Life Sciences Commons^™

Discovery Of Pharmaceutical Agents Towards The Inhibition Of P38 Mitogen- Activated Protein Kinase (Mapk): In Silico And In Vitro Studies, Utid Suriya

Chulalongkorn University Theses and Dissertations (Chula ETD)

In this research, we combined the accelerated in silico screening methods alongside experimental evaluations to search for novel anti-non-small cell lung cancer (NSCLC) drug candidates specifically targeting the up-regulated kinases including epidermal growth factor receptor tyrosine kinase (EGFR TK), p38 mitogen-activated protein kinase (p38 MAPK) and Janus kinase 2 (JAK2). A novel compound scaffold, quinoxalinones including CPD4, CPD15, CPD16, and CPD21 was identified to inhibit the EGFR (L858R/T790M/C797S) TK with an IC50 value down to a nanomolar scale as well as showing a cytotoxic effect against NSCLC cell lines. Apart from that, we employed a drug repositioning platform to elucidate …

Characteristics Of Mononucleotide A-Repeats Around Transcription Start Sites In Vertebrates, Jatuphol Pholtaisong

Chulalongkorn University Theses and Dissertations (Chula ETD)

Tandem repeats (TRs) promote a wide variety of phenotypic traits. The imbalance of mononucleotide A-repeats around transcription start sites (TSSs) in human, mouse and rat in previous study. In this study, the imbalance of A-repeats in 60 vertebrates was investigated, and the potential function of upstream A-repeats was identified. We discovered that some vertebrates were the origin of the imbalance pattern. Mammals and birds had a similar pattern, while amphibians and reptiles did not. We hypothesized that the enriched A-repeats upstream of TSSs is a novel hallmark of endotherms or warm-blooded animals. Gene ontology analysis indicates that the primary function …

Software Architecture For Smart Farming (Fasal), Muhammad Asad Bin Khalid

MSCS Research Projects

The paper represents the urge of making this application for betterment of agricultural sector of Pakistan and how this application could result in the growth of the sector. Moreover, the paper presents the research papers work consulted during the research and also contains the flow of application made for POC. Future work is also illustrated at the end to make sure there is a gap that needs to be filled for a proper mature application.

Phenotype-Genotype Analysis Of Caucasian Patients With High Risk Of Osteoarthritis, Yanfei Wang, Jacqueline Chyr, Pora Kim, Weiling Zhao, Xiaobo Zhou

Faculty, Staff and Student Publications

Background: Osteoarthritis (OA) is a common cause of disability and pain around the world. Epidemiologic studies of family history have revealed evidence of genetic influence on OA. Although many efforts have been devoted to exploring genetic biomarkers, the mechanism behind this complex disease remains unclear. The identified genetic risk variants only explain a small proportion of the disease phenotype. Traditional genome-wide association study (GWAS) focuses on radiographic evidence of OA and excludes sex chromosome information in the analysis. However, gender differences in OA are multifactorial, with a higher frequency in women, indicating that the chromosome X plays an essential role …

Automated Identification Of Missing Is-A Relations In The Human Phenotype Ontology, Maryamsadat Mohtashamian, Ran Hu, Rashmie Abeysinghe, Xubing Hao, Hua Xu, Licong Cui

Faculty, Staff and Student Publications

Auditing the Human Phenotype Ontology (HPO) is necessary to provide accurate terminology for its use in clinical research. We investigate an approach leveraging the lexical features of concepts in HPO to identify missing IS-A relations among HPO concepts. We first model the names of HPO concepts as sets of words in lower case. Then, we generate two types of concept-pairs which have at least a single common word: (1) Linked concept-pairs generated from concept-pairs having an IS-A relation; (2) Unlinked concept-pairs generated from concept-pairs without an IS- A relation. Concept-pairs generate Derived Term Pairs (DTPs) emphasizing unique lexical information of …

Cost Of Care For Asylum Seekers And Refugees Entering The United States: The Case Of Volunteer Medical Providers In El Paso, Texas, Rigoberto I Delgado, Manuel De La Rosa, Marlon A Picado, Lisa Ayoub-Rodriguez, Celia E Gonzalez, Leopold Gemoets

