RSC Advances

Nile Red is widely used for the detection of microplastics because its fluorescence emission is sensitive to local polarity and can distinguish hydrophobic plastics from hydrophilic ones. The fluorescence of the molecular rotor, 9-(dicyanovinyl)-julolidine (DCVJ) is less sensitive to polarity but more to viscosity. DCVJ is less widely used for microplastic analysis, although it has been used to detect polystyrene nanobeads. Here, we compared these dyes with standard samples from the Hawaii Pacific University Polymer Kit 1.0 and confirmed that Nile Red, in general, was better for the detection and identification of microplastics. Fluorescence emission was analyzed using photography, as well as spectroscopy. The color and peak emission wavelength of some stained environmental microplastics were affected by additives. Raman spectroscopy was used to confirm the chemical identity of such samples. Although DCVJ emits green fluorescence on binding to some microplastics, a peak at 620 nm has been reported with polystyrene nanobeads, attributed to dimer/excimer formation. We confirmed this property and directly observed diffraction-limited spots using fluorescence microscopy, attributed to single or just a few nanobeads. Nile Red also stains polystyrene nanobeads and gave stronger signals than with DCVJ, but Nile Red was prone to false positives due to dye aggregation in aqueous solutions.

Mycosporine-like amino acids (MAAs) are among nature's most effective sunscreen compounds, capable of converting harmful ultraviolet radiation into harmless heat, and are widely distributed in marine organisms such as red macroalgae. Although decades of research have led to numerous discoveries, the rate of new MAA identifications has declined. To address this, we considerably expanded our previously developed combinatorial MAA database, increasing the number of covered structures tenfold. Following a comprehensive literature search for plausible but undescribed building blocks, the database now incorporates an extensive set of proteinogenic and non-proteinogenic amino acids, as well as other marine organic osmolytes, in combination with all (currently) known MAA scaffolds. This expanded resource was integrated into our identification platform, which combines UHPLC-VWD-HRMS2 analysis, feature-based molecular networking, and bioinformatics-driven annotation. Application of this updated workflow enabled the isolation and structural elucidation of a novel MAA, mycosporine-cysteinolic acid, from the red marine macroalga Vertebrata lanosa. Altogether, this study provides a valuable extension of the bioinformatics-based MAA screening pipeline, enhancing the annotation and discovery of novel MAAs in natural matrices.

Cancer is a serious public health problem, and is becoming more common, with a projected increase in deaths and more than 25 million new cases in coming decades. A number of molecular mechanisms are involved in the tumoral process, with one of them, never in mitosis A-related kinase 2 (NEK2), a serine/threonine protein kinase, being a frequent target of amplification in various malignancies that is responsible for chromosomal instability, aneuploidy and activation of several oncogenic pathways. Available kinase inhibitors are not yet optimized in terms of their pharmacokinetic properties for clinical use, and current therapies, such as chemotherapeutic agents or immunotherapies are often limited by their resistance. In silico methods represent an effective tool to search for novel potent inhibitors, before testing in animals, with time constraints and limited resources. To find new inhibitors of NEK2, we used E pharmacophore-based modeling and structure based virtual screening in this study. NEK2 was chosen as the target for therapeutic intervention and an energy optimized pharmacophore model was employed to screen the Enamine REAL library of millions of compounds. Pharmacodynamic and Pharmacokinetic properties of the Top hits were tested using ADMET profiling. These were further screened using molecular docking (standard precision and extra precision) and virtual screening to obtain three lead compounds 1, 2, and 3 which have docking score of -7.414, -8.037 and -7.562 respectively. MM-GBSA calculations were used to estimate the binding free energies for these complexes, which were determined to be -54.92, -54.18 and -49.23 kcal/mol. Lastly, 100 ns molecular dynamics simulations have been run to evaluate complex stability in dynamic situations. The overall results of the MD showed the overall stability of the NEK2-ligand complexes, and thus these three compounds are promising NEK2 inhibitor candidates and could be further validated in vitro and in vivo for clinical application. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/742111v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@14e9cfeorg.highwire.dtl.DTLVardef@24ec78org.highwire.dtl.DTLVardef@20be75org.highwire.dtl.DTLVardef@1b80c02_HPS_FORMAT_FIGEXP M_FIG C_FIG

