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Customers prefer to book the hotels through the same site where they are more satisfied. They generally go through the hotel aggregator's website or hotel website. Hotel aggregators and hotel websites are trying to attract customers. However, it depends upon several factors, like website service qualityand customer experience from the website. The study examines what factors influence customer preference for hotel booking. The study analyzed 471customers' datafrom Delhi NCR, who have used the OYO app, a screening question was employed that wether they have booked the hotel room through OYO website/app. The results show that functional value, the information provided, conditional values, services quality, and services offered by the hotel aggregators attract customers to book through the hotel aggregators' websites or hotel’ websites and also influence customers’ intention of rebooking from the same site. The research is useful for the tourism industries, hotel aggregators, and hotels to strategize updates in their services.

Insider trading is the act of buying or selling securities by individuals who have access to non-public information. This practice is considered illegal and is a violation of securities laws in many countries, including the United States. The Securities and Exchange Commission (SEC) defines insider trading as "buying or selling a security, in breach of a fiduciary duty or other relationship of trust and confidence, while in possession of material, nonpublic information about the security" (SEC, n.d.). The impact of insider trading on financial markets and the effectiveness of legal and regulatory frameworks in preventing insider trading have been the subject of much research. In this systematic literature review, we will examine the existing literature on insider trading and summarize the key findings. Methodology To identify relevant articles, we conducted a comprehensive search of academic literature using databases such as Google Scholar, JSTOR, and EBSCOhost. We used a combination of keywords such as "insider trading," "illegal trading," "securities fraud," and "financial crimes" to identify relevant articles. We also searched the reference lists of identified articles to find additional sources. We included articles published between 2010 and 2023 that provided insights into insider trading. We analyzed the articles using a thematic approach, and we organized our findings according to key themes. Results Our search yielded 334 articles, and after applying inclusion and exclusion criteria, we selected 7 articles for analysis. The articles covered a range of topics related to insider trading, including the prevalence of insider trading, the impact of insider trading on financial markets, and the effectiveness of legal and regulatory frameworks in preventing insider trading.

list of DEGs (fold change >1.5, FDR

Table S5J-part3, expression values (transcripts per million; T.P.M.) of all genes (rows) in each sample (columns).

Mutational detail in EBV (S3) and host samples(S4). Full legend is provided in Supplementary methods.

Detailed information on different computational analysis performed in this study. This file additionally includes supplementary table legends and Table S2.

Shown are information related to EBV integration, EBV gene manipulation, and EBV mutational patterns. These are additional supporting information for Figure 1-4.

Shown are information on host mutational pattern and host response networks to EBV infection. These are additional supporting information for Figure 5-6.

Purpose: Local food buying is an essential phenomenon in tourist activity. This paper analyses tourists' intentions to buy local food by applying the theory of planned behaviour (TPB). Concerning this framework, the authors investigate the moderating effect of food neophobia. (FN). Design/Methodology/ Approach: A survey of 266 tourists was used to collect data who visited the state of Uttarakhand, India, from distinct parts of the country. The data was evaluated using SPSS 23 and AMOS 23. Findings: The findings reveal that subjective norm (SN), attitude (ATT), and perceived behaviour control (PBC) have a positive and significant influence on tourists' buying intention for local food, where both subjective norm and attitude are moderated by food neophobia on their relationship with buying intentions. Practical implications: This study provides essential information to the food business, enabling entrepreneurs or decision-makers to accurately analyse the buying intent (BI) for locally produced food that is both widely accessible and exclusive to distinct groups of buyers. Originality/value: The current research is the first attempt to comprehensively analyse tourist buying behaviour towards local food in Uttarakhand, India. This is one of the few research studies in India that has employed food neophobia as a moderator to study the effect on local food buying intention among tourists. Keywords: Food neophobia, Local food, Tourist, Buying intention, theory of planned behaviour

Astrocytes regulate the response of the central nervous system to disease and injury and have been hypothesized to actively kill neurons in neurodegenerative disease1–6. Here we report an approach to isolate one component of the long-sought astrocyte-derived toxic factor5,6. Notably, instead of a protein, saturated lipids contained in APOE and APOJ lipoparticles mediate astrocyte-induced toxicity. Eliminating the formation of long-chain saturated lipids by astrocyte-specific knockout of the saturated lipid synthesis enzyme ELOVL1 mitigates astrocyte-mediated toxicity in vitro as well as in a model of acute axonal injury in vivo. These results suggest a mechanism by which astrocytes kill cells in the central nervous system. Astrocytes can respond to diseases and injuries of the central nervous system by driving the death of neurons and mature oligodendrocytes through the delivery of long-chain saturated fatty acids contained in lipoparticles.

