Publications by authors named "J Sanford"

554 Publications

Evaluation of electroacupuncture for symptom modification in a rodent model of spontaneous osteoarthritis.

Acupunct Med 2021 Jun 9:9645284211020755. Epub 2021 Jun 9.

Department of Microbiology, Immunology, & Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA.

Objective: Faced with the frustration of chronic discomfort and restricted mobility due to osteoarthritis (OA), many individuals have turned to acupuncture for relief. However, the efficacy of acupuncture for OA is uncertain, as much of the evidence is inconclusive. The purpose of this study was to evaluate electroacupuncture (EA) in a rodent model of OA such that conclusions regarding its effectiveness for symptom or disease modification could be drawn.

Methods: Ten 12-month-old male Hartley guinea pigs-which characteristically have moderate to advanced OA at this age-were randomly assigned to receive EA for knee OA (n = 5) or anesthesia only (control group, n = 5). Treatments were performed three times weekly for 3 weeks, followed by euthanasia 2 weeks later. Gait analysis and enclosure monitoring were performed weekly to evaluate changes in movement. Serum was collected for inflammatory biomarker testing. Knee joints were collected for histology and gene expression.

Results: Animals receiving EA had significantly greater changes in movement parameters compared to those receiving anesthesia only. There was a tendency toward decreased serum protein concentrations of complement component 3 (C3) in the EA group compared to the control group. Structural and antioxidant gene transcripts in articular cartilage were increased by EA. There was no significant difference in total joint histology scores between groups.

Conclusion: This study provides evidence that EA has a positive effect on symptom, but not disease, modification in a rodent model of OA. Further investigations into mechanistic pathways that may explain the efficacy of EA in this animal model are needed.
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http://dx.doi.org/10.1177/09645284211020755DOI Listing
June 2021

Assessment of TMT Labeling Efficiency in Large-Scale Quantitative Proteomics: The Critical Effect of Sample pH.

ACS Omega 2021 May 6;6(19):12660-12666. Epub 2021 May 6.

Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

Isobaric labeling via tandem mass tag (TMT) reagents enables sample multiplexing prior to LC-MS/MS, facilitating high-throughput large-scale quantitative proteomics. Consistent and efficient labeling reactions are essential to achieve robust quantification; therefore, embedded in our clinical proteomic protocol is a quality control (QC) sample that contains a small aliquot from each sample within a TMT set, referred to as "Mixing QC." This Mixing QC enables the detection of TMT labeling issues by LC-MS/MS before combining the full samples to allow for salvaging of poor TMT labeling reactions. While TMT labeling is a valuable tool, factors leading to poor reactions are not fully studied. We observed that relabeling does not necessarily rescue TMT reactions and that peptide samples sometimes remained acidic after resuspending in 50 mM HEPES buffer (pH 8.5), which coincided with low labeling efficiency (LE) and relatively low median reporter ion intensities (MRIIs). To obtain a more resilient TMT labeling procedure, we investigated LE, reporter ion missingness, the ratio of mean TMT set MRII to individual channel MRII, and the distribution of log 2 reporter ion ratios of Mixing QC samples. We discovered that sample pH is a critical factor in LE, and increasing the buffer concentration in poorly labeled samples before relabeling resulted in the successful rescue of TMT labeling reactions. Moreover, resuspending peptides in 500 mM HEPES buffer for TMT labeling resulted in consistently higher LE and lower missing data. By better controlling the sample pH for labeling and implementing multiple methods for assessing labeling quality before combining samples, we demonstrate that robust TMT labeling for large-scale quantitative studies is achievable.
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http://dx.doi.org/10.1021/acsomega.1c00776DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8154127PMC
May 2021

PPE training and the effectiveness of universal masking in preventing exposures: The importance of the relationship between anesthesia and infection prevention.

Am J Infect Control 2021 May 19. Epub 2021 May 19.

Department of Infection Prevention, Emory University Hospital, Midtown. Atlanta, GA. Electronic address:

Early in the pandemic, infection prevention (IP), in collaboration with our local anesthesia leadership, took the approach of ensuring all members of the Anesthesia Department understood the importance of universal masking, were individually trained on the use of the Controlled Air Purifier Respirator, as well the appropriate method for donning/doffing N95 respirators. Multiple providers in the department tested positive for COVID, resulting in the IP Department to conduct the routine contact tracing investigation. During the investigation, it was determined that all persons who met the CDC (Centers for Disease Control & Prevention) contact exposure guidelines would undergo COVID testing, which consequently was 109 team members due to the exposure risk identified in the break room space. IP worked with the Anesthesia Preoperative Clinic to test all team members identified over a 3-day period (approximately 5-7 days postexposure). Out of the 109 team members who were tested postexposure, there were 0 conversions. The department attributes this to the consistency in personal protective equipment training, support and collaboration between anesthesia and IP which led to successful care for COVID patients with a limited provider infection rate.
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http://dx.doi.org/10.1016/j.ajic.2021.05.003DOI Listing
May 2021

Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.

Informatics (MDPI) 2021 Mar 3;8(1). Epub 2021 Mar 3.

Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
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http://dx.doi.org/10.3390/informatics8010016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112729PMC
March 2021

A new SARS-CoV-2 lineage that shares mutations with known Variants of Concern is rejected by automated sequence repository quality control.

bioRxiv 2021 Apr 6. Epub 2021 Apr 6.

We report a SARS-CoV-2 lineage that shares N501Y, P681H, and other mutations with known variants of concern, such as B.1.1.7. This lineage, which we refer to as B.1.x (COG-UK sometimes references similar samples as B.1.324.1), is present in at least 20 states across the USA and in at least six countries. However, a large deletion causes the sequence to be automatically rejected from repositories, suggesting that the frequency of this new lineage is underestimated using public data. Recent dynamics based on 339 samples obtained in Santa Cruz County, CA, USA suggest that B.1.x may be increasing in frequency at a rate similar to that of B.1.1.7 in Southern California. At present the functional differences between this variant B.1.x and other circulating SARS-CoV-2 variants are unknown, and further studies on secondary attack rates, viral loads, immune evasion and/or disease severity are needed to determine if it poses a public health concern. Nonetheless, given what is known from well-studied circulating variants of concern, it seems unlikely that the lineage could pose larger concerns for human health than many already globally distributed lineages. Our work highlights a need for rapid turnaround time from sequence generation to submission and improved sequence quality control that removes submission bias. We identify promising paths toward this goal.
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http://dx.doi.org/10.1101/2021.04.05.438352DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043452PMC
April 2021
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