RESUMO
Condensins play important roles in maintaining bacterial chromatin integrity. In mycobacteria, three types of condensins have been characterized: a homolog of SMC and two MksB-like proteins, the recently identified MksB and EptC. Previous studies suggest that EptC contributes to defending against foreign DNA, while SMC and MksB may play roles in chromosome organization. Here, we report for the first time that the condensins, SMC and MksB, are involved in various DNA transactions during the cell cycle of Mycobacterium smegmatis (currently named Mycolicibacterium smegmatis). SMC appears to be required during the last steps of the cell cycle, where it contributes to sister chromosome separation. Intriguingly, in contrast to other bacteria, mycobacterial MksB follows replication forks during chromosome replication and hence may be involved in organizing newly replicated DNA.
Assuntos
Adenosina Trifosfatases , Proteínas de Bactérias , Replicação do DNA , Proteínas de Ligação a DNA , Complexos Multiproteicos , Mycobacterium smegmatis , Proteínas de Ligação a DNA/metabolismo , Proteínas de Ligação a DNA/genética , Mycobacterium smegmatis/metabolismo , Mycobacterium smegmatis/genética , Proteínas de Bactérias/metabolismo , Proteínas de Bactérias/genética , Adenosina Trifosfatases/metabolismo , Complexos Multiproteicos/metabolismo , Cromossomos Bacterianos/metabolismo , Cromossomos Bacterianos/genética , DNA Bacteriano/metabolismo , DNA Bacteriano/genética , Ciclo Celular , Proteínas de Ciclo Celular/metabolismo , Proteínas de Ciclo Celular/genéticaRESUMO
CONTEXT: The presence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed. OBJECTIVE: To develop machine learning (ML) models for risk assessment of MASLD occurrence in patients with DM. METHODS: Feature selection determined the discriminative parameters, utilized to classify DM patients as those with and without MASLD. The performance of the multiple logistic regression model was quantified by sensitivity, specificity, and percentage of correctly classified patients, and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) assessed the model's net benefit for alternative treatments. RESULTS: We studied 2000 patients with DM (mean age 58.85 ± 17.37 years; 48% women). Eight parameters: age, body mass index, type of DM, alanine aminotransferase, aspartate aminotransferase, platelet count, hyperuricaemia, and treatment with metformin were identified as discriminative. The experiments for 1735 patients show that 744/991 (75.08%) and 586/744 (78.76%) patients with/without MASLD were correctly identified (sensitivity/specificity: 0.75/0.79). The area under ROC (AUC) was 0.84 (95% CI, 0.82-0.86), while DCA showed a higher clinical utility of the model, ranging from 30% to 84% threshold probability. Results for 265 test patients confirm the model's generalizability (sensitivity/specificity: 0.80/0.74; AUC: 0.81 [95% CI, 0.76-0.87]), whereas unsupervised clustering identified high-risk patients. CONCLUSION: A ML approach demonstrated high performance in identifying MASLD in patients with DM. This approach may facilitate better risk stratification and cardiovascular risk prevention strategies for high-risk patients with DM at risk of MASLD.
Assuntos
Aprendizado de Máquina , Humanos , Feminino , Masculino , Pessoa de Meia-Idade , Idoso , Adulto , Medição de Risco/métodos , Curva ROC , Diabetes Mellitus/epidemiologia , Diabetes Mellitus/metabolismo , Diabetes Mellitus/sangue , Fígado Gorduroso/diagnóstico , Fígado Gorduroso/complicações , Fígado Gorduroso/epidemiologia , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/metabolismo , Fatores de Risco , Complicações do Diabetes/diagnóstico , Complicações do Diabetes/epidemiologiaRESUMO
BACKGROUND: Diabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with or without MASLD. This study aims to develop machine learning (ML) models for assessing the risk of the HF occurrence in patients with DM with and without MASLD. RESEARCH DESIGN AND METHODS: In the Silesia Diabetes-Heart Project (NCT05626413), patients with DM with and without MASLD were analyzed to identify the most important HF risk factors with the use of a ML approach. The multiple logistic regression (MLR) classifier exploiting the most discriminative patient's parameters selected by the χ2 test following the Monte Carlo strategy was implemented. The classification capabilities of the ML models were quantified using sensitivity, specificity, and the percentage of correctly classified (CC) high- and low-risk patients. RESULTS: We studied 2000 patients with DM (mean age 58.85 ± SD 17.37 years; 48% women). In the feature selection process, we identified 5 parameters: age, type of DM, atrial fibrillation (AF), hyperuricemia and estimated glomerular filtration rate (eGFR). In the case of MASLD( +) patients, the same criterion was met by 3 features: AF, hyperuricemia and eGFR, and for MASLD(-) patients, by 2 features: age and eGFR. Amongst all patients, sensitivity and specificity were 0.81 and 0.70, respectively, with the area under the receiver operating curve (AUC) of 0.84 (95% CI 0.82-0.86). CONCLUSION: A ML approach demonstrated high performance in identifying HF in patients with DM independently of their MASLD status, as well as both in patients with and without MASLD based on easy-to-obtain patient parameters.
Assuntos
Fibrilação Atrial , Diabetes Mellitus , Fígado Gorduroso , Insuficiência Cardíaca , Hiperuricemia , Doenças Metabólicas , Humanos , Feminino , Pessoa de Meia-Idade , Masculino , Insuficiência Cardíaca/diagnóstico , Insuficiência Cardíaca/epidemiologia , Insuficiência Cardíaca/etiologia , Fatores de Risco , Aprendizado de MáquinaRESUMO
We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015-2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months. An end-to-end ML technique which exploits the neighborhood component analysis for elaborating discriminative predictors, followed by a hybrid sampling/boosting classification algorithm, multiple logistic regression (MLR), or unsupervised hierarchical clustering was proposed. In 1735 patients with diabetes (53% female), there were 150 (8.65%) ones with a new CV event in the follow-up. Twelve most discriminative patients' parameters included coronary artery disease, heart failure, peripheral artery disease, stroke, diabetic foot disease, chronic kidney disease, eosinophil count, serum potassium level, and being treated with clopidogrel, heparin, proton pump inhibitor, and loop diuretic. Utilizing those variables resulted in the area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95% Confidence Interval [CI] 0.56-0.68, P < 0.01) to 0.72 (95% CI 0.66-0.77, P < 0.01) across 5 nonoverlapping test folds, whereas MLR correctly determined 111/150 (74.00%) high-risk patients, and 989/1585 (62.40%) low-risk patients, resulting in 1100/1735 (63.40%) correctly classified patients (AUC: 0.72, 95% CI 0.66-0.77). ML algorithms can identify patients with diabetes at a high risk of new CV events based on a small number of interpretable and easy-to-obtain patients' parameters.