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1.
Dongbing Lai Emma C. Johnson Sarah Colbert Gayathri Pandey Grace Chan Lance Bauer Meredith W. Francis Victor Hesselbrock Chella Kamarajan John Kramer Weipeng Kuang Sally Kuo Samuel Kuperman Yunlong Liu Vivia McCutcheon Zhiping Pang Martin H. Plawecki Marc Schuckit Jay Tischfield Leah Wetherill Yong Zang Howard J. Edenberg Bernice Porjesz Arpana Agrawal Tatiana Foroud 《Alcoholism, clinical and experimental research》2022,46(3):374-383
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Andy KH Lim 《World journal of gastroenterology : WJG》2020,26(10):1020-1028
Rhabdomyolysis is a syndrome of skeletal muscle injury with release of cellular constituents such as potassium,phosphate,urate and intracellular proteins such as myoglobin into the circulation,which may cause complications including acute kidney injury,electrolyte disturbance and cardiac instability.Abnormal liver function tests are frequently observed in cases of severe rhabdomyolysis.Typically,there is an increase in serum aminotransferases,namely aspartate aminotransferase and alanine aminotransferase.This raises the question of liver injury and often triggers a pathway of investigation which may lead to a liver biopsy.However,muscle can also be a source of the increased aminotransferase activity.This review discusses the dilemma of finding abnormal liver function tests in the setting of muscle injury and the potential implications of such an association.It delves into some of the clinical and experimental evidence for correlating muscle injury to raised aminotransferases,and discusses pathophysiological mechanisms such as oxidative stress which may cause actual liver injury.Serum aminotransferases lack tissue specificity to allow clinicians to distinguish primary liver injury from muscle injury.This review also explores potential approaches to improve the accuracy of our diagnostic tools,so that excessive or unnecessary liver investigations can be avoided. 相似文献
4.
Danielle E Whittier Elizabeth J Samelson Marian T Hannan Lauren A Burt David A Hanley Emmanuel Biver Pawel Szulc Elisabeth Sornay-Rendu Blandine Merle Roland Chapurlat Eric Lespessailles Andy Kin On Wong David Goltzman Sundeep Khosla Serge Ferrari Mary L Bouxsein Douglas P Kiel Steven K Boyd 《Journal of bone and mineral research》2022,37(3):428-439
Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR). 相似文献
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Dupuytren’s disease with severe finger contractures and recurrent contractures following previous surgery often have extensive skin involvement. In these severe cases, excision of the diseased chord along with the involved skin is a good option to reduce the risk of recurrance. The resulting skin defect can be covered with a full thickness skin graft (FTSG) or a cross finger flap. Cross finger flaps have donor finger morbidity and hence a full thickness graft is usually preferred. The FTSG extending to the midlateral margins on both sides of the finger reduces the risk of joint contracture due to graft shrinkage. Once the FTSG is sutured in place, the standard practice is to compress and secure the graft to its recipient bed with a tie-over dressing and this can be time consuming. We present a simple dressing technique to secure the FTSG without the need for a tie-over dressing. 相似文献
7.
Alexander Real Chierika Ukogu Divya Krishnamoorthy Nicole Zubizarreta Samuel K. Cho Andrew C. Hecht James C. Iatridis 《The spine journal》2019,19(2):225-231
Background Context
Low back pain (LBP) is a common complaint in clinical practice of multifactorial origin. Although obesity has been thought to contribute to LBP primarily by altering the distribution of mechanical loads on the spine, the additional contribution of obesity-related conditions such as diabetes mellitus (DM) to LBP has not been thoroughly examined.Purpose
To determine if there is a relationship between DM and LBP that is independent of body mass index (BMI) in a large cohort of adult survey participants.Study Design
Retrospective analysis of prospectively collected National Health and Nutrition Examination Survey (NHANES) data to characterize associations between LBP, DM, and BMI in adults subdivided into 6 subpopulations: normal weight (BMI 18.5–25), overweight (BMI 25–30), and obese (BMI >30) diabetics and nondiabetics. Diabetes was defined with glycohemoglobin A1c (HbA1c) ≥6.5%.Patient Sample
11,756 participants from NHANES cohort.Outcome Measures
Percentage of LBP reported.Methods
LBP reported in the 1999-2004 miscellaneous pain NHANES questionnaire was the dependent variable examined. Covariates included HbA1c, BMI, age, and family income ratio to poverty as continuous variables as well as race, gender, and smoking as binary variables. Individuals were further subdivided by weight class and diabetes status. Regression and graphical analyses were performed on the study population as a whole and also on subpopulations.Results
Increasing HbA1c did not increase the odds of reporting LBP in the full cohort. However, multivariate logistic regression of the 6 subpopulations revealed that the odds of LBP significantly increased with increasing HbA1c levels in normal weight diabetics. No other subpopulations reported significant relationships between LBP and HbA1c. LBP was also significantly associated with BMI for normal weight diabetics and also for obese subjects regardless of their DM status.Conclusions
LBP is significantly related to DM status, but this relationship is complex and may interact with BMI. These results support the concept that LBP may be improved in normal weight diabetic subjects with improved glycemic control and weight loss, and that all obese LBP subjects may benefit from improved weight loss alone. 相似文献8.
Evaluation of training nurses to perform semi‐automated three‐dimensional left ventricular ejection fraction using a customised workstation‐based training protocol
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Darren R. Feldman MD Yasser Ged MBBS Chung-Han Lee PhD Andrea Knezevic MS Ana M. Molina MD Ying-Bei Chen PhD Joshua Chaim DO Devyn T. Coskey MS Samuel Murray MS Satish K. Tickoo MD Victor E. Reuter MD Sujata Patil PhD Han Xiao MD Jahan Aghalar MD Arlyn J. Apollo MD Maria I. Carlo MD Robert J. Motzer MD Martin H. Voss MD 《Cancer》2020,126(24):5247-5255