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Lipases are enzymes that hydrolyze long-chain carboxylic esters, and in the presence of organic solvents, they catalyze organic synthesis reactions. However, the use of solvents in these processes often results in enzyme denaturation, leading to a reduction in enzymatic activity. Consequently, there is significant interest in identifying new lipases that are resistant to denaturing conditions, with extremozymes emerging as promising candidates for this purpose. Lip7, a lipase from Geobacillus sp. ID17, a thermophilic microorganism isolated from Deception Island, Antarctica, was recombinantly expressed in E. coli C41 (DE3) in functional soluble form. Its purification was achieved with 96% purity and 23% yield. Enzymatic characterization revealed Lip7 to be a thermo-alkaline enzyme, reaching a maximum rate of 3350 U mg−1 at 50 °C and pH 11.0, using p-nitrophenyl laurate substrate. Notably, its kinetics displayed a sigmoidal behavior, with a higher kinetic efficiency (kcat/Km) for substrates of 12-carbon atom chain. In terms of thermal stability, Lip7 demonstrates stability up to 60 °C at pH 8.0 and up to 50 °C at pH 11.0. Remarkably, it showed high stability in the presence of organic solvents, and under certain conditions even exhibited enzymatic activation, reaching up to 2.5-fold and 1.35-fold after incubation in 50% v/v ethanol and 70% v/v isopropanol, respectively. Lip7 represents one of the first lipases from the bacterial subfamily I.5 and genus Geobacillus with activity and stability at pH 11.0. Its compatibility with organic solvents makes it a compelling candidate for future research in biocatalysis and various biotechnological applications.

This article presents a study on the implementation of a virtual escape-room game as a novel teaching methodology in biochemistry education. The game aimed to engage students in producing monoclonal antibodies against SARS-CoV-2 while reinforcing theoretical concepts and fostering teamwork. Three versions of the game were tested, incorporating modifications to address student feedback on and improve the overall experience. The study employed a satisfaction survey to gather insights from students regarding their perception of the game. Results showed that the implementation of answer flexibility using RegEx had a significant positive impact on student satisfaction and motivation. The introduction of RegEx allowed for a more realistic and immersive gaming experience, as students could provide varied answers while still being evaluated correctly. Overall, the findings highlight the effectiveness of the game's design, the suitability of the Google Forms platform for distance learning, and the importance of incorporating answer flexibility through RegEx. These results provide valuable guidance for educators seeking to enhance student engagement and satisfaction through the use of escape-room games in biochemistry education.

Se registra un nuevo caso de Aura Tiñosa, Cathartes aura (Linnaeus, 1758), con leucismo parcial para el archipiélago cubano. La observación se hizo en el área Paisaje Natural Protegido Escaleras de Jaruco ubicado en los municipios de Jaruco y San José de las Lajas, en las Alturas de Tapaste, en la actual provincia de Mayabeque. El avistamiento ocurrió en horas de la mañana (10:30 a.m.), sobre el penacho de una palma real (Roystonea regia, H.B.K. Cook). Este es considerado como el primer registro para la región occidental cubana y el cuarto para el país. La distribución actual de individuos leucísticos en Cuba comprende las regiones: occidental (el presente registro), central (con dos registros) y un registro que data del siglo XIX, donde no se precisa la localidad.

The standard first-line therapy for ovarian cancer is a combination of surgery and carboplatin/paclitaxel-based chemotherapy. Patients with longer survival and improved response to chemotherapy usually present T-cell inflamed tumours. The presence of tumour-infiltrating T cells (TILs) notably varies among the different subtypes of ovarian tumours, being highest in high-grade serous ovarian carcinoma, intermediate in endometrioid tumours, and lowest in low-grade serous, mucinous and clear cell tumours. Interestingly, the presence of TILs is often accompanied by a strong immunosuppressive tumour environment. A better understanding of the immune response against ovarian cancer and the tumour immune evasion mechanisms will enable improved prognostication, response prediction and immunotherapy of this disease. This article provides an overview of some ovarian cancer cell features relevant for antitumour response, such as tumour-associated antigens, including neoantigens, expression of inhibitory molecules, and other mechanisms of immune evasion. Moreover, we describe relevant immune cell types found in epithelial ovarian tumours, including T and B lymphocytes, regulatory T cells, natural killer cells, tumour-associated macrophages, myeloid-derived suppressor cells and neutrophils. We focus on how these components influence the burden of the tumour and the clinical outcome.

Specimens of the Neotropical dog-faced bats of the genus Cynomops Thomas, 1920 are poorly represented in museum collections, and the geographical distributions of the eight species are not well known. We report voucher specimens that confirm the presence of C. greenhalli Goodwin, 1958 in the Tropical Wet Forest of Costa Rica. These specimens represent an extension of the distribution into the Caribbean Central America.

Nile tilapia (Oreochromis niloticus) is one of the most cultivated and economically important species in world aquaculture. Intensive production promotes the use of monosex animals, due to an important dimorphism that favors male growth. Currently, the main mechanism to obtain all-male populations is the use of hormones in feeding during larval and fry phases. Identifying genomic regions associated with sex determination in Nile tilapia is a research topic of great interest. The objective of this study was to identify genomic variants associated with sex determination in three commercial populations of Nile tilapia. Whole-genome sequencing of 326 individuals was performed, and a total of 2.4 million high-quality bi-allelic single nucleotide polymorphisms (SNPs) were identified after quality control. A genome-wide association study (GWAS) was conducted to identify markers associated with the binary sex trait (males = 1; females = 0). A mixed logistic regression GWAS model was fitted and a genome-wide significant signal comprising 36 SNPs, spanning a genomic region of 536 kb in chromosome 23 was identified. Ten out of these 36 genetic variants intercept the anti-Müllerian (Amh) hormone gene. Other significant SNPs were located in the neighboring Amh gene region. This gene has been strongly associated with sex determination in several vertebrate species, playing an essential role in the differentiation of male and female reproductive tissue in early stages of development. This finding provides useful information to better understand the genetic mechanisms underlying sex determination in Nile tilapia.

