Analysis of possible risk predictors in patients with coronavirus disease 2019: a retrospective cohort study.

OBJECTIVE: This study aimed to analyze the clinical-epidemiological profile, possible risk predictors, and outcomes of patients with coronavirus disease 2019 admitted to the ward of a tertiary care hospital in southern Brazil. Specifically, we describe the demographic characteristics, comorbidities, baseline laboratory findings, clinical course, and survival of these patients. METHODS: This is an observational, retrospective cohort study, performed from January to March 2022, on medical records of patients hospitalized between April 2020 and December 2021 in the coronavirus disease 2019 ward of a tertiary hospital in southern Brazil. RESULTS: Data from 502 hospitalized patients were analyzed, of which 60.2% were male, with a median age of 56 years and 31.7% were over 65 years old. The main symptoms presented were dyspnea/respiratory discomfort (69.9%) and cough (63.1%). The most common comorbidities were obesity, systemic arterial hypertension, and diabetes mellitus. A proportion of 55.8% of 493 patients had PaO2/FiO2<300 mmHg in the first examination performed after admission and 46.0% had a neutrophil/lymphocyte ratio>6.8. Oxygen therapy by Venturi mask or mask with reservoir was used in 34.7% of the patients, and non-invasive ventilation was used in 10.0% of the patients. The majority of the patients (98.4%) used corticosteroids, and the outcome of 82.5% of the hospitalized patients was home discharge. CONCLUSION: After analyzing the clinical and epidemiological profile, it can be concluded that age greater than 65 years and pulmonary involvement >50% are predictors of a worse prognosis for coronavirus disease 2019, as is the need for high-flow oxygen therapy. Corticotherapy, however, proved to be beneficial in the treatment of the disease.

Tidal lung hysteresis to interpret PEEP-induced changes in compliance in ARDS patients.

BACKGROUND: In ARDS, the PEEP level associated with the best respiratory system compliance is often selected; however, intra-tidal recruitment can increase compliance, falsely suggesting improvement in baseline mechanics. Tidal lung hysteresis increases with intra-tidal recruitment and can help interpreting changes in compliance. This study aims to assess tidal recruitment in ARDS patients and to test a combined approach, based on tidal hysteresis and compliance, to interpret decremental PEEP trials. METHODS: A decremental PEEP trial was performed in 38 COVID-19 moderate to severe ARDS patients. At each step, we performed a low-flow inflation-deflation manoeuvre between PEEP and a constant plateau pressure, to measure tidal hysteresis and compliance. RESULTS: According to changes of tidal hysteresis, three typical patterns were observed: 10 (26%) patients showed consistently high tidal-recruitment, 12 (32%) consistently low tidal-recruitment and 16 (42%) displayed a biphasic pattern moving from low to high tidal-recruitment below a certain PEEP. Compliance increased after 82% of PEEP step decreases and this was associated to a large increase of tidal hysteresis in 44% of cases. Agreement between best compliance and combined approaches was accordingly poor (K = 0.024). The combined approach suggested to increase PEEP in high tidal-recruiters, mainly to keep PEEP constant in biphasic pattern and to decrease PEEP in low tidal-recruiters. PEEP based on the combined approach was associated with lower tidal hysteresis (92.7 ± 20.9 vs. 204.7 ± 110.0 mL; p < 0.001) and lower dissipated energy per breath (0.1 ± 0.1 vs. 0.4 ± 0.2 J; p < 0.001) compared to the best compliance approach. Tidal hysteresis ≥ 100 mL was highly predictive of tidal recruitment at next PEEP step reduction (AUC 0.97; p < 0.001). CONCLUSIONS: Assessment of tidal hysteresis improves the interpretation of decremental PEEP trials and may help limiting tidal recruitment and energy dissipated into the respiratory system during mechanical ventilation of ARDS patients.

Ophthalmic manifestations of monkeypox infection.

