Molecular Epidemiology of Clostridioides difficile Colonization in Families With Infants.

Background: Community-associated Clostridioides difficile infection is a major public health hazard to adults and older children. Infants frequently excrete toxigenic C difficile asymptomatically in their stool, but their importance as a community reservoir of C difficile is uncertain. Methods: Families of healthy infants were recruited at the baby's 4-month well child visit and were followed longitudinally until the baby was approximately 9 months old. Babies and mothers submitted stool or rectal swabs every 2 weeks that were cultivated for C difficile; fathers' participation was encouraged but not required. Clostridioides difficile isolates were strain-typed by fluorescent polymerase chain reaction ribotyping and by core genome multilocus sequence typing, and the number of families in whom the same strain was cultivated from >1 family member ("strain sharing") was assessed. Results: Thirty families were enrolled, including 33 infants (3 sets of twins) and 30 mothers; 19 fathers also participated. Clostridioides difficile was identified in 28 of these 30 families over the course of the study, and strain sharing was identified in 17 of these 28. In 3 families, 2 separate strains were shared. The infant was involved in 17 of 20 instances of strain sharing, and in 13 of these, the baby was identified first, with or without a concomitantly excreting adult. Excretion of shared strains usually was persistent. Conclusions: Clostridioides difficile strain sharing was frequent in healthy families caring for an infant, increasing the likelihood that asymptomatically excreting babies and their families represent a reservoir of the organism in the community.

Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 Among Residents and Employees in a Veterans Affairs Community Living Center: A 42-Month Prospective Cohort Study.

Background: Understanding routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in long-term care facilities is essential for the development of effective control measures. Methods: Between March 1, 2020, and August 31, 2023, we identified coronavirus disease 2019 (COVID-19) cases among residents and employees in a Veterans Affairs community living center that conducted routine screening for asymptomatic COVID-19. Contact tracing was conducted to identify suspected transmission events, and whole genome sequencing was performed to determine the relatedness of SARS-CoV-2 samples. Results: During the 42-month study period, 269 cases of COVID-19 were diagnosed, including 199 employees and 70 residents. A total of 48 (24.1%) employees and 30 (42.9%) residents were asymptomatic. Sequencing analysis provided support for multiple events in which employees transmitted SARS-CoV-2 to co-workers and residents. There was 1 episode of likely transmission of SARS-CoV-2 from one resident to another resident, but no documented transmissions from residents to employees. Conclusions: Transmission of SARS-CoV-2 in the community living center predominantly involved transmission from employees to co-workers and residents. There is a need for improved measures to prevent transmission of SARS-CoV-2 by healthcare personnel.

EPI-SF: essential protein identification in protein interaction networks using sequence features.

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Evolution of a Distinct SARS-CoV-2 Lineage Identified during an Investigation of a Hospital Outbreak.

The SARS-CoV-2 virus steadily evolves, and numerous antigenically distinct variants have emerged over the past three years. Tracking the evolution of the virus would help us understand the process that generates the diverse variants and predict the future evolutionary trajectory of SARS-CoV-2. Here, we report the evolutionary trajectory of a unique Omicron lineage identified during an outbreak investigation that occurred in a residence unit in the healthcare system. The new lineage had four distinct non-synonymous and two distinct synonymous mutations apart from its parental lineage. Since this lineage of virus was exclusively found during the outbreak, we were able to track the detailed evolutionary history of the entire lineage along the transmission path. Furthermore, we estimated the evolutionary rate of the SARS-CoV-2 Omicron variant from the analysis of the evolution of the lineage. This new Omicron sub-lineage acquired 3 mutations in a 12-day period, and the evolutionary rate was estimated as 3.05 × 10-3 subs/site/year. This study provides more insight into an ever-evolving virus.

Computational drug repurposing for viral infectious diseases: a case study on monkeypox.

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

UV-A-Induced Photoisomerization and Photodimerization of Curcumin: An Ion Mobility Mass Spectrometry Study.

Curcumin, the bioactive compound present in spice plant turmeric, has been shown to exhibit selective phototoxic activities toward mammalian cancer cells, and it is being used extensively as a photosensitizer (PS) in photodynamic therapies (PDT). However, so far, the fate of curcumin toward photochemical transformations is not well understood. Here we report our findings of a number of novel photochemical reaction channels of curcumin in water-methanol mixture, like photoisomerization, photodimerization, and photooxidation (H2-loss). The reaction was performed by irradiating the curcumin solution with ultraviolet (UV) light of wavelength 350 nm, which is abundant in the earth's troposphere. Product identification and structure elucidation are done by employing an integrated method of drift tube ion mobility mass spectrometry (DTIMS) in combination with high-performance liquid chromatography (HPLC) and collision-induced dissociation (CID) of the mass-selected molecular ions. Two photoisomers of curcumin produced as a result of trans-cis configurational changes about C═C double bonds in the excited state have been identified, and it has been shown that they could serve as the precursors for formation of isomeric dimers via [2 + 2] cycloaddition and H2-loss products. Comparisons of the experimentally measured collision cross-section (CCS) values of the reactant and product ions obtained by the DTIMS method with those predicted by the electronic structure theory are found to be very effective for the discrimination of the produced photoisomers. The observed photochemical reaction channels are potentially significant toward uses of curcumin as a photosensitizer in photodynamic therapy.

