Advances in machine intelligence-driven virtual screening approaches for big-data.

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

The spectrum of psychiatric manifestations in subacute sclerosing panencephalitis: A systematic review of published case reports and case series.

Data related to psychiatric manifestations in subacute sclerosing panencephalitis (SSPE) is currently available only in the form of isolated case reports. In this systematic review, we evaluated the spectrum of psychiatric manifestations and their impact on the course and outcome of SSPE. Data were obtained from 4 databases (PubMed, Embase, Scopus, and Google Scholar), with the most recent search conducted on March 27, 2023. The PRISMA guidelines were followed, and the PROSPERO registration number for the protocol is CRD42023408227. SSPE was diagnosed using Dyken's criteria. Extracted data were recorded in an Excel spreadsheet. To evaluate the quality of the data, the Joanna Briggs Institute Critical Appraisal tool was employed. Our search resulted in 30 published reports of 32 patients. The mean age was 17.9 years. Schizophrenia, catatonia, and poorly characterized psychotic illnesses were the 3 most common psychiatric presentations that were seen in 63% (20/32) of cases. Catatonia was seen in 4 patients. Affective disorders, mania, and depression were reported among 22% (7/32) cases. In approximately 81% (26/32) cases, the course of SSPE was acute fulminant. Treatment with antipsychotic drugs had poor or no response. Out of 17 patients, who received antipsychotic drugs, 6 patients noted severe extrapyramidal adverse effects. SSPE often masquerades as a psychiatric disorder. Unresponsive psychiatric symptoms, early extrapyramidal signs, and progressive encephalopathy indicate SSPE.

Clinical features, pathogenesis, pathology, neuroimaging, clinical course and outcome of measles inclusion-body encephalitis: a systematic review of published case reports and case series.

Measles inclusion-body encephalitis (MIBE) is rare, with insights largely from case studies. We systematically analyzed subacute Sclerosing Panencephalitis (SSPE) cases in immunocompromised patients, identifying distinctive clinical and neuroimaging features. These findings could facilitate MIBE diagnosis without the need for brain biopsies. Our systematic review on MIBE and HIV-related SSPE adhered to PRISMA guidelines and was registered with PROSPERO. We searched multiple databases and followed a detailed inclusion process with independent reviews and quality assessment. Data on patient demographics, clinical features, and outcomes were compiled. A review of 39 studies on 49 MIBE patients and 8 reports on HIV-positive SSPE patients was conducted. Acute lymphoblastic leukemia, HIV, organ transplants, and malignancies were common precursors to MIBE. Perinatal HIV was prevalent among SSPE cases. Seizures were the primary symptom in MIBE, often drug-resistant and progressing to status epilepticus or epilepsia partialis continua, whereas periodic myoclonus was universal in SSPE. Neuroimaging showed distinct patterns for each group, and histopathology confirmed measles virus presence in 39% of MIBE cases. MIBE patients typically progressed to coma and death. In conclusion, MIBE and SSPE in HIV-infected patients present with distinct clinical pictures but identical brain pathological abnormalities.

The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression.

This letter provides a critical assessment of a previous study on the utility of whole tumor apparent diffusion coefficient (ADC) histogram characteristics in predicting meningioma progesterone receptor expression. While acknowledging the benefits of employing classical diffusion-weighted imaging (DWI) for non-invasive tumor evaluation, it also emphasizes significant drawbacks. Advanced imaging techniques such as diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) were not used in the study, which could have provided a more comprehensive understanding of tumor microstructure and heterogeneity. Furthermore, the inclusion of necrotic and cystic areas in ADC analysis may distort results due to their different diffusion properties. While focusing on first-order ADC histogram characteristics is useful, it ignores the potential insights gained from higher-order features and texture analysis. These limitations indicate that future research should combine improved imaging modalities with thorough analytical methodologies to increase the predictive value of imaging biomarkers for meningioma features and progesterone receptor expression.

Epidemiological Predictors of Financial Toxicity in Surgical Burn Injuries: A Multicenter, Longitudinal, Cohort Study.

