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Phosphate recovery from wastewater is vital for both environmental sustainability and resource conservation, offering the dual benefit of reducing phosphate pollution while providing a valuable source of this essential nutrient. We previously reported an approach for synthesizing hydrous manganese oxide (HMO) nanoparticles within a polymeric cation-exchange membrane (CEM) to achieve a phosphate-selective mixed-matrix membrane (PhSMMM); however, the phosphate flux was lower than desired. Herein, we demonstrate a next-generation PhSMMM membrane with enhanced phosphate flux and selectivity. Experimental results confirm the successful incorporation of up to 28 wt% HMO nanoparticles into the polymeric CEM. The new PhSMMM exhibits a phosphate flux of 1.57 mmol∙m–2.hr–1 (an 8.5X enhancement), with selectivity over chloride, nitrate, and sulfate ions of 9, 11, and 104, respectively. This significant enhancement in phosphate flux marks a promising advancement in a sustainable solution for phosphate removal and recovery from wastewater.

Summary: Global warming concerns have led to emission regulations and various incentives for low-carbon technologies. Energy system models, which are used to examine how investments affect our ability to meet energy demand, are typically based on two assumptions: key parameters are assumed to be known deterministically and a multi-period energy transition plan is determined at one point in time. We argue that for a systematic generation and analysis of energy transition pathways, these assumptions should be relaxed and, accordingly, we propose methods to achieve that. First, we use stochastic programming (SP) to account for uncertainty in key parameters. Second, we pair SP with a sequential decision-making approach that represents how decisions can be updated as uncertainties unfold. Third, we use simulation-based methods to evaluate the quality of energy transitions. Importantly, we find that accounting for uncertainty, proactively and through feedback, yields pathways with diverse technology portfolios that are resilient to uncertainty.

In this work, we address a nationwide tactical planning for industrial gas supply chains, particularly argon. The proposed approaches follow as extensions of our previous work (Comp. & Chem. Eng., 161 (2022) 107778) in which a regional argon supply chain problem is addressed; in that work, both production and distribution could be represented in detail. Two different types of deliveries from the Air Separating Units (ASU) to customers, which involve single driver deliveries for short distance trips and sleeper team that require multiple days. The nationwide problem requires simplifications to keep the problem mathematically tractable, primarily the representation of production sites with different tier costs and the aggregation of customers in clusters. The regional problem addressed in our previous work is used as a benchmark case study for benchmarking. We then focus on a real-world problem that represents a nationwide argon supply chain. Despite the size of the models, near optimal solutions could be found in reasonable times. Finally, we highlight important features of the proposed approaches.

Protein–Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques. These methods not only improve predictive accuracy but also provide insights into functional sites, such as binding and catalytic residues. However, challenges such as limited high-resolution structural data and the need for effective negative sampling persist. Through the integration of experimental and computational tools, structure-based prediction paves the way for comprehensive proteomic network analysis, holding promise for advancements in drug discovery, biomarker identification, and personalized medicine. Future directions include enhancing scalability and dataset reliability to expand these approaches across diverse proteomes.

(1) Background: “Kidney Disease: Improving Global Outcomes” (KDIGO) provides guidelines for identifying the stages of acute kidney injury (AKI) and chronic kidney disease (CKD). A data-driven rule-based engine was developed to determine KDIGO staging compared to KD-related keywords in discharge letters. (2) Methods: To assess potential differences in outcomes, we compare the patient subgroups with exact KDIGO staging to imprecise or missing staging for all-cause mortality, in-hospital mortality, selection bias and costs by applying Kaplan–Meier analysis and the Cox proportional hazards regression model. We analysed 63,105 in-patient cases from 2016 to 2023 at a tertiary hospital with AKI, CKD and acute-on-chronic KD. (3) Results: Imprecise and missing CKD staging were associated with an 85% higher risk of all-cause and in-hospital mortality (CI: 1.7 to 2.0 and 1.66 to 2.03, respectively) compared to exact staging for any given disease status; imprecise or missing AKI staging increased in-hospital mortality risk by 56% and 57% (CI: 1.43 to 1.70 and 1.37 to 1.81, respectively) in patients with AKI. (4) Conclusions: Exact staging is associated with better outcomes in KD management. Our study provides valuable insight into potential quality and outcome improvements and lower costs, considering elderly patients, women and patients with acute-on-chronic KD as the most vulnerable.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza, and respiratory syncytial virus (RSV) are significant global health threats. The need for low-cost, easily synthesized oral drugs for rapid deployment during outbreaks is crucial. Broad-spectrum therapeutics, or pan-antivirals, are designed to target multiple viral pathogens simultaneously by focusing on shared molecular features, such as common metal cofactors or conserved residues in viral catalytic domains. This study introduces a new generation of potent sartans, known as bisartans, engineered in our laboratories with negative charges from carboxylate or tetrazolate groups. These anionic tetrazoles interact strongly with cationic arginine residues or metal cations (e.g., Zn2+) within viral and host target sites, including the SARS-CoV-2 ACE2 receptor, influenza H1N1 neuraminidases, and the RSV fusion protein. Using virtual ligand docking and molecular dynamics, we investigated how bisartans and their analogs bind to these viral receptors, potentially blocking infection through a pan-antiviral mechanism. Bisartan, ACC519TT, demonstrated stable and high-affinity docking to key catalytic domains of the SARS-CoV-2 NSP3, H1N1 neuraminidase, and RSV fusion protein, outperforming FDA-approved drugs like Paxlovid and oseltamivir. It also showed strong binding to the arginine-rich furin cleavage sites S1/S2 and S2′, suggesting interference with SARS-CoV-2’s spike protein cleavage. The results highlight the potential of tetrazole-based bisartans as promising candidates for developing broad-spectrum antiviral therapies.

Abstract Background Asymptomatic Plasmodium falciparum parasitaemia forms a reservoir for the transmission of malaria disease in West Africa. Certain haemoglobin variants are known to protect against severe malaria infection. However, data on the potential roles of haemoglobin variants and nongenetic factors in asymptomatic malaria infection is scarce and controversial. Therefore, this study investigated the associations of iron homeostasis, inflammation, nutrition, and haemoglobin mutations with parasitaemia in an asymptomatic cohort from a P. falciparum-endemic region during the high transmission season. Methods A sub-study population of 688 asymptomatic individuals (predominantly children and adolescents under 15 years, n = 516) from rural Burkina Faso previously recruited by the NOVAC trial (NCT03176719) between June and October 2017 was analysed. Parasitaemia was quantified with conventional haemocytometry. The haemoglobin genotype was determined by reverse hybridization assays targeting a selection of 21 HBA and 22 HBB mutations. Demographics, inflammatory markers (interleukins 6 and 10, hepcidin), nutritional status (mid upper-arm circumference and body mass index), and anaemia (total haemoglobin, ferritin, soluble transferrin receptor) were assessed as potential predictors through logistic regression. Results Malaria parasites were detected in 56% of subjects. Parasitaemia was associated most strongly with malnutrition. The effect size increased with malnutrition severity (OR = 6.26, CI95: 2.45–19.4, p

Zinc is a multipurpose trace element for the human body, as it plays a crucial part in various physiological processes, such as cell growth and development, metabolism, cognitive, reproductive, and immune system function. Its significance in human health is widely acknowledged, and this has led the scientific community towards more research that aims to uncover all of its beneficial properties, especially when compared to other essential metal ions. One notable area where zinc has shown beneficial effects is in the prevention and treatment of various diseases, including cancer. This review aims to explain the involvement of zinc in specific health conditions such as cancer, coronavirus disease 2019 (COVID-19) and neurological disorders like Alzheimer's disease, as well as its impact on the gut microbiome.

