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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.

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

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.

Large eddy simulation of flow past a circular cylinder of low aspect ratio ( $AR=1$ and $3$ ), spanning subcritical, critical and supercritical regimes, is carried out for $2\times 10^3 \le Re \le 4\times 10^5$ . The end walls restrict three-dimensionality of the flow. The critical $Re$ for the onset of the critical regime is significantly lower for small aspect ratio cylinders. The evolution of secondary vortex (SV), laminar separation bubble (LSB) and the related transition of boundary layer with $Re$ is investigated. The plateau in the surface pressure due to LSB is modified by the presence of SV. Proper orthogonal decomposition of surface pressure reveals that although the vortex shedding mode is most dominant throughout the $Re$ regime studied, significant energy of the flow lies in a symmetric mode that corresponds to expansion–contraction of the vortex formation region and is responsible for bursts of weak vortex shedding. A triple decomposition of the time signals comprising of contributions from shear layer vortices, von Kármán vortex shedding and low frequency modulation due to the symmetric mode of flow is proposed. A moving average, with appropriate size of window, is utilized to estimate the component due to vortex shedding. It is used to assess the variation, with $Re$ , of strength of vortex shedding as well as its coherence along the span. Weakening of vortex shedding in the high subcritical and critical regime is followed by its rejuvenation in the supercritical regime. Its spanwise correlation is high in the subcritical regime, decreases in the critical regime and improves again in the supercritical regime.

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.

Purpose E-learning has become an increasingly prevalent learning approach in higher educational institutions due to the fast growth of internet technologies in India. This paper aims to mainly focus on evaluating the effectiveness of the e-learning experience from students’ perceptive. Design/methodology/approach “Survey” method has been used to collect the data with the help of a structured questionnaire from the students who have registered on COURSERA (www.coursera.org/) website for e-learning. The questionnaire consisted of two sections e-learning system and e-learning effectiveness. E-learning system included items related to system quality, information quality and service quality. E-learning effectiveness dimension included user satisfaction and net benefits. The items in this section were measured on a five-point Likert scale ranging from strongly disagrees to strongly agree. The data collected have been analyzed using the SPSS version 17.0 and AMOS version 21.0. Findings Results show that system quality and service quality contribute more in e-learning system compared to information quality. Students perception may be that information available on the website may not be very useful as it’s a one-way mode of communication. The researcher also found that the three dimensions (system quality, service quality and information quality) of e-learning system contributes to user satisfaction and net benefits. Students are satisfied with e-learning websites and intent to continue to use it in future as well. They also found it beneficial as it helps them in career growth and making them employable. Originality/value This paper proposed a second-order model of e-learning system and a second-order model e-learning effectiveness. E-learning system has been defined by three first-order constructs: a system quality, service quality and information quality. E-learning effectiveness has been defined by two first-order constructs: a user satisfaction and net benefits. The predictability of the proposed model is high to explain the impact of e-learning system on e-learning effectiveness.

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/.

Diagnostic ion-molecule reactions using tandem mass spectrometry can differentiate between isomeric compounds unlike a popular collision-activated dissociation methodology for the identification of previously unknown mixtures. Selected neutral reagents, such as 2-methoxypropene (MOP) are introduced into an ion trap mass spectrometer and react with protonated analytes to produce product ions diagnostic of the functional groups present in the analyte. However, the interpretation and understanding of specific reactions are challenging and time-consuming for chemical characterization. Here, we introduce a first bootstrapped decision tree model trained on 36 known ion-molecule reactions with MOP using graph-based connectivity of analyte’s functional groups as input. A Cohen Kappa statistic of 0.72 was achieved, suggesting substantial inter-model reliability on limited training data. Prospective diagnostic product predictions were made and validated for 14 previously unpublished analytes . Chemical reactivity flowcharts were introduced to understand the decisions made by the machine learning method that will be useful for chemists.

A 42 year old female with type 2 diabetes mellitus (T2DM), presented with angina on exertion and left ventricular (LV) dysfunction (global LV ejection fraction (EF)=26%). Patient was subjected to coronary angiography which revealed triple vessel disease. Patient was started on usual standard of care heart failure (HF) medications, including sodium-glucose co-transporter-2 (SGLT-2) inhibitor dapagliflozin which is promising new class of drug for treating T2DM and HF. Patient was advised myocardial revascularization in form of percutaneous transluminal coronary angioplasty (PTCA). Post angioplasty patient developed metabolic acidosis (high anion gap with normal lactate and increased ketone levels). Patient was diagnosed as case of euglycemic diabetic ketoacidosis (DKA) and patient was treated by volume resuscitation and insulin infusion.

