Biomedical Context Research Articles

Optogenetic-controlled immunotherapeutic designer cells for post-surgical cancer immunotherapy

Surgical resection is the main treatment option for most solid tumors, yet cancer recurrence after surgical resection remains a significant challenge in cancer therapy. Recent advances in cancer immunotherapy are enabling radical cures for many tumor patients, but these technologies remain challenging to apply because of side effects related to uncontrollable immune system activation. Here, we develop far-red light-controlled immunomodulatory engineered cells (FLICs) that we load into a hydrogel scaffold, enabling the precise optogenetic control of cytokines release (IFN-β, TNF-α, and IL-12) upon illumination. Experiments with a B16F10 melanoma resection mouse model show that FLICs-loaded hydrogel implants placed at the surgical wound site achieve sustainable release of immunomodulatory cytokines, leading to prevention of tumor recurrence and increased animal survival. Moreover, the FLICs-loaded hydrogel implants elicit long-term immunological memory that prevents against tumor recurrence. Our findings illustrate that this optogenetic perioperative immunotherapy with FLICs-loaded hydrogel implants offers a safe treatment option for solid tumors based on activating host innate and adaptive immune systems to inhibit tumor recurrence after surgery. Beyond extending the optogenetics toolbox for immunotherapy, we envision that our optogenetic-controlled living cell factory platform could be deployed for other biomedical contexts requiring precision induction of bio-therapeutic dosage.

Biomedical Image Classification via Dynamically Early Stopped Artificial Neural Network

It is well known that biomedical imaging analysis plays a crucial role in the healthcare sector and produces a huge quantity of data. These data can be exploited to study diseases and their evolution in a deeper way or to predict their onsets. In particular, image classification represents one of the main problems in the biomedical imaging context. Due to the data complexity, biomedical image classification can be carried out by trainable mathematical models, such as artificial neural networks. When employing a neural network, one of the main challenges is to determine the optimal duration of the training phase to achieve the best performance. This paper introduces a new adaptive early stopping technique to set the optimal training time based on dynamic selection strategies to fix the learning rate and the mini-batch size of the stochastic gradient method exploited as the optimizer. The numerical experiments, carried out on different artificial neural networks for image classification, show that the developed adaptive early stopping procedure leads to the same literature performance while finalizing the training in fewer epochs. The numerical examples have been performed on the CIFAR100 dataset and on two distinct MedMNIST2D datasets which are the large-scale lightweight benchmark for biomedical image classification.

A manifesto on explainability for artificial intelligence in medicine

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.

Deep Image Prior for medical image denoising, a study about parameter initialization

Convolutional Neural Networks are widely known and used architectures in image processing contexts, in particular for medical images. These Deep Learning techniques, known for their ability to extract high-level features, almost always require a labeled dataset, a process that can be computationally expensive. Most of the time in the biomedical context, when images are used they are noisy and the ground-truth is unknown. For this reason, and in the context of Green Artificial Intelligence, recently, an unsupervised method that employs Convolutional Neural Networks, or more precisely autoencoders, has appeared in the panorama of Deep Learning. This technique, called Deep Image Prior (DIP) by the authors, can be used in areas such as denoising, superresolution, and inpainting. Starting from these assumptions, this work analyses the robustness of these networks with respect to different types of initialization. First of all, we analyze the different types of parameters: related to the Batch Norm and the Convolutional layers. For the results, we focus on the speed of convergence and the maximum performance obtained. However, this paper aims to apply acquired information on Computer Tomography noised images. In fact, the final purpose is to test the best initializations of the first phase on a phantom image and then on a real Computer Tomography one. In fact, Computer Tomography together with Magnetic Resonance Imaging and Positron Emission Tomography are some of the diagnostic tools currently available to neuroscientists and oncologists. This work shows how initializations affect final performances and, in addition, how they should be used in the medical image reconstruction field. The section on numerical experiments shows results that on the one hand confirm the importance of a good initialization to obtain fast convergence and high performance; on the other hand, it shows how the method is robust to the processing of different image types: natural and medical. Not a single good initialization is discovered, but many of them could be chosen, according to specific necessities of the single problem.

Examining Science and Technology/Engineering Educators' Views of Teaching Biomedical Concepts Through Physical Computing.

