Data-Driven, Explainable Machine Learning Model for Predicting Volatile Organic Compounds' Standard Vaporization Enthalpy.

The accurate prediction of standard vaporization enthalpy (ΔvapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔvapHm° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications. In this work, we present a data driven, explainable supervised ML model to predict ΔvapHm° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' ΔvapHm° with a mean absolute error of 3.02 kJ mol-1 and a 94% test score. The model was successfully validated through the prediction of ΔvapHm° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.

Intranasal Resveratrol Nanoparticles Enhance Neuroprotection in a Model of Multiple Sclerosis.

PURPOSE: Resveratrol is a natural polyphenol which has a very low bioavailability but whose antioxidant, anti-inflammatory and anti-apoptotic properties may have therapeutic potential for the treatment of neurodegenerative diseases such as multiple sclerosis (MS). Previously, we reported the oral administration of resveratrol nanoparticles (RNs) elicited a neuroprotective effect in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS, at significantly lower doses than unconjugated resveratrol (RSV) due to enhanced bioavailability. Furthermore, we demonstrated that the intranasal administration of a cell-derived secretome-based therapy at low concentrations leads to the selective neuroprotection of the optic nerve in EAE mice. The current study sought to assess the potential selective efficacy of lower concentrations of intranasal RNs for attenuating optic nerve damage in EAE mice. METHODS: EAE mice received either a daily intranasal vehicle, RNs or unconjugated resveratrol (RSV) for a period of thirty days beginning on the day of EAE induction. Mice were assessed daily for limb paralysis and weekly for visual function using the optokinetic response (OKR) by observers masked to treatment regimes. After sacrifice at day 30, spinal cords and optic nerves were stained to assess inflammation and demyelination, and retinas were immunostained to quantify retinal ganglion cell (RGC) survival. RESULTS: Intranasal RNs significantly increased RGC survival at half the dose previously shown to be required when given orally, reducing the risk of systemic side effects associated with prolonged use. Both intranasal RSV and RN therapies enhanced RGC survival trends, however, only the effects of intranasal RNs were significant. RGC loss was prevented even in the presence of inflammatory and demyelinating changes induced by EAE in optic nerves. CONCLUSIONS: The intranasal administration of RNs is able to reduce RGC loss independent of the inflammatory and demyelinating effects on the optic nerve and the spinal cord. The concentration of RNs needed to achieve neuroprotection is lower than previously demonstrated with oral administration, suggesting intranasal drug delivery combined with nanoparticle conjugation warrants further exploration as a potential neuroprotective strategy for the treatment of optic neuritis, alone as well as in combination with glucocorticoids.

Magnetic Ionic Liquids: Current Achievements and Future Perspectives with a Focus on Computational Approaches.

Magnetic ionic liquids (MILs) stand out as a remarkable subclass of ionic liquids (ILs), combining the desirable features of traditional ILs with the unique ability to respond to external magnetic fields. The incorporation of paramagnetic species into their structures endows them with additional attractive features, including thermochromic behavior and luminescence. These exceptional properties position MILs as highly promising materials for diverse applications, such as gas capture, DNA extractions, and sensing technologies. The present Review synthesizes key experimental findings, offering insights into the structural, thermal, magnetic, and optical properties across various MIL families. Special emphasis is placed on unraveling the influence of different paramagnetic species on MILs' behavior and functionality. Additionally, the Review highlights recent advancements in computational approaches applied to MIL research. By leveraging molecular dynamics (MD) simulations and density functional theory (DFT) calculations, these computational techniques have provided invaluable insights into the underlying mechanisms governing MILs' behavior, facilitating accurate property predictions. In conclusion, this Review provides a comprehensive overview of the current state of research on MILs, showcasing their special properties and potential applications while highlighting the indispensable role of computational methods in unraveling the complexities of these intriguing materials. The Review concludes with a forward-looking perspective on the future directions of research in the field of magnetic ionic liquids.

