In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report.

With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n = 37) with soil degradation property (DT50) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal = R2adj: 0.721-0.861, Q2LOO: 0.609-0.757, and external = Q2Fn = 0.597-0.933, MAEext = 0.174-0.260). Further, the leverage approach of applicability domain assured the model's reliability. The veterinary pharmaceuticals with no experimental values were classified based on their persistence level. Further, the terrestrial toxicity analysis of persistent veterinary pharmaceuticals was done using toxicity prediction by computer assisted technology and in-house built quantitative structure toxicity relationship models to prioritize the toxic and persistent veterinary pharmaceuticals. This study will be helpful in estimation of persistence and toxicity of existing and upcoming veterinary pharmaceuticals.

Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization.

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.

Calcutta position: A new modified supine decubitus for supine PCNL.

INTRODUCTION: PCNL (percutaneous nephrolithotripsy) is considered the gold standard treatment for renal stone more than 2 cm. In today's scenario, supine PCNL is considered equally effective as prone PCNL. The ideal position for supine PCNL is still debatable. We hereby describe our initial experiences of supine PCNL in a novel position. METHODS AND MATERIALS: This prospective study includes 60 patients who underwent supine PCNL in the 'Calcutta position' in our institute from August 2021 to August 2022. Successful procedure was defined as a complete stone free rate or a clinically insignificant residual stone (<4 mm). RESULTS: Average Operative room (OR) occupancy time was 130.9 ± 19.63 min. The immediate stone free rate was 84.2%, 71.4% and 37.5% for single, multiple and staghorn calculus respectively. Complications include fever, requirement of blood transfusions and renal colic. The average hospital stay was 83.6 ± 17.42 h. Eight patients (13.3%) required secondary procedures like extracorporeal shock wave lithotripsy (ESWL) or relook PCNL. At 3 months average stone free rates were 92%, 85%, 75% for single, multiple and staghorn calculus respectively. We performed supine PCNL in Calcutta position in obese, kyphoscoliosis, poliomyelitis, autosomal polycystic kidney disease (ADPKD), malrotated kidney and diverticular stone with comparable success. CONCLUSION: Supine PCNL in Calcutta position is a safe and effective option for nephrolithiasis management. Apart from the inherent advantages of supine PCNL it also has the advantages of better C-Arm and nephroscope manoeuvrability. Supine PCNL in Calcutta position was performed in a variety of scenarios with comparable results.

Network pharmacology and bioinformatics based investigation of Phyllanthus fraternus: herb-drug interaction study.

Phyllanthus fraternus (PF), a plant from the Euphorbiaceae family, is used extensively in ayurvedic formulations for its significant medicinal properties. When PF is administered alongside conventional drugs, there could be potential herb-drug interactions between the active compounds and the genes involved in drug transport and metabolism. Hence, this study was designed to investigate potential herb-drug interactions, focusing on elucidating their functional and pharmacological mechanisms, using an integrated approach of metabolite profiling and network pharmacology. We utilized LC-MS to generate metabolite profiling of PF and network pharmacology for predicting key targets and pathways. This comprehensive analysis involved the construction of networks illustrating the relationships among compounds, targets, and pathways and the exploration of protein-protein interactions and protein-ligand interactions. In this study, a total of 79 compounds were identified in LC-MS, such as alkaloids, steroids, saponins, flavonoids, lignans, phenolic acids, tannins, terpenoids, and fatty acids. The identified compound's physicochemical properties were predicted using SwissADME. Network analysis predicted 1076 PF-related genes and 1497 genes associated with drug transport and metabolism, identifying 417 overlapping genes, including 51 related to drug transport and metabolism. Based on the degree of interaction the hub targets like ABCB1, CYP1A1, CYP1A2, CYP2C9, and CYP3A4 were identified. In the compound-target-pathway network, 2,4-bis(1,1-dimethyl ethyl)-phenol; 5-Methoxy-N-[(5-Methylpyridin-2-yl) sulfonyl]-1h-Indole-2-Carboxamide; and E,E,Z-1,3,12-Nonadecatriene-5,14-diol possessed more interactions with the targets. This study helps identify bioactive compounds, essential targets, and pathways potentially implicated in these interactions, laying the foundation for future studies (in vitro and in vivo) to verify their potential to explore their clinical implications.Communicated by Ramaswamy H. Sarma.

