Prerpints.org logo
Preprint
Article

Systematic Surveillance and Meta-Analysis of Antimicrobial Resistance

Altmetrics

Downloads

236

Views

138

Comments

0

  * Corresponding authors with equal contribution.

This version is not peer-reviewed

Antimicrobial Resistance in the Time of COVID-19

Submitted:

17 May 2023

Posted:

19 May 2023

You are already at the latest version

Alerts
Abstract
Human exposure to antimicrobial resistance (AMR) through food is now very imperfectly understood, creating a significant gap in the design of interventions. The interchange of AMR genes and the transfer of AMR bacteria from animals to humans through the food chain necessitate comprehensive methods to risk reduction. With a focus on AMR in bacterial species isolated from food products, foods (of both animal and non-animal origin), and ambient samples, the current meta-analysis gathered up-to-date information on the epidemiology of AMR in animal-source food chain. As a result, the combined prevalence of AMR across the various food sources was calculated. From the 18,784 food samples obtained as a result of selected publications, 7,676 (40.9%) samples were contaminated, including 4343 (56.6%) and 3363 (43.4%) samples from Taiwan and India, respectively. Meat (chicken, pork, and beef), fish and milk all have moderate to medium potential for AMR exposure to both Gram-positive and Gram-negative foodborne pathogens such S. aureus, Clostridium, E. coli, Salmonella, etc. Antibiotic resistance to β-lactam, fluoroquinolone, carbapenem etc, is present in the majority of food samples. The results of this study emphasize the persistent danger of antimicrobial residue in animal-derived foods in Taiwan, India, and other nations with comparable customs.
Keywords: 
Subject: Biology and Life Sciences  -   Immunology and Microbiology

1. Introduction

Highlights:
  • A meta-analysis was conducted in order to assess the spread of AMR in different food sources.
  • Both India and Taiwan shows high contamination in meat (chicken, pork and beef) against E. coli.
  • The combined prevalence of AMR for bacterium-antibiotic combinations in different food samples was reported against β-lactam and fluoroquinolones.
  • In the majority of the food samples from both nations, multidrug resistance was found.
Antimicrobial drugs are used on both humans and animals and are crucial for maintaining public health all over the world, but now it has become a serious global health issue, impacting every country to some extent. It is an unavoidable result of the improper use of antibiotics in people and animals. AMR has been identified as a global hazard to public health due to its links to higher rates of morbidity, mortality, and financial costs.(Dadgostar 2019; Shrestha et al. 2018) By 2050, it is predicted that AMR will cause 10 million deaths annually, reduce the global GDP by 3% to 4%, and cost $100 trillion US dollars.(Antimicrobial Resistance (AMR) n.d.; New report calls for urgent action to avert antimicrobial resistance crisis n.d.) Therefore, AMR is among the top priorities in achieving the 2030 UN Sustainable Development Goals (SDGs)(Grace 2015; Tang et al. 2017) Additionally, WHO has developed several of approaches to counter the rise of antibiotic resistance, including enhancing sanitation and hygiene to lower overall infection rates and optimizing the use of antibiotics (and avoiding their abuse) in both humans and animals.(Global antimicrobial resistance surveillance system (GLASS) report: early implementation 2020 n.d.)
Though the factors influencing the occurrence of AMR are difficult and complicated, it is undeniable that AMR is linked to the irrational or incorrect use of antimicrobial agents (specifically antibiotics) in humans, livestock, agriculture, and aquaculture combined with the self-mutating capacity of the organisms.(Aidara-Kane et al. 2018) Antibiotic resistance can develop in animals due to bacteria that have been treated with antibiotics. These bacteria that may carry resistance genes can subsequently be passed from animals to humans. This inter-species transfer of bacteria from animals to humans can occur due to direct contact, food, or common environmental sources such as contaminated water, etc. (Ghimpețeanu et al. 2022; Malijan et al. 2022) There are shreds of evidences of several connections between humans, animals, and the larger environment that permit not just the movement of bacteria but also that of mobile genetic elements (MGEs) of microorganisms, furthering the spread of AMR.(Woolhouse et al. 2015) (Figure 1).
Livestock raised for food is given antibiotics for a various reasons, including non-therapeutic uses, disease management within a herd or flock, and the treatment of infectious diseases.(H. C. Lee et al. 2018; S. Sivaraman and Parady 2018) This leads to a wide range of potential antibiotic limitation strategies. The least restrictive strategies would limit the use of antibiotics to only certain non-therapeutic purposes, such as using antibiotics for the fast growth of broilers, layers, cattle, etc. For example, in E. coli and S. aureus recovered from the community and unknown origin blood and urine specimens, the rates of resistance to methicillin β-lactam antibiotic and cephalosporin (3rd generation antibiotic) were reported by several countries, territories, and areas, respectively (Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report 2020; A. S. Lee et al. 2018)
Antibiotics are frequently used as a preventative measure in food-producing animals (such as cattle, poultry, and pigs); it is estimated that such use will increase by about 67% globally by 2030.(Van Boeckel et al. 2015) As a result, humans consuming food products originating from agricultural farms, livestock and aquaculture may develop resistance against these specific anti-microbial’s due to AMR residues being introduced in them.(Cheng et al. 2019; Hudson et al. 2017) India has one of the highest rates of multidrug-resistant diseases and has the highest burden of drug-resistant microorganisms. According to the Scoping Report on Antimicrobial Resistance in India, from 2000 to 2030, it is expected that poultry consumption will increase by 577%, whereas the use of antibiotics in animal feed will increase by 82%. Some of the most heavily used antibiotics to meet up this load are penicillin, tetracycline and quinolones.(S. Sivaraman and Parady 2018)
In India, chicken is highly consumed and one of the most common contaminated food sources. Poultry and livestock can be infected by water or food contaminated with human or animal feces, or through human carrier workers. One possible entry point for numerous microorganisms could be inappropriate sanitary procedures used during the handling and processing of meat and meat products. Raw meat and unfermented milk, are especially prone to bacterial contamination.(Lorenzo et al. 2018) In comparison to India chicken is a highly consumed food as well in Taiwan which is also thought to have a substantial negative impact on the formation of a number of health problems. To understand the current scenario of AMR we collect data not only from animal-based products but also other RTE food categories with high consumption in Taiwan, including fresh-cut fruits/vegetables, ice desserts, rice products and low-water activity foods. Various investigations in human patients, food animals, foods, and the environment revealed the risk of losing valuable treatments. However, because the studies are not thorough and difficult to compare, it can be difficult to provide a complete picture of the problem. A compilation of current information is required to adequately analyze the situation and direct our limited resources toward minimizing knowledge gaps.
To investigate the situation of antimicrobial residues in different food sources, we have performed a meta-analysis of two representative countries, India (A developing nation) and Taiwan (A developed nation). The present study aimed at retrieving and analyzing the existing information on AMR, focusing on bacteria species in the animal and non-animal-source food chain by including foods (animal origin and non-animal origin), aquaculture and agriculture.