Prevalence of comorbidities with the potential to increase the risk of nonadherence to topical ocular hypotensive medication in patients with open-angle glaucoma.

OBJECTIVE: To evaluate the prevalence of comorbidities that may limit or prevent adherence to topical ocular hypotensive therapy in patients with open-angle glaucoma (OAG). METHODS: The UK Clinical Practice Research Datalink (CPRD) database of primary and secondary care and prescription records was analyzed to identify patients with a first (index) diagnosis of OAG during 2016-2020. The primary care records of these patients were screened for diagnostic terms linked to prespecified (qualifying) comorbidities considered to have the potential to impact patients' ability to instill eye drops. The prevalence of each of 10 categories of qualifying comorbidity recorded within the period from 5 years before to 2 years after the index OAG diagnosis was analyzed. RESULTS: A total of 100,968 patients with OAG were included in the analysis. Among the patients in the OAG cohort, 13,962 (13.8%) were aged 40-54 years, 32,145 (31.8%) were aged 55-69 years, 42,042 (41.6%) were aged 70-84 years, and 12,819 (12.7%) were aged 85+ years. Within the OAG population, 82.7%, 14.6%, and 2.7% of patients had no category, one category, and two or more categories of qualifying comorbidity, respectively. Qualifying comorbidities were most common in older patients. The most prevalent qualifying comorbidities were categorized as degenerative, traumatic, or pathological central nervous system disorder disrupting cognitive function (5.2%), movement disorder (4.4%), and low vision (4.1%). The prevalence of arthropathies and injuries affecting upper limbs (including arthritis in the hands) was 2.4%. CONCLUSIONS: The presence of comorbidities should be considered when determining whether eye drops are suitable treatment for glaucoma. Neurodegenerative disease affecting cognition and memory, motor disease, and low vision are common comorbidities that may impact adherence to eye drops, and affected patients may benefit from non-drop treatment modalities.

Effects of COVID-19 pandemic on glaucoma appointment scheduling in a tertiary hospital in London, UK.

PURPOSE: To investigate the impact of the delay in patient appointments caused by the COVID-19 pandemic and the triage system on the glaucomatous disease of patients in a London tertiary hospital. METHODS: Observational retrospective study that randomly selected 200 glaucoma patients with more than 3 months of unintended delay for their post-COVID visit and other inclusion and exclusion criteria. Demographic information, clinical data, number of drugs, best-corrected visual acuity (BCVA), intraocular pressure (IOP), visual field (VF) mean deviation (MD), and global peripapillary retinal nerve fibre layer (pRNFL) thickness were obtained from the pre- and post-COVID visit. At the post-COVID visit, the clinical outcomes subjective clinical concern and change of treatment or need for surgery were also annotated. The variables were stratified by glaucoma severity (according to the MD into early, moderate and advanced) and by delay time (more and less than 12 months) and analysed using SPSS. RESULTS: We included 121 eyes (from 71 patients). The median patient age was 74 years (interquartile range -IQR- 15), 54% were males and 52% Caucasians. Different glaucoma types and all glaucoma severities were included. When data was stratified for glaucoma severity, at the pre-COVID visit, significant differences in BCVA, CCT and IOP were observed and there were significantly higher values in the early glaucoma group. The median follow-up delay was 11 months (IQR 8), did not differ between the glaucoma severity groups and did not correlate to the glaucoma severity. At the post-COVID visit, significant differences in BCVA, IOP, and Global pRNFL thickness were observed between the glaucoma severity groups, as lower BCVA and higher IOP and pRNFL thickness were observed in the early glaucoma group. At the post-COVID visit there was cause for concern in 40 eyes: 5 were followed more closely, 22 had a change of treatment and 13 were booked for surgery (3 for cataract and 10 for glaucoma surgery). However, the number of eyes with causes for concern were similar between the glaucoma severity groups and there was no correlation between these clinical outcomes and the delay of the post-COVID visit. The number of topical hypotensive medications increased significantly after the post-COVID visit, higher number of medications were observed in the advanced glaucoma group. When differences of IOP, MD and pRNFL thickness between the pre and post-COVID visit, only the MD difference was significantly different between the glaucoma severity groups because it was higher in the severe group. When data was stratified for delay longer or shorter than 12 months, no differences were observed between the groups except at the pre-COVID visit, when the numbers of patients with MD deviation >-6 dB had longer delay time. When differences in IOP, MD and RNFL thickness were calculated, only the pRNFL thickness showed significant differences between the delay groups, because it was higher in the longer delay group. Finally, when paired analysis of the variables at the pre- and post-COVID visits, stratified by glaucoma severity and delay were conducted, although there were no significant differences in IOP in any group, the BCVA decreased significantly in the overall group and in the longer delay groups, the number of hypotensive drugs increased significantly overall and in the moderate and advanced glaucoma, the MD of the VF worsened significantly in the overall group and in the early glaucoma and longer delay groups and the pRNFL thickness decreased significantly in all groups. CONCLUSIONS: We document that delayed care impacts negatively on the glaucomatous disease of our patients because at the post-COVID visit there were reasons for clinical concern in a third of eyes that resulted in change of treatment or surgery. However, these clinical consequences were not related to IOP, glaucoma severity or delay time and reflect that the triage methods implemented worked adequately. The most sensitive parameter to indicate progression in our sample was the pRNFL thickness.