BACKGROUND: Burns constitute a major global health challenge, causing over 11 million injuries and 300,000 deaths annually and surpassing the economic burden of cervical cancer and HIV combined. Despite this, patient-level financial consequences of burn injuries remain poorly quantified, with a significant gap in data from low- and middle-income countries. In this study, we evaluate financial toxicity in burn patients. METHODS: A prospective, multicenter cohort study was conducted across two tertiary care hospitals in India, assessing 123 adult surgical in-patients undergoing operative interventions for burn injuries. Patient sociodemographic, clinical, and financial data were collected through surveys and electronic records during hospitalization and at 1, 3, and 6 months postoperatively. Out-of-pocket costs (OOPCs) for surgical burn treatment were evaluated during hospitalization. Longitudinal changes in income, employment status, and affordability of basic subsistence needs were assessed at the 1-, 3-, and 6-month postoperative time point. Degree of financial toxicity was calculated using a combination of the metrics catastrophic health expenditure and financial hardship. Development of financial toxicity was compared by sociodemographic and clinical characteristics using logistic regression models. RESULTS: Of the cohort, 60% experienced financial toxicity. Median OOPCs was US$555.32 with the majority of OOPCs stemming from direct nonmedical costs (US$318.45). Cost of initial hospitalization exceeded monthly annual income by 80%. Following surgical burn care, income decreased by US$318.18 within 6 months, accompanied by a 53% increase in unemployment rates. At least 40% of the cohort consistently reported inability to afford basic subsistence needs within the 6-month perioperative period. Significant predictors of developing financial toxicity included male gender (odds ratio, 4.17; 95% confidence interval, 1.25-14.29; P = 0.02) and hospital stays exceeding 20 days (odds ratio, 11.17; 95% confidence interval, 2.11-59.22; P ≤ 0.01). CONCLUSIONS: Surgical treatment for burn injuries is associated with substantial financial toxicity. National and local policies must expand their scope beyond direct medical costs to address direct nonmedical and indirect costs. These include burn care insurance, teleconsultation follow-ups, hospital-affiliated subsidized lodging, and resources for occupational support and rehabilitation. These measures are crucial to alleviate the financial burden of burn care, particularly during the perioperative period.

Copper and nanocopper toxicity using integrated biomarker response in Pangasianodon hypophthalmus.

The current study focused on assessing the toxicological effects of copper (Cu) and copper nanoparticles (Cu-NPs) in acute condition on Pangasianodon hypophthalmus. The median lethal concentration (LC50 ) for Cu and Cu-NPs were determined as 8.04 and 3.85 mg L-1 , respectively. For the subsequent definitive test, varying concentrations were selected: 7.0, 7.5, 8.0, 8.5, and 9.0 mg L-1 for Cu, and 3.0, 3.3, 3.6, 3.9, and 4.2 mg L-1 for Cu-NPs. To encompass these concentration levels and assess their toxic effects, biomarkers associated with toxicological studies like oxidative stress, neurotransmission, and cellular metabolism were measured in the liver, kidney, and gill tissues. Notably, during the acute test, the activities of catalase, superoxide dismutase, glutathione-s-transferase, glutathione peroxidase, and lipid peroxide in the liver, gill, and kidney tissues were significantly increased due to exposure to Cu and Cu-NPs. Similarly, acetylcholinesterase activity in the brain was notably inhibited in the presence of Cu and Cu-NPs when compared to the control group. Cellular metabolic stress was greatly influenced by the exposure to Cu and Cu-NPs, evident from the considerable elevation of cortisol, HSP 70, and blood glucose levels in the treated groups. Furthermore, integrated biomarker response, genotoxicity, DNA damage in gill tissue, karyotyping in kidney tissue, and histopathology in gill and liver were investigated, revealing tissue damage attributed to exposure to Cu and Cu-NPs. In conclusion, this study determined that elevated concentrations of essential trace elements, namely Cu and Cu-NPs, induce toxicity and disrupt cellular metabolic activities in fish.

Utility of Itraconazole in Combination with Liposomal Amphotericin B in Rhizopus oryzae Associated Mucormycosis-An Exploratory Study.

BACKGROUND: During the COVID-19 pandemic-associated mucor epidemic, acute antifungal drug shortage necessitated the exploration of other antifungals based on culture sensitivity. Itraconazole is a cheap, safe, and effective antifungal in sensitive cases. METHODOLOGY: We enrolled itraconazole-sensitive COVID-19-associated mucormycosis during the mucormycosis pandemic. After the intensive phase course of liposomal amphotericin B, Itraconazole was offered in susceptible cases during the maintenance phase along with standard of care. These patients were clinically and radiologically followed for 6 months. RESULTS: We enrolled 14 patients (Male: Female-11:3) of Rhino-orbito-cerebral mucormycosis (ROCM) which included 12 diabetics. All patients had facial swelling, orbital swelling, visual impairment, and headache. MRI showed involvement of bilateral sinus (10/14), orbital extension (13/14), cavernous sinus (5/14), cerebral part of the internal carotid artery (3/14), and brain infarcts (4/14). All 14 patients showed sensitivity to Itraconazole with 12 having minimum inhibitory concentration (MIC) ≤ 1 µg/ml and 2 having MIC ≤ 2 µg/ml. Follow-up at 6 months showed clinical improvement in the majority (11/14) and radiological improvement in six out of seven scanned patients. CONCLUSION: Our study shows the potential therapeutic role of oral Itraconazole in ROCM.

