Isolation and characterization of natural nano starch from amaranth starch

Nano starch exhibits many advantages for application in diverse fields. Amaranth starch consisted of starch particle aggregates, isolated amaranth starch, and few natural nano starch (NNS), while NNS (0.92 ± 0.12 μm) was successfully isolated for the first time. Compared with the isolated amaranth starch, NNS showed smaller particle size but larger molecular weight, suggesting that the molecules arranged densely. NNS had a weak A-type crystal structure because of its more content of short starch chains, but higher amylose content resulted in the increase of its gelatinization temperature. The special NNS, owning several different physicochemical properties from amaranth starch, can open new ways for the production and application of nano biomass materials.

Regulated High-Spin State and Constrained Charge Behavior of Active Cobalt Sites in Covalent Organic Frameworks for Promoting Electrocatalytic Oxygen Reduction

A novel type of covalent organic frameworks has been developed by assembling definite cobalt-nitrogen-carbon configurations onto carbon nanotubes using linkers that have varying electronic effects. This innovative approach has resulted in an efficient electrocatalyst for oxygen reduction, which is understood by a combination of in situ spectroelectrochemistry and the bond order theorem. The strong interaction between the electron-donating carbon nanotubes and the electron-accepting linker mitigates the trend of charge loss at cobalt sites, while inducing the generation of high spin state. This enhances the adsorption strength and electron transfer between the cobalt center and reactants/intermediates, leading to an improved oxygen reduction capability. This work not only presents an effective strategy for developing efficient non-noble metal electrocatalysts through reticular chemistry, but also provides valuable insights into regulating the electronic configuration and charge behavior of active sites in designing high-performance electrocatalysts.

Towards the molecular era of discriminating multiple lung cancers

In the era of histopathology-based diagnosis, the discrimination between multiple lung cancers (MLCs) poses significant uncertainties and has thus become a clinical dilemma. However, recent significant advances and increased application of molecular technologies in clonal relatedness assessment have led to more precision in distinguishing between multiple primary lung cancers (MPLCs) and intrapulmonary metastasis (IPMs). This review summarizes recent advances in the molecular identification of MLCs and compares various methods based on somatic mutations, chromosome alterations, microRNAs, and tumor microenvironment markers. The paper also discusses current challenges at the forefront of genomics-based discrimination, including the selection of detection technology, application of next-generation sequencing, and intratumoral heterogeneity (ITH). In summary, this paper highlights an entrance into the primary stage of molecule-based diagnostics.

XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores

BACKGROUND

Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over a predicted property. For this purpose, some XAI techniques calculate attribution scores associated with tokens of SMILES strings or with atoms of a molecule. While an association of a score with an atom can be directly visually represented on a molecule diagram, scores computed for SMILES non-atom tokens cannot. For instance, a substring [N+] contains 3 non-atom tokens, i.e., [, [Formula: see text], and ], and their attributions, depending on the model, are not necessarily revealing an influence of the nitrogen atom over the predicted property; for that reason, it is not possible to represent the scores on a molecule diagram. Moreover, SMILES's notation is complex, foregrounding the need for techniques to facilitate the analysis of explanations associated with their tokens.

RESULTS

We propose XSMILES, an interactive visualization technique, to explore explainable artificial intelligence attributions scores and support the interpretation of SMILES. Users can input any type of score attributed to atom and non-atom tokens and visualize them on top of a 2D molecule diagram coordinated with a bar chart that represents a SMILES string. We demonstrate how attributions calculated for SMILES strings can be evaluated and better interpreted through interactivity with two use cases.

CONCLUSIONS

Data scientists can use XSMILES to understand their models' behavior and compare multiple modeling approaches. The tool provides a set of parameters to adapt the visualization to users' needs and it can be integrated into different platforms. We believe XSMILES can support data scientists to develop, improve, and communicate their models by making it easier to identify patterns and compare attributions through interactive exploratory visualization.

Host immune response against leishmaniasis and parasite persistence strategies: A review and assessment of recent research

Leishmaniasis, a neglected parasitic disease caused by a unicellular protozoan of the genus Leishmania, is transmitted through the bite of a female sandfly. The disease remains a major public health problem and is linked to tropical and subtropical regions, with an endemic picture in several regions, including East Africa, the Mediterranean basin and South America. The different causative species display a diversity of clinical presentations; therefore, the immunological data on leishmaniasis are both scarce and controversial for the different forms and infecting species of the parasite. The present review highlights the main immune parameters associated with leishmaniasis that might contribute to a better understanding of the pathogenicity of the parasite and the clinical outcomes of the disease. Our aim was to provide a concise overview of the immunobiology of the disease and the factors that influence it, as this knowledge may be helpful in developing novel chemotherapeutic and vaccine strategies.

