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Mechanical characterization supposes a key step in the development of cultured meat to help mimicking the sensorial properties of already existing commercial products based on traditional meat. This work presents two well stablished methods that can help studying cultured meat mechanical characteristics: texture profile analysis (double compression test) and rheology. These techniques provide data about the elastic and viscous behaviour of the samples but also values about other texture characteristics such as springiness, cohesiveness, chewiness and resilience. In this work, we present a comparison of cultured meat-based samples with commercial of the shelf common meat products (sausage, turkey and chicken breast). Results show that both Young’s and Shear modulus in the cultured meat samples can be compared to commercial products in order to understand its properties. The texture characteristics for the cultured meat studied, show values within the range of commercial products. These results demonstrate the applicability of this methodology for the adjustment of mechanical properties of cultured meat products.

Over the last few years, there has been growing interest in measuring the contractile force (CF) of engineered muscle tissues to evaluate their functionality. However, there are still no standards available for selecting the most suitable experimental platform, measuring system, culture protocol, or stimulation patterns. Consequently, the high variability of published data hinders any comparison between different studies. We have identified that cantilever deflection, post deflection, and force transducers are the most commonly used configurations for CF assessment in 2D and 3D models. Additionally, we have discussed the most relevant emerging technologies that would greatly complement CF evaluation with intracellular and localized analysis. This review provides a comprehensive analysis of the most significant advances in CF evaluation and its critical parameters. In order to compare contractile performance across experimental platforms, we have used the specific force (sF, kN/m 2 ), CF normalized to the calculated cross-sectional area (CSA). However, this parameter presents a high variability throughout the different studies, which indicates the need to identify additional parameters and complementary analysis suitable for proper comparison. We propose that future contractility studies in skeletal muscle constructs report detailed information about construct size, contractile area, maturity level, sarcomere length, and, ideally, the tetanus-to-twitch ratio. These studies will hopefully shed light on the relative impact of these variables on muscle force performance of engineered muscle constructs. Prospective advances in muscle tissue engineering, particularly in muscle disease models, will require a joint effort to develop standardized methodologies for assessing CF of engineered muscle tissues.

Most methods for the detection of nucleic acids require many reagents and expensive and bulky instrumentation. Here, we report the development and testing of a graphene-based field-effect transistor that uses clustered regularly interspaced short palindromic repeats (CRISPR) technology to enable the digital detection of a target sequence within intact genomic material. Termed CRISPR–Chip, the biosensor uses the gene-targeting capacity of catalytically deactivated CRISPR-associated protein 9 (Cas9) complexed with a specific single-guide RNA and immobilized on the transistor to yield a label-free nucleic-acid-testing device whose output signal can be measured with a simple handheld reader. We used CRISPR–Chip to analyse DNA samples collected from HEK293T cell lines expressing blue fluorescent protein, and clinical samples of DNA with two distinct mutations at exons commonly deleted in individuals with Duchenne muscular dystrophy. In the presence of genomic DNA containing the target gene, CRISPR–Chip generates, within 15 min, with a sensitivity of 1.7 fM and without the need for amplification, a significant enhancement in output signal relative to samples lacking the target sequence. CRISPR–Chip expands the applications of CRISPR–Cas9 technology to the on-chip electrical detection of nucleic acids. An electrical biosensor combining CRISPR–Cas9 and a graphene field-effect transistor detects target genes in purified genomic samples at high sensitivity, within 15 minutes, and without the need for amplification.

Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient’s cognition, eventually leading to the inability to perform everyday tasks without assistance. Since dementia is an incurable disease, early detection plays an important role in delaying its progression. Because of this, tools and methods have been developed to help accurately diagnose patients in their early stages. State-of-the-art methods have shown that the use of syntactic-type linguistic features provides a sensitive and noninvasive tool for detecting dementia in its early stages. However, these methods lack relevant semantic information. In this work, we propose a novel methodology, based on the semantic features approach, by using sentence embeddings computed by Siamese BERT networks (SBERT), along with support vector machine (SVM), K-nearest neighbors (KNN), random forest, and an artificial neural network (ANN) as classifiers. Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer’s disease, provided by the DementiaBank Pitt Corpus database. To quantify the relevance of the extracted features for the dementia classification task, we calculated the mutual information score, which demonstrates a dependence between our features and the MMSE score. The experimental classification performance metrics, such as the accuracy, precision, recall, and F1 score (77, 80, 80, and 80%, respectively), validate that our methodology performs better than syntax-based methods and the BERT approach when only the linguistic features are used.

Electrical impedance has emerged as a powerful tool for real-time, label-free, and non-invasive monitoring of cellular processes. Here, we employed an impedance-based assay to characterize the myogenic process of control and dystrophic human myoblasts. First, we conducted a comprehensive analysis of control myoblast differentiation, assessing the effects of initial seeding density and various extracellular matrix coatings. We also evaluated the influence of electrode presence and current application, both of which improved myoblast alignment. Immortalized myoblasts from Duchenne muscular dystrophy patients exhibited marked alterations in early differentiation and maturation, which were readily detected via impedance measurements. We further compared two differentiation protocols using one control and one dystrophic representative cell line. While both protocols supported the formation of mature myotubes, impedance profiles differed depending on the culture medium. Notably, we identified the protocol with superior impedance profile reproducibility over the culture lifespan. Finally, we successfully assessed calcium homeostasis in control and dystrophic myotubes differentiated on 96-well impedance plates. Our findings underscore the potential of impedance-based assays for monitoring myogenesis and identifying disease-associated phenotypes. Moreover, 96-well impedance plates represent a robust tool for high-throughput and high-content functional analysis in muscle disease modeling and therapeutic screening.