Fusarium oxysporum f. sp. lentis (Fol) is considered the most destructive disease for lentil (Lens culinaris Medik.) worldwide. Despite the extensive studies elucidating plants’ metabolic response to fungal agents, there is a knowledge gap in the biochemical mechanisms governing Fol-resistance in lentil. Τhis study aimed at comparatively evaluating the metabolic response of two lentil genotypes, with contrasting phenotypes for Fol-resistance, to Fol-inoculation. Apart from gaining insights into the metabolic reprogramming in response to Fol-inoculation, the study focused on discovering novel biomarkers to improve early selection for Fol-resistance. GC-MS-mediated metabolic profiling of leaves and roots was employed to monitor changes across genotypes and treatments as well as their interaction. In total, the analysis yielded 178 quantifiable compounds, of which the vast majority belonged to the groups of carbohydrates, amino acids, polyols and organic acids. Despite the magnitude of metabolic fluctuations in response to Fol-inoculation in both genotypes under study, significant alterations were noted in the content of 18 compounds, of which 10 and 8 compounds referred to roots and shoots, respectively. Overall data underline the crucial contribution of palatinitol and L-proline in the metabolic response of roots and shoots, respectively, thus offering possibilities for their exploitation as metabolic biomarkers for Fol-resistance in lentil. To the best of our knowledge, this is the first metabolomics-based approach to unraveling the effects of Fol-inoculation on lentil’s metabolome, thus providing crucial information related to key aspects of lentil–Fol interaction. Future investigations in metabolic aspects of lentil–Fol interactions will undoubtedly revolutionize the search for metabolites underlying Fol-resistance, thus paving the way towards upgrading breeding efforts to combat fusarium wilt in lentil.

Metal complexes of benzotriazole-type ligands continue to attract the intense interest of many inorganic chemistry groups around the world for a variety of reasons, including their aesthetically beautiful structures, physical properties and applications. 1-methylbenzotriazole (Mebta) is the N-substituted archetype of the parent 1H-benzotriazole. The first attempt to build a “periodic table” of Mebta, which includes its complexes with several metal ions, is described in this work. This, at first glance, trivial ligand has led to interesting results in terms of the chemistry, structures and properties of its metal complexes. This work reviews the to-date published coordination chemistry of Mebta with Mn(II), Fe(II), Fe(III), Co(II), Ni(II), Cu(I), Cu(II), Zn(II), Pd(II), Au(I) and {UVIO2}2+, with emphasis on their preparations, reactivity, structures and properties. Unpublished results from our group comprising other Co(II), Ni(II), Cu(II) and Zn(II) complexes, as well as Cd(II), Hg(II), Ag(I), In(III) and Sn(IV) ones are briefly reported. Mebta can also provide access to 1D and 3D heterometallic thiocyanato-bridged Co(II)/Hg(II) and Ni(II)/Hg(II) compounds. In almost all cases, Mebta behaves as a monodentate ligand with the nitrogen of position 3 of the azole ring as the donor atom. However, there are two copper complexes in which this molecule adopts a bidentate bridging coordination behavior. Our efforts to complete the “periodic table” of Mebta are continued.

P-values have played a central role in the advancement of research in virtually all scientific fields; however, there has been significant controversy over their use. “The ASA president’s task force statement on statistical significance and replicability” has provided a solid basis for resolving the quarrel, but although the significance part is clearly dealt with, the replicability part raises further discussions. Given the clear statement regarding significance, in this article, we consider the validity of p-value use for statistical inference as de facto. We briefly review the bibliography regarding the relevant controversy in recent years and illustrate how already proposed approaches, or slight adaptations thereof, can be readily implemented to address both significance and reproducibility, adding credibility to empirical study findings. The definitions used for the notions of replicability and reproducibility are also clearly described. We argue that any p-value must be reported along with its corresponding s-value followed by (1−α)% confidence intervals and the rejection replication index.

Abstract Energy crops for biofuel production, especially switchgrass (Panicum virgatum), are of interest from a climate change perspective. Here, we use outputs from a crop growth model and life cycle assessment (LCA) to examine the global warming intensity (GWI; g CO2 MJ−1) and greenhouse gas (GHG) mitigation potential (Mg CO2 year−1) of biofuel systems based on a spatially explicit analysis of switchgrass grown on marginal land (abandoned former cropland) in Michigan, USA. We find that marginal lands in Michigan can annually produce over 0.57 hm3 of liquid biofuel derived from nitrogen‐fertilized switchgrass, mitigating 1.2–1.5 Tg of CO2 year−1. About 96% of these biofuels can meet the Renewable Fuel Standard (60% reduction in lifecycle GHG emissions compared with conventional gasoline; GWI ≤37.2 g CO2 MJ−1). Furthermore, 73%–75% of these biofuels are carbon‐negative (GWI less than zero) due to enhanced soil organic carbon (SOC) sequestration. However, simulations indicate that SOC levels would fail to increase and even decrease on the 11% of lands where SOC stocks >>200 Mg C ha−1, leading to carbon intensities greater than gasoline. Results highlight the strong climate mitigation potential of switchgrass grown on marginal lands as well as the needs to avoid carbon rich soils such as histosols and wetlands and to ensure that productivity will be sufficient to provide net mitigation.

Furins are serine endoproteases that process precursor proteins into their biologically active forms, and they play essential roles in normal metabolism and disease presentation, including promoting expression of bacterial virulence factors and viral pathogenesis. Thus, furins represent vital targets for development of antimicrobial and antiviral therapeutics. Recent experimental evidence indicated that dichlorophenyl (DCP)-pyridine “BOS” drugs (e.g., BOS-318) competitively inhibit human furin by an induced-fit mechanism in which tryptophan W254 in the furin catalytic cleft (FCC) functions as a molecular gate, rotating nearly 180o through a steep energy barrier about its chi-1 dihedral to an “open” orientation, exposing a buried (i.e., cryptic) hydrophobic pocket 1. Once exposed, the non-polar DCP group of BOS-318, and similar halo-phenyl groups of analogs, enter the cryptic pocket, stabilizing drug binding. Here, we demonstrate flexible-receptor docking of BOS-318 (and various analogs) was unable to emulate the induced-fit motif, even when tryptophan was replaced with less bulky phenylalanine or glycine. While either substitution allowed access to the hydrophobic pocket for most ligands tested, optimal binding was observed only for W254, inferring a stabilizing effect of the indole sidechain. Furthermore, non-equilibrium steered molecular dynamics (sMD) in which the bound drugs (or their fragments) were extracted from the FCC did not cause closure of the open W254 gate, consistent with the thermodynamic stability of the open or closed W254 orientations. Finally, interactive molecular dynamics (iMD) revealed two putative conduits of drug entry and binding into the FCC, each coupled with W254 dihedral rotation and opening of the cryptic pocket. The iMD simulations further revealed ligand entry and binding in the FCC is likely driven in part by energy fluxes stemming from disruption and re-formation of ligand and protein solvation shells during drug migration from the solution phase into the FCC.

