Upper respiratory tract microbiome profiles in SARS-CoV-2 Delta and Omicron infected patients exhibit variant specific patterns and robust prediction of disease groups.

IMPORTANCE: The role of the upper respiratory tract (URT) microbiome in predicting lung health has been documented in several studies. The dysbiosis in COVID patients has been associated with disease outcomes by modulating the host immune system. However, although it has been known that different SARS-CoV-2 variants manifest distinct transmissibility and mortality rates in human populations, their effect on the composition and diversity of the URT microbiome has not been studied to date. Unlike the older variant (Delta), the newer variant (Omicron) have become more transmissible with lesser mortality and the symptoms have also changed significantly. Hence, in the present study, we have investigated the change in the URT microbiome associated with Delta and Omicron variants and identified variant-specific signatures that will be useful in the assessment of lung health and can be utilized for nasal probiotic therapy in the future.

Contrasting Distribution of SARS-CoV-2 Lineages across Multiple Rounds of Pandemic Waves in West Bengal, the Gateway of East and North-East States of India.

The evolution of viral variants and their impact on viral transmission have been an area of considerable importance in this pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed the viral variants in different phases of the pandemic in West Bengal, a state in India that is important geographically, and compared the variants with other states like Delhi, Maharashtra, and Karnataka, located in other regions of the country. We have identified 57 pango-lineages in 3,198 SARS-CoV-2 genomes, alteration in their distribution, as well as contrasting profiles of amino acid mutational dynamics across different waves in different states. The evolving characteristics of Delta (B.1.617.2) sublineages and alterations in hydrophobicity profiles of the viral proteins caused by these mutations were also studied. Additionally, implications of predictive host miRNA binding/unbinding to emerging spike or nucleocapsid mutations were highlighted. Our results throw considerable light on interesting aspects of the viral genomic variation and provide valuable information for improved understanding of wave-defining mutations in unfolding the pandemic. IMPORTANCE Multiple waves of infection were observed in many states in India during the coronavirus disease 2019 (COVID19) pandemic. Fine-scale evolution of major SARS-CoV-2 lineages and sublineages during four wave-window categories: Pre-Wave 1, Wave 1, Pre-Wave 2, and Wave 2 in four major states of India: Delhi (North), Maharashtra (West), Karnataka (South), and West Bengal (East) was studied using large-scale virus genome sequencing data. Our comprehensive analysis reveals contrasting molecular profiles of the wave-defining mutations and their implications in host miRNA binding/unbinding of the lineages in the major states of India.

Surgical Site Infection by Methicillin Resistant Staphylococcus aureus- on Decline?

INTRODUCTION: Surgical Site Infection (SSI) is the most common healthcare associated infection that could be averted by antibiotics prophylaxis against the probable offending organisms. As Staphylococcus aureus has been playing a substantial role in the aetiology of SSIs, Methicillin Resistant Staphylococcus aureus (MRSA) happens to be a problem while dealing with the postoperative wound infection. AIM: To determine the prevalence of SSI caused by MRSA and the antibiotic sensitivity pattern of MRSA. MATERIALS AND METHODS: A cross-sectional study was conducted at Nil Ratan Sircar Medical College, Kolkata, West Bengal from July 2009 to December 2012. A total of 19,359 surgical procedures were done of which 3003 culture positive SSIs have been documented. The clinical samples were collected from patients of both sexes and all ages suspected to be suffering from SSI from different specialities. Samples were processed according to CLSI, 2007 guidelines. The isolated strains of Staphylococcus aureus were screened for MRSA by detection of resistance to Cefoxitin disc (zone of inhibition was ≤21 mm) and slidex staph latex agglutination tests were done on cefoxitin resistant strains to spot phenotypic expression of mec A gene. Then PCR was performed for detection of mecA gene. Antibiotic sensitivity test was done following Kirby Bauer technique. RESULTS: In this 3½ year study, 1049 Staphylococcus aureus (34.93%) were reported from 3003 cases of SSI followed by Escherichia coli (20.34%), Klebsiella spp. (18.08%), Pseudomonas spp. (7.99%), Acinetobacter spp. (7.49%) respectively. Among the Staphylococcus aureus, 267 strains were derived as MRSA (25.45%). MRSA were isolated from 167 (62.54%) male patients and 100 (37.45%) female patients having surgical site infections. Inpatients and outpatients distribution of MRSA were 235 (88.01%) and 32 (11.98%) respectively. Majority of the MRSA cases were reported from Surgery (12.49%) and Orthopaedics (11.85%) departments in the age group above 75 years (15.63%). The MRSA strains have been found to be 100% sensitive to linezolid and tigecycline followed by fucidin (92.51%), mupirocin (88.39%), levofloxacin (75.66%) and doxycycline (72.28%). No vancomycin resistant strains were detected, but 3 strains (1.12%) were found to be intermediately susceptible to it (VISA). Incidence of MRSA in SSI has been decreased by 15.17 % in 2012 in comparison to 2009. PCR revealed mecA gene was present in 96.25% of cefoxitin resistant Staphylococcus aureus strains. CONCLUSION: Staphylococcus aureus being the predominant organism causing SSIs, MRSA needs the attention for its resistance to commonly used antibiotics in the hospital like penicillin, cephalosporin group of drugs. Regular monitoring of the MRSA, involved in the SSI of a particular setup is the basic requirement to trim down the incidence of the postoperative wound infections by proper antibiotic prophylaxis.

A neuro-fuzzy framework for inferencing.

Earlier we proposed a connectionist implementation of compositional rule of inference (COI) for rules with antecedents having a single clause. We first review this net, then generalize it so that it can deal with rules with antecedent having multiple clauses. We call it COIN, the compositional rule of inferencing network. Given a relational representation of a set of rules, the proposed architecture can realize the COI. The outcome of COI depends on the choice of both the implication function and the inferencing scheme. The problem of choosing an appropriate implication function is avoided through neural learning. COIN can automatically find a 'good' relation to represent a set of fuzzy rules. We model the connection weights so as to ensure learned weights lie in [0,1]. We demonstrate through extensive numerical examples that the proposed neural realization can find a much better representation of the rules than that by usual implication and hence results in much better conclusions than the usual COI.