A Novel Averaging Principle Provides Insights in the Impact of Intratumoral Heterogeneity on Tumor Progression

Typically stochastic differential equations (SDEs) involve an additive or multiplicative noise term. Here, we are interested in stochastic differential equations for which the white noise is nonlinearly integrated into the corresponding evolution term, typically termed as random ordinary differential equations (RODEs). The classical averaging methods fail to treat such RODEs. Therefore, we introduce a novel averaging method appropriate to be applied to a specific class of RODEs. To exemplify the importance of our method, we apply it to an important biomedical problem, in particular, we implement the method to the assessment of intratumoral heterogeneity impact on tumor dynamics. Precisely, we model gliomas according to a well-known Go or Grow (GoG) model, and tumor heterogeneity is modeled as a stochastic process. It has been shown that the corresponding deterministic GoG model exhibits an emerging Allee effect (bistability). In contrast, we analytically and computationally show that the introduction of white noise, as a model of intratumoral heterogeneity, leads to monostable tumor growth. This monostability behavior is also derived even when spatial cell diffusion is taken into account.

The adhesion capability of S. aureus cells is heterogeneously distributed over the cell envelope

Understanding and controlling microbial adhesion is an important biomedical problem. However, many properties of the adhesion process of bacteria are still unknown, for example the distribution of adhesive strength over the cell wall. While a patchy colloid model for adhesion has been developed recently for Gram-negative Escherichia coli cells, a comparable model for Gram-positive cells is not known. Here, we use single-cell force spectroscopy to measure the adhesion of Staphylococcus aureus at different positions on tailored surfaces. We find a heterogenous distribution of the adhesion forces with varying degrees of intensity. By comparing these results to simulations, we obtain the distribution of adhesive strength on the cell wall: The cells have several distinct spots of high adhesion capability, similar to the patchy colloid model. We discuss implications of our results for the development of new materials and the design and analysis of future studies.

selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks

Predicting potential links in heterogeneous biomedical networks (HBNs) can greatly benefit various important biomedical problem. However, the self-supervised representation learning for link prediction in HBNs has been slightly explored in previous researches. Therefore, this study proposes a two-level self-supervised representation learning, namely selfRL, for link prediction in heterogeneous biomedical networks. The meta path detection-based self-supervised learning task is proposed to learn representation vectors that can capture the global-level structure and semantic feature in HBNs. The vertex entity mask-based self-supervised learning mechanism is designed to enhance local association of vertices. Finally, the representations from two tasks are concatenated to generate high-quality representation vectors. The results of link prediction on six datasets show selfRL outperforms 25 state-of-the-art methods. In particular, selfRL reveals great performance with results close to 1 in terms of AUC and AUPR on the NeoDTI-net dataset. In addition, the PubMed publications demonstrate that nine out of ten drugs screened by selfRL can inhibit the cytokine storm in COVID-19 patients. In summary, selfRL provides a general frame-work that develops self-supervised learning tasks with unlabeled data to obtain promising representations for improving link prediction.

Interrogating the Brain

Can torture be studied as a biomedical problem? I contend that it most certainly can. Torture can be approached as a biomedical problem in a variety of ways. Torture can be examined in terms of its consequences (e.g., in torture patients); torture can be modeled, using special populations (e.g., elite soldiers) undergoing particular training and combat regimes; torture can be treated deductively, in an analytic fashion, by investigating the consequences of stressors focused on shared aspects of neuropsychological function. Predictions can be made, based on contemporary models of the effects of stress on neurocognitive function, and outcomes tested in differing populations. It is an epistemic mistake to think that torture can only be approached using randomized-control trial methodologies, as this misunderstands the nature of empirical-deductive science. Torture is a demonstrably poor truth-seeking practice, for reasons rooted deep in our shared psychology and neurobiology. Besides being morally abhorrent and manifestly illegal, torture is a demonstrable failure on its own terms. The extreme stressors employed during torture negatively affect the integrated psychobiological functioning of our brains and bodies. Hence the intelligence yield from torture both in recent experience, and historically, has typically been very poor. Finally, I examine outdated ideas regarding human behavior, but which live on in practice (“zombie” ideas). I conclude with a discussion of the centrality of the behavioral and brain sciences as evolving research domains central to solving the problem of reliably and ethically gathering information from other human beings, with a focus on the importance of evidence-based policy formation.