2. Materials and methods

2.1. Data extraction and search strategy

Systematic searches were conducted through a number of electronic databases, including Google Scholar, Science Direct, and PubMed, to find relevant published works that were published between January 2013 and December 2022. The process of Data retrieval and inclusion procedures closely followed to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards. The databases listed above were searched for all pertinent articles using pertinent medical subject heading (MeSH) terms and keywords. The MeSH phrases and keywords were: “AMR/antibiotic from food sources in India/Taiwan”, “AMR/antibiotic prevalence from food sources in India/Taiwan”, “E. coli from food sources in India/Taiwan”, “Salmonella and India/Taiwan”, “Staphylococcus and India/Taiwan”, “Listeria and India/Taiwan”, “AMR/antibiotic prevalence in animals/livestock and India/Taiwan”, “E. coli in animals and India/Taiwan”, “Salmonella in animals/livestock and India/Taiwan”, “Staphylococcus in animals/livestock and India/Taiwan”, AMR/anti-biotic in foods and India/Taiwan”, “AMR/antibiotic prevalence in foods and India/Taiwan”, “E. coli in foods and India/Taiwan”, “Salmonella in foods and India/Taiwan”, “Staphylococcus in foods and India/Taiwan”, “Listeria in foods and India/Taiwan”, “E. coli from water and India/Taiwan”, “Salmonella water and India/Taiwan”, etc. The search queries were applied using the Boolean operators "AND", "OR", “NOT” and “AND NOT”.

2.2. Inclusion and exclusion criteria

The literature search defined by the aforementioned strategy yielded 53 articles, and we used pre-defined inclusion and exclusion criteria to identify eligible articles. The inclusion criteria were as follows: 1) abstracts and full text only available in English 2) the source of samples for bacteria isolation investigations, which should include food handlers, live food animals, foods (animal origin or non-animal source foods), 3) longitudinal/cross-sectional studies that report the proportion of bacterial pathogens and AMR in food sources, 4) an acceptable representative sample size with a clear report on the proportion of resistant and vulnerable organisms, 5) reliable methods for detecting bacterial species (phenotypic identification using culture and biochemical tests, molecular detection methods) and 6) similarly reliable methods for assessing AMR using disc diffusion, minimum inhibitory concentration (MIC) inhibition, or molecular techniques in accordance with international manuals and AMR cut-off standards. Furthermore, research lumped AMR results into big categories such as "Gram-negative organisms," and lacked the essential comprehensive and explicit information such as types of samples from which the bacteria were isolated, as well as studies done outside of India/Taiwan or studies not following the above-mentioned inclusion criteria were eliminated.

2.3. Extraction of data

The necessary information from the publications was retrieved and validated for consistency and data quality. Data extraction templates were created in Excel (Microsoft® office excel 2013) and pre-tested before the entire extraction by extracting random papers and making any required revisions. The variables extracted were the year of publication (2013-2022), sample sources (foods, food producing animals, and non-animal source food), sample size (number of study units/samples), number of positive food samples (to calculate prevalence) and species of microorganism investigated, microbiological and AMR detection methods (disc diffusion, MIC and molecular techniques) for which antimicrobials were tested, name and number of susceptible and resistant pathogens. Papers were managed using Mendeley (version 1.19.8, Elsevier, London, UK)

2.4. Food categories

Food samples from twelve different categories were evaluated which includes: Fish, Pork, Chicken, Egg, Turkey, Beef, Sea food, Ready to eat food (RTE), Fruits and vegetables, Milk, Mutton and Others (Frog, Goat, Duck, Geese, Raw meat, Duck, Emu, Slaughter house environment swabs/sewage, Chopping boards and knives).

3. Result

3.1. Data acquisition

The preliminary search yielded 687 articles published between January 2013 and December 2022. After excluding articles that did not meet the inclusion criteria, 53 reports (34 from India and 19 from Taiwan) were identified as eligible for data extraction and qualitative analysis, as shown in Figure 2. Prevalence estimates from these studies were calculated from 18,784 samples that meet the meta-analysis inclusion criteria on AMR from different food sources (food-producing animals and non-animal source food, agriculture, and aquaculture). Out of 18,784 samples, 9,456 food samples were from India, and 9,328 samples were from Taiwan. The included articles had tested for resistance to 14 different bacteria involved in 12 different food contamination.

3.2. Meta-analysis

Out of the total 18,784 samples acquired for meta-analysis from different studies, 7,676 positive samples were found (4,313 were from India and 3,363 were from Taiwan) were obtained from various bacteria found in various food items. A total of 18 different antimicrobial agents/drugs and 16 different bacteria had resistance. The sample sources, species of bacteria, and their proportions are represented in Table 1 and Table 2.

3.2.1. Prevalence of pathogenic bacteria in India

From 34 research articles,(Access et al. 2015; Adesiji, Deekshit, and Karunasagar 2014; Dutta et al. 2016; Hussain et al. 2017; Jeyasanta and Patterson 2016; Kar et al. 2015; Kaushik et al. 2018; Kerala 2017; Koovapra et al. 2016; Kumar et al. 2021; Kumari and Sarkar 2014; Mahanti et al. 2013; Mailk et al. 2013; Maloo et al. 2017; Mistry et al. 2016; Nirupama et al. 2018; Preethirani et al. 2015; Rasheed et al. 2014b; Rashid et al. 2013; Sadashiv and Kaliwal 2014; Saksena, Malik, and Gaind 2020; Samanta et al. 2014; Samples, Rathore, and Abulreesh 2018; Savariraj et al. 2018; Shah et al. 2019; Sharma et al. 2019; Singh 2016; G. K. Sivaraman et al. 2020; Sudha et al. 2014; Suresh et al. 2018; Verma et al. 2018; Visnuvinayagam et al. 2015; Zehra et al. 2019) 9,456 food samples were acquired, out of which 4,313 (45.6%) samples were contaminated with pathogenic bacteria’s. The maximum contamination was found in Beef (881; 62.8%) and Milk (1036; 60.7%) followed by Chicken (944; (30.6%). The prevalence of contamination in other food products was in Fruit and vegetables, Fish, RTE, Pork, and Sea food which accounts for (404, 57.7%; 323, 81%; 151, 54.5%; 362, 34.7%; and 45, 33.8%). The minimum amount of pathogenic bacteria was found in others (83, 25.4%); egg (48, 25.4%); and mutton (36, 18%).
The maximum pathogenic bacteria found in India from different food sources were in E. coli (1760, 18.6%) and Staphylococcus aureus (969, 10.2%); followed by Klebsiella (370, 3.9%), Bacillus (365, 3.9%), Campylobacter (209; 2.2%), Salmonella (205, 2.2%), Vibrio spp. (159,1.7%) while the lesser number of pathogenic microbes were of Acinetobacter spp. (97,1.0%); Other (Citrobacter spp., Proteus spp., Providencia spp., Aeromonas spp., Enterococcus spp.) (43, 0.4%); Enterobacter spp., (38, 0.4%) Streptococcus, (35, 0.4%); Pseudomonas spp. (15, 0.2%) (Table 1 and 2).

