Antimicrobial resistance (AMR) is a global health challenge and antimicrobial use (AMU) in the livestock sector has been considered as one of the contributing factors towards the development of AMR in bacteria. This study summarizes the results of a point prevalence survey conducted to monitor farm-level AMU in commercial broiler chicken farms in Punjab and Khyber Pakhtunkhwa (KPK) provinces of Pakistan. A cross-sectional study was conducted to quantify AMU and to check seasonal variations of AMU in 12 commercial broiler chicken farms (six from each province) during the summer and winter seasons of the year 2020–2021. AMU was recorded using three AMU metrics: kg, mg per population correction unit (mg/PCU), and mg/kg of final flock weight. A total of 22 antimicrobial drugs (348.59 kg) were used for therapeutic or prophylactic purposes in surveyed broiler chicken farms. The total combined AMU for all the broiler chicken farms was 462.57 mg/PCU. The use of most of the antimicrobials increased during winter flocks compared to summer. The top three antimicrobial drugs used during the summer were neomycin (111.39 mg/PCU), doxycycline (91.91 mg/PCU), and tilmicosin (77.22 mg/PCU), whereas doxycycline (196.81 mg/PCU), neomycin (136.74 mg/PCU), and amoxicillin (115.04 mg/PCU) during the winter. Overall, 60% of the antibiotics used in broiler chicken were critically important antimicrobial classes (CIA) for human medicine as characterized by the World Health Organization. Our findings showed high AMU in broiler chicken production and a call for urgent actions to regulate CIA use in food animals in Pakistan. This baseline survey is critical for the design and implementation of a subsequent national level AMU surveys that can include additional farming types, animals’ species, and geographical locations over a longer period of time.

Intensive livestock farming has become indispensable to meet the rapidly increasing demand for animal-based nutrition in low- and middle-income countries (LMICs) where antimicrobials are frequently used for treatment and prophylactic or metaphylactic purposes. However, very little is known about the trends of antimicrobial use (AMU) in dairy animals in LMICs. The objective of this study was to quantify AMU in two large commercial dairy farms in Pakistan. A retrospective study was conducted at two large corporate commercial dairy farms located in Punjab province for the year 2018. AMU was calculated using three metrics: active ingredient (AI; kg) and milligrams per population unit (mg/PU; mg/kg), which quantifies the amount of AI used, and antimicrobial treatment incidence (ATI; DDDA/1,000 cow-days), which estimates the per-day number of treatments to 1,000 cows. Total on-farm AMU was found to be 138.34 kg, 65.88 mg/kg, and 47.71 DDDA/1,000 cow-days. Measured in ATI, aminoglycosides (11.05 DDDA/1,000 cow-days), penicillins (8.29 DDDA/1,000 cow-days), and tetracyclines (8.1 DDDA/1,000 cow-days) were the most frequently used antimicrobial classes. A total of 42.46% of all the antimicrobials used belonged to the critically important antimicrobials for human medicine as defined by the World Health Organization. Considerably high AMU was found compared to other farm-level studies across the world. This was the first study to quantify AMU in the dairy industry in Pakistan. Our results showed that corporate commercial dairy management practices are associated with increased antimicrobial consumption and highlight the need for antimicrobial stewardship programs to encourage prudent use of antimicrobials in commercial dairy.

No abstract

Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods.

No abstract