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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.

Purpose: The median time from the event leading to the spinal cord injury (SCI) to the time of decompressive surgery is estimated to be 6.9 days in Iran, which is much longer than the proposed ideal time (less than 24 h) in published guidelines. The current qualitative study aimed to determine the reasons for the observed decompression surgery delay in Iran from the perspective of neurosurgeons. Methods: This qualitative study is designed to perform content analysis on the gathered data from face-to-face semi-structured interviews with 12 Iranian neurosurgeons. Results: The findings of the current study suggest that patient-related factors constitute more than half of the codes extracted from the interviews. Overall, the type of injury, presence of polytrauma, and surgeons' wrong attitude are the main factors causing delayed spinal cord decompression in Iranian patients from the perspective of neurosurgeons. Other notable factors include delay in transferring patients to the trauma center, delay in availability of necessary equipment, and scarce medical personnel. Conclusion: In the perspective of neurosurgeons, the type of injury, presence of polytrauma, and surgeons' wrong attitude are the leading reasons for delayed decompressive surgery of individuals with SCI in Iran.

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.

Background and objective: Nowadays, novel biotechnological methods are preferred for flavoring productions since traditional methods include disadvantages. The aim of this study was to assess in situ biosynthesis of natural fruity flavors in fermented milks using microbial co-cultures and lipase enzyme. Materials and methods: Trans esterification of milk fats with ethanol was carried out to develop fruity flavors in ultra-high-temperature whole milks using lipase of Palatase coupled with ethanol fermentation. Kluyveromyces marxianus NCYC 1425 was used to produce in situ ethanol in co-cultures with Lactobacillus fermentum PCC or Lactobacillus paracasei L26. Effects of co-culturing on cell viability and fermentation progress were assessed using enumeration of viable cells and measurement of pH in samples at 0, 24 (Palatase addition) and 48 h (end of fermentation). Headspace solid phase microextraction-gas chromatography (SPME)-MS/FID was used for ester, ethanol and free fatty acid analyses at the beginning and end of the fermentation. Standard curve of ethanol was used to assess the products in terms of being Halal. Results and conclusion: Kluyveromyces marxianus included synergistic effects on Lactobacillus paracasei growth as well as antagonistic effects on Lactobacillus fermentum growth. Antimicrobial effects were seen in Kluyveromyces marxianus-Lactobacillus paracasei co-cultures when Palatase was added. Palatase significantly increased ester levels of the fermented samples. The co-cultures did not include significant differences in shorter chain ester levels (esters of 4-7 carbon chain fatty acids); in contrast, Kluyveromyces marxianus- Lactobacillus fermentum resulted in higher levels of longer chain esters. Although the Kluyveromyces marxianus cultures resulted in higher ester levels compared to that its co-cultures did, the cultures can be used as appropriate adjunct cultures with Lactobacillus cultures to boost flavor ester synthesis. This flavor synthesis can be an appropriate alternative for artificial flavoring agents.

Abstract Sensor platforms that exploit the fibrous textile threads as substrates offer great promise since they can be directly sewn, woven or stitched on to any clothing. They can also be placed directly in intimate contact with the skin. In this work, we present a thread-based sensing platform in the form of a multiplexed sensing patch for continuous simultaneous on-skin monitoring of sweat. The patch performs real-time, on-body measurements of important biomarkers present in sweat such as electrolytes (sodium and ammonium ions), metabolites (lactate) and acidity (pH). Flexible threads coated with conductive inks were used as sensing electrodes. Selective potentiometric detection of electrolytes and pH was made possible through ion-selective membrane deposition and pH-sensitive polyaniline coating on threads, respectively. An amperometric enzymatic sensing scheme with lactate oxidase was used for the detection of lactate. An array of the thread sensors is integrated onto a patch with connectivity to a miniaturized circuit module containing a potentiostat, microprocessor and wireless circuitry for wireless smartphone readout. Extensive in vitro validation and an in vivo human pilot study involving a maximal exertion test show the promise of this platform for real-time physiological monitoring of human performance/fitness under stress, as well as diagnostic monitoring through sweat analysis.