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.

Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔGsol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔGsol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔGsol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol-1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.

Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature.

INTRODUCTION: Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED: The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION: Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

Nanoparticles Enhance Solubility and Neuroprotective Effects of Resveratrol in Demyelinating Disease.

Resveratrol is a natural polyphenol which may be useful for treating neurodegenerative diseases such as multiple sclerosis (MS). To date, current immunomodulatory treatments for MS aim to reduce inflammation with limited effects on the neurodegenerative component of this disease. The purpose of the current study is to develop a novel nanoparticle formulation of resveratrol to increase its solubility, and to assess its ability to prevent optic nerve and spinal cord degeneration in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Resveratrol nanoparticles (RNs) were made using a thin rehydration technique. EAE mice received a daily oral administration of vehicle, RNs or unconjugated resveratrol for one month. They were assessed daily for clinical signs of paralysis and weekly for their visual acuity with optokinetic responses (OKR). After one month, their spinal cords and optic nerves were stained for inflammation and demyelination and retinal ganglion cells immunostained for Brn3a. RNs were stable for three months. The administration of RNs did not have any effect on clinical manifestation of EAE and did not preserve OKR scores but reduced the intensity of the disease. It did not reduce inflammation and demyelination in the spinal cord and the optic nerve. However, RNs were able to decrease RGC loss compared to the vehicle. Results demonstrate that resveratrol is neuroprotective by reducing RGC loss. Interestingly, neuroprotective effects and decreased disease severity occurred without reduction of inflammation or demyelination, suggesting this therapy may fill an unmet need to limit the neurodegenerative component of MS.

Predicting the ecotoxicity of endocrine disruptive chemicals: Multitasking in silico approaches towards global models.

Manufactured substances known as endocrine disrupting chemicals (EDCs) released in the environment, through the use of cosmetic products or pesticides, can cause severe eco and cytotoxicity that may induce trans-generational as well as long-term deleterious effects on several biological species at relatively low doses, unlike other classical toxins. As the need for effective, affordable and fast EDCs environmental risk assessment has become increasingly pressing, the present work introduces the first moving average-based multitasking quantitative structure-toxicity relationship (MA-mtk QSTR) modeling specifically developed for predicting the ecotoxicity of EDCs against 170 biological species belonging to six groups. Based on 2,301 data-points with high structural and experimental diversity, as well as on the usage of various advanced machine learning methods, the novel most predictive QSTR models display overall accuracies > 87% in both training and prediction sets. However, maximum external predictivity was achieved when a new multitasking consensus modeling approach was applied to these models. Additionally, the developed linear model provided means to investigate the determining factors for eliciting higher ecotoxicity by the EDCs towards different biological species, identifying several factors such as solvation, molecular mass and surface area as well as the number of specific molecular fragments (e.g.: aromatic hydroxy and aliphatic aldehyde). The resource to non-commercial open-access tools to develop the models is a useful step towards library screening to speed up regulatory decision on discovery of safe alternatives to reduce the hazards of EDCs.