Electrochemical transformations catalyzed by cytochrome P450s and peroxidases.

Cytochrome P450s (Cyt P450s) and peroxidases are enzymes featuring iron heme cofactors that have wide applicability as biocatalysts in chemical syntheses. Cyt P450s are a family of monooxygenases that oxidize fatty acids, steroids, and xenobiotics, synthesize hormones, and convert drugs and other chemicals to metabolites. Peroxidases are involved in breaking down hydrogen peroxide and can oxidize organic compounds during this process. Both heme-containing enzymes utilize active FeIVO intermediates to oxidize reactants. By incorporating these enzymes in stable thin films on electrodes, Cyt P450s and peroxidases can accept electrons from an electrode, albeit by different mechanisms, and catalyze organic transformations in a feasible and cost-effective way. This is an advantageous approach, often called bioelectrocatalysis, compared to their biological pathways in solution that require expensive biochemical reductants such as NADPH or additional enzymes to recycle NADPH for Cyt P450s. Bioelectrocatalysis also serves as an ex situ platform to investigate metabolism of drugs and bio-relevant chemicals. In this paper we review biocatalytic electrochemical reactions using Cyt P450s including C-H activation, S-oxidation, epoxidation, N-hydroxylation, and oxidative N-, and O-dealkylation; as well as reactions catalyzed by peroxidases including synthetically important oxidations of organic compounds. Design aspects of these bioelectrocatalytic reactions are presented and discussed, including enzyme film formation on electrodes, temperature, pH, solvents, and activation of the enzymes. Finally, we discuss challenges and future perspective of these two important bioelectrocatalytic systems.

Exploring the Therapeutic Potential of Petiveria alliacea L. Phytochemicals: A Computational Study on Inhibiting SARS-CoV-2's Main Protease (Mpro).

The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of -8.9, -8.7 and -8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials.

Protective role of selenium and selenium-nanoparticles against multiple stresses in Pangasianodon hypophthalmus.

Pollution and climate change pose significant threats to aquatic ecosystems, with adverse impacts on aquatic animals, including fish. Climate change increases the toxicity of metal in aquatic ecosystems. To understand the severity of metal pollution and climate change, an experiment was conducted to delineate the mitigation potential of selenium (Se) and selenium nanoparticles (Se-NPs) against lead (Pb) and high temperature stress in Pangasianodon hypophthalmus. For the experiment, five isonitrogenous and isocaloric diets were prepared, varying in selenium supplementation as Se at 0, 1, and 2 mg kg-1 diet, and Se-NPs at 1 and 2 mg kg-1 diet. The fish in stressor groups were exposed to Pb (1/20th of LC50 concentration, 4 ppm) and high temperature (34 °C) throughout the experiment. The results demonstrated that dietary supplementation of Se at 1 and 2 mg kg-1 diet, as well as Se-NPs at 1 mg kg-1 diet, significantly reduced (p < 0.01) the levels of lactate dehydrogenase and malate dehydrogenase in both liver and muscle tissues. Additionally, the levels of alanine aminotransferase and aspartate aminotransferase in both gill and liver tissues were significantly decreased (p < 0.01) with the inclusion of Se and Se-NPs in the diets. Furthermore, the enzymes glucose-6-phosphate dehydrogenase in gill and liver tissues, fructose 1,6-bisphosphatase in liver and muscle tissues, and acid phosphatase in liver tissue were remarkably reduced (p < 0.01) due to the supplementation of Se and Se-NPs. Moreover, dietary supplementation of Se and Se-NPs significantly enhanced (p < 0.01) the activity of pyruvate kinase, glucokinase, hexokinase, alkaline phosphatase, ATPase, protease, amylase, lipase, and RNA/DNA ratio in the fish. Histopathological examination of gill and liver tissues also indicated that Se and Se-NPs protected against structural damage caused by lead and high-temperature stress. Moreover, the study examined the bioaccumulation of selenium and lead in muscle, water, and diets. The aim of the study revealed that Se and Se-NPs effectively protected the fish from lead toxicity and high-temperature stress, while also improving the function of cellular metabolic enzymes in P. hypophthalmus.

Speciation-specific chromium bioaccumulation and detoxification in fish using hydrogel microencapsulated biogenic nanosilver and zeolite synergizing with biomarkers.

