Search

Abstract Mesothelioma is an aggressive cancer of the mesothelial layer associated with an extensive fibrotic response. The latter is in large part mediated by cancer-associated fibroblasts which mediate tumour progression and poor prognosis. However, understanding of the crosstalk between cancer cells and fibroblasts in this disease is mostly lacking. Here, using co-cultures of patient-derived mesothelioma cell lines and lung fibroblasts, we demonstrate that fibroblast activation is a self-propagated process producing a fibrotic extracellular matrix (ECM) and triggering drug resistance in mesothelioma cells. Following characterisation of mesothelioma cells/fibroblasts signalling crosstalk, we identify several FDA-approved targeted therapies as far more potent than standard-of-care Cisplatin/Pemetrexed in ECM-embedded co-culture spheroid models. In particular, the SRC family kinase inhibitor, Saracatinib, extends overall survival well beyond standard-of-care in a mesothelioma genetically-engineered mouse model. In short, we lay the foundation for the rational design of novel therapeutic strategies targeting mesothelioma/fibroblast communication for the treatment of mesothelioma patients.

Abstract Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiotherapy outcomes due to its accessibility by bioactive molecules within foods. Even though a few radioresponse modulators have been identified using experimental techniques, trying to experimentally identify all potential modulators is intractable. Here we introduce a machine learning (ML) approach to interrogate the space of bioactive molecules within food for potential modulators of radiotherapy response and provide phytochemically-enriched recipes that encapsulate the benefits of discovered radiotherapy modulators. Potential radioresponse modulators were identified using a genomic-driven network ML approach, metric learning and domain knowledge. Then, recipes from the Recipe1M database were optimized to provide ingredient substitutions maximizing the number of predicted modulators whilst preserving the recipe’s culinary attributes. This work provides a pipeline for the design of genomic-driven nutritional interventions to improve outcomes of rectal cancer patients undergoing radiotherapy.

Abstract Alzheimer’s disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

Pathological processes often change the mechanical properties of cells. Increased rigidity could be a marker of cellular malfunction. Erythrocytes are a type of cell that deforms to squeeze through tiny capillaries; changes in their rigidity can dramatically affect their functionality. Furthermore, differences in the homeostatic elasticity of the cell can be used as a tool for diagnosis and even for choosing the adequate treatment for some illnesses. More accurate types of equipment needed to study biomechanical phenomena at the single-cell level are very costly and thus out of reach for many laboratories around the world. This study presents a simple and low-cost technique to study the rigidity of red blood cells (RBCs) through the application of electric fields in a hand-made microfluidic chamber that uses a capacitor principle. As RBCs are deformed with the application of voltage, cells are observed under a light microscope. From mechanical force vs. deformation data, the elastic constant of the cells is determined. The results obtained with the capacitor-based method were compared with those obtained using optical tweezers, finding good agreement. In addition, P. falciparum-infected erythrocytes were tested with the electric field applicator. Our technique provides a simple means of testing the mechanical properties of individual cells.

People with Down syndrome have more visual problems than the general population. They experience premature ageing, and they are expected to also have an acceleration in worsening visual function. A prospective observational study which includes visual acuity, refractive error, accommodation, binocular and colour vision was performed on young adults with (n = 69) and without (n = 65) Down syndrome and on a senior group (n = 55) without Down syndrome. Results showed significant differences in visual acuity between groups (p < 0.001), and it can be improved with a new prescription in 40% of the participants with Down syndrome. Regarding the accommodative state, no significant differences were found between groups of young people. Concerning binocular vision, 64.7% of strabismus was observed in the group with Down syndrome (p < 0.001). Visual abnormalities are significant in young adults with Down syndrome and are different from those of older people without Down syndrome, some of which can be improved by providing the optimal prescription as well as regular eye examinations.

Abstract Background Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the classification task. Additionally, due to the disjoint nature of the tasks, the employed approach proved cumbersome to optimize, requiring testing of thousands of hyperparameter combinations and significant computational resources.To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task. To solve this task, inspired by the success of graph neural networks in graph classification problems, we use an end-to-end graph neural network model operating directly on the graphs, which learns drug representations to optimize model performance in the prediction of anticancer therapeutics. Results The proposed model outperforms the baseline approach in the anticancer therapeutic prediction task, achieving an F1 score of 67.99%±2.52% and an AUPR of 73.91%±3.49%. It is also shown that the model is able to capture knowledge of biological pathways to predict anticancer molecules based on the molecules’ effects on cancer-related pathways. Conclusions We introduce an end-to-end graph convolutional model to predict cancer-beating molecules within food. The introduced model outperforms the existing baseline approach, and shows interpretability, paving the way to the future of a personalized nutritional science approach allowing the development of nutrition strategies for cancer prevention and/or therapeutics.

Abstract In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80–85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

The COVID-19 pandemic upended family life, forcing many families to reorganize their daily routines. Hispanic families have been especially affected by the pandemic, experiencing cumulative stressors and increased risks of contracting the virus, hospitalization, and morbidity. To date, there is limited research examining home life within Hispanic families during the pandemic. Given the extended amount of time for which families have been isolated at home together, identifying factors that may enhance or detract from well-being within the home is important in advancing efforts to support at-risk families. In this qualitative study, 29 Hispanic parents (primarily mothers) living in California participated in one of eight focus groups conducted in Spanish. Parents described activities and behaviors during the first year of the COVID-19 pandemic. The following six themes were identified using reflexive thematic analysis: (1) parents focused on family time; (2) children adapted to the changes of the pandemic; (3) parents and children engaged in physical activity; (4) children mainly entertained themselves with screens; (5) COVID-19 media coverage was accessible in the home; and (6) parents worried about the virus, and its effects on the future. While findings include anxiety around the virus and its attendant effects, family strengths were also present throughout the discussions. Public policy should consider ways to leverage family strengths to preserve family relationships and routines during future public health crises.