Acute myeloid leukemia (AML) is an aggressive malignancy with only a handful of therapeutic options. About 30% of AML patients harbor mutated FLT3 kinase, and thus, this cancer-driver has become a hotly pursued AML target. Herein we report a new class of FLT3 inhibitors, which potently inhibit the proliferation of acute myeloid leukemia (AML) cells at nanomolar concentrations.

We present a deep learning method for identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra without the use of any database, pre-established rules, procedures, or peak-matching methods. We derive patterns and correlations directly from spectral data representing multiple functional groups as a multi-class, multi-label problem. For practical usability, we introduce two new metrics (Molecular F1 score and Molecular Perfection rate) to measure the performance by identifying all functional groups on molecules. Our optimized model has a Molecular F1 score of 0.92 and a Molecular Perfection rate of 72%. Backpropagation of our model reveals IR patterns typically used by human chemists suggesting “learning” of known spectral features. We show that the introduction of new functional groups does not decrease model performance. Finally, we show redundancy in FTIR and MS data by encoding combined data in a latent space that retains the accuracy of the original model. Our results reveal the importance of deep learning for rapid identification of functional groups to realize autonomous analytical processes in the future.

This paper focuses on the importance of continuing education among employees in information technology companies in India. The study identifies the factors which influence employees for continuing education and its impact on their career growth and development. A systematic survey of the 315 employees working in the top ten IT companies was conducted to accumulate primary data. The analysis of the study stipulates that factors including - financial investment, time requirements for completing the course, employment opportunities and acquiring new skills play an important role among employees for continuing education. This study also reveals that continuing education has a positive impact on career growth and development. With the help of continuing education, employees get professional growth (better career opportunities, job security, salary increment, and promotion) and personal growth (personality development, development of interpersonal skills, development of technical skills and self-confidence).

Background: Following Myocardial Infarction some proteins and enzymes, CPK-MB/ Troponin-I, T, are released into the blood from the necrotic heart muscle. Serum Uric Acid (SUA) may be a risk factor and negative prognostic marker for cardiovascular diseases. Aim of the study was to study serum uric acid levels in patients of acute Myocardial infarction with congestive heart failure, its relation with stages of congestive heart failure as per Killip classification and the role of serum uric acid levels as a marker of mortality. Methods: The case control study was conducted on 120 patients divided into two groups. Group A included 60 patients of acute Myocardial infarction. Group A was further divided into two categories. One includes 30 patients of with congestive heart failure and another includes 30 patients without congestive heart failure. Group B consists of 60 control patients. Serum uric acid levels were measured in Group A on 1 st , 3 rd and 7 th day of hospital admission and in Group B on 1 st day. Results: The study showed females have higher degree of hyperuricemia than males. SUA was significantly higher in patients of acute myocardial infarction than control group patients. SUA were also higher in patients with history of IHD, in patients with BNP >100 and it correlates with Killip class and mortality rates. Patients of acute myocardial infarction with diabetes mellitus had higher degree of hyperuricemia than nondiabetic and control group. No significant difference in SUA levels were observed with regard to age, alcohol intake, lipid profile, ejection fraction and hypertension. Conclusions: In acute myocardial infarction, patients with hyperuricemia had higher mortality and may be considered as poor prognostic biomarker.