While various mass spectrometric approaches have been applied to lipid analysis, unraveling the extensive structural diversity of lipids remains a significant challenge. Notably, these approaches often fail to differentiate between isomeric lipids - a challenge that is particularly acute for branched-chain fatty acids (FAs) that often share similar (or identical) mass spectra to their straight-chain isomers. Here, we utilize charge-switching strategies that combine ligated magnesium dications with deprotonated fatty acid anions. Subsequent activation of these charge inverted anions yields mass spectra that differentiate anteiso branched- from straight-chain and iso branched-chain FA isomers with the predictable fragmentation enabling de novo assignment of anteiso branch points. Application of these charge-inversion chemistries in both gas- and solution-phase modalities is demonstrated to assign the position of anteiso-methyl branch-points in FAs, and, with the aid of liquid chromatography, can be extended to de novo assignment of additional branching sites via predictable fragmentation patterns as methyl branching site(s) move closer to the carboxyl carbon. The gas phase approach is shown to be compatible with top-down structure elucidation of complex lipids such as phosphatidylcholines, while integration of solution-phase charge-inversion with reversed phase liquid chromatography enables separation and unambiguous identification of FA structures within isomeric mixtures. Taken together, the presented charge switching MS-based technique, in combination with liquid chromatography enables the structural identification of branched-chain FA without the requirement of authentic methyl branched FA reference standards.

Money laundering is a defiance of law and order within the Real Estate (RE) sector in Canada. Laundering of illicit funds impacts the stability, reliability, and integrity of the government, financial institutions, and the economic sectors. Due to its mysterious nature, there are more unknowns about the quantum of snow washing in the property market or its effects on price inflation. Using data from the Cullen Commission about Money Laundering in Canada, this chapter is an effort to examine the nuances that law enforcement encounters while investigating illegal investment operations. This chapter illuminates a broad range of problems around the investigations of on unlawful investments and recommends pragmatic solutions to the illicit flow of money in RE in Canada.

India loses thousands of metric tons of tomato crop every year due to pests and diseases. Tomato leaf disease is a major issue that causes significant losses to farmers and possess a threat to the agriculture sector. Understanding how does an algorithm learn to classify different types of tomato leaf disease will help scientist and engineers built accurate models for tomato leaf disease detection. Convolutional neural networks with backpropagation algorithms have achieved great success in diagnosing various plant diseases. However, human benchmarks in diagnosing plant disease have still not been displayed by any computer vision method. Under different conditions, the accuracy of the plant identification system is much lower than expected by algorithms. This study performs analysis on features learned by the backpropagation algorithm and studies the state-of-the-art results achieved by image-based classification methods. The analysis is shown through gradient-based visualization methods. In our analysis, the most descriptive approach to generated attention maps is Grad-CAM. Moreover, it is also shown that using a different learning algorithm than backpropagation is also possible to achieve comparable accuracy to that of deep learning models. Hence, state-of-the-art results might show that Convolutional Neural Network achieves human comparable accuracy in tomato leaf disease classification through supervised learning. But, both genetic algorithms and semi-supervised models hold the potential to built precise systems for tomato leaf detection.