Programming and automation continue to evolve rapidly and advance the capabilities of science, technology, engineering, and mathematics (STEM) fields. However, physical computing (the integration of programming and interactive physical devices) integrated within biomedical contexts remains an area of limited focus in secondary STEM education programs. As this is an emerging area, many educators may not be well prepared to teach physical computing concepts within authentic biomedical contexts. This shortcoming provided the rationale for this study, to examine if professional development (PD) had a noticeable influence on high school science and technology and engineering (T&E) teachers’ (1) perceptions of teaching biomedical and computational thinking (CT) concepts and (2) plans to integrate physical computing within the context of authentic biomedical engineering challenges. The findings revealed a significant difference in the amount of biomedical and CT concepts that teachers planned to implement as a result of the PD. Using a modified version of the Science Teaching Efficacy Belief Instrument (STEBI-A) Riggs and Enochs in Science Education, 74(6), 625–637 (1990), analyses revealed significant gains in teachers’ self-efficacy toward teaching both biomedical and CT concepts from the PD. Further analyses revealed that teachers reported increases in their perceived knowledge of biomedical and CT concepts and a significant increase in their intent to collaborate with a science or T&E educator outside of their content area. This study provides implications for researchers and educators to integrate more biomedical and physical computing instruction at the secondary education level.

Influence of crosslinking on the drug release of PLLA/gelatin nonwovens

Abstract Electrospun fibers based on synthetic and natural polymers represent a promising approach in order to mimic the mechanical stability and the required cell-polymer interaction of the natural extracellular matrix (ECM), . In this biomedical context, the biodegradable medical grade poly-llactide (PLLA) and the natural gelatin are already well established. However, to obtain a stabilized structured PLLA/gelatin blend, crosslinking of gelatin is required. In this study, the influence of the post processed crosslinking via Glutaraldehyde (GTA) vapor and GTA solution on the morphological and physicochemical properties of the blend, as well as on the in vitrorelease of previously incorporated dexamethasone (DMS), was investigated. The addition of gelatin decreased the fiber diameter and increased the surface hydrophilicity, which is furthermore unaffected by the crosslinking process. The in vitrorelease of DMS was decisively changed by both, the addition of gelatin and the subsequent crosslinking processes. For all modified samples, a very rapid burst release of DMS was detected within the first 24 h, which is crucial for biomedical applications such as tumor therapy.

Reversion is most likely under high mutation supply when compensatory mutations do not fully restore fitness costs.

The dynamics of adaptation, reversion, and compensation have been central topics in microbial evolution, and several studies have attempted to resolve the population genetics underlying how these dynamics occur. However, questions remain regarding how certain features-the evolution of mutators and whether compensatory mutations alleviate costs fully or partially-may influence the evolutionary dynamics of compensation and reversion. In this study, we attempt to explain findings from experimental evolution by utilizing computational and theoretical approaches toward a more refined understanding of how mutation rate and the fitness effects of compensatory mutations influence adaptive dynamics. We find that high mutation rates increase the probability of reversion toward the wild type when compensation is only partial. However, the existence of even a single fully compensatory mutation is associated with a dramatically decreased probability of reversion to the wild type. These findings help to explain specific results from experimental evolution, where compensation was observed in nonmutator strains, but reversion (sometimes with compensation) was observed in mutator strains, indicating that real-world compensatory mutations are often unable to fully alleviate the costs associated with adaptation. Our findings emphasize the potential role of the supply and quality of mutations in crafting the dynamics of adaptation and reversal, with implications for theoretical population genetics and for biomedical contexts like the evolution of antibiotic resistance.

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine.

Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

An ISB-consistent Denavit-Hartenberg model of the human upper limb for joint kinematics optimization: validation on synthetic and robot data during a typical rehabilitation gesture.

