The Gordon Life Science Institute

195 Background: As part of the quality improvement initiative, the Northwell Health Cancer Institute aimed to improve the average time to first appointment (TTA) for all new breast and thoracic cancer patients to 7 days or less. Average TTAs were 11.3 days for breast and 10 days for thoracic cancer patients. This initiative was conducted in collaboration with the ASCO Quality Training Program. Methods: Data collection included interviews, surveys, and chart reviews, with input from nurse navigators, schedulers, and social work. Data sources included the new patient unit, navigator and social work databases. Pareto analysis was used to identify key barriers to timely appointments. We chose two interventions that were high impact and relatively easy to implement: increasing tele-visit appointment availability and scheduling provider visits before all work up was completed or resulted. Results: After three Plan-Do-Study-Act (PDSA) cycles, TTA for thoracic cancer patients showed improvement: PDSA 2 TTA 9.1 days, PDSA 2 TTA 7.9 days. For breast cancer patients, similar interventions were not able to improve TTA: PDSA 2 TTA 12.9 days and PDSA 3 TTA 11.7 days. Pareto analysis revealed that delays were often due to patient preference to wait for Oncotype results or patient/ family availability before the initial oncology visit. Review of triple-negative, HER2-positive, metastatic breast cancer, and breast cancer in pregnancy indicated improved TTA for this subgroup: PDSA Cycle 2 TTA was 7.7 days and PDSA Cycle 3 TTA was 5.9 days. Discussion: Improved awareness and adoption of interventions can improve TTA. Challenges include patient preference and hospitalization, which are not amenable to change. Future steps involve refining data collection templates, addressing specific barriers such as Oncotype testing delays, and re-evaluating the appropriateness of the 7-day TTA metric for hormone positive, Her2 negative breast cancer or chemoprevention patients. Conclusions: TTA for thoracic and some subtypes of breast oncology patients improved. Oncotype or genomic testing for hormone positive breast patients is a vital part of the medical oncology visit where most patients prefer to wait for results and delay TTA.

The “Gordon Life Science” or “GLS” Institute is the first Internet Institute in the world. Established by Prof. Dr. Kuo-Chen Chou in 2003, right after he was retired from “Pfizer Global Research and Development” in San Diego, California. There is a very interesting story for the name of Institute as elaborated below. After the “gangs of four” (including Mao Zedong's wife) was smashed and the complete failure of Mao “Cultural Revolution”, China was under the leadership of Deng Xiaoping, who took the policy of “韬光养晦”starting to open China’s door, the founder was invited by Professor Sture Forsén, the then “Chairman of Nobel Prize Committee”, to work in Chemical Center of Lund University as a Visiting Professor. It was very difficult for Swedish people to pronounce “Kuo-Chen Chou”. In order for his colleagues and friends there easier to pronounce his name, Professor Chou used “Gordon” as his name in Sweden. In 2003, about a quarter of century later, the same name was used for the Institute, meaning that Deng’s “韬光养晦” policy can stimulate a lot of great creativities.

Introduction Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification.Methods The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called “iHyd-LysSite (EPSV)” (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins.Results The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold cross-validation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%.Conclusion The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.

In 2015 a very powerful web-server predictor has been established for identifying N 6 -methyladenosine (m 6 A), which is one of the most abundant modifications in RNA [1].

N-Glycosylation is one of the most important post-translational mechanisms in eukaryotes. N-glycosylation predominantly occurs in N-X-[S/T] sequon where X is any amino acid other than proline. However, not all N-X-[S/T] sequons in proteins are glycosylated. Therefore, accurate prediction of N-glycosylation sites is essential to understand Nglycosylation mechanism. In this article, our motivation is to develop a computational method to predict Nglycosylation sites in eukaryotic protein sequences. In this article, we report a random forest method, Nglyc, to predict N-glycosylation site from protein sequence, using 315 sequence features. The method was trained using a dataset of 600 N-glycosylation sites and 600 non-glycosylation sites and tested on the dataset containing 295 Nglycosylation sites and 253 non-glycosylation sites. Nglyc prediction was compared with NetNGlyc, EnsembleGly and GPP methods. Further, the performance of Nglyc was evaluated using human and mouse N-glycosylation sites. Nglyc method achieved an overall training accuracy of 0.8033 with all 315 features. Performance comparison with NetNGlyc, EnsembleGly and GPP methods shows that Nglyc performs better than the other methods with high sensitivity and specificity rate. Our method achieved an overall accuracy of 0.8248 with 0.8305 sensitivity and 0.8182 specificity. Comparison study shows that our method performs better than the other methods. Applicability and success of our method was further evaluated using human and mouse N-glycosylation sites. Nglyc method is freely available at https://github.com/bioinformaticsML/ Ngly.

In this paper, the chemical mechanism of the coronavirus disease 2019 (COVID-19) has been explored and clearly revealed.

In this short review paper, the significant and profound impacts of the Chou’s “wenxiang diagram” have been briefly presented with crystal clear convincingness.

Identification of the sites of post-translational modifications (PTMs) in protein, RNA, and DNA sequences is currently a very hot topic. This is because the information thus obtained is very useful for in-depth understanding the biological processes at the cellular level and for developing effective drugs against major diseases including cancers as well. Although this can be done by means of various experimental techniques, it is both time-consuming and costly to determine the PTM sites purely based on experiments. With the avalanche of biological sequences generated in the post-genomic age, it is highly desired to develop bioinformatics tools for rapidly and effectively identifying the PTM sites. In the last few years, many efforts have been made in this regard, and considerable progresses have been achieved. This review is focused on those prediction methods that have the following two features. (1) They have been developed by strictly observing the 5-steps rule so that they each have a user-friendly web-server for the majority of experimental scientists to easily get their desired data without the need to go through the detailed mathematics involved. (2) Their cornerstones have been based on Pseudo Amino Acid Composition (PseAAC) or Pseudo K-tuple Nucleotide Composition (PseKNC), and hence the prediction quality is generally higher than most of the other PTM prediction methods.

BackgroundIn various biological processes and cell functions, Post Translational Modifications (PTMs) bear critical significance. Hydroxylation of proline residue is one kind of PTM, which occurs following protein synthesis. The experimental determination of hydroxyproline sites in an uncharacterized protein sequence requires extensive, time-consuming and expensive tests.MethodsWith the torrential slide of protein sequences produced in the post-genomic age, certain remarkable computational strategies are desired to overwhelm the issue. Keeping in view the composition and sequence order effect within polypeptide chains, an innovative in-silico> predictor via a mathematical model is proposed.ResultsLater, it was stringently verified using self-consistency, cross-validation and jackknife tests on benchmark datasets. It was established after a rigorous jackknife test that the new predictor values are superior to the values predicted by previous methodologies.ConclusionThis new mathematical technique is the most appropriate and encouraging as compared with the existing models.