In silico Strategy: A Promising Implement in the Development of Multitarget Drugs against Neurodegenerative Diseases.

Multi-target drug development (MTDD) is the demand of the recent era, especially in the case of multi-factorial conditions such as cancer, depression, neurodegenerative diseases (NDs), etc. The MTDD approaches have many advantages; avoidance of drug-drug interactions, predictable pharmacokinetic profile, and less drug resistance. The wet lab practice in MTDD is very challenging for the researchers, and the chances of late-stage failure are obvious. Identification of an appropriate target (Target fishing) is another challenging task in the development of multi-target drugs. The in silico tools will be one of the promising tools in the MTDD for the NDs. Therefore the outlook of the review comprises a short description of NDs, target associated with different NDs, in silico studies so far done for MTDD for various NDs. The main thrust of this review is to explore the present and future aspects of in silico tools used in MTDD for different NDs in combating the challenge of drug development and the application of various in silico tools to solve the problem of target fishing.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population.

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.

Infrared Signatures of Phycobilins within the Phycocyanin 645 Complex.

Aquatic photosynthetic organisms evolved to use a variety of light frequencies to perform photosynthesis. Phycobiliprotein phycocyanin 645 (PC645) is a light-harvesting complex in cryptophyte algae able to transfer the absorbed green solar light to other antennas with over 99% efficiency. The infrared signatures of the phycobilin pigments embedded in PC645 are difficult to access and could provide useful information to understand the mechanism behind the high efficiency of energy transfer in PC645. We use visible-pump IR-probe and two-dimensional electronic vibrational spectroscopy to study the dynamical evolution and assign the fingerprint mid-infrared signatures to each pigment in PC645. Here, we report the pigment-specific vibrational markers that enable us to track the spatial flow of excitation energy between the phycobilin pigment pairs. We speculate that two high-frequency modes (1588 and 1596 cm-1) are involved in the vibronic coupling leading to fast (

Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT.

Currently, numerous potent chemotherapeutic agents are available in the market but most of them show poor pharmacokinetics, lethal effects and drug resistance during their enduring use. The increased cancer cases, deaths and need of better treatment stimulates us to give newer lifesaving anticancer drugs. The phthalimide derivatives are structurally diverse and exert potential anticancer activity. In this regard, the 3D QSAR Pharmacophore model was developed and validated using fifty-eight phthalimide derivatives. The validation parameters corroborated the reliability and statistical robustness of CEASER Hypo 1. Three databases-NCI Open, Drug Bank, and Asinex were submitted to ADMET and drug-like filtering; 117893 drug-like compounds were mapped on CEASER Hypo 1; and 362 hits with IC50 <1 µM were discovered. These hits were docked on VEGFR2-TK, and in the form of results fifteen hits exhibited greater affinity than sorafenib. The top lead ASN 03206926 was subjected for MD simulation (100 ns) and RMSD, Rg, RMSF, number of hydrogen bonds, and SASA verified that the complex was stable, rigid and highly compact. Results demonstrated GLU885, PHE918, CYS919, LYS920, HIS1026, CYS1045, ASP1046 are the essential residues for favourable interactions. The binding free energy calculations support the affinity and stability revealed by docking and MD simulation. The DFT calculations, negative binding energy and lower HOMO-LUMO band gap revealed that the process is spontaneous and ASN 03206926 is very reactive. Following extensive analysis we suggest that the ASN 03206926 might be employed as a new VEGFR2-TK inhibitor for the treatment of breast and VEGFR2-TK associated cancers.Communicated by Ramaswamy H. Sarma.

Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.

Crowd Characterization in Surveillance Videos Using Deep-Graph Convolutional Neural Network.

Crowd behavior is a natural phenomenon that can provide valuable insight into the crowd characterization process. Modeling the visual appearance of a large crowd gathering can reveal meaningful information about its dynamics. Parametric modeling can be used to develop efficient and robust crowd monitoring systems. A crowd can be structured or unstructured based on the organization. In this article, crowd characterization has been mapped to a graph classification problem to classify movements based on order parameter ( ϕ ), active force components, and steadiness (Reynolds number). The graphs are constructed from the motion groups obtained using an active Langevin framework. These graphs are processed using a deep graph convolutional neural network for crowd characterization. For experimentation, we have prepared a dataset comprising of videos from popular publicly available datasets and our own recorded videos. The proposed framework has been compared with the latest deep learning-based frameworks in terms of accuracy and area under the curve (AUC). We have obtained a 4%-5% improvement in accuracy and AUC values over the existing frameworks. The insights obtained from the proposed framework can be used for better crowd monitoring and management.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach.

