Light Chain Multiple Myeloma Presenting As Secondary Cutaneous Amyloidosis: A Case Report on an Uncommon Systemic Manifestation.

Light chain multiple myeloma presenting as secondary cutaneous amyloidosis is an uncommon systemic manifestation, posing diagnostic challenges. We present a case of an elderly woman with a history of hemorrhoidal disease, who sought medical attention for what she thought was rectal bleeding. Initial examination revealed an ulcerative vulvar lesion. After extensive evaluation by different medical fields, two skin and a bone marrow biopsies, the diagnosis was finally confirmed. This case emphasizes interdisciplinary collaboration, comprehensive evaluation, and awareness of rare multiple myeloma manifestations. It highlights the importance of considering systemic implications even in localized presentations.

Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

Evaluating Deep Learning models for predicting ALK-5 inhibition.

Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the performance of Deep Learning models in comparison to Random Forest, and Support Vector Regression for predicting the biological activity (pIC50) of ALK-5 inhibitors as candidates to treat cancer. The generalization power of the models was assessed by internal and external validation procedures. A deep neural network model obtained the best performance in this comparative study, achieving a coefficient of determination of 0.658 on the external validation set with mean square error and mean absolute error of 0.373 and 0.450, respectively. Additionally, the relevance of the chemical descriptors for the prediction of biological activity was estimated using Permutation Importance. We can conclude that the forecast model obtained by the deep neural network is suitable for the problem and can be employed to predict the biological activity of new ALK-5 inhibitors.

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery.

Discovering (or planning) a new drug candidate involves many parameters, which makes this process slow, costly, and leading to failures at the end in some cases. In the last decades, we have witnessed a revolution in the computational area (hardware, software, large-scale computing, etc.), as well as an explosion in data generation (big data), which raises the need for more sophisticated algorithms to analyze this myriad of data. In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data. According to IEEE, computational intelligence involves the theory, the design, the application, and the development of biologically and linguistically motivated computational paradigms. In addition, CI encompasses three main methodologies: neural networks (NN), fuzzy systems, and evolutionary computation. In particular, artificial neural networks have been successfully applied in medicinal chemistry studies. A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data. Therefore, in this mini-review we will briefly outline the present scope, advances, and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative structure-activity relationships (QSAR) and virtual screening (VS) of databases containing thousands of compounds.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges.

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r2training = 0.761, q2 = 0.656, r2test = 0.746, MSEtest = 0.132 and MAEtest = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSEtest values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r2test = 0.824, MSEtest = 0.088 and MAEtest = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r2test = 0.811, MSEtest = 0.100 and MAEtest = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and µ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Influence of contact points on the performance of caries detection methods in approximal surfaces of primary molars: an in vivo study.

This in vivo study aimed to evaluate the influence of contact points on the approximal caries detection in primary molars, by comparing the performance of the DIAGNOdent pen and visual-tactile examination after tooth separation to bitewing radiography (BW). A total of 112 children were examined and 33 children were selected. In three periods (a, b, and c), 209 approximal surfaces were examined: (a) examiner 1 performed visual-tactile examination using the Nyvad criteria (EX1); examiner 2 used DIAGNOdent pen (LF1) and took BW; (b) 1 week later, after tooth separation, examiner 1 performed the second visual-tactile examination (EX2) and examiner 2 used DIAGNOdent again (LF2); (c) after tooth exfoliation, surfaces were directly examined using DIAGNOdent (LF3). Teeth were examined by computed microtomography as a reference standard. Analyses were based on diagnostic thresholds: D1: D 0 = health, D 1 ­D 4 = disease; D2: D 0 , D 1 = health, D 2 ­D 4 = disease; D3: D 0 ­D 2 = health, D 3 , D 4 = disease. At D1, the highest sensitivity/specificity were observed for EX1 (1.00)/LF3 (0.68), respectively. At D2, the highest sensitivity/ specificity were observed for LF3 (0.69)/BW (1.00), respectively. At D3, the highest sensitivity/specificity were observed for LF3 (0.78)/EX1, EX2 and BW (1.00). EX1 showed higher accuracy values than LF1, and EX2 showed similar values to LF2. We concluded that the visual-tactile examination showed better results in detecting sound surfaces and approximal caries lesions without tooth separation. However, the effectiveness of approximal caries lesion detection of both methods was increased by the absence of contact points. Therefore, regardless of the method of detection, orthodontic separating elastics should be used as a complementary tool for the diagnosis of approximal noncavitated lesions in primary molars.

Copulation is necessary for the completion of a gonotrophic cycle in the tick Rhipicephalus sanguineus (Latreille, 1806) (Acari: Ixodidae).

Ovarian development and egg maturation are essential stages in animal reproduction. For bisexual ixodid ticks, copulation is an important prerequisite for the completion of the gonotrophic cycle. In this study, we aimed to characterize the morpho-histological changes in the ovary and oocytes of the tick Rhipicephalus sanguineus, together with the identification of feeding and reproductive parameters associated with mating. Virgin and cross-mated females (with R. turanicus males) weighed 60% less at full engorgement than females mated conspecifically. In addition, the oocytes of these females did not develop to the same advanced stages as those of the conspecifically mated females. Sequencing of a 250-bp ITS-2 fragment in eggs that originated from a cross between an R. sanguineus female and an R. turanicus male showed a genotype similar (except by a deletion of 1 thymine) to that observed in the mother, arguing against fertilization by a trans-specific male. These findings suggest that male sex peptides are species-specific molecules that influence both full engorgement and oocyte maturation. Mechanical stimulation of the gonopore alone was insufficient for the completion of the entire process of vitellogenesis.

Influência dos anti-inflamatórios na movimentação ortodôntica / The influence of antinflammatory drugs on orthodontical movement

Foi realizada revisão bibliográfica quanto à influência dos anti-inflamatórios e da dieta na fisiologia óssea e taxa de movimentação dentária. Os trabalhos analisados mostraram que quando diferentes classes de medicamentos são utilizadas por longo tempo ocorre influência na remodelação óssea, mas as modificações na taxa de movimentação dentária dependem também de parâmetros cinéticos do fármaco e do metabolismo basal do animal experimental ou do ser humano estudado. Esta relação não se mostrou linear, sendo importante para o ortodontista ter exatidão no planejamento e no controle clínico radiográfico, individualizando o tratamento ortodôntico dos pacientes que utilizam medicações sistêmicas de uso contínuo.