Faculty, Staff and Student Publications

BACKGROUND: Between October 2018, and February 2020, the United States saw an unprecedented increase in the number of asylum seekers and refugees arriving unexpectedly at international crossings along the US-Mexico Border. Many of these migrants needed proper medical attention, and consequently created significant pressure on local health systems. In El Paso, Texas, volunteer clinicians, collaborating closely with religious organizations and non-governmental organizations, provided outpatient medical care for the new arrivals; the county hospital provided in-patient care at local tax payers' expense. The objective of this study was to estimate costs of healthcare services offered by these volunteers in order to …

A Nomogram For Predicting Upper Urinary Tract Damage Risk In Children With Neurogenic Bladder, Qi Li, Miao Cai, Qingsong Pu, Shengde Wu, Xing Liu, Tao Lin, Dawei He, Jianguo Wen, Guanghui Wei

Faculty, Staff and Student Publications

PURPOSE: To establish a predictive model for upper urinary tract damage (UUTD) in children with neurogenic bladder (NB) and verify its efficacy.

METHODS: A retrospective study was conducted that consisted of a training cohort with 167 NB patients and a validation cohort with 100 NB children. The clinical data of the two groups were compared first, and then univariate and multivariate logistic regression analyses were performed on the training cohort to identify predictors and develop the nomogram. The accuracy and clinical usefulness of the nomogram were verified by receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses.

RESULTS: …

Racial And Socioeconomic Disparities In Patients With Meningioma: A Retrospective Cohort Study, Hudin N Jackson, Caroline C Hadley, A Basit Khan, Ron Gadot, James C Bayley, Arya Shetty, Jacob Mandel, Ali Jalali, K Kelly Gallagher, Alex D Sweeney, Arif O Harmanci, Akdes S Harmanci, Tiemo Klisch, Shankar P Gopinath, Ganesh Rao, Daniel Yoshor, Akash J Patel

Faculty, Staff and Student Publications

BACKGROUND: Meningiomas are the most common intracranial neoplasms. Although genomic analysis has helped elucidate differences in survival, there is evidence that racial disparities may influence outcomes. African Americans have a higher incidence of meningiomas and poorer survival outcomes. The etiology of these disparities remains unclear, but may include a combination of pathophysiology and other factors.

OBJECTIVE: To determine factors that contribute to different clinical outcomes in racial populations.

METHODS: We retrospectively reviewed 305 patients who underwent resection for meningiomas at a single tertiary care facility. We used descriptive statistics and univariate, multivariable, and Kaplan-Meier analyses to study clinical, radiographical, and …

Risk Of Alzheimer's Disease Following Influenza Vaccination: A Claims-Based Cohort Study Using Propensity Score Matching, Avram S Bukhbinder, Yaobin Ling, Omar Hasan, Xiaoqian Jiang, Yejin Kim, Kamal N Phelps, Rosemarie E Schmandt, Albert Amran, Ryan Coburn, Srivathsan Ramesh, Qian Xiao, Paul E Schulz

Faculty, Staff and Student Publications

BACKGROUND: Prior studies have found a reduced risk of dementia of any etiology following influenza vaccination in selected populations, including veterans and patients with serious chronic health conditions. However, the effect of influenza vaccination on Alzheimer's disease (AD) risk in a general cohort of older US adults has not been characterized.

OBJECTIVE: To compare the risk of incident AD between patients with and without prior influenza vaccination in a large US claims database.

METHODS: Deidentified claims data spanning September 1, 2009 through August 31, 2019 were used. Eligible patients were free of dementia during the 6-year look-back period and≥65 years …

Fairly Predicting Graft Failure In Liver Transplant For Organ Assigning, Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse …

Facilitating Federated Genomic Data Analysis By Identifying Record Correlations While Ensuring Privacy, Leonard Dervishi, Xinyue Wang, Wentao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday

Faculty, Staff and Student Publications

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record …

Caries Risk Documentation And Prevention: Emeasures For Dental Electronic Health Records, Suhasini Bangar, Ana Neumann, Joel M White, Alfa Yansane, Todd R Johnson, Gregory W Olson, Shwetha V Kumar, Krishna K Kookal, Aram Kim, Enihomo Obadan-Udoh, Elizabeth Mertz, Kristen Simmons, Joanna Mullins, Ryan Brandon, Muhammad F Walji, Elsbeth Kalenderian

Faculty, Staff and Student Publications

BACKGROUND: Longitudinal patient level data available in the electronic health record (EHR) allows for the development, implementation, and validations of dental quality measures (eMeasures).

