Food Chemicals and Epigenetic Targets: An Epi Food Chemical Database.

There is increasing awareness of epigenetics's importance in understanding disease etiologies and developing novel therapeutics. An increasing number of publications in the past few years reflect the renewed interest in epigenetic processes and their relationship with food chemicals. However, there needs to be a recent study that accounts for the most recent advances in the area by associating the chemical structures of food and natural product components with their biological activity. Here, we analyze the status of food chemicals and their intersection with natural products in epigenetic research. Using chemoinformatics tools, we compared quantitatively the chemical contents, structural diversity, and coverage in the chemical space of food chemicals with reported epigenetic activity. As part of this work, we built and curated a compound database of food and natural product chemicals annotated with structural information, an epigenetic target activity profile, and the main source of the food chemical or natural product, among other relevant features. The compounds are cross-linked with identifiers from other major public databases such as FooDB and the collection of open natural products, COCONUT. The compound database, the "Epi Food Chemical Database", is accessible in HTML and CSV formats at https://github.com/DIFACQUIM/Epi_food_Chemical_Database.

Chemical Multiverse and Diversity of Food Chemicals.

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Art driven by visual representations of chemical space.

Science and art have been connected for centuries. With the development of new computational methods, new scientific disciplines have emerged, such as computational chemistry, and related fields, such as cheminformatics. Chemoinformatics is grounded on the chemical space concept: a multi-descriptor space in which chemical structures are described. In several practical applications, visual representations of the chemical space of compound datasets are low-dimensional plots helpful in identifying patterns. However, the authors propose that the plots can also be used as artistic expressions. This manuscript introduces an approach to merging art with chemoinformatics through visual and artistic representations of chemical space. As case studies, we portray the chemical space of food chemicals and other compounds to generate visually appealing graphs with twofold benefits: sharing chemical knowledge and developing pieces of art driven by chemoinformatics. The art driven by chemical space visualization will help increase the application of chemistry and art and contribute to general education and dissemination of chemoinformatics and chemistry through artistic expressions. All the code and data sets to reproduce the visual representation of the chemical space presented in the manuscript are freely available at https://github.com/DIFACQUIM/Art-Driven-by-Visual-Representations-of-Chemical-Space- . Scientific contribution: Chemical space as a concept to create digital art and as a tool to train and introduce students to cheminformatics.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Moderate intensity aerobic training reduces the signs of peripheral sensitization in a mouse model of type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) is a global health problem for many reasons including the comorbidities, such as diabetic neuropathy (DPN), which is the most common. It has been suggested that aerobic training can improve metabolic health in individuals with T2DM. Still, the effect of aerobic training on DPN signs and its relationship with serum levels of tumor necrosis tumor alpha (TNF-α), an essential molecule in T2DM development, is unknown. We evaluated the effect of two intensities of aerobic training in adult male C57BL/6 mice divided into six groups: sedentary control (CTRL), control with low-intensity training (CTRL-LI), control with moderate-intensity training (CTRL-MI), T2DM sedentary (T2DM), T2DM with low-intensity training (T2DM-LI), and T2DM with moderate-intensity training (T2DM-MI). We induced the T2DM model by combining a hypercaloric diet and low doses of streptozotocin. We measured serum TNF-α levels and correlated them with peripheral sensitization and the cardinal signs of T2DM in mice. Moderate intensity aerobic training decreased the symptoms of DPN and improved metabolic health in T2DM. Interestingly, decreased TNF-α serum levels correlated with reduced peripheral thermal sensitivity and mechanical sensitivity by aerobic training. Moderate intensity aerobic training counteracts the development and symptoms of DPN and improve metabolic health in T2DM. Decreased TNF-α correlates with reduced peripheral thermal sensitivity and mechanical sensitivity by aerobic training.

Chemical Multiverse: An Expanded View of Chemical Space.

Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community's attention. The large- and ultra-large chemical spaces are associated with the significant increase in the number of compounds that can potentially be made and exist and the increasing number of experimental and calculated descriptors, that are emerging that encode the molecular structure and/or property aspects of the molecules. Due to the importance and continued evolution of compound libraries, herein, we discuss definitions proposed in the literature for chemical space and emphasize the convenience, discussed in the literature to use complementary descriptors to obtain a comprehensive view of the chemical space of compound data sets. In this regard, we introduce the term chemical multiverse to refer to the comprehensive analysis of compound data sets through several chemical spaces, each defined by a different set of chemical representations. The chemical multiverse is contrasted with a related idea: consensus chemical space.

Design, development, and experimental setup of near-field electrospinning with a sharp electrode: Influence of procedural parameters on the 3D nanofiber structure.

A near-field electrospinning configuration has been developed to fabricate 3D structures by layer-by-layer stacking. The system or experimental setup consists of a high voltage source, a syringe pump, and the electrospinning equipment which has been designed and built. It works with Arduino Uno as a controller for adjusting the procedural parameters through OpenBuilds CONTROL software using a firmware preloaded on the Arduino Uno. The proposed experimental configuration consists of a collinear arrangement between the spinner and the sharp electrode, which move in the XY directions, keeping the same disposition; this arrangement is designed with the aim of manipulating the electric field (EF) and reducing instabilities associated with the process. The displacement speed (DS) and the distance of work adjust automatically to modify nanofiber features, which improves the flexibility of the system. In order to be efficient and set the EF profile, this was simulated using COMSOL Multiphysics® software. Nylon 6,6 polymeric fiber films have been electrospun to evaluate the efficiency of the system setup and the influence of parameters. The fiber morphology is studied by scanning electron microscopy and the chemical structure features are studied by infrared spectroscopy. Parameters such as voltage and DS are studied experimentally and analyzed to determine their effects on the control of fiber deposition. Stacking of up to 15 layers was obtained where the structural characteristics notably depend on the operating parameters.

Efficient Chemical Surface Modification Protocol on SiO2 Transducers Applied to MMP9 Biosensing.

The bioreceptor immobilization process (biofunctionalization) turns to be one of the bottlenecks when developing a competent and high sensitivity label-free biosensor. Classical approaches seem to be effective but not efficient. Although biosensing capacities are shown in many cases, the performance of the biosensor is truncated by the inefficacious biofunctionalization protocol and the lack of reproducibility. In this work, we describe a unique biofunctionalization protocol based on chemical surface modification through silane chemistry on SiO2 optical sensing transducers. Even though silane chemistry is commonly used for sensing applications, here we present a different mode of operation, applying an unusual silane compound used for this purpose (3-Ethoxydimethylsilyl)propylamine, APDMS, able to create ordered monolayers, and minimizing fouling events. To endorse this protocol as a feasible method for biofunctionalization, we performed multiple surface characterization techniques after all the process steps: Contact angle (CA), X-ray photoelectron spectroscopy (XPS), ellipsometry, and fluorescence microscopy. Finally, to evidence the outputs from the SiO2 surface characterization, we used those SiO2 surfaces as optical transducers for the label-free biosensing of matrix metalloproteinase 9 (MMP9). We found and demonstrated that the originally designed protocol is reproducible, stable, and suitable for SiO2-based optical sensing transducers.

Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products.

Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material.

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop.

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

Acute Inflammatory Mediators in Young Adult Patients with COVID-19 in Mexico.