After the global COVID-19 pandemic, there has been an alarming concern with the monkeypox (mpox) outbreak, which has affected more than 110 countries worldwide. Monkeypox virus is a doublestranded DNA virus of the genus Orthopox of the Poxviridae family, which causes this zoonotic disease. Recently, the mpox outbreak was declared by the World Health Organization (WHO) as a public health emergency of international concern (PHEIC). Monkeypox patients can present with ophthalmic manifestation and ophthalmologists have a role to play in managing this rare entity. Apart from causing systemic involvement such as skin lesions, respiratory infection and involvement of body fluids, Monkeypox related ophthalmic disease (MPXROD) causes varied ocular manifestations such as lid and adnexal involvement, periorbital and lid lesion, periorbital rash, conjunctivitis, blepharocounctivitis and keratitis. A detailed literature review shows few reports on MPXROD infections with limited overview on management strategies. The current review article is aimed to provide the ophthalmologist with an overview of the disease with a spotlight on ophthalmic features. We briefly discuss the morphology of the MPX, various modes of transmission, an infectious pathway of the virus, and the host immune response. A brief overview of the systemic manifestations and complications has also been elucidated. We especially highlight the detailed ophthalmic manifestations of mpox, their management, and prevention of vision threatening sequelae.

Scent dog identification of SARS-CoV-2 infections in different body fluids.

BACKGROUND: The main strategy to contain the current SARS-CoV-2 pandemic remains to implement a comprehensive testing, tracing and quarantining strategy until vaccination of the population is adequate. Scent dogs could support current testing strategies. METHODS: Ten dogs were trained for 8 days to detect SARS-CoV-2 infections in beta-propiolactone inactivated saliva samples. The subsequent cognitive transfer performance for the recognition of non-inactivated samples were tested on three different body fluids (saliva, urine, and sweat) in a randomised, double-blind controlled study. RESULTS: Dogs were tested on a total of 5242 randomised sample presentations. Dogs detected non-inactivated saliva samples with a diagnostic sensitivity of 84% (95% CI: 62.5-94.44%) and specificity of 95% (95% CI: 93.4-96%). In a subsequent experiment to compare the scent recognition between the three non-inactivated body fluids, diagnostic sensitivity and specificity were 95% (95% CI: 66.67-100%) and 98% (95% CI: 94.87-100%) for urine, 91% (95% CI: 71.43-100%) and 94% (95% CI: 90.91-97.78%) for sweat, 82% (95% CI: 64.29-95.24%), and 96% (95% CI: 94.95-98.9%) for saliva respectively. CONCLUSIONS: The scent cognitive transfer performance between inactivated and non-inactivated samples as well as between different sample materials indicates that global, specific SARS-CoV-2-associated volatile compounds are released across different body secretions, independently from the patient's symptoms. All tested body fluids appear to be similarly suited for reliable detection of SARS-CoV-2 infected individuals.

Impact of public sentiments on the transmission of COVID-19 across a geographical gradient.

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual's belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people's sentiments (positive and negative) which accounts for the influence of disinformation. People's sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.

Absorption, distribution, metabolism and excretion of 14C-Emvododstat following a single oral dose in rats and dogs.

Emvododstat is a potent inhibitor of dihydroorotate dehydrogenase and is now in clinical development for the treatment of acute myeloid leukaemia and COVID-19.Following an oral dose administration in Long-Evans rats, 14C-emvododstat-derived radioactivity was widely distributed throughout the body, with the highest distribution in the endocrine, fatty, and secretory tissues and the lowest in central nervous system.Following a single oral dose of 14C-emvododstat in rats, 54.7% of the dose was recovered in faeces while less than 0.4% of dose was recovered in urine 7 days post-dose. Emvododstat was the dominant radioactive component in plasma and faeces.Following a single oral dose of 14C-emvododstat in dogs, 75.2% of the dose was recovered in faeces while 0.5% of dose was recovered in urine 8 days post-dose. Emvododstat was the dominant radioactive component in faeces, while emvododstat and its two metabolites (O-desmethyl emvododstat and emvododstat amide bond hydrolysis product) were the major circulating radioactivity in dog plasma.

