Sepsis globally accounts for an alarming annual toll of 48.9 million cases, resulting in 11 million deaths, and inflicts an economic burden of approximately USD 38 billion on the United States healthcare system. The rise of multidrug-resistant organisms (MDROs) has elevated the urgency surrounding the management of multidrug-resistant (MDR) sepsis, evolving into a critical global health concern. This review aims to provide a comprehensive overview of the current epidemiology of (MDR) sepsis and its associated healthcare challenges, particularly in critically ill hospitalized patients. Highlighted findings demonstrated the complex nature of (MDR) sepsis pathophysiology and the resulting immune responses, which significantly hinder sepsis treatment. Studies also revealed that aging, antibiotic overuse or abuse, inadequate empiric antibiotic therapy, and underlying comorbidities contribute significantly to recurrent sepsis, thereby leading to septic shock, multi-organ failure, and ultimately immune paralysis, which all contribute to high mortality rates among sepsis patients. Moreover, studies confirmed a correlation between elevated readmission rates and an increased risk of cognitive and organ dysfunction among sepsis patients, amplifying hospital-associated costs. To mitigate the impact of sepsis burden, researchers have directed their efforts towards innovative diagnostic methods like point-of-care testing (POCT) devices for rapid, accurate, and particularly bedside detection of sepsis; however, these methods are currently limited to detecting only a few resistance biomarkers, thus warranting further exploration. Numerous interventions have also been introduced to treat MDR sepsis, including combination therapy with antibiotics from two different classes and precision therapy, which involves personalized treatment strategies tailored to individual needs. Finally, addressing MDR-associated healthcare challenges at regional levels based on local pathogen resistance patterns emerges as a critical strategy for effective sepsis treatment and minimizing adverse effects.

A new rapid Immunochromatographic test kit(SD MPT64TB Ag Kit) for detection of MPT 64 Antigen in M. tuberculosis isolates using mouse monoclonal MPT 64 Antibody developed by SD Bioline, South Korea was evaluated for rapid identification of M. tuberculosis isolates. We also assessed the sensitivity, specificity and predictive values of this kit. The test kit has an excellent sensitivity, specificity, negative predictive value & positive predictive value. This rapid method is found to be a reliable, rapid and cheaper method for confirming MTB culture isolates in resource poor laboratories. Material/methods: 54 culture isolates of M. tuberculosis in broth & on LJ medium, 12 Non mycobacterial isolates, 10 Non tubercular (NTM) rapidly growing Mycobacteria isolated from pus & 5 smear positive sputum samples were tested for detection of MPT64 antigen using the SD Bioline immunochromatography (ICT)test kit. H37 RV strain was employed as the positive reference control.

Exacerbation due to antimicrobial-drug-resistant bacteria among chronic obstructive pulmonary disease (AECOPD) patients contributes to mortality and morbidity. We examined the prevalence of the bacterial organisms and trends in drug resistance in AECOPD. In this retrospective study, between January 2016 to December 2020, among 3027 AECOPD patients, 432 (14.3%) had bacteria isolated. The regression and generalized estimating equations (GEE) were used for trends in the resistance patterns over five years, adjusting for age, gender, and comorbidities. Klebsiella pneumoniae (32.4%), Pseudomonas aeruginosa (17.8%), Acinetobacter baumannii (14.4%), Escherichia coli (10.4%), and Staphylococcus aureus (2.5%) were common. We observed high levels of drug resistance in AECOPD patients admitted to ICU (87.8%) and non-ICU (86.5%). A Cox proportional hazard analysis, observed infection with Acinetobacter baumannii and female sex as independent predictors of mortality. Acinetobacter baumannii had 2.64 (95% confidence interval (CI): 1.08–6.43) higher odds of death, compared to Klebsiella pneumoniae. Females had 2.89 (95% CI: 1.47–5.70) higher odds of death, compared to males. A high proportion of bacterial AECOPD was due to drug-resistant bacteria. An increasing trend in drug resistance was observed among females.