Molecular Chains: Arranging and Programming Logic Gates

One particularly fascinating vision for charge-operated devices is the controlled assembly of structures from single surface-deposited molecules. Here, we report on the assembly of linear clusters that consist of phthalocyanine (H₂Pc) molecules on a Ag(111) surface. The molecules are imaged as well as manipulated with a low-temperature scanning tunneling microscope (STM). Upon deprotonation of every second H₂Pc, the resulting HPc molecule exhibits an isomeric bistability which can be used as inputs in logic gates. Combining our STM measurements with density functional theory calculations we show that the HPc isomers exhibit a repulsive electrostatic interaction with adjacent H₂Pc molecules which, due to the asymmetric charge distribution on HPc, results in a counterclockwise or clockwise molecule tilt of the latter, thereby defining the logic 0 and 1 of the output. It is shown that information can be relayed along molecule chains over distances equivalent to at least nine molecules.

Protein molecular defect detection method based on a neural network algorithm

Proteins, as the largest macromolecules in the body, are among the most important components of the body and play very vital and important roles. These substances are made up of a series of amino acid chains that, depending on the type of protein, the number of these amino acids can reach several thousand. Proteins function differently depending on the type and location of their presence, including enzymatic activity to catalyze the process, identify microbes and cancer cells, transport substances such as respiratory gases, and signalize. In the biochemical experiment, the problem of optimizing the detection of protein molecular defects, because of the randomness of the information, parameters, selection and setting, limits the detection accuracy of protein molecular defects. Based on the characteristics of fast learning speed and a robust network of neural network algorithm, a protein molecular defect detection method based on a neural network algorithm was proposed. Firstly, the protein secondary structure was predicted by the method of protein secondary structure prediction based on the generalized regression neural network to obtain the protein structural features; secondly, the protein defective molecular sequence classification model based on the neural network was used to classify the protein defective molecular sequence to achieve the protein molecular defect detection. The results showed that the detection accuracy of the proposed method was very high, which meets the needs of protein molecular defect detection, and has some application advantages compared with similar detection methods.

Direct Observation of the Reduction of a Molecule on Nitrogen Pairs in Doped Graphene

Incorporating functional atomic sites in graphene is essential for realizing advanced two-dimensional materials. Doping graphene with nitrogen offers the opportunity to tune its chemical activity with significant charge redistribution occurring between molecules and substrate. The necessary atomic scale understanding of how this depends on the spatial distribution of dopants, as well as their positions relative to the molecule, can be provided by scanning tunneling microscopy. Here we show that a noncovalently bonded molecule such as CoPc undergoes a variable charge transfer when placed on N-doped graphene; on a nitrogen pair, it undergoes a redox reaction with an integral charge transfer whereas a lower fractional charge transfer occurs over a single nitrogen. Thus, the charge state of molecules can be tuned by suitably tailoring the conformation of dopant atoms.

PhyteByte: identification of foods containing compounds with specific pharmacological properties

Background

Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds.

Results

PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator.

Conclusions

PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

Genetic and molecular basis of epilepsy-related cognitive dysfunction

Epilepsy is a common neurological disease characterized by recurrent seizures. About 70 million people were affected by epilepsy or epileptic seizures. Epilepsy is a complicated complex or symptomatic syndromes induced by structural, functional, and genetic causes. Meanwhile, several comorbidities are accompanied by epileptic seizures. Cognitive dysfunction is a long-standing complication associated with epileptic seizures, which severely impairs quality of life. Although the definitive pathogenic mechanisms underlying epilepsy-related cognitive dysfunction remain unclear, accumulating evidence indicates that multiple risk factors are probably involved in the development and progression of cognitive dysfunction in patients with epilepsy. These factors include the underlying etiology, recurrent seizures or status epilepticus, structural damage that induced secondary epilepsy, genetic variants, and molecular alterations. In this review, we summarize several theories that may explain the genetic and molecular basis of epilepsy-related cognitive dysfunction.

Binding of Nucleic Acid Components to the Serpentinite-Hosted Hydrothermal Mineral Brucite

The adsorption of nucleic acid components onto the serpentinite-hosted hydrothermal mineral brucite has been investigated experimentally by determining the equilibrium adsorption isotherms in aqueous solution. Thermodynamic characterization of the adsorption data has been performed using the extended triple-layer model (ETLM) to establish a model for the stoichiometry and equilibrium constants of surface complexes. Infrared characterization of the molecule-mineral complexes has helped gain insight into the molecular functional groups directly interacting with the mineral surface. Quantum mechanical calculations have been carried out to identify the possible complexes formed on surfaces by nucleic acid components and their binding configurations on mineral surfaces, both in the presence of water molecules and in water-free conditions. The results indicate that brucite favors adsorption of nucleotides with respect to nucleosides and nucleobases from dilute aqueous environments. The surface of this mineral is able to induce well-defined orientations of the molecules through specific molecule-mineral interactions. This result suggests plausible roles of the mineral brucite in assisting prebiotic molecular self-organization. Furthermore, the detection of the infrared spectroscopic features of such building blocks of life adsorbed on brucite at very low degrees of coverage provides important support to life detection investigations.