University teachers have adapted to different situations during the development of distance learning due to the pandemic caused by the COVID-19 virus. This study was conducted by assigning a data collection instrument to 993 teachers who are part of 15 technological universities (TUs) and 7 polytechnic universities (PUs) to determine how they were affected by COVID-19. The questions asked were related to the social, economic, academic, emotional, and health effects experienced. The results show that 63% of the teachers working online complained that online teaching invaded their family privacy; 56% pointed out that working from home and the virtual classes affected their performance as teachers; 90% of the teachers thought that they dedicated too much extra to preparing for their classes; 15% were stressed; 4% felt negative under the new teaching scheme of virtual classes; finally, 38% of the teachers stated that repeated interaction with electronic devices had a lot of negative impacts on their emotional wellbeing. By means of a G-test, it was determined that gender was independent from the studied effects. Through a multiple correspondence analysis (MCA), it was determined that, of the total number of teachers who responded to the questionnaire, half were comfortable with the online teaching model and the other half were not. The most impacted effects were the economic, training and connectivity independently from the gender.

The contingency measures put in place by the government during COVID-19 exposed the students to a new condition to which they must adapt. To understand how the students perceive and cope during the unplanned, changed learning mode, we conducted a study using an evaluation tool which seeks to understand the effect of the contingency measures associated with the emergence of the COVID-19 virus on the students. By assigning a data collection instrument to students who are part of 15 technological universities (TUs) and 7 polytechnic universities (PUs), we determined how they were affected by COVID-19. The questions intended to evaluate the social, economic, academic, emotional, and health effects experienced. A total of 6596 students were assessed in the study representing an appropriate percentage of the Mexican students. The outcome of the study showed that 12% of the students agreed with the online approach to learn adopted because of the contingency. A total of 39% reported that they have a good environmental space for online learning, 32% reported that they mostly take their classes via their mobile phones, and 3% said they lacked access to an internet facility and as such could not take their classes. A total of 14% reported that they have little access to the internet, while 42% reported that they regularly have internet, and both complained that internet fluctuation significantly affects their academic performance. Comparing the different modes of teaching, 52% believe an in-person class is the best approach to learning, but 22% agreed that a hybrid system will be effective. Through a multiple correspondence analysis (MCA) it was determined that, in the effects, there was no significant difference in relation to gender. The effects that most impacted the students were economic, connectivity, and a lack of physical activity.

The objective of this study is to evaluate the relationship between public expenditure for education and human capital on economic growth in Honduras from 1990 to 2020, using the instrumental variables (IV) method, which incorporates the components of public spending on education and human capital, in addition to a set of control variables. The time series were extracted from the World Bank online databases. The results show that there is no correlation between public expenditure for education and economic growth; they also suggest that human capital is not contributing to economic growth, confirming that human capital accumulation is not fully developing. Finally, of the set of control variables considered key by the literature and on which social and economic development depends to a large extent, these would be preventing sustained economic growth, so the government and the population have enormous challenges to overcome.

The main purpose of this study is to assess the effect of current public expenditure on education and human capital on economic growth in Central America between 1992 and 2021. In this context, data on education spending and human capital for Guatemala, Honduras, El Salvador, Nicaragua, and Costa Rica were analyzed using a panel data approach with Driscoll–Kraay standard errors. The time series were primarily obtained from the online databases of the World Bank, UNESCO, and national public sources. The results show a positive and significant effect of current education expenditure and human capital formation on the economic growth of Central America. This research provides empirical evidence on a topic that has been scarcely examined in the Central American regional context, and its findings constitute relevant input for scholars, practitioners, and policymakers.

The main focus of this research was to develop an approach using statistical tools and Data envelopment analysis (DEA) to tackling productivity measurements and benchmarking problems in electrical conductor manufacturing environment. In the present work, a tooling efficiency study was carried out with a nozzle used for the manufacture of 23-AWG wires. The efficiency of five types of tooling, four non-Mexican-manufactured types and one Mexican-manufactured type, were compared. Analysis of Variance (ANOVA) and the Tukey test were applied. Six factors were considered that influence of the performance of the tooling during the manufacturing process: productivity, quality, time, machine, operator, and color of the insulating material, but the research work focuses on the efficiency of the tooling die-nozzle. The results demonstrated that two die-nozzle models exhibited the best performance; one of them was the Mexican model, surpassed by a non-Mexican model, the capability process index Cpk = 1.26 manifested a better performance for the 3DND die-nozzle according to the statistical analysis and the tests performed. Subsequently, through a super-efficiency DEA model of inputs-oriented with non-decreasing returns to scale (NDRS). The results obtained in the statistical analysis were corroborated using this technique, its application combined with statistical tools represents an innovation for knowledge in manufacturing processes of electrical conductors. Input data were obtained at a manufacturer of electrical conductors supplier of the automotive sector in the Querétaro City of Mexico.