The coordination chemistry of 2-pyridyl ketoximes continues to attract the interest of many inorganic chemistry groups around the world for a variety of reasons. Cadmium(II) complexes of such ligands have provided models of solvent extraction of this toxic metal ion from aqueous environments using 2-pyridyl ketoxime extractants. Di-2-pyridyl ketone oxime (dpkoxH) is a unique member of this family of ligands because its substituent on the oxime carbon bears another potential donor site, i.e., a second 2-pyridyl group. The goal of this study was to investigate the reactions of cadmium(II) halides and dpkoxH in order to assess the structural role (if any) of the halogeno ligand and compare the products with their zinc(II) analogs. The synthetic studies provided access to complexes {[CdCl2(dpkoxH)∙2H2O]}n (1∙2H2O), {[CdBr2(dpkoxH)]}n (2) and {[CdI2(dpkoxH)]}n (3) in 50–60% yields. The structures of the complexes were determined by single-crystal X-ray crystallography. The compounds consist of structurally similar 1D zigzag chains, but only 2 and 3 are strictly isomorphous. Neighboring CdII atoms are alternately doubly bridged by halogeno and dpkoxH ligands, the latter adopting the η1:η1:η1:μ (or 2.0111 using Harris notation) coordination mode. A terminal halogeno group completes distorted octahedral coordination at each metal ion, and the coordination sphere of the CdII atoms is {CdII(η1 − X)(μ − X)2(Npyridyl)2(Noxime)} (X = Cl, Br, I). The trans-donor–atom pairs in 1∙2H2O are Clterminal/Noxime and two Clbridging/Npyridyl; on the contrary, these donor–atom pairs are Xterminal/Npyridyl, Xbridging/Noxime, and Xbridging/Npyridyl (X = Br, I). There are intrachain H-bonding interactions in the structures. The packing of the chains in 1∙2H2O is achieved via π-π stacking interactions, while the 3D architecture of the isomorphous 2 and 3 is built via C-H∙∙∙Cg (Cg is the centroid of one pyridyl ring) and π-π overlaps. The molecular structures of 1∙2H2O and 2 are different compared with their [ZnX2(dpkoxH)] (X = Cl, Br) analogs. The polymeric compounds were characterized by IR and Raman spectroscopies in the solid state, and the data were interpreted in terms of the known molecular structures. The solid-state structures of the complexes are not retained in DMSO, as proven via NMR (1H, 13C, and 113Cd NMR) spectroscopy and molar conductivity data. The complexes completely release the coordinated dpkoxH molecule, and the dominant species in solution seem to be [Cd(DMSO)6]2+ in the case of the chloro and bromo complexes and [CdI2(DMSO)4].

Open-source devices are nowadays used in a vast number of research fields like medicine, education, agriculture, and sports, among others. In this work, an open-source, portable, low-cost pH logger, appropriate for in situ measurements, was designed and developed to assist in experiments on agricultural produce manufacturing. Τhe device was calibrated manually using pH buffers for values of 4.01 and 7.01. Then, it was tested by manually measuring the pH from the juice of citrus fruits. A waterproof temperature sensor was added to the device for temperature compensation when measuring the pH. A formal method comparison process between the open-source device and a Hanna HI9024 Waterproof pH Meter was designed to assess their agreement. We derived indices of agreement and graphical assessment tools using mixed-effects models. The advantages and disadvantages of interpreting agreement through the proposed procedure are discussed. In our illustration, the indices reported mediocre agreement and the subsequent similarity analysis revealed a fixed bias of 0.22 pH units. After recalibration, agreement between the devices improved to excellent levels. The process can be followed in general to avoid misleading or over-simplistic results of studies reporting solely correlation coefficients for formal comparison purposes.

This study explores the relationship between the stock of knowledge generated by sectoral groups of the economy, calculated using Research and Development (R&D) expenditure, and knowledge output as measured by official patent applications. Using sector-specific R&D expenditure data published by the OECD, we calculate the total domestic R&D spending across the manufacturing, non-manufacturing, government, and educational sectors. Constructing a consistent 15-year panel dataset for the 17 most significant countries in R&D, we employ econometric subsampling, various estimators, and consider different rates of knowledge depreciation. Our findings reveal that the stock of knowledge in the private manufacturing, government, and educational sectors has a robust positive effect on patent generation.

This paper investigates the relationship between the gross value added (GVA) of Greece’s agricultural sector and the GVAs of the other sectors. The research considers both the relationship between value levels and the cycles of GVAs. Dynamic analysis using ARDL modeling shows that there is no cointegration between agricultural GVA and the other GVAs. However, there is an estimated cointegrating relationship between business cycles of agriculture and those of the rest of the economic sectors, with the cycles of services being the significant variable. Moreover, econometric analysis using NARDL modeling shows that there is a cointegrating relationship between the levels of GVAs as well, when asymmetricity—with respect to GVA changes of the services sector—is introduced.

The integration of renewable rnergy sources (RESs) into the power grid involves operational challenges due to the inherent RES energy-production variability. Imbalances between actual power generation and scheduled production can lead to grid instability and revenue loss for RES operators and aggregators. To address this risk, in this paper, we introduce a mutually beneficial bilateral trading scheme between a RES and a DR aggregator to internally offset real-time energy imbalances before resorting to the flexibility market. We consider that the DR aggregator manages the energy demand of users, characterized by uncertainty in their participation in DR events and thus the actual provision of flexibility, subject to their offered monetary incentives. Given that the RES aggregator faces penalties according to dual pricing for positive or negative imbalances, we develop an optimization framework to achieve the required flexibility while addressing the trade-off between maximizing the profit of the RES and DR aggregators and appropriately incentivizing the users. By using appropriate parameterization of the solution, the achievable revenue for the imbalance offsetting can be shared between the RES and the DR aggregators while keeping users satisfied. Our analysis highlights the interdependencies of the demand–production energy imbalance on user characteristics and the RES and DR aggregator profits. Based on our results, we show that a win–win outcome (for the RES and DR aggregators and the users) is possible for a wide range of cases, and we provide guidelines so that such bilateral agreements between RES and DR aggregators could emerge in practical settings.

In silico approaches were employed to examine the characteristics of interactions between human mitochondrial thioredoxin 2 (HsTrx2) and its 38 previously identified mitochondrial protein ligands. All interactions appeared driven mainly by electrostatic forces. The statistically significant residues of HsTrx2 for interactions were characterized as “contact hot spots”. Since these were identical/adjacent to putative thermodynamic hot spots, an energy network approach identified their neighbors to highlight possible contact interfaces. Three distinct areas for binding emerged: (i) one around the active site for covalent interactions, (ii) another antipodal to the active site for strong non-covalent interactions, and (iii) a third area involved in both kinds of interactions. The contact interfaces of HsTrx2 were projected as respective interfaces for Escherichia coli Trx1 (EcoTrx1), 2, and HsTrx1. Comparison of the interfaces and contact hot spots of HsTrx2 to the contact residues of EcoTx1 and HsTrx1 from existing crystal complexes with protein ligands supported the hypothesis, except for a part of the cleft/groove adjacent to Trp30 preceding the active site. The outcomes of this study raise the possibility for the rational design of selective inhibitors for the interactions of HsTrx2 with specific protein ligands without affecting the entirety of the functions of the Trx system.

