Impaired CO2 sensitivity of astrocytes in a mouse model of Rett syndrome

Rett syndrome is a prototypical neurological disorder characterised by abnormal breathing pattern and reduced ventilatory CO2 sensitivity. Medullary astrocytes are a crucial component of central CO2 /pH chemosensitivity. This study tested the hypotheses that methyl-CpG-binding protein 2 (MeCP2) deficient medullary astrocytes are (i) unable to produce/release appropriate amounts of lactate, and/or (ii) unable to sense changes in PCO2/[H(+) ]. We found no differences in tonic or hypoxia-induced release of lactate from the ventral surface of the medulla oblongata or cerebral cortex between MeCP2-knockout and wild-type mice. Respiratory acidosis triggered robust [Ca(2+) ]i responses in wild-type astrocytes residing near the ventral surface of the medulla oblongata. CO2 -induced [Ca(2+) ]i responses in astrocytes were dramatically reduced in conditions of MeCP2 deficiency. These data suggest that (i) 'metabolic' function of astrocytes in releasing lactate into the extracellular space is not affected by MeCP2 deficiency, and (ii) MeCP2 deficiency impairs the ability of medullary astrocytes to sense changes in PCO2/[H(+) ]

An experimental comparison of several PWM controllers for a single-phase AC-DC converter

Published versio

Epidemiological evaluation of subclinical mastitis of dairy cows in Greece

ΔΕΝ ΔΙΑΤΙΘΕΤΑΙ ΠΕΡΙΛΗΨΗSubclinical mastitis, diagnosed by elevated somatic cell count (SCC) in milk, is an important monitoring parameter of dairy cows’ udder health, related to their productivity and welfare. The present retrospective study aims to evaluate the epidemiology of subclinical mastitis (SCM) among the 37 herds of the Holstein Association of Greece participating in the milk quality recording system “ΙΩ”, from the start of 2015 until the end of 2018. The herds’ inclusion criterion was the consistency of monthly SCC recording throughout at least one full year between 2015 and 2018, with a maximum interval of 61 days between two consecutive monthly SCC recordings. Twenty-six herds (8630 cows) in 2015, thirty herds (10763 cows) in 2016, thirty herds (10945 cows) in 2017 and twenty-six herds (9597 cows) in 2018 were included. The prevalence of SCM and chronic SCM, the incidence rate of new cases of SCM, as well as the average somatic cell score and bulk tank milk SCC were determined for each of the four years. The results indicate a progressive deterioration of udder health from the onset of the cow’s productive life until culling. A year-over-year increase in the number of cows with subclinical mastitis led to an overall SCM prevalence of 34.5%, chronic SCM prevalence of 26.9% and a bulk tank milk SCC of 463000 cells/mL, in 2018. The average somatic cell score, a base 2logarithm of individual cow’s SCC, was found persistently above the subclinical mastitis indicative cut-off in all four years, with a peak in 2018. At herd level, the incidence rate of new SCM cases was 12 new cases / 100 cows / month; the highest incidence rate was observed in the early lactation stage group (1-60 days-in-milk), in all four years, reaching a peak of 31 new cases / 100 cows / month, in 2018. In 2018, prevalence of heifers’ SCM and chronic SCM was23.4% and 16.9%, respectively. Despite the adequate average 305-days milk yield (9608 kg in 2018), the results were indicative of poor udder health status, pointed out by reduced duration of cows’ productive life (less than 3 lactations)and lower milk quality (elevated SCC). The severity and wide spreading of subclinical mastitis in Greek dairy herds highlights the necessity of a national mastitis control program, aiming to improve the productive efficacy, management decisions accuracy and quality of produced milk

Novel drug-target interactions via link prediction and network embedding

BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. RESULTS: We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. CONCLUSIONS: The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04650-w

A tool kit for rapid cloning and expression of recombinant antibodies

Over the last four decades, molecular cloning has evolved tremendously. Efficient products allowing assembly of multiple DNA fragments have become available. However, cost-effective tools for engineering antibodies of different specificities, isotypes and species are still needed for many research and clinical applications in academia. Here, we report a method for one-step assembly of antibody heavy- and light-chain DNAs into a single mammalian expression vector, starting from DNAs encoding the desired variable and constant regions, which allows antibodies of different isotypes and specificity to be rapidly generated. As a proof of principle we have cloned, expressed and characterized functional recombinant tumor-associated antigen-specific chimeric IgE/κ and IgG(1)/κ, as well as recombinant grass pollen allergen Phl p 7 specific fully human IgE/λ and IgG(4)/λ antibodies. This method utilizing the antibody expression vectors, available at Addgene, has many applications, including the potential to support simultaneous processing of antibody panels, to facilitate mechanistic studies of antigen-antibody interactions and to conduct early evaluations of antibody functions