While various mass spectrometric approaches have been applied to lipid analysis, unraveling the extensive structural diversity of lipids remains a significant challenge. Notably, these approaches often fail to differentiate between isomeric lipids - a challenge that is particularly acute for branched-chain fatty acids (FAs) that often share similar (or identical) mass spectra to their straight-chain isomers. Here, we utilize charge-switching strategies that combine ligated magnesium dications with deprotonated fatty acid anions. Subsequent activation of these charge inverted anions yields mass spectra that differentiate anteiso branched- from straight-chain and iso branched-chain FA isomers with the predictable fragmentation enabling de novo assignment of anteiso branch points. Application of these charge-inversion chemistries in both gas- and solution-phase modalities is demonstrated to assign the position of anteiso-methyl branch-points in FAs, and can be extended to de novo assignment of additional branching sites via predictable fragmentation patterns as methyl branching site(s) move closer to the carboxyl carbon. The gas phase approach is shown to be compatible with top-down structure elucidation of complex lipids such as phosphatidylcholines, while integration of solution-phase charge-inversion with reversed phase liquid chromatography enables separation and unambiguous identification of FA structures within isomeric mixtures. Taken together, the presented charge switching MS-based technique, in combination with liquid chromatography enables the structural identification of branched-chain FA without the requirement of authentic methyl branched FA reference standards.

BACKGROUND Cirrhosis is a chronic disease of liver marked by degeneration of cells, inflammation and fibrous thickening of the tissue. It is a common cause of morbidity and mortality. Globally, the incidence of liver cirrhosis is on the rise. Therefore, the exact knowledge of aetiology, mode of clinical presentation, and pattern of development of complications of cirrhosis will help in optimal and costeffective control measures of this disease. We undertook this study to evaluate the cause and mode of presentation of patients with liver cirrhosis presenting in a tertiary care hospital of North India. We also wanted to determine biochemical changes in liver cirrhosis. METHODS This observational prospective study was conducted in the Department of Medicine over a period of one year, it included 122 cirrhotic patients of both sexes between 18 and 75 years irrespective of aetiology who fulfilled inclusion criteria. After detailed medical history and thorough physical examination, relevant laboratory investigations, ultrasound of abdomen, and upper GI endoscopy was done. Child-Turcotte-Pugh (CTP) score and model for end-stage liver disease ( MELD) score was calculated. The recorded data was analysed statistically. RESULTS Out of 122 patients, 85.2 % were males and 14.8 % were females. Maximum number of patients 33.60 % were in 41 - 50 years of age. Alcohol was found to be the most common aetiological factor in 80.3 % patients. The most common presenting symptom was abdominal distension in 89.9 % patients. Various complications such as spontaneous bacterial peritonitis was present in 13.9 %, anaemia in 93.4 %, thrombocytopenia 75.4 %, deranged international normalized ratio (INR) 78.6 %, deranged renal function 45.9 %, hyponatremia in 35.2 % patients. Maximum number of patients 72.9 % were in CTP class C. 68 % patientshad MELD score > 14. CONCLUSIONS Males are mainly affected by liver cirrhosis due to excessive alcohol consumption therefore proper preventive measures have to be taken. The raised total leucocyte count (TLC), serum bilirubin, serum creatinine, decreased serum albumin, serum sodium and coagulopathy were found to be poor prognostic markers. KEYWORDS Cirrhosis, Aetiology, Complications, Prognosis

PurposeThis study integrates the providers' perspective as well as the patient's perspective in developing and validating a scale to measure hospital service quality in multispecialty hospitals.Design/methodology/approachAn exploratory sequential mixed-method approach was used in this study. The strategies used included a thematic literature review, semi-structured interviews, modified Delphi and confirmatory factor analysis.FindingsThe reliability coefficient of 41 item scale was 0.963 with each attribute, that is, pivotal, core and peripheral, having a Cronbach's alpha of 0.907, 0.91 and 0.891, with scale content validity (S-CVI Ave) of 0.9151. The composite reliability scores of all constructs were greater than 0.7, with an Average Variance Explained (AVE) of all items greater than 0.5.Originality/valueThe instrument can be used to measure the difference between what service providers believe customers expect and customers’ actual needs and expectations. The scale can be used to measure the difference between what is delivered (as perceived by the provider) and what customers perceive they have received (because they are unable to accurately evaluate service quality). The dyadic approach of administering this questionnaire in measuring hospital service quality will lead to the identification of a knowledge gap and a perception gap in delivering hospital service quality.

