Sex-Related Differences in the Immune System Drive Differential Responses to Anti-PD-1 Immunotherapy.

Immune checkpoint inhibitors, such as anti-PD-1 antibodies, represent a significant advancement in cancer immunotherapy, but their efficacy varies notably between individuals, influenced by complex biological systems. Recent evidence suggests that sex-related biological differences play a pivotal role in modulating these responses. This study uses a systems biology approach to examine how sex-specific differences in the immune system contribute to variability in the response to treatment.

Investigating nano-sized tumor-derived extracellular vesicles in enhancing anti-PD-1 immunotherapy.

Anti-PD1 immune checkpoint blockade (ICB) has shown promising results for treating several aggressive cancers, enhancing patient survival rates. The variability in clinical response to anti-PD1 ICB is thought to be driven by patient-specific biology and heterogeneity within the tumor microenvironment. Tumor-derived extracellular vesicles (TDEVs), nano-sized particles released from tumor cells, can modulate the tumor microenvironment, leading to immunosuppression and tumor progression.

An Effective Method for Decellularization of Human Foreskin: Implications for Skin Regeneration in Small Wounds.

Objective: Acellular matrices of different allogeneic or xenogeneic origins are widely used as structural scaffolds in regenerative medicine. The main goal of this research was to optimize a method for decellularization of foreskin for skin regeneration in small wounds.

Materials And Methods: In this experimental study, the dermal layers of foreskin were divided into two sections and subjected to two different decellularization methods: the sodium dodecyl sulfate method (SDS-M), and our optimized foreskin decellularization method (OFD-M).

An integrative systems biology approach to overcome venetoclax resistance in acute myeloid leukemia.

The over-expression of the Bcl-2 protein is a common feature of many solid cancers and hematological malignancies, and it is typically associated with poor prognosis and resistance to chemotherapy. Bcl-2-specific inhibitors, such as venetoclax, have recently been approved for the treatment of chronic lymphocytic leukemia and small lymphocytic lymphoma, and they are showing promise in clinical trials as a targeted therapy for patients with relapsed or refractory acute myeloid leukemia (AML). However, successful treatment of AML with Bcl-2-specific inhibitors is often followed by the rapid development of drug resistance.

A novel statistical method predicts mutability of the genomic segments of the SARS-CoV-2 virus.

The SARS-CoV-2 virus has made the largest pandemic of the 21st century, with hundreds of millions of cases and tens of millions of fatalities. Scientists all around the world are racing to develop vaccines and new pharmaceuticals to overcome the pandemic and offer effective treatments for COVID-19 disease. Consequently, there is an essential need to better understand how the pathogenesis of SARS-CoV-2 is affected by viral mutations and to determine the conserved segments in the viral genome that can serve as stable targets for novel therapeutics.

Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy.

The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. In practice, it is often prohibitively difficult to ensure the validity of patient-specific parameterizations of these models for any particular patient. As a result, sensitivities to these particular parameters can result in therapeutic dosing schedules that are optimal in principle not performing well on particular patients.

Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade.

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient's immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate.

A mean-field approach for modeling the propagation of perturbations in biochemical reaction networks.

Often, the time evolution of a biochemical reaction network is crucial for determining the effects of combining multiple pharmaceuticals. Here we illustrate a mathematical framework for modeling the dominant temporal behaviour of a complicated molecular pathway or biochemical reaction network in response to an arbitrary perturbation, such as resulting from the administration of a therapeutic agent. The method enables the determination of the temporal evolution of a target protein as the perturbation propagates through its regulatory network.

Modeling the impact of public response on the COVID-19 pandemic in Ontario.

The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses.

Integrating Systems Biology and an Ex Vivo Human Tumor Model Elucidates PD-1 Blockade Response Dynamics.

Ex vivo human tumor models have emerged as promising, yet complex tools to study cancer immunotherapy response dynamics. Here, we present a strategy that integrates empirical data from an ex vivo human system with computational models to interpret the response dynamics of a clinically prescribed PD-1 inhibitor, nivolumab, in head and neck squamous cell carcinoma (HNSCC) biopsies (N = 50). Using biological assays, we show that drug-induced variance stratifies samples by T helper type 1 (Th1)-related pathways.

Mathematical modelling of cancer stem cell-targeted immunotherapy.

The cancer stem cell hypothesis states that tumors are heterogeneous and comprised of several different cell types that have a range of reproductive potentials. Cancer stem cells (CSCs), represent one class of cells that has both reproductive potential and the ability to differentiate. These cells are thought to drive the progression of aggressive and recurring cancers since they give rise to all other constituent cells within a tumor.

Fluctuations in Hertz chains at equilibrium.

We examine the long-term behavior of nonintegrable, energy-conserved, one-dimensional systems of macroscopic grains interacting via a contact-only generalized Hertz potential and held between stationary walls. Such systems can be set up to have no phononic background excitation and represent examples of a sonic vacuum. Existing dynamical studies showed the absence of energy equipartitioning in such systems, hence their long-term dynamics was described as quasiequilibrium.

Granular chains with soft boundaries: Slowing the transition to quasiequilibrium.

We present here a detailed numerical study of the dynamical behavior of "soft" uncompressed grains in a granular chain where the grains interact via the intrinsically nonlinear Hertz force. It is well known that such a chain supports the formation of solitary waves (SWs). Here, however, the system response to the material properties of the grains and boundaries is explored further.