3.2.2. Prevalence of Pathogenic bacteria in Taiwan

From 19 research articles,(Chang et al. 2022; Chen et al. 2017; Chu et al. 2017; Fan et al. 2016; Hsieh, Yen, and Chuang 2019; Hsueh et al. 2020; S. I. Lee et al. 2020; W. C. Lee and Yeh 2017; Lin et al. 2017a; Lo et al. 2017; Nguyen et al. 2017; Publication 2017; Su et al. 2016; J. Wang, Hussain, and Huang 2021; Y. T. Wang et al. 2019; C. J. Wu et al. 2019; Y. C. Wu et al. 2017; Yeh et al. 2018) 9328 food samples were found out of which 3,363 (36%) samples were contaminated with pathogenic bacteria. The maximum contamination was found in Fish (388; 71.2%) and Pork (896; 50.8%) followed by Chicken (651; (48.5%). The prevalence of contamination in other food products was in others (Frog, Goat, Duck, and Geese), (118, 66.7%); RTE, (535, 38%); Sea food, (150, 36.7%); Beef, (96, 29.6%); Milk, (242, 22.2%) Turkey (285, 13.5%) and Fruit and vegetables (5, 4.2%) The only dietary item completely devoid of harmful microorganisms was eggs (0, 0.0%).
The maximum pathogenic bacteria found in Taiwan from different food sources were E. coli (1467, 15.7%) and Staphylococcus aureus (596, 6.4%); followed by Salmonella (444, 4.8%), Aeromonas spp. (382, 4.1%), Streptococcus spp. (262, 2.8%), Clostridium (142, 1.5%) while the lesser number of pathogenic microbes was of Acinetobacter spp. (22, 0.2%); Bacillus (21, 0.2%), Enterobacteriaceae spp., (15, 0.2%) Pseudomonas spp. (15, 0.1%). (Table 1 and 2)
When we compare the antimicrobial-resistant bacteria (AMRB) prevalence in both countries, 9676 AMR isolates were yielded (4313 and 3363 were from India and Taiwan, respectively) (Figure 3). The majority of AMR isolate samples in India were from E. coli (41% in India and 43% in Taiwan), which shows prevalence in the maximum of the food samples in both countries. Followed by Staphylococcus aureus that originated from meat products (beef, pork, and chicken), vegetables, fish, and others, yielding 22% of the samples in India and 18% in Taiwan. This was followed by Klebsiella (9%), Bacillus (8%), and Campylobacter (5%), Vibrio (4%), Acinetobacter (2%), in India, whereas in Taiwan, the AMR isolates were followed by Salmonella (13%), Aeromonas (11%), Streptococcus (8%), and Clostridium (4%) (Figure 3).

3.3. Comparison of AMR prevalence in India and Taiwan

To further understand the spread of AMR, we looked at the key species of foodborne pathogens and indicator bacteria from India and Taiwan separately (Figure 4 and Figure 5). In India, most of the pathogenic bacteria show resistance against β-lactam and cephalosporin, whereas in Taiwan, the most resistance was seen against quinolone and fluoroquinolones (Figure 4 and Figure 5), followed by amino glycoside. β-lactam shows more resistance in Gram-positive bacteria, while quinolones were more likely in Gram-negative bacteria.

3.3.1. Gram positive bacteria

3.3.1.1. Staphylococcus

Among other species, S. aureus was the most prominent, with two strains isolated: Methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-susceptible Staphylococcus aureus (MSSA). Bacteria were regularly found in milk and other animal products such as pork, chicken, beef, RTE, and others. The country with the most tainted food samples was India. AMR was most common for the following drugs: β-lactam, aminoglycosides, and tetracycline, whereas, folic-acid inhibitors, and fluoroquinolones were less common.

3.3.1.2. Streptococcus

Streptococcus agalactiae, Streptococcus dysgalactiae and Streptococcus uberis were the genus found in Streptococcus. The most prevalent contaminated foods were milk and fish. In Taiwan, 8% of the isolates was streptococcus. AMR was observed against β-lactam, cephalosporin, and aminoglycoside, whereas less resistance was shown against macrolide and sulfonamide.

3.3.1.3. Listeria spp.

Listeria is an important bacterial infection that occurs in animals and humans and arises mainly from the ingesting contaminated food. From the data, about 1% of the total positive samples were contaminated with listeria. It is commonly found in beef and sea food. High resistance was seen against aminoglycoside, monocarboxylic and quinolone, and less resistance was seen against β-lactam, tetracycline, and chloramphenicol.

3.3.1.4. Bacillus

Bacillus is responsible for the spoilage of most of the dairy products and have been associated with foodborne outbreaks. B. cereus, Bacillus weihenstephanensis, and Bacillus cytotoxicus are closely related species. About 8 % of the positive samples of Bacillus were seen in India. AMR was observed against β-lactam, cephalosporin, and macrolide. Less resistance was shown against tetracycline and fluoroquinolones drugs.

3.3.1.5. Clostridium

C. perfringens infection is one of the most common foodborne diseases in humans, along with Salmonella and Campylobacter infections. It has been claimed that chicken meat is responsible for about 30% of C. perfringens human infections.(la Mora et al. 2020) From the data, around 4% of the positive samples were from Taiwan. AMR was observed against β-lactam, macrolide, lincosamide and tetracycline. Less prevalence was seen against fluoroquinolones and phenicol.

3.3.2. Gram negative bacteria

3.3.2.1. Escherichia coli

Pathogenic E. coli is the leading cause of infection both in humans and animals. About 41% in India and 44% in Taiwan of the total positive samples were contaminated with E. coli. The most prevalent contaminated food was pork, chicken, beef, fish, egg, mutton and milk. Across the entire data set of E. coli isolates were found resistant against quinolone, fluoroquinolones, and β-lactam, whereas just a small proportion of total E. coli were identified to be resistant to tetracycline, and folic acid inhibitors.

3.3.2.2. Salmonella

Salmonella is one of the most significant zoonotic bacterial foodborne pathogens of humans that cause diarrhea, focal suppurative infections and bacteraemia. Pork, turkey, chicken, beef, sea food, fruit and vegetables, and mutton have maximum salmonella contamination. The most prevalent antibiotic to which the various Salmonella serovars were resistant to β-lactam, tetracycline, and aminoglycoside. Lower resistance was observed for quinolone, polymyxin, and cephalosporin.

3.3.2.3. Campylobacter

Campylobacter spp. has been attributed globally as one of the four key causes of diarrheal diseases.(Campylobacter n.d.) From the data, about 2.2% of the total positive sample shows the bacterial prevalence of campylobacter in India. Presence of Campylobacter spp. isolates in chicken meat samples were seen maximum. The isolates showed higher resistance against tetracycline, sulfonamides, and cephalosporin, whereas a small proportion of the sample was resistant against monocarboxylic, and macrolide.

3.3.2.4. Acinetobacter

In recent decades, Acinetobacter spp. has become a significant global source of nosocomial infections. From the data, the most prevalent contaminated food sources were fruits and vegetable and REF in India and Taiwan, respectively. The isolates shows higher resistance against carbapenems, cephalosporin, and β-lactam. In contrast, just small proportion was seen resistant against tetracycline, polymyxin and folic acid inhibitors.