Grass carp intestinal waste-mediated biosynthesized nanosilver (AgNPs) was valorized using guaran and zeolite matrices, resulting in AgNPs-guaran, AgNPs-zeolite, and AgNPs-guaran -zeolite composites. The valorized products were examined using Environmental Scanning Electron Microscopy, Energy Dispersive X-ray analysis and X-ray Diffraction analysis to confirm uniform dispersion and entrapment of AgNPs within the matrixes. These valorized products were evaluated for their efficacy in detoxifying the ubiquitous and toxic hexavalent chromium (Cr6+) in aquatic environments, with Anabas testudineus exposed to 2 mg l-1 of Cr6+ for 60 days. Remarkable reduction of Cr6+ concentration to 0.86 ± 0.007 mg l-1 was achieved with AgNPs-guaran-zeolite composite, indicating successful reclamation of contaminated water and food safety assurance. Consistency in results was further corroborated by minimal stress-related alterations in fish physiological parameters and integrated biomarker response within the experimental group treated with the AgNPs-guaran-zeolite composite. Despite observed chromium accumulation in fish tissues, evidence of physiological stability was apparent, potentially attributable to trivalent chromium accumulation, serving as an essential nutrient for the fish. Additionally, the challenge study involving Anabas testudineus exposed to Aeromonas hydrophila exhibited the lowest cumulative mortality (11.11%) and highest survival rate (87.5%) within the same experimental group. The current study presents a novel approach encompassing the valorization of AgNPs for Cr6+ detoxification under neutral to alkaline pH conditions, offering a comprehensive framework for environmental remediation.

An unusual case of duplicated left brachiocephalic vein with right sided aortic arch and aberrant origin of LSCA in a patient undergoing implantable cardioverter defibrillator (ICD) implantation.

BACKGROUND: Anomalous left brachiocephalic vein (ALBCV) is a rare venous anomaly. Double Left brachiocephalic vein is the rarest type of ALBCV anomaly. CASE REPORT: Here we report a case of gentleman with post myocardial infarction ventricular tachycardia who underwent ICD implantation, where we could not place the lead initially through left side. CT angiography revealed presence of a duplicated circumaortic left BCV. It's cranial limb coursing normally anterior to arch and compressed at its confluence with RBCV and the caudal limb with a subaortic course draining into the RSVC. We report this first case of double LBCV along with right sided aortic arch and aberrant origin of LSCA arising from Kommerel's diverticulum. CONCLUSION: This case highlights that interventional cardiologists should be aware of these venous anomalies for proper planning and implantation of CIED successfully via transvenous approach.

Parametric separation of phase-locked and non-phase-locked activity.

Brain dynamics recorded via electroencephalography (EEG) is conceptualized as a sum of two components: "phase-locked" and "non-phase-locked" to the stimulus. Phase-locked activity is often implicitly studied as event-related potentials (ERPs), and the trial-averaged estimates-evoked potentials (EP) considered both time-locked and phase-locked to the stimulus. The non-phase-locked activity, on the other hand, refers to an increase in power in a narrow band or broadband frequencies in the signal emerging at variable phases from stimulus initiation. Both components are understood to stem from different neuronal mechanisms; hence, accurately characterizing them is of immense importance to neuroscientific studies. Here, we discuss the drawbacks of currently used methods to separate the phase-locked and non-phase-locked activity and propose a novel concurrent phaser method (CPM) that simultaneously decomposes the two components. First, we establish that the single-trial separation of phase-locked and non-phase-locked power is an ill-posed problem. Second, using simulations where ground truth validation is possible, we elucidate how the estimation of non-phase-locked power gets biased by phase-locked power in the state-of-the-art averaging method and ways to resolve the issue using CPM. Next, we use two experimental EEG datasets-audio oddball and auditory steady-state responses (ASSR) to show that empirical signal-to-noise estimates warrant the usage of CPM to separate phase-locked and non-phase-locked activity. Thus, using ground truth validation from simulations and demonstration in real experimental scenarios, the efficacy of the proposed CPM is established.NEW & NOTEWORTHY Parametric models for estimation of phase-locked and non-phase-locked brain signals reveals how estimation of non-phase-locked component is biased by the variability of phase-locked component and at the level of single trial becomes an ill-posed problem. Furthermore, the modeling framework delimits the boundaries where traditional averaging approach can be trusted to estimate the phase-locked and non-phase-locked components.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival.

PURPOSE: PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples. METHODS: We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival. RESULTS: Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival. CONCLUSION: Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Multiple mobile excitons manifested as sidebands in quasi-one-dimensional metallic TaSe3.