Abstract We have recently introduced a new semiconductor laser design which is based on an extreme, 99%, reduction of the laser mode absorption losses. In previous reports, we showed that this was achieved by a laser mode design which confines the great majority of the modal energy (> 99%) in a low-loss Silicon guiding layer rather than in highly-doped, thus lossy, III–V p $${}^+$$ + and n $${}^+$$ + layers, which is the case with traditional III–V lasers. The resulting reduced electron-field interaction was shown to lead to a commensurate reduction of the spontaneous emission rate by the excited conduction band electrons into the laser mode and thus to a reduction of the frequency noise spectral density of the laser field often characterized by the Schawlow–Townes linewidth. In this paper, we demonstrate theoretically and present experimental evidence of yet another major beneficial consequence of the new laser design: a near total elimination of the contribution of amplitude-phase coupling (the Henry $$\alpha $$ α parameter) to the frequency noise at “high” frequencies. This is due to an order of magnitude lowering of the relaxation resonance frequency of the laser. Here, we show that the practical elimination of this coupling enables yet another order of magnitude reduction of the frequency noise at high frequencies, resulting in a quantum-limited frequency noise spectral density of 130 Hz $$^2$$ 2 /Hz (linewidth of 0.4 kHz) for frequencies beyond the relaxation resonance frequency 680 MHz. This development is of key importance in the development of semiconductor lasers with higher coherence, particularly in the context of integrated photonics with a small laser footprint without requiring any sort of external cavity.

The discovery and facile synthesis of a new class of sartan-like arterial antihypertensive drugs (angiotensin receptor blockers [ARBs]), subsequently referred to as “bisartans” is reported. In vivo results and complementary molecular modelling presented in this communication indicate bisartans may be beneficial for the treatment of not only heart disease, diabetes, renal dysfunction, and related illnesses, but possibly COVID-19. Bisartans are novel bis-alkylated imidazole sartan derivatives bearing dual symmetric anionic biphenyl tetrazole moieties. In silico docking and molecular dynamics studies revealed bisartans exhibited higher binding affinities for the ACE2/spike protein complex (PDB 6LZG) compared to all other known sartans. They also underwent stable docking to the Zn2+ domain of the ACE2 catalytic site as well as the critical interfacial region between ACE2 and the SARS-CoV-2 receptor binding domain. Additionally, semi-stable docking of bisartans at the arginine-rich furin-cleavage site of the SARS-CoV-2 spike protein (residues 681–686) required for virus entry into host cells, suggest bisartans may inhibit furin action thereby retarding viral entry into host cells. Bisartan tetrazole groups surpass nitrile, the pharmacophoric “warhead” of PF-07321332, in its ability to disrupt the cysteine charge relay system of 3CLpro. However, despite the apparent targeting of multifunctional sites, bisartans do not inhibit SARS-CoV-2 infection in bioassays as effectively as PF-07321332 (Paxlovid).

Several artificial larval diets have been developed, evaluated and used for mass-rearing of the Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann) (Diptera: Teprhitidae). There are several efforts to reduce the cost of rearing and optimize the quality of the produced sterile males that are destined for release in sterile insect release programs. Survival, growth, longevity and reproductive capacity of sterile males are strongly connected with the most expensive ingredient, the brewer’s yeast (protein), in the larval diet. The current study focused on settling the optimal content of brewer’s yeast in a liquid diet and a gel diet. Egg hatch rates, developmental duration of immatures, pupation rate, pupae and adult survival were recorded as indicators of quantity and quality of the produced adults. Egg hatch was higher and larval developmental duration longer in the gel diet. In contrast to the liquid diet, an increase in brewer’s yeast concentration was correlated with increased pupation rate and pupae survival in the gel diet. Reducing brewer’s yeast up to 50% of its initial quantity had no significant effect on the survival of the emerging adults regardless of the diet type. Our findings may contribute to the production of low-cost and effective diets for use in mass-rearing facilities of medflies.

The potential of targeting the Renin-Angiotensin-Aldosterone System (RAAS) as a treatment for the coronavirus disease 2019 (COVID-19) is currently under investigation. One way to combat this disease involves the repurposing of angiotensin receptor blockers (ARBs), which are antihypertensive drugs, because they bind to angiotensin-converting enzyme 2 (ACE2), which in turn interacts with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. However, there has been no in silico analysis of the potential toxicity risks associated with the use of these drugs for the treatment of COVID-19. To address this, a network-based bioinformatics methodology was used to investigate the potential side effects of known Food and Drug Administration (FDA)-approved antihypertensive drugs, Sartans. This involved identifying the human proteins targeted by these drugs, their first neighbors, and any drugs that bind to them using publicly available experimentally supported data, and subsequently constructing proteomes and protein–drug interactomes. This methodology was also applied to Pfizer’s Paxlovid, an antiviral drug approved by the FDA for emergency use in mild-to-moderate COVID-19 treatment. The study compares the results for both drug categories and examines the potential for off-target effects, undesirable involvement in various biological processes and diseases, possible drug interactions, and the potential reduction in drug efficiency resulting from proteoform identification.

Protein contact networks (PCNs) have been used for the study of protein structure and function for the past decade. In PCNs, each amino acid is considered as a node while the contacts among amino acids are the links/edges. We examined the possible correlation between the closeness centrality measure of amino acids within PCNs and their mobility as known from NMR spin relaxation experiments and molecular dynamic (MD) simulations. The pivotal observation was that plasticity within a protein stretch correlated inversely to closeness centrality. Effects on protein conformational plasticity caused by the formation of disulfide bonds or protein–protein interactions were also identified by the PCN analysis measure closeness centrality and the hereby introduced percentage of closeness centrality perturbation (% CCP). All the comparisons between PCN measures, NMR data, and MDs were performed in a set of proteins of different biological functions and structures: the core protease domain of anthrax lethal factor, the N-terminal RING domain of E3 Ub ligase Arkadia, the reduced and oxidized forms of human thioredoxin 1, and the ubiquitin molecules (Ub) of the catalytic Ub–RING–E3–E2–Ub complex of E3 ligase Ark2.The graph theory analysis of PCNs could thus provide a general method for assessing the conformational dynamics of free proteins and putative plasticity changes between different protein forms (apo/complexed or reduced/oxidized).

The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach.

SARS-CoV-2 infection was announced as a pandemic in March 2020. Since then, several scientists have focused on the low prevalence of smokers among hospitalized COVID-19 patients. These findings led to our hypothesis that the Nicotinic Cholinergic System (NCS) plays a crucial role in the manifestation of COVID-19 and its severe symptoms. Molecular modeling revealed that the SARS-CoV-2 Spike glycoprotein might bind to nicotinic acetylcholine receptors (nAChRs) through a cryptic epitope homologous to snake toxins, substrates well documented and known for their affinity to the nAChRs. This binding model could provide logical explanations for the acute inflammatory disorder in patients with COVID-19, which may be linked to severe dysregulation of NCS. In this study, we present a series of complexes with cholinergic agonists that can potentially prevent SARS-CoV-2 Spike glycoprotein from binding to nAChRs, avoiding dysregulation of the NCS and moderating the symptoms and clinical manifestations of COVID-19. If our hypothesis is verified by in vitro and in vivo studies, repurposing agents currently approved for smoking cessation and neurological conditions could provide the scientific community with a therapeutic option in severe COVID-19.

