Confinement tonicity on epidemic spreading.

Emerging and re-emerging pathogens are latent threats in our society with the risk of killing millions of people worldwide, without forgetting the severe economic and educational backlogs. From COVID-19, we learned that self isolation and quarantine restrictions (confinement) were the main way of protection till availability of vaccines. However, abrupt lifting of social confinement would result in new waves of new infection cases and high death tolls. Here, inspired by how an extracellular solution can make water move into or out of a cell through osmosis, we define confinement tonicity. This can serve as a standalone measurement for the net direction and magnitude of flows between the confined and deconfined susceptible compartments. Numerical results offer insights on the effects of easing quarantine restrictions.

Biological activities of 4H-thiochromen-4-one 1,1-dioxide derivatives against tropical disease parasites: A target-based drug design approach.

A promising strategy for developing novel therapies against tropical diseases, including malaria, leishmaniasis, and trypanosomiasis, is to detect biological targets such as trypanothione reductase, a vital parasite enzyme that regulates oxidative stress. This enzyme is highly selective and conserved in the Trypanosotidae family and has an ortholog in the Plasmodium genus. Previous studies have established that an isosteric replacement of naphthoquinone's carbonyl group with a sulfone group leads to compounds with high bioactivity and selectivity (half-maximal inhibitory concentration = 3 µM against intracellular amastigotes of L. panamensis, selectivity index = 153 over monocytes U-937). In this study, we analyzed the reactive oxygen species (ROS) levels of parasites through indirect measurements of the tryparedoxin system after treatment with these isosteric compounds. This strategy proved that a significant increase in the ROS levels and strong mitochondrial perturbation led to the death of parasites due to cell homeostatic imbalance, confirming the compounds' effectiveness in disrupting this important metabolic pathway. To improve understanding of the parasite-molecule interaction, 27 new compounds were synthesized and assessed against parasites of the three principal tropical diseases (malaria, leishmaniasis, and trypanosomiasis), displaying an EC50 below 10 µM and good correlation with in-silico studies, indicating that the 4H-thiochromen-4-one 1,1-dioxide core is a special allosteric modulator. It can interact in the binding pocket through key amino acids like Ser-14, Leu-17, Trp-21, Ser-109, Tyr-110, and Met-113, leading to interhelical disruption.

Topological data analysis of antibody dynamics of severe and non-severe patients with COVID-19.

The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system's role in the disease's severity level. Here, based on antibody kinetic data of severe and non-severe COVID-19 patients, topological data analysis (TDA) highlights that severity is not binary. However, there are differences in the shape of antibody responses that further classify COVID-19 patients into non-severe, severe, and intermediate cases of severity. Based on the results of TDA, different mathematical models were developed to represent the dynamics between the different severity groups. The best model was the one with the lowest average value of the Akaike Information Criterion for all groups of patients. Our results suggest that different immune mechanisms drive differences between the severity groups. Further inclusion of different components of the immune system will be central for a holistic way of tackling COVID-19.

Effect of neoadjuvant chemotherapy on tumor immune infiltration in breast cancer patients: Systematic review and meta-analysis.

The tumor immune infiltrate has an impact on cancer control and progression, additionally a growing body of evidence has proposed the role of neoadjuvant chemotherapy in modulating the contexture of the tumor immune infiltrate. Here, we performed a systematic review to evaluate the effect of chemotherapy in the immune infiltration of breast cancer tumors. We systematically searched Pubmed/MEDLINE, EMBASE, CENTRAL, and BVS databases with a cutoff date of 11/06/2022. Studies in patients with pathological diagnosis of BC, whose first line of treatment was only NAC, were included. Only published experimental studies that measured tumor immune infiltrate before and after NAC by hematoxylin and eosin (H&E) staining, immunohistochemistry (IHQ), or transcriptome were included. Reviews, studies with animal models and in-vitro models were excluded. Studies in which BC was not the primary tumor or studies with patients who received other types of neoadjuvant therapy were also excluded. The NIH quality assessment tool for before and after studies without control was used. We included 32 articles that evaluated the proximal tumor microenvironment before and after neoadjuvant chemotherapy in 2072 patients who received NAC as first line of treatment and who were evaluated for immune infiltrate in the pre- and post-chemotherapy tumor sample. Results were divided into two major categories immune cells and in-situ expression of immune checkpoints and cytokines. Qualitative synthesis was performed with the 32 articles included, and in nine of them a quantitative analysis was achieved, resulting in six meta-analyses. Despite high heterogeneity among the articles regarding treatment received, type of tumor reported, and techniques used to evaluate immune infiltrate, we found a significant decrease of TILs and FoxP3 expression after neoadjuvant chemotherapy. The study protocol was registered in PROSPERO 2021 (Protocol ID: CRD42021243784) on 6/29/2021.

