In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the `therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate pure
The question of whether or not neutron therapy works has been answered. It is a qualified yes, as is the case with all of radiation therapy. But, neutron therapy has not kept pace with the rest of radiation therapy in terms of beam delivery techniques. Modern photon and proton based external beam radiotherapy routinely implements image-guidance, beam intensity-modulation and 3-dimensional treatment planning. The current iteration of fast neutron radiotherapy does not. Addressing these deficiencies, however, is not a matter of technology or understanding, but resources. The future of neutron therapy lies in better understanding the interaction processes of radiation with living tissue. A combination of radiobiology and computer simulations is required in order to optimize the use of neutron therapy. The questions that need to be answered are: Can we connect the macroscopic with the microscopic? What is the optimum energy? What is the optimum energy spectrum? Can we map the sensitivity of the various tissues of the human body and use that knowledge to our advantage? And once we gain a better understanding of the above radiobiological issues will we be able to capitalize on this under
In this paper we describe briefly the basic aspects of magnetic resonance therapy, registered as TMR therapy. Clinical studies have shown that application of this therapy significantly accelerates wound healing and, in particular, healing of the diabetic foot disease. To understand the working principle of this therapy, we analyze relevant to it biological effects produced by magnetic fields. Based on these data, we show that there is a hierarchy of the possible physical mechanisms, which can produce such effects. The mutual interplay between the mechanisms can lead to a synergetic outcome delayed in time, which can affect the physiological state of the organism. In particular, we show that soliton mediated charge transport during the redox processes in living organisms is sensitive to magnetic fields, so that such fields can facilitate redox processes in particular, and can stimulate the healing effect of the organism in general. This and other non-thermal resonant mechanisms of the biological effects of magnetic fields are summarized as the working principle of the magnetic resonance therapy. We support our approach by some biological and histological data (both in vitro and in v
Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Since the Scopus database contains a larger number of journals and covers also the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is due to (i) the larger number of journals covered by Scopus and (ii) the historical record of citations older than ten years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.
One of the barriers to the development of effective adoptive cell transfer therapies (ACT), specifically for genetically engineered T-cell receptors (TCRs), and chimeric antigen receptor (CAR) T-cells, is target antigen heterogeneity. It is thought that intratumor heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model to rationalize the outcomes of two types of ACT therapies over heterogeneous tumors: antigen specific ACT therapy and multi-antigen recognition ACT therapy. We found that one dose of antigen specific ACT therapy should be expected to reduce the tumor size as well as its growth rate, however it may not be enough to completely eliminate it. A second dose also reduced the tumor size as well as the tumor growth rate, but, due to the intratumor heterogeneity, it turned out to be less effective than the previous dose. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of c
Fear of flying is a serious problem that affects millions of individuals. Exposure therapy for fear of flying is an effective therapy technique. However, exposure therapy is also expensive, logistically difficult to arrange, and presents significant problems of patient confidentiality and potential embarrassment. We have developed a virtual airplane for use in fear of flying therapy. Using the virtual airplane for exposure therapy is a potential solution to many of the current problems of fear of flying exposure therapy. We describe the design of the virtual airplane and present a case report on its use for fear of flying exposure therapy.
Stuttering is a complex speech disorder that negatively affects an individual's ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the condition and seek help through therapy. Fluency shaping is a therapy approach where PWSs learn to modify their speech to help them to overcome their stutter. Mastering such speech techniques takes time and practice, even after therapy. Shortly after therapy, success is evaluated highly, but relapse rates are high. To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency. Monitoring could create the ability to intervene early by detecting lapses in fluency. To the best of our knowledge, no public dataset is available that contains speech from people who underwent stuttering therapy that changed the style of speaking. This work introduces the Kassel State of Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The clips were labeled with six stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and
Soft robotics is attractive for wearable applications that require conformal interactions with the human body. Soft wearable robotic garments hold promise for supplying dynamic compression or massage therapies, such as are applied for disorders affecting lymphatic and blood circulation. In this paper, we present a wearable robot capable of supplying dynamic compression and massage therapy via peristaltic motion of finger-sized soft, fluidic actuators. We show that this peristaltic wearable robot can supply dynamic compression pressures exceeding 22 kPa at frequencies of 14 Hz or more, meeting requirements for compression and massage therapy. A large variety of software-programmable compression wave patterns can be generated by varying frequency, amplitude, phase delay, and duration parameters. We first demonstrate the utility of this peristaltic wearable robot for compression therapy, showing fluid transport in a laboratory model of the upper limb. We theoretically and empirically identify driving regimes that optimize fluid transport. We second demonstrate the utility of this garment for dynamic massage therapy. These findings show the potential of such a wearable robot for the tr