Probing the Allosteric Modulation of P-Glycoprotein: A Medicinal Chemistry Approach Toward the Identification of Noncompetitive P-Gp Inhibitors.

A medicinal chemistry approach combining in silico and in vitro methodologies was performed aiming at identifying and characterizing putative allosteric drug-binding sites (aDBSs) at the interface of the transmembrane- and nucleotide-binding domains (TMD-NBD) of P-glycoprotein. Two aDBSs were identified, one in TMD1/NBD1 and another one in TMD2/NBD2, by means of in silico fragment-based molecular dynamics and characterized in terms of size, polarity, and lining residues. From a small library of thioxanthone and flavanone derivatives, experimentally described to bind at the TMD-NBD interfaces, several compounds were identified to be able to decrease the verapamil-stimulated ATPase activity. An IC50 of 81 ± 6.6 µM is reported for a flavanone derivative in the ATPase assays, providing evidence for an allosteric efflux modulation in P-glycoprotein. Molecular docking and molecular dynamics gave additional insights on the binding mode on how flavanone derivatives may act as allosteric inhibitors.

Long-range communication between transmembrane- and nucleotide-binding domains does not depend on drug binding to mutant P-glycoprotein.

In this study, the impact of four P-gp mutations (G185V, G830V, F978A and ΔF335) on drug-binding and efflux-related signal-transmission mechanism was comprehensively evaluated in the presence of ligands within the drug-binding pocket (DBP), experimentally related with changes in their drug efflux profiles. The severe repacking of the transmembrane helices (TMH), induced by mutations and exacerbated by the presence of ligands, indicates that P-gp is sensitive to perturbations in the transmembrane region. Alterations on drug-binding were also observed as a consequence of the TMH repacking, but were not always correlated with alterations on ligands binding mode and/or binding affinity. Finally, and although all P-gp variants holo systems showed considerable changes in the intracellular coupling helices/nucleotide-binding domain (ICH-NBD) interactions, they seem to be primarily induced by the mutation itself rather than by the presence of ligands within the DBP. The data further suggest that the changes in drug efflux experimentally reported are mostly related with changes on drug specificity rather than effects on signal-transmission mechanism. We also hypothesize that an increase in the drug-binding affinity may also be related with the decreased drug efflux, while minor changes in binding affinities are possibly related with the increased drug efflux observed in transfected cells.Communicated by Ramaswamy H. Sarma.

Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD.

Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity towards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the management of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.

Quantitative brain-derived neurotrophic factor lateral flow assay for point-of-care detection of glaucoma.

Glaucoma, a ruinous group of eye diseases with progressive degeneration of the optic nerve and vision loss, is the leading cause of irreversible blindness. Accurate and timely diagnosis of glaucoma is critical to promote secondary prevention and early disease-modifying therapies. Reliable, cheap, and rapid tests for measuring disease activities are highly required. Brain-derived neurotrophic factor (BDNF) plays an important role in maintaining the function and survival of the central nervous system. Decreased BDNF levels in tear fluid can be seen in glaucoma patients, which indicates that BDNF can be regarded as a novel biomarker for glaucoma. Conventional ELISA is the standard method to measure the BDNF level, but the multi-step operation and strict storage conditions limit its usage in point-of-care settings. Herein, a one-step and a portable glaucoma detection method was developed based on the lateral flow assay (LFA) to quantify the BDNF concentration in artificial tear fluids. The results of the LFA were analyzed by using a portable and low-cost system consisting of a smartphone camera and a dark readout box fabricated by 3D printing. The concentration of BDNF was quantified by analyzing the colorimetric intensity of the test line and the control line. This assay yields reliable quantitative results from 25 to 300 pg mL-1 with an experimental detection limit of 14.12 pg mL-1. The LFA shows a high selectivity for BDNF and high stability in different pH environments. It can be readily adapted for sensitive and quantitative testing of BDNF in a point-of-care setting. The BDNF LFA strip shows it has great potential to be used in early glaucoma detection.