The growth of therapeutic monoclonal antibodies continues to accelerate due to their success as treatments for many diseases. As new therapeutics are developed, it is increasingly important to have robust bioanalytical methods to measure the pharmacokinetics of the circulating therapeutic monoclonal antibodies (mAbs) in serum. Ligand-binding assays such as enzyme-linked immunosorbent assays (ELISA) utilizing anti-idiotypic (anti-ID) antibodies against the variable regions of the antibody, are a sensitive and specific bioanalytical method routinely used to measure levels of therapeutic antibodies in a biological matrix. Screening and selecting optimal reagents and assay format are critical steps in the development of robust assays. An additional complication exists for soluble circulating drug, mAb targets that could interfere with the anti-IDs binding to the therapeutic mAb resulting in underestimation of total drug concentrations. Therefore, the selection of anti-IDs and the assay format that are not impacted by soluble antigen is paramount to development of a successful pharmacokinetic (PK) assay. We have developed an automated workflow and scoring system that allows for ranking of candidate anti-IDs on a variety of criteria. A primary generic automated indirect ELISA was utilized to shortlist the anti-IDs that need to be labeled and screened in pairs. A secondary screen using a sandwich ELISA with labeled-anti-ID pairings tested multiple PK assay formats to identify the best anti-ID pairing/PK assay format. We demonstrate the use of automation and a scoring system that allows for screening of anti-IDs and identification of the most robust PK assay format in a significantly reduced timeframe compared to standard approaches.

Predicting the outcome of chemical reactions using machine learning models has emerged as a promising research area in chemical science. However, the use of such models to prospectively test new reactions by interpreting chemical reactivity is limited. We have developed a new fast and one-pot multicomponent reaction of N-sulfonylimines with heterogenous reactivity. Fast reaction times (N-sulfonylimine lead us to develop a human-interpretable machine learning model using positive and negative reaction profiles. We introduce chemical reactivity flowcharts to help chemists interpret the decisions made by the machine learning model for understanding heterogeneous reactivity of N-sulfonylimines. The model learns chemical patterns to accurately predict the reactivity of N-sulfonylimine with different carboxylic acids and can be used to suggest new reactions to elucidate the substrate scope of the reaction. We believe our human-interpretable machine learning approach is a general strategy that is useful to understand chemical reactivity of components for any multicomponent reaction to enhance synthesis of drug-like libraries.

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking data sets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly create benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to the protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking data sets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 min to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify this to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking data sets in virtual screening. The DUBS package is available on GitHub at https://github.com/chopralab/lemon/tree/master/dubs.

N,N‐dimethyl formamide (DMF) is an extensively used organic solvent but is also a potent pollutant. Certain bacterial species from genera such as Paracoccus, Pseudomonas, and Alcaligenes have evolved to use DMF as a sole carbon and nitrogen source for growth via degradation by a dimethylformamidase (DMFase). We show that DMFase from Paracoccus sp. strain DMF is a halophilic and thermostable enzyme comprising a multimeric complex of the α2β2 or (α2β2)2 type. One of the three domains of the large subunit and the small subunit are hitherto undescribed protein folds of unknown evolutionary origin. The active site consists of a mononuclear iron coordinated by two Tyr side‐chain phenolates and one carboxylate from Glu. The Fe3+ ion in the active site catalyzes the hydrolytic cleavage of the amide bond in DMF. Kinetic characterization reveals that the enzyme shows cooperativity between subunits, and mutagenesis and structural data provide clues to the catalytic mechanism., Break it down again: Certain bacteria can break down N,N‐dimethyl formamide (DMF) through the action of a dimethylformamidase (DMFase). The structure and mechanism of a DMFase from a bacterium of the genus Paracoccus are presented. The enzyme comprises a multimeric α2β2 or (α2β2)2 complex, and the active site consists of FeIII coordinated by two Tyr side‐chain phenolates and one carboxylate from Glu in an unusual square pyramidal geometry.

Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. For peptide products, it is common to measure physical stability via aggregation or fibrillation using the fluorescent reporter Thioflavin T. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a product, we introduce a machine learning based model for predicting the chemical stability over time using both the formulation conditions as well as the aggregation curve. In this work, we explore the relationships between the formulation, stability time point, and the measurements of chemical stability and achieve a coefficient of determination on a random test set of 0.945 and a mean absolute error (MAE) of 0.421 when using a multilayer perceptron (MLP) neural network for total degradation. Similarly, we achieve a coefficient of determination of 0.908 and an MAE of 1.435 when predicting the potency using a random forest model. When measurements of physical stability are included into the model, the MAE in the prediction of TD decreases to 0.148 for the MLP model. Using a similar strategy for the prediction of potency, the MAE decreases to 0.705 for the random forest model. Therefore, we can conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability.

Epstein–Barr virus (EBV) is a complex oncogenic symbiont. The molecular mechanisms governing EBV carcinogenesis remain elusive and the functional interactions between virus and host cells are incompletely defined. Here we present a comprehensive map of the host cell–pathogen interactome in EBV-associated cancers. We systematically analyzed RNA sequencing from >1,000 patients with 15 different cancer types, comparing virus and host factors of EBV+ to EBV− tissues. EBV preferentially integrated at highly accessible regions of the cancer genome, with significant enrichment in super-enhancer architecture. Twelve EBV transcripts, including LMP1 and LMP2, correlated inversely with EBV reactivation signature. Overexpression of these genes significantly suppressed viral reactivation, consistent with a “virostatic” function. In cancer samples, hundreds of novel frequent missense and nonsense variations in virostatic genes were identified, and variant genes failed to regulate their viral and cellular targets in cancer. For example, one-third of patients with EBV+ NK/T-cell lymphoma carried two novel nonsense variants (Q322X, G342X) of LMP1 and both variant proteins failed to restrict viral reactivation, confirming loss of virostatic function. Host cell transcriptional changes in response to EBV infection classified tumors into two molecular subtypes based on patterns of IFN signature genes and immune checkpoint markers, such as PD-L1 and IDO1. Overall, these findings uncover novel points of interaction between a common oncovirus and the human genome and identify novel regulatory nodes and druggable targets for individualized EBV and cancer-specific therapies. Significance: This study provides a comprehensive map of the host cell-pathogen interactome in EBV+ malignancies. See related commentary by Mbulaiteye and Prokunina-Olsson, p. 5917

Live cell surface functionalization has been increasingly explored in recent years because of its emerging bio- medical and therapeutic applications. Known methods to functionalize live-cell surface includes time-consuming metabolic docking of non-natural functional groups on cell surfaces that are hard to use. Here, we report an easy and direct functionalization method for cell surface and cell-cell conjugation utilizing native phosphate groups on cell membranes. We used three-dimensional single-molecule localization microscopy to confirm that the chemical conjugate binds preferentially on the cell surface. We show an application of this method for live-cell imaging and enhancement of cell-cell interaction leading to increased T cell proliferation. Our simple and reversible cell-conjugation strategy would be widely useful to explore and optimize cell-cell contact-based proliferation for any cell-based therapeutic applications.

The Programmed Cell Death Protein 1/Programmed Death-Ligand 1 (PD-1/PD-L1) interaction is an immune checkpoint utilized by cancer cells to enhance immune suppression. There is a huge need to develop small molecule drugs that are fast acting, cost effective, and readily bioavailable compared to antibodies. Unfortunately, synthesizing and validating large libraries of small- molecules to inhibit PD-1/PD-L1 interaction in a blind manner is both time-consuming and expensive. To improve this drug discovery pipeline, we have developed a machine learning methodology trained on patent data to identify, synthesize, and validate PD-1/PD-L1 small molecule inhibitors. Our model incorporates two features: docking scores to represent the energy of binding (E) as a global feature and sub-graph features through a graph neural network (GNN) of molecular topology to represent local features. This interaction energy-based Graph Neural Network (EGNN) model outperforms traditional machine learning methods and a simple GNN with a F1 score of 0.9524 and Cohen’s kappa score of 0.8861 for the hold out test set, suggesting that the topology of the small molecule, the structural interaction in the binding pocket, and chemical diversity of the training data are all important considerations for enhancing model performance. A Bootstrapped EGNN model was used to select compounds for synthesis and experimental validation with predicted high and low potency to inhibit PD-1/PD-L1 interaction. The potent inhibitor, (4-((3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2- methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine, is a hybrid of two known bioactive scaffolds, with an IC50 of 339.9 nM that is comparatively better than the known bioactive compound. We conclude that our bootstrapped EGNN model will be useful to identify target-specific high potency molecules designed by scaffold hopping, a well-known medicinal chemistry technique.