Charge neutrality and their expected itinerant nature makes excitons potential transmitters of information. However, exciton mobility remains inaccessible to traditional optical experiments that only create and detect excitons with negligible momentum. Here, using angle-resolved photoemission spectroscopy, we detect dispersing excitons in the quasi-one-dimensional metallic trichalcogenide, TaSe3. The low density of conduction electrons and the low dimensionality in TaSe3 combined with a polaronic renormalization of the conduction band and the poorly screened interaction between these polarons and photo-induced valence holes leads to various excitonic bound states that we interpret as intrachain and interchain excitons, and possibly trions. The thresholds for the formation of a photo-hole together with an exciton appear as side valence bands with dispersions nearly parallel to the main valence band, but shifted to lower excitation energies. The energy separation between side and main valence bands can be controlled by surface doping, enabling the tuning of certain exciton properties.

Immune-dysregulation in subacute sclerosing panencephalitis: An exploratory case-control study.

Subacute sclerosing panencephalitis (SSPE) is a chronic progressive neurological condition caused by a defective measles virus. It is postulated that immune-dysregulation might result in persistent infection (immune evasion) as well as initiation of autoimmune phenomenon (via natural killer cells) leading to panencephalitis. The primary objective of this case-control study was to analyse the pattern of immune dysregulation in cases with SSPE. The secondary objective was to assess the correlation between the measured immunological variables and disability/death at 6 months. This was an exploratory case-control study conducted at a tertiary-care referral-facility from January 2020 to September 2021. Thirty consecutive patients fulfilling the Dyken's criteria for SSPE and 30 age-and-sex-matched healthy controls were enrolled. Immunological profile constituted by lymphocyte subset analysis, immunoglobulin levels and complement levels were done in all cases and controls. Cases were staged as per Jabbour's system; disability was assessed using the modified Rankin Scale (mRS). Patients with SSPE had a mean age of 14.76 years (±6.9 years). There were 25 males and 5 females; 6.7% cases belonged to Jabbour's first stage, 40% to second stage and 53.3% to third stage. At least 1/4th had evidence of measles vaccination. Levels of absolute lymphocyte count, B-cells, T cells, helper T-cells, and cytotoxic T-cells were significantly higher in cases. IgG, IgM, and IgE levels were significantly higher while IgD levels were significantly lower in cases. At baseline, 13.3% of cases had a mRS score of 0-2 and 86.7% had a score of 3-6; at 6 months 10% had a mRS score 0-2 (favorable outcome) while 90% had a mRS score 3-6 (poor outcome). Higher IgE levels were found to correlate significantly with favorable outcome. Immune-dysregulation may play a significant role in shaping one's response to measles infection as well as in determining vaccine-efficacy.

Abiotic and biotic stress alleviating effects of the medicinal and aromatic plant-derived product on striped catfish Pangasianodonhypophthalmus.

Intensification of aquaculture production leads to abiotic and biotic stresses, which are further induced by the impact of climate change. Thus, it is important to explore a combined strategy to alleviate multiple stresses in fish. In the present investigation, the removal of nitrogenous metabolites from aquaponics water was studied using products derived from five different locally available medicinal and aromatic plants (MAPs) namely banana stem (Musa Accuminata), Aloe vera, Mint (Minata sepicata), Indian beech/Pongameoil-tree/Karanj (Pongamia pinnatum), and Coat Button/Mexican daisy/Dagadipala (Tridax procrumbens) in the batch experiment for 24 h. A reduction of 91-94% in the ammonia level and 75-80% removal of nitrite-N in the treatments with products derived from mint, banana stem, and aloe vera has been observed. Mint product was also found to be bactericidal against fish pathogenic bacteria. Based on this dual function of mint, further study was conducted under wet-lab conditions to evaluate the possible protective role of the mint-based product in dry powder form against abiotic stresses caused by nitrogenous toxicants for 24 h and 45 days multiple stresses caused by nitrogenous toxicants in freshwater stocked with eight fingerlings of Pangasianodon hypophthalmus of the initial weight of 8 ± 0.5g for 24 h and 8.82 ± 0.75g for 45 days. The results revealed that mint-derived plant product has better anti-stress properties in terms of their bioremediation effectiveness in lowering ammonia and nitrite. Mint also improved the fish growth performance with better physiological responses and anti-oxidative status and reduced the cellular metabolic stress in fish reared under ammonia, as further indicated by reduced oxidative stress parameters, and tissue biochemical indices. Overall, mint showed its main properties as a growth promoter, and immune enhancer, where this medicinal plant product acts as a bioremediation and antibacterial agent to the host immune system. This has potential applications in the environmental and health management of aquaculture.

Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties.

Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 µM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.