An improved expression and purification protocol enables the structural characterization of Mnt1, an antifungal target from Candida albicans.

BACKGROUND: Candida albicans is one of the most prevalent fungi causing infections in the world. Mnt1 is a mannosyltransferase that participates in both the cell wall biogenesis and biofilm growth of C. albicans. While the cell wall performs crucial functions in pathogenesis, biofilm growth is correlated with sequestration of drugs by the extracellular matrix. Therefore, antifungals targeting CaMnt1 can compromise fungal development and potentially also render Candida susceptible to drug therapy. Despite its importance, CaMnt1 has not yet been purified to high standards and its biophysical properties are lacking. RESULTS: We describe a new protocol to obtain high yield of recombinant CaMnt1 in Komagataella phaffii using methanol induction. The purified protein's identity was confirmed by MALDI-TOF/TOF mass spectroscopy. The Far-UV circular dichroism (CD) spectra demonstrate that the secondary structure of CaMnt1 is compatible with a protein formed by α-helices and ß-sheets at pH 7.0. The fluorescence spectroscopy results show that the tertiary structure of CaMnt1 is pH-dependent, with a greater intensity of fluorescence emission at pH 7.0. Using our molecular modeling protocol, we depict for the first time the ternary complex of CaMnt1 bound to its two substrates, which has enabled the identification of residues involved in substrate specificity and catalytic reaction. Our results corroborate the hypothesis that Tyr209 stabilizes the formation of an oxocarbenium ion-like intermediate during nucleophilic attack of the acceptor sugar, opposing the double displacement mechanism proposed by other reports. CONCLUSIONS: The methodology presented here can substantially improve the yield of recombinant CaMnt1 expressed in flask-grown yeasts. In addition, the structural characterization of the fungal mannosyltransferase presents novelties that can be exploited for new antifungal drug's development.

ECG Forecasting System Based on Long Short-Term Memory.

Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals' nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal's structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model's accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.

Crystal structure and interconversion of monomers and domain-swapped dimers of the walnut tree phytocystatin.

Biotechnological applications of phytocystatins have garnered significant interest due to their potential applications in crop protection and improve crop resistance to abiotic stress factors. Cof1 and Wal1 are phytocystatins derived from Coffea arabica and Juglans regia, respectively. These plants hold significant economic value due to coffee's global demand and the walnut tree's production of valuable timber and widely consumed walnuts with culinary and nutritional benefits. The study involved the heterologous expression in E. coli Lemo 21(DE3), purification by immobilized metal ion affinity and size exclusion chromatography, and biophysical characterization of both phytocystatins, focusing on isolating and interconverting their monomers and dimers. The crystal structure of the domain-swapped dimer of Wal1 was determined revealing two domain-swapped dimers in the asymmetric unit, an arrangement reminiscent of the human cystatin C structure. Alphafold models of monomers and Alphafold-Multimer models of domain-swapped dimers of Cof1 and Wal1 were analyzed in the context of the crystal structure. The methodology and data presented here contribute to a deeper understanding of the oligomerization mechanisms of phytocystatins and their potential biotechnological applications in agriculture.

Multiple keratotic projections on the palms and soles.

Spiny keratoderma (SKD) is a rare palmoplantar keratoderma that presents with few to numerous millimetric hyperkeratotic projections on the palms and soles. It has been described with both hereditary and acquired variants. The acquired form, which presents in older adults, has been associated with a variety of systemic diseases and malignant conditions. In patients suspected of having acquired spiny keratoderma, an evaluation for malignant conditions may be warranted. Treatment with topical keratolytics or topical and oral retinoids is usually insufficient. Herein, we present the case of a 58-year-old man diagnosed with idiopathic SKD.

Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices.

In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...].

A review on multimodal machine learning in medical diagnostics.

Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.

Identification of novel Zika virus NS3 protease inhibitors with different inhibition modes by integrative experimental and computational approaches.

