The relationship between Salusin-α, Salusin-ß, and Klotho levels with subclinical atherosclerosis in ankylosing spondylitis.

OBJECTIVE: Subclinical atherosclerosis (SA) is often observed in ankylosing spondylitis (AS) patients; Salusin-α (Sal-α), Salusin-ß (Sal-ß), and Klotho hormones are thought to be associated with atherosclerosis. This study aims to evaluate the relationship between Sal-α, Sal-ß, and Klotho levels with SA in AS. PATIENTS AND METHODS: The study included patients older than 18 years who applied between August 1, 2019, and September 1, 2019. Patients with AS were included in the AS group, and patients without a known disease were included in the healthy group. Epicardial adipose tissue thickness (EATT) and carotid intima-media thickness (CIMT) measurements were used to assess SA. RESULTS: The study group included 38 (40.9%) patients diagnosed with AS, and the control group included 55 (59.1%) participants. CIMT and EATT levels were higher in the AS group than in the healthy group [0.37 (0.17) vs. 0.54±0.18, p<0.001; 0.44±0.11 vs. 0.54 (0.18), p=0.004, respectively]. There was no significant difference in Sal-α, Sal-ß, and Klotho levels between the AS and healthy groups (p>0.05). Furthermore, there was no observed relationship between EATT or CIMT and Klotho, Sal-α, or Sal-ß in either group (p>0.05). CONCLUSIONS: Although SA level was higher in AS patients, there was no relationship between SA and Sal-α, Sal-ß, and Klotho levels.

Can artificial intelligence predict COVID-19 mortality?

OBJECTIVE: COVID-19 infection rapidly spread across the globe and evolved into a pandemic. Artificial intelligence (AI) has been used to predict the spread of the virus and diagnose and treat the disease. In this study, we aimed to predict whether patients admitted to the intensive care unit (ICU) due to COVID-19 infection will result in mortality. PATIENTS AND METHODS: Ninety parameters were used for each 589 ICU patient. The nine parameters with the highest effect on mortality were determined. Four hundred seventy-one patients were used to train the AI with these nine parameters. AI was tested with 118 patient data. RESULTS: AI estimated mortality with 83% sensitivity, 84% specificity, 84% accuracy, and 0.81 F1 score. Therefore, the area under the curve was calculated as 0.91. The results indicate that mortality among COVID-19 patients admitted to the ICU can be predicted based on their laboratory parameters on the first day. CONCLUSIONS: These findings underscore the potential benefits of utilizing AI in managing pandemics.

The effect of thrombosis-related laboratory values on mortality in COVID-19 infection.

OBJECTIVE: COVID-19 may cause thrombosis in both venous and arterial systems. Familiarity with the signs and symptoms of thrombosis and its treatment is essential in treating COVID-19 infection and its complications. D-Dimer and mean platelet volume (MPV) are measurements related to the development of thrombosis. This study investigates whether MPV and D-Dimer values could be used to determine the risk of thrombosis and mortality in the COVID-19 early stages. PATIENTS AND METHODS: 424 patients who were COVID-19 positive, according to the World Health Organization (WHO) guidelines, were randomly and retrospectively included in the study. Demographic and clinical characteristics such as age, gender, and length of hospitalization were obtained from the digital records of participants. Participants were divided into living and deceased groups. The patients' biochemical, hormonal, and hematological parameters were analyzed retrospectively. RESULTS: White blood cells (WBC), neutrophils, and monocytes were significantly different in the two groups (p-value <0.001), and their values were lower in the living group than in the deceased group. MPV median values did not differ according to prognosis (p-value = 0.994). While the median value was 9.9 in the survivors, it was 10 in the deceased. Creatinine, procalcitonin, ferritin, and the number of hospitalization days in living patients were significantly lower than in patients who died (p-value <0.001). Median values of D-dimer (mg/L) differ according to prognosis (p-value <0.001). While the median value was 0.63 in the survivors, it was found as 438 in the deceased. CONCLUSIONS: Our results did not show any significant relationship between the mortality of COVID-19 patients and their MPV levels. However, a significant association between D-Dimer and mortality in COVID-19 patients was observed.

Associations of serum Magnesium levels with diabetes mellitus and diabetic complications.

BACKGROUND: Magnesium (Mg) deficiency is a common problem in diabetic patients. Deficiency of Mg may increase the incidence of diabetes mellitus (DM) and occurrence of diabetic complications. In this study, our aim was to evaluate an association between serum Mg level,  glycemic regulation, and diabetic complications. MATERIAL-METHODS: In this retrospective study 673 diabetic patients were evaluated. According to Mg levels, the patients were divided into two groups; as normomagnesemic patients and hypomagnesemic patients. RESULTS: Among the patients, 57.8% were men and 42.2% were women. Mean age was 55.6 years and the mean duration of diabetes was 81 ± 86.9 months. The mean glycosylated hemoglobin (HbA1c) was 9.0 ±2.4 % (4.5-18); mean magnesium level was 1.97 ± 0.25 (1.13 to 3.0) mg / dl.  There were 55 patients (8.2%) with diabetic retinopathy and 95 patients (14.1%) with diabetic neuropathy. Five hundred patients (74.3%)  had normoalbuminuria; 133 patients (19. 8%) had microalbuminuria (MA) and 40 patients (5.9%) had overt proteinuria. One hundred and seventy one patients (25.4%) had HbA1c levels equal or below 7%; and 502 patients (74.6%) had HbA1c levels above 7%. There was no statistical difference in age or duration of diabetes between the groups formed according to Mg levels. Although there were no differences between the groups for retinopathy and neuropathy, MA was more common in hypomagnesemic patients (p =0.004). HbA1c levels did not differ between the groups (p =0.243). However there was a weak negative correlation between serum Mg and HbA1c levels (r =-0.110, p =0.004) and also between serum Mg and urine protein level  (r =-0.127, p =0.018). CONCLUSION: Mg depletion is a common problem in patients with DM. It affects both glycemic regulation and the occurence of complications. Also, poor glycemic regulation affects serum Mg levels. Hippokratia 2015; 19 (2):153-157.