Young adults (18-40 years old) are an active population with high risk of infection and transmission of COVID-19. They are considered a low-risk population due to its low 1.0% case fatality rate (CFR). Despite their high clinical usefulness to prevent fatal cases, inflammatory and coagulation biomarkers studies are limited. For this reason, we performed a retrospective cohort study with COVID-19 patients in Hermosillo, Mexico, to assess inflammation, coagulopathy profile, and severity outcomes in young adults. We analyzed blood samples to determine the neutrophil/lymphocyte ratio (NLR), neutrophil/monocyte ratio (NMR), lymphocyte/monocyte ratio (LMR), platelet/lymphocyte ratio (PLR), and C-reactive protein (C-RP). We included epidemiological features and comorbidities, and compared them to the severity status. Only 359 COVID-19-confirmed young adults were included in the ambulatory (44.8%), hospitalized (42.9%), and death (12%) severity groups. Laboratory results showed an increase in NMR, LMR, and C-RP associated with the aggravated patients. Additionally, obesity, arterial hypertension, and type-2 diabetes mellitus (T2DM) were associated with the COVID-19 severity outcome. We found that 9.1% and 30.3% of young adults presented the novel COVID-19-associated coagulopathy (CAC) and the risk of CAC, respectively. These parameters can be considered independent biomarkers reflecting an enhanced inflammatory process related to the COVID-19 prognosis.

Knowledge and perceptions about Covid-19 in the marginalized population of southeastern Mexico.

Not available.

Fragment Library of Natural Products and Compound Databases for Drug Discovery.

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.

A Fragment Library of Natural Products and its Comparative Chemoinformatic Characterization.

We report a comprehensive fragment library with 205,903 fragments derived from the recently published Collection of Open Natural Products (COCONUT) data set with more than 400,000 non-redundant natural products. The natural products-based fragment library was compared with other two fragment libraries herein generated from ChEMBL (biologically relevant compounds) and Enamine-REAL (a large on-demand collection of synthetic compounds), both used as reference data sets with relevance in drug discovery. It was found that there is a large diversity of unique fragments derived from natural products and that the entire structures and fragments derived from natural products are more diverse and structurally complex than the two reference compound collections. During this work we introduced a novel visual representation of the chemical space based on the recently published concept of statistical-based database fingerprint. The compounds and fragments libraries from natural products generated and analyzed in this work are freely available.

Engineering vertically interrogated interferometric sensors for optical label-free biosensing.

In this work, we review the technology of vertically interrogated optical biosensors from the point of view of engineering. Vertical sensors present several advantages in the fabrication processes and in the light coupling systems, compared with other interferometric sensors. Four different interrelated aspects of the design are identified and described: sensing cell design, optical techniques used in the interrogation, fabrication processes, fluidics, and biofunctionalization of the sensing surface. The designer of a vertical sensor should decide carefully which solution to adopt on each aspect prior to finally integrating all the components in a single platform. Complexity, cost, and reliability of this platform will be determined by the decisions taken on each of the design process. We focus on the research and experience acquired by our group during last years in the field of optical biosensors.

Identification of inhalable rutile and polycyclic aromatic hydrocarbons (PAHs) nanoparticles in the atmospheric dust.

Addressing the presence of rutile nanoparticles (NPs) in the air is a work in progress, and the development of methodologies for the identification of NPs in atmospheric dust is essential for the assessment of its toxicological effects. To address this issue, we selected the fast growing desertic city of Hermosillo in northern Mexico. Road dust (n = 266) and soils (n = 10) were sampled and bulk Ti-contents were tested by portable X-ray fluorescence. NPs were extracted from atmospheric dust by PM1.0-PTFE filters and further characterized by Confocal Raman Microscopy, Energy-dispersive X-ray spectroscopy (EDS) coupled to Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). Results showed (i) the average concentration of Ti in road dust (3447 mg kg-1) was similar to natural values and worldwide urban dusts; (ii) the bulk geochemistry was not satisfactory for Ti-NPs identification; (iii) 76% of the total extracted PM1.0 sample corresponded to NPs; (iv) mono-microaggregates of rutile NPs were identified; (v) ubiquitous polycyclic aromatic hydrocarbons (PAHs) were linked to NPs. The genotoxicity of rutile and PAHs, in connection with NPs content, make us aware of a crucial emerging environmental issue of significant health concern, justifying further research in this field.

Effect of antimicrobial nanocomposites on Vibrio cholerae lifestyles: Pellicle biofilm, planktonic and surface-attached biofilm.