Integrating drawings into health curricula: university educators’ perspectives

The literature has identified promising findings regarding the application of arts-based initiatives to enhance healthcare professional (HCP) training. Research shows that drawings offer a window into the authentic, insider view of health and illness, with potential to be a platform for healthcare student and HCP learning. In addition, drawings may also have a place in health communication. Our previous work provides support for the educational application of patients’ drawings in bringing HCPs closer to the patient’s lived experience. Subsequently, this study aimed to explore university educators’ opinions regarding the implementation of drawings as an educational tool for higher education healthcare students. The objective of this study was to explore pathways for using drawings as an art form in an educational context, and provide recommendations for developing curricula and resources for further evaluation. Findings from focus group interviews with nine university educators revealed support for the use of drawings as a novel medium as they offer rich insights into the patient’s perspective while encouraging creative and critical thinking. Key perceived benefits were that drawings foster student appreciation of (1) the holistic impact of illness, (2) the importance of patients’ priorities and (3) the value of learning from the patient. Patients’ drawings of their experiences would offer needed opportunities for students to explicitly reflect about the ‘person’ holistically rather than view the patient as a ‘biomedical problem’. Shifting students’ perspectives and possible assumptions to be better aligned with and appreciative of the patient’s experiences was noted as central to adopting a person-centred approach to healthcare practice. Our findings suggest that incorporating drawings, or indeed other art forms, as educational tools would be a valuable addition to the health curricula.

A novel averaging principle provides insights in the impact of intratumoral heterogeneity on tumor progression

Typically stochastic differential equations (SDEs) involve an additive or multiplicative noise term. Here, we are interested in stochastic differential equations for which the white noise is non-linearly integrated in the corresponding evolution term, typically termed as random ordinary differential equations (RODEs). The classical averaging methods fail to treat such RODEs. Therefore, we introduce a novel averaging method appropriate to be applied on RODEs. To exemplify the importance of our method, we apply it in an important biomedical problem, i.e. the assessment of intratumoral heterogeneity impact on tumor dynamics. In particular, we model gliomas according to a well-known Go or Grow (GoG) model and tumor heterogeneity is modelled as a stochastic process. It has been shown that this GoG model exhibits an emerging Allee effect (bistability). We analytically and computationally show that the introduction of white noise, as a model of intratumoral heterogeneity, leads to a monostable tumor growth. This monostability behaviour is also derived even when spatial cell diffusion is taking into account.

On the mean field approximation of a stochastic model of tumour-induced angiogenesis

In the field of Life Sciences, it is very common to deal with extremely complex systems, from both analytical and computational points of view, due to the unavoidable coupling of different interacting structures. As an example, angiogenesis has revealed to be an highly complex, and extremely interesting biomedical problem, due to the strong coupling between the kinetic parameters of the relevant branching – growth – anastomosis stochastic processes of the capillary network, at the microscale, and the family of interacting underlying biochemical fields, at the macroscale. In this paper, an original revisited conceptual stochastic model of tumour-driven angiogenesis has been proposed, for which it has been shown that it is possible to reduce complexity by taking advantage of the intrinsic multiscale structure of the system; one may keep the stochasticity of the dynamics of the vessel tips at their natural microscale, whereas the dynamics of the underlying fields is given by a deterministic mean field approximation obtained by an averaging at a suitable mesoscale. While in previous papers, only an heuristic justification of this approach had been offered; in this paper, a rigorous proof is given of the so called ‘propagation of chaos’, which leads to a mean field approximation of the stochastic relevant measures associated with the vessel dynamics, and consequently of the underlying tumour angiogenic factor (TAF) field. As a side, though important result, the non-extinction of the random process of tips has been proven during any finite time interval.

Towards Semantic Biomedical Problem Solving

Biological and medical intelligence (BMI) has been studied in solos, lacking a systematic methodology. In this paper, we describe how Semantic Computing can enhance biological and medical intelligence. Specifically, we show how Structured Natural Language (SNL) can express many problems in BMI with a finite number of sentence patterns, and show how biological tools, OLAP, data mining tools and statistical analysis tools may be linked to solve problems related to biomedical data.