A new computational approach, based on images trajectories, to identify the subjacent heterogeneity of sperm to the effects of ketanserin.

The identification of kinematic subpopulations is of paramount importance to understanding the biological nature of the sperm heterogeneity. Nowadays, the data of motility parameters obtained by a computer-assisted sperm analysis (CASA) system has been used as input to distinct algorithms to identify kinematic subpopulations. In contrast, the images of the trajectories were depicted only as examples of the patterns of motility in each subpopulation. Here, python code was written to reconstruct the images of trajectories, from their coordinates, then the images of trajectories were used as input to a machine learning clustering algorithm of classification, and the subpopulations were described statistically by the motility parameters. Finally, the images of trajectories in each subpopulation were displayed in a way we called Pollock plots. Semen samples of boar sperm were treated with distinct concentrations of ketanserin (an antagonist of the 5-HT2 receptor of serotonin) and untreated samples were used as a control. The motility of sperm in each sample was analyzed at 0 and 30 min of incubation. Six subpopulations were found. The subpopulation 2 presented the highest values of velocities at 0 or 30 min. After 30 min of incubation, the ketanserin increased the values of the curvilinear velocity at high concentrations, whereas the linearity and the straight velocity decreased. Our computational model permits better identification of the kinematic subpopulations than the traditional approach and provides insights onto the heterogeneity of the response to ketanserin; thus, it could significantly impact the research on the relationship between sperm heterogeneity-fertility.

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning.

The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing of the models. We show here that lung viral load, neutrophil counts, cytokines (such as gamma interferon [IFN-γ] and interleukin 6 [IL-6]), and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models showed that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. IMPORTANCE During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host's lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.

Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly.

Seasonal influenza outbreaks, especially in high-risk groups such as the elderly, represent an important public health problem. Prevailing inadequate efficacy of seasonal vaccines is a crucial bottleneck. Understanding the immunological and molecular mechanisms underpinning differential influenza vaccine responsiveness is essential to improve vaccination strategies. Here we show comprehensive characterization of the immune response of randomly selected elderly participants (≥ 65 years), immunized with the adjuvanted influenza vaccine Fluad. In-depth analyses by serology, multi-parametric flow cytometry, multiplex and transcriptome analysis, coupled to bioinformatics and mathematical modelling, reveal distinguishing immunological and molecular features between responders and non-responders defined by vaccine-induced seroconversion. Non-responders are specifically characterized by multiple suppressive immune mechanisms. The generated comprehensive high dimensional dataset enables the identification of putative mechanisms and nodes responsible for vaccine non-responsiveness independently of confounding age-related effects, with the potential to facilitate development of tailored vaccination strategies for the elderly.

Anaerobiosis favors biosynthesis of single and multi-element nanostructures.

Herein we report the use of an environmental multimetal(loid)-resistant strain, MF05, to biosynthesize single- or multi-element nanostructures under anaerobic conditions. Inorganic nanostructure synthesis typically requires methodologies and conditions that are harsh and environmentally hazardous. Thus, green/eco-friendly procedures are desirable, where the use of microorganisms and their extracts as bionanofactories is a reliable strategy. First, MF05 was entirely sequenced and identified as an Escherichia coli-related strain with some genetic differences from the traditional BW25113. Secondly, we compared the CdS nanostructure biosynthesis by whole-cell in a design defined minimal culture medium containing sulfite as the only sulfur source to obtain sulfide reduction from a low-cost chalcogen reactant. Under anaerobic conditions, this process was greatly favored, and irregular CdS (ex. 370 nm; em. 520-530 nm) was obtained. When other chalcogenites were tested (selenite and tellurite), only spherical Se0 and elongated Te0 nanostructures were observed by TEM and analyzed by SEM-EDX. In addition, enzymatic-mediated chalcogenite (sulfite, selenite, and tellurite) reduction was assessed by using MF05 crude extracts in anaerobiosis; similar results for nanostructures were obtained; however Se0 and Te0 formation were more regular in shape and cleaner (with less background). Finally, the in vitro nanostructure biosynthesis was assessed with salts of Ag, Au, Cd, and Li alone or in combination with chalcogenites. Several single or binary nanostructures were detected. Our results showed that MF05 is a versatile anaerobic bionanofactory for different types of inorganic NS. synthesis.