Several biomedical contexts such as diagnosis, rehabilitation, and ergonomics require an accurate estimate of human upper limbs kinematics. Wearable inertial measurement units (IMU s) represent a suitable solution because of their unobtrusiveness, portability, and low-cost. However, the time-integration of the gyroscope angular velocity leads to an unbounded orientation drift affecting both angular and linear displacements over long observation interval. In this work, a Denavit-Hartenberg model of the upper limb was defined in accordance with the guidelines of the International Society of Biomechanics and exploited to design an optimization kinematics process. This procedure estimated the joint angles by minimizing the difference between the modelled and IMU-driven orientation of upper arm and forearm. In addition, reasonable constraints were added to limit the drift influence on the final joint kinematics accuracy. The validity of the procedure was tested on synthetic and experimental data acquired with a robotic arm over 20 minutes. Average rms errors amounted to 2.8 deg and 1.1 for synthetic and robot data, respectively. Clinical Relevance - The proposed method has the potential to improve robustness and accuracy of multi-joint kinematics estimation in the general contexts of home-based tele-rehabilitation interventions. In this respect adoption of multi-segmental kinematic model along with physiological joint constraints could contribute to address current limitations associated to unsupervised analysis in terms of monitoring and outcome assessment.

Amphiphilic Gold Nanoparticles: A Biomimetic Tool to Gain Mechanistic Insights into Peptide-Lipid Interactions.

Functional peptides are now widely used in a myriad of biomedical and clinical contexts, from cancer therapy and tumor targeting to the treatment of bacterial and viral infections. Underlying this diverse range of applications are the non-specific interactions that can occur between peptides and cell membranes, which, in many contexts, result in spontaneous internalization of the peptide within cells by avoiding energy-driven endocytosis. For this to occur, the amphipathicity and surface structural flexibility of the peptides play a crucial role and can be regulated by the presence of specific molecular residues that give rise to precise molecular events. Nevertheless, most of the mechanistic details regulating the encounter between peptides and the membranes of bacterial or animal cells are still poorly understood, thus greatly limiting the biomimetic potential of these therapeutic molecules. In this arena, finely engineered nanomaterials—such as small amphiphilic gold nanoparticles (AuNPs) protected by a mixed thiol monolayer—can provide a powerful tool for mimicking and investigating the physicochemical processes underlying peptide-lipid interactions. Within this perspective, we present here a critical review of membrane effects induced by both amphiphilic AuNPs and well-known amphiphilic peptide families, such as cell-penetrating peptides and antimicrobial peptides. Our discussion is focused particularly on the effects provoked on widely studied model cell membranes, such as supported lipid bilayers and lipid vesicles. Remarkable similarities in the peptide or nanoparticle membrane behavior are critically analyzed. Overall, our work provides an overview of the use of amphiphilic AuNPs as a highly promising tailor-made model to decipher the molecular events behind non-specific peptide-lipid interactions and highlights the main affinities observed both theoretically and experimentally. The knowledge resulting from this biomimetic approach could pave the way for the design of synthetic peptides with tailored functionalities for next-generation biomedical applications, such as highly efficient intracellular delivery systems.

Shame as a moral mood in medicine.

The emotional underpinnings that facilitate and complicate the practice of ethical principles like respect warrant sustained interdisciplinary attention. In this article, I suggest that shame is a requisite component of the emotional repertoire than makes respect for persons possible. I use person-centered interview data from a sample of 54 physicians (including 35 surgeons), 60% of whom are women, to examine the emergence and endurance of shame as a mood with moral significance. Drawing on anthropologist Throop's concept of a moral mood, I explore physicians' first-person narratives of the endurance of shame experiences. Narratives demonstrate that shame inheres in biomedical contexts that reinforce the physician's responsibilization and culpability for events beyond their control. As a persistent cognitive and affective state, mooded shame is a recursive and compulsory motive force for a physician's dynamic evolution as a moral actor. Variably distressing, looming and commonplace, mooded shame becomes an atmospheric and imaginative mode through which physicians contemplate their responsibilities and connections to patients. Sometimes in a hypercognized manner that conceals its emotional roots, physicians link the mood of shame to their incessant efforts to fulfill responsibilities to each unique patient. I suggest that through reflection made possible within mooded shame, physicians develop a sense of being both accountable to and alongside patients, and I explore the ties between this position and philosophical concepts of respect.

State Estimation with Model Reduction and Shape Variability. Application to biomedical problems

We develop a mathematical and numerical framework to solve state estimation problems for applications that present variations in the shape of the spatial domain. This situation arises typically in a biomedical context where inverse problems are posed on certain organs or portions of the body which inevitably involve morphological variations. If one wants to provide fast reconstruction methods, the algorithms must take into account the geometric variability. We develop and analyze a method which allows to take this variability into account without needing any a priori knowledge on a parametrization of the geometrical variations. For this, we rely on morphometric techniques involving Multidimensional Scaling, and couple them with reconstruction algorithms that make use of reduced model spaces pre-computed on a database of geometries. We prove the potential of the method on a synthetic test problem inspired from the reconstruction of blood flows and quantities of medical interest with Doppler ultrasound imaging.

