time series. Here, we push a fresh perspective toward white noise that, grounded on combinatorial considerations, contributes to giving new interesting insights both for modeling and theoretical purposes. To this aim, we incorporate the ordinal pattern analysis approach, which allows us to abstract a time series as a sequence of patterns and their associated permutations, and introduce a simple functional over permutations that partitions them into classes encoding their level of asymmetry. We compute the exact probability mass function (p.m.f.) of this functional over the symmetric group of degree n, thus providing the description for the case of an infinite white noise realization. This p.m.f. can be conveniently approximated by a continuous probability density from an exponential family, the Gaussian, hence providing natural sufficient statistics that render a convenient and simple statistical analysis through ordinal patterns. Such analysis is exemplified on experimental data for the spatial increments from tracks of gold nanoparticles in 3D diffusion.

PMID:36587330 | DOI:10.1063/5.0097187

Chaos. 2022 Dec;32(12):123123. doi: 10.1063/5.0118404.

ABSTRACT

A fractional order susceptible-exposed-infected-quarantined-recovered model is established on the complex networks. We calculate a specific expression for the basic reproduction number R₀, prove the existence and uniqueness with respect to the solution, and prove the Ulam-Hyers stability of the model. Using the Latin hypercube sampling-partial rank correlation coefficient method, the influence of parameters on the R₀ is analyzed. Based on the results of the analysis, the optimal control of the model is investigated as the control variables with vaccination rate and quarantine rate applying Pontryagin’s minimum principle. The effects of α, degree of nodes, and network size on the model dynamics are simulated separately by the prediction correction method.

PMID:36587321 | DOI:10.1063/5.0118404

Chaos. 2022 Dec;32(12):123102. doi: 10.1063/5.0122184.

ABSTRACT

Key features of biological activity can often be captured by transitions between a finite number of semi-stable states that correspond to behaviors or decisions. We present here a broad class of dynamical systems that are ideal for modeling such activity. The models we propose are chaotic heteroclinic networks with nontrivial intersections of stable and unstable manifolds. Due to the sensitive dependence on initial conditions, transitions between states are seemingly random. Dwell times, exit distributions, and other transition statistics can be built into the model through geometric design and can be controlled by tunable parameters. To test our model’s ability to simulate realistic biological phenomena, we turned to one of the most studied organisms, C. elegans, well known for its limited behavioral states. We reconstructed experimental data from two laboratories, demonstrating the model’s ability to quantitatively reproduce dwell times and transition statistics under a variety of conditions. Stochastic switching between dominant states in complex dynamical systems has been extensively studied and is often modeled as Markov chains. As an alternative, we propose here a new paradigm, namely, chaotic heteroclinic networks generated by deterministic rules (without the necessity for noise). Chaotic heteroclinic networks can be used to model systems with arbitrary architecture and size without a commensurate increase in phase dimension. They are highly flexible and able to capture a wide range of transition characteristics that can be adjusted through control parameters.

PMID:36587320 | DOI:10.1063/5.0122184