Diabetes mellitus in rheumatic diseases: clinical characteristics and treatment considerations

Fedorchenko, Yuliya; Mahmudov, Khaiyom; Abenov, Zhumabek; Zimba, Olena; Yessirkepov, Marlen

doi:10.1007/s00296-023-05453-9

Cited by 8 publications

(5 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the use of GC, an anti-inflammatory drug with ADR of glucose metabolism, is highly likely to add to the problem. 4 Therefore, there is an urgent need to develop new, more selective GCs that have similar anti-inflammatory effects but with greatly reduced or no ADRs. Pharmacological studies of ginsenosides have shown that GCK is the major metabolite and the ultimate absorbed form of diol-type ginsenosides in the human gut.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…1 Disorders of glucose metabolism and elevated blood glucose levels are common adverse drug reactions (ADR) after long-term use of traditional glucocorticoid drugs. 3,4 This disorder of glucose metabolism is thought to be related to the transcriptional activation of GR. Whether GCK can cause glucose metabolism disorders and elevated blood sugar levels when it exerts an antiinflammatory effect like traditional glucocorticoid drugs is still unclear.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ginsenoside compound K plays an anti-inflammatory effect without inducing glucose metabolism disorder in adjuvant-induced arthritis rats

Mao,

Liu,

et al. 2024

Food Funct.

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ginsenoside compound K plays an anti-inflammatory effect without inducing glucose metabolism disorder in adjuvant-induced arthritis rats

Mao,

Liu,

et al. 2024

Food Funct.

View full text Add to dashboard Cite

show abstract

“…Diabetes mellitus (DM) represents a systemic endocrinological disorder characterized by multifaceted manifestations and complications, including renal and ocular involvement, cardiovascular complications, as well as neurological and musculoskeletal manifestations [5,6]. Table 2 represents diabetes mellitus-related musculoskeletal disorders.…”

Section: Discussionmentioning

confidence: 99%

Limited Joint Mobility of the Hand: Could It Be the Aging Hand?

Gokcen

2023

AAEE

View full text Add to dashboard Cite

Background: The progressive diminution of manual dexterity and functional capacity in the hands is an inherent aspect of the aging process. However, when assessing hand dysfunction and limitations in elderly individuals, it is imperative to exclude alternative etiologies before attributing such manifestations solely to age-related deterioration. Case Report: This report presents the case of a 65-year-old female patient exhibiting skin thickening and restricted joint mobility in both hands. Subsequent comprehensive evaluation led to a diagnosis of diabetic cheiroarthropathy, commonly known as limited joint mobility syndrome. Conclusion: The observed decline in hand function among elderly individuals with diabetes mellitus may be attributable to diabetic cheiroarthropathy. This underscores the importance of considering diabetes-related musculoskeletal disorders in the differential diagnosis of age-associated functional deterioration in the hands.

show abstract

“…In a brief period of time, 85% of patients may develop sepsis-related serious complications including urosepsis [6]. Septic shock has up to a 50% fatality rate, which is extremely detrimental to both individuals and society [7]. As a consequence, the key to arresting the progression of renal empyema is the early identi cation [8,9].…”

Section: Introductionmentioning

confidence: 99%

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

Yang,

Zhong,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.

show abstract

Diabetes mellitus in rheumatic diseases: clinical characteristics and treatment considerations

Cited by 8 publications

References 80 publications

Ginsenoside compound K plays an anti-inflammatory effect without inducing glucose metabolism disorder in adjuvant-induced arthritis rats

Ginsenoside compound K plays an anti-inflammatory effect without inducing glucose metabolism disorder in adjuvant-induced arthritis rats

Limited Joint Mobility of the Hand: Could It Be the Aging Hand?

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

Contact Info

Product

Resources

About