In COVID-19-induced acute respiratory distress syndrome, the lungs are incapable of filling with sufficient air, leading to hypoxemia that results in high mortality among hospitalized patients. In clinical trials, low-molecular-weight heparin was administered via a specially designed soft-mist inhaler device in an investigator initiated, single-center, open-label, phase-IIb clinical trial. Patients with evidently worse clinical presentations were classed as the “Device Group”; 40 patients were given low-molecular-weight heparin via a soft mist inhaler at a dose of 4000 IU per administration, twice a day. The Control Group, also made up of 40 patients, received the standard therapy. The predetermined severity of hypoxemia and the peripheral oxygen saturation of patients were measured on the 1st and 10th days of treatment. The improvement was particularly striking in cases of severe hypoxemia. In the 10-day treatment, low-molecular-weight heparin was shown to significantly improve breathing capability when delivered via a soft-mist inhaler.

BACKGROUND: Acute cholecystitis (AC) is a common emergency seen by general surgeons. Optimal treatment is laparoscopic cholecystectomy (LC); however, in cases where surgery cannot be performed due to high risk of morbidity and mortality, such as in elderly patients with comorbid diseases, other treatment modalities may be used. Percutaneous cholecystostomy (PC) is one alternative method to treat AC. PC can be used to provide drainage of the gall bladder and control infection. Subsequently, interval cholecystectomy can be performed when there are better conditions. Presently described is experience and results with PC in high risk, elderly patients with AC.

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Background Stress triggers a cascade of reactions that alter the organism's dynamic steady state. There is a scarcity of interventional studies that show cortisol variability on stress over time in groups of patients with chronic noncommunicable diseases and comorbidities. Purpose In this research, we aimed to examine salivary cortisol changes in the cognitive stress response of patients with Hypertension and Diabetes Mellitus (HT & DM) and patients with Hypertension (HT) and to determine the differences between them. Methods The research was conducted by solving an arithmetic task as a stress test in 62 patients with HT&DM and HT that are being treated in the outpatient clinic of Medical Pharmacology and Clinical Pharmacology Department in Istanbul University, Istanbul Medical Faculty Hospital. Results There was no statistically significant difference among HT&DM and HT groups on systolic and diastolic blood pressure (BP) values (p = 0.331 and p = 0.058). When measured by repeated ANOVA, salivary cortisol level [F (1.842, 60) = 8.771, p < 0.0001], systolic blood pressure [F (2.185, 60) = 12.080, p < 0.0001], diastolic blood pressure [F (2.793, 60) = 6.043, p = 0.001] and the heart rate [F (2.073, 60) = 13.259, p < 0.0001] were statistically significant for the main effect (time), while the effect of the group*time interaction factor was statistically not significant (p = 0.773; p = 0.751; p = 0.713 and p = 0.506, respectively). Main conclusions In conclusion, the arithmetic problem-solving task used in the HT&DM and HT patients were found to be useful as an acute stress test in the laboratory environment. There was no statistically significant difference was found in terms of group*time interaction factor between the HT&DM and HT groups, however the salivary cortisol and BP values increased significantly after acute stress within each group.

Objective: This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success. Methods: Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints. Results: The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (−0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (−0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (−0.12), glucose (−0.12), hemoglobin A1c (−0.12), uric acid (−0.09) and creatinine (−0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients. Conclusion: Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models.