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Precision Medicine

Precission Medicine: Harnessing the Power of Personalized Healthcare.
Precision medicine is an innovative approach that tailors medical treatment to the individual characteristics of each patient.
By leveraging advanced technologies and data analysis, this field aims to optimize disease prevention, diagnosis, and treatment for improved patient outcomes.
Through the integration of genomics, molecular profiling, and cutting-edge therapies, precision medicine empowers healthcare providers to deliver more targeted, effective, and efficient care.
This holistic, patient-centered model holds great promise in revolutionizing the management of a wide range of diseases, from cancer to rare genetic disorders.

Most cited protocols related to «Precision Medicine»

Our analysis of the three Precision Medicine and Public Health course components resembles a “nested case-control design”, which situates a case-control study in the context of a cohort study [28 ]. Here, we nest a brief qualitative study of the student responses in the Precision Medicine and Public Health case study and class exercise between the above narrative description of previous course elements touching on precision health, and an examination of the precision medicine and public health topics covered and student comments rendered in the Precision Medicine Initiative didactic session. The two aims of this analysis are the following: (1) show how precision health, the PMI in particular, can be incorporated into a public health genetics policy course; and (2) assess the extent to which this inclusion has allowed our students to formulate policy in the precision public health area.
Knowing that precision public health was a new area with lasting implications, the course instructors took extensive notes on student–instructor and student–student dialogue during the three course components. Verbatim records of student presentations are kept during class exercises to enable accurated grading. The instructors have inspected and analyzed for issues and policy content relevant data for the three Precision Medicine Initiative-related sessions: (1) class notes containing what each participant said—for all three sessions; (2) student hand-ins for the class exercise; and (3) the topical areas covered in the class materials of the didactic session. The didactic session topical areas were inspected, following analysis of the case study and class exercise, for content that reinforces and extends the themes elicited by the case study and class exercise, and illustrative student comments.
In the analysis, student responses for the case study and class exercise, both oral and written, were categorized into thematic areas. The pre-categories were formed by assorting themes in the ten shared case study/class exercise questions (page 3 of the case study and class exercise hand-outs) into issue and policy areas. The post-categories were formed by manually labeling sentences in the case study and class exercise student responses for major themes. Categories were cross-checked by the three authors. The authors recorded frequency of mentions in the pre-and post-categories for the following: (1) case study student responses; (2) in-class exercise student responses; and (3) class exercise hand-ins, which were used to select exemplary quotations within the major categories. In keeping with the course policy orientation, we have analyzed student responses to determine our students’ ability to formulate issue areas into policy, that is, to satisfy aim 2, rather than to systematically explore the various categories of responses. We also include student comments on the Precision Medicine and Public Health class exercise, collected as part of the overall end-of-semester course evaluation, to show whether students felt the class exercise was useful and what changes could be made in the future. The evaluation did not include questions on the case study and didactic session.
In writing this piece, the authors have inspected the precision medicine and precision public health technical, program-related, and socio-ethical literature we collected at the time of the 2015/2016 classes, and supplemented this inspection with additional current PubMed and NIH website searches. The PMI has evolved into the NIH All of Us Research Program, which began beta testing in June 2017 and had a full national roll-out of the cohort-based program and extensive provider network in Spring 2018. This article is written from the standpoint of what has taken place in the national program as of June 2018.
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Publication 2018
Feelings Precision Medicine Student Wellness Programs
We performed DNA extraction using a Chemagic 360 (PerkinElmer) with the use of the low-volume kit CMG-1491 and the buffy-coat kit CMG-714 (Chemagen), respectively. For genotyping, we used the Global Screening Array (GSA), version 2.0 (Illumina), which contains 712,189 variants before quality control. Details on genotyping and quality-control procedures are provided in the Supplementary Methods section in Supplementary Appendix 1. To maximize genetic coverage, we performed single-nucleotide polymorphism (SNP) imputation on genome build GRCh38 using the Michigan Imputation Server and 194,512 haplotypes generated by the Trans-Omics for Precision Medicine (TOPMed) program (freeze 5).16 After the exclusion of samples during quality control (the majority of which were due to population outliers; see the Supplementary Methods section and Table S1B and S1C), the final case–control data sets comprised 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain. A total of 8,965,091 SNPs were included in the Italian cohort and 9,140,716 SNPs in the Spanish cohort.
Publication 2020
Birth Freezing Genome Haplotypes Hispanic or Latino Patients Precision Medicine Single Nucleotide Polymorphism

