Drug Discovery by Automated Adaptation of Chemical Structure and Identity.

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito.

Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: -48.75%, for CO, -45.76%, for SO2, -42.17%, for PM2.5, and -63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.

The effect of national protest in Ecuador on PM pollution.

Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall - 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Chemical approaches in the development of natural nontoxic peptide Polybia-MP1 as a potential dual antimicrobial and antitumor agent.

Polybia-MP1 is a well-known natural antimicrobial peptide that has been intensively studied recently due to its therapeutic potential. MP1 exhibited not only potent antibacterial activity but also antifungal and anticancer properties. More importantly, MP1 shows relatively low hemolytic activity compared to other antimicrobial peptides having a similar origin. Thus, besides investigating possible mechanisms of action, great efforts have been invested to develop this peptide to become more "druggable". In this review, we summarized all the chemical approaches, both success and failure, that using MP1 as a lead compound to create modified analogs with better pharmacological properties. As there have been thousands of natural AMPs found and deposited in numerous databases, such useful information in both the success and failure will provide insight into the research and development of antimicrobial peptides and guiding for the next steps.

Association between carotid intima-media thickness and bone mineral density: a cross-sectional study in Vietnamese men and women aged 50 years and older.

OBJECTIVES: The association between osteoporosis and atherosclerosis remains controversial. We sought to define the relationship between carotid intima-media thickness and bone mineral density (BMD) in individuals of Vietnamese background. DESIGN AND SETTING: Cross-sectional study in Ho Chi Minh City, Vietnam. PARTICIPANTS: The study involved 1460 individuals (559 men) aged 50 years and older (average age 59 years) who were randomly recruited from the community. OUTCOME MEASURES: BMD at the femoral neck and lumbar spine was measured by dual-energy X-ray absorptiometry (Hologic, Waltham, Massachusetts, USA). Carotid intima-media thickness (cIMT) was measured using a Philips Ultrasonography (HD7XE). The presence of atherosclerotic plaque was ascertained for each individual. The association between cIMT and BMD was analysed by a multiple linear regression model. RESULTS: In unadjusted analysis, cIMT was positively associated with femoral neck BMD in men (p=0.005), but not in women (p=0.242). After adjusting for age, smoking, diabetes and hypertension, the association remained statistically significant in men (partial R2=0.005; p=0.015) but not in women (partial R2=0.008; p=0.369). When the analysis was limited to individuals aged 60 years and older, the association between cIMT and BMD was no longer statistically significant. There was no statistically significant association between cIMT and lumbar spine BMD in either men or women. CONCLUSIONS: In Vietnamese individuals aged 50 years and older, there is a clinically non-significant but statistically significant association between carotid intima-media thickness and BMD in men, not in women.

Relationship between mechanism of injury and neurocognitive functioning in OEF/OIF service members with mild traumatic brain injuries.

Military personnel deployed to combat theaters in Iraq and Afghanistan are at risk of sustaining mild traumatic brain injuries (mTBI) from causes such as improvised explosive devices, motor vehicle accidents, and falls. Despite the high incidence of mTBI in deployed personnel, questions remain about the effects of blast-related vs. non-blast-related mTBI on acute and long-term sequelae. This investigation is a retrospective review of service members who presented for evaluation of suspected mTBI and underwent neurocognitive screening evaluation, mTBI diagnosis was made by semistructured clinical interview. Only individuals in whom mechanism of injury could be determined (blast vs. non-blast) were included. Sixty individuals were included in the final sample: 32 with blast mTBI and 28 with non-blast mTBI. There were no differences between the blast-related and non-blast-related mTBI groups on age, time since injury, combat stress symptoms, or headache. Analysis of variance showed no significant between-group differences on any of the neurocognitive performance domains. Although speculation remains that the effects of primary blast exposure are unique, the results of this study are consistent with prior research suggesting that blast-related mTBI does not differ from other mechanisms of injury with respect to cognitive sequelae in the postacute phase.