Rapid Detection of SARS-CoV-2 Spike RBD Protein in Body Fluid: Based on Special Calcium Ion-Mediated Gold Nanoparticles Modified by Bromide Ions.

The receptor-binding domain of the SARS-CoV-2 spike mediates the key to binding the virus to the host receptor, but capturing the molecular signal of this spike RBD remains a formidable challenge. Here, we report a new surface-enhanced Raman spectroscopy (SERS) approach, which used gold nanoparticles prepared by low-speed constant-temperature centrifugation by bromine and calcium ions in two cleaning steps as the enhanced substrate to rapidly and accurately detect spike RBD large protein molecules in body fluids. The detection signal was extremely stable, and the orientation of the spike RBD on the enhanced substrate surface was also determined. This approach was specific in distinguishing different SARS-CoV-2 variants of spike RBD, including Delta, Beta, Gamma, and Omicron. Additionally, the enhanced substrate can identify biologically active or inactive spike RBD. This two-step cleaning enhanced substrate opens up opportunities not only for early diagnostics of SARS-CoV-2 virus but also for developing targeted drugs against viruses.

Delhi's network for surveillance of antimicrobial resistance: The journey, challenges and output from first year.

PURPOSE: Antimicrobial resistance [AMR] has emerged as a global and national priority and establishing an effective surveillance system for antimicrobial resistance is an essential prerequisite for generating evidence for informed policymaking at both national and state levels. METHODS: Twenty-four laboratories were enrolled after assessment in the WHO-IAMM Network for Surveillance of Antimicrobial Resistance in Delhi [WINSAR-D]. The NARS- NET standard operating procedures were adopted along with its priority pathogen lists and antibiotic panels. The members were trained to use WHONET software and monthly data files were collected, collated, and analyzed. RESULTS: Multiple logistic issues such as procurement, erratic supply of consumables, non-availability of standard guidelines, lack of automated systems, high workload and low manpower were reported by the majority of member laboratories. Microbiological challenges such as differentiation between colonization and pathogen in absence of patient details, lack of confirmation of resistance, identification of isolates and lack of dedicated computer and genuine windows software for data were common to most laboratories. The total number of isolates of priority pathogens in 2020 was 31,463. Of these, 50.1% isolates were from urine 20.6% were from blood and 28.3% were from pus aspirate and other sterile body fluids. High levels of resistance were observed for all antibiotics. CONCLUSION: There are many challenges in generating quality AMR data in lower-middle-income countries. There is a need for resource allocation and capacity building at all levels to ensure the collection of quality assured data.

Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.

Detection of SARS-CoV-2 in exhaled breath from non-hospitalized COVID-19-infected individuals.

The diagnosis of COVID-19 is based on detection of SARS-CoV-2 in oro-/nasopharyngel swabs, but due to discomfort and minor risk during the swab procedure, detection of SARS-CoV-2 has been investigated in other biological matrixes. In this proof-of-concept study, individuals with confirmed SARS-CoV-2 infection performed a daily air sample for five days. Air samples were obtained through a non-invasive electrostatic air sampler. Detection of SARS-CoV-2 RNA was determined with qRT-PCR. The association of positive samples with different exposures was evaluated through mixed-effect models. We obtained 665 air samples from 111 included participants with confirmed SARS-CoV-2 infection. Overall, 52 individuals (46.8%) had at least one positive air sample, and 129 (19.4%) air samples were positive for SARS-CoV-2. Participants with symptoms or a symptom duration ≤ four days had significantly higher odds of having a positive air sample. Cycle threshold values were significantly lower in samples obtained ≤ 4 days from symptom onset. Neither variant of SARS-CoV-2 nor method of air sampling were associated with a positive air sample. We demonstrate that SARS-CoV-2 is detectable in human breath by electrostatic air sampling with the highest detection rate closest to symptom onset. We suggest further evaluation of the air sampling technique to increase sensitivity.

Rapid Colorimetric Detection of Wound Infection with a Fluidic Paper Device.