Abstract Knockdown and mortality of adults of the red flour beetle, Tribolium castaneum (Herbst) and the confused flour beetle, Tribolium confusum Jacquelin du Val, were assessed after exposure to two contact insecticides, chlorfenapyr and cyfluthrin, on a concrete surface. Individuals were rated on a scale for knockdown of exposed adults according to their mobility from 1, representing immobilized adults to 5, representing normally moving (similar to the controls). Only cyfluthrin gave immediate knockdown. Adults were rated at 1, 3 and 7 days post-exposure. After the final assessment, adults were discarded and the same procedure was repeated for 5 consecutive weeks with new adults exposed on the same treated surfaces. Despite initial knockdown, many individuals did not eventually die after exposure to cyfluthrin. In contrast, adults exposed to chlorfenapyr were not initially knocked down after exposure but most died after 7 days. These trends were similar during the entire 5-week residual testing period. The storage of the treated dishes in illuminated or non-illuminated conditions did not affect the insecticidal effect of either insecticide. The results of the present study can be further implemented towards the design of a “lethality index” that can serve as a quick indicator of knockdown and mortality rates caused after exposure to insecticides.

In recent years, cannabis has raised public awareness in many countries worldwide about its medical uses. The European cannabis market reached its peak during 2018 with investments of 500 million €. Greece introduced Law 4523/2018, which regulates the production and processing of medical cannabis. A start-up business plan was drawn up for a medical cannabis operation owned a 1 ha greenhouse to estimate the start-up costs and the costs of cultivating and processing medical cannabis under greenhouse conditions in Greece. The results of the present study revealed that the investment was estimated at 4,960,044 €. Net profit in the first year amounted to 3,584,621.70 €, while the annual net profits from the second to the tenth year amounted to approximately 7.07 billion. The Net Present Value with a value of 45,425,241.24 € is positive and the internal rate of return (IRR) is 94.14%, which means that the investment can be characterized as profitable.

In this work, yellow pine (YP, softwood) and switchgrass (SG, grass) lignins were extracted as a coproduct of an organosolv γ-valerolactone (GVL) biorefinery that also produces biofuels and furfural. The extracted lignins were converted to carbon precursors for synthesizing porous activated carbon electrodes for high energy-density supercapacitors. This research details the impact of lignin composition on the derived porous structures and electrochemical properties of activated carbons. Lignin precursors with various syringyl (S) to guaiacyl (G) contents were characterized using 31P nuclear magnetic resonance (NMR) and two-dimensional 1H‒13C NMR. A two-step activation process, using steam and carbon dioxide as the activating agents, enabled the formation of porous carbons structures with high surface area. The capacitive behavior of supercapacitors was systematically characterized by cyclic voltammetry, charge-discharge cycling, and electrochemical impedance spectroscopy. The specific capacitance of YP and SG capacitors reached 367 and 221 F g−1, respectively. Both types of capacitors demonstrated remarkably stable capacitance (capacitance retention >90%) along with excellent Coulombic efficiency (>99%) over 10,000 cycles. Compared to SG electrode, the better electrochemical performance achieved with YP electrodes was mainly due to shorter diffusion path, improved ionic mobility, and increased active surface area. The inexpensive lignin-based porous electrodes synthesized in this work can be used for various electrochemical devices for improved performance, decreased cost, and enhanced durability. This work also demonstrates that the selection of feedstock and appropriate processing conditions can tailor the structure of carbon composites for targeted applications. Techno-economic analysis indicates that YP and SG activated carbons can be produced at a minimum selling price of $8,493 and $6,670 per ton, respectively, which is competitive with the commercially available supercapacitor-grade activated carbons.

Age-specific and diurnal patterns of locomotory activity, can be considered as biomarkers of aging in model organisms and vary across the lifetime of individuals. Τhe Mediterranean fruit fly (medfly), Ceratitis capitata, is a commonly used model-species in studies regarding demography and aging. In the present study, we introduce a modification of the automated locomotory activity electronic device LAM25system (Locomotory Activity Monitor)–Trikinetics, commonly used in short time studies, to record the daily locomotory activity patterns of adult medflies throughout the life. Additionally, fecundity rates and survival of adult medflies were recorded. Male and female medflies were kept in the system tubes and had access to an agar-based gel diet, which provided water and nutrients. The locomotory activity was recorded at every minute by three monitors in the electronic device. The locomotory activity of females was higher than that of males across the different ages. For both sexes locomotory rates were high during the first 20 days of the adult life and decreased in older ages. The activity of males was high in the morning and late afternoon hours, while that of females was constantly high throughout the photophase. Negligible locomotory activity was recorded for both sexes during the nighttime. Males outlived females. Fecundity of females was higher in younger ages. Our results support the adoption of LAM25system in studies addressing aging of insects using medfly as a model organism.

Background, Aims, Methods, Results, Conclusions: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global challenge due to its ability to mutate into variants that spread more rapidly than the wild-type virus. The molecular biology of this virus has been extensively studied and computational methods applied are an example paradigm for novel antiviral drug therapies. The rapid evolution of SARS-CoV-2 in the human population is driven, in part, by mutations in the receptor-binding domain (RBD) of the spike (S-) protein, some of which enable tighter binding to angiotensin-converting enzyme (ACE2). More stable RBD-ACE2 association is coupled with accelerated hydrolysis by proteases, such as furin, trypsin, and the Transmembrane Serine Protease 2 (TMPRSS2) that augment infection rates, while inhibition of the 3-chymotrypsin-like protease (3CLpro) can prevent the viral replication. Additionally, non-RBD and non-interfacial mutations may assist the S-protein in adopting thermodynamically favorable conformations for stronger binding. This study aimed to report variant distribution of SARS-CoV-2 across European Union (EU)/European Economic Area (EEA) countries and relate mutations with the driving forces that trigger infections. Variants’ distribution data for SARS-CoV-2 across EU/EEA countries were mined from the European Centre for Disease Prevention and Control (ECDC) based on the sequence or genotyping data that are deposited in the Global Science Initiative for providing genomic data (GISAID) and The European Surveillance System (TESSy) databases. Docking studies performed with AutoDock VINA revealed stabilizing interactions of putative antiviral drugs, e.g., selected anionic imidazole biphenyl tetrazoles, with the ACE2 receptor in the RBD-ACE2 complex. The driving forces of key mutations for Alpha, Beta, Gamma, Delta, Epsilon, Kappa, Lambda, and Omicron variants, which stabilize the RBD-ACE2 complex, were investigated by computational approaches. Arginine is the critical amino acid in the polybasic furin cleavage sites S1/S2 (681-PRRARS-686) S2′ (814-KRS-816). Critical mutations into arginine residues that were found in the delta variant (L452R, P681R) and may be responsible for the increased transmissibility and morbidity are also present in two widely spreading omicron variants, named BA.4.6 and BQ.1, where mutation R346T in the S-protein potentially contributes to neutralization escape. Arginine binders, such as Angiotensin Receptor Blockers (ARBs), could be a class of novel drugs for treating COVID-19.