3.3.2.5. Enterobacter

Enterobacter spp. is a food-borne pathogen that has been related to infantile meningitis, septicemia, and necrotizing colitis after consuming contaminated powdered infant meals. From the data, the most prevalent contaminated food sources were fruits and vegetable and REF. The isolates show higher resistance against β-lactam, polymyxin, and cephalosporin,. Lower resistance was observed against folic acid inhibitors, fluoroquinolones, and tetracycline.

3.3.2.6. Klebsiella

In humans, Klebsiella pneumoniae is a significant food-borne pathogen that can lead to septicemia, liver abscesses, and diarrhea. (Davis and Price 2016; Liao, Huang, and Hsueh 2022) From data, the prevalence was seen in fish, fruits and vegetables and RTE, about 9% of the total positive sample was found in India. Most of the resistance was seen against aminoglycoside, β-lactam, and cephalosporin, while lesser resistance was seen against tetracycline, lincosamide and sulfonamide.

3.3.2.7. Vibrio spp.

Vibrio species have been identified as human entero-pathogens and as the etiological agents of gastroenteritis, septicemia, and wound infection that is transmitted via shellfish. (Baker-Austin et al. 2018) From our data, the prevalence of vibrio spp. was seen in sea food particularly in shellfish. About 4% of the total positive sample was seen in India. The most prevalent antibiotic to which the various vibrio spp. were resistant to cephalosporin, β-lactam and quinolone, the less resistance was seen against tetracycline, fluoroquinolones and aminoglycoside.

3.3.2.8. Aeromonas

Aeromonas spp. is opportunistic bacteria that cause a wide range of human disorders, including gastroenteritis, urinary tract infections, etc., which is transmitted via food-borne pathogens.(Piotrowska and Popowska 2014) From the data, about 11% of the total positive was seen in Taiwan. The most contaminated food was fish and sea food. The highest resistance was seen against cephalosporin, tetracycline, and carbapenems.

3.3.2.9. S. maltophillia

Stenotrophomonas maltophilia is an important opportunistic pathogen that has been reported to be associated with a wide variety of infections; it is ubiquitously present in aqueous environments and REF.(Li et al. 2019) From the data, about 1% of the total positive samples were contaminated. It shows high resistance against chloramphenicol, cephalosporin, folic acid inhibitor and less resistance was seen against β-lactam, and fluoroquinolones.

3.3.2.10. Pseudomonas

Transmission of Pseudomonas aeruginosa along the food chain could cause gastrointestinal infections. From the data, about 1% of the samples were contaminated in both against, particularly against fruit and vegetables and RTE. The isolates show higher resistance against cephalosporin, β-lactam, and folic acid inhibitor.

4. Discussion

The threat posed by AMR to both human and animal health is significant, and it has garnered attention from all around the world in an effort to stop its spread before it is too late. Due to a lack of surveillance networks, laboratory capacity, and adequate diagnostics, AMR is typically not consistently monitored in many nations.(Iskandar et al. 2021) This is worsened by overuse, misuse and improper use of antibiotics by animal handlers, farmers, etc.
The goal of this systematic data analysis was to quantify AMR exposure in various foods from butcher shops and retail food markets in India and Taiwan. The study's goal is to give a literature-based analysis of AMR bacteria occurrence and food vulnerability. The AMR exposure assessment serves as the foundation for developing an AMR action plan that incorporates risk analysis into systemic surveillance systems at the consumer and retail levels. This meta-analysis is essential in providing answers that can influence antibiotic usage strategies in livestock.
The meta-analysis study suggests that most of the food samples were contaminated with E.coli and Staphylococcus in both India and Taiwan. A high contamination rate was seen in chicken and beef in India, which is highly consumed, whereas in Taiwan, the resistance was seen against fish and pork. This indicates that proper hygiene practices are not being followed consistently. The data provided revealed that contamination occurred in the food most often consumed in each country. The isolated bacteria were mostly resistant to β-lactam, fluoroquinolone and quinolone. Resistance to carbapenem, tetracycline, and cephalosprin was moderate, whereas resistance to macrolides, phenicol, folic acid inhibitors, fusidane, lincosamide, glycylcycline, and monobactam was low. Raw foods consumption makes it more serious to consumers because resistant germs can thrive in untreated food.
In India beef, milk, pork and chicken are the most contaminated food as their consumption is high due to low prices. Raw foods contaminated with pathogens (meat, eggs, milk) and diseased animals are major sources of human diseases.(Rincón-Gamboa, Poutou-Piñales, and Carrascal-Camacho 2021) Furthermore, antimicrobial drugs are employed in animals not only for diagnosis and prevention of bacterial illness, but also for growth promotion also a major cause.(Chattopadhyay 2014) In addition, different animal-adapted serotypes are far more likely to cause invasive illness in humans. Similarly, vegetables may be contaminated by inadequately treated water and fertilizers, or they may be harmed by the use of bio-control agents during cultivation. (Rasheed et al. 2014a)
However in Taiwan, food such as pork, chicken and meat is mostly contaminated by pathogens. In addition intake of raw milk, uncooked meat, the frozen fish ready for human consumption, some are employed as fermentation starters in meat, or other food processing, shows principal carriers of AMR.(Hoel, Vadstein, and Jakobsen 2017) According to one estimate, over 150,000 different varieties of food and drink are served on the streets and in night markets around the country.(Lin et al. 2017b) The culture of street food has always been linked to food poisoning and other digestive disorders and is associated with the food chain.(Alimi 2016; Huynh-Van et al. 2022) This suggests that the food supply chain could be a significant route for resistant genes to spread. As a result, examining the genetic arrangement of AMR genes is crucial for determining AMR risk and should be done more thoroughly.
It is essential to understand how the AMR is developing worldwide, and the current severity of the problem if it is unchecked. Similar to disease surveillance, AMR surveillance and meta-analysis offers data to guide intervention options. For instance, AMR surveillance can assist in evaluating of the effects of interventions on antibiotic usage guidelines or infection control. Additionally, effective surveillance systems can give decision-makers information on the emergence and prevalence of AMR.(Johnson 2015) Various countries including India and Taiwan have implemented policies like introduction monitoring systems and the One Health approach(Hernando-Amado et al. 2019) of resistance in food animals considering the public health risks of possible transfer of resistant bacteria or genes from animals or the environment to humans.(Carlos Franco et al. 2022; Gunjan et al. 2023).
Therefore, devising successful measures to target a reduction in the formation and spread of AMR requires an understanding of the epidemiology of AMR. As a result, organized multi-sectorial, collaborative AMR surveillance is critical for decision-making at the national, regional, and worldwide levels. It aids in tracking the burden of AMR and resistance trends in order for countries to develop cost-effective, evidence-based AMR response strategies. It will be beneficial in analyzing foodborne pathogens from multiple sources and reducing the knowledge gap.