State of the Art of Chemosensors in a Biomedical Context

Healthcare is undergoing large transformations, and it is imperative to leverage new technologies to support the advent of personalized medicine and disease prevention. It is now well accepted that the levels of certain biological molecules found in blood and other bodily fluids, as well as in exhaled breath, are an indication of the onset of many human diseases and reflect the health status of the person. Blood, urine, sweat, or saliva biomarkers can therefore serve in early diagnosis of diseases such as cancer, but also in monitoring disease progression, detecting metabolic disfunctions, and predicting response to a given therapy. For most point-of-care sensors, the requirement that patients themselves can use and apply them is crucial not only regarding the diagnostic part, but also at the sample collection level. This has stimulated the development of such diagnostic approaches for the non-invasive analysis of disease-relevant analytes. Considering these timely efforts, this review article focuses on novel, sensitive, and selective sensing systems for the detection of different endogenous target biomarkers in bodily fluids as well as in exhaled breath, which are associated with human diseases.

Evolution and Biogeography of Freshwater Snails of the Genus Bulinus (Gastropoda) in Afromontane Extreme Environments

Mountains are considered island-like systems often characterized by exceptional biodiversity and endemism. There are many highly isolated mountain ranges in Africa that collectively have been called the Afromontane archipelago. Freshwater snails of the genus Bulinus are composed of many veterinary and biomedical important species. These intermediate hosts for schistosomiasis parasites are wide spread and some of the species are considered to be highly adapted to very harsh environmental conditions such as droughts or low temperatures. However, the extent to which the Bulinus have adapted to live in high-altitudes and factors influencing these range shifts are not well investigated. In this study, we analyzed pan-African Bulinus species from various habitats across different altitudinal ranges, focusing on the high mountains or “sky islands” to examine how the contemporary climate and historical geological factors affect species distributions and evolutionary processes. Using a fossil-calibrated multigene phylogeny composed of two mitochondrial genes (cox1, 16S) and two nuclear genes (ITS2 and H3), we tested: 1) how often and when extreme altitudes were colonized, 2) what are the biogeographical affinities and degree of isolation of high-altitude species, 3) which lineages diversified and evolved endemism in the Afromontane environments, and 4) whether the Afromontane regions represent “sky islands”. Bayesian phylogenetic inference employing a fossil-calibrated molecular clock resulted in a strongly supported phylogeny resolving the relationships between the four Bulinus groups. High-altitude colonization exists exclusively within the Bulinus truncatus/tropicus complex. Several independent colonization events occurred in the Pliocene and Pleistocene throughout Africa, mostly from nearby regions of the respective mountain ranges. Most species evolved in low to mid-altitudinal ranges. Endemism is pronounced in the Ethiopian Highlands and those of Kenya and Lesotho. A previously unknown species was found at an extreme altitude (∼4,000 m a.s.l.) on Mt. Elgon/Uganda extending the formerly known altitudinal maximum of the genus by roughly 900 m. The endemic species has already diverged in the Pliocene (∼4 myr) and is currently characterized by low genetic diversity. There is further cryptic diversity in mountain ranges of Lesotho. Our findings are discussed in a biogeographical, conservation and biomedical context.

A Simple Fabrication, Low Noise, Capacitive Tactile Sensor for Use in Inexpensive and Smart Healthcare Systems

Tactile sensors are among the most important devices used in industrial and biomedical fields. Sensors’ profiles are significantly affected by their structures and material used. This article presents a robust, low-cost, low noise, accurate and simple fabrication capacitive tactile sensor as a single taxel fabricated on foam. This highly scalable design provides excellent noise immunity, accuracy, and due to a unique printable elastic conductor, it is flexible and stretchable with more than 200% strain. Furthermore, the taxel is based on the capacitive Wheatstone bridge. As a result, noise immunity and stability in case of temperature fluctuation is accomplished. Additionally, the sensor’s innovative, simple fabrication, made of Polyurethane foam and printable elastic conductor, allows the system to adapt and achieve relevant results necessary for the purpose of the sensor’s application. Therefore, the proposed sensor has potential applications in industrial and biomedical contexts, such as sleep monitoring, etc.