Minimizing the epidemic final size while containing the infected peak prevalence in SIR systems.

Mathematical models are critical to understand the spread of pathogens in a population and evaluate the effectiveness of non-pharmaceutical interventions (NPIs). A plethora of optimal strategies has been recently developed to minimize either the infected peak prevalence ( I P P ) or the epidemic final size ( E F S ). While most of them optimize a simple cost function along a fixed finite-time horizon, no consensus has been reached about how to simultaneously handle the I P P and the E F S , while minimizing the intervention's side effects. In this work, based on a new characterization of the dynamical behaviour of SIR-type models under control actions (including the stability of equilibrium sets in terms of herd immunity), we study how to minimize the E F S while keeping the I P P controlled at any time. A procedure is proposed to tailor NPIs by separating transient from stationary control objectives: the potential benefits of the strategy are illustrated by a detailed analysis and simulation results related to the COVID-19 pandemic.

Computational simulations to dissect the cell immune response dynamics for severe and critical cases of SARS-CoV-2 infection.

BACKGROUND: COVID-19 is a global pandemic leading to high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe, and critical cases. In particular, studies have highlighted the relationship between lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. However, a quantitative understanding of the immune responses in COVID-19 patients is still missing. OBJECTIVES: In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical cases. The dynamics of different immune cells are taken into account in mechanistic models to elucidate those that contribute to the worsening of the disease. METHODS: Several mathematical models based on ordinary differential equations are proposed to represent data sets of different immune response cells dynamics such as CD8+ T cells, NK cells, and also CD4+ T cells in patients with SARS-CoV-2 infection. Parameter fitting is performed using the differential evolution algorithm. Non-parametric bootstrap approach is introduced to abstract the stochastic environment of the infection. RESULTS: The mathematical model that represents the data more appropriately is considering CD8+ T cell dynamics. This model had a good fit to reported experimental data, and in accordance with values found in the literature. The NK cells and CD4+ T cells did not contribute enough to explain the dynamics of the immune responses. CONCLUSIONS: Our computational results highlight that a low viral clearance rate by CD8+ T cells could lead to the severity of the disease. This deregulated clearance suggests that it is necessary immunomodulatory strategies during the course of the infection to avoid critical states in COVID-19 patients.

Network models to evaluate vaccine strategies towards herd immunity in COVID-19.

Vaccination remains a critical element in the eventual solution to the COVID-19 public health crisis. Many vaccines are already being mass produced and supplied in many countries. However, the COVID-19 vaccination programme will be the biggest in history. Reaching herd immunity will require an unprecedented mass immunisation campaign that will take several months and millions of dollars. Using different network models, COVID-19 pandemic dynamics of different countries can be recapitulated such as in Italy. Stochastic computational simulations highlight that peak epidemic sizes in a population strongly depend on the network structure. Assuming a vaccine efficacy of at least 80% in a mass vaccination program, at least 70% of a given population should be vaccinated to obtain herd immunity, independently of the network structure. If the vaccine efficacy reports lower levels of efficacy in practice, then the coverage of vaccination would be needed to be even higher. Simulations suggest that the "Ring of Vaccination" strategy, vaccinating susceptible contact and contact of contacts, would prevent new waves of COVID -19 meanwhile a high percent of the population is vaccinated.

Dynamical characterization of antiviral effects in COVID-19.

Mathematical models describing SARS-CoV-2 dynamics and the corresponding immune responses in patients with COVID-19 can be critical to evaluate possible clinical outcomes of antiviral treatments. In this work, based on the concept of virus spreadability in the host, antiviral effectiveness thresholds are determined to establish whether or not a treatment will be able to clear the infection. In addition, the virus dynamic in the host - including the time-to-peak and the final monotonically decreasing behavior - is characterized as a function of the time to treatment initiation. Simulation results, based on nine patient data, show the potential clinical benefits of a treatment classification according to patient critical parameters. This study is aimed at paving the way for the different antivirals being developed to tackle SARS-CoV-2.