Praziquantel, which has limited efficacy against young parasites and reduces susceptibility, is the principal therapy for schistosomiasis, a neglected tropical illness. This work identified fungal-derived compounds with potential dual inhibitory action against Schistosoma mansoni DHODH and cathepsin B1 using an integrated computational method. From 1,831 fungal metabolites, 120 were selected for molecular docking after structure standardisation, drug-likeness evaluation, ADMET, and toxicity screening. Ganoderlactone B (CID_122184973) and Spiroapplanatumine F (CID_132962217) strongly bound to DHODH and CB1, with docking affinities of -10.6 and -10.0 kcal/mol and -9.0 and -8.9, respectively. Docking validation showed good discrimination between active compounds and decoys with area-under-the-curve values of 0.98 for DHODH and 0.96 for CB1. The protein-ligand interaction study showed hydrogen bonding, hydrophobic interactions, and van der Waals contacts at the binding sites. In 100 ns molecular dynamics simulations, protein complexes exhibited modest backbone deviations and maintained compactness, although CID_132962217 showed sustained binding and a constrained conformation. MM-PBSA analysis identified CID_122184973 as the best common ligand for both targets. At the B3LYP/6-31G level, density functional theory calculations showed that CID_139584993 had the lowest HOMO-LUMO energy gap and hardness values of 3.552 and 1.776 eV, respectively, and the highest softness value of 0.282 eV-1, indicating greater electronic reactivity. Electrostatic potential mapping and QSAR predictions supported their interaction and antiparasitic potential. Overall, CID_122184973 and CID_132962217 are promising fungal scaffolds for dual-target anti-schistosomal drug development. Experimental enzyme inhibition, parasite viability, toxicity, and in vivo research are needed to confirm computational findings.

Proper alignment of cells is crucial for functioning of various tissues such as skeletal muscle tissues, neural cells, adipose-derived stem cells, etc. Current in-vitro fabrication methods to replicate the cellular environment, e.g., photolithography and 3D printing, are not cost-effective and cannot capture the complexity of the surfaces to which these cells are exposed to. In this work, we used bio-mimicked leaf templates to closely resemble the in-vivo environment the muscle cells and cultured C2C12 cells, myoblast cell lines, on modified PDMS substrates fabricated using these leaf templates. Using image analysis software, we analyzed the degree of alignment of cells, aspect ratio and the area projected by individual cells cultured on these surfaces. The C2C12 cells cultured on the PDMS substrates formed utilizing the front and back sides of the leaves of Musaceae Banana were found to have an Aspect Ratio of 6.3 and 8.3, the highest among the surfaces studied in this paper. C2C12 cells cultured had the highest degree of alignment on the negative replica of the back side of Dracaena Sanderiana. Due to the availability of a wide range of leaf templates and bio-mimicked surface structures to measure cell response, it is difficult to find the optimal design. Hence, we have also tried to create a catalog using 15 different leaf surfaces and characterized these surfaces into various categories based on the grooves on the surfaces to provide a more comprehensive set of surface designs for studying cell behavior. To quantitatively analyze the groove pattern, 2D FFT analysis was also performed to find the dominant wavelength of the grooves. In surface characterization, hydrophobicity is also a parameter that needs to be considered; hence, the water contact angle of these surfaces was also measured. Our findings highlight the importance of surface topography and hydrophobicity in influencing cell alignment and can contribute to developing biomimetic surfaces for tissue engineering applications.

