Objective

A better understanding of the biobehavioral mechanisms underlying depression in cancer is required to translate biomarker findings into clinical interventions. We tested for associations between cytokines and the somatic and psychological symptoms of depression in cancer patients and their healthy caregivers.

Patients and methods

The GRID Hamilton Rating Scale for Depression (Ham-D) was administered to 61 cancer patients of mixed type and stage, 26 primary caregivers and 38 healthy controls. Concurrently, blood was drawn for multiplexed plasma assays of 15 cytokines. Multiple linear regression, adjusted for biobehavioral variables, identified cytokine associations with the psychological (Ham-Dep) and somatic (Ham-Som) subfactors of the Ham-D.

Results

The Ham-Dep scores of cancer patients were similar to their caregivers, but their Ham-Som scores were significantly higher (twofold, p=0.016). Ham-Som was positively associated with IL-1ra (coefficient: 1.27, p≤0.001) in cancer patients, and negatively associated with IL-2 (coefficient: -0.68, p=0.018) in caregivers. Ham-Dep was negatively associated with IL-4 (coefficient: -0.67, p=0.004) in cancer patients and negatively associated with IL-17 (coefficient: -1.81, p=0.002) in caregivers.

Conclusion

The differential severity of somatic symptoms of depression in cancer patients and caregivers and the unique cytokine associations identified with each group suggests the potential for targeted interventions based on phenomenology and biology. The clinical implication is that depressive symptoms in cancer patients can arise from biological stressors, which is an important message to help destigmatize the development of depression in cancer patients.

This work deals with transport and general electronic phenomena in low-dimensional systems. The first chapter is dedicated to Variable Range Hopping. It starts with a brief review of the general hopping formalism, based on previous work. Next, new methods and results are presented and discussed. In particular, studies of both Ohmic and non-Ohmic regime are performed and the stark differences between the two are elucidated. In addition, apparent power law dependence of current on voltage in disordered one-dimensional materials is analyzed. The results obtained compare favorably with the experiments. Finally, the behavior of the conducting network in d dimensions is discussed using the percolation approach. The second chapter deals with plasmonic effects in graphene. After giving a short introduction to graphene and plasmonic behavior, current work is presented. Charge oscillations in graphene half-plane are discussed and compared with experimental results obtained from near-field microscopy. In addition, plasmonic oscillations in a "narrow-flake" geometry are analyzed analytically and numerically, showing good agreement between the two methods

It has been shown experimentally that cratered surfaces may have better adhesion properties than flat ones. However, the suction effect produced by the craters, which may be chiefly responsible for the improved adhesion, has not been properly modelled. This paper combines experimental, numerical simulation and analytical approaches towards developing a framework for quantifying the suction effect produced by isolated craters and cratered surfaces. The modelling approach emphasizes the essential role of large elastic deformation, while the airflow dynamics, microscopic mechanisms, like surface tension and air permeation, and rate-dependence are neglected. This approach is validated using experimental data for isolated hemi-spherical craters. The modelling approach is further applied to spherical cap (not necessarily hemi-spherical) craters with the objective of identifying optimal geometric and material properties, as well as the minimum preload necessary for attaining the maximum suction force. It is determined that stiff polymers with deep craters are capable of producing large suction forces. For soft materials, central to biomedical applications, large suction forces can be attained by reinforcing deep craters with thin stiff layers. Parametric optimization studies of reinforced craters reveal that some of them perform beyond common expectations. However, those high-performance reinforced craters are prone to surface instabilities, and therefore the practical use of such craters may be problematic.

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

Recently, plasmons in graphene have been observed experimentally using scattering scanning near-field optical microscopy. In this paper, we develop a simplified analytical approach to describe the behavior in triangular samples. Replacing Coulomb interaction by a short-range one reduces the problem to a Helmholtz equation, amenable to analytical treatment. We demonstrate that even with our simplifications, the system still exhibits the key features seen in the experiment.

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.

We investigate near-field infrared spectroscopy and superfluid polariton imaging experiments on conventional and unconventional superconductors. Our modeling shows that near-field spectroscopy can measure the magnitude of the superconducting energy gap in Bardeen-Cooper-Schrieffer-like superconductors with nanoscale spatial resolution. We demonstrate how the same technique can measure the c-axis plasma frequency, and thus the c-axis superfluid density, of layered unconventional superconductors with a similar spatial resolution. Our modeling also shows that near-field techniques can image superfluid surface mode interference patterns near physical and electronic boundaries. We describe how these images can be used to extract the collective mode dispersion of anisotropic superconductors with subdiffractional spatial resolution. © 2014 American Physical Society.

Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the networks input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.

The field of valleytronics has promised greater control of electronic and spintronic systems with an additional valley degree of freedom. However, conventional and two-dimensional valleytronic systems pose practical challenges in the utilization of this valley degree of freedom. Here we show experimental evidences of the valley effect in a bulk, ambient, and bias-free model system of Tin(II) sulfide. We elucidate the direct access and identification of different sets of valleys, based primarily on the selectivity in absorption and emission of linearly polarized light by optical reflection/transmission and photoluminescence measurements, and demonstrate strong optical dichroic anisotropy of up to 600% and nominal polarization degrees of up to 96% for the two valleys with band-gap values 1.28 and 1.48 eV, respectively; the ease of valley selection further manifested in their non-degenerate nature. Such discovery enables a new platform for better access and control of valley polarization.

Numerous drugs are currently under accelerated clinical investigation for the treatment of coronavirus disease 2019 (COVID-19); however, well-established safety and efficacy data for these drugs are limited. The goal of this study was to predict the potential of 25 small molecule drugs in clinical trials for COVID-19 to cause clinically relevant drug-drug interactions (DDIs), which could lead to potential adverse drug reactions (ADRs) with the use of concomitant medications. We focused on 11 transporters, which are targets for DDIs. In vitro potency studies in membrane vesicles or HEK293 cells expressing the transporters coupled with DDI risk assessment methods revealed that 20 of the 25 drugs met the criteria from regulatory authorities to trigger consideration of a DDI clinical trial. Analyses of real-world data from electronic health records, including a database representing nearly 120,000 patients with COVID-19, were consistent with several of the drugs causing transporter-mediated DDIs (e.g., sildenafil, chloroquine, and hydroxychloroquine). This study suggests that patients with COVID-19, who are often older and on various concomitant medications, should be carefully monitored for ADRs. Future clinical studies are needed to determine whether the drugs that are predicted to inhibit transporters at clinically relevant concentrations, actually result in DDIs.