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Publication 2014
Kidney Injury, Acute Patients Precision Medicine
The confirmation human brain proteomes were profiled from the dPFC of post-mortem brain samples from 198 participants of European descent recruited by the Banner Sun Health Research Institute (Banner). Participants in this study were recruited from the retirement communities in the greater Phoenix, Arizona, USA. All enrolled participants or their legal representatives signed an informed consent and the study was approved by the Institutional Review Board of Banner Sun Health Research Institute. Participants consented to annual standardized medical, neurological, and neuropsychological testing. Research diagnoses were made using approved research guidelines and a final clinicopathological diagnosis was made after review of all clinical, medical records, and neuropathological findings6 (link). Only subjects with a final diagnosis of normal cognition or AD were included in the proteomic analysis. Proteomic profiling was performed using the same approach as described above for the discovery proteomes with two differences: only MS2 scans were obtained and MS2 spectra were searched against the UniProtKB human brain proteome database downloaded in April 2015. Due to different databases, exact Uniprot IDs were used when comparing the discovery and confirmation results. In total, there were 11,518 proteins quantified. We applied the same quality control procedure as was done in the discovery proteomic dataset to the confirmation proteomic data. Likewise, we used regression to remove the effects of proteomic sequencing batch, age, sex, post-mortem interval, and final clinical diagnosis of cognitive status from the confirmatory proteomic profiles before estimating the protein weights.
Genotyping was performed using the Affymetrix Precision Medicine Array using DNA extracted from the brain with the Qiagen GenePure kit. We applied the same approach to quality control as described for the discovery dataset, including removing individuals based on data completeness or relatedness, removing sites with evidence of deviation from Hardy Weinberg equilibrium, missingness above 5%, minor allele frequency below 1%, or are not a SNP. Genotyping was imputed to the 1000 Genome Project Phase 326 (link) using the Michigan Imputation Server27 (link). SNPs with imputation R2> 0.3 were retained. Finally, only sites included in the linkage disequilibrium reference panel were used in our confirmation PWAS, as recommended by the FUSION pipeline. After quality control, there were 152 subjects with both proteomic and genetic data to include in our confirmation analyses.
Publication 2021
Autopsy Brain Cognition Diagnosis Ethics Committees, Research Europeans Genome Homo sapiens Precision Medicine Proteins Proteome Radionuclide Imaging Single Nucleotide Polymorphism
The training set was a subset of CMap consisting of gene-expression data and known DILI status for 190 small molecules (130 of which had been found to cause DILI in patients). The test set consisted of an additional 86 small molecules. The CMap gene-expression data were generated using Affymetrix gene-expression microarrays. In Phase I, we used the Single Channel Array Normalization (SCAN) algorithm [14 (link)]—a single-sample normalization method—to process the individual CEL files (raw data), which we downloaded from the CMap website (https://portals.broadinstitute.org/cmap/). As part of the normalization process, we used BrainArray annotations to discard faulty probes and to summarize the values at the gene level (using Entrez Gene identifiers) [15 (link)]. We wrote custom Python scripts (https://python.org) to summarize the data and execute analytical steps. The scripts we used to normalize and prepare the data can be found here: https://osf.io/v3qyg/.
For each treatment on each cell line, CMap provides gene-expression data for multiple biological replicates of vehicle-treated cells. For simplicity, we averaged gene-expression values across the multiple vehicle files. We then subtracted these values from the corresponding gene expression values for the compounds of interest. Finally, we merged the vehicle-adjusted data into separate files for MCF7 and PC3, respectively.
The SCAN algorithm is designed for precision-medicine workflows in which biological samples may arrive serially and thus may need to be processed one sample at a time [14 (link)]. This approach provides logistical advantages and ensures that the data distribution of each sample is similar, but it does not attempt to adjust for systematic differences that may be observed across samples. Therefore, during Phase II, we generated an alternative version of the data, which we normalized using the FARMS algorithm [16 (link)]—a multi-sample normalization method. This enabled us to evaluate whether the single-sample nature of the SCAN algorithm may have negatively affected classification accuracy in Phase I. Irrespective of normalization method, it is possible that batch effects can bias a machine-learning analysis. Indeed, the CMap data were processed in many batches. Therefore, for SCAN and FARMS, we created an additional version of the expression data by adjusting for batch effects using the ComBat algorithm [17 (link)].
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Publication 2020
Biopharmaceuticals Cell Lines Cells Gene Expression Genes MCF-7 Cells Microarray Analysis Patients Precision Medicine Python