Abstract Background Influenza infections produce a spectrum of disease severity, ranging from a mild respiratory illness to respiratory failure and death. The host-response pathways associated with the progression to severe influenza disease are not well understood. Methods To gain insight into the disease mechanisms associated with progression to severe infection, we analyzed the leukocyte transcriptome in severe and moderate influenza patients and healthy control subjects. Pathway analysis on differentially expressed genes was performed using a topology-based pathway analysis tool that takes into account the interaction between multiple cellular pathways. The pathway profiles between moderate and severe influenza were then compared to delineate the biological mechanisms underpinning the progression from moderate to severe influenza. Results 107 patients (44 severe and 63 moderate influenza patients) and 52 healthy control subjects were included in the study. Severe influenza was associated with upregulation in several neutrophil-related pathways, including pathways involved in neutrophil differentiation, migration, degranulation and neutrophil extracellular trap (NET) formation. The degree of upregulation in neutrophil-related pathways were significantly higher in severely infected patients compared to moderately infected patients. Severe influenza was also associated with downregulation in immune response pathways, including pathways involved in antigen presentation such as CD4+ T-cell co-stimulation, CD8+ T cell and Natural Killer (NK) cells effector functions. Apoptosis pathways were also downregulated in severe influenza patients compare to moderate and healthy controls. Conclusions These findings showed that there are changes in gene expression profile that may highlight distinct pathogenic mechanisms associated with progression from moderate to severe influenza infection.

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.

Abstract Objectives Hospitalized patients who presented within the last 24 h with a bacterial infection were recruited. Participants were assigned into sepsis and uncomplicated infection groups. In addition, healthy volunteers were recruited as controls. RNA was prepared from whole blood, depleted from beta-globin mRNA and sequenced. This dataset represents a highly valuable resource to better understand the biology of sepsis and to identify biomarkers for severe sepsis in humans. Data description The data presented here consists of raw and processed transcriptome data obtained by next generation RNA sequencing from 105 peripheral blood samples from patients with uncomplicated infections, patients who developed sepsis, septic shock patients, and healthy controls. It is provided as raw sequenced reads and as normalized log2 transformed relative expression levels. This data will allow performing detailed analyses of gene expression changes between uncomplicated infections and sepsis patients, such as identification of differentially expressed genes, co-regulated modules as well as pathway activation studies.

Identification of host factors associated with severe influenza infection could provide insights into treatment options. Here, the authors provide transcriptomic analyses of blood from >100 influenza infected patients and show that changes in circulating neutrophils are associated with severe influenza infection.

Introduction Accurate triage is an important first step to effectively manage the clinical treatment of severe cases in a pandemic outbreak. In the current COVID-19 global pandemic, there is a lack of reliable clinical tools to assist clinicians to perform accurate triage. Host response biomarkers have recently shown promise in risk stratification of disease progression; however, the role of these biomarkers in predicting disease progression in patients with COVID-19 is unknown. Here, we present a protocol outlining a prospective validation study to evaluate the biomarkers’ performance in predicting clinical outcomes of patients with COVID-19.Methods and analysis This prospective validation study assesses patients infected with COVID-19, in whom blood samples are prospectively collected. Recruited patients include a range of infection severity from asymptomatic to critically ill patients, recruited from the community, outpatient clinics, emergency departments and hospitals. Study samples consist of peripheral blood samples collected into RNA-preserving (PAXgene/Tempus) tubes on patient presentation or immediately on study enrolment. Real-time PCR (RT-PCR) will be performed on total RNA extracted from collected blood samples using primers specific to host response gene expression biomarkers that have been previously identified in studies of respiratory viral infections. The RT-PCR data will be analysed to assess the diagnostic performance of individual biomarkers in predicting COVID-19-related outcomes, such as viral pneumonia, acute respiratory distress syndrome or bacterial pneumonia. Biomarker performance will be evaluated using sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operating characteristic curve.Ethics and dissemination This research protocol aims to study the host response gene expression biomarkers in severe respiratory viral infections with a pandemic potential (COVID-19). It has been approved by the local ethics committee with approval number 2020/ETH00886. The results of this project will be disseminated in international peer-reviewed scientific journals.

Abstract Background Respiratory viruses circulate constantly in the ambient air. The risk of opportunistic infection from these viruses can be increased in mechanically ventilated patients. The present study evaluates the feasibility of detecting airborne respiratory viruses in mechanically ventilated patients using a novel sample collection method involving ventilator filters. Methods We collected inspiratory and expiratory filters from the ventilator circuits of mechanically ventilated patients in an intensive care unit over a 14-month period. To evaluate whether we could detect respiratory viruses collected in these filters, we performed a reverse transcription polymerase chain reaction on the extracted filter membrane with primers specific for rhinovirus, respiratory syncytial virus, influenza virus A and B, parainfluenza virus (type 1, 2 and 3) and human metapneumovirus. For each patient, we also performed a full virology screen (virus particles, antibody titres and virus-induced biomarkers) on respiratory samples (nasopharyngeal swab, tracheal aspirate or bronchoalveolar fluid) and blood samples. Results Respiratory viruses were detected in the ventilator filters of nearly half the patients in the study cohort (n = 33/70). The most common virus detected was influenza A virus (n = 29). There were more viruses detected in the inspiratory filters (n = 18) than in the expiratory filters (n = 15). A third of the patients with a positive virus detection in the ventilator filters had a hospital laboratory confirmed viral infection. In the remaining cases, the detected viruses were different from viruses already identified in the same patient, suggesting that these additional viruses come from the ambient air or from cross-contamination (staff or visitors). In patients in whom new viruses were detected in the ventilator filters, there was no evidence of clinical signs of an active viral infection. Additionally, the levels of virus-induced biomarker in these patients were not statistically different from those of non-infected patients (p = 0.33). Conclusions Respiratory viruses were present within the ventilator circuits of patients receiving mechanical ventilation. Although no adverse clinical effect was evident in these patients, further studies are warranted, given the small sample size of the study and the recognition that ventilated patients are potentially susceptible to opportunistic infection from airborne respiratory viruses.