OBJECTIVE: We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year.

METHODS: Both eMeasures define the numerator and denominator beginning at the patient level, populations' specifications, and validated the automated queries. For …

Improving Pharmacovigilance Signal Detection From Clinical Notes With Locality Sensitive Neural Concept Embeddings, Justin Mower, Elmer Bernstam, Hua Xu, Sahiti Myneni, Devika Subramanian, Trevor Cohen

Faculty, Staff and Student Publications

Although pharmaceutical products undergo clinical trials to profile efficacy and safety, some adverse drug reactions (ADRs) are only discovered after release to market. Post-market drug safety surveillance - pharmacovigilance - leverages information from various sources to proactively identify such ADRs. Clinical notes are one source of observational data that could assist this process, but their inherent complexity can obfuscate possible ADR signals. In previous research, embeddings trained on observational reports have improved detection of such signals over commonly used statistical measures. Moreover, neural embedding methods which further encode juxtapositional information have shown promise on analogical retrieval tasks, suggesting proximity-based alternatives …

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, And Beyond, Robert El-Kareh, Dean F Sittig

Faculty, Staff and Student Publications

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

Advances In Non-Invasive Biosensing Measures To Monitor Wound Healing Progression, Walker D Short, Oluyinka O Olutoye, Benjamin W Padon, Umang M Parikh, Daniel Colchado, Hima Vangapandu, Shayan Shams, Taiyun Chi, Jangwook P Jung, Swathi Balaji

Faculty, Staff and Student Publications

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and …

A Novel Group Of Genes That Cause Endocrine Resistance In Breast Cancer Identified By Dynamic Gene Expression Analysis, Arvand Asghari, Katherine Wall, Michael Gill, Natascha Del Vecchio, Farnaz Allahbakhsh, Jacky Wu, Nan Deng, W Jim Zheng, Hulin Wu, Michihisa Umetani, Vahed Maroufy

Faculty, Staff and Student Publications

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of …

A Deep Learning-Based Framework For Supporting Clinical Diagnosis Of Glioblastoma Subtypes, Sana Munquad, Tapas Si, Saurav Mallik, Asim Bikas Das, Zhongming Zhao

Faculty, Staff and Student Publications

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms …

Crucial Roles Of Microrna-16-5p And Microrna-27b-3p In Ameloblast Differentiation Through Regulation Of Genes Associated With Amelogenesis Imperfecta, Akiko Suzuki, Hiroki Yoshioka, Teng Liu, Aania Gull, Naina Singh, Thanh Le, Zhongming Zhao, Junichi Iwata

Faculty, Staff and Student Publications

Amelogenesis imperfecta is a congenital disorder within a heterogeneous group of conditions characterized by enamel hypoplasia. Patients suffer from early tooth loss, social embarrassment, eating difficulties, and pain due to an abnormally thin, soft, fragile, and discolored enamel with poor aesthetics and functionality. The etiology of amelogenesis imperfecta is complicated by genetic interactions. To identify mouse amelogenesis imperfecta-related genes (mAIGenes) and their respective phenotypes, we conducted a systematic literature review and database search and found and curated 70 mAIGenes across all of the databases. Our pathway enrichment analysis indicated that these genes were enriched in tooth development-associated pathways, forming four …

Dimensionality Reduction And Louvain Agglomerative Hierarchical Clustering For Cluster-Specified Frequent Biomarker Discovery In Single-Cell Sequencing Data, Soumita Seth, Saurav Mallik, Tapas Bhadra, Zhongming Zhao