Most recents protocols related to «Precision Medicine»

Example 7

To perform PLA with PDX samples, the glioblastoma patient derived FFPE samples were used (provided by Samsung Seoul hospital in Seoul, Korea). After FFPE sample were de-paraffinized and performed heat induced antigen retrieval for 15 minutes at 100° C. Slides were blocked with blocking solution provided by Duolink and incubated with rabbit anti-CXCR4 (1:200, Thermoscientific, PA3305), mouse anti-ADRB2 (1:200, Santacruz, Sc-271322), at 37° C. for 1 h in a humidifying chamber. The other process was same as described above (PLA with PDC).

In the FIG. 15A, nuclei were visualized with DAPI staining, and CXCR4-ADRB4 heteromers were stained with PLA as small dots. As shown in FIG. 15B, PLA ratio is different according to the patient and based on this result, indicating that it is possible to perform personalized medicine by the companion diagnostics.

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Patent 2024
ADRB2 protein, human Antigens Cell Nucleus Companions CXCR4 protein, human DAPI Diagnosis Glioblastoma Mus Patients Precision Medicine Rabbits
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Example 9

These include any type of insulin pumps, chemotherapy pumps, pain killers-administration pump, intrathecal pumps, diuretic pumps, and alike, which work via a closed-loop integrating into their operating systems methods or algorithms which incorporate personalized-variability pattern(s).

Managing patients with severe insulin resistance is challenging because it is difficult to achieve good glycemic control using conventional treatment approaches. The algorithm identifies patterns of variability in glucose levels in each subject and implement them into a treatment schedule of the basal-bolus insulin pump which alters the regularity of the dosages and intervals between dosages, as a method for alleviating resistance and reducing overall insulin requirement. For pain-killer pumps, the goal is reducing the amount of painkillers administered to the patient by implementing inherent variability patterns into the treatment algorithm.

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Patent 2024
Analgesics Diuretics Glucose Glycemic Control Insulin Insulin Resistance Management, Pain Pain Patients Pharmacotherapy Precision Medicine Treatment Protocols
The Taiwan Precision Medicine Initiative (TPMI) is a collaborative plan involving clinical research between Academia Sinica and top medical centers in Taiwan for the establishment of a large and comprehensive nationwide population-based database of genetic and clinical information, specific for the Han Chinese population (https://tpmi.ibms.sinica.edu.tw/www/en/). Our study population was obtained from TPMI participants recruited by Taichung Veterans General Hospital (TCVGH). The study was approved by the Institutional Review Boards of TCVGH (IRB number: SF19153A). Informed consents were obtained from each participant. Forty-two thousand, six-hundred sixty-eight (42,668) participants possessing genotype data were included in our study. Among these participants, the patients with a diagnosis of AMD were identified according to the International Classification of Diseases, ninth Revision, Clinical Modification (ICD-9-CM) codes 362.51 (non-exudative AMD) and 362.52 (exudative AMD). The electronic medical records for each patient were reviewed by ophthalmologists for confirmation of AMD. The control cohort was identified from the patients without AMD or other eye diseases (ICD-9-CM: 360.xx—369.xx). For matching between AMD case and non-AMD controls to achieve the subset selection of control cohorts, 10 matched controls were randomly selected for each patient with AMD according to gender and age (case-control matching ratio of 1:10) using the R package MatchIt (Ho et al., 2011 (link)). Nearest neighbor matching was implemented without replacement in MatchIt (method = “nearest”). The AMD case-control population was used for our study.
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Publication 2023
Chinese Diagnosis Eye Disorders Genotype Myositis, Inclusion Body Ophthalmologists Patients Precision Medicine
Metastatic melanoma patients carrying a BRAFV600 mutation can be treated with targeted therapies (BRAF and MEK inhibitors) but resistance occurs. Predicting patient response to targeted therapies is crucial to guide clinical decision, since these patients may also be directed to first‐line immunotherapy. Mouse patient‐derived xenograft (PDX) models are incompatible with personalized medicine approaches because of their long timeframe.
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Publication 2023
BRAF protein, human Heterografts Immunotherapy inhibitors Melanoma Mus Mutation Patients Precision Medicine
We provide proof‐of‐concept that the AVI‐PDXTM models the diversity of responses of melanoma patients to BRAFi/MEKi, within days, hence positioning it as a valuable tool for the design of personalized medicine assays.
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Publication 2023
Biological Assay Melanoma Patients Precision Medicine