Serum Concentration of the Phytohormone Abscisic Acid Is Associated With Immune-Regulatory Mediators and Is a Potential Biomarker of Disease Severity in Chronic Obstructive Pulmonary Disease.

COPD and asthma are two distinct but sometimes overlapping diseases exhibiting varying degrees and types of inflammation on different stages of the disease. Although several biomarkers are defined to estimate the inflammatory endotype and stages in these diseases, there is still a need for new markers and potential therapeutic targets. We investigated the levels of a phytohormone, abscisic acid (ABA) and its receptor, LANCL2, in COPD patients and asthmatics. In addition, PPAR-γ that is activated by ABA in a ligand-binding domain-independent manner was also included in the study. In this study, we correlated ABA with COPD-propagating factors to define the possible role of ABA, in terms of immune regulation, inflammation, and disease stages. We collected blood from 101 COPD patients, 52 asthmatics, and 57 controls. Bronchoscopy was performed on five COPD patients and 29 controls. We employed (i) liquid chromatography-tandem mass spectrometry and HPLC to determine the ABA and indoleamine 2,3-dioxygenase levels, respectively; (ii) real-time PCR to quantify the gene expression of LANCL2 and PPAR-γ; (iii) Flow cytometry to quantify adipocytokines; and (iv) immunoturbidimetry and ELISA to measure CRP and cytokines, respectively. Finally, a multinomial regression model was used to predict the probability of using ABA as a biomarker. Blood ABA levels were significantly reduced in COPD patients and asthmatics compared to age- and gender-matched normal controls. However, PPAR-γ was elevated in COPD patients. Intriguingly, ABA was positively correlated with immune-regulatory factors and was negatively correlated with inflammatory markers, in COPD. Of note, ABA was increased in advanced COPD stages. We thereby conclude that ABA might be involved in regulation of COPD pathogenesis and might be regarded as a potential biomarker for COPD stages.

Quantum Sequential Hypothesis Testing.

We introduce sequential analysis in quantum information processing, by focusing on the fundamental task of quantum hypothesis testing. In particular, our goal is to discriminate between two arbitrary quantum states with a prescribed error threshold ε when copies of the states can be required on demand. We obtain ultimate lower bounds on the average number of copies needed to accomplish the task. We give a block-sampling strategy that allows us to achieve the lower bound for some classes of states. The bound is optimal in both the symmetric as well as the asymmetric setting in the sense that it requires the least mean number of copies out of all other procedures, including the ones that fix the number of copies ahead of time. For qubit states we derive explicit expressions for the minimum average number of copies and show that a sequential strategy based on fixed local measurements outperforms the best collective measurement on a predetermined number of copies. Whereas for general states the number of copies increases as log1/ε, for pure states sequential strategies require a finite average number of samples even in the case of perfect discrimination, i.e., ε=0.

Personalized neoantigen-based cancer immunotherapy. A literature review / Inmunoterapia personalizada contra el cáncer basada en neoantígenos. Revisión de la literatura

Abstract Introduction: The progress made in cancer immunotherapy and the clinical response of patients who have undergone this type of therapy have made it the fourth pillar of cancer treatment. Objective: To briefly describe the biological rationale of personalized neoantigen-based cancer immunotherapy, the current perspectives regarding its development, and some of the clinical outcomes achieved with this therapy. Materials and methods: A literature search was performed in PubMed, Scopus and EBSCO using the following search strategy: type of articles: original experimental studies, clinical trials, and narrative and systematic reviews addressing methods to identify mutations found in tumors and cancer immunotherapy strategies based on neoantigen-based vaccines; study population: humans and animal models; publication period: January 1989 - December 2019; language: English and Spanish; search terms: "Immunotherapy", "Neoplasms", "Mutation" and "Cancer Vaccines". Results: The initial search started with 1 344 records. Once duplicates were removed (n=176), 780 studies were excluded after reading their abstract and title. The full text of 338 articles was read to confirm which met the inclusion criteria, finally including 73 studies for full analysis. All articles retrieved were published in English and were mainly conducted in the USA (43.83%) and Germany (23.65%). In the case of original studies (n=43), 20 were performed in humans only, 9 in animals only, 2 in both models, and 12 used in silico methodology. Conclusion: Personalized cancer immunotherapy with tumor neoantigen-based vaccines is strongly emerging as a new alternative to treat cancer. However, to achieve its appropriate implementation, it is necessary to use it in combination with conventional treatments, produce more knowledge that helps clarify cancer immunobiology, and reduce the costs associated with its production.