Faculty, Staff and Student Publications

The major interest domains of single-cell RNA sequential analysis are identification of existing and novel types of cells, depiction of cells, cell fate prediction, classification of several types of tumor, and investigation of heterogeneity in different cells. Single-cell clustering plays an important role to solve the aforementioned questions of interest. Cluster identification in high dimensional single-cell sequencing data faces some challenges due to its nature. Dimensionality reduction models can solve the problem. Here, we introduce a potential cluster specified frequent biomarkers discovery framework using dimensionality reduction and hierarchical agglomerative clustering Louvain for single-cell RNA sequencing data analysis. First, we pre-filtered …

Explanation-Driven Deep Learning Model For Prediction Of Brain Tumour Status Using Mri Image Data, Loveleen Gaur, Mohan Bhandari, Tanvi Razdan, Saurav Mallik, Zhongming Zhao

Faculty, Staff and Student Publications

Cancer research has seen explosive development exploring deep learning (DL) techniques for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We have observed a substantial gap in explanation, interpretability, and high accuracy for DL models. Consequently, we propose an explanation-driven DL model by utilising a convolutional neural network (CNN), local interpretable model-agnostic explanation (LIME), and Shapley additive explanation (SHAP) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an MRI image dataset. Unlike previous models, our model used a dual-input CNN approach to prevail over the classification challenge with images of inferior quality …

Breast Cancer Detection: Shallow Convolutional Neural Network Against Deep Convolutional Neural Networks Based Approach, Himanish Shekhar Das, Akalpita Das, Anupal Neog, Saurav Mallik, Kangkana Bora, Zhongming Zhao

Faculty, Staff and Student Publications

Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).

Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help …

The Wnt/Β-Catenin Pathway Regulates Expression Of The Genes Involved In Cell Cycle Progression And Mitochondrial Oxidative Phosphorylation In The Postmitotic Cardiac Myocytes, Melis Olcum, Sirisha M Cheedipudi, Leila Rouhi, Siyang Fan, Hyun-Hwan Jeong, Zhongming Zhao, Priyatansh Gurha, Ali J Marian

Faculty, Staff and Student Publications

INTRODUCTION: Aging is associated with cardiac myocyte loss, sarcopenia, and cardiac dysfunction. Adult cardiac myocytes are postmitotic cells with an insufficient proliferative capacity to compensate for myocyte loss. The canonical WNT (cWNT) pathway is involved in the regulation of cell cycle reentry in various cell types. The effects of the cWNT pathway on the expression of genes involved in cell cycle reentry in the postmitotic cardiac myocytes are unknown.

AIM: The aim of the study was to identify genes whose expression is regulated by the β-catenin, the indispensable component to the cWNT signaling, in the postmitotic myocytes.

METHODS AND RESULTS: …

Prediction Of Intensive Care Unit Admission (>24h) After Surgery In Elective Noncardiac Surgical Patients Using Machine Learning Algorithms, Lan Lan, Fangwei Chen, Jiawei Luo, Mengjiao Li, Xuechao Hao, Yao Hu, Jin Yin, Tao Zhu, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources.

METHOD: Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, …

Distinguish Bipolar And Major Depressive Disorder In Adolescents Based On Multimodal Neuroimaging: Results From The Adolescent Brain Cognitive Development Stud, Yujun Liu, Kai Chen, Yangyang Luo, Jiqiu Wu, Qu Xiang, Li Peng, Jian Zhang, Weiling Zhao, Mingliang Li, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

METHODS: We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of …

Prediction Of Fluid Intelligence From T1-W Mri Images: A Precise Two-Step Deep Learning Framework, Mingliang Li, Mingfeng Jiang, Guangming Zhang, Yujun Liu, Xiaobo Zhou

Faculty, Staff and Student Publications

The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. In this work, a two-step deep learning pipeline is proposed to improve the prediction accuracy of fluid intelligence scores. In terms of the first step, the main contributions of this study include the following: (1) the concepts of the residual network (ResNet) and the squeeze-and-excitation network (SENet) are utilized to improve the original 3D U-Net; (2) in the segmentation process, the pixels in symmetrical brain regions are assigned the same label; …

รูปแบบการถ่ายทอดการขยายนิวคลีโอไทด์ซ้ำในโรคลมชักแบบกระตุกที่เริ่มเป็นในวัยผู้ใหญ่และถ่ายทอดในครอบครัวแบบไม่รุนแรงด้วยเทคโนโลยีการหาลำดับเบสแบบสายยาว, ศุภกร รักวงศ์ขจร