Current procedures for the assessment of chronic wound infection are time-consuming and require complex instruments and trained personnel. The incidence of chronic wounds worldwide, and the associated economic burden, urge for simple and cheap point-of-care testing (PoCT) devices for fast on-site diagnosis to enable appropriate early treatment. The enzyme myeloperoxidase (MPO), whose activity in infected wounds is about ten times higher than in non-infected wounds, appears to be a suitable biomarker for wound infection diagnosis. Herein, we develop a single-component foldable paper-based device for the detection of MPO in wound fluids. The analyte detection is achieved in two steps: (i) selective immunocapture of MPO, and (ii) reaction of a specific dye with the captured MPO, yielding a purple color with increasing intensity as a function of the MPO activity in infected wounds in the range of 20-85 U/mL. Ex vivo experiments with wound fluids validated the analytic efficiency of the paper-based device, and the results strongly correlate with a spectrophotometric assay.

Hygroscopic growth of simulated lung fluid aerosol particles under ambient environmental conditions.

The hygroscopicity of respiratory aerosol determines their particle size distribution and regulates solute concentrations to which entrained microorganisms are exposed. Here, we report the hygroscopicity of simulated lung fluid (SLF) particles. While the response of aqueous particles follow simple mixing rules based on composition, we observe phase hysteresis with increasing and decreasing relative humidity (RH) and clear uptake of water prior to deliquescence. These results indicate that RH history may control the state of respiratory aerosol in the environment and influence the viability of microorganisms.

Serum glial fibrillary acidic protein is a body fluid biomarker: A valuable prognostic for neurological disease - A systematic review.

Astrocytes are the most abundant cell type in the human central nervous system, and they play an important role in the regulation of neuronal physiology. In neurological disorders, astrocyte disintegration leads to the release of glial fibrillary acidic protein (GFAP) from tissue into the bloodstream. Elevated serum levels of GFAP can serve as blood biomarkers, and a useful prognostic tool to facilitate the early diagnosis of several neurological diseases ranging from stroke to neurodegenerative disorders. This systematic review synthesizes studies published between January 2012 and September 2021 that used GFAP as a potential blood biomarker to detect neurological disorders. The following electronic databases were accessed: MEDLINE, Scopus, and Web of Science. In all the databases, the following search strategy was used: ¨GFAP¨ OR ¨glial fibrillary acidic protein¨ AND ¨neurological¨ OR ¨neurodegenerative¨ AND ¨plasma¨ OR ¨serum¨. The initial search identified 1152 articles. After the exclusion criteria were applied, 48 publications that reported GFAP levels in neurological disorders were identified. A total of16 different neurological disorders that have plasmatic GFAP levels as a possible biomarker for the disease were described in the articles, being: multiple sclerosis, frontotemporal lobar degeneration, Alzheimer's disease, Parkinson disease, COVID-19, epileptic seizures, Wilson Disease, diabetic ketoacidosis, schizophrenia, autism spectrum disorders, major depressive disorder, glioblastoma, spinal cord injury, asthma, neuromyelitis optica spectrum disorder and Friedreich's ataxia. Our review shows an association between GFAP levels and the disease being studied, suggesting that elevated GFAP levels are a potentially valuable diagnostic biomarker in the evaluation of different neurological diseases.

Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases.

Rapid and precise diagnostic methods are required to control emerging infectious diseases effectively. Human body fluids are attractive clinical samples for discovering diagnostic targets because they reflect the clinical statuses of patients and most of them can be obtained with minimally invasive sampling processes. Body fluids are good reservoirs for infectious parasites, bacteria, and viruses. Therefore, recent clinical proteomics methods have focused on body fluids when aiming to discover human- or pathogen-originated diagnostic markers. Cutting-edge liquid chromatography-mass spectrometry (LC-MS)-based proteomics has been applied in this regard; it is considered one of the most sensitive and specific proteomics approaches. Here, the clinical characteristics of each body fluid, recent tandem mass spectroscopy (MS/MS) data-acquisition methods, and applications of body fluids for proteomics regarding infectious diseases (including the coronavirus disease of 2019 [COVID-19]), are summarized and discussed.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors.

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.