Background: During the last six decades, extensive use of antibiotics has selected resistant strains, increasing the rate of fatal infectious diseases, and exerting an economic burden on society. This situation is widely accepted as a global problem, yet its degree is not well elucidated in many regions of the world. Up till now, no systemic analysis of Antimicrobial resistance (AMR) in Pakistan has been published. The current study aims to describe the antibiotic-resistance scenario of Pakistan from human samples of the last 10 y, to find the gaps in surveillances and methodology and recommendations for researchers and prescribers founded on these outcomes. Methods: Original research articles analyzed the pattern of Antibiotic resistance of any World Health Organization (WHO) enlisted priority pathogens in Pakistan (published onward 2009 till March 2020), were collected from PubMed, Google scholar, and PakMedi Net search engines. These articles were selected based on predefined inclusion and exclusion criteria. Data about the study characteristics and antibiotic-resistance for a given bacterium were excluded from literature. Antibiotic resistance to a particular bacterium was calculated as a median resistance with 95% Confidence Interval (CI). Results: Studies published in the last 10 y showed that Urinary Tract Infection (UTI) is the most reported clinical diagnosis (16.1%) in Pakistan. E. coli were reported in 28 (30.11%) studies showing high resistance to antibiotics’ first line. Methicillin-resistant Staphylococcus aureus (MRSA) was found in 49% of S. aureus’ total reported cases. Phenotypic resistance pattern has mostly been evaluated by Disk Diffusion Method (DDM) (82.8%), taken Clinical Laboratory Standards Institute (CLSI) as a breakpoint reference guideline (in 79.6% studies). Only 28 (30.11%) studies have made molecular identification of the resistance gene. blaTEM (78.94% in Shigella spp) and blaNDM-1 (32.75% in Klebsiella spp) are the prominent reported resistant genes followed by VanA (45.53% in Enterococcus spp), mcr-1 (1.61% in Acinetobacter spp), and blaKPC-2 (31.67% in E. coli). Most of the studies were from Sindh (40.86%), followed by Punjab (35.48%), while Baluchistan’s AMR data was not available. Conclusion: Outcomes of our study emphasize that most of the pathogens show high resistance to commonly used antibiotics; also, we find gaps in surveillances and breaches in methodological data. Based on these findings, we recommend the regularization of surveillance practice and precise actions to combat the region’s AMR.

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Keywords: Multiple drug resistance; Colistin resistance; mcr-1; Acinetobacter baumannii; Pseudomonas aeruginosa

Antifungal resistance to Candida pathogens increases morbidity and mortality of immunosuppressive patients, an emerging crisis worldwide. Understanding the Candida prevalence and antifungal susceptibility pattern is necessary to control and treat candidiasis. We aimed to systematically analyse the susceptibility profiles of Candida species published in the last ten years (December 2011 to December 2021) from mainland China. The studies were collected from PubMed, Google Scholar, and Science Direct search engines. Out of 89 included studies, a total of 44,716 Candida isolates were collected, mainly comprising C. albicans (49.36%), C. tropicalis (21.89%), C. parapsilosis (13.92%), and C. glabrata (11.37%). The lowest susceptibility was detected for azole group; fluconazole susceptibilities against C. parapsilosis, C. albicans, C. glabrata, C. tropicalis, C. guilliermondii, C. pelliculosa, and C. auris were 93.25%, 91.6%, 79.4%, 77.95%, 76%, 50%, and 0% respectively. Amphotericin B and anidulafungin were the most susceptible drugs for all Candida species. Resistance to azole was mainly linked with mutations in ERG11, ERG3, ERG4, MRR1–2, MSH-2, and PDR-1 genes. Mutation in FKS-1 and FKS-2 in C. auris and C. glabrata causing resistance to echinocandins was stated in two studies. Gaps in the studies’ characteristics were detected, such as 79.77%, 47.19 %, 26.97%, 7.86%, and 4.49% studies did not mention the mortality rates, age, gender, breakpoint reference guidelines, and fungal identification method, respectively. The current study demonstrates the overall antifungal susceptibility pattern of Candida species, gaps in surveillance studies and risk-reduction strategies that could be supportive in candidiasis therapy and for the researchers in their future studies.

The emergence of pathogens is conferring resistance to last-resort therapies such as tigecycline, colistin, and carbapenems, limiting the therapeutic options, and raising concerns about the emergence of new “superbugs.” This study reports the first incident of a bla_NDM–5 and tet(X4) co-harboring Escherichia coli with resistance to carbapenem and tigecycline recovered as the causative agent of a urinary tract infection in a 94-year-old patient. The E. coli strain ECCL209 carries multiple resistance genes [i.e., bla_TEM–1B, bla_NDM–5, bla_CMY–2, aadA22, florR, erm(B), mph(A), erm(42), lnuG, qnrS1, and sul2] and exhibits resistance to almost all clinically used antibiotics. MLST analysis found that the strain belongs to ST648, considered a worldwide high-risk pandemic clone. Moreover, multiple plasmid incompatibility types were detected, i.e., IncHI1A, IncHI1B, IncFII, IncFIA, IncFIB, IncQ1, Col, and IncX4. Genetic analysis revealed that bla_NDM–5 and tet(X4) genes were localized on two hybrid plasmids with multiple replicons. Continuous monitoring studies are suggested to quantify the antimicrobial resistance and assess the dissemination of such superbugs into a human healthcare setting.

1. Candidiasis is a common fungal infection caused by the Candida species that can affect several parts of the body, including the skin, mouth, throat, and even genital areas (Lopes and Lionakis 20...