Robot-Assisted Therapy (RAT) has successfully been used in Human Robot Interaction (HRI) research by including social robots in health-care interventions by virtue of their ability to engage human users in both social and emotional dimensions. Robots used for these tasks must be designed with several user groups in mind, including both individuals receiving therapy and care professionals responsible for the treatment. These robots must also be able to perceive their context of use, recognize human actions and intentions, and follow the therapeutic goals to perform meaningful and personalized treatment. Effective interactions require for robots to be capable of coordinated, timely behavior in response to social cues. This means being able to estimate and predict levels of engagement, attention, intentionality and emotional state during human-robot interactions. An additional challenge for social robots in therapy and care is the wide range of needs and conditions the different users can have during their interventions, even if they may share the same pathologies their current requirements and the objectives of their therapies can varied extensively. Therefore, it becomes crucial for
Antimicrobial resistance is a threat to public health with millions of deaths linked to drug resistant infections every year. To mitigate resistance, common strategies that are used are combination therapies and therapy switching. However, the stochastic nature of pathogenic mutation makes the optimization of these strategies challenging. Here, we propose a two-scale stochastic model that considers the effective evolution of therapies in a multidimensional efficacy space, where each dimension represents the efficacy of a specific drug in the therapy. The diffusion of therapies within this space is subject to stochastic resets, representing therapy switches. The boundaries of the space, inferred from coarser pathogen-host dynamics, can be either reflecting or absorbing. Reflecting boundaries impede full recovery of the host, while absorbing boundaries represent the development of antimicrobial resistance, leading to therapy failure. We derive analytical expressions for the average absorption times, accounting for both continuous and discrete genomic changes using the frameworks of Langevin and Master equations, respectively. These expressions allow us to evaluate the relevance of ti
Chimeric antigen receptor T-cell (CAR-T) therapy is considered a promising cancer treatment. The dynamic response to this therapy can be broadly divided into a short-term phase, ranging from weeks to months, and a long-term phase, ranging from months to years. While the short-term response, encompassing the multiphasic kinetics of CAR-T cells, is better understood, the mechanisms underlying the outcomes of the long-term response, characterized by sustained remission, relapse, or disease progression, remain less understood due to limited clinical data. Here, we analyze the long-term dynamics of a previously validated mathematical model of CAR-T cell therapy. We perform a comprehensive stability and bifurcation analysis, examining model equilibria and their dynamics over the entire parameter space. Our results show that therapy failure results from a combination of insufficient CAR-T cell proliferation and increased tumor immunosuppression. By combining different techniques of nonlinear dynamics, we identify Hopf and Bogdanov-Takens bifurcations, which allow to elucidate the mechanisms behind oscillatory remissions and transitions to tumor escape. In particular, rapid expansion of CA
Selective accumulation of B-10 compound in tumour tissue is a fundamental condition for the achievement of BNCT (Boron Neutron Capture Therapy), since the effectiveness of therapy irradiation derives just from neutron capture reaction of B-10. Hence, the determination of the B-10 concentration ratio, between tumour and healthy tissue, and a control of this ratio, during the therapy, are essential to optimise the effectiveness of the BNCT, which it is known to be based on the selective uptake of B-10 compound. In this work, experimental methods are proposed and evaluated for the determination in vivo of B-10 compound in biological samples, in particular based on neutron radiography and gammaray spectroscopy by telescopic system. Measures and Monte Carlo calculations have been performed to investigate the possibility of executing imaging of the 10B distribution, both by radiography with thermal neutrons, using 6LiF/ZnS:Ag scintillator screen and a CCD camera, and by spectroscopy, based on the revelation of gamma-ray reaction products from B-10 and the H. A rebuilding algorithm has been implemented. The present study has been done for the standard case of B-10 uptake, as well as for p
Intermittent Androgen Suppression (IAS) is a treatment strategy for delaying or even preventing time to relapse of advanced prostate cancer. IAS consists of alternating cycles of therapy (in the form of androgen suppression) and off-treatment periods. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. In spite of extensive recent clinical experience with IAS, the design of an ideal protocol for any given patient remains one of the main challenges associated with effectively implementing this therapy. We use a threshold-based policy for optimal IAS therapy design that is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. We apply Infinitesimal Perturbation Analysis (IPA) to a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under IAS and derive unbiased estimators of the cost metric gradient with respect to various model and therapy parameters. These estimators are subsequently used for system analysis. By evaluating sensitivity estimates
While the use of combination therapy is increasing in prevalence for cancer treatment, it is often difficult to predict the exact interactions between different treatment forms, and their synergistic/antagonistic effects on patient health and therapy outcome. In this research, a system of ordinary differential equations is constructed to model nonlinear dynamics between tumor cells, immune cells, and three forms of therapy: chemotherapy, immunotherapy, and radiotherapy. This model is then used to generate optimized combination therapy plans using optimal control theory. In-silico experiments are conducted to simulate the response of the patient model to various treatment plans. This is the first mathematical model in current literature to introduce radiotherapy as an option alongside immuno- and chemotherapy, permitting more flexible and effective treatment plans that reflect modern therapeutic approaches.