The SARS-CoV-2 infection was previously associated with the expression of the dopamine biosynthetic enzyme L-Dopa decarboxylase (DDC). Specifically, a negative correlation was detected between DDC mRNA and SARS-CoV-2 RNA levels in in vitro infected epithelial cells and the nasopharyngeal tissue of COVID-19 patients with mild/no symptoms. However, DDC, among other genes related to both DDC expression and SARS-CoV-2-infection (ACE2, dACE2, EPO), was upregulated in these patients, possibly attributed to an orchestrated host antiviral response. Herein, by comparing DDC expression in the nasopharyngeal swab samples of severe/critical to mild COVID-19 cases, we showed a 20 mean-fold reduction, highlighting the importance of the expression of this gene as a potential marker of COVID-19 severity. Moreover, we identified an association of SARS-CoV-2 infection with the expression of key catecholamine biosynthesis/metabolism-related genes, in whole blood samples from hospitalized patients and in cultured cells. Specifically, viral infection downregulated the biosynthetic part of the dopamine pathway (reduction in DDC expression up to 7.5 mean-fold), while enhanced the catabolizing part (increase in monoamine oxidases A and B expression up to 15 and 10 mean-fold, respectively) in vivo, irrespectively of the presence of comorbidities. In accordance, dopamine levels in the sera of severe cases were reduced (up to 3.8 mean-fold). Additionally, a moderate positive correlation between DDC and MAOA mRNA levels (r = 0.527, p < 00001) in the blood was identified upon SARS-CoV-2-infection. These observations were consistent to the gene expression data from SARS-CoV-2-infected Vero E6 and A549 epithelial cells. Furthermore, L-Dopa or dopamine treatment of infected cells attenuated the virus-derived cytopathic effect by 55% and 59%, respectively. The SARS-CoV-2 mediated suppression of dopamine biosynthesis in cell culture was, at least in part, attributed to hypoxia-like conditions triggered by viral infection. These findings suggest that L-Dopa/dopamine intake may have a preventive or therapeutic value for COVID-19 patients.

Cadmium (Cd) is a toxic metal for the human organism and for all ecosystems. Cd is naturally found at low levels; however, higher amounts of Cd in the environment result from human activities as it spreads into the air and water in the form of micropollutants as a consequence of industrial processes, pollution, waste incineration, and electronic waste recycling. The human body has a limited ability to respond to Cd exposure since the metal does not undergo metabolic degradation into less toxic species and is only poorly excreted. The extremely long biological half-life of Cd essentially makes it a cumulative toxin; chronic exposure causes harmful effects from the metal stored in the organs. The present paper considers exposure and potential health concerns due to environmental cadmium. Exposure to Cd compounds is primarily associated with an elevated risk of lung, kidney, prostate, and pancreatic cancer. Cd has also been linked to cancers of the breast, urinary system, and bladder. The multiple mechanisms of Cd-induced carcinogenesis include oxidative stress with the inhibition of antioxidant enzymes, the promotion of lipid peroxidation, and interference with DNA repair systems. Cd2+ can also replace essential metal ions, including redox-active ones. A total of 12 cancer types associated with specific genes coding for the Cd-metalloproteome were identified in this work. In addition, we summarize the proper treatments of Cd poisoning, based on the use of selected Cd detoxifying agents and chelators, and the potential for preventive approaches to counteract its chronic exposure.

The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network–based method (“Bacterial Wars”) was developed to handle sequence similarities of predicted AMPs among UniProt-derived protein sequences from different bacterial taxa, while a resulting parameter (“Die” score) suggested which taxa would prevail in a defined microbiome. T he working hypothesis was examined by correlating the calculated Die scores, to the abundance of bacterial taxa from gut microbiomes from different states of health and disease. Eleven publicly available 16S rRNA datasets and a dataset from a full shotgun metagenomics served for the analysis. The overall conclusion was that AMPs encrypted within bacterial proteomes affected the predominance of bacterial taxa in chemospheres.

An economic analysis of conventional and organic olive oil cultivation was examined in one of the most important olive-growing regions in Greece. Based on structured interviews and open-ended questions of 60 farmers from both organic and conventional systems, this study aimed to clarify the cost and returns of organic olive oil cultivation in comparison with conventional farming. Olive oil cultivation cost was calculated as 2.60 € kg-1 in organic and 2.25 € kg-1 in conventional system. The selling price for organic olive production was 4.19 € kg-1 compared to 3.63 € kg-1 for conventional production. Net profit from organic olive oil cultivation was 7,043.43 € ha-1, which was 3,664.58 € ha-1 higher than the net profit from conventional cultivation. Finally, in both types of olive farming the results of financial analysis attested to a positive convenience; however, organic farming is more advantageous providing a financially competitive alternative to conventional.

Summary: Although several “renewable” strategies have been recently proposed to produce high-volume as well as new (replacement) chemicals, the identification of good targets for such strategies remains challenging. Such chemicals that are expensive to obtain today from fossil fuel feedstocks would have an advantage if produced cheaply using alternative methods in the future. In this work we identify the characteristics of such potentially promising replacement chemicals. We also identify the characteristic of promising bio-based replacement chemicals that are relatively easy to obtain through bio-conversions. This work provides insights into the development of renewable chemicals to support a sustainable economy. : Chemical Engineering; Industrial Chemistry; Energy Sustainability Subject Areas: Chemical Engineering, Industrial Chemistry, Energy Sustainability

The thioredoxin system, consisting of thioredoxin (Trx), thioredoxin reductase (TrxR), and NADPH, plays a fundamental role in the control of antioxidant defenses, cell proliferation, redox states, and apoptosis. Aberrations in the Trx system may lead to increased oxidative stress toxicity and neurodegenerative processes. This study reviews the role of the Trx system in the pathophysiology and treatment of Alzheimer’s, Parkinson’s and Huntington’s diseases, brain stroke, and multiple sclerosis. Trx system plays an important role in the pathophysiology of those disorders via multiple interactions through oxidative stress, apoptotic, neuro-immune, and pro-survival pathways. Multiple aberrations in Trx and TrxR systems related to other redox systems and their multiple reciprocal relationships with the neurodegenerative, neuro-inflammatory, and neuro-oxidative pathways are here analyzed. Genetic and environmental factors (nutrition, metals, and toxins) may impact the function of the Trx system, thereby contributing to neuropsychiatric disease. Aberrations in the Trx and TrxR systems could be a promising drug target to prevent and treat neurodegenerative, neuro-inflammatory, neuro-oxidative stress processes, and related brain disorders.

Angiotensin receptor blockers (ARBs) used in the treatment of hypertension and potentially in SARS-CoV-2 infection exhibit inverse agonist effects at angiotensin AR1 receptors, suggesting the receptor may have evolved to accommodate naturally occurring angiotensin ‘antipeptides’. Screening of the human genome has identified a peptide (EGVYVHPV) encoded by mRNA, complementary to that encoding ANG II itself, which is an inverse agonist. Thus, opposite strands of DNA encode peptides with opposite effects at AR1 receptors. Agonism and inverse agonism at AR1 receptors can be explained by a receptor ‘switching’ between an activated state invoking receptor dimerization/G protein coupling and an inverse agonist state mediated by an alternative/second messenger that is slow to reverse. Both receptor states appear to be driven by the formation of the ANG II charge-relay system involving TyrOH-His/imidazole-Carboxylate (analogous to serine proteases). In this system, tyrosinate species formed are essential for activating AT1 and AT2 receptors. ANGII is also known to bind to the zinc-coordinated metalloprotease angiotensin converting enzyme 2 (ACE2) used by the COVID-19 virus to enter cells. Here we report in silico results demonstrating the binding of a new class of anionic biphenyl-tetrazole sartans (‘Bisartans’) to the active site zinc atom of the endopeptidase Neprilysin (NEP) involved in regulating hypertension, by modulating humoral levels of beneficial vasoactive peptides in the RAS such as vasodilator angiotensin (1–7). In vivo and modeling evidence further suggest Bisartans can inhibit ANG II-induced pulmonary edema and may be useful in combatting SARS-CoV-2 infection by inhibiting ACE2-mediated viral entry to cells.