La cultura formadora de emprendedores capaces de ofrecer servicios de manera independiente, crear empresas o innovar en las existentes, es sin duda una de las tareas universitarias. El objetivo de esta investigación fue sentar bases para el diseño de formación emprendedora a partir de elaborar un perfil. Se describen los valores para características personales de estudiantes de licenciatura en Administración en dos universidades públicas en diferentes países. Los resultados indican que existe una estrecha relación entre algunas dimensiones. La comparación posibilita identificar áreas de oportunidad para ser aprovechadas por los estudiantes mexicanos y colombianos. Las conclusiones orientarán la cultura emprendedora de instituciones educativas.

AIM: To determine the repeatability and agreement of stereoacuity measurements made using some of the most widely used clinical tests: Frisby, TNO, Randot and Titmus. METHODS: Stereoacuity was measured in two different sessions separated by a time interval of at least 24h but no longer than 1wk in 74 subjects of mean age 20.6y using the four methods. The study participants were divided into two groups: subjects with normal binocular vision and subjects with abnormal binocular vision. RESULTS: Best repeatability was shown by the Frisby and Titmus [coefficient of repeatability (COR): ±13 and ±12s arc respectively] in the subjects with normal binocular vision though a clear ceiling effect was noted. In the subjects with abnormal binocular vision, best repeatability was shown by the Frisby (COR: ±69s arc) and Randot (COR: ±72s arc). In both groups, the TNO test showed poorest agreement with the other tests. CONCLUSION: The repeatability of stereoacuity measures was low in subjects with poor binocular vision yet fairly good in subjects with normal binocular vision with the exception of the TNO test. The reduced agreement detected between the tests indicates they cannot be used interchangeably.

The technological advances in the digital field have facilitated the access to virtual space or cyberspace; a non-physical place where countless situations of everyday life are occurring. In this place, the transport network companies are transforming the traditional practice of urban mobility in the main cities of Mexico. This paper discusses the influence of Uber in the daily movements, the incursion, boom and later decline of its initial conditions in its transport service. The paper uses a methodological analysis of the content and the quasi-ethnographic practice, consisting of participant observations, formal and informal interviews and netnographic exercises in the Metropolitan Area of Guadalajara. The results suggest that we are experiencing variations in the exercise of automobilility, through complex hybridizations of the modus normalis and this new option of transport management, which disincentives a little the practice of motorized urban mobility.

Contemporary cities have been characterized by architectural designs mostly focused users with average physical capacities have and excluded fractions of the population that do not meet the requirements of the standard user and even have reduced opportunities for access to public space and the means of transport, although the recent recommendations have suggested modifications to the traditional design, to allow a greater social inclusion. This thinking has not finished smear of everything in the responsible for the design and construction of the city. The task of obtaining a greater accessibility possible in favor of the greatest number of people, involves interventions that deletion of the architectural barriers, reduce the hostility of the built environment and to facilitate, to the extent possible, the autonomy of the user in their daily itineraries. This paper analyzes the current conditions of the infrastructure for access to public space of the historical center of the municipality of Guadalajara and how they have influenced the dependence on the displacement of a good part of people with reduced mobility.

Abstract Background The increasing demand for remote medical care, driven by digital healthcare advancements and the COVID-19 pandemic, necessitates effective solutions tailored to patients and healthcare practitioners. Co-design, involving collaboration between software developers, patients, and healthcare practitioners, prioritizes end-user needs. Research indicates that integrating patient perspectives enhances user experience and usability. However, its application in healthcare has been limited to small projects. This work focuses on co-designing a technological solution to enhance the monitoring and visual rehabilitation of individuals with Age-Related Macular Degeneration (AMD), a condition that significantly impacts the quality of life in people over 60. Current vision rehabilitation systems lack personalization, motivation, and effective progress monitoring. Involving patients and healthcare practitioners in the design process aims to ensure the final product meets their needs. Methods The project employs iterative and collaborative principles, involving a vision rehabilitation expert and two AMD patients as active users in the application’s development and validation. The process begins by establishing requirements for user accounts and rehabilitation exercises. It continues with an initial approach extended through user validation. Co-design is facilitated by specific workshops marking each project iteration, totaling four workshops, along with continuous communication sessions between experts and developers to validate design decisions. Initial requirements gathering and constant feedback from end-users, the visual rehabilitator, and patients are crucial for refining the product effectively. Results The workshops produced a prototype featuring a test to monitor changes and progression and 15 visual rehabilitation exercises. Numerous patient and vision rehabilitation-driven software modifications led to a final design that is responsive and adaptive to end-user needs. Conclusions The Rehab-AMD pilot project aims to develop a collaborative and adaptive software solution for AMD rehabilitation by actively involving stakeholders and applying iterative design principles. Co-design in the Rehab-AMD solution proves to be a methodology that identifies usability issues and needs from the initial design stages. This approach ensures that software developers create a final product that is genuinely useful and manageable for people with AMD and the targeted vision rehabilitators.