Center for Applied Genomics

Applied Therapeutics to Present Baseline Data from Phase 3 ARISE-HF Study Evaluating AT-001 at the 2023 EASD Annual Meeting

Retrieved on: 
Thursday, September 28, 2023
Heart failure, Diabetes, Diabetic cardiomyopathy, Center for Applied Genomics, European Association for the Study of Diabetes, Doctor of Philosophy, EASD, MD, Patient, Risk, Symposium, Annual general meeting, Therapy, Disease, Pharmaceutical industry

NEW YORK, Sept. 28, 2023 (GLOBE NEWSWIRE) -- Applied Therapeutics, Inc. (Nasdaq: APLT), a clinical-stage biopharmaceutical company developing a pipeline of novel drug candidates against validated molecular targets in indications of high unmet medical need, today announced it will participate in a Symposium presentation, entitled Diabetic Cardiomyopathy (DbCM): a severe complication of diabetes, at the 59th European Association for the Study of Diabetes (EASD) Annual Meeting to take place October 2-6, 2023 in Hamburg, Germany and online.

Key Points: 
  • NEW YORK, Sept. 28, 2023 (GLOBE NEWSWIRE) -- Applied Therapeutics, Inc. (Nasdaq: APLT), a clinical-stage biopharmaceutical company developing a pipeline of novel drug candidates against validated molecular targets in indications of high unmet medical need, today announced it will participate in a Symposium presentation, entitled Diabetic Cardiomyopathy (DbCM): a severe complication of diabetes, at the 59th European Association for the Study of Diabetes (EASD) Annual Meeting to take place October 2-6, 2023 in Hamburg, Germany and online.
  • “Patients with diabetes are at a high risk of developing overt heart failure even when optimal glucose control is achieved,” said Riccardo Perfetti, MD, PhD, Chief Medical Officer at Applied Therapeutics.
  • “Diabetic Cardiomyopathy (DbCM) is a severe disease affecting approximately 20% of patients with diabetes.
  • The Phase 3 ARISE-HF study is investigating the potential benefit of AT-001 in treating DbCM, and we look forward to the data readout later this year.”

CHOP and NJIT Researchers Develop New Tool for Studying Multiple Characteristics of a Single Cell

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Wednesday, December 21, 2022
Online, Hospital, Protein, NJIT, Philadelphia, U.S. News & World Report, Carnegie Classification of Institutions of Higher Education, Mutation, Cell, RNA, Student, New Jersey Institute of Technology, National Institutes of Health, News, Classification, Children's Hospital of Philadelphia, B cell, Organelle, Spanish National Center for Biotechnology, Health, National Science Foundation, National Center for Advancing Translational Sciences, Gene expression, XSEDE, Prussia, The Princeton Review, CHOP, Center for Applied Genomics, Doctor of Philosophy, TeraGrid, Software, Hakon Hakonarson, Research, Genomics, University, Machine learning, Vaccine, Mobile phone, Management, MD

PHILADELPHIA, Dec. 21, 2022 /PRNewswire/ -- Researchers from Children's Hospital of Philadelphia (CHOP) and New Jersey Institute of Technology (NJIT) developed new software that integrates a variety of information from a single cell, allowing researchers to see how one change in a cell can lead to several others and providing important clues for pinpointing the exact causes of genetic-based diseases.

Key Points: 
  • Single-cell sequencing allows researchers to look at specific aspects of a cell to determine how it interacts with its microenvironment.
  • At the single- cell level, researchers can study gene expression as well as messenger RNA, proteins and even organelles within the cells in much greater detail and resolution than before.
  • The software, referred to as single-cell multimodal deep clustering (scMDC), uses machine learning to analyze data about different characteristics of a single cell.
  • The researchers conducted extensive simulation and real-data experiments and found that scMDC outperformed existing single cell single-modal and multimodal clustering methods on single-cell multimodal data sets.

