Stability of extemporaneously prepared sitagliptin phosphate solution

xlomafota13 shared this article with you from Inoreader Stability of extemporaneously prepared sitagliptin phosphate solution Via swallowing exercises PLoS One. 2022 Mar 16;17(3):e0262068. doi: 10.1371/journal.pone.0262068. eCollection 2022. ABSTRACT Sitagliptin is a dipeptidyl peptidase-4 (DPP-4) inhibitor that is used orally in conjunction with diet and exercise to control sugar levels in type 2 Diabetes Mellitus patients. This study aimed to extemporaneously prepare SiP solution (1% w/v) using pure Sitagliptin phosphate (SiP) powder and assess its stability according to pharmaceutical regulatory guidelines. Four SiP solutions, coded T1, T2, T3, and T4, were extemporaneously prepared using pure SiP powder as a source of API. The most suitable one, in terms of general organoleptic properties, was selected for further investigations, including stability studies. For this last purpose, samples of the T4 solution were kept under two storage conditions, room temperature (25˚C and 60% Relative Humidity) and accelerated stability conditions (40˚C and 75% Relative Humidity). Assay, pH, organoleptic properties, related substances, and microbial contamination were evaluated for 4 consecutive weeks. A High-Performance Liquid Chromatography (HPLC) analytical method was developed and validated to be used for the analysis and quantification of SiP in selected solution formulation. The adopted formula had a pH on the average of 3 to 4. During the stability tests, all pH values remained constant. Furthermore, after 4 weeks of storage under both conditions, the SiP concentration was close to 100%. A stable SiP extempora neous solution was successfully prepared using pure SiP powder. Patients with swallowing problems who use feeding tubes and are unable to take oral solid dosage forms may benefit from this research. Community pharmacists can prepare the solution using sitagliptin powder as the source of the active ingredient. PMID:35294449 | DOI:10.1371/journal.pone.0262068 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Introducing Zirconium Organic Gels for Efficient Radioiodine Gas Removal

xlomafota13 shared this article with you from Inoreader Introducing Zirconium Organic Gels for Efficient Radioiodine Gas Removal Via radioactive iodine Inorg Chem. 2022 Mar 15. doi: 10.1021/acs.inorgchem.1c03159. Online ahead of print. ABSTRACT Iodine radioisotope, as one of the most important fission products of uranium, may cause severe damage to human health when it is accidentally discharged into the environment. Hence, efficient removal of radioactive iodine is one of the most critical issues for both used nuclear fuel (UNF) reprocessing and environmental remediation. In this work, three metal-organic gels (MOGs) were introdu ced for iodine removal. The presented zirconium-based MOGs, namely, CWNU, CWNU-NH2, and CWNU-2NH2, were prepared via moderate solvothermal reactions. These MOGs all exhibit excellent chemical stability and reusability, marked iodine sorption capability, and favorable machinability, which can even rival commercial ones. The sorption capacities are determined to be 3.36, 4.10, and 4.20 g/g, respectively. The increased amount of amino group is considered to be responsible for the elevated iodine sorption capacity and kinetics, as confirmed by combined sorption studies and XPS analysis. The presented work sheds light on the utilization of MOGs for radioiodine capture. PMID:3528 9614 | DOI:10.1021/acs.inorgchem.1c03159 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Mycobacterium avium and Mycobacterium intracellulare in potable water in the USA

xlomafota.13 shared this article with you from Inoreader Occurrence revisited: Mycobacterium avium and Mycobacterium intracellulare in potable water in the USA Via Latest Results for Applied Microbiology and Biotechnology Abstract Nontuberculous mycobacterium (NTM) infections are increasing in the USA and have a high cost burden associated with treatment. Thus, it is necessary to understand what changes could be contributing to this increase in NTM disease rate. Water samples from 40 sites were collected from around the USA. They represented three water types: groundwater disinfected with chlorine and surface water disinfected with chlorine or monochloramine. Two methods, culture and qPCR, were used to measure M. avium and M. intracellulare. Heterotrophic bacteria and NTM counts were also measured. M. avium and M. intracellulare were molecularly detected in 25% (73/292) and 35% (102/292) of samples. The mean concentrations of M. avium and M. intracellulare were 2.8 × 103 and 4.0 × 103 genomic units (GU) L−1. The Northeast sites had the highest sample positively rate for both M. avium and M. intracellulare. The highest NTM counts and M. avium concentrations were observed in the surface water treated with chloramine. Geographic location and source water/disinfectant type were observed to significantly influence M. avium and M. intracellulare occurrence rates. These studies can help improve public health risk management by balancing disinfectant treatments and diverse microbial loads in drinking water. Key points • M. avium (MA) culture rate increased significantly: 1% (1999) to 13%. • Culture versus qPCR method: 13% vs 31% for MA and 6% vs 35% for MI. • The results of each method type tell two different stories of MA and MI occurrence. Graphical abstract View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG

xlomafota.13 shared this article with you from Inoreader Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG Via Latest Results for Cardiovascular Engineering and Technology Abstract Objectives Sleep apnea is the most common sleep disorder that leads to serious health complications if not treated early. Forecasting apnea occurrence ahead in time provides the opportunity to take appropriate actions to control and manage it. Methods A novel framework for forecasting the occurrence of apnea from single-lead electrocardiogram (ECG) based on deep recurrent neural networks is proposed. ECG R-peak amplitudes and R-R intervals are extracted and aligned using power spectral analysis, and recurrent deep learning models are developed to extract the most predictive ECG features and forecast the occurrence of apnea. Results The performance of the proposed approach was validated in forecasting apnea events up to five minutes in future on a dataset of 70 sleep recordings. A forecasting accuracy of up to 94.95% was achieved which was higher than the performance of conventional multilayer perceptron (p < 0.05) and other state-of-the-art techniques. Conclusions The proposed deep learning approach was successful in forecasting the occurrence of sleep apnea from single-lead ECG. It can therefore be adopted in wearable sleep monitors for the management of sleep apnea. Our developed algorithms are publicly available on GitHub. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.