Nursing home wastewater surveillance for early warning of SARS-CoV-2-positive occupants-Insights from a pilot project at 8 facilities.

BACKGROUND: Wastewater surveillance for SARS-CoV-2 has been used widely in the United States for indication of community incidence during the COVID-19 pandemic, but less is known about the feasibility of its use on a building level in nursing homes to provide early warning and prevent transmission. METHODS: A pilot study was conducted at 8 Department of Veterans Affairs nursing homes across the United States to examine operational feasibility. Wastewater from the participating facilities was sampled daily during the week for 6 months (January 11, 2021-July 2, 2021) and analyzed for SARS-CoV-2 genetic material. Wastewater results were compared to new SARS-CoV-2 infections in nursing home residents and employees to determine if wastewater surveillance could provide early warning of a COVID-19-positive occupant. RESULTS: All 8 nursing homes had wastewater samples positive for SARS-CoV-2 and COVID-19-positive occupants. The sensitivity of wastewater surveillance for early warning of COVID-19-positive residents was 60% (3/5) and for COVID-19-positive employees was 46% (13/28). CONCLUSIONS: Wastewater surveillance may provide additional information for reinforcing infection control practices and lead to preventing transmission in a setting with high-risk residents. The low sensitivity for early warning in this real-world pilot highlights limitations and insights for applicability in buildings.

Understanding the significance of microbiota recovered from health care surfaces.

BACKGROUND: Microbial contamination of hospital surfaces remains despite adherence to routine disinfection. Our study demonstrates bioburden from various types of hospital high-touch surfaces and the pathogenicity of all bacteria recovered. METHODS: Several high-touch hospital surfaces from a single medical-surgical unit were sampled and cultured using replicate organism detection and counting (RODAC) Tryptic Soy agar plates. Colonies were then subcultured to blood agar plates and speciated using MALDI-TOF. The local microbiology laboratory database was queried for any clinical isolate match with the environmental samples recovered. RESULTS: Manikins, bed rails, and workstations-on-wheels were the most contaminated surfaces with the largest variety of bacteria isolated from manikins and bed rails. A total of 60 different types of pathogens were isolated, 18 of which were well-known pathogens, and 7 were classified as important in the health care setting by CDC. Our clinical microbiology laboratory identified 29 of 60 hospital surface bacteria in clinical isolates. Urine, soft tissue, and blood were the most common sources of clinical isolates. CONCLUSIONS: Surfaces in the health care environment harbor both well-known and not-so-well-known human pathogens. Several not-so-well-known pathogens are skin flora or environmental bacteria, which in the right setting, can become pathogenic and cause diseases including meningitis, brain abscess, endocarditis, and bacteremia.

Using a handheld UV device for disinfection in the patient care environment: A descriptive qualitative study.

Frontline nurses play a critical role in the prevention of healthcare-acquired infections (HAIs) through daily practices of hand hygiene and decontamination of surfaces. Despite these practices, environmental contamination and HAIs persist. Emerging use of UV light at wavelengths safe for human exposure provides additional strategies for disinfecting the patient care environment. The purpose of this qualitative study is to explore frontline nursing feedback regarding a novel handheld UV device prototype. A convenience sample of nurses were invited to participate in facilitated individual or small group discussions led by one member of the research team. Thematic analysis of discussion transcripts was completed by two members of the research team. Sixteen registered nurses participated. Four themes found in the study were time considerations, complexity, safety (patient and nurse), and characteristics of technology to improve patient care. Findings suggest that while nursing staff are willing to use technology, it must be considered valuable to patient care and should not hinder the provision of care. Inclusion of inputs from nursing staff for development of technology identifies potential barriers to acceptance and use in the practice environment.

Efficacy of a filtered far-UVC handheld disinfection device in reducing the microbial bioburden of hospital surfaces.

