First Description of Marinoquinoline Derivatives' Activity against Toxoplasma gondii.

Toxoplasmosis is a globally prevalent zoonotic disease with significant clinical implications, including neurotoxoplasmosis, a leading cause of cerebral lesions in AIDS patients. The current pharmacological treatments for toxoplasmosis face clinical limitations, necessitating the urgent development of new therapeutics. Natural sources have yielded diverse bioactive compounds, serving as the foundation for clinically used derivatives. The exploration of marine bacteria-derived natural products has led to marinoquinolines, which feature a pyrroloquinoline core and demonstrate in vitro and in vivo anti-Plasmodium activity. This study investigates the in vitro anti-Toxoplasma gondii potential of six marinoquinoline derivatives. Additionally, it conducts absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions, and evaluates the in vivo efficacy of one selected compound. The compounds displayed half-maximal effective concentration (EC50) values between 1.31 and 3.78 µM and half-maximal cytotoxic concentration (CC50) values ranging from 4.16 to 30.51 µM, resulting in selectivity indices (SI) from 3.18 to 20.85. MQ-1 exhibiting the highest in vitro SI, significantly reduced tachyzoite numbers in the peritoneum of RH-infected Swiss mice when it was orally administered at 12.5 mg/kg/day for eight consecutive days. Also, MQ-1 significantly reduced the cerebral parasite burden in chronically ME49 infected C57BL/6 mice when it was orally administered at 25 mg/kg/day for 10 consecutive days. These findings underscore the promising anti-T. gondii activity of marinoquinolines and their potential as novel therapeutic agents against this disease.

Alkylation of 6-thiopurine derivatives by the Mitsunobu reaction.

Alkylation of thiopurine derivatives with alcohols by the Mitsunobu reaction are reported in moderated to good yields. The method was applied in synthesis of number of thiopurine and thiopurine ribosides derivatives.

Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists' diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

N, N', N″-trisubstituted guanidines: Synthesis, characterization and evaluation of their leishmanicidal activity.

Leishmaniasis is a group of diseases caused by protozoan parasites from the genus Leishmania. There are estimated 1.3 million new cases annually with a mortality of 20,000-30,000 per year, when patients are left untreated. Current chemotherapeutic drugs available present high toxicity and low efficacy, the latter mainly due to the emergence of drug-resistant parasites, which makes discovery of novel, safe, and efficacious antileishmanial drugs mandatory. The present work reports the synthesis, characterization by ESI-MS, 1H and 13C NMR, and FTIR techniques as well as in vitro and in vivo evaluation of leishmanicidal activity of guanidines derivatives presenting lower toxicity. Among ten investigated compounds, all being guanidines containing a benzoyl, a benzyl, and a substituted phenyl moiety, LQOF-G2 (IC50-ama 5.6 µM; SI = 131.8) and LQOF-G7 (IC50-ama 7.1 µM; SI = 87.1) were the most active against L. amazonensis intracellular amastigote, showing low cytotoxicity to the host cells according to their selectivity index. The most promising compound, LQOF-G2, was further evaluated in an in vivo model and was able to decrease 60% of the parasite load in foot lesions at a dose of 0.25 mg/kg/day. Moreover, this guanidine derivative demonstrated reduced hepatotoxicity compared to other leishmanicidal compounds and did not show nephrotoxicity, as determined by the analyses of biomarkers of hepatic damage and renal function, which make this compound a potential new hit for therapy against leishmaniasis.

Principal Component Analysis applied to digital image compression / Utilização da Análise de Componentes Principais na compressão de imagens digitais

Objective: To describe the use of a statistical tool (Principal Component Analysis - PCA) for the recognition of patterns and compression, applying these concepts to digital images used in Medicine. Methods: The description of Principal Component Analysis is made by means of the explanation of eigenvalues and eigenvectors of a matrix. This concept is presented on a digital image collected in the clinical routine of a hospital, based on the functional aspects of a matrix. The analysis of potential for recovery of the original image was made in terms of the rate of compression obtained. Results: The compressed medical images maintain the principal characteristics until approximately one-fourth of their original size, highlighting the use of Principal Component Analysis as a tool for image compression. Secondarily, the parameter obtained may reflect the complexity and potentially, the texture of the original image. Conclusion: The quantity of principal components used in the compression influences the recovery of the original image from the final (compacted) image.

Objetivo: Descrever a utilização de uma ferramenta estatística (Análise de Componentes Principais ou Principal Component Analysis - PCA) para reconhecimento de padrões e compressão, aplicando esses conceitos em imagens digitais utilizadas na medicina. Métodos: A descrição da Análise de Componentes Principais é realizada por meio da explanação de autovalores e autovetores de uma matriz. Esse conceito é apresentado em uma imagem digital coletada na rotina clínica de um hospital, a partir dos aspectos funcionais de uma matriz. Foi feita a análise de potencial para recuperação da imagem original em termos de taxa de compressão obtida. Resultados: As imagens médicas comprimidas mantêm as características principais até aproximadamente um quarto de seu volume original, destacando o emprego da Análise de Componentes Principais como ferramenta de compressão da imagem. Secundariamente, o parâmetro obtido pode refletir a complexidade e, potencialmente, a textura da imagem original. Conclusão: A quantidade de componentes principais utilizada na compressão influencia a recuperação da imagem original a partir da imagem final (compactada).

Radial basis functions-simulated annealing classification of mammographic calcifications.

We investigated several approaches to classify mammographic calcifications as malignant or benign: a supervised classifier, multi-layer perceptron (MLP), a supervised and unsupervised classifier, a classifier based upon adaptive resonance theory with linear discriminant analysis (ART2LDA), and a classifier based upon nonlinear and combinational optimization techniques: RBF (radial basis functions)-simulated annealing. The classifiers were trained using shape factors extracted from 143 mammographic calcifications (79 malignant and 64 benign), adopting the leave-one-cut procedure. The classifiers' performance was compared in terms of the area under the ROC curve. The best result of 0.97 was obtained with RBF-simulated annealing, which was significantly better than the results obtained with MLP and ART2LDA, which were, respectively, 0.70 and 0.71.