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q2LOO = 0.620-0.663) and externally (Q2Fn = 0.693-0.868, CCCext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO2, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.

Decoding the cognitive states of attention and distraction in a real-life setting using EEG.

Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.

Pharmacophore based virtual screening of cholinesterase inhibitors: search of new potential drug candidates as antialzheimer agents.

Alzheimer's disease (AD) is a distinctive medical condition characterized by loss of memory, orientation, and cognitive impairments, which is an exceptionally universal form of neurodegenerative disease. The statistical data suggested that it is the 3rd major cause of death in older persons. Butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) inhibitors play a vital role in the treatment of AD. Coumarins, natural derivatives, are reported as cholinesterase inhibitors and emerges as a promising scaffold for design of ligands targeting enzymes and pathological alterations related to AD. In this regard, the 3D QSAR pharmacophore models were developed for coumarin scaffold containing BChE and AChE inhibitors. Several 3D QSAR pharmacophore models were developed with FAST, BEST, and CEASER methods, and finally, statistically robust models (based on correlation coefficient, cost value, and RMSE value) were selected for further analysis for both targets. The important features ((HBA 1, HBA 2, HY, RA (BChE) HBA 1, HBA 2, HY, PI, (AChE)) were identified for good inhibitory activity of coumarin derivatives. Finally, the selected models were applied to various database compounds to find potential BChE and AChE inhibitors, and we found 13 for BChE and 1 potent compound for AChE with an estimated activity of IC50 < 10 µM. Further, the Lipinski filters, and ADMET analysis supports the selected compounds to become a drug candidate. These selected BChE and AChE inhibitors can be used in the treatment of AD. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-022-00133-1.

Interference between Franck-Condon and Herzberg-Teller Terms in the Condensed-Phase Molecular Spectra of Metal-Based Tetrapyrrole Derivatives.

The commonly used Franck-Condon (FC) approximation is inadequate for explaining the electronic spectra of compounds that possess vibrations with substantial Herzberg-Teller (HT) couplings. Metal-based tetrapyrrole derivatives, which are ubiquitous natural pigments, often exhibit prominent HT activity. In this paper, we compare the condensed phase spectra of zinc-tetraphenylporphyrin (ZnTPP) and zinc-phthalocyanine (ZnPc), which exhibit vastly different spectral features in spite of sharing a common tetrapyrrole backbone. The absorption and emission spectra of ZnTPP are characterized by a lack of mirror symmetry and nontrivial temperature dependence. In contrast, mirror symmetry is restored, and the nontrivial temperature-dependent features disappear in ZnPc. We attribute these differences to FC-HT interference, which is less pronounced in ZnPc because of a larger FC component in the dipole moment that leads to FC-dominated transitions. A single minimalistic FC-HT vibronic model reproduces all the experimental spectral features of these molecules. These observations suggest that FC-HT interference is highly susceptible to chemical modification.

Enhancing object detection in aerial images.

Unmanned Aerial Vehicles have proven to be helpful in domains like defence and agriculture and will play a vital role in implementing smart cities in the upcoming years. Object detection is an essential feature in any such application. This work addresses the challenges of object detection in aerial images like improving the accuracy of small and dense object detection, handling the class-imbalance problem, and using contextual information to boost the performance. We have used a density map-based approach on the drone dataset VisDrone-2019 accompanied with increased receptive field architecture such that it can detect small objects properly. Further, to address the class imbalance problem, we have picked out the images with classes occurring fewer times and augmented them back into the dataset with rotations. Subsequently, we have used RetinaNet with adjusted anchor parameters instead of other conventional detectors to detect aerial imagery objects accurately and efficiently. The performance of the proposed three step pipeline of implementing object detection in aerial images is a significant improvement over the existing methods. Future work may include improvement in the computations of the proposed method, and minimising the effect of perspective distortions and occlusions.

Sexual well-being and fertility in male renal transplant recipients: A study in a tertiary care centre.

