Knowledge about the pathogenesis and pathophysiology of chronic obstructive pulmonary disease (COPD) has advanced dramatically over the last 30 years. Unfortunately, this has had little impact in terms of new treatments. Over the same time frame, only one new class of medication for COPD has been introduced. Even worse, the rate at which new treatments are being developed is slowing. The development of new tools for the assessment of new treatments has not kept pace with understanding of the disease. In part, this is because drug development tools require a regulatory review, and no interested party has been in a position to undertake such a process. In order to facilitate the development of novel tools to assess new treatments, the Food and Drug Administration, in collaboration with the COPD Foundation, the National Heart Lung and Blood Institute and scientists from the pharmaceutical industry and academia conducted a workshop to survey the available information that could contribute to new tools. Based on this, a collaborative project, the COPD Biomarkers Qualification Consortium, was initiated. The Consortium in now actively preparing integrated data sets from existing resources that can address the problem of drug development tools for COPD.
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http://dx.doi.org/10.3109/15412555.2012.752807 | DOI Listing |
Lab Anim
January 2025
Kastamonu University, Faculty of Medicine, Department of Physiology, Kastamonu, Turkey.
Diabetes mellitus, characterized by insufficient insulin secretion and impaired insulin efficacy, disrupts carbohydrate, protein, and lipid metabolism. The global diabetic population is expected to double by 2025, from 380 million, posing a significant health challenge. Most diabetic individuals fall into the type 1 or type 2 categories, and diabetes adversely affects various organs, such as the kidneys, liver, nervous system, reproductive system, and eyes.
View Article and Find Full Text PDFCurr Med Chem
January 2025
Shree S K Patel College of Pharmaceutical Education and Research, Ganpat University, Mahesana, Gujarat, 384012, India.
Therapeutic hurdles persist in the fight against lung cancer, although it is a leading cause of cancer-related deaths worldwide. Results are still not up to par, even with the best efforts of conventional medicine, thus new avenues of investigation are required. Examining how immunotherapy, precision medicine, and AI are being used to manage lung cancer, this review shows how these tools can change the game for patients and increase their chances of survival.
View Article and Find Full Text PDFCurr Med Chem
January 2025
Department of Electronics & Communication Engineering, Jaypee University of Information Technology, Solan, H.P., India.
A planktonic population of bacteria can form a biofilm by adhesion and colonization. Proteins known as "adhesins" can bind to certain environmental structures, such as sugars, which will cause the bacteria to attach to the substrate. Quorum sensing is used to establish the population is dense enough to form a biofilm.
View Article and Find Full Text PDFAnn Transl Med
December 2024
Post-Graduation Department, Faculty of Medical Sciences of Minas Gerais, Belo Horizonte, Brazil.
Background And Objective: Sarcopenia, characterized by the progressive loss of skeletal muscle mass (MM) and muscle function, is a common and debilitating condition in cancer patients, significantly impacting their quality of life, treatment outcomes, and overall survival. The pathophysiology of sarcopenia is multifactorial, involving metabolic, hormonal, and inflammatory changes. Recent research highlights the role of chronic inflammation in the development and progression of sarcopenia, with pro-inflammatory cytokines being key mediators of muscle catabolism.
View Article and Find Full Text PDFSens Diagn
December 2024
Department of Bioengineering, Rice University Houston TX 77030 USA
CRISPR-Cas-based lateral flow assays (LFAs) have emerged as a promising diagnostic tool for ultrasensitive detection of nucleic acids, offering improved speed, simplicity and cost-effectiveness compared to polymerase chain reaction (PCR)-based assays. However, visual interpretation of CRISPR-Cas-based LFA test results is prone to human error, potentially leading to false-positive or false-negative outcomes when analyzing test/control lines. To address this limitation, we have developed two neural network models: one based on a fully convolutional neural network and the other on a lightweight mobile-optimized neural network for automated interpretation of CRISPR-Cas-based LFA test results.
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