Microbiological Diversity and Associated Enzymatic Activities in Honey and Pollen from Stingless Bees from Northern Argentina.

Honey and pollen from Tetragonisca fiebrigi and Scaptotrigona jujuyensis, stingless bees from northern Argentina, presented a particular microbiological profile and associated enzymatic activities. The cultured bacteria were mostly Bacillus spp. (44%) and Escherichia spp. (31%). The phylogenetic analysis showed a taxonomic distribution according to the type of bee that was similar in both species. Microbial enzymatic activities were studied using hierarchical clustering. Bacillus spp. was the main bacterium responsible for enzyme production. Isolates with xylanolytic activity mostly presented cellulolytic activity and, in fewer cases, lipolytic activity. Amylolytic activity was associated with proteolytic activity. None of the isolated strains produced multiple hydrolytic enzymes in substantial amounts, and bacteria were classified according to their primary hydrolytic activity. These findings add to the limited knowledge of microbiological diversity in honey and pollen from stingless bees and also provide a physiological perspective of this community to assess its biotechnological potential in the food industry.

Machine learning enables non-Gaussian investigation of changes to peripheral nerves related to electrical stimulation.

Electrical stimulation of the peripheral nervous system (PNS) is becoming increasingly important for the therapeutic treatment of numerous disorders. Thus, as peripheral nerves are increasingly the target of electrical stimulation, it is critical to determine how, and when, electrical stimulation results in anatomical changes in neural tissue. We introduce here a convolutional neural network and support vector machines for cell segmentation and analysis of histological samples of the sciatic nerve of rats stimulated with varying current intensities. We describe the methodologies and present results that highlight the validity of the approach: machine learning enabled highly efficient nerve measurement collection, while multivariate analysis revealed notable changes to nerves' anatomy, even when subjected to levels of stimulation thought to be safe according to the Shannon current limits.

Comparative oil extraction from mutt (Myliobatis goodei) liver by enzymatic hydrolysis: free versus immobilized biocatalyst.

BACKGROUND: The development and fine-tuning of biotechnological processes for fish oil extraction constitute a very important focus to contribute to the development of a food industry based on fish consumption. This work lies in a comparative analysis of the oil extraction yield of Myliobatis goodei livers using free and immobilized enzymes. RESULTS: An immobilized biocatalyst was designed from the cell-free extract of a Bacillus sp. Mcn4. A complete factorial design was used to study the components of the bacterial culture medium and select the condition with the highest titers of extracellular enzymatic activities. Wheat bran had a significant effect on the culture medium composition for enzymatic production. The immobilized biocatalyst was designed by covalent binding of the proteins present in the cocktail retaining a percentage of different types of enzymatic activities (Mult.Enz@MgFe2 O4 ). Among the biocatalyst used, Alcalase® 2.4 L and Purazyme® AS 60 L (free commercial proteases) showed extraction yields of 87.39% and 84.25%, respectively, while Mult.Enz@MgFe2 O4 achieved a better one of 89.97%. The oils obtained did not show significant differences in their physical-chemical properties while regarding the fatty acid content, the oil extracted with Purazyme® AS 60 L showed a comparatively lower proportion of polyunsaturated fatty acids. CONCLUSIONS: Our results suggest that the use of by-products of M. goodei is a valid alternative and encourages the use of immobilized multienzyme biocatalysts for the treatment of complex substrates in the fishing industry. © 2023 Society of Chemical Industry.

Physical activity levels in various university majors amidst the COVID-19 pandemic: a comparative study / Nivel de actividad física en varias carreras universitarias en medio de la pandemia de COVID-19: Un estudio comparativo / Níveis de atividade física em várias carreiras universitárias em meio à pandemia de COVID-19: Um estudo comparativo