Zika virus (ZIKV) infection is associated with severe neurological disorders and congenital malformation. Despite efforts to eradicate the disease, there is still neither vaccine nor approved drugs to treat ZIKV infection. The NS2B-NS3 protease is a validated drug target since it is essential to polyprotein virus maturation. In the present study, we describe an experimental screening of 2,320 compounds from the chemical library of the Muséum National d'Histoire Naturelle of Paris on ZIKV NS2B-NS3 protease. A total of 96 hits were identified with 90% or more of inhibitory activity at 10 µM. Amongst the most active compounds, five were analyzed for their inhibitory mechanisms by kinetics assays and computational approaches such as molecular docking. 2-(3-methoxyphenoxy) benzoic acid (compound 945) show characteristics of a competitive inhibition (Ki = 0.49 µM) that was corroborated by its molecular docking at the active site of the NS2B-NS3 protease. Taxifolin (compound 2292) behaves as an allosteric inhibitor whereas 3,8,9-trihydroxy-2-methyl-1H-phenalen-1-one (compound 128), harmol (compound 368) and anthrapurpurin (compound 1499) show uncompetitive inhibitions. These new NS2B-NS3 protease inhibitors are valuable hits to further hit-to-lead optimization.

COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review.

Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals' evolution of the disease.

Agreement between mechanical and digital skinfold callipers.

Background: Skinfold callipers are often used in clinical practice to estimate subcutaneous adipose tissue thickness. Recently, LipoTool emerged as a potential digital system to measure skinfolds, however comparisons with competing equipment are lacking. Aim: The aim of this study was to test the agreement between two competing skinfold callipers (digital and mechanical). Methods: The sample included 22 healthy male adult participants. A certified observer measured eight skinfolds twice using different skinfold callipers (digital and mechanical). Differences between equipment were tested using Wilcoxon signed rank test The distribution of error was examined using the normality test Results: Differences between skinfold callipers were significantly in five skinfolds: triceps (Z = -3.546; P < 0.001), subscapular (Z = -3.984; P < 0.001), suprailiac (Z = 3.024; P = 0.002), supraspinale (Z = 3.885; P < 0.001), abdominal (Z z = -2.937; P = 0.003), thigh (Z = -2.224; P = 0.026) and calf (Z = -2.052; P = 0.040). Differences between callipers were constant. Conclusions: Mechanical and digital callipers tended to record different values of skinfold thickness. Clinical examination should consider equipment-related variation in fat mass estimation.

Interaction models between peptide substrate and Alphavirus Protease nsP2 of Chikungunya and Mayaro and implications to the mechanism of action.

The Arbovirus (Arthropod-borne virus) is a group which comprises viruses whose transmission is carried out by arthropod vectors infecting vertebrates. Some arboviruses related to human diseases have been given considerable relevance as Chikungunya and Mayaro of the family Togaviridae, genus Alphavirus. The lack of proper specific treatment has prompted the requirement for deeper structural studies that could unveil leads to new drugs. Among possible targets, viral proteases are recognized as proteins with big potential. These proteins, termed nsP2 in Alphavirus, have the function of cleaving certain regions of the viral polyprotein, being vital to the viral cycle. In this research, we used docking and molecular dynamics to analyze the contact between the protease nsP2 of Alphavirus Chikungunya and Mayaro and substrates formed by peptides with ten amino acid residues. A model of the Mayaro nsP2 was constructed based on homologous proteases. Our study suggests that the glycine specificity motif, a region where a highly conserved glycine residue in position P2 of the protease substrate is positioned, facilitates the nucleophilic attack by assisting in placing the P1 carbonyl group carbon. Stabilization of different substrate regions maybe explained by relevant contacts with the enzyme. Besides that, the phi and psi angles in the outlier region of the Ramachandran plot found for the P2 glycine of the Chikungunya substrate seems to indicate the necessity of this residue that can accommodate angles not allowed to other residues.Communicated by Ramaswamy H. Sarma.

Contribution of Home Gardens to Sustainable Development: Perspectives from A Supported Opinion Essay.

Home gardening has a long history that started when humans became sedentary, being traditionally considered an accessible source of food and medicinal plants to treat common illnesses. With trends towards urbanization and industrialization, particularly in the post-World War II period, the importance of home gardens as important spaces for growing food and medicinal plants reduced and they began to be increasingly seen as decorative and leisure spaces. However, the growing awareness of the negative impacts of agricultural intensification and urbanization for human health, food quality, ecosystem resilience, and biodiversity conservation motivated the emergence of new approaches concerning home gardens. Societies began to question the potential of nearby green infrastructures to human wellbeing, food provisioning, and the conservation of traditional varieties, as well as providers of important services, such as ecological corridors for wild species and carbon sinks. In this context. and to foster adaptive and resilient social-ecological systems, our supported viewpoint intends to be more than an exhaustive set of perceptions, but a reflection of ideas about the important contribution of home gardens to sustainable development. We envision these humble spaces strengthening social and ecological components, by providing a set of diversified and intermingled goods and services for an increasingly urban population.