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In this study, we present a label-free non-faradaic impedimetric biosensor to detect bacterial cells using microfabricated gold interdigitated electrode (IDE). Silver nanoparticles (AgNP) are green synthesized using aqueous neem extract and characterized using Attenuated Total Reflectance- Fourier Transform Infrared spectra (ATR-FTIR), Dynamic Light Scattering (DLS), Scanning Electron Microscopy (SEM), and UV–Visible spectroscopy techniques. The synthesized AgNPs are well dispersed with an average size of 84 nm and showed an extensive antibacterial property indicated by a standard bioassay against Escherichia coli (E. coli). Gold IDEs are microfabricated by lithography on borosilicate glass wafers. The biofunctionalization of gold IDE is carried out using thiol‑gold covalent chemistry with mercaptohexanol (MCH). The self-assembled monolayer (SAM) of MCH facilitates drop-cast deposition of AgNP on the surface forming an MCH-AgNP. The functionalized IDE is electrochemically stable for further experiments and was validated by open circuit potential measurements. The objective of developing a label-free approach is confirmed by cyclic voltammetry analysis. Non-faradaic electrochemical impedance spectroscopy (nf-EIS) is carried out to detect E.coli cells suspended in water. The antibacterial property of AgNP is exploited to detect the decrease in cell concentration using nf-EIS. The impedance signatures corresponding to the trapping of cells are recorded with respect to time. Bacterial growth is a major challenge in maintaining water quality. The results demonstrated in this work would help to mitigate this problem effectively in a quick time without the need for skilled labor and sophisticated instruments required in traditional antibacterial testing.

Abstract This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Blueberry consumption has been a burgeoning interest attributed to their nutritional and health benefits. However, the main limitation to blueberry marketability is their perishability due to water loss, fungal, and mechanical damage during post-harvest preservation. In this study, pulsed light (PL) treatments with doses of 3, 6 and 9 J cm-2 were applied in the 6-week storage of highbush blueberry (cv. Draper) at 0.5°C under 90-95% relative humidity (RH) conditions. The quality changes of berry were assessed by using physicochemical attributes and antioxidant activity bi-weekly at week 0, 2, 4, 6. The results show that in spite of the partial loss of the antioxidant activity and total soluble solids (TSS), PL at dose of 6 J cm-2 was found to increase firmness and titratable acidity (TA), and decrease weight loss, rot incidence, and pH during post-harvest storage, which lead to a better maintenance of blueberry quality and shelf-life extension.

Background: Persistent pulmonary hypertension of the newborn (PPHN) is caused by the inability of the pulmonary arteries to dilate at birth, which leads to severe hypoxemia. Several risk factors have been identified in association with its occurrence and prognosis. The present study aimed to determine the incidence of PPHN, describe neonates’ characteristics, and evaluate the etiology, as well as mortality risk factors in newborns hospitalized due to PPHN at Bahrami Children’s Hospital, Tehran, Iran, from 2017 to 2020. Methods: A total of 49 neonates were included in the present study. The PPHN diagnosis was based on clinical criteria and echocardiography provided by neonatologists. Therefore, a complete history, physical examination, and laboratory data were gathered. Afterward, PPHN etiology was determined, and the patients were followed up for six months. Finally, PPHN prevalence was calculated and probable risk factors for its complications were investigated by using logistic regression analysis. Results: The findings revealed that the prevalence of PPHN was 3.5% in the center under study, and the mortality rate, as well as complete recovery, were 24.5% and 63%, respectively. It was also found that factors, such as male gender, abnormal Apgar score at birth, and cesarean delivery, were high in a percentage of PPHN patients; however, they were not associated with PPHN mortality and morbidity. The only variable that had a significant association with mortality and morbidity was an abnormal white blood cell count. Conclusion: The need for inotropic support was associated with poor outcomes. From the aspect of etiology, there exists higher mortality rates, complications, and poor prognosis among PPHN patients with underdevelopment, compared to the ones with maldevelopment.

Abstract Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

PurposeRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.ResultsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.ConclusionThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.

BackgroundCoronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals display a wide spectrum of disease severity, as defined by the World Health Organization (WHO). One of the main factors underlying this heterogeneity is the host immune response, with severe COVID-19 often associated with a hyperinflammatory state.AimOur current study aimed to pinpoint the specific genes and pathways underlying differences in the disease spectrum and outcomes observed, through in-depth analyses of whole blood transcriptomics in a large cohort of COVID-19 participants.ResultsAll WHO severity levels were well represented and mild and severe disease displaying distinct gene expression profiles. WHO severity levels 1-4 were grouped as mild disease, and signatures from these participants were different from those with WHO severity levels 6-9 classified as severe disease. Severity level 5 (moderate cases) presented a unique transitional gene signature between severity levels 2-4 (mild/moderate) and 6-9 (severe) and hence might represent the turning point for better or worse disease outcome. Gene expression changes are very distinct when comparing mild/moderate or severe cases to healthy controls. In particular, we demonstrated the hallmark down-regulation of adaptive immune response pathways and activation of neutrophil pathways in severe compared to mild/moderate cases, as well as activation of blood coagulation pathways.ConclusionsOur data revealed discrete gene signatures associated with mild, moderate, and severe COVID-19 identifying valuable candidates for future biomarker discovery.

Worldwide, a substantial economic loss in agricultural products is caused by plant pathogens. The increased losses in agriculture have drawn attention towards the development of miniaturized pathogen detection systems for phytopathology. This review paper's main selling point supports recent research (from 2015 to 2022) and technological advancements in the field of plant pathogen detection. The article discusses in depth important developments in the loop-mediated isothermal amplification (LAMP) assay, microfluidics, Molecular Imprinted Polymer (MIP) based biosensors, digital droplet PCR (ddPCR), disposable all-printed electronics, and nanoparticle-based sensors for instantaneous pathogen detection in agricultural applications. Utilizing nanoparticles to identify agricultural pathogens is a crucial topic that is explored. A brief on various commercially available detection systems worldwide have been listed. Finally, we discuss the perspective in the development of portable miniaturized systems and novel assay technologies based on advanced nanomaterials. Gold standard techniques: Although Polymerase Chain Reaction (PCR) and culture counting have been widely used for plant pathogen detection, they are not appropriate for measurements made in the field due to their higher installation costs, lack of portability, need for well-equipped laboratories, and requirement of skilled personnel. Therefore, these recent trends are overtaking the traditional methods in Agri-diagnostics because of their superior performances and suitability for the task.

Several months have passed since the onset of the COVID-19 pandemic. Multiple characteristics have been proposed as prognostic factors so far. This study aims to provide evidence on the association of neutrophil-to-lymphocyte ratio (NLR) at the hospitalization time and three desired outcomes (mortality, prolonged hospitalization, and intensive care unit [ICU] admission). We designed a single-centre retrospective observational study in Baharloo Hospital (Tehran, Iran) from 20 February to 19 April 2020. Patients with confirmed COVID-19 diagnosis via rt-PCR or chest CT imaging were included. Demographic and clinical data were obtained. The sample was divided into three groups, using tertile boundaries of initial NLR. The differences in mortality, comorbidities, hospitalization duration, drug administration, and ICU admission between these three groups were investigated. The identified confounding factors were adjusted to calculate the odds ratio of death, ICU admission, and prolonged hospitalization. Nine hundred sixty-three patients were included. In total, 151 and 212 participants experienced mortality and ICU admission, respectively. In multivariate logistic regression models, the adjusted odds ratio for mortality event in the second and third tertile of initial NLR after full adjustment were 1.89 (95% CI:1.07-3.32) and 2.57 (95% CI:1.48-4.43) and for ICU admission were 1.85 (95% CI:1.14-3.01) and 2.88 (95% CI:1.79-4.61), respectively. The optimal cut-off value of the initial NLR for predicting mortality was 4.27. Initial NLR can predict mortality and ICU admission in COVID-19 patients. Further investigations for curating the calculated cut-off can propose initial NLR as an indicator of poor prognosis for COVID-19 patients.