This study determined levels of physical activity in students by comparing them based on gender, faculty, and major among university students during the Covid-19 pandemic. The research followed a quantitative approach with a descriptive-comparative design. The study was conducted once per student, with the participation of 582 students of both genders. The International Physical Activity Questionnaire (IPAQ) was administered to the students using Google Forms, distributed through their institutional emails. The collected data were analyzed using the statistical software SPSS V.22.0. The independent samples t-test was employed to compare the energy expenditure between males and females, along with Cohen's d statistic to assess the effect size. Prior to these analyses, the Kolmogorov-Smirnov normality test and Levene's test were conducted. Results were considered significant when the p-value was <0.05. The findings indicate that males allocate more time to work than females. Additionally, it was observed that males exhibit a higher level of physical activity than females within the engineering field. Lastly, majors with the highest levels of physical activity per week were Physical Education and Nutrition. These outcomes shed light on the reality of physical activity levels among Chilean university students based on faculty and major. University authorities should consider promoting physical activity programs, particularly emphasizing women and majors such as engineering, architecture, and mathematics, which have shown lower levels of physical activity.

Este estudio determinó los niveles de actividad física en estudiantes comparando por sexo, facultad y carrera en estudiantes universitarios en tiempos de pandemia por de Covid-19. Este estudio tiene un enfoque de investigación cuantitativa con diseño descriptivo-comparativo. Este estudio se aplicó una sola vez por estudiante contando con la participación de 582 estudiantes de ambos sexos. A los estudiantes se les aplicó el cuestionario Internacional de Actividad Física (IPAQ) mediante Google forms haciéndoselos llegar sus correos institucionales. Los datos obtenidos se analizaron en el programa estadístico SPSS V.22.0. Se utilizo la prueba estadística T-student para muestras independientes para comparar el coste energético entre hombres y mujeres, además del estadístico d de Cohen para evaluar el tamaño del efecto. Antes de realizar estas evaluaciones se realizó la prueba de normalidad Kolmogorov Smirnov y prueba de Levene. Se consideraron resultados significativos cuando el valor de p fue <0.05. Los resultados indican que los hombres destinan mayor tiempo a trabajar que las mujeres, además se encontró que los hombres poseen mayor nivel de actividad física que las mujeres en ingeniería. Finalmente, los estudiantes de las carreras con mayor nivel de actividad física a la semana fueron Educación física y nutrición. Estos resultados dan cuenta de la realidad en universitarios chilenos respecto al nivel de actividad física por facultad y por carrera. Las autoridades universitarias deberían promover programas de actividad física enfatizando en mujeres y en carreras de ingeniería, arquitectura y matemática que han mostrado menores niveles de actividad física.

Este estudo determinou os níveis de atividade física em estudantes, comparando por gênero, faculdade e curso entre estudantes universitários durante a pandemia de Covid-19. A pesquisa seguiu uma abordagem quantitativa com um design descritivo-comparativo. O estudo foi conduzido uma única vez por estudante, com a participação de 582 estudantes de ambos os gêneros. O Questionário Internacional de Atividade Física (IPAQ) foi administrado aos estudantes por meio do Google Forms, distribuído por meio de seus e-mails institucionais. Os dados coletados foram analisados usando o software estatístico SPSS V.22.0. O teste t de amostras independentes foi empregado para comparar o gasto energético entre homens e mulheres, juntamente com a estatística d de Cohen para avaliar o tamanho do efeito. Antes dessas análises, o teste de normalidade de Kolmogorov-Smirnov e o teste de Levene foram conduzidos. Resultados foram considerados significantes quando o valor de p foi <0.05. Os resultados indicam que os homens dedicam mais tempo ao trabalho do que as mulheres. Adicionalmente, observou-se que os homens apresentam um nível mais elevado de atividade física do que as mulheres no campo da engenharia. Por fim, os cursos com os níveis mais altos de atividade física por semana foram Educação Física e Nutrição. Estes resultados lançam luz sobre a realidade dos níveis de atividade física entre estudantes universitários chilenos com base na faculdade e no curso. As autoridades universitárias devem considerar a promoção de programas de atividade física, especialmente enfatizando as mulheres e os cursos como engenharia, arquitetura e matemática, que mostraram níveis mais baixos de atividade física.

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling.

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

Tuning surface interactions on MgFe2O4 nanoparticles to induce interfacial hyperactivation in Candida rugosa lipase immobilization.