BackgroundMicrobial chain-elongation by Clostridium kluyveri using the products (acetate and ethanol) derived from the electrocatalytic CO2 reduction reaction (CO2RR) represents a unique sustainable strategy for producing C4-C6 chemicals from CO2. However, direct integration of electrocatalytic effluents with anaerobic bioprocesses is often impeded by the physiological incompatibility between electrocatalytic product streams and microbial metabolism. Specifically, CO2RR effluents commonly contain formate, which cannot be utilized by C. kluyveri for chain elongation and therefore reduces the overall carbon efficiency of CO2 conversion to C4-C6 chemicals. Moreover, both formate and the elevated phosphate concentrations typical of electrochemical reaction solutions may inhibit microbial growth. ResultsWe show that formate at concentrations of up to 50 mM did not inhibit the growth of or the chain elongation by C. kluyveri. Based on this finding, we developed a modular two-step bioprocess. In the first step, the acetogen Clostridium ljungdahlii converts formate in CO2RR product mixtures into acetate, thereby generating additional substrates for second-step C. kluyveri-driven chain elongation, thus increasing the CO2RR carbon-conversion efficiency to C- C6 chemicals. To address the issue of C. ljungdahliis inhibition by high phosphate concentrations in electrocatalytic solutions, we explored the use of C. ljungdahlii biofilms for the first, i.e. the formate-conversion, step. C. ljungdahlii biofilms exhibit tolerance to concentrated electrolytes, enabling the conversion of up to 50 mM formate in CO2RR solutions. ConclusionsThe demonstrated two-step process constitutes the basis for the development of a robust and carbon-efficient biological process for the scalable upgrading of C1-C2 CO2RR products into higher-value C4-C6 chemicals.

The delivery of small-molecule therapeutics across cellular membranes remains a fundamental challenge in drug development, particularly for compounds whose physicochemical properties limit intracellular bioavailability. Inspired by the low toxicity, biodegradability, and synthetic accessibility of triethanolamine-derived lipids, we investigated a series of C18 aminotriester lipids as excipients for the codelivery of anticancer therapeutics. Four structurally related triester lipids bearing saturated, cis-monounsaturated, trans-monounsaturated, and di-unsaturated C18 tails were synthesized through straightforward esterification reactions with triethanolamine. These lipids were evaluated for their ability to enhance the antiproliferative activity of several anticancer agents, including paclitaxel, doxorubicin, amonafide, homoharringtonine, camptothecin, and SN-38. Across multiple drug classes, the aminotriester lipids exhibited minimal intrinsic cytotoxicity and generally produced limited enhancement of drug potency. However, the fully saturated tris-stearate aminotriester lipid selectively enhanced the antiproliferative activity of amonafide in HCT-116 colorectal cancer cells, particularly at lower concentrations. In contrast, unsaturated lipid analogues displayed negligible enhancement effects, and the activity of the tris-stearate lipid was substantially attenuated in A549 lung adenocarcinoma cells, weakly observed in HT-29 colorectal cancer cells, and absent in CT26 murine colorectal cancer cells. Lactate dehydrogenase release assays indicated that these effects were not associated with membrane lysis or nonspecific cytotoxicity. Further, fluorescence microscopy time course experiments suggest improved membrane trafficking of amonafide when codelivered stoichiometrically with fully saturated C18 aminotriester lipids. Collectively, these findings demonstrate that saturated triethanolamine-based aminotriester lipids may function as selective and biocompatible codelivery excipients for certain small-molecule therapeutics. The observed dependence on both lipid saturation state and cellular context suggests that membrane organization and lipid packing may play important roles in mediating these effects. These results establish a foundation for further mechanistic investigations into aminotriester lipid-assisted small-molecule delivery systems.

Monitoring pH and extracellular acidification rate (ECA) in biological samples containing live mammalian cells can provide valuable information on the glycolytic activity and bioenergetic status of cells. Compared to pH electrodes, optochemical pH sensors look more advantageous, since they allow rapid, non-invasive parallel analysis of multiple samples with stable readout of pH. We have developed new fluorescent pH sensors based on hydrophobic protonable metal-free porphyrins,OEP and OEPK, embedded in a plasticized PVC matrix containing a proton transfer agent. These pH sensors provide internally-referenced calibration-free operation, both in ratiometric intensity and lifetime-based detection modes. Sensor development included optimization of the indicator dye and its photophysical characteristics, screening of different proton transfer agents to minimize sensor toxicity, tuning of the protonation range and pKa, long-term storage stability and response time studies. Optimised pH sensor coatings were then deposited on plastic substrates (96-well microplates) and used for real-time monitoring of Extracellular Acidification Rate (ECAR) for cultured cancer cells and 3D spheroid structures on standard laboratory equipment (multi-label plate reader and confocal FLIM microscope). The advanced pH sensors tailored for use with biological samples have high potential for cell analysis and related applications.

