ABSTRACT. Endophytic microorganisms represent promising alternatives for obtaining new drugs of biotechnological importance. In this study, the endophytic species Acremonium cavaraeanum (A1a) isolated from Cocos nucifera was cultivated for the production of secondary metabolites, and its extracts and fractions were evaluated by the dilution method (MIC). The EtOAc extracts and MeOH fractions were tested against Gram-positive and -negative bacteria, and had an MIC of 125 µg/mL when evaluated in the EtOAc extract (EBI). The EtOAc extract (EBII) had an MIC of 62.25 µg/mL for Staphylococcus aureus and an MIC between 125 and 250 µg/mL for Gram-negative bacteria. The methanolic fractions showed activity with MIC between 125 and 250 µg/mL for all bacteria tested. The IGS region of the rDNA repeat unit of genomic DNA was analyzed by PCR/RFLPs, including endonucleases PstI, BamHII, HinfI, and EcoRI. The physical maps showed different restriction sites for the 6 Acremonium sp isolates, and revealed 5 RFLP patterns. The results showed that isolates of the same Acremonium species exhibited variation in this specific region. The sequences of ITS1-5.8S-ITS2 regions were aligned by Clustal W using the neighbor joining method, which grouped the isolates into 5 distinct clusters. This study aimed to evaluate the genetic diversity of A. cavaraeanum crops exhibiting antibacterial activity. The results of this study indicate that different fungal genetic isolates have biotechnological potential for the production of active bio-compounds against several human pathogens.

The study of Externally Visible Characteristics (EVC) of pigmentation associated with SNPs (Single Nucleotide Polymorphisms) has become a target in the forensic field due to the possibility of phenotypically characterizing an individual. In Brazil, there are few data that shows the evaluation of some these markers, so further studies are necessary to understand better the pigmentation process related to genetic markers. The aim of this study was to test the association between 8 SNPs  present in HIrisplex tool and EVC to provide a starting point for the development of prediction models for heterogeneous populations like the one in Pernambuco. Were evaluated 176 individuals by associations between self-reported eye, hair and skin color data and polymorphisms. Artificial intelligence tools were used for the prediction models. Significant associations were found between rs1800404 (OCA2), rs6058017 (ASIP), rs16891982 (SLC45A2) and rs1426654 (SLC24A5) with (EVC). The prediction models evaluated showed satisfactory prediction rates, rates above 60% for skin color and above 70% for eyes and hair. The associations found in our data show the importance of SNPs evaluation used in DNA Phenotyping, because of its ability to provide new information in the context of criminal investigations. Our data indicate that is possible to use molecular information to predict phenotypes in miscigenated populations, like the Brazilian population. These polymorphisms could be possible phenotypic predictors for the Pernambuco population.

Background Effective management of patients with infected wounds is a crucial concern. A delay in prescribing the appropriate antibacterial agent can lead to life-threatening clinical complications. Thus, the electronic nose technique (eNose) can provide a diagnostic aid tool that allows rapid and accurate identification of pathogens. Results This study examines the effectiveness of using eNoses to aid in the diagnosis of bacterially infected wounds. The systematic search in the literature retrieved 3,326 publications, of which 97 were for a complete review, and of these, 09 comprised the sample of this study. These studies involved the analysis of seven types of wounds, the most common being the infected skin wound. The most frequent bacteria were P. aeruginosa, E. coli and methicillin-susceptible Staphylococcus aureus (MSSA). The average accuracy of the eNoses in identifying these microorganisms was 95.13% for the training set and 91.5% for the test set, including the ability to differentiate between bacteria of the same genus but sensitive or resistant to antibiotics. Among the Artificial Intelligence techniques used to classify the models, the Support Vector Machine (SVM) was the most commonly used in the experiments. Conclusion The eNoses devices observed may have broad applicability in aiding diagnosis of wound infection through their high efficacy values. However, further research needs to explore the reduction of interferences in the accuracy of the application of Machine Learning algorithms.

Segundo dados da Organização Mundial da saúde (OMS), as doenças crônicas não transmissíveis (DCNT) são responsáveis por cerca de 71% dos óbitos em todo o mundo. Desse modo, ao longo dos anos algumas medidas vêm sendo tomadas para tentar reduzir esse índice. No que diz respeito ao uso de tecnologias nesse processo, existem algumas iniciativas no contexto do Aprendizado de Máquina (AM) que tentam encontrar formas que vão desde o auxílio ao diagnóstico até o suporte em determinados tipos de tratamentos. Visando isso, esse projeto tem como intuito apresentar uma ferramenta, baseada em um modelo de aprendizado de máquina, para auxiliar profissionais da saúde no diagnóstico das DCNT usando dados sintomáticos derivados da base “Chronic illness” da plataforma Kaggle. Como melhor resultado desse processo, foi escolhido um modelo de aprendizado baseado em técnicas de ensemble, onde a melhor precisão obtida chegou a ≈ 71,63 % para um número de 20 patologias, sendo esse modelo usado como base para a aplicação Chronic Illness Diagnosis Helper (CIDH), desenvolvida para uma prova de conceito inicial.

Abstract. Natural Interaction can be a good alternative to diversify

The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused by Candida spp., remains challenging in clinical practice. Blood culture, the current gold standard technique, suffers from lengthy turnaround times and limited sensitivity. To address these limitations, we propose a novel approach utilizing an Electronic Nose (E-nose) combined with Time Series-based classification techniques to analyze and identify Candida spp. rapidly from blood, using culture species of C. albicans, C.kodamaea ohmeri, C. glabrara, C. haemulonii, C. parapsilosis e C. krusei as control samples. This innovative method not only enhances diagnostic accuracy and reduces decision time for healthcare professionals in selecting appropriate treatments but also offers the potential for expanded usage and cost reduction due to the E-nose’s low production costs. Our experimental results demonstrate promising outcomes, with the Inception Time classifier achieving an impressive average accuracy of 97.46% during the test phase. This paper presents a groundbreaking advancement in the field, empowering medical practitioners with an efficient and reliable tool for early and precise identification of candidemia, ultimately leading to improved patient outcomes.