Targeting live cell organelles is essential for imaging, understanding, and controlling specific biochemical processes. Typically, fluorescent probes with distinct structural scaffolds are used to target specific cell organelles. Here, we have designed a modular one-step synthetic strategy using a common reaction intermediate to develop new lysosomal, mitochondrial, and nucleus-targeting pH-activable fluorescent probes that are all based on a single boron dipyrromethane scaffold. The divergent cell organelle targeting was achieved by synthesizing probes with specific functional group changes to the central scaffold resulting in differential fluorescence and pKa . Specifically, we show that the functional group transformation of the same scaffold influences cellular localization and specificity of pH-activable fluorescent probes in live primary microglial cells with pKa values ranging from ∼3.2-6.0. We introduce a structure-organelle-relationship (SOR) framework to target nuclei (NucShine), lysosomes (LysoShine), and mitochondria (MitoShine) in live microglia. This work will result in future applications of SOR beyond imaging to target and control organelle-specific biochemical processes in disease-specific models.

The study aims to measure the effectiveness of the project management information system (PMIS) and its impact on financial wellness in rural areas. The study uses DeLone and McLean’s updated information success model to measure the net impacts of the PMIS on the community. The dynamics between the PMIS and financial wellness have not yet been clarified, as the available literature on the concerned domain is very limited; thus, further research is required to report the effect of the PMIS on financial wellness. A total of 628 samples were analyzed from 666 collected through structured questionnaires and stratified sampling from 21 hill blocks from the hills of Uttarakhand. Exploratory-confirmatory factor analysis and path analysis were both conducted using SPSS and AMOS. The study found that PMIS quality, information quality, and service quality are significantly important for the PMIS, and its impact on the net benefits derived from PMIS was studied. The results also reveal that the net benefits of the system impact the financial behavior and financial attitudes of SHGs and cooperative members, consequently significantly impacting financial wellness. This study proposes and tests the information system (IS) financial wellness (FW) model for community-based development programs in the lines of the logical framework approach and the stimulus-organism-response framework.

Phagocytosis by glial cells is essential to regulate brain function during health and disease. Therapies for Alzheimer's disease (AD) have primarily focused on targeting antibodies to amyloid β (Aβ) or inhibitng enzymes that make it, and while removal of Aβ by phagocytosis is protective early in AD it remains poorly understood. Impaired phagocytic function of glial cells during later stages of AD likely contributes to worsened disease outcome, but the underlying mechanisms of how this occurs remain unknown. We have developed a human Aβ1–42 analogue (AβpH) that exhibits green fluorescence upon internalization into the acidic organelles of cells but is non-fluorescent at physiological pH. This allowed us to image, for the first time, glial uptake of AβpH in real time in live animals. We find that microglia phagocytose more AβpH than astrocytes in culture, in brain slices and in vivo. AβpH can be used to investigate the phagocytic mechanisms responsible for removing Aβ from the extracellular space, and thus could become a useful tool to study Aβ clearance at different stages of AD., Glial cell phagocytosis of pH-dependent amyloid-β, AβpH, in live and fixed cultures, brain tissue sections, retina, cortex and in live animals useful for studying function in health and disease.

In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future.