Multidrug-resistant sepsis (MDR) is a pressing concern in intensive care unit (ICU) settings, specifically among geriatric patients who experience age-related immune system changes and comorbidities. The aim of this review is to explore the clinical impact of MDR sepsis in geriatric ICU patients and shed light on healthcare challenges associated with its management. We conducted a comprehensive literature search using the National Center for Biotechnology Information (NCBI) and Google Scholar search engines. Our search incorporated keywords such as “multidrug-resistant sepsis” OR “MDR sepsis”, “geriatric ICU patients” OR “elderly ICU patients”, and “complications”, “healthcare burdens”, “diagnostic challenges”, and “healthcare challenges” associated with MDR sepsis in “ICU patients” and “geriatric/elderly ICU patients”. This review explores the specific risk factors contributing to MDR sepsis, the complexities of diagnostic challenges, and the healthcare burden faced by elderly ICU patients. Notably, the elderly population bears a higher burden of MDR sepsis (57.5%), influenced by various factors, including comorbidities, immunosuppression, age-related immune changes, and resource-limited ICU settings. Furthermore, sepsis imposes a significant economic burden on healthcare systems, with annual costs exceeding $27 billion in the USA. These findings underscore the urgency of addressing MDR sepsis in geriatric ICU patients and the need for tailored interventions to improve outcomes and reduce healthcare costs.

Recent studies have documented an increase in the incidence of antifungal resistance in newly emerging species closely related to C. albicans, and the coexistence of genotypic variants. Hence, an application of PCR-based molecular typing is crucial in identifying these fungi. Our study used molecular methods to characterize the latest genotypic subgroups of C. albicans and analysed if there was a relationship between the genotypes and the antifungal resistance pattern. The study was conducted in JSS Hospital, Mysuru, Karnataka between July 2018 and December 2020. A total of 1427 Candida species were isolated from clinical samples. Candida albicans were isolated and confirmed using Germ tube test, ID VITEK 2 and PCR (ITS primer). DNA extraction was done using the Hi-Media Yeast DNA Extraction Kit. The amplified products were analysed using Agarose gel electrophoresis (2%). Among 1427 Candida species, 282 were Candida albicans. The following resistance was exhibited to major antifungals – Caspofungin (3.5%), Amphotericin B (1.4%), flucytosine (2.8%) Fluconazole (6%) Micafungin (2.8%) Voriconazole (3.1%) and all were sensitive to miconazole. ABC genotyping showed Genotype A (450 bp) predominant (87.58%) followed by genotype B (840bp) (9.92 %) and genotype C (450bp and 840 bp) (0.2%). Genotype D and E were not observed. Our study showed the growing antifungal resistance in clinical isolates. Genotype A was predominant in South Karnataka region followed by Genotype B and C. There was no correlation between genotyping and antifungal resistance. However, a study with greater number of samples from diverse geographical locations may give more insight.

Extended-spectrum β-lactamase-producing Escherichia coli poses a global public health threat. Here, we performed a hospital-based study that reinforced the necessity for rapid antimicrobial resistance (AMR) and virulence gene mapping of clinical E. coli isolates. Whole-genome sequencing of 18 sepsis-causing E. coli strains was performed to identify multidrug resistance (MDR) and virulence factor genes and to correlate these with antibiotic use in patients with sepsis. We identified various global and emerging MDR sequence types, utilizing a supervised machine learning approach to elucidate the relationship between genome content and AMR profiles across 17 antimicrobial classes, ensuring unbiased analysis. Known AMR genes were correlated with resistance phenotypes, and several crucial and novel AMR genes were identified. The feature selection methodology involved processing the genome into overlapping 13 bp k-mer features using a two-step selection process. Logistic regression with nested cross-validation and synthetic minority oversampling technique confirmed the robustness of the model. The combination of Machine Learning (ML) algorithms facilitates the discovery of nonlinear interactions and complex patterns within genomic data, which may not be readily apparent using conventional genomic analysis alone. This will enable the identification of novel biomarkers and genetic determinants of AMR profiles. The integration of genomic data with ML models can be used to quickly predict AMR, allowing for more targeted and personalized treatment strategies that are not typically achieved by traditional AMR surveillance methods. Our findings tailor the research approaches for patients with sepsis, particularly with AMR E. coli, highlighting the importance of prompt surveillance, robust infection control, optimized antibiotic stewardship and integrated genomic and epidemiological analysis to control MDR bacteria transmission, ultimately improving patient outcomes and safeguarding public health.