Reactions of Bis(oxy)enamines with O-Nucleophiles in the Presence of Metal Salts
NO donors are an emerging class of pharmaceutical compounds, with many important functions in the cardiovascular, nervous and immune systems. With great therapeutic potential, the development of new NO donor compounds would be of great medicinal value, potentially opening a whole class of drugs to be used to treat various ailments. This project studies a specific class of compounds, substituted cyclic oxime ethers, which have proven to be useful intermediates in fields such as medicine and biochemistry. The cyclic structure along with a determinable substitutable group at the C3 position is highly valuable, as it allows the oxime ether to act as a convenient precursor for a variety of useful products, playing key components in many drugs. And with a substituted nitrate group, which is an O-nucleophile, the oxime ether has the potential to become an NO-donor, and hence become a possible intermediate in a wide array of NO donor drugs. Co(NO)3 was used in the synthesis of the cyclic oxime ether, directly from a phenyl substituted bis(oxy)enamine intermediate, producing an entirely new compound: α-hydroxyoxime nitrates, the oxime ether being substituted with a nitrate group. This new reaction of the synthesis of α-hydroxyoxime nitrates was further studied for optimization purposes, in order to open a new class of NO donor precursors. In addition, other nucleophiles were also explored in this class of reactions, forming important bonds such as C-N and C-S bonds, with key structures for other types of synthesis intermediates and precursors. Different metal nitrates, or various other nucleophiles in place of the nitrates, were used in reaction with bis(oxy)enamine, and the yield and structure of the final products were determined by NMR spectra. Successful optimization of the synthesis of α-hydroxyoxime nitrates has been achieved, where the conditions for optimum synthesis involve using Cr(NO3)3•9H2O which achieved a high yield of 76%, dissolved in THF with the bis(oxy)enamine starting compound. It has been determined that the metal in the salt affects the reaction pathway, as the nature of the metal cation affects its efficiency to cleave the N-O bond in the starting compound (with d-block elements being the best performing), and H+ ions can promote the reaction as well. Also, the reaction proceeds with different types of bis(oxy)enamines, meaning the substrate scope can be expanded to give a variety of products. The reaction can also proceed to form other products with different nucleophiles other than the nitrate group, where the C-N and C-S bonds were successfully formed in the reactions from bis(oxy)enamine to oxime ether. Thus, this class of reaction in converting the bis(oxy)enamine to a cyclic oxime ether has potentially opened a new class of NO donor compounds, and further possesses the potential to form a wide variety of products to be used in other important synthesis procedures.
告訴你「拉午耳」 「亨利」的壓力有多大 ─自製簡易的 IC 電路板來討論拉?
This research is aimed to make an in-depth exploration into Raoul’s Law and Henry’s Law by using an accurate but simple vapor pressure gauge. The gauge is constructed from non-complicated electronics components- electronics IC PCB, film resistor, digital multi-meter, and battery. In the first step, we measured the vapor pressure of six kinds of liquids and 3 liquid mixtures- water, ethanol, chloroform, acetone, benzene, toluene, mixture of water and ethanol, mixture of chloroform and acetone, mixture benzene and toluene. From the results of this experiments, the vapor pressures of water and ethanol liquid mixture, and chloroform and acetone liquid mixture were slightly lower than their theoretic values-called negative deviation solution, while the vapor pressure of the benzene and toluene liquid mixture was quite close to its theoretic value-near an ideal solution. In the second step, the individual vapor pressures of water, ethanol, and chloroform were measured at various temperatures; the vapor heat(ΔH) were calculated by using the lausius-Clapeyron equation. In the final step, we used the gauge and other non-commercial instruments to measure the B.O.D. values of water from the Kaohsiung Love River, found the P-T correlation using Gay-Lussac’s Law, and calculated the absolute zero temperature value by extrapolation. 本研究是利用一些簡易的電子元件－電路IC板、電阻膜、數位三用電表和電瓶來組裝一 個準確、簡易的氣壓量測器。我們將此量測器用來深入探討「拉午耳定律」及「亨利定律」 。 首先，我們測量了水、乙醇、氯仿、丙酮、苯、甲苯等六種純液體的蒸氣壓，並測量了(水＋乙醇)、(氯仿＋丙酮)、(苯＋甲苯)等兩成份系溶液的混合蒸氣壓。結果發現(水＋乙醇) 、(氯仿＋丙酮)的混合蒸氣壓都比理論值低了一些，此稱為負偏差溶液；(苯＋甲苯)的混合蒸氣壓與理論值差不多，較接近理想溶液。 接下來，我們還測量了不同溫度下水、酒精及丙酮的蒸氣壓，並利用clausius－clapeyron equation求出液體純質的汽化熱( H Δ )。 最後，我們還搭配了自製的儀器裝置，來測定愛河水質的B.O.D.值(生化需氧量)以及探討氣體的給呂薩克定律(P~T關係)，並利用外差法來推求絕對零度。