5. Conclusion

The systematic meta-analysis depicted the overall antimicrobial drug resistance problem across different food samples with higher estimates of clinically important resistance from the pork in Taiwan and chicken in India. It also emphasized the importance of understanding the probable linkage, transmission mechanisms, and flow pattern of resistant bacteria in the human-animal-environmental interface in order to reduce the potential risk to public health. As a result, measures to lessen the burden of antimicrobial resistance should be based on a thorough knowledge of the essence of a coordinated One Health strategy.
AMR is becoming a serious health issue in the country as some widely used antimicrobials are not effective in the treating bacterial infections in animals and humans. Fluoroquinolone and β-lactam shows the highest drug resistance. Therefore, drug susceptibility for treating bacterial infections should be practiced as much as possible. To overcome or eliminate the risk of Antimicrobial resistance bacteria in the food chain, two important stages must be monitored: antimicrobial usage in foods and AMRB arising from livestock and agricultural practices. On-time revision and implementation of standard treatment guidelines, shall be prioritized in livestock as strategy to tackle AMR in the respective countries. The proposed methods should be policy-based, universally applied, and fully supported by legislative measures. A single nation cannot solve the AMR issues affecting the world's food supply, but a worldwide collaborative effort can solve it.

Funding

Project grants (SCRPD1L0221); DOXABIO-CGU, R.O.C., (SCRPD1K0131), and the CGU project grant (UZRPD1L0011).

Acknowledgements

This work was supported through industry-academia collaboration projects, VtR Inc-CGU, R.O.C., project grant (SCRPD1L0221), DOXABIO-CGU, R.O.C., project grant (SCRPD1K0131), and also the CGU project grant (UZRPD1L0011).

Ethical consideration

Not Applicable.

Competing interest

Not Applicable.