Introduction: Preoperative anxiety is associated with problems such as difficult venous access, jaw relaxation and coughing during induction of anaesthesia, autonomic fluctuations and increased anaesthesia requirements. It has also been correlated with increased pain, nausea and vomiting in the postoperative period, prolonged recovery and increased risk of infection. Most patients experience preoperative anxiety which is a major concern before surgery and it has been suggested that the reduction in preoperative anxiety levels might be associated with better surgical outcomes, shorten hospital stay and minimise lifestyle disruptions. Aim: The aim of the present study was to assess the pre and post procedure anxiety level of the patient undergoing surgery under spinal anaesthesia, after being shown a video of how the procedure is performed and to assess the ease of the anaesthesiologist performing the Subarachnoid Block (SAB). Materials and Methods: The present study included 100 patients divided in two groups. One group who had a face-toface interview with the anaesthesiologist and the other group were shown a audio-visual video of the procedure, posted for elective surgery under SAB. The anxiety levels of the patients was evaluated using Visual Analogue Scale (VAS) scores, State-Trait Anxiety Inventory (STAI) questionnaire and the satisfaction level of the anaesthesiologist using Likert’s scale. The postoperative anxiety values were assessed when the patients were shifted to recovery room. The statistical tests used were Student’s t-test for continuous data, chi-square test for categorical variables. One-way repeated ANOVA to analyse the significance. Results: The VAS scores in between Group 1 and Group 2 showed that the pre-intervention, pre-procedure and postprocedure were statistically not significant. The pre-intervention VAS score was 3.23±0.92 in Group 1 and 2.78±0.82 in Group 2 (p-value=0.4), the pre-procedure VAS score was 3.04±0.84 in Group 1 and 2.67±0.76 in Group 2 (p-value=0.6), the postprocedure VAS score was 3.84±0.96 in Group 1 and 2.88±0.9 in Group 2 (p-value=0.5). The state anxiety between the preintervention, pre-procedure and post-procedure in Group 1 and Group 2 the data was statistically significant (p=0.01 and p=0.02). The trait anxiety between pre-intervention, pre-procedure and post-procedure in Group 1 and Group 2 showed that the data was statistically significant (p=0.03 and p=0.01) respectively. The Likert’s score which was assessed by the anaesthesiologist performing the block, there was statistically highly significant difference present between the groups (p=0.00). Conclusion: Educating the patient is important for helping in reducing the fear and anxiety of patient, face-to-face interview is a good way of providing information to the patient but multimedia information is better as patients are more receptive to audio-visual information.

Introduction: Knee arthroscopy is associated with variable amount of postoperative pain. In an attempt to improve postoperative analgesia, intra-articular injection of local anaesthetic in combination with other agent have been studied. However, the best combination is not known. Aim: To compare the analgesic efficacy of intra-articular injection of morphine and dexmedetomidine when added with levobupivacaine in arthroscopic knee surgeries. Materials and Methods: Seventy eight patients, scheduled to undergo elective arthroscopic procedure under spinal anaesthesia were recruited for the study. All the patients received 18 ml of 0.25% levobupivacaine however in addition to this Group M patients received 8 mg (2 ml) morphine, Group D patients received 100µg (2 ml) of dexmedetomidine while Group C patients received 2 ml of isotonic saline intraarticularly. Postoperatively the intensity of pain was assessed using Numerical Rating Scale (NRS). Rescue analgesia was given at NRS ≥ 4. The duration of analgesia and total diclofenac consumption was noted. Results: The mean duration of analgesia was longest in Group M (576.20±67.09 minutes) followed by Group D (460.93±38.95 minutes) and Group C (370.27±58.80 minutes) statistically this difference was found to be highly significant (p-value < 0.001). Total consumption of diclofenac in 24 hours was found lowest in group M (86.25±27.48 mg) followed by group D (110.87±44.48 mg) and group C (141.35±44.13 mg) this difference was found to be highly significant (p-value < 0.001). Conclusion: Morphine when added with levobupivacaine in patients undergoing arthroscopic knee surgery improves the quality and prolongs the duration of postoperative analgesia.

Background: To compare general and spinal anaesthesia with respect to neonatal outcome and observation of post operative adverse effects born by elective caesarean section. Methods: 60 female parturients women were posted for elective caesarean section (CS) at appropriate gestational age under spinal anaesthesia (SA) and general anaesthesia (GA). There were equal no. of patients (n=30) for both the groups, GA and SA. Maternal age, weight, height, ASA grading, neonatal Apgar score (1 and 5 minute) were recorded.(1) Early neonatal neurobehavioural assessment using Scanlon's ENNS (Early Neonatal Neurobehavioural Scale) was done 8-10 hours post birth at the nursery.(2) Post operative adverse effects-Nausea, Vomiting, Pruritis, Shivering, Sedation and Post dural puncture headache (PDPH) were documented. Statistical analysis performed using student't' test and coefficient of variance. Results: Maternal age, weight, height and ASA grading were not different between the study groups. Apgar scores were overall better in SA group at 1 minute and similar at 5 minute between both the groups. But no statistical significance seen between the groups. Early neurobehavioural assessment was found to be different with regard to Moro's, Sucking and Startle reflexes.

Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.

Introduction Lumbar radiculopathy is a major health problem often treated by surgery or guided lumbar epidural steroids for pain relief. We have used Platelet Rich Plasma (PRP) a novel therapeutic tool of autologous nature that has emerged strongly in recent years to treat patients of prolapsed intervertebral disc. Aim To evaluate the efficacy of PRP via interlaminar epidural route in treatment of pain in patients with prolapsed inter vertebral disc. Materials and methods Ten patients were injected with five ml of autologous platelet rich plasma under fluoroscopic guidance via interlaminar lumbar epidural injection into area of affected nerve root. They were followed using VAS (Visual Analogue Scale), SLRT (Straight Leg Raising Test) and MODQ (Modified Oswestry Disability Questionnaire) for clinical improvement. Results Patients who had received epidural injections of autologous PRP showed improvements in their scores of evaluation tools. Improvement was sustained during the 3 month study period and was not associated with any complications. Conclusion Autologous PRP can be considered as a good alternative to epidural steroids and surgery in management of patients with chronic prolapsed intervertebral disc.

The KoBaMIN web server provides an online interface to a simple, consistent and computationally efficient protein structure refinement protocol based on minimization of a knowledge-based potential of mean force. The server can be used to refine either a single protein structure or an ensemble of proteins starting from their unrefined coordinates in PDB format. The refinement method is particularly fast and accurate due to the underlying knowledge-based potential derived from structures deposited in the PDB; as such, the energy function implicitly includes the effects of solvent and the crystal environment. Our server allows for an optional but recommended step that optimizes stereochemistry using the MESHI software. The KoBaMIN server also allows comparison of the refined structures with a provided reference structure to assess the changes brought about by the refinement protocol. The performance of KoBaMIN has been benchmarked widely on a large set of decoys, all models generated at the seventh worldwide experiments on critical assessment of techniques for protein structure prediction (CASP7) and it was also shown to produce top-ranking predictions in the refinement category at both CASP8 and CASP9, yielding consistently good results across a broad range of model quality values. The web server is fully functional and freely available at http://csb.stanford.edu/kobamin.

Based on the knowledge that microRNAs (miRNAs) are dysregulated in diseases such as cancer, various attempts have been explored to develop miRNA-based cancer therapeutics. Although many strategies have been used to restore the levels of therapeutically relevant microRNAs, clinical delivery of the processed or mature miRNA to cancer cells still remains a challenge. To overcome this challenge, innovative methods are being explored to increase the pools of tumor-suppressive miRNAs, such as enhancing miRNA biogenesis. This is especially true for the let-7 family of tumor-suppressive miRNAs, which has an additional layer of regulation over the canonical miRNA biogenesis pathway. In particular, the DROSHA and DICER cleavage steps are blocked when unprocessed let-7 is bound by the RNA-binding protein LIN28. LIN28 interacts specifically with unprocessed let-7 via a sequence-specific motif, GGAG, contained in most let-7 family members. Because previous attempts to restore mature let-7 levels through increasing let-7 pools have been quite successful at reducing tumor burden, identifying novel and clinically relevant ways to increase the level of this tumor-suppressive miRNA represents a critical need. In this study, we hypothesize that small-molecule inhibitors that disrupt the LIN28-let-7 interaction will lead to enhanced processing and increased levels of mature, tumor-suppressive let-7. Thus, an in vitro high-throughput fluorescence polarization screen has been conducted using His-tagged LIN28 and a Cy5 fluorophore-tagged let-7 RNA probe. The nine-nucleotide RNA probe was designed through in silico modeling to include the GGAG motif, which associates with LIN28 through the zinc knuckle domain of LIN28. Due to the size difference between unbound let-7 probe and LIN28-bound let-7 probe, a wide window of polarization values was achieved, resulting in a Z′ score of 0.61, indicating that the assay was robust to proceed with the screen. The screen was performed against 23,680 compounds, which consist of FDA-approved LOPAC and blood-brain barrier-permeable CNS library compounds. Eight compounds were identified as positive hits from the screen. These compounds are currently being characterized in secondary assays both in vitro and in cell culture. The significance of this research is that small-molecule inhibitors may provide novel therapeutic strategies to increase the pool of mature, tumor-suppressive let-7 in tumors with elevated LIN28. Citation Format: Sunghyun Myoung, Sergey Savinov, Lan Chen, Gaurav Chopra, Larisa Avramova, James Welch, Bradley Loren, David Thompson, Andrea L. Kasinski. Development of an RNA-based cancer therapeutic targeting the let-7-LIN28 interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4656.