Chulalongkorn University Theses and Dissertations (Chula ETD)

โรคลมชักแบบกระตุกที่เริ่มเป็นในวัยผู้ใหญ่และถ่ายทอดในครอบครัวแบบไม่รุนแรง (BAFME) เป็นโรคทางระบบประสาทที่ถ่ายทอดแบบยีนเด่นบนโครโมโซมร่างกาย โดยมีการสั่นและ/หรือชักเกร็งเป็นลักษณะของโรค ซึ่งมีช่วงอายุที่เริ่มแสดงอาการของโรคตั้งแต่วัยรุ่นจนถึงวัยผู้ใหญ่ สาเหตุของโรคเกิดจากการขยายเบสซ้ำชนิด TTTTA และตามด้วยการแทรกตัวของลำดับเบสชนิด TTTCA ในบริเวณอินทรอนของยีนมากมาย รวมถึงอินทรอน 4 ของยีน SAMD12 ในโรคลมชักแบบกระตุกที่เริ่มเป็นในวัยผู้ใหญ่และถ่ายทอดในครอบครัวแบบไม่รุนแรงชนิดที่ 1 (BAFME1) และอินทรอน 1 ของยีน YEATS2 ในโรคลมชักแบบกระตุกที่เริ่มเป็นในวัยผู้ใหญ่และถ่ายทอดในครอบครัวแบบไม่รุนแรงชนิดที่ 4 (BAFME4) จากการศึกษาที่ผ่านมาพบว่าความยาวของจำนวนซ้ำที่มากขึ้นทำให้อาการของโรครุนแรงขึ้น วัตถุประสงค์ของการศึกษานี้ คือการใช้เทคโนโลยีการหาลำดับเบสแบบสายยาวของ Pacific Biosciences ในการระบุจำนวนซ้ำ การศึกษาการถ่ายทอดของการขยายเบสซ้ำ และหาความสัมพันธ์ระหว่างจีโนไทป์และฟีโนไทป์ในครอบครัวผู้ป่วยชาวไทยที่เป็น BAFME1 และ BAFME4 ผลการศึกษาพบว่าในผู้ป่วยที่เป็นพี่น้องกัน จำนวนซ้ำชนิด TTTCA มีความแตกต่างอย่างมีนัยสำคัญเมื่อเทียบกับจำนวนซ้ำชนิด TTTTA สำหรับจำนวนซ้ำ TTTTA มีจำนวนลดลงในขณะที่จำนวนซ้ำ TTTCA มีจำนวนเพิ่มขึ้น เมื่อมีการส่งถ่ายทอดจากผู้ป่วยรุ่นพ่อแม่ไปยังผู้ป่วยรุ่นลูก และพบความสัมพันธ์แบบผกผันระหว่างจำนวนซ้ำ TTTTA จำนวนซ้ำ TTTCA และจำนวนซ้ำทั้งหมด กับอายุที่เริ่มแสดงอาการของโรคในครอบครัว BAFME1 แต่ไม่พบความสัมพันธ์ดังกล่าวในครอบครัว BAFME4 นอกจากนี้ยังพบโครงสร้างรูปแบบใหม่ ((TTTTA)exp(CAAAA)ins(CCAAA)ins(TTTCA)ins) ในผู้ป่วย BAFME4 โดยการศึกษานี้แสดงให้เห็นถึงพฤติกรรมของการขยายเบสซ้ำที่มีทั้งความซับซ้อนและความหลากหลายระหว่างชนิดของ BAFME ที่ต่างกัน