References

  1. Adesiji, Yemisi O., Vijaya Kumar Deekshit, and Indrani Karunasagar. 2014. “ Antimicrobial-resistant Genes Associated with Salmonella Spp. Isolated from Human, Poultry, and Seafood Sources .” Food Science & Nutrition 2(4): 436–42. [CrossRef]
  2. Aidara-Kane, Awa et al. 2018. “World Health Organization (WHO) Guidelines on Use of Medically Important Antimicrobials in Food-Producing Animals.” Antimicrobial Resistance and Infection Control 7(1). /pmc/articles/PMC5772708/ (January 26, 2023). [CrossRef]
  3. Alimi, Buliyaminu Adegbemiro. 2016. “Risk Factors in Street Food Practices in Developing Countries: A Review.” Food Science and Human Wellness 5(3): 141–48. [CrossRef]
  4. “Antimicrobial Resistance (AMR).” https://www.worldbank.org/en/topic/health/brief/antimicrobial-resistance-amr (January 26, 2023).
  5. Baker-Austin, Craig et al. 2018. “Vibrio Spp. Infections.” Nature Reviews Disease Primers 2018 4:1 4(1): 1–19. https://www.nature.com/articles/s41572-018-0005-8 (January 29, 2023).
  6. “Campylobacter.” https://www.who.int/news-room/fact-sheets/detail/campylobacter (January 29, 2023).
  7. Carlos Franco, and M et al. 2022. “Systematic Surveillance and Meta-Analysis of Antimicrobial Resistance and Food Sources from China and the USA.” Antibiotics 2022, Vol. 11, Page 1471 11(11): 1471. https://www.mdpi.com/2079-6382/11/11/1471/htm (January 31, 2023). [CrossRef]
  8. Chang, Yi-jung, Chyi-liang Chen, Hsin-ping Yang, and Cheng-hsun Chiu. 2022. “Prevalence , Serotypes , and Antimicrobial Resistance Patterns of Non-Typhoid Salmonella in Food in Northern Taiwan.” : 1–9.
  9. Chattopadhyay, Madhab K. 2014. “Use of Antibiotics as Feed Additives: A Burning Question.” Frontiers in Microbiology 5(JULY): 334. [CrossRef]
  10. Chen, Chih Ming et al. 2017. “High Diversity of Antimicrobial Resistance Genes, Class 1 Integrons, and Genotypes of Multidrug-Resistant Escherichia Coli in Beef Carcasses.” Microbial Drug Resistance 23(7): 915–24. [CrossRef]
  11. Cheng, Guyue et al. 2019. “Selection and Dissemination of Antimicrobial Resistance in Agri-Food Production.” Antimicrobial Resistance & Infection Control 2019 8:1 8(1): 1–13. https://link.springer.com/articles/10.1186/s13756-019-0623-2 (January 28, 2023). [CrossRef]
  12. Chu, Chi Shih et al. 2017. “Investigation into Antimicrobial Resistance, Enterotoxin and Cassette Chromosome Gene of Staphylococcus Aureus Isolates from Humans, Cows and Goats in Taiwan.” Thai Journal of Veterinary Medicine 47(4): 481–92. [CrossRef]
  13. Chowdhury NK, Deepika, Choudhury R, Sonawane GA, Mavinamar S, Lyu X, Pandey RP, Chang CM. Nanoparticles as an effective drug delivery system in COVID-19. Biomed Pharmacother. 2021 Nov;143:112162. [CrossRef]
  14. Dadgostar, Porooshat. 2019. “Antimicrobial Resistance: Implications and Costs.” Infection and Drug Resistance 12: 3903–10. https://www.tandfonline.com/action/journalInformation?journalCode=didr20 (January 26, 2023). [CrossRef]
  15. Davis, Gregg S., and Lance B. Price. 2016. “Recent Research Examining Links Among Klebsiella Pneumoniae from Food, Food Animals, and Human Extraintestinal Infections.” Current environmental health reports 3(2): 128–35. https://pubmed.ncbi.nlm.nih.gov/27022987/ (January 29, 2023). [CrossRef]
  16. Dutta, Shanta et al. 2016. “Non-Typhoidal Salmonella Isolates from Livestock and Food Samples, Kolkata, India Conclusion: High Prevalence of NTS in Poultry and Meat Samples in Kolkata Poses a Potential Risk of Foodborne Disease in Humans.” Journal of Microbiology and Infectious Diseases 66(33): 113–20. http://dergipark.gov.tr/download/article-file/325637.
  17. Fan, Yang Chi et al. 2016. “Incidence and Antimicrobial Susceptibility to Clostridium Perfringens in Premarket Broilers in Taiwan.” Avian Diseases 60(2): 444–49.
  18. Ghimpețeanu, Oana Mărgărita et al. 2022. “Antibiotic Use in Livestock and Residues in Food—A Public Health Threat: A Review.” Foods 2022, Vol. 11, Page 1430 11(10): 1430. https://www.mdpi.com/2304-8158/11/10/1430/htm (January 26, 2023). [CrossRef]
  19. Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report. 2020.
  20. “Global Antimicrobial Resistance Surveillance System (GLASS) Report: Early Implementation 2020.” https://apps.who.int/iris/handle/10665/332081 (January 26, 2023).
  21. Grace, Delia. 2015. “Review of Evidence on Antimicrobial Resistance and Animal Agriculture in Developing Countries.” https://www.gov.uk/research-for-development-outputs/review-of-evidence-on-antimicrobial-resistance-and-animal-agriculture-in-developing-countries-201309 (January 26, 2023).
  22. Gunjan et al. 2023. “Comparative Meta-Analysis of Antimicrobial Resistance from Different Food Sources along with One Health Approach in Italy and Thailand.” One Health 16: 100477. [CrossRef]
  23. Hernando-Amado, Sara, Teresa M. Coque, Fernando Baquero, and José L. Martínez. 2019. “Defining and Combating Antibiotic Resistance from One Health and Global Health Perspectives.” Nature Microbiology 2019 4:9 4(9): 1432–42. https://www.nature.com/articles/s41564-019-0503-9 (January 31, 2023). [CrossRef]
  24. Hoel, Sunniva, Olav Vadstein, and Anita N. Jakobsen. 2017. “Species Distribution and Prevalence of Putative Virulence Factors in Mesophilic Aeromonas Spp. Isolated from Fresh Retail Sushi.” Frontiers in Microbiology 8(MAY): 931. [CrossRef]
  25. Hsieh, Jui Chun, Yu Shan Yen, and Shih Te Chuang. 2019. “Identification of Streptococcus Spp. Isolated from Bovine Milk and Characterization of Their Antimicrobial Susceptibility Profiles in Taiwan.” Thai Journal of Veterinary Medicine 49(1): 57–63. [CrossRef]
  26. Hsueh, Shun Chung et al. 2020. “Molecular Evidence for Intra- and Inter-Farm Spread of Porcine Mcr-1-Carrying Escherichia Coli in Taiwan.” Frontiers in Microbiology 11(August): 1–9. [CrossRef]
  27. Hudson, John A. et al. 2017. “The Agri-Food Chain and Antimicrobial Resistance: A Review.” Trends in Food Science & Technology 69: 131–47. [CrossRef]
  28. Hussain, Arif et al. 2017. “Risk of Transmission of Antimicrobial Resistant Escherichia Coli from Commercial Broiler and Free-Range Retail Chicken in India.” 8(November). [CrossRef]
  29. Huynh-Van, Ba et al. 2022. “Factors Associated with Food Safety Compliance among Street Food Vendors in Can Tho City, Vietnam: Implications for Intervention Activity Design and Implementation.” BMC Public Health 22(1): 1–11. https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-12497-2 (January 31, 2023). [CrossRef]
  30. Iskandar, Katia et al. 2021. “Surveillance of Antimicrobial Resistance in Low- and Middle-Income Countries: A Scattered Picture.” Antimicrobial Resistance & Infection Control 2021 10:1 10(1): 1–19. https://aricjournal.biomedcentral.com/articles/10.1186/s13756-021-00931-w (January 29, 2023). [CrossRef]
  31. Jeyasanta, K Immaculate, and Jamila Patterson. 2016. “Prevalence of Antibiotic Resistant Listeria Monocytogenes in Sea Foods of Tuticorin Coast , Southeastern India.” 8(6): 356–64.
  32. Johnson, Alan P. 2015. “Surveillance of Antibiotic Resistance.” Philosophical Transactions of the Royal Society B: Biological Sciences 370(1670). /pmc/articles/PMC4424431/ (January 31, 2023). [CrossRef]