Chronic exposure to arsenic (As) compounds leads to its accumulation in the body, with skin lesions and cancer being the most typical outcomes. Treating As-induced diseases continues to be challenging as there is no specific, safe, and efficacious therapeutic management. Therapeutic and preventive measures available to combat As toxicity refer to chelation therapy, antioxidant therapy, and the intake of natural dietary compounds. Although chelation therapy is the most commonly used method for detoxifying As, it has several side effects resulting in various toxicities such as hepatotoxicity, neurotoxicity, and other adverse consequences. Drugs of plant origin and natural dietary compounds show efficient and progressive relief from As-mediated toxicity without any particular side effects. These natural compounds have also been found to aid the elimination of As from the body and, therefore, can be more effective than conventional therapeutic agents in ameliorating As toxicity. This review provides an overview of the recently updated knowledge on treating As poisoning through natural dietary compounds. This updated information may serve as a basis for defining novel prophylactic and therapeutic formulations.

Diminazene aceturate (DIZE) is a putative angiotensin-converting enzyme 2 (ACE2) activator and angiotensin type 1 receptor antagonist (AT1R). Its simple chemical structure possesses a negatively charged triazene segment that is homologous to the tetrazole of angiotensin receptor blockers (ARB), which explains its AT1R antagonistic activity. Additionally, the activation of ACE2 by DIZE converts the toxic octapeptide angiotensin II (AngII) to the heptapeptides angiotensin 1–7 and alamandine, which promote vasodilation and maintains homeostatic balance. Due to DIZE’s protective cardiovascular and pulmonary effects and its ability to target ACE2 (the predominant receptor utilized by severe acute respiratory syndrome coronavirus 2 to enter host cells), it is a promising treatment for coronavirus 2019 (COVID-19). To determine DIZE’s ability to inhibit AngII constriction, in vitro isometric tension analysis was conducted on rabbit iliac arteries incubated with DIZE or candesartan and constricted with cumulative doses of AngII. In silico docking and ligand interaction studies were performed to investigate potential interactions between DIZE and other ARBs with AT1R and the spike protein/ACE2 complex. DIZE, similar to the other ARBs investigated, was able to abolish vasoconstriction in response to AngII and exhibited a binding affinity for the spike protein/ACE2 complex (PDB 6LZ6). These results support the potential of DIZE as a treatment for COVID-19.

SARS-CoV-2 is a global challenge due to its ability to mutate into variants that spread more rapidly than the wild-type virus. Because the molecular biology of this virus has been studied in such great detail, it represents an archetypal paradigm for research into new antiviral drug therapies. The rapid evolution of SARS-CoV-2 in the human population is driven, in part, by mutations in the receptor-binding domain (RBD) of the spike (S-) protein, some of which enable tighter binding to angiotensin-converting enzyme (ACE2). More stable RBD-ACE2 association is coupled with accelerated hydrolysis of furin and 3CLpro cleavage sites that augment infection. Non-RBD and non-interfacial mutations assist the S-protein in adopting thermodynamically favorable conformations for stronger binding. The driving forces of key mutations for Alpha, Beta, Gamma, Delta, Kappa, Lambda and Omicron variants, which stabilize the RBD-ACE2 complex, are investigated by free-energy computational approaches, as well as equilibrium and steered molecular dynamic simulations. Considered also are the structural hydropathy traits of the residues in the interface between SARS-CoV-2 RBD and ACE2 protein. Salt bridges and π-π interactions are critical forces that create stronger complexes between the RBD and ACE2. The trend of mutations is the replacement of non-polar hydrophobic interactions with polar hydrophilic interactions, which enhance binding of RBD with ACE2. However, this is not always the case, as conformational landscapes also contribute to a stronger binding. Arginine, the most polar and hydrophilic among the natural amino acids, is the most aggressive mutant amino acid for stronger binding. Arginine blockers, such as traditional sartans that bear anionic tetrazoles and carboxylates, may be ideal candidate drugs for retarding viral infection by weakening S-protein RBD binding to ACE2 and discouraging hydrolysis of cleavage sites. Based on our computational results it is suggested that a new generation of “supersartans”, called “bisartans”, bearing two anionic biphenyl-tetrazole pharmacophores, are superior to carboxylates in terms of their interactions with viral targets, suggesting their potential as drugs in the treatment of COVID-19. In Brief: This in silico study reviews our understanding of molecular driving forces that trigger mutations in the SARS-CoV-2 virus. It also reports further studies on a new class of “supersartans” referred to herein as “bisartans”, bearing two anionic biphenyltetrazole moieties that show potential in models for blocking critical amino acids of mutants, such as arginine, in the Delta variant. Bisartans may also act at other targets essential for viral infection and replication (i.e., ACE2, furin cleavage site and 3CLpro), rendering them potential new drugs for additional experimentation and translation to human clinical trials.

The aim of our study was to assess the immunogenicity of the third dose of the BNT162b2 mRNA COVID-19 vaccine (Comirnaty) in a cohort of 129 health-care workers in Greece whose anti-S1 RBD IgG titers were monitored over the course of nine months. Titers were measured for each participant just before the third dose (nine months after the second dose) and also one month after the third dose. Of the 129 participants, 19 had been previously infected before starting the vaccination scheme. The SARS-CoV-2 IgG II Quant assay on the Architect System was employed to longitudinally assess the titers of IgG against the receptor-binding domain of the S1 subunit of the spike protein (anti-S1 RBD). Boosters raised Geometric Mean Concentrations (GMCs) by a factor of approximately 47 relative to levels at 9 months and by a factor of approximately 23 relative to levels at 6 months. The immune response one month after the third dose was significantly higher than the response achieved one month after the second dose (p = 0.008). In conclusion, our findings verify the potent immunogenicity elicited by the third dose in all age and prior COVID-19 status groups, suggesting that the timely administration of the third (booster) dose maximizes the immunogenic potential of the vaccine.

Myelin in humans is composed of about 80% lipids and 20% protein. Initially, myelin protein composition was considered low, but various recent proteome analyses have identified additional myelin proteins. Although, the myelin proteome is qualitatively and quantitatively identified through complementary proteomic approaches, the corresponding Protein–Protein Interaction (PPI) network of myelin is not yet available. In the present work, the PPI network was constructed based on available experimentally supported protein interactions of myelin in PPI databases. The network comprised 2017 PPIs between 567 myelin proteins. Interestingly, structure-based in silico analysis revealed that 20% of the myelin proteins that are interconnected in the proposed PPI network are metal-binding proteins/enzymes that construct the main sub-PPI network of myelin proteome. Finally, the PPI networks of the myelin proteome and sub-metalloproteome were analyzed ontologically to identify the biochemical processes of the myelin proteins and the interconnectivity of myelin-associated diseases in the interactomes. The presented PPI dataset could provide a useful resource to the scientific community to further our understanding of human myelin biology and serve as a basis for future studies of myelin-related neurological diseases and particular autoimmune diseases such as multiple sclerosis where myelin epitopes are implicated.