Glaucoma Rose Plot Analysis: Detecting Early Structural Progression Using Angular Histograms.

PURPOSE: To evaluate the novel Rose Plot Analysis (RPA) in the analysis and presentation of glaucoma structural progression data. DESIGN: Case-control image analysis study using retrospective retinal imaging series. SUBJECTS: Subjects with open-angle glaucoma with at least 5 registered spectral-domain OCT scans. METHODS: Glaucoma RPA was developed, combining a novel application of angular histograms and dynamic cluster analysis of circumpapillary retinal nerve fiber layer (cRNFL) OCT data. Rose Plot Analysis plots were created for each eye and each visit. Significant clusters of progression were indicated in red. Three masked clinicians categorized all RPA plots (progressing, not progressing), in addition to measuring the significant RPA area. A masked OCT series assessment with linear regression of averaged global and sectoral cRNFL thicknesses was conducted as the clinical imaging standard. MAIN OUTCOME MEASURES: Interobserver agreement was compared between RPA and the clinical imaging standard. Discriminative ability was assessed using receiver-operating characteristic curves. The time to detection of progression was compared using a Kaplan-Meier survival analysis, and the agreement of RPA with the clinical imaging standard was calculated. RESULTS: Seven hundred fourty-three scans from 98 eyes were included. Interobserver agreement was significantly greater when categorizing RPA (κ, 0.86; 95% confidence interval [CI], 0.81-0.91) compared with OCT image series (κ, 0.66; 95% CI, 0.54-0.77). The discriminative power of RPA to differentiate between eyes that were progressing and not progressing (area under the curve [AUC], 0.97; 95% CI, 0.92-1.00) was greater than that of global cRNFL thickness (AUC, 0.71; 95% CI, 0.59-0.82; P < 0.0001) and equivalent to that of sectoral cRNFL regression (AUC, 0.97; 95% CI, 0.92-1.00). A Kaplan-Meier survival analysis showed that progression was detected 8.7 months sooner by RPA than by global cRNFL linear regression (P < 0.0001) in progressing eyes but was not sooner than with sectoral cRNFL (P = 0.06). Rose Plot Analysis showed substantial agreement with the presence of significant thinning on sectoral cRNFL linear regression (κ, 0.715; 95% CI, 0.578-0.853). CONCLUSIONS: Rose Plot Analysis has been shown to provide accurate and intuitive, at-a-glance data analysis and presentation that improve interobserver agreement and may aid early diagnosis of glaucomatous disease progression.

Microglia: Key Players in Retinal Ageing and Neurodegeneration.

Microglia are the resident immune cells of the central nervous system (CNS) and play a key role in maintaining the normal function of the retina and brain. During early development, microglia migrate into the retina, transform into a highly ramified phenotype, and scan their environment constantly. Microglia can be activated by any homeostatic disturbance that may endanger neurons and threaten tissue integrity. Once activated, the young microglia exhibit a high diversity in their phenotypes as well as their functions, which relate to either beneficial or harmful consequences. Microglial activation is associated with the release of cytokines, chemokines, and growth factors that can determine pathological outcomes. As the professional phagocytes in the retina, microglia are responsible for the clearance of pathogens, dead cells, and protein aggregates. However, their phenotypic diversity and phagocytic capacity is compromised with ageing. This may result in the accumulation of protein aggregates and myelin debris leading to retinal neuroinflammation and neurodegeneration. In this review, we describe microglial phenotypes and functions in the context of the young and ageing retina, and the mechanisms underlying changes in ageing. Additionally, we review microglia-mediated retinal neuroinflammation and discuss the mechanisms of microglial involvement in retinal neurodegenerative diseases.