ฮอร์โมนเอสโตรเจนเหนี่ยวนำให้เกิดการแทรกของ Line-1 ดีเอ็นเอเข้าไปในจีโนมของเซลล์มะเร็งเต้านม, กุลจิรา โรจน์ไพศาลกิจ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในปัจจุบันโรคมะเร็งเต้านมเป็นมะเร็งที่มีอุบัติการณ์และอัตราการตายสูงที่สุดในเพศหญิงทั่วโลกรวมถึงในประเทศไทยด้วย โดยพบว่า LINE-1 ถือว่าเป็น Retrotransposon element ที่พบมากที่สุดถึง 17 % ของจีโนมและมีความสามารถในการคัดลอกแล้วแทรกตัวเองในบริเวณอื่นของจีโนมซึ่งเป็นสาเหตุของทำให้เกิดความไม่สเถียรของจีโนมอันเป็นสาเหตุของการเกิดโรคมะเร็ง และยังพบว่าการแสดงออกของLINE-1 มีผลต่อการลุกลาม, ขนาด, และระยะของโรคมะเร็งเต้านมด้วย นอกจากนี้ยังพบว่าผู้หญิงที่การได้รับฮอร์โมนเอสโตรเจนต่อเนื่องเป็นระยะเวลานานทำให้ความเสี่ยงของโรคมะเร็งเต้านมเพิ่มมากขึ้น ถึงกระนั้นยังคงไม่ทราบถึงกลไกการทำงานของเอสโตรเจน ดังนั้นผู้วิจัยจึงตั้งสมมุติฐานว่า เอสโตรเจนสามารถเหนี่ยวนำให้เกิดการแทรกของ LINE-1 ดีเอ็นเอเข้าไปในจีโนมของเซลล์มะเร็งเต้านม โดยทำการศึกษาบผลของเอสโตรเจนที่ได้รับต่อการแสดงออกของ LINE-1 mRNA และ LINE-1 โปรตีน รวมถึงผลของเอสโตรเจนที่ได้รับต่อการแทรกของ LINE-1 ดีเอ็นเอโดยวิเคราะห์จากปริมาณดีเอ็นเอในเซลล์มะเร็งเต้านม T47D ผลการทดลองพบว่าที่เอสโตรเจนมีผลทำให้การแสดงออกของ LINE-1 mRNA เพิ่มขึ้นสูงที่สุดอย่างมีนัยสำคัญที่ความเข้มข้นของเอสโตรเจน 5 nM ด้วยวิธี Realtime RT-PCR ในเซลล์มะเร็งเต้านม T47D และผลทำให้การแสดงออกของ LINE-1 mRNA เพิ่มขึ้นสูงที่สุดอย่างมีนัยสำคัญที่ 12 ชม. ด้วยวิธี Realtime RT-PCR ในเซลล์มะเร็งเต้านม T47D และเอสโตรเจนมีผลทำให้การแสดงออกของโปรตีน LINE-1 ORF1p เพิ่มขึ้นสูงที่สุดอย่างมีนัยสำคัญที่ 48 และ 72 ชม. ในเซลล์มะเร็งเต้านม T47D ด้วยวิธี Western blot โดยพบว่าการแสดงออกของโปรตีน LINE-1 ORF1p เพิ่มขึ้นสูงที่สุดอย่างมีนัยสำคัญ 72 ชม. นอกจากนี้เอสโตรเจนมีผลทำให้ปริมาณของ LINE-1 ดีเอ็นเอบริเวณ ORF2 เพิ่มขึ้นสูงที่สุดอย่างมีนัยสำคัญที่ 72 ชม.ในเซลล์มะเร็งเต้านม T47D มากที่สุดอย่างมีนัยสำคัญด้วยวิธี Real-time PCR แสดงให้เห็นว่าเอสโตรเจนมีผลทำให้เกิดการแทรกของ LINE-1 ดีเอ็นเอเข้าไปในจีโนมได้ จากผลการทดลองทั้งหมดจึงสรุปได้ว่า เอสโตรเจนมีผลต่อการเพิ่มแสดงออกของ LINE-1 mRNA และ LINE-1 โปรตีน รวมถึงเพิ่มปริมาณ LINE-1 ดีเอ็นเอ อย่างมีนัยสำคัญในเซลล์มะเร็งเต้านม T47D แสดงว่าเอสโตรเจนสามารถเหนี่ยวนำให้เกิดการแทรกของ LINE-1 ดีเอ็นเอเข้าไปในจีโนมนำไปสู่การกลายพันธุ์ของยีนและความไม่เสถียรของจีโนม