We are in a golden age of progress in artificial intelligence (AI). Radiotherapy, due to its technology-intensive nature as well as direct human-machine interactions, is perfectly suited for benefitting from AI to enhance accuracy and efficiency. Over the past few years, a vast majority of AI research have already been published in the field of photon therapy, while the applications of AI specifically targeted for particle therapy remain scarcely investigated. There are two distinct differences between photon therapy and particle therapy: beam interaction physics (photons vs. charged particles) and beam delivery mode (e.g. IMRT/VMAT vs. pencil beam scanning). As a result, different strategies of AI deployment are required between these two radiotherapy modalities. In this article, we aim to present a comprehensive survey of recent literatures exclusively focusing on AI-powered particle therapy. Six major aspects are included: treatment planning, dose calculation, range and dose verification, image guidance, quality assurance and adaptive replanning. A number of perspectives as well as potential challenges and common pitfalls, are also discussed.
Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of co
Phage therapy is an alternative treatment method for bacterial infections. It has shown particular promise in reducing bacterial load while preventing antibiotic resistance. Here, we develop a mathematical model of a bacterial infection within a host to study phage therapy. It incorporates interactions between phages, bacteria, the immune system, and antibiotics. Additionally, the model includes bacterial social dynamics that provide protection from treatments and the innate immune response. We analytically and numerically identify all of the equilibria of the model and derive insights regarding the overall effectiveness of phage therapy. Without phage therapy, the model exhibits bistability: bacteria populations above a threshold grow and become entrenched, while those below it can be effectively suppressed by the immune system. We find that that phages destabilize the former equilibrium, and thus in combination with the immune system are able to suppress the bacteria. We conducted bifurcation analyses, which show that the equilibrium with a suppressed population of bacteria can become unstable. In this scenario, the system undergoes oscillations. However, these oscillations -- wh
In this work, we study the correlation between interdisciplinarity of papers within physical sciences and their citations by using meta data of articles published in American Physical Society's Physical Review journals between 1985 to 2012. We use the Weitzman diversity index to measure the diversity of papers and authors, exploiting the hierarchical structure of PACS (Physics and Astronomy Classification Scheme) codes. We find that the fraction of authors with high diversity is increasing with time, where as the fraction of least diversity are decreasing, and moderate diversity authors have higher tendency to switch over to other diversity groups. The diversity index of papers is correlated with the citations they received in a given time period from their publication year. Papers with lower and higher end of diversity index receive lesser citations than the moderate diversity papers.
A previous study of symmetric collisions of massive nuclei has shown that current models of multi-nucleon transfer (MNT) reactions do not adequately describe the transfer product yields. To gain further insight into this problem, we have measured the yields of MNT products in the interaction of 977 (E/A = 4.79 MeV) and 1143 MeV (E/A = 5.60 MeV) $^{204}$Hg with $^{208}$Pb. We find that the yield of multi-nucleon transfer products are similar in these two reactions and are substantially lower than those observed in the reaction of 1257 MeV (E/A = 6.16 MeV) $^{204}$Hg + $^{198}$Pt. We compare our measurements with the predictions of the GRAZING-F, di-nuclear systems (DNS) and improved quantum molecular dynamics (ImQMD) models. For the observed isotopes of the elements Au, Hg, Tl, Pb and Bi, the measured values of the MNT cross sections are orders of magnitude larger than the predicted values. Furthermore, the various models predict the formation of nuclides near the N=126 shell, which are not observed.
This paper, the second in a series of two, provides a set of recommendations that the American Astronomical Society (AAS) can take to create a more diverse and inclusive professional society for astronomers, with a focus on women astronomers. As noted in Paper I, now is the time for the AAS to take decisive action to transform astronomy into a diverse and inclusive profession. By combining the results of our 2019 survey, which is described in Paper I, peer-reviewed academic literature, and findings from many of the white papers submitted to Astro2020, the CSWA has developed 26 specific actions the AAS can take to help end harassment and bullying in astronomy; advance career development for astronomers who are women, members of other underrepresented groups, and intersections of these populations; and improve the climate and culture of AAS meetings. Actions to reduce rates of harassment and bullying include improvements to the AAS's anti-harassment policies and procedures and the development of astronomy-specific anti-harassment training resources. Actions to advance career development include creating a compensation database, improving how jobs are posted in the AAS Job Register, a