Biomedical waste segregation compliance scoring system: to analyze, strengthen, monitor, and step up waste management in healthcare facilities.

Introduction: segregation of biomedical waste (BMW) is the foremost and prime step for effective BMW management. This study was taken up to assess the BMW segregation compliance in patient care areas using a checklist-based scoring system to analyze the segregation compliance and establish feedback-based training programs. Methods: this study was conducted between January 2020 to December 2021 at a government tertiary care hospital in Hyderabad. The compliance was calculated by giving a score of one for each day, such that if there was no noncompliance (NC) the score was 100% for a given location at the end of the month. A score of minus one was given for each day a noncompliance was noted and transfigured into percentages. A score of 100% was considered good, and below 95% was considered an action point necessitating root cause analysis and training. Results: the BMW segregation compliance scores of the hospital for the year 2020 (96.5%) were compared with 2021 scores (97.5%). The outpatient department (OPD) and ICU had the poorest compliance rate of 93.7% and 93.6% respectively, compared to wards (96.2%). The most common factors influencing NC in BMW segregation were the joining of new staff, relocation, or new establishment of wards. The most common segregation error was found in the yellow disposal bags pertaining to the disposal of personal protective equipment. Conclusion: this easy and simple scoring system was established to improve the segregation compliance of BMW. End of each month an area wise compliance is easily made such that areas with low scores could be trained.

Detection of SARS-CoV-2 in the air in Indian hospitals and houses of COVID-19 patients.

To understand the transmission characteristics of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) through air, samples from different locations occupied by coronavirus disease (COVID-19) patients were analyzed. Three sampling strategies were used to understand the presence of virus in the air in different environmental conditions. In the first strategy, which involved hospital settings, air samples were collected from several areas of hospitals like COVID-intensive-care units (ICUs), nurse-stations, COVID-wards, corridors, non-COVID-wards, personal protective equipment (PPE) doffing areas, COVID rooms, out-patient (OP) corridors, mortuary, COVID casualty areas, non-COVID ICUs and doctors' rooms. Out of the 80 air samples collected from 6 hospitals from two Indian cities- Hyderabad and Mohali, 30 samples showed the presence of SARS-CoV-2 nucleic acids. In the second sampling strategy, that involved indoor settings, one or more COVID-19 patients were asked to spend a short duration of time in a closed room. Out of 17 samples, 5 samples, including 4 samples collected after the departure of three symptomatic patients from the room, showed the presence of SARS-CoV-2 nucleic acids. In the third strategy, involving indoor settings, air samples were collected from rooms of houses of home-quarantined COVID-19 patients and it was observed that SARS-CoV-2 RNA could be detected in the air in the rooms occupied by COVID-19 patients but not in the other rooms of the houses. Taken together, we observed that the air around COVID-19 patients frequently showed the presence of SARS-CoV-2 RNA in both hospital and indoor residential settings and the positivity rate was higher when 2 or more COVID-19 patients occupied the room. In hospitals, SARS-CoV-2 RNA could be detected in ICUs as well as in non-ICUs, suggesting that the viral shedding happened irrespective of the severity of the infection. This study provides evidence for the viability of SARS-CoV-2 and its long-range transport through the air. Thus, airborne transmission could be a major mode of transmission for SARS-CoV-2 and appropriate precautions need to be followed to prevent the spread of infection through the air.

Artificial Intelligence-Based Portable Bioelectronics Platform for SARS-CoV-2 Diagnosis with Multi-nucleotide Probe Assay for Clinical Decisions.

In the context of the recent pandemic, the necessity of inexpensive and easily accessible rapid-test kits is well understood and need not be stressed further. In light of this, we report a multi-nucleotide probe-based diagnosis of SARS-CoV-2 using a bioelectronics platform, comprising low-cost chemiresistive biochips, a portable electronic readout, and an Android application for data acquisition with machine-learning-based decision making. The platform performs the desired diagnosis from standard nasopharyngeal and/or oral swabs (both on extracted and non-extracted RNA samples) without amplifying the viral load. Being a reverse transcription polymerase chain reaction-free hybridization assay, the proposed approach offers inexpensive, fast (time-to-result: ≤ 30 min), and early diagnosis, as opposed to most of the existing SARS-CoV-2 diagnosis protocols recommended by the WHO. For the extracted RNA samples, the assay accounts for 87 and 95.2% test accuracies, using a heuristic approach and a machine-learning-based classification method, respectively. In case of the non-extracted RNA samples, 95.6% decision accuracy is achieved using the heuristic approach, with the machine-learning-based best-fit model producing 100% accuracy. Furthermore, the availability of the handheld readout and the Android application-based simple user interface facilitates easy accessibility and portable applications. Besides, by eliminating viral RNA extraction from samples as a pre-requisite for specific detection, the proposed approach presents itself as an ideal candidate for point-of-care SARS-CoV-2 diagnosis.

Comparison of antimicrobial susceptibility interpretation among Enterobacteriaceae using CLSI and EUCAST breakpoints.

PURPOSE: To determine the difference in antimicrobial susceptibility of various antibiotics using the CLSI & EUCAST breakpoints. METHODS: In this non interventional, retrospective observational study, we reviewed minimum inhibitory concentrations (MIC) of various antibiotics routinely reported for Enterobacteriaceae clinical isolates, from an automated microbiology identification system (VITEK-2). These MICs were then analysed using both CLSI 2019 and EUCAST 2019 guidelines and classified as per the breakpoints into various categories. RESULTS: The concordance rates of the antimicrobial susceptibility for various drugs ranged from 78.2% to 100% among two breakpoints. Perfect agreement with κ = 1 (p < 0.001) was observed for only three antimicrobials ceftriaxone, levofloxacin and trimethoprim-sulfamethoxazole. The changes in antimicrobial susceptibility interpretation for cefepime, ciprofloxacin, amoxicillin clavulanic acid was majorly in Intermediate category. CONCLUSION: The change in interpretation of the susceptibility will lead to change in the usage of antibiotics especially due to recent change in definition of I by EUCAST. There is need of more studies in this aspect to ascertain clinical implication of change in antimicrobial susceptibility.