Direct quantitative measurement of the C═O⋅⋅⋅H-C bond by atomic force microscopy

The hydrogen atom-the smallest and most abundant atom-is of utmost importance in physics and chemistry. Although many analysis methods have been applied to its study, direct observation of hydrogen atoms in a single molecule remains largely unexplored. We use atomic force microscopy (AFM) to resolve the outermost hydrogen atoms of propellane molecules via very weak C═O⋅⋅⋅H-C hydrogen bonding just before the onset of Pauli repulsion. The direct measurement of the interaction with a hydrogen atom paves the way for the identification of three-dimensional molecules such as DNAs and polymers, building the capabilities of AFM toward quantitative probing of local chemical reactivity.

A Real Life Comparison between Allergenic Extracts and Allergenic Molecules

Serum IgE assay is a mainstay step in the allergy work up. Allergenic extracts and molecular components are available at present. This real life study compared the serum specific IgE levels against allergenic extracts with allergenic molecules in patients allergic to Parietaria, Betulaceae, and mites. This retrospective real life study included 489 subjects with respiratory allergy. Inclusion criteria were 1) documented diagnosis of allergic rhinitis (AR) and/or allergic asthma, and 2) documented allergy to Parietaria judaica (Par j) 2 (216 patients: 112 females, mean age 42 years), or to Betula verrucosa (Bet v) 1 (62 patients: 35 females, mean age 3 years), or Dermatophagoides pteronyssinus (Der p) 1 (211 patients: 107 females, mean age 34 years); and mono-allergy. Serum IgE, specific both for total/crude allergen extracts and individual purified/recombinant allergens, were assessed by ImmunoCap system. The serum IgE levels to birch extract were very strongly (R2=0.96) related to IgE to Bet v 1. There was a strong (R2=0.71) correlation between Dermatophagoides pteronyssinus IgE and Der p 1. A very strong (R2=0.87) correlation also existed between Parietaria extract IgE and Par j 2 IgE levels. However, there was discrepancy between percentages of positivity between allergenic extracts and molecules. Therefore, allergen molecular diagnostics may represent a useful way in allergy work up, but deserves caution in particular circumstances.

Intraspecific venom variation in the medically significant Southern Pacific Rattlesnake (Crotalus oreganus helleri): biodiscovery, clinical and evolutionary implications

Due to the extreme variation of venom, which consequently results in drastically variable degrees of neutralization by CroFab antivenom, the management and treatment of envenoming by Crotalus oreganus helleri (the Southern Pacific Rattlesnake), one of the most medically significant snake species in all of North America, has been a clinician's nightmare. This snake has also been the subject of sensational news stories regarding supposed rapid (within the last few decades) evolution of its venom. This research demonstrates for the first time that variable evolutionary selection pressures sculpt the intraspecific molecular diversity of venom components in C. o. helleri. We show that myotoxic β-defensin peptides (aka: crotamines/small basic myotoxic peptides) are secreted in large amounts by all populations. However, the mature toxin-encoding nucleotide regions evolve under the constraints of negative selection, likely as a result of their non-specific mode of action which doesn't enforce them to follow the regime of the classic predator-prey chemical arms race. The hemorrhagic and tissue destroying snake venom metalloproteinases (SVMPs) were secreted in larger amounts by the Catalina Island and Phelan rattlesnake populations, in moderate amounts in the Loma Linda population and in only trace levels by the Idyllwild population. Only the Idyllwild population in the San Jacinto Mountains contained potent presynaptic neurotoxic phospholipase A2 complex characteristic of Mohave Rattlesnake (Crotalus scutulatus) and Neotropical Rattlesnake (Crotalus durissus terrificus). The derived heterodimeric lectin toxins characteristic of viper venoms, which exhibit a diversity of biological activities, including anticoagulation, agonism/antagonism of platelet activation, or procoagulation, appear to have evolved under extremely variable selection pressures. While most lectin α- and β-chains evolved rapidly under the influence of positive Darwinian selection, the β-chain lectin of the Catalina Island population appears to have evolved under the constraint of negative selection. Both lectin chains were conspicuously absent in both the proteomics and transcriptomics of the Idyllwild population. Thus, we not only highlight the tremendous biochemical diversity in C. o. helleri's venom-arsenal, but we also show that they experience remarkably variable strengths of evolutionary selection pressures, within each toxin class among populations and among toxin classes within each population. The mapping of geographical venom variation not only provides additional information regarding venom evolution, but also has direct medical implications by allowing prediction of the clinical effects of rattlesnake bites from different regions. Such information, however, also points to these highly variable venoms as being a rich source of novel toxins which may ultimately prove to be useful in drug design and development.

BIOLOGICAL SIGNIFICANCE

These results have direct implications for the treatment of envenomed patients. The variable venom profile of Crotalus oreganus helleri underscores the biodiscovery potential of novel snake venoms.

Leptin resistance and insulin resistance

Leptin resistance and insulin resistance are the common pathology of various metabolic diseases, and make great contribution to the metabolic syndrome. Many researches have proved that the two processes affected each other, whereas the exploration of the resistance mechanism was often independently carried out. Here, we'd like to make a review focusing on the relationship between leptin resistance and insulin resistance, and their interactions at multiple levels.