The development of NIR fluorescent dyes with favorable optical properties is of great importance especially for applications within the field of optical imaging. In this study, we report the design, synthesis and photophysical characterization of novel BODIPY-Xanthene hybrid dyes obtained via Knoevenagel condensation between an asymmetrical BODIPY core and the functionalized xanthene aldehydes intermediates. The synthesized dyes ex-hibit absorption (667 nm) and emission bands (720-722 nm) within the NIR region. Detailed UV-Vis and fluo-rescence studies were performed and molar extinction coefficients and stokes shifts also calculated. Moreover, findings highlighting the influence of structural modifications on the optical behavior of the hybrid dyes and their potential utility in optical imaging applications are reported.

The spread and evolution of antimicrobial resistance (AMR) pose significant threats to public health, food safety, and sustainable development. Metallo bea lactamases (MBLs), a class of carbapenemases that hydrolyze carbapenems, often considered last resort antibiotics, are of particular concern and underscore the urgent need to develop effective inhibitors. Recent advancements in bicyclic boronate inhibitors represent significant progress toward this goal. Although the mechanistic basis of NDM1 inhibition by bicyclic boronate inhibitors remains incompletely elucidated, this study systematically characterizes the binding profiles and intermolecular interaction landscapes between NDM1 and four cyclic boronic acid based inhibitors, vaborbactam (RPX7009), taniborbactam (VNRX 5133), xeruborbactam (QPX7728), and ledaborbactam (VNRX-5236). We established the NDM-1-mediated hydrolysis of two representative carbapenem antibiotics, meropenem and imipenem, as the reference system for comparative analysis. While conventional docking workflows often struggle to model metalloenzyme active sites accurately, the AMDock (Assisted Molecular Docking) platform, integrated with AutoDock optimized zinc force field, enables robust and reliable docking simulations for this enzyme family. Our AMDock based molecular docking not only identified the critical NDM1 binding site residues governing hydrogen-bond networks with ligands, but also revealed that bicyclic boronate inhibitors show substantially greater binding stability than both carbapenems and their monocyclic boronate counterparts. A follow-up 100 ns all atom molecular dynamics simulation in GROMACS provided atomistic level evidence for this enhanced performance: RMSD, RMSF, radius of gyration, continuous hydrogen bonding occupancy, and 2D free energy landscape analyses all consistently supported the higher binding affinity of bicyclic boronates. Ultimately, this study characterizes two promising drug candidates, QPX7728 and VNRX5133, as competitive inhibitors that can effectively rescue carbapenem activity against NDM1 MBL mediated antimicrobial resistance. This visualization elucidates interactions between NDM1 and its two ligand classes, carbapenem antibiotics and cyclic boronate inhibitors. It reveals the stability and interaction strength underlying the distinct inhibition pattern of bicyclic boronate inhibitors in the enzyme active site, offering a promising strategy to combat antibiotic resistance.

Vanadium (V) is a potential insulinomimetic that can modulate carbohydrate metabolism, but its biological effects are sensitive to chemical form, concentration, and sex. Chelation of vanadium with organic ligands, in particular citrate, allows to increase its bioavailability and optimize pharmacokinetic properties. The aim of the study was to evaluate tissue-, dose-, and sex-dependent changes in physiological parameters and activity of the key glycolytic enzyme - lactate dehydrogenase (LDH) - under the influence of vanadium citrate. The study was conducted on 6-week-old Wistar rats of both sexes. The animals received vanadium citrate orally for 36-38 days at doses of 3, 12.5, and 50 g VCit/kg body weight. LDH activity in skeletal muscle, liver, kidney, and pancreas was investigated. No pronounced toxic effect on physiological parameters was detected: body weight dynamics corresponded to age norms, no behavioral changes were observed. LDH activity demonstrated pronounced sexual dimorphism and depended on the dose received. It was established that the optimal dose, which provides a modulating effect without signs of metabolic stress, for females is 12.5 g VCit/kg, while for males - 3 g VCit/kg. The most significant changes in LDH activity were recorded in the pancreas at a dose of 50 g V/kg, where the indicators decreased from 0.81 to 0.31 mol/(min x mg protein) in females and from 1.02 to 0.28 mol/(min x mg protein) in males. The effect of vanadium citrate on carbohydrate metabolism, as well as its dose-, tissue- and sex-specific nature, is likely determined by a dual action: the insulin-like effect of vanadium (redirecting pyruvate to oxidation) and the allosteric inhibition of glycolysis by the citrate ligand (substrate limitation for LDH). The obtained results emphasize the importance of considering sex and dose in the research and development of metabolically active compounds.