Abstract Background Recent advances in metabolic engineering enable the production of chemicals from sugars through microbial bio-conversion. Terpenes have attracted substantial attention due to their relatively high prices and wide applications in different industries. To this end, we synthesize and assess processes for microbial production of terpenes. Results To explain a counterintuitive experimental phenomenon where terpenes such as limonene (normal boiling point 176 °C) are often found to be 100% present in the vapor phase after bio-conversion (operating at only ~ 30 °C), we first analyze the vapor–liquid equilibrium for systems containing terpenes. Then, we propose alternative production configurations, which are further studied, using limonene as an example, in several case studies. Next, we perform economic assessment of the alternative processes and identify the major cost components. Finally, we extend the assessment to account for different process parameters, terpene products, ways to address terpene toxicity (microbial engineering vs. solvent use), and cellulosic biomass as a feedstock. We identify the key cost drivers to be (1) feed glucose concentration (wt%), (2) product yield (% of maximum theoretical yield) and (3) VVM (Volume of air per Volume of broth liquid per Minute, i.e., aeration rate in min−1). The production of limonene, based on current experimental data, is found to be economically infeasible (production cost ~ 465 $/kg vs. market selling price ~ 7 $/kg), but higher glucose concentration and yield can lower the cost. Among 12 terpenes studied, limonene appears to be the most reasonable short-term target because of its large market size (~ 160 million $/year in the US) and the relatively easier to achieve break-even yield (~ 30%, assuming a 14 wt% feed glucose concentration and 0.1 min−1 VVM). Conclusions The methods proposed in this work are applicable to a range of terpenes as well as other extracellular insoluble chemicals with density lower than that of water, such as fatty acids. The results provide guidance for future research in metabolic engineering toward terpenes production in terms of setting targets for key design parameters.

The present work describes the reactions of CdI2 with 2-pyridyl aldoxime (2paoH), 3-pyridyl aldoxime (3paoH), 4-pyridyl aldoxime (4paoH), 2-6-diacetylpyridine dioxime (dapdoH2) and 2,6-pyridyl diamidoxime (LH4). The primary goal was to contribute to understanding the molecular basis of the very good liquid extraction ability of 2-pyridyl ketoximes with long aliphatic chains towards toxic Cd(II) and the inability of their 4-pyridyl isomers for this extraction. Our systematic investigation provided access to coordination complexes [CdI2(2paoH)2] (1), {[CdI2(3paoH)2]}n (2), {[CdI2(4paoH)2]}n (3) and [CdI2(dapdoH2)] (4). The reaction of CdI2 and LH4 in EtOH resulted in a Cd(II)-involving reaction of the bis(amidoxime) and isolation of [CdI2(L’H2)] (5), where L’H2 is the new ligand 2,6-bis(ethoxy)pyridine diimine. A mechanism of this transformation has been proposed. The structures of 1, 2, 3, 4·2EtOH and 5 were determined by single-crystal X-ray crystallography. The complexes have been characterized by FT-IR and FT-Raman spectra in the solid state and the data are discussed in terms of structural features. The stability of the complexes in DMSO was investigated by 1H NMR spectroscopy. Our studies confirm that the excellent extraction ability of 2-pyridyl ketoximes is due to the chelating nature of the extractants leading to thermodynamically stable Cd(II) complexes. The monodentate coordination of 4-pyridyl ketoximes (as confirmed in our model complexes with 4paoH and 3paoH) seems to be responsible for their poor performance as extractants.

The biosorption of pollutants using microbial organisms has received growing interest in the last decades. Diatoms, the most dominant group of phytoplankton in oceans, are (i) pollution tolerant species, (ii) excellent biological indicators of water quality, and (iii) efficient models in assimilation and detoxification of toxic metal ions. Published research articles connecting proteomics with the capacity of diatoms for toxic metal removal are very limited. In this work, we employed a structural based systematic approach to predict and analyze the metalloproteome of six species of marine diatoms: Thalassiosira pseudonana, Phaeodactylum tricornutum, Fragilariopsis cylindrus, Thalassiosira oceanica, Fistulifera solaris, and Pseudo-nitzschia multistriata. The results indicate that the metalloproteome constitutes a significant proportion (~13%) of the total diatom proteome for all species investigated, and the proteins binding non-essential metals (Cd, Hg, Pb, Cr, As, and Ba) are significantly more than those identified for essential metals (Zn, Cu, Fe, Ca, Mg, Mn, Co, and Ni). These findings are most likely related to the well-known toxic metal tolerance of diatoms. In this study, metalloproteomes that may be involved in metabolic processes and in the mechanisms of bioaccumulation and detoxification of toxic metals of diatoms after exposure to toxic metals were identified and described.

Multiple Sclerosis (MS) is a serious autoimmune disease. The patient in an advanced state of the disease has restrained mobility and remains handicapped. It is therefore understandable that there is a great need for novel drugs and vaccines for the treatment of MS. Herein we summarise two major approaches applied for the treatment of the disease using peptide molecules alone or conjugated with mannan. The first approach focuses on selective myelin epitope peptide or peptide mimetic therapy alone or conjugated with mannan, and the second on immune-therapy by preventing or controlling disease through the release of appropriate cytokines. In both approaches the use of cyclic peptides offers the advantage of increased stability from proteolytic enzymes. In these approaches, the synthesis of myelin epitope peptides conjugated to mannan is of particular interest as this was found to protect mice against experimental autoimmune encephalomyelitis, an animal model of MS, in prophylactic and therapeutic protocols. Protection was peptide-specific and associated with reduced antigen-specific T cell proliferation. The aim of the studies of these peptide epitope analogs is to understand their molecular basis of interactions with human autoimmune T-cell receptor and a MS-associated human leucocyte antigen (HLA)-DR2b. This knowledge will lead the rational design to new beneficial non-peptide mimetic analogs for the treatment of MS. Some issues of the use of nanotechnology will also be addressed as a future trend to tackle the disease. We highlight novel immunomodulation and vaccine-based research against MS based on myelin epitope peptides and strategies developed in our laboratories.

Abstract Background Bioseparations can contribute to more than 70% in the total production cost of a bio-based chemical, and if the desired chemical is localized intracellularly, there can be additional challenges associated with its recovery. Based on the properties of the desired chemical and other components in the stream, there can be multiple feasible options for product recovery. These options are composed of several alternative technologies, performing similar tasks. The suitability of a technology for a particular chemical depends on (1) its performance parameters, such as separation efficiency; (2) cost or amount of added separating agent; (3) properties of the bioreactor effluent (e.g., biomass titer, product content); and (4) final product specifications. Our goal is to first synthesize alternative separation options and then analyze how technology selection affects the overall process economics. To achieve this, we propose an optimization-based framework that helps in identifying the critical technologies and parameters. Results We study the separation networks for two representative classes of chemicals based on their properties. The separation network is divided into three stages: cell and product isolation (stage I), product concentration (II), and product purification and refining (III). Each stage exploits differences in specific product properties for achieving the desired product quality. The cost contribution analysis for the two cases (intracellular insoluble and intracellular soluble) reveals that stage I is the key cost contributor (>70% of the overall cost). Further analysis suggests that changes in input conditions and technology performance parameters lead to new designs primarily in stage I. Conclusions The proposed framework provides significant insights for technology selection and assists in making informed decisions regarding technologies that should be used in combination for a given set of stream/product properties and final output specifications. Additionally, the parametric sensitivity provides an opportunity to make crucial design and selection decisions in a comprehensive and rational manner. This will prove valuable in the selection of chemicals to be produced using bioconversions (bioproducts) as well as in creating better bioseparation flow sheets for detailed economic assessment and process implementation on the commercial scale.