Cancer is a heterogeneous disease that can be understood as a combination of several pathways common to different cancer types or other non-cancer related diseases. Cancer drug resistance and variability in drug efficacy, a major problem in the field of cancer therapy, occurs due to heterogeneity in cancer cells specific pathways (networks) resulting in an ‘arms race' due to progression of disease pathogenesis. Identifying cancer cell specific protein network to target from a long list of known targets for each cancer type is an essential solution that may also be useful for other cancers types with similar overlapping networks. Unfortunately, the traditional process of drug design does not consider identification and targeting a network of proteins in a cancer cell specific manner. To our knowledge, no method exists to simultaneously identify the targeted network and design non-toxic, potent synthetic libraries in a cancer cell specific manner. We have developed a method to use machine learning, modeling and experimental cellular data to identify the cell specific targeted network while simultaneously design and synthesize compound library to degrade the identified network of proteins for anticancer potency. We will present our approach for Castration Resistant Prostate Cancer (CRPC) using all known targets for prostate cancer. We have designed and synthesized non-toxic potent anticancer molecules for prostate cancer cells (LnCaP and C4-2 cells with most potent IC50 ~ 0.72 nM on C4-2 and non-toxic in tissue-specific epithelial cells and in mice at high-dose). We selected compound designs for synthesis that were predicted to be active by training the machine learning algorithm on the set of active (IC50≤100nM) and inactive compounds tested experimentally for prostate cancer cell types in an iterative manner. Our potent anticancer molecules degraded the androgen receptor (AR, known prostate cancer target) more than known potent AR inhibitor (Enzalutamide), even though it was not explicitly developed to target AR. Furthermore, we identified major “features” (proteins) based on the compound-proteome interaction profile that is common to all potent compounds from our entire library. We also identified compound-specific protein networks predicted to be inhibited by each potent anticancer compound. Finally, we experimentally validated degradation of our targeted network predictions prospectively, and identified a network of RORγ, AKR1C4, CYP17A1, SHBG and AR as differential targets for our most potent, non-toxic synthetic molecule. Our results suggest that potent non-toxic molecules prefer specific protein targeted network which may not rank the known “traditional” target (e.g. AR) as the top target to develop molecules for effective anticancer potency in a cell-specific manner. We conclude that our chemical screening based machine learning method can be used more generally to simultaneously identify protein-targeted networks (pathways), guide selection and synthesis of specific compounds for desired cell-specific activity (e.g. anticancer potency), as well as, suggest a rational way to approach drug combinations based on selected cell-specific networks that may be used to combat intra- and inter-cancer heterogeneity. Citation Format: Joydeb Majumder, Jonathan A. Fine, Travis C. Lantz, Cory J. Conder, Gaurav Chopra. Cancer cell specific lethality by degrading specific protein target network identified using a chemical screening based machine learning method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-076.

Increased demand in the performance of missiles has led to extensive research in the field of slender body aerodynamics techniques for missile control. The major characteristics of a missile are its range and maneuverability. Usually, maneuvering is done by the deflection of a set of control surfaces attached to the body, most common are canard control, wing control and tail control. Deflected nose is an alternative to canard wing control with advantages regarding downwash. Deflection in nose also causes the generation of the vortices at the end of the missile. These vortices cause a significant amount of effect over the force generation in the missile. This report includes the computational and experimental study of a deflected nose missile at subsonic speed and in comparison with the normal missile. In this report we are only studying missile with deflected nose. The research was done at various angles of nose deflection and at different angles of attack at subsonic speed for quantitative and qualitative study to understand the effect precisely. The computation was carried out in Fluent using unsteady laminar flow and design was created in Auto-CAD and Gambit. Results consist of lift, moment, pressure magnitude and vorticity contours over the missile of many nose deflection configurations and angles of attack. Results depict good effects of deflected nose over the missile in comparison to the missile without any nose deflection in terms of force and momentum generation. All the experiments and computations have been performed at subsonic speeds. The overall study reflects good explanation for the aerodynamics flow phenomena over the missile with deflected nose. These studies show as high a moment generated over the missile when compared to canard control. The deflected nose also produces less or no downwash on the sides which effects over the wing or any other control surface. The vorticity contour shows good results.