Top products related to «Precision Medicine»

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The HiSeq 2000 is a high-throughput DNA sequencing system designed by Illumina. It utilizes sequencing-by-synthesis technology to generate large volumes of sequence data. The HiSeq 2000 is capable of producing up to 600 gigabases of sequence data per run.
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The HiSeq 4000 is a high-throughput sequencing system designed for generating large volumes of DNA sequence data. It utilizes Illumina's proven sequencing-by-synthesis technology to produce accurate and reliable results. The HiSeq 4000 has the capability to generate up to 1.5 terabytes of data per run, making it suitable for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis.
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The HiSeq X system is a high-throughput DNA sequencing platform developed by Illumina. It is designed to perform large-scale genomic sequencing projects. The HiSeq X system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality DNA sequencing data.
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AMPure XP beads are a magnetic bead-based product used for the purification of nucleic acids, such as DNA and RNA, from various samples. The beads are designed to selectively bind to nucleic acids, allowing for the removal of contaminants and unwanted molecules during the purification process. The core function of AMPure XP beads is to provide an efficient and reliable method for the cleanup and concentration of nucleic acids in preparation for downstream applications.
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
The Axiom Precision Medicine Diversity Array is a high-throughput, comprehensive genotyping platform designed for genetic research. It provides a broad coverage of genetic variants associated with diverse populations and precision medicine applications.
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The TapeStation is a laboratory instrument that provides automated analysis of DNA and RNA samples. It measures the size, concentration, and integrity of nucleic acid samples in a quick and efficient manner.
The GenePure kit is a laboratory equipment product designed for the purification of genetic material, such as DNA and RNA, from various biological samples. It provides a reliable and efficient method for extracting and purifying nucleic acids for further analysis and experimentation.
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The 2100 Bioanalyzer is a lab equipment product from Agilent Technologies. It is a microfluidic platform designed for the analysis of DNA, RNA, and proteins. The 2100 Bioanalyzer utilizes a lab-on-a-chip technology to perform automated electrophoretic separations and detection.
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The Axiom Precision Medicine Research Array is a high-density genotyping array designed for comprehensive, genome-wide analysis. It provides comprehensive coverage of genetic variants associated with a wide range of diseases and traits, enabling researchers to conduct genetic association studies.

More about "Precision Medicine"

Precision medicine, personalized healthcare, and individualized therapy are innovative approaches that leverage advanced technologies and data analysis to optimize disease prevention, diagnosis, and treatment for improved patient outcomes.
This holistic, patient-centered model aims to deliver more targeted, effective, and efficient care by integrating genomics, molecular profiling, and cutting-edge therapies.
Key aspects of precision medicine include the use of high-throughput sequencing platforms like the HiSeq 2000, HiSeq 4000, and HiSeq X system to analyze genetic and molecular data.
Purifcation and quality control techniques, such as AMPure XP beads, RNeasy Mini Kit, and the 2100 Bioanalyzer, play a crucial role in sample preparation.
Specialized arrays, like the Axiom Precision Medicine Diversity Array and Axiom Precision Medicine Research Array, enable comprehensive genotyping and analysis of genetic variants linked to disease risk and drug response.
By harnessing the power of these advanced technologies and tools, precision medicine empowers healthcare providers to deliver more personalized and effective interventions.
This paradigm shift holds great promise in revolutionizing the management of a wide range of diseases, from cancer and rare genetic disorders to complex chronic conditions.
With the aid of intelligent platforms like PubCompare.ai, researchers and clinicians can easily locate cutting-edge protocols and leverage AI-powered comparisons to identify the optimal approaches for their precision medicine research and clinical practice.