การตรวจหายีนดื้อยากลุ่ม Β-Lactams จากมูลสุกรโดยตรง ด้วยเทคนิค Multiplex Polymerase Chain Reaction ร่วมกับ Lateral Flow Assay และเทคนิค Nested Polymerase Chain Reaction, นุชบา สุจันทา

Chulalongkorn University Theses and Dissertations (Chula ETD)

สุกรเป็นอีกหนึ่งแหล่งกักเก็บเชื้อแบคทีเรียที่มียีนดื้อยาสร้างเอนไซม์ Extended-Spectrum β-Lactamases (ESBLs) ซึ่งสามารถแพร่กระจายยีนดื้อยามาสู่มนุษย์ สัตว์ และสิ่งแวดล้อมได้ ในการศึกษานี้จึงได้พัฒนาการตรวจหายีนสร้างเอนไซม์ ESBLs จากมูลสุกรโดยตรง ด้วยเทคนิค Multiplex polymerase chain reaction (mPCR), mPCR-Lateral flow assay (mPCR-LFA) และเทคนิค Nested PCR (nPCR) เปรียบเทียบกับการตรวจจากเชื้อ ESBL-producing Escherichia coli ซึ่งเพาะแยกจากมูลสุกรตัวอย่างเดียวกัน ด้วยเทคนิค PCR-sequencing เชื้อ ESBL-producing E. coli จำนวน 149 isolates ที่เพาะแยกจากมูลสุกรทั้งหมด 139 ตัวอย่าง พบความชุกของยีนชนิด blaCTX-M-15 (34.2%) มากที่สุด รองลงมาคือ blaCTX-M-15+TEM-1 (17.5%) และ blaCTX-M-55+TEM-1 (13.4%) โดยเชื้อ ESBL-producing E. coli ส่วนใหญ่มีการดื้อยาหลายขนาน โดยดื้อต่อยา Ceftriaxone (100%), Cefotaxime (100%), Ciprofloxacin (31.5%) และ Trimethoprim/Sulfamethoxazole (30.9%) ความไว ความจำเพาะ และขีดจำกัดของการตรวจจากเชื้อ ESBL-producing E. coli ด้วยเทคนิค mPCR-LFA เท่ากับ 100% และ 0.1-1 ng ตามลำดับ เทคนิค mPCR ที่พัฒนาขึ้น สำหรับตรวจจากมูลสุกรโดยตรง พบว่าต้องใช้ปริมาณเชื้อมากถึง 107 CFU จึงจะตรวจพบ โดยมีความไวในการตรวจยีน blaCTX-M และ blaTEM เท่ากับ 30.43% และ 87.65% ตามลำดับ ขีดจำกัดและความไวของการตรวจพบยีน blaCTX-M โดยตรง …

A Framework For Near-Real Time Monitoring Of Diversity Patterns Based On Indirect Remote Sensing, With An Application In The Brazilian Atlantic Rainforest, Andrea Paz, Thiago S. Silva, Ana C. Carnaval

Publications and Research

Monitoring biodiversity change is key to effective conservation policy. While it is difficult to establish in situ biodiversity monitoring programs at broad geographical scales, remote sensing advances allow for near-real time Earth observations that may help with this goal. We combine periodical and freely available remote sensing information describing temperature and precipitation with curated biological information from several groups of animals and plants in the Brazilian Atlantic rainforest to design an indirect remote sensing framework that monitors potential loss and gain of biodiversity in near-real time. Using data from biological collections and information from repeated field inventories, we demonstrate that …

A Horor Theory For Introspective Consciousness, Adriana Renero, Richard Brown

Publications and Research

Higher-order theories of consciousness typically account for introspection in terms of one’s higher-order thoughts being conscious, which would require a third-order thought—i.e., a thought about a thought about a mental state. In this work, we offer an alternative account of introspection that builds on the recent Higher-Order Representation of a Representation (HOROR) theory of phenomenal consciousness. According to HOROR theory, phenomenal consciousness consists in having the right kind of higher-order representation. We claim that this theory can be extended to introspection by recognizing that there is a distinctive kind of consciousness—i.e., introspective consciousness—which can be accounted for as the theory …