Rhabdomyolysis

Cabral, Brian Michael I.; Edding, Sherida; Portocarrero, Juan P.; Lerma, Edgar V.

doi:10.1016/j.disamonth.2020.101015

Cited by 155 publications

(226 citation statements)

References 41 publications

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“…Myoglobin and CK are the main products of the breakdown of skeletal muscle bres in a glycerol-induced RM model [30]. Myoglobin-induced renal toxicity plays a key role in RM-induced AKI by activating the complement system and increasing oxidative stress, in ammation, endothelial dysfunction, vasoconstriction, and apoptosis [10][11][12]. The clinical symptoms of RM are characterised by elevated serum CK and myoglobin [1].…”

Section: Discussionmentioning

confidence: 99%

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Exogenous biological renal support improves kidney function in rhabdomyolysis-induced acute kidney injury in mice

Liu

Chi

Geng

et al. 2021

Preprint

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BackgroundRhabdomyolysis (RM) is a clinical syndrome characterised by the breakdown of skeletal muscle fibres and release of their contents into the circulation. Myoglobin-induced acute kidney injury (AKI) is one of the most severe complications of RM. Based on our previous study, exogenous biological renal support alleviates renal ischaemia-reperfusion injury (IRI) in elderly mice. This study aimed to determine whether exogenous biological renal support promoted renal recovery from RM-induced AKI and to preliminarily explore the mechanisms involved.MethodsA parabiosis animal model was established to investigate effects of exogenous biological renal support on RM-induced AKI. Male wild-type C57BL/6 mice and C57BL/6-TgN (ACTb-EGFP) transgenic mice were used to determine whether shared circulation was established among parabiotic pairs 3 weeks after parabiosis surgery. Mice were divided into three groups: the control group (sterile saline injected); RM group (glycerol (8 mL/kg) injected); and parabiosis + RM group (three weeks after the parabiosis model was established, the recipient mouse was injected with glycerol). Blood samples and kidney tissue were collected for further processing 48 hours after RM induction. Bioinformatics analysis was conducted with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, functional enrichment analysis and clustering analysis.ResultsAt 48 hours after the procedure, all mice survived. Exogenous biological renal support attenuated the histological and functional deterioration in RM-induced AKI in mice. Bioinformatics analysis identified key pathways and proteins involved in this process. We further demonstrated that exogenous biological renal support ameliorated kidney injury through multiple pathways, including suppressing the complement system; attenuating oxidative stress, inflammation, and apoptosis; and increasing proliferation.ConclusionsExogenous biological renal support provided by parabiosis can improve renal function in RM-induced AKI by suppressing the complement system; decreasing oxidative stress, inflammation, and apoptosis; and promoting tubular cell proliferation. Our study provides new ideas for effectively preventing and treating RM-induced AKI and provides basic research evidence for the use of bioartificial kidneys to treat RM-induced AKI.

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Section: Discussionmentioning

confidence: 99%

“…Myoglobin and its oxygen-carrying moiety haem play a key role in the development of AKI. AKI can cause renal tubular obstruction, renal vasoconstriction, and the direct activation of oxidative stress, lipid peroxidation and macrophages, which injure proximal tubular cells [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Exogenous biological renal support improves kidney function in rhabdomyolysis-induced acute kidney injury in mice

Liu

Chi

Geng

et al. 2021

Preprint

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“…On the one hand, it may be associated with heart injury in critically ill patients with COVID-19 [34]. On the other hand, this indicator was related to rhabdomyolysis [35][36].Several cases of rhabdomyolysis were reported in COVID-19 severe patients, with a marked increase of CK [37][38][39].…”

Section: Discussionmentioning

confidence: 99%

Risk Factors Analysis of COVID-19 Patients with ARDS and Prediction Based on Machine Learning

Xu¹,

Sun²,

Gao

et al. 2020

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COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the two groups were elaborately compared and both traditional machine learning algorithms and deep learning-based methods were used to build the prediction models. Results indicated the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI>25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, leukocyte counting, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, sensitivity, and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.

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“…Meanwhile, 5 patients in the psittacosis group and 1 in the LP group were complicated with rhabdomyolysis. The diagnosis of rhabdomyolysis was con rmed when the level of serum creatine kinase (CK) was found higher than 1000 U/L [13]. Clinical manifestations of the patients are shown in Table 2.…”

Section: Clinical Characteristicsmentioning

confidence: 99%

Comparative analysis of clinical presentation of community-acquired pneumonia induced by Chlamydia psittaci and Legionella Diagnosed through Metagenomic Next-Generation Sequencing

Zeng

et al. 2020

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Background Legionella and Chlamydia psittaci cause atypical community-acquired pneumonia, which mimic each other. Our aim was to compare the clinical characteristics of Legionella pneumonia (LP) and psittacosis and assess whether metagenomic next-generation sequencing (mNGS) is an effective method for early diagnosis. Methods We conducted a retrospective study and compared seventeen patients with Chlamydia psittaci pneumonia and nine patients with LP, diagnosed by mNGS. This study was carried out in the First Affiliated Hospital of Wenzhou Medical University, China, from July 2018 to May 2020. mNGS was carried out from bronchial alveolar lavage fluid (BALF) and/or lung tissues. Results 76.5% of psittacosis cases and 0% of legionellosis cases had an avian exposure history (p < 0.001). Compared with LP patients, psittacosis patients had significantly higher hemoglobin (118.9 ± 20.2 vs. 93.2 ± 23.2 g/L, p = 0.007), serum sodium (138.9 ± 5.4 vs. 133.9 ± 6.5 mmol/L, p = 0.047), and higher proportion of elevated aspartate aminotransferase levels (88.2 vs. 44.4%, p = 0.028). Other clinical parameters were similar in psittacosis and LP patients: normal or slightly elevated leucocytes (10.5 vs. 9.5 × 109/L, p > 0.05), neutrophils (9.4 vs. 8.3 × 109/L, p > 0.05), and procalcitonin (0.8 vs 5.1 ng/mL, p > 0.05); highly increased C-reactive protein levels (205.1 vs. 234.9 mg/L, p > 0.05); and decreased lymphocytes (0.7 vs. 0.6 × 109/L, p > 0.05). Extra-pulmonary manifestations and mortality (11.8% in psittacosis vs. 22.2% in LP group, p = 0.591) were also similar in both the groups. mNGS detected Chlamydia psittaci in 17/17 BALF samples, and Legionella in 8/8 BALF samples and 1/1 lung tissue sample. Conclusions Apart from sharing many features with legionellosis, psittacosis is a vital differential diagnosis for LP, especially in patients with avian exposure history. mNGS is a sensitive and promising method for early diagnosis of both psittacosis and LP.

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Rhabdomyolysis

Cited by 155 publications

References 41 publications

Exogenous biological renal support improves kidney function in rhabdomyolysis-induced acute kidney injury in mice

Exogenous biological renal support improves kidney function in rhabdomyolysis-induced acute kidney injury in mice

Risk Factors Analysis of COVID-19 Patients with ARDS and Prediction Based on Machine Learning

Comparative analysis of clinical presentation of community-acquired pneumonia induced by Chlamydia psittaci and Legionella Diagnosed through Metagenomic Next-Generation Sequencing

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