Biofilm Growth under Elastic Confinement.

Bacteria often form surface-bound communities, embedded in a self-produced extracellular matrix, called biofilms. Quantitative studies of bioflim growth have typically focused on unconfined expansion above solid or semisolid surfaces, leading to exponential radial growth. This geometry does not accurately reflect the natural or biomedical contexts in which biofilms grow in confined spaces. Here, we consider one of the simplest confined geometries: a biofilm growing laterally in the space between a solid surface and an overlying elastic sheet. A poroelastic framework is utilized to derive the radial growth rate of the biofilm; it reveals an additional self-similar expansion regime, governed by the Poisson's ratio of the matrix, leading to a finite maximum radius, consistent with our experimental observations of growing Bacillus subtilis biofilms confined by polydimethylsiloxane.

The role of statistical significance testing in public law and health risk assessment.

SummaryFollowing a fundamental statement made in 2016 by the American Statistical Associations and broad and consistent changes in data analysis and interpretation methodology in public health and other sciences, statistical significance/null hypothesis testing is being increasingly criticized and abandoned in the reporting and interpretation of the results of biomedical research. This shift in favor of a more comprehensive and non-dichotomous approach in the assessment of causal relationships may have a major impact on human health risk assessment. It is interesting to see, however, that authoritative opinions by the Supreme Court of the United States and European regulatory agencies have somehow anticipated this tide of criticism of statistical significance testing, thus providing additional support to its demise. Current methodological evidence further warrants abandoning this approach in both the biomedical and public law contexts, in favor of a more comprehensive and flexible method of assessing the effects of toxicological exposure on human and environmental health.

"Something I Have Created": Breastfeeding and Motherhood Trauma in Thi Bui's The Best We Could Do.

This essay examines the challenge of breastfeeding in Thi Bui's The Best We Could Do: An Illustrated Memoir, specifically how the process of learning to feed, significant in both her own and her mother's traumatic entrances into motherhood, enables Bui to face the "terrifying thought" that, upon giving birth, "FAMILY is now something [she has] created." By situating her narrative in the context of feeding her son, Bui amplifies the associations between the act of providing nourishment and the matrilineal responsibility of satiating a hunger for familial connection. In illustrating this association, Bui depicts the nuanced layers of breastfeeding difficulty and failure, especially for women mothering in the context of generational trauma. Within a biomedical context, Bui's narrative illustrates how breastfeeding difficulties, and their emotional toll, may be reminiscent of diasporic losses of cultural and familial connection, prompting practitioners to consider more broadly the roots of their patients' breastfeeding anxieties.

COT: an efficient and accurate method for detecting marker genes among many subtypes.

MotivationIdeally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others—so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG.ResultsWe report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods.Availability and implementationThe Python COT software with a detailed user’s manual and a vignette are freely available at https://github.com/MintaYLu/COT.Supplementary information Supplementary data are available at Bioinformatics Advances online.

The nebula of chronicity: dealing with metastatic breast cancer in the UK

In this article, I explore how the concept of chronicity is mobilised by different actors in reference to metastatic breast cancer (MBC) and the transformation of the condition as a consequence of medical innovations. I do so by using data collected in the UK between 2017 and 2019 through in-depth interviews with medical professionals involved in the treatment of MBC and with patients living with MBC. I show how chronicity appears as a multidimensional and uncertain concept, which I analyse through the image of the nebula. While the medical literature tends to consider MBC chronic or on route to chronicisation, the medical professionals interviewed were uncertain as to whether MBC can be considered a chronic disease, and attempted to discuss chronicity through survival times, the kind of management possible for the disease, and how it compares to other conditions more commonly considered chronic. In some cases, the patients considered the idea of chronicity a source of hope or a way to link their condition to those of people with other diseases; however, they generally rejected the definition as inappropriate for their experience of the illness. Analysing the fluid uses of the concept of chronicity in the case of MBC contributes to the debate within medical anthropology on how medical categories acquire different values and uses and on the circulation of meanings between the biomedical context and the patient experience.