Labrys portucalensis F11 efficiently degrades Di-(2-ethylhexyl) Phthalate

info:eu-repo/semantics/publishedVersio

Intraspecific effects of short-term elevated atmospheric co2 in yield and nutritional profile of phaseolus vulgaris

Legumes are key contributors of essential nutrients for human health, namely iron (Fe) and zinc (Zn), but they are one of the most sensitive plant families to elevated concentrations of atmospheric CO2 (eCO2), a major threat to global agriculture and human nutrition. Therefore, unravelling the effects underlying eCO2 responses on biomass yield and nutritional value is of utmost importance to anticipate potential negative effects on human nutrition and expedite mitigation strategies.info:eu-repo/semantics/publishedVersio

Common bean (Phaseolus vulgaris L.) nutritional profile & soil microbiome community is affected by elevated atmospheric carbon dioxide (CO2)

info:eu-repo/semantics/publishedVersio

More than a meat- or synthetic nitrogen fertiliser-substitute: a review of legume phytochemicals as drivers of ‘One Health’ via their influence on the functional diversity of soil- and gut-microbes

Legumes are essential to healthy agroecosystems, with a rich phytochemical content that impacts overall human and animal well-being and environmental sustainability. While these phytochemicals can have both positive and negative effects, legumes have traditionally been bred to produce genotypes with lower levels of certain plant phytochemicals, specifically those commonly termed as ‘antifeedants’ including phenolic compounds, saponins, alkaloids, tannins, and raffinose family oligosaccharides (RFOs). However, when incorporated into a balanced diet, such legume phytochemicals can offer health benefits for both humans and animals. They can positively influence the human gut microbiome by promoting the growth of beneficial bacteria, contributing to gut health, and demonstrating anti-inflammatory and antioxidant properties. Beyond their nutritional value, legume phytochemicals also play a vital role in soil health. The phytochemical containing residues from their shoots and roots usually remain in-field to positively affect soil nutrient status and microbiome diversity, so enhancing soil functions and benefiting performance and yield of following crops. This review explores the role of legume phytochemicals from a ‘one health’ perspective, examining their on soil- and gut-microbial ecology, bridging the gap between human nutrition and agroecological science

Hybrid model for early identification post-Covid-19 sequelae

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.info:eu-repo/semantics/publishedVersio

Challenges in the diagnosis and management of acromegaly : a focus on comorbidities

Q2Q1Introduction: Acromegaly is a rare, insidious disease resulting from the overproduction of growth hormone (GH) and insulin-like growth factor 1 (IGF-1), and is associated with a range of comorbidities. The extent of associated complications and mortality risk is related to length of exposure to the excess GH and IGF-1, thus early diagnosis and treatment is imperative. Unfortunately, acromegaly is often diagnosed late, when patients already have a wide range of comorbidities. The presence of comorbid conditions contributes significantly to patient morbidity/mortality and impaired quality of life. Methods: We conducted a retrospective literature review for information relating to the diagnosis of acromegaly, and its associated comorbidities using PubMed. The main aim of this review is to highlight the issues of comorbidities in acromegaly, and to reinforce the importance of early diagnosis and treatment. Findings and conclusions: Successful management of acromegaly goes beyond treating the disease itself, since many patients are diagnosed late in disease evolution, they present with a range of comorbid conditions, such as cardiovascular disease, diabetes, hypertension, and sleep apnea. It is important that patients are screened carefully at diagnosis (and thereafter), for common associated complications, and that biochemical control does not become the only treatment goal. Mortality and morbidities in acromegaly can be reduced successfully if patients are treated using a multimodal approach with comprehensive comorbidity management.https://orcid.org/0000-0002-8433-5435N/

Analysis of Synaptic Proteins in the Cerebrospinal Fluid as a New Tool in the Study of Inborn Errors of Neurotransmission

Abstract In a few rare diseases, specialised studies in cerebrospinal fluid (CSF) are required to identify the underlying metabolic disorder. We aimed to explore the possibility of detecting key synaptic proteins in the CSF, in particular dopaminergic and gabaergic, as new procedures that could be useful for both pathophysiological and diagnostic purposes in investigation of inherited disorders of neurotransmission. Dopamine receptor type 2 (D2R), dopamine transporter (DAT) and vesicular monoamine transporter type 2 (VMAT2) were analysed in CSF samplesfrom 30 healthy controls (11 days to 17 years) by western blot analysis. Because VMAT2 was the only protein with intracellular localisation, and in order to compare results, GABA vesicular transporter, which is another intracellular protein, was also studied. Spearman’s correlation and Student’s t tests were applied to compare optical density signals between different proteins. All these synaptic proteins could be easily detected and quantified in the CSF. DAT, D2R and GABA VT expression decrease with age, particularly in the first months of life, reflecting the expected intense synaptic activity and neuronal circuitry formation. A statistically significant relationship was found between D2R and DAT expression, reinforcing the previous evidence of DAT regulation by D2R. To our knowledge, there are no previous studies on human CSF reporting a reliable analysis of these proteins. These kinds of studies could help elucidate new causes of disturbed dopaminergic and gabaergic transmission as well as understanding different responses to L-dopa in inherited disorders affecting dopamine metabolism. Moreover, this approach to synaptic activity in vivo can be extended to different groups of proteins and diseases

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science