INTRODUCTION: Sexual dysfunction is a common problem among chronic kidney disease (CKD) patients. The uraemia, comorbid conditions, hormonal disturbances, autonomic neuropathy, side effects of medication and psycho-social factors contribute to sexual dysfunction. These factors also affect fertility of CKD patients. It starts early in CKD and gradually deteriorates with time. Renal transplant corrects most of the issues and leads to improvement of sexual function which ultimately leads to improved fertility outcomes. METHODS: It was a cross sectional study performed in a single institution including 135 male renal transplant recipients. A questionnaire was used to evaluate the socio-economic status, fertility and developmental condition of the off-springs. The International index of erectile dysfunction (IIEF) was used for assessment of sexual well-being of the patient. The data were statistically analysed by SPSS 25.0 version. RESULTS: The mean age at transplant was 40.9 ± 9.9 years and the duration of haemodialysis received 13.6 ± 7.43 months. Among 135 recipients 63 (46.67%) desired but only 49 were successful to father a child. Upper middle class being the largest group to receive renal transplant also had the highest fertility rate in our study. The children born had no development anomaly. Sexual function improved in 85 patients, worsened in 15 and remain unchanged among 35 patients after renal transplant. Overall there was significant improvement in all five parameters of IIEF. CONCLUSION: Renal transplant corrects most of the metabolic abnormality as well gives a psychological boost to the CKD patients. These lead to improvement of sexual functions which in turn improves the fertility in renal transplant recipients. But the overall fertility rate among the male renal transplant recipients was comparable to the general population.

In silico selectivity modeling of pyridine and pyrimidine based CYP11B1 and CYP11B2 inhibitors: A case study.

DESIGN: of selective drug candidates for highly structural similar targets is a challenging task for researchers. The main objective of this study was to explore the selectivity modeling of pyridine and pyrimidine scaffold towards the highly homologous targets CYP11B1 and CYP11B2 enzymes by in silico (Molecular docking and QSAR) approaches. In this regard, a big dataset (n = 228) of CYP11B1 and CYP11B2 inhibitors were gathered and classified based on heterocyclic ring and the exhaustive analysis was carried out for pyridine and pyrimidinescaffolds. The LibDock algorithm was used to explore the binding pattern, screening, and identify the structural feature responsible for the selectivity of the ligands towards the studied targets. Finally, QSAR analysis was done to explore the correlation between various binding parameters and structural features responsible for the inhibitory activity and selectivity of the ligands in a quantitative way. The docking and QSAR analysis clearly revealed and distinguished the importance of structural features, functional groups attached for CYP11B2 and CYP11B1 selectivity for pyridine and pyrimidine analogs. Additionally, the docking analysis highlighted the differentiating amino acids residues for selectivity for ligands for each of the enzymes. The results obtained from this research work will be helpful in designing the selective CYP11B1/CYP11B2 inhibitors.

Franck-Condon and Herzberg-Teller Signatures in Molecular Absorption and Emission Spectra.

Some molecules of chemical and biological significance possess vibrations with significant Herzberg-Teller (HT) couplings, which render the Franck-Condon (FC) approximation inadequate and cause the breakdown of the well-known mirror-image symmetry between linear absorption and emission spectra. Using a model two-state system with displaced harmonic potential surfaces, we show analytically that the FC-HT interference gives rise to asymmetric intensity modification, which has the same sign for all transitions on one side of the 0-0 absorption line and the opposite sign in the equivalent fluorescence transitions, while the trend is exactly reversed for all transitions on the other side the 0-0 line. We examine the dependence of the absorption-emission asymmetry on the mode frequency, Huang-Rhys factor, and dipole moment parameters to show the recovery of symmetry with particular combinations of parameters and a crossover from fluorescence to absorption dominance. We illustrate the analytical predictions through numerically exact calculations in models of one and two discrete vibrational modes and in the presence of a harmonic dissipative bath. In addition to homogeneous broadening effects, we identify large asymmetric shifts of absorption and emission band maxima, which can produce the illusion of unequal frequencies in the ground and excited potential surfaces as well as a nontrivial modulation of spectral asymmetry by temperature, which results from the enhancement of transitions on one side of the 0-0 line. These findings will aid the interpretation of experimental spectra in HT-active molecular systems.

Synthetic Control of Exciton Dynamics in Bioinspired Cofacial Porphyrin Dimers.