Prostate cancer (PCa) ranks second in terms of cancer-related deaths among men in the United States. The primary cause is the emergence of castration resistant prostate cancer (CRPC) and subsequent metastasis and chemoresistance. There is no known cure for CRPC with a median survival rate of about 18 months. CRPC tumors are either intrinsically resistant or rapidly develop resistance to chemotherapy. Traditional approaches to drug discovery are highly specific to single targets (molecules and indications) and are time-consuming trial-and-error approaches, which are often ineffective and unsafe for humans. This approach has resulted in a dearth of novel drugs (currently less than thirty are approved each year) and the time and costs to develop and bring one to market are prohibitive ($2.6 billion in 2013). A solution is to repurpose existing drugs with known safety profiles that interact with therapeutic targets and can be rapidly deployed for use in mono and multi-drug therapies. However, there is a problem in consistently identifying high-confidence drugs for CRPC. Many computational tools have been developed for analyses of high-throughput genomics data, and these methods have led to improved understanding of cancer biology. However, despite the generation of enormous amounts of data, improved treatments and diagnostics take a long time, and a substantial amount of research is needed to translate a genomic discovery to the bedside. We have developed an integrated drug discovery/repurposing platform that analyzes compound-protein structural interaction signatures across multiple proteomes to determine drug behavior, in contrast to traditional single (or few) target approaches. The platform implements a modeling pipeline that generates an interaction between ‘all’ (currently 3,733) human approved drugs and ‘all’ (currently 48,278) proteins using our interactome docking with dynamics program to compute ~ 1 billion proteome-compound interactions. We have prospectively validated our predictions with in vitro and in vivo preclinical studies for more than 10 different diseases including immunological, metabolic, infectious and genetic indications. Recently, we applied our integrated drug discovery pipeline and found strong relationship between CRPC with breast cancer, hypertension and inflammation to repurpose human approved drugs for CRPC. We have newly identified an FDA approved drug with IC50 less than 10nM activity on LnCaP and C4-2 cell proliferation. The repurposed lead is tissue specific and its toxicity is very less compared to other chemotherapeutic agents used for cancer. Using our computational design approach of taking into account target and anti-target binding profile we are modifying and synthesizing new analogues of this potent anti-cancer human approved drug for immunomodulation without effecting anti-cancer potency. We conclude that compared to traditional single target drug discovery that is slow and error prone, interactome based drug discovery that takes into account cancer as a heterogeneous combination of other diseases will help bring anticancer drugs faster to the clinic. Citation Format: Joydeb Majumder, Travis Lantz, Jonathan Fine, Gaurav Chopra. Drug repurposing for castration resistant prostate cancer based on disease-disease relationships. [abstract]. In: Proceedings of the AACR Special Conference on Translational Control of Cancer: A New Frontier in Cancer Biology and Therapy; 2016 Oct 27-30; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2017;77(6 Suppl):Abstract nr A35.

Sacrococcygeal teratomas are tumors that arise in the sacrococcygeal area containing tissue from all three germ layers. Sacrococcygeal teratoma has an incidence of ~one in 27 000 live births, with malignant transformation with increasing age. We report a unique case of a 6-year-old child with a large sacrococcygeal mass with rudimentary genitals with an intraoperative intraoral misplacement of the endotracheal tube during positioning.

We describe a patient with long-standing ankylosing spondylitis who underwent percutaneous nephrolithotomy under spinal anesthesia. At preoperative assessment, it was considered that intubation of the trachea was likely to be difficult. Fiberoptic bronchoscopy was attempted, but without success. As the standard technique for spinal anesthesia failed, a variation of the paramedian approach in the lumbosacral approach, also known as Taylor's approach was successfully attempted. This resulted in adequate sensory and motor blockade for the surgical procedure. The patient did not require airway interventions, but equipment and aids to secure airway were available.

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.