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We describe a dataset on the crustacean Order Tanaidacea from the coasts of the Iberian Peninsula and surrounding seas, including the archipelagos of the Azores, Madeira, Savage, and the Canary Islands. The dataset gathers the records from all available sources published between 1828 to 2019, which were collected following a standardized Google Scholar search and cross checking each article’s reference lists. For each record, the dataset includes taxonomic, geographical, and ecological information, as well as remarks regarding the sampling methods. The dataset was further completed with 52 additional unpublished records obtained from screening the collections of the University Complutense of Madrid gathered from 35 shallow water surveys. Furthermore, 698 records from different oceanographic deep-sea campaigns have also been included. In total, 3456 records from 186 species in 22 families have been compiled. The dataset organises the current published and unpublished knowledge on tanaidaceans in the area and, by making it open access, it will allow comparisons of the distribution of tanaidaceans in zoogeographic studies.

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care. [ABSTRACT FROM AUTHOR]

Glioblastoma invasion is the primary mechanism responsible for its dismal prognosis and is the direct result of interactions between glioblastoma cells and the tumor vasculature. The dysregulated microvasculature in glioblastoma tumors and vessels co-opted from surrounding brain tissue support rapid tumor growth and are utilized as pathways for invasive cancer cells. Attempts to target the glioblastoma vasculature with antiangiogenic agents (eg, bevacizumab) have nonetheless shown limited and inconsistent efficacy, and the underlying causes of such heterogeneous responses remain unknown. Several studies have identified that patients with glioblastoma who develop hypertension following treatment with bevacizumab show significant improvement in overall survival compared with normotensive nonresponders. Here we review these findings and discuss the potential of hypertension as a biomarker for glioblastoma treatment response in individual patients and the role of hypertension as a modulator of interactions between tumor cells and cells in the perivascular niche. We suggest that a better understanding of the actions of bevacizumab and hypertension at the cellular level will contribute to developing more effective personalized therapies that address glioblastoma tumor cell invasion. [ABSTRACT FROM AUTHOR]

Background: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. Results: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). Conclusions: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients. [ABSTRACT FROM AUTHOR]

The chloroquine resistance transporter (PfCRT) confers resistance to a wide range of quinoline and quinoline-like antimalarial drugs in Plasmodium falciparum, with local drug histories driving its evolution and, hence, the drug transport specificities. For example, the change in prescription practice from chloroquine (CQ) to piperaquine (PPQ) in Southeast Asia has resulted in PfCRT variants that carry an additional mutation, leading to PPQ resistance and, concomitantly, to CQ re-sensitization. How this additional amino acid substitution guides such opposing changes in drug susceptibility is largely unclear. Here, we show by detailed kinetic analyses that both the CQ- and the PPQ-resistance conferring PfCRT variants can bind and transport both drugs. Surprisingly, the kinetic profiles revealed subtle yet significant differences, defining a threshold for in vivo CQ and PPQ resistance. Competition kinetics, together with docking and molecular dynamics simulations, show that the PfCRT variant from the Southeast Asian P. falciparum strain Dd2 can accept simultaneously both CQ and PPQ at distinct but allosterically interacting sites. Furthermore, combining existing mutations associated with PPQ resistance created a PfCRT isoform with unprecedented non-Michaelis-Menten kinetics and superior transport efficiency for both CQ and PPQ. Our study provides additional insights into the organization of the substrate binding cavity of PfCRT and, in addition, reveals perspectives for PfCRT variants with equal transport efficiencies for both PPQ and CQ. Author summary: Chloroquine (CQ) used to be the drug of choice against malaria until the parasite responsible for the disease became resistant. In the 1970s, piperaquine (PPQ) was introduced in areas where resistance to CQ was wide spread. In the following decade, an estimated 140 million doses were distributed, which substantially reduced the malaria burden, particularly in China, but created an environment in which PPQ resistant strains of the human malaria parasite Plasmodium falciparum emerged and spread. Interestingly, the PPQ resistant parasites displayed an increased CQ sensitivity. The main genetic determinant of both CQ and PPQ resistance in P. falciparum is a drug transporter, termed PfCRT. In this study, we used biochemical and bioinformatics approaches to understand how mutational changes in PfCRT influence the interaction of the carrier with CQ and PPQ. We found that PfCRT from CQ resistant parasites is better at transporting CQ than are PfCRT variants from PPQ resistant parasites, while the opposite is true for PPQ. We also found that PfCRT can be engineered such that it transports both antimalarials equally well. Our study offers insights into how PfCRT has evolved in response to changing drug pressure, and raises concerns regarding how it may evolve in the future. [ABSTRACT FROM AUTHOR]

Community organizing brings Latina/o families together to enhance repertoires of culturally relevant practices to promote health and curriculum. The Healthy Schools Campaign, a 4-year environmental justice partnership between public health researchers and Latina/o organizations in 2 neighborhoods of Chicago, was formed to confront the epidemics of asthma and obesity in children that result in premature death. The partnership supported diverse organizing and addressed health inequity through inquiry with parents who created knowledge, took action, and reflected on outcomes in schools and in the community. Community funds of knowledge were created and exchanged through the processes of "compromiso," "confianza," and "colaboracion."