In 1998 we predicted percent human intestinal absorption (HIA) for 86 drug and drug-like compounds from six molecular descriptors chosen by a genetic algorithm with a small neural network serving as the fitness function. That result rested on one division of the compounds into three sets: training, cross-validation and external prediction. Repeating the division shows it was a rather fortunate one. An extraction is one random draw from a wide distribution, so we repeat every experiment 250 times and report the result as a distribution. Across 250 stratified extractions, 14 match the published cross-validation error and only one matches the published external prediction error. Reconstructed this way, the published six descriptors give a mean extraction error roughly twice the external prediction value we reported in 1998 (Table 1). We then applied the same six descriptors to a larger set of 780 compounds from a public compilation. Under the 1998 network they give 22.50 %HIA units on training compounds against 25.77 on extracted compounds, whereas on the 86 they give 13.43 against 30.39. More compounds close the gap between fitting and prediction. Four datasets are modeled here: 780 small molecules with HIA measured in man, 3,771 small molecules and 1,281 cyclic peptides measured across Caco-2 cell monolayers, and 7,298 cyclic peptides measured across artificial membranes. Each is its own prediction problem, and one procedure is applied to all four. A genetic algorithm chooses which descriptors to use and how many, and a random forest grades each choice on compounds its own trees never saw. The descriptors chosen most often across the 250 extractions form one consensus set for each dataset. For absorption, 25 consensus descriptors under a random forest reach an extraction rms of 20.47 %HIA units at R 0.76. On the same compounds and the same extractions, the six 1998 descriptors under the 1998 network reach 25.77 at R 0.59. The four models are served at predicthia.ai, where a structure can be drawn or pasted, scored against its training set for similarity, and the training data with fitted models downloaded. Each model served is its fixed consensus set fitted on every compound in its dataset; the accuracy quoted beside a prediction comes from evaluating that same set across 250 extractions (Table 3). The fixed sets hold 25 descriptors for absorption, 55 for Caco-2 small-molecule permeability, 50 for Caco-2 peptide permeability and 25 for artificial-membrane permeability. Keywords: human intestinal absorption; QSPR; genetic algorithm; descriptor selection; cyclic peptides; membrane permeability; oral bioavailability; repeated extraction

Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process management and cause delays in decision-making. This study develops a machine learning-enabled Raman spectroscopy framework for Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) applications in bioprocess manufacturing. Five predictive modeling techniques--Partial Least Squares (PLS) regression, Support Vector Regression (SVR), Random Forest, Extreme Gradient Boosting (XGBoost), and Neural Networks--were used to analyze Raman spectral data from an Escherichia coli fermentation dataset. The models were assessed using the coefficient of determination (R{superscript 2}), root mean square error (RMSE), and mean absolute error (MAE) to predict two crucial fermentation parameters: the concentrations of glucose and acetate. The superior performance of PLS regression for glucose prediction and the improved prediction accuracy of XGBoost for acetate concentration demonstrated the importance of selecting modeling techniques based on biological complexity. Explainable artificial intelligence using SHAP analysis was incorporated to improve model transparency by identifying Raman spectral regions contributing to predictions. The suggested architecture shows how Raman spectroscopy and machine learning can be combined to assist automated process monitoring, enhance process comprehension, and hasten the implementation of real-time quality judgments in next-generation biomanufacturing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/740653v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@6a460aorg.highwire.dtl.DTLVardef@11c68e5org.highwire.dtl.DTLVardef@2acf7aorg.highwire.dtl.DTLVardef@9b890a_HPS_FORMAT_FIGEXP M_FIG C_FIG Overall workflow of the Raman spectroscopy-based machine learning framework for PAT and RTRT implementation. Raman spectra collected from E. coli fermentation were preprocessed and analyzed using multiple machine learning algorithms for the prediction of glucose and acetate concentrations. Model performance evaluation and SHAP-based explainable AI analysis enabled the identification of important spectral features for real-time bioprocess monitoring. HighlightsO_LIDeveloped a Raman spectroscopy-based machine learning framework for real-time monitoring of critical bioprocess parameters. C_LIO_LICompared traditional chemometric modeling (PLS regression) with advanced machine learning approaches, including SVR, Random Forest, XGBoost, and neural networks. C_LIO_LIShowed that the biochemical target affects the models performance, with XGBoost improving acetate prediction and PLS offering better glucose prediction. C_LIO_LIIntegrated explainable artificial intelligence to identify Raman spectral regions contributing to bioprocess predictions. C_LIO_LIEstablished a pathway toward interpretable Raman-based Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) implementation. C_LI