Resumen Introducción. Los avances que se han hecho en inmunoterapia contra el cáncer y la respuesta clínica de los pacientes que han recibido este tipo de terapia la han convertido en el cuarto pilar para el tratamiento del cáncer. Objetivo. Describir brevemente el fundamento biológico de la inmunoterapia personalizada contra el cáncer basada en neoantígenos, las perspectivas actuales de su desarrollo y algunos resultados clínicos de esta terapia. Materiales y métodos. Se realizó una búsqueda de la literatura en PubMed, Scopus y EBSCO utilizando la siguiente estrategia de búsqueda: tipo de artículos: estudios experimentales originales, ensayos clínicos y revisiones narrativas y sistemáticas sobre métodos de identificación de mutaciones generadas en los tumores y estrategias de inmunoterapia del cáncer con vacunas basadas en neoantígenos; población de estudio: humanos y modelos animales; periodo de publicación: enero de 1989 a julio de 2019; idioma: inglés y español; términos de búsqueda: "Immunotherapy", "Neoplasms", "Mutation" y "Cancer Vaccines". Resultados. La búsqueda inicial arrojó 1 344 registros; luego de remover duplicados (n = 176), 780 fueron excluidos después de leer su resumen y título, y se evaluó el texto completo de 338 para verificar cuáles cumplían con los criterios de inclusión, seleccionándose finalmente 73 estudios para análisis completo. Todos los artículos recuperados se publicaron en inglés, y fueron realizados principalmente en EE. A (43.83%) y Alemania (23.65%). En el caso de los estudios originales (n=43), 20 se realizaron únicamente en humanos, 9 solo en animales, 2 en ambos modelos, y 12 usaron metodología in silico. Conclusión. La inmunoterapia personalizada contra el cáncer con vacunas basadas en neoantígenos tumorales se está convirtiendo de forma contundente en una nueva alternativa para tratar el cáncer. Sin embargo, para lograr su implementación adecuada, es necesario usarla en combinación con tratamientos convencionales, generar más conocimiento que contribuya a aclarar la inmunobiología del cáncer y reducir los costos asociados con su producción.

Uncovering antibody cross-reaction dynamics in influenza A infections.

MOTIVATION: Influenza viruses are a cause of large outbreaks and pandemics with high death tolls. A key obstacle is that flu vaccines have inconsistent performance, in the best cases up to 60% effectiveness, but it can be as low as 10%. Uncovering the hidden pathways of how antibodies (Abs) induced by one influenza strain are effective against another, cross-reaction, is a central vexation for the design of universal flu vaccines. RESULTS: We conceive a stochastic model that successfully represents the antibody cross-reactive data from mice infected with H3N2 influenza strains and further validation with cross-reaction data of H1N1 strains. Using a High-Performance Computing cluster, several aspects and parameters in the model were tested. Computational simulations highlight that changes in time of infection and the B-cells population are relevant, however, the affinity threshold of B-cells between consecutive infections is a necessary condition for the successful Abs cross-reaction. Our results suggest a 3-D reformulation of the current influenza antibody landscape for the representation and modeling of cross-reactive data. AVAILABILITY AND IMPLEMENTATION: The full code as a testing/simulation platform is freely available here: https://github.com/systemsmedicine/Antibody_cross-reaction_dynamics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stability analysis in COVID-19 within-host model with immune response.

The 2019 coronavirus disease (COVID-19) is now a global pandemic. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the causative pathogen of COVID-19. Here, we study an in-host model that highlights the effector T cell response to SARS-CoV-2. The stability of a unique positive equilibrium point, with viral load V * , suggests that the virus may replicate fast enough to overcome T cell response and cause infection. This overcoming is the bifurcation point, near which the orders of magnitude for V * can be sensitive to numerical changes in the parameter values. Our work offers a mathematical insight into how SARS-CoV-2 causes the disease.