BACKGROUND: The filtered far-UV-C (FFUV) handheld disinfection device is a small portable device that emits far UV-C at 222 nm. The objective of this study was to evaluate the device's ability to kill microbial pathogens on hospital surfaces and compare it to manual disinfection using germicidal sodium hypochlorite wipes. METHODS: A total of 344 observations (4 observations from 86 objects' surfaces) were sampled with 2 paired samples per surface: a pre- and a post-sodium hypochlorite and FFUV sample. The results were analyzed via a Bayesian multilevel negative binomial regression model. RESULTS: The estimated mean colony counts for the sodium hypochlorite control and treatment groups were 20.5 (95% uncertainty interval: 11.7-36.0) and 0.1 (0.0-0.2) colony forming units (CFUs), respectively. The FFUV control and treatment groups had mean colony counts of 22.2 (12.5-40.1) and 4.1 (2.3-7.2) CFUs. The sodium hypochlorite group and the FFUV group had an estimated 99.4% (99.0%-99.7%) and 81.4% (76.2%-85.7%) reduction in colony counts, respectively. CONCLUSIONS: The FFUV handheld device effectively reduced the microbial bioburden on surfaces in the health care setting. The major benefit of FFUV is likely seen when manual disinfection is not possible or when supplementing cleaners or disinfectants with the low-level disinfection properties.

Assessment of microbial bioburden on portable medical equipment in a hospital setting.

Objectives: Although routine disinfection of portable medical equipment is required in most hospitals, frontline staff may not be able to disinfect portable medical equipment at a rate that adequately maintains low bioburden on high-use equipment. This study quantified bioburden over an extended time period for two types of portable medical equipment, workstations on wheels and vitals machines, across three hospital wards. Methods: Bioburden was quantified via press plate samples taken from high touch surfaces on 10 workstations on wheels and 5 vitals machines on each of 3 medical surgical units. The samples were taken at three timepoints each day over a 4-week period, with random rotation of timepoints and portable medical equipment, such that frontline staff were not aware at which timepoint their portable medical equipment would be sampled. The mean bioburden from the different locations and portable medical equipment was estimated and compared with Bayesian multilevel negative binomial regression models. Results: Model estimated mean colony counts (95% credible interval) were 14.4 (7.7-26.7) for vitals machines and 29.2 (16.1-51.1) for workstations on wheels. For the workstations on wheel, colony counts were lower on the mouse, 0.22 (0.16-0.29), tray, 0.29 (0.22, 0.38), and keyboard, 0.43 (0.32-0.55), when compared to the arm, as assessed by incident rate ratios. Conclusions: Although routine disinfection is required, bioburden is still present across portable medical equipment on a variety of surfaces. The difference in bioburden levels among surfaces likely reflects differences in touch patterns for the different portable medical equipment and surfaces on the portable medical equipment. Although the association of portable medical equipment bioburden to healthcare-associated infection transmission was not assessed, this study provides evidence for the potential of portable medical equipment as a vector for healthcare-associated infection transmission despite hospital disinfection requirements.

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host-pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host-pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host-pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host-pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

Standardized uptake value of normal organs on routine clinical [18F]FDG PET/CT: impact of tumor metabolism and patient-related factors.

BACKGROUND: To evaluate the effect of patient-related factors such as age, gender, body mass index (BMI), blood glucose (BG), diabetes, serum creatinine and injected dose on 18F-Fluorodeoxyglucose ([18F]FDG) uptake of tumor and normal organs, as well impact of [18F]FDG uptake of tumor on normal organs, in clinical positron emission tomography-computed tomography (PET/CT). MATERIAL AND METHODS: In this retrospective study, data of 200 patients who underwent clinical [18F]FDG PET/CT with (n = 192) and without (n = 8) intravenous contrast was evaluated. Ten target organs and tumor [18F]FDG uptake were measured with a standardized uptake value maximum (SUVmax). Pearson correlation coefficient was calculated for continuous variables while t-test/Wilcoxon rank sum tests were used to compare continuous outcomes. Multivariate linear regression analysis was done to exclude covariates, followed by posthoc multiple linear regression analysis after adjusting the levels of significance. RESULTS: Significant but weak positive correlation was seen between tumor [18F]FDG uptake with uptake in the pancreas (r = 0.43, p < 0.001) and heart (r = 0.19, p = 0.049), but not other organs. With age, a significant negative correlation was seen with the brain (r = -0.183, p = 0.009) and a positive correlation was seen with the blood pool (r = 0.205, p = 0.003). With BG, significant negative correlation was seen with the brain (r = -0.449, p < 0.0001) and heart (r = -0.15, p = 0.033), while a positive correlation was seen with fat (r = 0.143, p = 0.043). BMI showed a significant positive correlation with [18F]FDG uptake of all organs except the pancreas and heart, as well as tumor. No significant correlation was seen with serum creatinine and injected [18F]FDG dose. Significantly higher uptake was seen in the brain, spleen, and muscles of females. Between obese and non-obese, a significant difference was seen for all organs except for the pancreas and heart, and tumor. Comparison between non-diabetic and diabetic patients showed significant differences only for bone. Multivariate linear analysis adjusting for cofactors showed only BMI (p = 0.0009) and BG (p = 0.0002) to be independently correlated with [18F]FDG uptake. Post-hoc multiple regression analysis showed a significant positive correlation between [18F]FDG uptake of the brain (ß = 0.118, p < 0.001), liver (ß = 0.02, p = 0.002), and fat (ß = 0.01, p < 0.0006) with BMI, and significant negative correlation of brain uptake with BG (ß = 0.03, p < 0.0001). CONCLUSIONS: Tumor [18F]FDG uptake has no significant effect on the uptake in organs, except for the pancreas and heart. Age, gender, BMI, and BG, but not creatinine and injected [18F]FDG dose show correlation with uptake in tumor and organs. BG and BMI are independent significant factors, with a positive correlation of BMI with the brain, hepatic and fat uptake, and a negative correlation of BG with brain uptake.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms.

Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/.

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN.

Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein-protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques.

Tautomers and Rotamers of Curcumin: A Combined UV Spectroscopy, High-Performance Liquid Chromatography, Ion Mobility Mass Spectrometry, and Electronic Structure Theory Study.

The structures of tautomers and rotameric forms of curcumin, the bioactive compound present in spice plant turmeric, have been investigated using ion mobility mass spectrometry (IMMS) in conjunction with high-performance liquid chromatography (HPLC) and UV-visible spectroscopy. Two tautomeric forms of this ß-diketone compound, keto-enol and diketo, have been chromatographically separated, and the electronic absorption spectra for these two tautomeric forms in methanol solution have been recorded separately for the first time. The molecular identity of the HPLC-separated solution fractions is established unambiguously by recording the mass and fragmentation spectra simultaneously. The ion mobility spectrum for the deprotonated curcumin anion, [Cur-H]-, corresponding to the diketo tautomer, displays only one peak (P), and the collision cross-section (CCS) value is measured to be 185.9 Å2. However, the ion mobility spectrum corresponding to the HPLC-separated keto-enol tautomer shows three distinctly separated peaks, P, Q, and R, with CCS values of 185.9, 194.8, and 203.4 Å2, respectively, whereby peak R appears to be the most intense one, followed by peaks P and Q. The theoretically calculated CCS values of different isomers of [Cur-H]-, optimized by electronic structure theory methods, display satisfactory correlation with the experimentally observed values, corroborating our assignments. The spectral attributes also indicate the occurrence of structural rearrangements in the electrospray ionization process. With the aid of the electronic structure calculation, low-energy pathways for the occurrence of the structural isomerization to surpass the energy barrier are suggested, which are consistent with the assignments of the peaks observed in the IM spectra.

Effect of a "feedback prompt" from a disinfection tracking system on portable medical equipment disinfection.

BACKGROUND: Portable medical equipment (PME) may contribute to transmission of multidrug-resistant organisms without proper disinfection. We studied whether a Disinfection Tracking System (DTS) with feedback prompt, attached to PME, can increase the frequency of PME disinfection. METHODS: DTS devices were placed on 10 workstations-on-wheels (WOWs) and 5 vitals machine (VM). After a 25 day "screen-off" period, the DTS device screens were turned on to display the number of hours since the last recorded disinfection event for a 42 day period. We used a Bayesian multilevel zero-inflated negative binomial model to compare the number of events in the display "screen-off" to the "screen-on" period. RESULTS: During the "screen-off" period, there were 1.26 and 0.49 mean disinfection events and during the "screen-on" period, there were 1.76 and 0.50 mean disinfection events for WOWs and VM, respectively, per day. The model estimated mean events per device per day in the the "screen-on" period for WOW's were 1.32 (1.10 - 1.57) times greater than those in the "screen-off" period and the "screen-on" period for VM devices was 1.37 (0.89 - 2.01) times greater than those in the "screen-off" period. CONCLUSIONS: The rate of disinfection events for WOWs increased following the implementation of the DTS feedback prompt.

Computational modeling of human-nCoV protein-protein interaction network.

Novel coronavirus(SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein-protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (∼89% similarity). With this hypothesis, the present work focuses on developing a computational model for the nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in the SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered potential human targets for nCoV bait proteins. A gene-ontology-based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at a ∼99.98% specificity threshold. This also identifies 37 level-1 human spreaders for COVID-19 in the human protein-interaction network. 2474 level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using six potential FDA-listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.

The viral phoenix: enhanced infectivity and immunity evasion of SARS-CoV-2 variants.

SARS-CoV-2 pandemic continues with emergence of new variants of concerns. These variants are fueling the third and fourth waves of pandemic across many nations. Here we describe the new emerging variants of SARS-CoV-2 and why they have enhanced infectivity and possess the ability to evade immunity.