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.

Epithelial ovarian cancer (EOC) is still the most lethal gynecological cancer. Germline alterations in breast cancer 1 (gBRCA1) and breast cancer 2 (gBRCA2) genes have been identified in up to 18% of women diagnosed with EOC, and somatic mutations are found in an additional 7%. Testing of BRCA at the primary diagnosis of patients with EOC is recommended due to the implications in the genomic counseling of the patients and their families, as well as for the therapeutic implications. Indeed, the introduction of poly-(ADP ribose) polymerase inhibitors (PARPis) has changed the natural history of patients harboring a mutation in BRCA, and has resulted in a new era in the treatment of patients with ovarian cancer harboring a BRCA mutation.

Animal-plant interactions are ubiquitous and critical for tropical ecosystem functioning. Neotropical rodents perform key ecosystem functions such as seed dispersal and predation, however few information is available regarding their ecological interactions. Here, we reported the interaction between the Sumichrast’s Vesper Rat, Nyctomys sumichrasti, and the plant Jacaratia spinosa in the Central Caribbean of Costa Rica. Rodents were observed feeding on the flesh and seeds of the plants. Natural history observations such as this are critical to the knowledge on the feeding links of poorly known Neotropical rodents.

Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson's disease, whose ultimate goal is the detection by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contain a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades because the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to implement a classification system which uses two of the most well-known CNN architectures, LeNet and AlexNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.

BackgroundPerioperative chemotherapy (CT) or neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced gastric (GC) or gastroesophageal junction cancer (GEJC) has been shown to improve survival compared to an exclusive surgical approach. However, most patients retain a poor prognosis due to important relapse rates. Population pharmacokinetic-pharmacodynamic (PK/PD) modeling may allow identifying at risk-patients. We aimed to develop a mechanistic PK/PD model to characterize the relationship between the type of neoadjuvant therapy, histopathologic response and survival times in locally advanced GC and GEJC patients.MethodsPatients with locally advanced GC and GEJC treated with neoadjuvant CT with or without preoperative CRT were analyzed. Clinical response was assessed by CT-scan and EUS. Pathologic response was defined as a reduction on pTNM stage compared to baseline cTNM. Metastasis development risk and overall survival (OS) were described using the population approach with NONMEM 7.3. Model evaluation was performed through predictive checks.ResultsA low correlation was observed between clinical and pathologic TNM stage for both T (R = 0.32) and N (R = 0.19) categories. A low correlation between clinical and pathologic response was noticed (R = -0.29). The OS model adequately described the observed survival rates. Disease recurrence, cTNM stage ≥3 and linitis plastica absence, were correlated to a higher risk of death.ConclusionOur model adequately described clinical response profiles, though pathologic response could not be predicted. Although the risk of disease recurrence and survival were linked, the identification of alternative approaches aimed to tailor therapeutic strategies to the individual patient risk warrants further research.

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.

The rise of neuroimaging in research and clinical practice, together with the development of new machine learning techniques has strongly encouraged the Computer Aided Diagnosis (CAD) of different diseases and disorders. However, these algorithms are often tested in proprietary datasets to which the access is limited and, therefore, a direct comparison between CAD procedures is not possible. Furthermore, the sample size is often small for developing accurate machine learning methods. Multi-center initiatives are currently a very useful, although limited, tool in the recruitment of large populations and standardization of CAD evaluation. Conversely, we propose a brain image synthesis procedure intended to generate a new image set that share characteristics with an original one. Our system focuses on nuclear imaging modalities such as PET or SPECT brain images. We analyze the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, we can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space. The system has been evaluated on different functional neuroimaging datasets assessing the: resemblance of the synthetic images with the original ones, the differences between them, their generalization ability and the independence of the synthetic dataset with respect to the original. The synthetic images maintain the differences between groups found at the original dataset, with no significant differences when comparing them to real-world samples. Furthermore, they featured a similar performance and generalization capability to that of the original dataset. These results prove that these images are suitable for standardizing the evaluation of CAD pipelines, and providing data augmentation in machine learning systems -e.g. in deep learning-, or even to train future professionals at medical school.

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI.

18F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson's disease (PD) that allows us to examine postsynaptic dopamine D2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of 18F-DMFP-PET images is concentrated in the striatum. However, other regions can also be useful for diagnostic purposes. An appropriate delimitation of the regions of interest contained in 18F-DMFP-PET data is crucial to improve the automatic diagnosis of PD. In this manuscript we propose a novel methodology to preprocess 18F-DMFP-PET data that improves the accuracy of computer aided diagnosis systems for PD. First, the data were segmented using an algorithm based on Hidden Markov Random Field. As a result, each neuroimage was divided into 4 maps according to the intensity and the neighborhood of the voxels. The maps were then individually normalized so that the shape of their histograms could be modeled by a Gaussian distribution with equal parameters for all the neuroimages. This approach was evaluated using a dataset with neuroimaging data from 87 parkinsonian patients. After these preprocessing steps, a Support Vector Machine classifier was used to separate idiopathic and non-idiopathic PD. Data preprocessed by the proposed method provided higher accuracy results than the ones preprocessed with previous approaches.

The Rufous Tree Rat, Diplomys labilis, is known to range geographically from Central Panamá to Colombia and probably Ecuador. It occurs in a variety of habitat types where it moves by the branches in the high canopy at night. We report the Rufous Tree Rat for Costa Rica, ranging outside its geographical distribution about 490 km west from its type locality.