Issue 3 (June)

Background

The study investigates the in-vitro aerodynamic particle size distribution of Symbicort^® Turbuhaler^®, a dry powder inhaler containing budesonide and formoterol, at different inhalation flows.

Methods

The primary objective was to assess the effect of inhalation flow on drug delivery by analyzing the fine particle dose (FPD) and mass median aerodynamic diameter (MMAD) using an Andersen Cascade Impactor at 28.3 L/min and 60 L/min inhalation flows.

Results

The results reveal a statistically significant impact of inhalation flow on drug deposition (p<0.05). A higher inhalation flow (60 L/min) led to an increase in FPD, a decrease in MMAD, and a higher fine particle fraction (FPF), indicating improved lung deposition. Additionally, throat deposition decreased with increasing flows, suggesting better de-aggregation and dispersion of drug particles. Despite these changes, the ratio of budesonide (R and S epimers) to formoterol remained constant, ensuring consistent pharmacological efficacy.

Conclusions

The findings highlight the importance of flow considerations in dry powder inhaler performance and suggest that higher inspiratory flows may enhance drug delivery efficiency. This study supports the need for variable flow testing in pharmacopoeial standards to optimize inhaler design and ensure effective drug administration for patients with asthma and chronic obstructive pulmonary disease.

Background: A meta-analysis study was undertaken to examine antibiotic resistance, specifically by assessing the effectiveness of pharmacist interventions in influencing the rate of antibiotic prescriptions compared to their impact on adherence to antibiotic prescribing guidelines.

Objective: Evaluating the effectiveness of pharmacist interventions in influencing the rate of antibiotic prescriptions, in contrast to their impact on adherence to antibiotic prescribing guidelines.

Method: A comprehensive literature review up to the year 2016 was conducted, examining a total of 215 relevant studies. Among these, 15 specific studies were chosen for inclusion, encompassing a population of 298,339 individuals who initially demonstrated antibiotic resistance. Within this group, 134,004 individuals were exposed to interventions involving pharmacist participation, while 164,335 served as controls. The calculation of odds ratios (OR) and 95% confidence intervals (CIs) was employed to assess antibiotic resistance in pharmacists involved in antibiotic prescribing rates as compared to those involved in antibiotic prescribing adherence rates. This analysis utilized dichotomous approaches and employed both fixed and random models.

Result: When pharmacists participated in interventions targeting antibiotic prescribing rates, a considerable reduction in antibiotic resistance was observed (Odds Ratio, 0.86; 95% Confidence Interval, 0.78-0.95, p<0.00001). However, these findings exhibited a significant degree of heterogeneity (I2 = 90%). Conversely, in interventions focusing on improving antibiotic prescribing adherence rates involving pharmacists, a substantial increase in antibiotic resistance was noted (Odds Ratio, 1.96; 95% Confidence Interval, 1.56-2.45, p<0.00001), with similarly high heterogeneity in the results (I2 = 91%). These outcomes were specifically evident in individuals grappling with antibiotic resistance issues.

Conclusion: Pharmacist-led interventions targeting antibiotic prescribing rates led to a noteworthy decrease in antibiotic resistance compared to scenarios without pharmacist involvement in such interventions. Nonetheless, it is crucial to approach the interpretation of these results with caution, given the limited sample size in certain studies incorporated into the meta-analysis.

Background

An ocular ailment impacts the entirety of sensory functioning, and an undetected and untreated ocular ailment might result in visual impairment. The objective of this study was to create an automated system for detecting age-related macular degeneration (ARMD) using deep-learning (DL) method that combines optimal deep features selected using the arithmetic optimizer (AO).

Methods

The main stages of the proposed scheme include; image collection from the chosen dataset and resizing, feature extraction using the DL scheme, feature optimization with AO and serial features fusion, and bi-level data classification and five-fold cross validation. The performance of the developed DL system is verified using conventional-, optimal-, and fused-features and the importance of these methods are established based on the chosen performance metrics.

Results

This study is executed using 1200 numbers of optical coherence tomography images of Normal/ARMD class and the scheme helps in achieving accuracy of >90% and >91% with conventional- and optimal features. Further, the fused-features based classification provided an accuracy of >99% with the support vector machin.

Conclusions

The achieved results of this study confirm the importance of the developed procedure on the examination of retinal optical coherence tomography images.