Correction: An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers.

Correction for 'An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers' by David Ferro-Costas et al., Phys. Chem. Chem. Phys., 2022, DOI: 10.1039/d1cp03928h.

A simple electrochemical detection of atorvastatin based on disposable screen-printed carbon electrodes modified by molecularly imprinted polymer: Experiment and simulation.

Atorvastatin (ATV) is a statin member consumed in high quantities worldwide. In response to that, the occurrence of ATV in environmental waters has become a reality, highlighting the need of rapid and sensitive analytical devices for its monitoring. In this work, the first electrochemical molecularly imprinted polymer (MIP) sensor for the detection of ATV in water samples is presented. Computational studies were conducted based on quantum mechanical (QM) calculations and molecular dynamics (MD) simulations for rational selection of a suitable functional monomer and to study in detail the template-monomer interaction, respectively. The sensor was prepared by electropolymerisation of the selected 4-aminobenzoic acid (ABA) monomer with ATV, acting as template, on screen printed carbon electrode (SPCE). Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques were applied to characterise the modified electrode surfaces. The quantitative measurements were carried out with differential pulse voltammetry (DPV) in 0.1 M phosphate buffer (pH = 7). After investigation and optimisation of important experimental parameters, a linear working range down to 0.05 µmol L-1 was determined with a correlation coefficient of 0.9996 and a limit of detection (LOD) as low as 0.049 µmol L-1 (S/N = 3). High sensitivity and selectivity of the prepared sensor were demonstrated with the ability to recognise ATV molecules over its closer structural analogues. Moreover, the sensor was quickly and successfully applied in spiked water samples, proving its potential for future on-site monitoring of ATV in environmental waters.

An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers.

This work presents a protocol designed to study hydrogen abstraction reactions by atomic hydrogen in molecules with multiple conformations. The protocol starts with the search and location of the conformers of the equilibrium structures using the TorsiFlex program. By a simple modification of the starting geometry of reactants, a Python script generates the input for the hydrogen abstraction transition states. Initially, the search of the stationary points (reactants and transition states) is carried out at a low-level employing firstly a preconditioned search and secondly a random search. The low-level conformers were reoptimized using a higher level electronic structure method. This information allows the evaluation of the multistructural harmonic-oscillator partition functions, which are corrected for zero-point energy anharmonicity by the hybrid degeneracy-corrected second-order vibrational perturbation theory and for torsional anharmonicity by the multistructural torsional method, as implemented in the MsTor program. The structural information of the stationary points is used by Pilgrim to evaluate the multipath canonical variational transition state theory thermal rate constants with multidimensional small-curvature corrections for tunneling. Therefore, the thermal rate constants include variational (recrossing) and tunneling effects in addition to the effect of multiple conformations on the thermal rate constants. These features grant the applicability of the method to a wide range of temperatures. The method was applied to each of the hydrogen abstraction sites of the four isomers of butanol. The methodology employed allowed us to calculate the thermal rate constants in the temperature range of 250-2500 K and to accurately fit them to analytical expressions. The variety of abstraction sites shows that the protocol is robust and that it can be employed to study hydrogen abstraction reactions in molecules containing carbon and oxygen as heavy atoms.

Measures of disease activity in glaucoma.

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.

First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers.

OBJECTIVE: Acrylic acid derivatives are frequently used as dental monomers and their cytotoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental monomers, using quantitative structure-activity relationships (QSAR) modeling approaches. METHODS: A regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been reported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness. RESULTS: The performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter optimization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors responsible for higher cytotoxicity of the dental monomers. SIGNIFICANCE: This study is the first attempt on unveiling the cytotoxicity of dental monomers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.