  33. Kar, Debasish et al. 2015. “Molecular and Phylogenetic Characterization of Multidrug Resistant Extended Spectrum Beta-Lactamase Producing Escherichia Coli Isolated from Poultry and Cattle in Odisha, India.” Infection, Genetics and Evolution 29: 82–90. [CrossRef]
  34. Kaushik, Purushottam et al. 2018. “Antimicrobial Resistance and Molecular Characterisation of E. Coli from Poultry in Eastern India.” Veterinaria Italiana 54(3): 197–204. [CrossRef]
  35. Kerala, Slaughterhouses. 2017. “Occurrence and Antibiotic Susceptibility of Listeria Species and Staphylococcus Aureus in Cattle.” 14(10): 1–7. [CrossRef]
  36. Koovapra, S. et al. 2016. “Molecular Signature of Extended Spectrum β-Lactamase Producing Klebsiella Pneumoniae Isolated from Bovine Milk in Eastern and North-Eastern India.” Infection, Genetics and Evolution 44: 395–402. [CrossRef]
  37. Kumar, M Suman et al. 2021. “Occurrence and Antimicrobial Resistance of Campylobacter Isolates from Broiler Chicken and Slaughter House Environment in India.” Animal Biotechnology 0(0): 1–9. [CrossRef]
  38. Kumari, Sarita, and Prabir K Sarkar. 2014. “Prevalence and Characterization of Bacillus Cereus Group from Various Marketed Dairy Products in India.” 2013(Nddb 2013). [CrossRef]
  39. Lee, Andie S. et al. 2018. “Methicillin-Resistant Staphylococcus Aureus.” Nature Reviews Disease Primers 2018 4:1 4(1): 1–23. https://www.nature.com/articles/nrdp201833 (January 26, 2023).
  40. Lee, Hsin Chun, Chi Min Chen, Jen Ting Wei, and Hsiu Yi Chiu. 2018. “Analysis of Veterinary Drug Residue Monitoring Results for Commercial Livestock Products in Taiwan between 2011 and 2015.” Journal of Food and Drug Analysis 26(2): 565–71. [CrossRef]
  41. Lee, Soo In, Sun Do Kim, Ji Heon Park, and Soo-jin Yang. 2020. “Species Distribution , Antimicrobial Resistance , and Enterotoxigenicity of Non- Aureus Staphylococci in Retail Chicken Meat.”.
  42. Lee, Wan Chen, and Kuang Sheng Yeh. 2017. “Characteristics of Extended-Spectrum β-Lactamase-Producing Escherichia Coli Isolated from Fecal Samples of Piglets with Diarrhea in Central and Southern Taiwan in 2015.” BMC Veterinary Research 13(1). [CrossRef]
  43. Li, Dan, Chun Hong Wong, Mei Fang Seet, and Nicole Kuan. 2019. “Isolation, Characterization, and Inactivation of Stenotrophomonas Maltophilia From Leafy Green Vegetables and Urban Agriculture Systems.” Frontiers in Microbiology 10: 2718. [CrossRef]
  44. Liao, Chun Hsing, Yu Tsung Huang, and Po Ren Hsueh. 2022. “Multicenter Surveillance of Capsular Serotypes, Virulence Genes, and Antimicrobial Susceptibilities of Klebsiella Pneumoniae Causing Bacteremia in Taiwan, 2017-2019.” Frontiers in microbiology 13. https://pubmed.ncbi.nlm.nih.gov/35369508/ (January 29, 2023). [CrossRef]
  45. Lin, Lin et al. 2017a. “Antimicrobial Resistance and Genetic Diversity in Ceftazidime Non-Susceptible Bacterial Pathogens from Ready-to-Eat Street Foods in Three Taiwanese Cities.” Scientific Reports 7(1): 1–9. [CrossRef]
  46. Lin, Lin et al. 2017b. “Antimicrobial Resistance and Genetic Diversity in Ceftazidime Non-Susceptible Bacterial Pathogens from Ready-to-Eat Street Foods in Three Taiwanese Cities.” Scientific Reports 2017 7:1 7(1): 1–9. https://www.nature.com/articles/s41598-017-15627-8 (January 30, 2023). [CrossRef]
  47. Lo, Yung-tsun et al. 2017. “Prevalence and Antimicrobial Resistance of Salmonella in Market Raw Oysters in Taiwan.” 80(5): 734–39. [CrossRef]
  48. Lorenzo, Jose M. et al. 2018. “Main Groups of Microorganisms of Relevance for Food Safety and Stability: General Aspects and Overall Description.” Innovative Technologies for Food Preservation: 53. /pmc/articles/PMC7150063/ (January 30, 2023). [CrossRef]
  49. Mahanti, A. et al. 2013. “Isolation, Molecular Characterization and Antibiotic Resistance of Shiga Toxin-Producing Escherichia Coli (STEC) from Buffalo in India.” Letters in Applied Microbiology 56(4): 291–98. [CrossRef]
  50. Mailk, Subhash et al. 2013. “Incidence and Drug Resistance Pattern of Collibacillosis in Cattle and Buffaloe Calves in Western Utter Pradesh in India.” Journal of Animal Health and Production 1(2): 15–19.
  51. Malijan, Greco Mark et al. 2022. “A Scoping Review of Antibiotic Use Practices and Drivers of Inappropriate Antibiotic Use in Animal Farms in WHO Southeast Asia Region.” One Health 15: 100412. [CrossRef]
  52. Maloo, Aayushi, Abhay B Fulke, Najmuddin Mulani, and Soniya Sukumaran. 2017. “Pathogenic Multiple Antimicrobial Resistant Escherichia Coli Serotypes in Recreational Waters of Mumbai , India : A Potential Public Health Risk.” : 11504–17.
  53. Mistry, Hiral et al. 2016. “Prevalence and Characterization of Oxacillin Susceptible MecA -Positive Clinical Isolates of Staphylococcus Aureus Causing Bovine Mastitis in India.” : 1–10.
  54. la Mora, Zuamí Villagrán De et al. 2020. “Clostridium Perfringens as Foodborne Pathogen in Broiler Production: Pathophysiology and Potential Strategies for Controlling Necrotic Enteritis.” Animals : an Open Access Journal from MDPI 10(9): 1–28. /pmc/articles/PMC7552638/ (January 29, 2023).
  55. “New Report Calls for Urgent Action to Avert Antimicrobial Resistance Crisis.” https://www.who.int/news/item/29-04-2019-new-report-calls-for-urgent-action-to-avert-antimicrobial-resistance-crisis (January 26, 2023).
  56. Nguyen, Thuy Thi Thu et al. 2017. “Genetic Diversity, Virulence Genes, and Antimicrobial Resistance of Streptococcus Dysgalactiae Isolates from Different Aquatic Animal Sources.” Aquaculture 479: 256–64. [CrossRef]
  57. Nirupama, K. R. et al. 2018. “Molecular Characterisation of BlaOXA-48 Carbapenemase-, Extended-Spectrum β-Lactamase- and Shiga Toxin-Producing Escherichia Coli Isolated from Farm Piglets in India.” Journal of Global Antimicrobial Resistance 13(2010): 201–5. [CrossRef]
  58. “Occurrence and Characteristics of Extended-Spectrum β-Lactamases Producing Escherichia Coli in Foods of Animal Origin and Human Clinical Samples in Chhattisgarh, India.Pdf.”.
  59. Piotrowska, Marta, and Magdalena Popowska. 2014. “The Prevalence of Antibiotic Resistance Genes among Aeromonas Species in Aquatic Environments.” Annals of Microbiology 64(3): 921–34. https://annalsmicrobiology.biomedcentral.com/articles/10.1007/s13213-014-0911-2 (January 29, 2023). [CrossRef]
  60. Preethirani, P. L. et al. 2015. “Isolation, Biochemical and Molecular Identification, and in-Vitro Antimicrobial Resistance Patterns of Bacteria Isolated from Bubaline Subclinical Mastitis in South India.” PLoS ONE 10(11): 1–15. [CrossRef]
  61. Publication, Advance. 2017. “The Journal of Veterinary Medical Science.”.
  62. Rasheed, Mohammed Uddin et al. 2014a. “Antimicrobial Drug Resistance in Strains of Escherichia Coli Isolated from Food Sources.” Revista do Instituto de Medicina Tropical de Sao Paulo 56(4): 341–46. https://pubmed.ncbi.nlm.nih.gov/25076436/ (January 30, 2023). [CrossRef]
  63. Rasheed, Mohammed Uddin et al. 2014b. “Resistência Microbiana a Drogas Em Linhagens de Escherichia Coli Isoladas de Fontes Alimentares.” Revista do Instituto de Medicina Tropical de Sao Paulo 56(4): 341–46.
  64. Rashid, Mohd, Sanjay Kumar Kotwal, M A Malik, and Maninder Singh. 2013. “Prevalence , Genetic Profile of Virulence Determinants and Multidrug Resistance of Escherichia Coli Isolates from Foods of Animal Origin.” 173204: 139–42.