Microplastic pollution from homopolymers such as polypropylene (PP), polystyrene (PS), and polyethylene (PE) poses growing health and environmental concerns, highlighting the need for robust polymer-degrading biocatalysts. We previously identified a naturally occurring cysteine-dependent hydrolase (CsdH) from Rhodococcus opacus, featuring a unique Cys160-Lys127-Asp39 catalytic triad and providing a novel mechanism for polyethylene terephthalate (PET) degradation. In this study, we have determined crystal structures of CsdH in complex with PP, PS, PE, and styrene and demonstrated the potential of the enzyme to directly degrade these homopolymers into their respective low molecular weight degradation products using LC-MS. Structural analysis reveals that the enzyme retains a conserved principal access tunnel while undergoing subtle substrate-dependent changes to accommodate different substrates. The enzyme exhibited broad plastic degradation activity with the highest weight loss observed for PE, followed by PP and PS. SEM analysis reveals surface roughness, cracks and grooves, confirming polymer degradation. Further, these results corroborate through FTIR and TGA analysis. These findings establish a strong foundation for enzyme engineering and aim at developing efficient microplastic degradation.

In the enzymatic toolbox of depollution, hydrolytic dehalogenases are of particular interest for the bioremediation of organohalogenated compounds. To better identify and characterize these enzymes, it is important to develop appropriate sensing methods. Fluorescent probes for dehalogenase detection have been developed in the past years, however, to this date none were available for application using the common FITC parameters (i.e. excitation laser at 488 nm). To address this limitation, we herein report a new fluorescent probe, COU4-Cl, excitable in the violet (405 nm) and blue (488 nm) wavelengths adapted for the detection and characterization of haloalkane dehalogenases. Its applicability is discussed and evaluated in comparison to the previously described COU2-Cl probe only excitable in the violet (405 nm). This study shows the advantages and complementarity of the two probes, and further demonstrate their application for the discrimination of dehalogenase activity in whole cell assays.

Three-dimensional (3D) spheroid culture provides a physiologically relevant alternative to conventional two-dimensional culture, but reliable spheroid formation in microwells depends on limiting cell-substrate adhesion. Pluronic F127 is an amphiphilic triblock copolymer that forms a hydrated surface layer, reducing protein adsorption. Here, we evaluated whether this intrinsic anti-fouling property could restore an anti-adhesive surface in used microwell plates to promote spheroid formation. Using chondrogenic ATDC5 and pancreatic {beta}-cell INS-1 cells, we characterized spheroid assembly kinetics, F127 cytotoxicity, surface hydrophilicity, protein adsorption, and spheroid morphology including size and shape factor. Both cell types formed compact spheroids within 24 hours on commercial anti-adhesive microwells. F127 coating markedly reduced water contact angle and protein adsorption, confirming increased surface hydrophilicity and reduced protein fouling. In microwells stripped of their original surface coating, F127 coating amounts of approximately 0.011-0.045 mg/cm2 consistently promoted spheroid formation across both cell types. Soluble F127 concentrations were confirmed to be non-cytotoxic up to 0.625% (w/v), while even complete dissolution of the highest tested coating amount would correspond to only 0.025% (w/v) F127. This simple, reproducible, and low-cost surface-modification strategy may provide an accessible approach for re-functionalizing microwell platforms for 3D cell culture.

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.