Astrocytes regulate the response of the central nervous system to disease and injury and have been hypothesized to actively kill neurons in neurodegenerative disease

Diffuse large B-cell lymphoma (DLBCL) is a common, aggressive cancer of notorious genotypic and phenotypic heterogeneity. A major challenge is predicting response to drug treatment that has typically been done using genomic tools alone with little success. A novel method that incorporates phenotypic profiling for predicting the effectiveness of therapy for individual patients is desperately needed. BioDynamic Imaging (BDI) is a technique for measuring time-dependent fluctuations in back-scattered light through living tumor tissues to identify critical changes in intracellular dynamics that are associated with phenotypic response to drugs. In this study, BDI and RNA sequencing (RNA-seq) data were collected on tumor samples from dogs with naturally occurring DLBCL, an animal model of increasingly recognized relevance to the human disease. BDI and RNA-seq data were combined to identify correlations between gene co-expression modules and linear combinations of biomarkers to provide biological mechanistic interpretations of BDI biomarkers. Using regularized multivariate logistic regression, we combined RNA-seq and BDI data to develop a novel machine learning model to accurately predict the clinical response of canine DLBCL to combination chemotherapy (i.e. CHOP). Our model incorporates data on the expression of 4 genes and 3 BDI-derived phenotypic biomarkers, capturing changes in transcription, microtubule related processes, and apoptosis. These results suggest that multi-scale genomic and phenotypic data integration can identify patients that respond to a given treatment a priori in a disease that has been difficult to treat. Our work provides an important framework for future development of strategies and treatments in precision cancer medicine. Key PointsO_LICombined intracellular Doppler spectra and RNA-seq classify DLBCL samples as sensitive or resistance to CHOP chemotherapy C_LIO_LIKey dynamic features were identified that can be used to classify dogs with naturally-occurring DLBCL as CHOP-sensitive or -resistant C_LI

Type 1 diabetes (T1D) is a disease characterized by destruction of the insulin-producing beta cells. Currently, there remains a critical gap in our understanding of how to reverse or prevent beta cell loss in individuals with T1D. Previous studies in mice discovered that pharmacologically inhibiting polyamine biosynthesis using difluoromethylornithine (DFMO) resulted in preserved beta cell function and mass. Similarly, treatment of non-obese diabetic mice with the tyrosine kinase inhibitor Imatinib mesylate reversed diabetes. The promising findings from these animal studies resulted in the initiation of two separate clinical trials that would repurpose either DFMO (NCT02384889) or Imatinib (NCT01781975) and determine effects on diabetes outcomes; however, whether these drugs directly stimulated beta cell growth remained unknown. To address this, we used the zebrafish model system to determine pharmacological impact on beta cell regeneration. After induction of beta cell death, zebrafish embryos were treated with either DFMO or Imatinib. Neither drug altered whole-body growth or exocrine pancreas length. Embryos treated with Imatinib showed no effect on beta cell regeneration; however, excitingly, DFMO enhanced beta cell regeneration. These data suggest that pharmacological inhibition of polyamine biosynthesis may be a promising therapeutic option to stimulate beta cell regeneration in the setting of diabetes.

Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

The Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD-1/PD-L1) interaction is an immune checkpoint utilized by cancer cells to enhance immune suppression. There exists a huge need to develop small molecules drugs that are fast acting, cheap, and readily bioavailable compared to antibodies. Unfortunately, synthesizing and validating large libraries of small-molecule to inhibit PD-1/PD-L1 interaction in a blind manner is a both time-consuming and expensive. To improve this drug discovery pipeline, we have developed a machine learning methodology trained on patent data to identify, synthesize and validate PD-1/PD-L1 small molecule inhibitors. Our model incorporates two features: docking scores to represent the energy of binding (E) as a global feature and sub-graph features through a graph neural network (GNN) to represent local features. This Energy-Graph Neural Network (EGNN) model outperforms traditional machine learning methods as well as a simple GNN with an average F1 score of 0.997 (± 0.004) suggesting that the topology of the small molecule, the structural interaction in the binding pocket, and chemical diversity of the training data are all important considerations for enhancing model performance. A Bootstrapped EGNN model was used to select compounds for synthesis and experimental validation with predicted high and low potency to inhibit PD-1/PD-L1 interaction. The new potent inhibitor, (4-((3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2-methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine, is a hybrid of two known bioactive scaffolds, and has an IC50 values of 339.9 nM that is comparatively better than the known bioactive compound. We conclude that our EGNN model can identify active molecules designed by scaffold hopping, a well-known medicinal chemistry technique and will be useful to identify new potent small molecule inhibitors for specific targets.