Diagnostic errors are common and have seemed intractable for decades. Although time pressure is often cited as a contributor to this problem, overconfidence on the part of the diagnostician may also play a role. Successful strategies for reducing error in the diagnostic process should include patients and families in innovative ways. Changing the existing paradigm of patients responding passively to one of patients participating actively has the potential to assist in achieving greater diagnostic accuracy. Providers should welcome patients' online research into their symptoms, succinct summaries of their course of illness and questions about the differential diagnoses that might be applicable. Systematic methods for following up after the initial diagnosis are essential for verifying accuracy as well as providing excellent patient care.
The quality of drug products in the United States, which are largely produced overseas, has been a matter of growing concern. Buyers and payers of pharmaceuticals, whether they are health-systems, insurers, PBMs, pharmacies, physicians, or patients, have little to no visibility into any quality metrics for the manufacturers of drug products or the products themselves. A system of quality scores is proposed to enable health-systems and other purchasers and payers of medication to differentiate among drug products according to evidence-based metrics. Metrics influencing the quality scores described herein include both broadly applicable regulatory information and more drug-specific, third-party chemical analysis information. The aggregation of these metrics through a proposed set of rules results in numerical values on a 0-100 scale that may be further simplified into a red/yellow/green designation. The simplicity of such scores enables seamless integration into existing healthcare systems and an integration scheme is proposed. Using real-world data from currently on-market valsartan drug products, this proposed system generated a variety of quality scores for six major manufacturers. These scores were further evaluated according to their current market price showing no significant correlation between quality score and price. The implementation of drug quality scores at healthcare institutions in the United States and their potential utilization by regulators, could create a much-needed, market-driven incentive for pharmaceutical manufacturers to produce quality medications that would reduce drug shortages and improve public health.
Abstract.-Stable free radicals have been prepared from purified plasma proteins, pituitary peptides, and simpler related structures like 5-OH tryptophan and melatonin by oxidation with the free-radical nitrosyl disulfonate in alkaline solution under controlled conditions. The presence of tyrosine or tryptophan amino acid residues in the protein was found essential for free-radical formation. These red-colored, stable free radicals showed electron spin resonance spectra in aqueous solutions at room temperature and maintained this characteristic for weeks when stored at 50C. Illumination, by visible light, of the free-radical proteins and peptides separated from excess nitrosyl disulfonate by salt fractionation or chromatography enhanced the free-radical concentration in the light. The increased signal decayed in the dark.Intravenous administration of the free-radical proteins or peptides into rabbits equipped with chronic cranial electrodes and sedated with a small dose of pentobarbital caused a sudden EEG arousal accompanied by behavioral changes indicative of brain excitation. Illumination of the free-radical compounds prior to administration enhanced the effects. Untreated control proteins or peptides had no effects. The observations are interpreted to suggest the involvement of free-radical structures in the transfer of energy in nervous tissue.Introduction.-In the chemical reactions of living systems, free energy is exchanged by the transformation of electronic energy that is present in chemical bonds. In photobiology and radiobiology, electron-excited states participate in the transformation reactions. It might be expected then that electron excitation and subsequent transfer reactions also could occur in mammalian biological systems in the dark. In this report a method is described for the conversion of plasma proteins and pituitary peptides or even simpler related structures into free radicals, stable for relatively long time periods in aqueous solutions. The injection of these stable free radicals into intact animals results in a neurophysiologically excited state, presumably caused by the transfer of the energy of these electron-excited molecules to structures which induce the brain excitatory effects.
Objective: Social media mining may provide surprising information about unknown effects of drugs. We endeavored to uncover such unknown drug-disease relationships by text mining of audio record transcripts from the popular NPR show, The People's Pharmacy.
Materials and Methods: We used Google Cloud to transcribe episodes of the NPR podcast into textual documents. We then built a pipeline for systematically pre-processing the text to ensure quality input to the core classification model. Finally, results of the model were filtered by a series of post-processing steps. Our classification model itself uses the FLAIR language model pre-trained on PubMed abstracts. The modular nature of our pipeline allows for ease of future developments in this area by substituting higher quality components at each stage of the pipeline. To validate the drug-disease relating assertions extracted from the podcast, we utilized the DrugCentral database and ROBOKOP biomedical knowledge graph, which capture drug-disease relationships for FDA approved medications.
Results: Our model identified 128 drug-disease pairs that were found in DrugCentral and 112 novel candidate pairs requiring expert review. To demonstrate the expert review process, we found literature evidence supporting the assertions for novel drug-disease pairs.
Discussion and Conclusion: Text mining of social media is increasingly used to uncover novel relationships between semantic concepts corresponding to biomedical concepts. However, mining audio transcripts of specialized podcast shows has not been explored previously for this purpose. Using this approach, we have identified several unknown drug-disease relationships with support in biomedical literature. The proposed approach can extend beyond radio podcasts and could be applied to any source of audio and textual data.
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