Understanding how the complex interplay among excitonic interactions, vibronic couplings, and reorganization energy determines coherence-enabled transport mechanisms is a grand challenge with both foundational implications and potential payoffs for energy science. We use a combined experimental and theoretical approach to show how a modest change in structure may be used to modify the exciton delocalization, tune electronic and vibrational coherences, and alter the mechanism of exciton transfer in covalently linked cofacial Zn-porphyrin dimers (meso-beta linked ABm-ß and meso-meso linked AAm-m). While both ABm-ß and AAm-m feature zinc porphyrins linked by a 1,2-phenylene bridge, differences in the interporphyrin connectivity set the lateral shift between macrocycles, reducing electronic coupling in ABm-ß and resulting in a localized exciton. Pump-probe experiments show that the exciton dynamics is faster by almost an order of magnitude in the strongly coupled AAm-m dimer, and two-dimensional electronic spectroscopy (2DES) identifies a vibronic coherence that is absent in ABm-ß. Theoretical studies indicate how the interchromophore interactions in these structures, and their system-bath couplings, influence excitonic delocalization and vibronic coherence-enabled rapid exciton transport dynamics. Real-time path integral calculations reproduce the exciton transfer kinetics observed experimentally and find that the linking-modulated exciton delocalization strongly enhances the contribution of vibronic coherences to the exciton transfer mechanism, and that this coherence accelerates the exciton transfer dynamics. These benchmark molecular design, 2DES, and theoretical studies provide a foundation for directed explorations of nonclassical effects on exciton dynamics in multiporphyrin assemblies.

The modulatory role of prime identified compounds in Geophila repens in mitigating scopolamine-induced neurotoxicity in experimental rats of Alzheimer's disease via attenuation of cholinesterase, ß-secretase, MAPt levels and inhibition of oxidative stress imparts inflammation.

ETHNOPHARMACOLOGICAL RELEVANCE: Geophila repens (L.) I.M. Johnst (Rubiaceae) is a small perennial creeper native to India, China, and other countries in Southeast Asia. The hot decoction of leaves is used orally for memory enhancing by the local folk of Andhra Pradesh, India. The ethnomedicinal claim of G. repens as memory enhancer was initially studied by the authors. Results demonstrated the important antioxidant and anticholinesterase activities of isolated molecule Pentylcurcumene and bioactive hydroalcohol extract of leaves of G. repens (GRHA). AIM OF THE STUDY: Based on the previous findings, additional research is needed to examine the efficacy of GRHA for memory enhancing properties. We therefore investigated the modulatory role of prime identified compounds in GRHA in mitigating scopolamine-induced neurotoxicity in experimental rats of Alzheimer's disease (AD) via attenuation of cholinesterase, ß-secretase, MAPt levels and inhibition of oxidative stress imparts inflammation. METHODS: Scopolamine (3 mg/kg) induced experimental rats of AD were treated with GRHA (300, 400 mg/kg) for 14 days. During the experimental period, elevated T-maze and locomotion-activity were performed to assess learning and memory efficacy of GRHA. At the end of the experiment, biochemical, neurochemical, neuroinflammation and histopathological observation of brain cortex were examined. GC-MS/MS analysis reported 31 compounds, among them 8 bioactive compounds possess antioxidant, neuroinflammation, neuroprotective activities, and were considered for docking analysis towards cholinesterase, ß-secretase activities in AD. RESULTS: GRHA 400 significantly improved learning and memory impairment with the improvement of oxidative stress (MDA, SOD, GSH, CAT), DNA damage (8-OHdG), neurochemical (AChE, BuChE, BACE1, BACE2, MAPt), neuroinflammation (IL-6, TNF-α) markers in neurotoxic rats. Docking studies of 8 compounds demonstrated negative binding energies for cholinesterase and ß-secretase indicating high affinity for target enzymes in AD. Test results were corroborated by the improvement of cellular tissue architecture of brain cortex in AD rats. CONCLUSION: Synergistic action of genistin, quercetin-3-D-galactoside, 9,12,15-octadecatrienoic-acid methyl-ester, phytol, retinal, stigmasterol, n-hexadecanoic acid, ß-sitosterol in GRHA restores memory-deficits via attenuation of cholinesterase, ß-secretase, MAPt level and inhibition of oxidative-stress imparts inflammation in AD.