Discovery, Cloning and Recombinant Expression of a Coral Peptide with anti-Bacteria activity
Inflammatory Bowel Disease (IBD) is a prevalent disease of the West which pathogenesis is driven by a combination interaction between bacteria and inflammatory cells. In this study, two Kazal domain peptide from Palythoa Caribaeorum were identified. They were found to exhibit serine protease inhibitory, anti-bacterial effects and low toxicity, making them ideal candidates for IBD treatment due to their ability to inhibit inflammatory cell migration and bacterial load. We amplified their coding DNA sequences via PCR and ligated the resulting PCR product into pGEX-4T3 vector. The recombinant plasmid was verified by sequencing, and restriction digest before being transformed into competent E.coli cells. Following transformation, we induced target peptides expression by IPTG to confirmed successful transformation and peptide production. Selected transformed bacterial colonies were expanded in LB broth before mixing with glycerol and frozen in -80°C freezer to complete the process of cell bank production.
IlluminaMed: Developing Novel Artificial Intelligence Techniques for the Use In a Biomedical Image Analysis Toolkit and Personalized Medicine Engine
Despite the multitude of biomedical scans conducted, there is still relatively low accuracy and standardization of diagnoses from these images. In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. The aim of my research was automatic segmentation of brain MRI scans to better analyze patients with tumors, multiple sclerosis, ALS, or Alzheimer’s. In particular, I aim to use this information, along with novel artificial intelligence algorithms, to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific covariate data that maximizes the expected survival time or clinical outcome. The result of the research was IlluminaMed, a biomedical image analysis toolkit that relies on the development of new artificial neural networks and training algorithms and novel research in fuzzy logic. The networks can detect patterns more complex than humans can identify and create patterns over long periods of time. IlluminaMed was trained by a dataset of professionally and manually segmented MRI scans from several prestigious hospitals and universities. I then developed an algorithmic framework to solve multistage decision problem with a varying number of stages that are subject to censoring in which the “rewards” are expected survival times. In specific, I developed a novel Q-learning algorithm that dynamically adjusts for these parameters. Furthermore, I found finite upper bounds on the generalized error of the treatment paths constructed by this algorithm. I have also shown that when the optimal Q-function is an element of the approximation space, the anticipated survival times for the treatment regime constructed by the algorithm will converge to the optimal treatment path. I demonstrated the performance of the proposed algorithmic framework via simulation studies and through the analysis of chronic depression data and a hypothetical clinical trial. IlluminaMed can automatically segment the scans with 98% accuracy, find tumors with 96% accuracy and approximate their volume within a 2% margin of error. It can also find lesions in MS and ALS, distinguishing them from tumors with 94% accuracy. IlluminaMed can, in addition, determine the tendency of a patient to develop Alzheimer’s several months before patients develop symptoms correlating the brain structure and its fluctuations. Lastly, the censored Q-learning algorithm I developed is more effective than the state of the art clinical decision support systems and is able to operate in environments when many covariate parameters may be unobtainable or censored. IlluminaMed is the only fully automatic biomedical image analysis toolkit and personalized medicine engine. The personalized medicine engine runs at a level that is comparable to the best physicians. It is less computationally complex than similar software and is unique in the fact that it can find new patterns in the brain with possible future diagnoses. IlluminaMed’s implications are not only great in terms of the biomedical field, but also in the field of artificial intelligence with new findings in neural networks and the relationships of fuzzy extensional subsets.