Computational safety evaluation of phytochemical libraries commonly relies on models trained on experimentally tested compounds and applied to much larger and chemically diverse collections. An important but often overlooked question is whether predicted compounds are adequately represented within the experimental chemical space underlying these models. Here, we examined this question for the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database using experimentally characterized datasets for three anti-target endpoints: hERG blockade, cytochrome P450 (CYP) inhibition, and pregnane X receptor (PXR) induction. After structure standardization, 14,956 unique IMPPAT compounds were compared with 8,877 hERG, 4,905 CYP, and 4,138 PXR reference compounds using consensus nearest-neighbor similarity based on Morgan and atom-pair fingerprints. At a Tanimoto similarity threshold of 0.80, only 0.92%, 0.30%, and 0.46% of IMPPAT compounds had close measured analogs in the hERG, CYP, and PXR datasets, respectively. Relative to the internal nearest-neighbor distributions of the reference datasets, a typical IMPPAT compound lay near the 1st percentile, indicating substantial chemical-space separation. Compounds with close experimental analogs also showed distinct molecular characteristics, with descriptor-based logistic regression yielding cross-validated AUC values of 0.77-0.80. Plant-level enrichment was markedly reduced after excluding widely distributed metabolites and was further restricted when the nearest measured analog was required to have pIC50 or pEC50 [≥] 5. The remaining signals were concentrated in a limited number of taxa, including Rauvolfia, Sinopodophyllum/Dysosma, Cinchona, and Catharanthus roseus. Overall, most IMPPAT phytochemicals lack close structural representation among currently measured anti-target compounds. Computational safety predictions for these molecules should therefore be interpreted as extrapolations and accompanied by explicit measures of structural support. Absence of a close experimental analog should be regarded as a gap in available evidence rather than evidence of safety.

2,4-Dinitrochlorobenzene (DNCB) is a well-characterized skin sensitizer that has been widely used in immunological and toxicological research and, historically, in clinical immunotherapy. Although it is a well-investigated chemical, this is the first study focusing on the dose response behavior at low-level concentrations. The aim was to reveal potential hormetic effects due to its known Nrf2 inducting activity. Therefore, THP-1 cells were treated with low doses of DNCB and two endpoints were evaluated for hormetic responses: metabolic activity using a resazurin-based assay and immune activation by measuring CD86 and CD54 expression using flow cytometry. The results showed a significant hormetic effect on the metabolic endpoint at the lower cell density for both analyzed time points, and a hormetic tendency at the higher cell density. Metabolic activity increased to approximately 125% of the control at 0.05 micromolar DNCB. For the immunological endpoint a slight decrease in CD86 and CD54 surface marker expression was observed, up to -16% and up to -12% compared to control at 0.5 micromolar DNCB. These findings highlight the importance of including low dose concentrations when characterizing chemical dose-response relationships and evaluating toxicological risk.

Fungal and oomycete plant pathogens are major drivers of yield losses worldwide and are spurring the search for sustainable alternatives to synthetic pesticides. Algae, in general, and microalgae, in particular, represent a promising source of bioactive compounds for crop protection. Despite this, their efficacy across different host-pathogen systems remains poorly characterised. This study evaluated the biocontrol potential of the aqueous extract of Chlorella sorokoniana against three economically important phytopathogens using complementary in vitro, ex vivo, and in planta assays. The strongest activity was observed against Magnaporthe oryzae, with the extract reducing fungal growth by approximately 70% in vitro, inhibiting appressorium formation by 55%, suppressing lesion development on detached rice leaves by more than 75%, and reducing rice blast severity by 64.5% as a preventive foliar treatment. In contrast, the extract showed little or no direct in vitro antifungal activity against Pythium ultimum and Rhizoctonia solani, yet it significantly reduced disease severity in planta by 13-37% and 48-55%, respectively. The contrasting responses among pathosystems suggest that C. sorokoniana aqueous extracts act through different mechanisms depending on the pathogen and the crop, combining direct antifungal activity against M. oryzae with plant-associated protective effects against soil-borne pathogens. These findings highlight the importance of evaluating candidate biocontrol products using complementary in vitro and in planta approaches, as laboratory antimicrobial assays alone may substantially underestimate their agricultural potential. The broad-spectrum protection achieved with an unrefined aqueous extract further supports C. sorokoniana as a promising source of sustainable crop protection products and provides a strong foundation for future mechanistic studies, formulation development, and field validation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/742703v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@394716org.highwire.dtl.DTLVardef@6a208corg.highwire.dtl.DTLVardef@17f0724org.highwire.dtl.DTLVardef@adda96_HPS_FORMAT_FIGEXP M_FIG C_FIG