Lipase adsorption on solid supports can be mediated by a precise balance of electrostatic and hydrophobic interactions. A suitable fine-tuning could allow the immobilized enzyme to display high catalytic activity. The objective of this work was to investigate how pH and ionic strength fluctuations affected protein-support interactions during immobilization via physical adsorption of a Candida rugosa lipase (CRL) on MgFe2O5. The highest amount of immobilized protein (IP) was measured at pH 4, and an ionic strength of 90 mM. However, these immobilization conditions did not register the highest hydrolytic activity (HA) in the biocatalyst (CRLa@MgFe2O4), finding the best values also at acidic pH but with a slight shift towards higher values of ionic strength around 110 mM. These findings were confirmed when the adsorption isotherms were examined under different immobilization conditions so that the maximum measurements of IP did not coincide with that of HA. Furthermore, when the recovered activity was examined, a strong interfacial hyperactivation of the lipase was detected towards acidic pH and highly charged surrounding environments. Spectroscopic studies, as well as in silico molecular docking analyses, revealed a considerable involvement of surface hydrophobic protein-carrier interactions, with aromatic aminoacids, especially phenylalanine residues, playing an important role. In light of these findings, this study significantly contributes to the body of knowledge and a better understanding of the factors that influence the lipase immobilization process on magnetic inorganic oxide nanoparticle surfaces.

Interfacial Hyperactivation of Candida rugosa Lipase onto Ca2Fe2O5 Nanoparticles: pH and Ionic Strength Fine-Tuning to Modulate Protein-Support Interactions.

The current study provides a comprehensive look of the adsorption process of Candida rugosa lipase (CRL) on Ca2Fe2O5 iron oxide nanoparticles (NPs). Protein-support interactions were identified across a broad range of pH and ionic strengths (mM) through a response surface methodology, surface charge determination, and spectroscopic and in silico analyses. The maximum quantity of immobilized protein was achieved at an ionic strength of 50 mM and pH 4. However, this condition did not allow for the greatest hydrolytic activity to be obtained. Indeed, it was recorded at acidic pH, but at 150 mM, where evaluation of the recovered activity revealed hyperactivation of the enzyme. These findings were supported by adsorption isotherms performed under different conditions. Based on zeta potential measurements, electrostatic interactions contributed differently to protein-support binding under the conditions tested, showing a strong correlation with experimentally determined immobilization parameters. Raman spectra revealed an increase in hydrophobicity around tryptophan residues, whereas the enzyme immobilization significantly reduced the phenylalanine signal in CRL. This suggests that this residue was involved in the interaction with Ca2Fe2O2 and molecular docking analysis confirmed these findings. Fluorescence spectroscopy showed distinct behaviors in the CRL emission patterns with the addition of Ca2Fe2O5 at pH 4 and 7. The calculated thermodynamic parameters indicated that the contact would be mediated by hydrophobic interactions at both pHs, as well as by ionic ones at pH 4. In this approach, this work adds to our understanding of the design of biocatalysts immobilized in iron oxide NPs.

Implementation of clinical assistants in a pediatric oncology department: An impact analysis.

Bureaucratic and administrative tasks associated with health care provision have historically fallen on health care professionals, which is one among the factors contributing to low job satisfaction and lower productivity. Incorporating new professional roles that help to better respond to the needs of both patients and professionals can increase the quality and efficiency of service provision. This article aims to evaluate the impact of the clinical assistant's introduction in the Sant Joan de Déu Barcelona Children's Hospital's pediatric oncology department, in terms of (i) displacement of activity loads carried out by this new professional role and the consequent time freed up for physicians, (ii) physicians' satisfaction and (iii) efficiency of the new care model. This is an observational and retrospective study using administrative data based on the type of activity performed by clinical assistants and the measurement of the time freed up in favor of the physicians. The potential skill mix productivity increase, survey of physicians' satisfaction, and reduction in costs with the new model was analyzed. During the first year of its implementation in the pediatric oncology department, clinical assistants have performed 13,553 requests (69% of the total), representing a total saving of 266.83 hours or 6.67 workweeks of 40 hours. They performed 74% of outpatient surgical requests in the oncology department, 87% of day hospital requests and 54% of total requests in the outpatient consultations area. Physicians are overall satisfied with the new role and think they can use the time gained to do other things such as research or improving the quality of care. The role change allows reducing the cost per request by 56% in relation to the conventional model. In conclusion, the introduction of clinical assistants in the oncology department could be efficient to the extent that it displaces a significant part of the bureaucratic and administrative tasks previously performed by health care professionals and thus enables to reduce the cost of these processes. This delegation allows them to work more closely to the maximum of their competences and the physicians to have more time for higher added value clinical tasks and increase professional satisfaction.

Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models.

Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human.

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models.

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Xenopus Oocytes as a Powerful Cellular Model to Study Foreign Fully-Processed Membrane Proteins.

The use of Xenopus oocytes in electrophysiological and biophysical research constitutes a long and successful story, providing major advances to the knowledge of the function and modulation of membrane proteins, mostly receptors, ion channels, and transporters. Earlier reports showed that these cells are capable of correctly expressing heterologous proteins after injecting the corresponding mRNA or cDNA. More recently, the Xenopus oocyte has become an outstanding host-cell model to carry out detailed studies on the function of fully-processed foreign membrane proteins after their microtransplantation to the oocyte. This review focused on the latter overall process of transplanting foreign membrane proteins to the oocyte after injecting plasma membranes or purified and reconstituted proteins. This experimental approach allows for the study of both the function of mature proteins, with their native stoichiometry and post-translational modifications, and their putative modulation by surrounding lipids, mostly when the protein is purified and reconstituted in lipid matrices of defined composition. Remarkably, this methodology enables functional microtransplantation to the oocyte of membrane receptors, ion channels, and transporters from different sources including human post-mortem tissue banks. Despite the large progress achieved over the last decades on the structure, function, and modulation of neuroreceptors and ion channels in healthy and pathological tissues, many unanswered questions remain and, most likely, Xenopus oocytes will continue to help provide valuable responses.

LipMetE (Lipophilic Metabolism Efficiency) as a Simple Guide for Half-Life and Dosing Regimen Prediction of Oral Drugs.

The in vivo half-life is a key property of every drug molecule, as it determines dosing regimens, peak-to-trough ratios and often dose. However, half-life optimization can be challenging due to its multifactorial nature, with in vitro metabolic turnover, plasma protein binding and volume of distribution all impacting half-life. We here propose that the medicinal chemistry design parameter Lipophilic Metabolism Efficiency (LipMetE) can greatly simplify half-life optimization of neutral and basic compounds. Using mathematical transformations, examples from preclinical GABAA projects and clinical compounds with human pharmacokinetic data, we show that LipMetE is directly proportional to the logarithm of half-life. As the design parameter LipMetE can be swiftly calculated using the readily available parameters LogD, intrinsic clearance and fraction unbound in human liver microsomes or hepatocytes, this approach enables rational half-life optimization from the early stages of drug discovery projects.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (Vss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of Vss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

Molecular Events behind the Selectivity and Inactivation Properties of Model NaK-Derived Ion Channels.

Y55W mutants of non-selective NaK and partly K+-selective NaK2K channels have been used to explore the conformational dynamics at the pore region of these channels as they interact with either Na+ or K+. A major conclusion is that these channels exhibit a remarkable pore conformational flexibility. Homo-FRET measurements reveal a large change in W55-W55 intersubunit distances, enabling the selectivity filter (SF) to admit different species, thus, favoring poor or no selectivity. Depending on the cation, these channels exhibit wide-open conformations of the SF in Na+, or tight induced-fit conformations in K+, most favored in the four binding sites containing NaK2K channels. Such conformational flexibility seems to arise from an altered pattern of restricting interactions between the SF and the protein scaffold behind it. Additionally, binding experiments provide clues to explain such poor selectivity. Compared to the K+-selective KcsA channel, these channels lack a high affinity K+ binding component and do not collapse in Na+. Thus, they cannot properly select K+ over competing cations, nor reject Na+ by collapsing, as K+-selective channels do. Finally, these channels do not show C-type inactivation, likely because their submillimolar K+ binding affinities prevent an efficient K+ loss from their SF, thus favoring permanently open channel states.

Vernalization Requirement, but Not Post-Vernalization Day Length, Conditions Flowering in Carrot (Daucus carota L.).