The neural network enables efficient solutions for Nondeterministic Polynomial-time (NP) hard problems, which are challenging for conventional von Neumann computing. The hardware implementation, i.e., neuromorphic computing, aspires to enhance this efficiency by custom hardware. Particularly, NP hard graphical constraint optimization problems are solved by a network of stochastic binary neurons to form a Boltzmann Machine (BM). The implementation of stochastic neurons in hardware is a major challenge. In this work, we demonstrate that the high to low resistance switching (set) process of a PrxCa1−xMnO3 (PCMO) based RRAM (Resistive Random Access Memory) is probabilistic. Additionally, the voltage-dependent probability distribution approximates a sigmoid function with 1.35%–3.5% error. Such a sigmoid function is required for a BM. Thus, the Analog Approximate Sigmoid (AAS) stochastic neuron is proposed to solve the maximum cut—an NP hard problem. It is compared with Digital Precision-controlled Sigmoid (DPS) implementation using (a) pure CMOS design and (b) hybrid (RRAM integrated with CMOS). The AAS design solves the problem with 98% accuracy, which is comparable with the DPS design but with 10× area and 4× energy advantage. Thus, ASIC neuro-processors based on novel analog neuromorphic devices based BM are promising for efficiently solving large scale NP hard optimization problems.

Chronic Hepatitis C infection is associated with a broad spectrum of liver pathologies, ranging from inflammation to fibrosis and liver cancer. Here Thabet et al. identified a polymorphism in the gene MBOAT7 that is associated with increased hepatic inflammation and higher risk of fibrosis development and progression.

Background and Aim: Vegetables are one of the major components of food basket and consumption of them is on the rise. On the other hand, diseases and outbreaks caused by this group of foodstuffs have increasing trend. This study was performed to investigate the microbial contamination of vegetables used in south of Tehran. Materials and Methods: Hundred fresh packaged and non packaged vegetable samples composed of 10 kinds, were examined based on the methods presented in Iran national standards. For each sample, enumeration of enterococcus and total microbial count was carried out by the pour plate technique in KF agar and plate count agar, respectively. Escherichia coli detection was also performed using lauryl sulfate broth, EC broth, peptone water and MacConkey agar mediums. Biochemical tests namely urea hydrolysis, motility, sulfide hydrogen (SH2) production, gas production and sugar fermentation were conducted as conformational tests of Escherichia coli. Results: All (100%) of mixed leafy vegetables, spinaches, peppers, kuku (Persian food) vegetables, wheat sprouts and mung been sprouts had contamination higher than acceptable limits. In the case of green onions, basils, kuku vegetables, mushrooms and broccolis, respectively 40, 60, 30, 90 and 30 % of the samples were in contravention with acceptable limits, in terms of at least one parameter. Conclusion: Our results show that consumption of vegetables specially those used as raw, need more attention at washing point and need to use hygienic rules of health department and health care centers, at shopping and consumption steps.

Remotely empowered wireless sensor networks use different energy resources including photovoltaic solar cells, wireless power transmission, and batteries. As another option the electromagnetic energy available in the ambient can be harvested to power these remote sensors. This is particularly valuable if it is desirable to harvest the ambient energy available in the wide range of electromagnetic spectrum. This has motivated the research for developing energy harvesting devices which can absorb this energy and produce a DC voltage. Rectenna, an antenna coupled with a rectifier, is the main component used for absorbing electromagnetic radiation at GHz and THz frequencies. Rectifying MIM tunnel diodes are able to operate at tens and hundreds of GHz frequency. As the preliminary steps towards development of high-frequency rectifiers, this paper presents fabrication and DC characterization of two new MIM diodes, Ti-TiO2-Al and Ti-TiO2-Pt. G-V analysis of the fabricated diodes verifies tunneling. Brinkman-Dynes-Rowell model is used to extract oxide thickness of which the derived value is around 9 nm. Ti-TiO2-Pt diode exhibits rectification ratio of 15 at 0.495 V, which is more than rectification ratio reported in earlier works.