Vibrio cholerae is an important human pathogen causing intestinal disease with a high incidence in developing countries. V. cholerae can switch between planktonic and biofilm lifestyles. Biofilm formation is determinant for transmission, virulence and antibiotic resistance. Due to the enhanced antibiotic resistance observed by bacterial pathogens, antimicrobial nanomaterials have been used to combat infections by stopping bacterial growth and preventing biofilm formation. In this study, the effect of the nanocomposites zeolite-embedded silver (Ag), copper (Cu), or zinc (Zn) nanoparticles (NPs) was evaluated in V. cholerae planktonic cells, and in two biofilm states: pellicle biofilm (PB), formed between air-liquid interphase, and surface-attached biofilm (SB), formed at solid-liquid interfaces. Each nanocomposite type had a distinctive antimicrobial effect altering each V. cholerae lifestyles differently. The ZEO-AgNPs nanocomposite inhibited PB formation at 4 µg/ml, and prevented SB formation and eliminated planktonic cells at 8 µg/ml. In contrast, the nanocomposites ZEO-CuNPs and ZEO-ZnNPs affect V. cholerae viability but did not completely avoid bacterial growth. At transcriptional level, depending on the nanoparticles and biofilm type, nanocomposites modified the relative expression of the vpsL, rbmA and bap1, genes involved in biofilm formation. Furthermore, the relative abundance of the outer membrane proteins OmpT, OmpU, OmpA and OmpW also differs among treatments in PB and SB. This work provides a basis for further study of the nanomaterials effect at structural, genetic and proteomic levels to understand the response mechanisms of V. cholerae against metallic nanoparticles.

Automated Chemical Sensing Unit Integration for Parallel Optical Interrogation.

We report the integration of an automated chemical optical sensing unit for the parallel interrogation of 12 BICELLs in a sensing chip. The work was accomplished under the European Project Enviguard (FP7-OCEAN-2013-614057) with the aim of demonstrating an optical nano-biosensing unit for the in-situ detection of various chemical pollutants simultaneously in oceanic waters. In this context, we designed an optical sensing chip based on resonant nanopillars (R-NPs) transducers organized in a layout of twelve biophotonic sensing cells (BICELLs). The sensing chip is interrogated in reflection with a 12-channels optical spectrometer equipped with an embedded computer-on-chip performing image processing for the simultaneous acquisition and analysis (resonant mode fitting) of the 12 spectra. A microfluidic chip and an automated flow control system composed of four pumps and a multi-path micro-valve makes it possible to drive different complex protocols. A rack was designed ad-hoc for the integration of all the modules. As a proof of concept, fluids of different refractive index (RI) were flowed in the system in order to measure the time response (sensogram) of the R-NPs under optical reflectance, and assess the sensors' bulk sensitivity (285.9 ± 16.4 nm/RIU) and Limit of Detection (LoD) (2.95 × 10-6 RIUS). The real-time response under continuous flow of a sensor chip based on R-NP is showed for the first time, obtaining 12 sensograms simultaneously, featuring the unit as a potential excellent multiplexed detection system. These results indicate the high potential of the developed chemical sensing unit to be used for in-situ, multiplex and automatic optical biosensing.

On the Determination of Uncertainty and Limit of Detection in Label-Free Biosensors.

A significant amount of noteworthy articles reviewing different label-free biosensors are being published in the last years. Most of the times, the comparison among the different biosensors is limited by the procedure used of calculating the limit of detection and the measurement uncertainty. This article clarifies and establishes a simple procedure to determine the calibration function and the uncertainty of the concentration measured at any point of the measuring interval of a generic label-free biosensor. The value of the limit of detection arises naturally from this model as the limit at which uncertainty tends when the concentration tends to zero. The need to provide additional information, such as the measurement interval and its linearity, among others, on the analytical systems and biosensor in addition to the detection limit is pointed out. Finally, the model is applied to curves that are typically obtained in immunoassays and a discussion is made on the application validity of the model and its limitations.