Carrots require a certain number of cold hours to become vernalized and proceed to the reproductive stage, and this phenomenon is genotype-dependent. Annual carrots require less cold than biennials to flower; however, quantitative variation within annuals and biennials also exists, defining a gradient for vernalization requirement (VR). The flowering response of carrots to day length, after vernalization has occurred, is controversial. This vegetable has been described both as a long-day and a neutral-day species. The objective of this study was to evaluate flowering time and frequency in response to different cold treatments and photoperiod regimes in various carrot genotypes. To this end, three annual genotypes from India, Brazil, and Pakistan, and a biennial carrot from Japan, were exposed to 7.5 °C during 30, 60, 90, or 120 days, and then transferred to either long day (LD) or short day (SD) conditions. Significant variation (p < 0.05) among the carrot genotypes and among cold treatments were found, with increased flowering rates and earlier onset of flowering being associated with longer cold exposures. No significant differences in response to photoperiod were found, suggesting that post-vernalization day length does not influence carrot flowering. These findings will likely impact carrot breeding and production of both root and seed, helping in the selection of adequate genotypes and sowing dates to manage cold exposure and day-length for different production purposes.

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery.

Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a "bottom-up" approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.

Translating pharmacology models effectively to predict therapeutic benefit.

Many in vitro and in vivo models are used in pharmacological research to evaluate the role of targeted proteins in a disease. Understanding the translational relevance and limitation of these models for analyzing a drug's disposition, pharmacokinetic/pharmacodynamic (PK/PD) profile, mechanism, and efficacy, is essential when selecting the most appropriate model of the disease of interest and predicting clinically efficacious doses of the investigational drug. Selected animal models used in ophthalmology, infectious diseases, oncology, autoimmune diseases, and neuroscience are reviewed here. Each area has specific challenges around translatability and determination of an efficacious dose: new patient-specific dosing methods may help overcome these limitations.

Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models.

Although electrical stimulation is an established treatment option for multiple central nervous and peripheral nervous system diseases, its effects on the tissue and subsequent safety of the stimulation are not well understood. Therefore, it is crucial to design stimulation protocols that maximize therapeutic efficacy while avoiding any potential tissue damage. Further, the stimulation levels need to be adjusted regularly to ensure that they are safe even with the changes to the nerve due to long-term stimulation. Using the latest advances in computing capabilities and machine learning approaches, we developed computational models of peripheral nerve stimulation based on very high-resolution cross-sectional images of the nerves. We generated nerve models constructed from non-stimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to examine how the current density distribution is affected by nerve damage. Using our in-house numerical solver, the Admittance Method (AM), we computed the induced current distribution inside the nerves and compared the current penetration for healthy and damaged nerves. Our computational results indicate that when the nerve is damaged, primarily evidenced by the decreased nerve fiber packing, the current penetrates deeper inside the nerve than in the healthy case. As safety limits for electrical stimulation of biological tissue are still debated, we ultimately aim to utilize our computational models to determine refined safety criteria and help design safer and more efficacious electrical stimulation protocols.

Hybridization and polyploidization effects on LTR-retrotransposon activation in potato genome.

Hybridization and polyploidization are major forces in plant evolution and potatoes are not an exception. It is proposed that the proliferation of Long Terminal Repeat-retrotransposons (LTR-RT) is related to genome reorganization caused by hybridization and/or polyploidization. The main purpose of the present work was to evaluate the effect of interspecific hybridization and polyploidization on the activation of LTR-RT. We evaluated the proliferation of putative active LTR-RT in a diploid hybrid between the cultivated potato Solanum tuberosum and the wild diploid potato species S. kurtzianum, allotetraploid lines derived from this interspecific hybrid and S. kurtzianum autotetraploid lines (ktz-autotetraploid) using the S-SAP (sequence-specific amplified polymorphism) technique and normalized copy number determination by qPCR. Twenty-nine LTR-RT copies were activated in the hybrid and present in the allotetraploid lines. Major LTR-RT activity was detected in Copia-27, Copia-12, Copia-14 and, Gypsy-22. According to our results, LTR-RT copies were activated principally in the hybrid, there was no activation in allotetraploid lines and only one copy was activated in the autotetraploid.