  65. Rincón-Gamboa, Sandra M., Raúl A. Poutou-Piñales, and Ana K. Carrascal-Camacho. 2021. “Antimicrobial Resistance of Non-Typhoid Salmonella in Meat and Meat Products.” Foods 10(8): 1731. https://www.mdpi.com/2304-8158/10/8/1731/htm (January 30, 2023). [CrossRef]
  66. Rosa UA, Ribeiro GO, Villanova F, Luchs A, Milagres FAP, Komninakis SV, Tahmasebi R, Lobato MCABS, Brustulin R, Chagas RTD, Abrão MFNDS, Soares CVDA, Tinker RJ, Pandey RP, Raj VS, Sabino EC, Deng X, Delwart E, Costa ACD, Leal É. First identification of mammalian orthoreovirus type 3 by gut virome analysis in diarrheic child in Brazil. Sci Rep. 2019 Dec 9;9(1):18599. [CrossRef]
  67. Sadashiv, S O, and B B Kaliwal. 2014. “Isolation, Characterization and Antibiotic Resistance of Bacillus Sps. from Bovine Mastitis in the Region of North Karnataka, India.” International Journal of Current Microbiology and Applied Sciences 3(4): 360–73. http://www.cabdirect.org/abstracts/20143173026.html?resultNumber=6&order=recent&start=0&q=(title:(antibiotic+OR+residue))+AND+(title:(India*)+OR+ab:(India*))+AND+(title:(bovine+OR+cattle+OR+cow+OR+%22dairy+cattle%22+OR+%22d.
  68. Sadhu S, Rizvi ZA, Pandey RP, Dalal R, Rathore DK, Kumar B, Pandey M, Kumar Y, Goel R, Maiti TK, Johri AK, Tiwari A, Pandey AK, Awasthi A. Gefitinib Results in Robust Host-Directed Immunity Against Salmonella Infection Through Proteo-Metabolomic Reprogramming. Front Immunol. 2021 Mar 31;12:648710. [CrossRef]
  69. Saksena, Rushika, Manika Malik, and Rajni Gaind. 2020. “Bacterial Contamination and Prevalence of Antimicrobial Resistance Phenotypes in Raw Fruits and Vegetables Sold in Delhi, India.” Journal of Food Safety 40(1): 1–8. [CrossRef]
  70. Samanta, I et al. 2014. “Prevalence and Antibiotic Resistance Profiles of Salmonella Serotypes Isolated from Backyard Poultry Flocks in West Bengal , India.” : 536–45.
  71. Samples, Related, Ram Swaroop Rathore, and Hussein Hasan Abulreesh. 2018. “Prevalence and Antibiotic Resistance Profiles.” XX(Xx): 1–8.
  72. Savariraj, Wilfred Ruban et al. 2018. “Prevalence , Antimicrobial Susceptibility and Virulence Genes of Staphylococcus Aureus Isolated from Pork Meat in Retail Outlets in India.” (October): 1–8.
  73. Shah, Muneeba Shafi et al. 2019. “Methicillin Resistance Genes and in Vitro Biofilm Formation among Staphylococcus Aureus Isolates from Bovine Mastitis in India.” Comparative Immunology, Microbiology and Infectious Diseases 64: 117–24. [CrossRef]
  74. Sharma, Jaishree et al. 2019. “Prevalence , Antimicrobial Resistance and Virulence Genes Characterization of Nontyphoidal Salmonella Isolated from Retail Chicken Meat Shops in Northern India.” 102(October 2018): 104–11.
  75. Shrestha, Poojan et al. 2018. “Enumerating the Economic Cost of Antimicrobial Resistance per Antibiotic Consumed to Inform the Evaluation of Interventions Affecting Their Use.” Antimicrobial Resistance and Infection Control 7(1).
  76. Singh, Bhoj Raj. 2016. “Prevalence of Multiple Drug-Resistant Salmonella and E . Coli in Table Eggs in North India.” (April).
  77. Sivaraman, G. K. et al. 2020. “Molecular Assessment of Antimicrobial Resistance and Virulence in Multi Drug Resistant ESBL-Producing Escherichia Coli and Klebsiella Pneumoniae from Food Fishes, Assam, India.” Microbial Pathogenesis 149(August): 104581. [CrossRef]
  78. Sivaraman, Satya, and Vida Parady. 2018. “Antibiotic Use in Food Animals: India Overview.” ReAct Asia-Pacific: 1–31. https://www.reactgroup.org/wp-content/uploads/2018/11/Antibiotic_Use_in_Food_Animals_India_LIGHT_2018_web.pdforg.
  79. Su, Yaochi et al. 2016. “Fluoroquinolone-Resistant and Extended-Spectrum β-Lactamase-Producing Escherichia Coli from the Milk of Cows with Clinical Mastitis in Southern Taiwan.” Journal of Microbiology, Immunology and Infection 49(6): 892–901. [CrossRef]
  80. Sudha, S, C Mridula, Reshma Silvester, and A A M Hatha. 2014. “Prevalence and Antibiotic Resistance of Pathogenic Vibrios in Shellfishes from Cochin Market.” 43(May): 815–24.
  81. Sudhanthiramani, Swetha C., and Bharathy 2015. “Prevalence of Antibiotic Resistant Staphylococcus Aureus from Raw Milk Samples Collected from the Local Vendors in the Region of Tirupathi ,.” 8: 478–81.
  82. Suresh, Y. et al. 2018. “Isolation, Molecular Characterization and Antimicrobial Resistance Patterns of Four Different Vibrio Species Isolated from Fresh Water Fishes.” International Journal of Current Microbiology and Applied Sciences 7(07): 3080–88. [CrossRef]
  83. Tang, Karen L. et al. 2017. “Restricting the Use of Antibiotics in Food-Producing Animals and Its Associations with Antibiotic Resistance in Food-Producing Animals and Human Beings: A Systematic Review and Meta-Analysis.” The Lancet. Planetary health 1(8): e316–27. https://pubmed.ncbi.nlm.nih.gov/29387833/ (January 26, 2023). [CrossRef]
  84. Tyagi R, Srivastava M, Jain P, Pandey RP, Asthana S, Kumar D, Raj VS. Development of potential proteasome inhibitors against Mycobacterium tuberculosis. J Biomol Struct Dyn. 2022 Mar;40(5):2189-2203.
  85. Van Boeckel, Thomas P. et al. 2015. “Global Trends in Antimicrobial Use in Food Animals.” Proceedings of the National Academy of Sciences of the United States of America 112(18): 5649–54. https://www.pnas.org/doi/abs/10.1073/pnas.1503141112 (January 26, 2023).
  86. Verma, P, V V Saharan, S Nimesh, and A P Singh. 2018. “Phenotypic and Virulence Traits of Escherichia Coli and Salmonella Strains Isolated from Vegetables and Fruits from India.”.
  87. Visnuvinayagam, S et al. 2015. “On Line On Line Co Py.” 36(May): 571–75.
  88. Wang, Jiun-ling, Bashir Hussain, and Shih-wei Huang. 2021. “Prevalence , Virulence-Gene Profiles , Antimicrobial Resistance , and Genetic Diversity of Human Pathogenic Aeromonas Spp . from Shellfish and Aquatic Environments ☆.” Environmental Pollution 287(September 2020): 117361. [CrossRef]
  89. Wang, Yu Ting et al. 2019. “Distribution of Antibiotic Resistance Genes among Staphylococcus Species Isolated from Ready-to-Eat Foods.” Journal of Food and Drug Analysis 27(4): 841–48. [CrossRef]
  90. Woolhouse, Mark, Melissa Ward, Bram Van Bunnik, and Jeremy Farrar. 2015. “Antimicrobial Resistance in Humans, Livestock and the Wider Environment.” Philosophical Transactions of the Royal Society B: Biological Sciences 370(1670). https://royalsocietypublishing.org/doi/10.1098/rstb.2014.0083 (January 26, 2023).
  91. Wu, Chi Jung et al. 2019. “Aeromonas Isolates from Fish and Patients in Tainan City, Taiwan: Genotypic and Phenotypic Characteristics.” Applied and Environmental Microbiology 85(21). [CrossRef]
  92. Wu, Ying Chen et al. 2017. “Prevalence and Molecular Characterization of Clostridium Difficile Isolates from a Pig Slaughterhouse, Pork, and Humans in Taiwan.” International Journal of Food Microbiology 242: 37–44. [CrossRef]
  93. Yeh, J. C. et al. 2018. “Comparison of Prevalence, Phenotype, and Antimicrobial Resistance of Salmonella Serovars Isolated from Turkeys in Taiwan.” Poultry Science 97(1): 279–88. [CrossRef]
  94. Zehra, Asima et al. 2019. “Journal of Global Antimicrobial Resistance Prevalence , Multidrug Resistance and Molecular Typing of Methicillin-Resistant Staphylococcus Aureus ( MRSA ) in Retail Meat from Punjab , India.” Integrative Medicine Research 16: 152–58. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated