Early Diagnosis of Alzheimer's Disease by NIRF Spectroscopy\ud and Nuclear Medicine\ud

Novel approaches to Early Diagnosis of Alzheimer's Disease by NIRF Spectroscopy and Nuclear Medicine are presented and related cognitive, as well as molecular and cellular, models are critically evaluated.\u

Early Diagnosis of Alzheimer's disease by NIRF Spectroscopy and Nuclear Medicine-v.4.0

There is an urgent need for the early detection of diseases such as Alzheimer’s (AD) and Cancers in order to enable their successful treatment. Cancer is the second major cause of death after Heart Disease, and AD is the third major cause of death with major, human and financial/economics trillion dollar consequences for the society. Nuclear Medicine is concerned with applications in Medicine of Nuclear Science and Engineering techniques and knowledge. Three major Nuclear Medicine techniques that are established for diagnostic and research purposes are: Positron Emission Tomography (PET) and CAT/CT, Nuclear Magnetic Resonance Imaging (NMRI/MRI). However, these three techniques have also major limitations in terms of either cost or image resolution, as well as patient irradiation in the case of CAT/CT and PET. On the other hand, Near Infrared Chemical Imaging Microspectroscopy and certain Fluorescence spectroscopic techniques are capable of single cancer cell and/or single molecule detection and/or imaging. Such powerful capabilities, combined with low cost of diagnostics, make these novel techniques very attractive means for early detection of diseases such as cancer and Alzheimer’s, that are promising to reduce the fatality rate of patients through adequate diagnosis and treatment of such diseases at early stages. 
Currently NIH provides only inadequate funding for the clinical and research aspects of these novel investigation and clinical diagnostic techniques by FT-NIRS and Fluorescence spectrocopy for early detection of Alzheimer’s and Cancers.
&#xa

Early Diagnosis of Alzheimer's disease by NIRF Spectroscopy and Nuclear Medicine

There is an urgent need for the early detection of diseases such as Alzheimer’s (AD) and Cancers in order to enable their successful treatment. Cancer is the second major cause of death after Heart Disease, and AD is the third major cause of death with major, human and financial/economics trillion dollar consequences for the society. Nuclear Medicine is concerned with applications in Medicine of Nuclear Science and Engineering techniques and knowledge. Three major Nuclear Medicine techniques that are established for diagnostic and research purposes are: Positron Emission Tomography (PET) and CAT/CT, Nuclear Magnetic Resonance Imaging (NMRI/MRI). However, these three techniques have also major limitations in terms of either cost or image resolution, as well as patient irradiation in the case of CAT/CT and PET. On the other hand, Near Infrared Chemical Imaging Microspectroscopy and certain Fluorescence spectroscopic techniques are capable of single cancer cell and/or single molecule detection and/or imaging. Such powerful capabilities, combined with low cost of diagnostics, make these novel techniques very attractive means for early detection of diseases such as cancer and Alzheimer’s, that are promising to reduce the fatality rate of patients through adequate diagnosis and treatment of such diseases at early stages. 
Currently NIH provides only inadequate funding for the clinical and research aspects of these novel investigation and clinical diagnostic techniques by FT-NIRS and Fluorescence spectrocopy for early detection of Alzheimer's and Cancers

The Foundational Model of Anatomy Ontology

Anatomy is the structure of biological organisms. The term also denotes the scientific discipline devoted to the study of anatomical entities and the structural and developmental relations that obtain among these entities during the lifespan of an organism. Anatomical entities are the independent continuants of biomedical reality on which physiological and disease processes depend, and which, in response to etiological agents, can transform themselves into pathological entities. For these reasons, hard copy and in silico information resources in virtually all fields of biology and medicine, as a rule, make extensive reference to anatomical entities. Because of the lack of a generalizable, computable representation of anatomy, developers of computable terminologies and ontologies in clinical medicine and biomedical research represented anatomy from their own more or less divergent viewpoints. The resulting heterogeneity presents a formidable impediment to correlating human anatomy not only across computational resources but also with the anatomy of model organisms used in biomedical experimentation. The Foundational Model of Anatomy (FMA) is being developed to fill the need for a generalizable anatomy ontology, which can be used and adapted by any computer-based application that requires anatomical information. Moreover it is evolving into a standard reference for divergent views of anatomy and a template for representing the anatomy of animals. A distinction is made between the FMA ontology as a theory of anatomy and the implementation of this theory as the FMA artifact. In either sense of the term, the FMA is a spatial-structural ontology of the entities and relations which together form the phenotypic structure of the human organism at all biologically salient levels of granularity. Making use of explicit ontological principles and sound methods, it is designed to be understandable by human beings and navigable by computers. The FMA’s ontological structure provides for machine-based inference, enabling powerful computational tools of the future to reason with biomedical data

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously.Comment: Accepted In The FEBS OPEN BIO (Q2, SCOPUS, SCIE, IF: 2.6, CS: 4.7), Wiley Journal, On FEB 25, 202

Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1–22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases

Generator breast datamart\u2014the novel breast cancer data discovery system for research and monitoring: Preliminary results and future perspectives

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into \u201cNot organized, not \u2018ontologized\u2019 data\u201d, \u201cOrganized, not \u2018ontologized\u2019 data\u201d, and \u201cOrganized and \u2018ontologized\u2019 data\u201d. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

A regulatory mutant on TRIM26 conferring the risk of nasopharyngeal carcinoma by inducing low immune response.

The major histocompatibility complex (MHC) is most closely associated with nasopharyngeal carcinoma (NPC), but the complexity of its genome structure has proven challenging for the discovery of causal MHC loci or genes. We conducted a targeted MHC sequencing in 40 Cantonese NPC patients followed by a two-stage replication in 1065 NPC cases and 2137 controls of Southern Chinese descendent. Quantitative RT-PCR analysis (qRT-PCR) was used to detect gene expression status in 108 NPC and 43 noncancerous nasopharyngeal (NP) samples. Luciferase reporter assay and chromatin immunoprecipitation (ChIP) were used to assess the transcription factor binding site. We discovered that a novel SNP rs117565607_A at TRIM26 displayed the strongest association (OR = 1.909, Pcombined = 2.750 × 10-19 ). We also observed that TRIM26 was significantly downregulated in NPC tissue samples with genotype AA/AT than TT. Immunohistochemistry (IHC) test also found the TRIM26 protein expression in NPC tissue samples with the genotype AA/AT was lower than TT. According to computational prediction, rs117565607 locus was a binding site for the transcription factor Yin Yang 1 (YY1). We observed that the luciferase activity of YY1 which is binding to the A allele of rs117565607 was suppressed. ChIP data showed that YY1 was binding with T not A allele. Significance analysis of microarray suggested that TRIM26 downregulation was related to low immune response in NPC. We have identified a novel gene TRIM26 and a novel SNP rs117565607_A associated with NPC risk by regulating transcriptional process and established a new functional link between TRIM26 downregulation and low immune response in NPC