Agendia Level 1B Evidence Shows MammaPrint® is the First and Only FDA-cleared Gene Expression Profiling Test to Predict Benefit from Extended Endocrine Therapy in Early-Stage Breast Cancer Patients

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Thursday, December 8, 2022
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Agendia, Inc. , a leader in gene expression profiling for early-stage breast cancer, today announced it will present late-breaking research at the 2022 San Antonio Breast Cancer Symposium (SABCS) that proves MammaPrint is the first FDA-cleared gene expression profiling test to predict an early-stage breast cancer patients benefit from extended endocrine therapy (EET).

Key Points: 
  • Agendia, Inc. , a leader in gene expression profiling for early-stage breast cancer, today announced it will present late-breaking research at the 2022 San Antonio Breast Cancer Symposium (SABCS) that proves MammaPrint is the first FDA-cleared gene expression profiling test to predict an early-stage breast cancer patients benefit from extended endocrine therapy (EET).
  • Additionally, MammaPrint helps identify those who will realize minimal to no benefit, offering patients the power of choice when discussing their treatment plan with their provider.
  • MammaPrint test results have now been used in level 1B evidence for extended endocrine treatment decisions, providing high confidence of its net benefit that providers and patients can rely on when making EET treatment decisions.
  • MammaPrint is a 70-gene prognostic test that, along with other clinicopathologic factors, determines a specific patients breast cancer recurrence risk.

CHOP Researchers Develop Algorithm to Help Distinguish ADHD Symptoms from Related Conditions

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Wednesday, July 13, 2022
Method, Phenotype, Hospital, EHR, Anxiety, Learning, Slaby, Health information management, Center, Sleep, NHGRI, Cohort study, Children's Hospital of Philadelphia, Lists of diseases, Prussia, Genomics, Electronic, Autism, Attention deficit hyperactivity disorder predominantly inattentive, Patient, National Human Genome Research Institute, CHOP, Laika (EHR testing framework), Health, Center for Applied Genomics, Philadelphia, ADHD, CAG, Neurodevelopmental disorder, Research, Doctor of Philosophy, Pharmaceutical industry, Child, MD

PHILADELPHIA, July 13, 2022 /PRNewswire/ -- Researchers from the Center for Applied Genomics (CAG) at Children's Hospital of Philadelphia (CHOP) have developed an algorithm that uses existing electronic health records to distinguish patients with attention-deficit/hyperactivity disorder (ADHD) alone versus patients who present with ADHD and a variety of related comorbid conditions. This method could help reduce the diagnostic odyssey that many patients with this condition face and help direct them toward more personalized treatment options. The findings were recently published in the journal Neurodevelopmental Disorders.

Key Points: 
  • This method could help reduce the diagnostic odyssey that many patients with this condition face and help direct them toward more personalized treatment options.
  • Because ADHD comes in three different types and exists on a spectrum, it can be difficult to diagnose.
  • To better distinguish these groups of patients, researchers developed an algorithm using existing electronic health records (EHR) to help distinguish ADHD from other related psychiatric disorders.
  • Slaby et al, "An electronic health record (EHR) phenotype algorithm to identify patients with attention defcit hyperactivity disorders (ADHD) and psychiatric comorbidities."

CHOP Researchers Use Deep Learning to Find Genetic Causes of Mental Health Disorders in Frequently Understudied African American Population

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Tuesday, February 1, 2022
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A new study from researchers at Children's Hospital of Philadelphia (CHOP) shows that a deep learning model has promising accuracy when helping to diagnose a variety of common mental health disorders in African American patients.

Key Points: 
  • A new study from researchers at Children's Hospital of Philadelphia (CHOP) shows that a deep learning model has promising accuracy when helping to diagnose a variety of common mental health disorders in African American patients.
  • Properly diagnosing mental disorders can be challenging, especially for young toddlers who are unable to complete questionnaires or rating scales.
  • Past genomic research has found several genomic signals for a variety of mental disorders, with some serving as potential therapeutic drug targets.
  • Liu et al, "Application of Deep Learning Algorithm on Whole Genome Sequencing Data Uncovers Structural Variants Associated with Multiple Mental Disorders in African American Patients."

PerkinElmer Launches NEXTFLEX Variant-Seq SARS-CoV-2 Kit v2 to Strengthen SARS-CoV-2 Surveillance

Retrieved on: 
Wednesday, December 8, 2021
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Using positive SARS-CoV-2 samples, the NEXTFLEX Variant-Seq SARS-CoV-2 Kit v2 detects mutations associated with all SARS-CoV-2 variants, including the latest Variant of Concern (VOC) identified by the World Health Organization, Omicron (B.1.1.529).

Key Points: 
  • Using positive SARS-CoV-2 samples, the NEXTFLEX Variant-Seq SARS-CoV-2 Kit v2 detects mutations associated with all SARS-CoV-2 variants, including the latest Variant of Concern (VOC) identified by the World Health Organization, Omicron (B.1.1.529).
  • The emergence of the Omicron variant has reaffirmed the important role that SARS-CoV-2 surveillance will play in ending the COVID-19 pandemic, said James Atwood, general manager of Applied Genomics, PerkinElmer.
  • The NEXTFLEX Variant-Seq SARS-CoV-2 Kit v2 workflow was specifically designed to meet the needs of the surveillance community by increasing speed from sample to result, increasing throughput and reducing workflow complexity.
  • Additionally, the NEXTFLEX Variant-Seq SARS-CoV-2 Kit v2 uses 1,536 Unique Dual Index (UDI) barcodes to enable the sequencing of 6,000 SARS-CoV-2 libraries in one flow cell.

Using Artificial Intelligence to Diagnose ADHD

Retrieved on: 
Wednesday, November 10, 2021
Intelligence, ADHD, Publishing, Center for Applied Genomics, CNV, Drug resistance, Experimental Biology and Medicine, Center, Method, Hospital, Experimental biology, Society, Artificial intelligence, Lists of diseases, Attention deficit hyperactivity disorder predominantly inattentive, AI, Children's Hospital of Philadelphia, Deep learning, CAG, Neoplasm, Genome, GLOBE, Child, Attention deficit hyperactivity disorder, Diagnosis, European Americans, Patient, Society for Experimental Biology and Medicine, Mental disorder, Medical device

Attention-deficit hyperactivity disorder (ADHD) is a common psychiatric disorder in children that causes lifelong impairments.

Key Points: 
  • Attention-deficit hyperactivity disorder (ADHD) is a common psychiatric disorder in children that causes lifelong impairments.
  • Accurate diagnosis of ADHD is impaired by disease heterogeneity as well as the long delay between symptom onset and diagnosis.
  • Artificial intelligence models have been used to analyze whole genome sequences from large numbers of patients and successfully predict drug resistance as well as classify tumors.However, these models have not been applied to the diagnosis of ADHD.
  • In this study, Dr. Liu and colleagues performed whole genome sequencing on 524 African American individuals, including 116 ADHD patients and 408 healthy controls.

Invitae Study of 143,000 Patients Shows Importance of Routinely Including Deletion and Duplication Detection with Sequencing in Genetic Testing

Retrieved on: 
Wednesday, June 13, 2018
Biology, Medicine, Genetics, Medical genetics, Copy-number variation, Genomics, Human evolution, Molecular biology, Genetic testing, Center for Applied Genomics, Causes of schizophrenia

The study found that these changes, also called exonic copy number variants (CNVs), are present in a substantial number of patients and suggested that CNV testing should be universally used in clinical genetic testing.

Key Points: 
  • The study found that these changes, also called exonic copy number variants (CNVs), are present in a substantial number of patients and suggested that CNV testing should be universally used in clinical genetic testing.
  • Ten percent of patients who received a report of a potentially disease-causing genetic change had CNVs.
  • Sensitive and reliable CNV testing is not a routine part of genetic testing for all genes at all clinical laboratories.
  • The large number of CNVs examined in this study provides novel insights into the biology underlying these genetic variants.