Search

[Display omitted] • New non-invasive method for COVID-19 diagnosis with fast turnaround time (<10 min). • Nanoparticle biosensors detect SARS-CoV-2 antigens trapped in surgical face masks. • Excellent sensitivity and specificity, even with asymptomatic patients. • Signals are read with a smartphone, ideal for decentralized mass screenings. • Potential for detecting other antigens as well as biomarkers of inflammation. Detecting SARS-CoV-2 antigens in respiratory tract samples has become a widespread method for screening new SARS-CoV-2 infections. This requires a nasopharyngeal swab performed by a trained healthcare worker, which puts strain on saturated healthcare services. In this manuscript we describe a new approach for non-invasive COVID-19 diagnosis. It consists of using mobile biosensors for detecting viral antigens trapped in surgical face masks worn by patients. The biosensors are made of filter paper containing a nanoparticle reservoir. The nanoparticles transfer from the biosensor to the mask on contact, where they generate colorimetric signals that are quantified with a smartphone app. Sample collection requires wearing a surgical mask for 30 min, and the total assay time is shorter than 10 min. When tested in a cohort of 27 patients with mild or no symptoms, an area under the receiving operating curve (AUROC) of 0.99 was obtained (96.2 % sensitivity and 100 % specificity). Serial measurements revealed a high sensitivity and specificity when masks were worn up to 6 days after diagnosis. Surgical face masks are inexpensive and widely available, which makes this approach easy to implement anywhere. The excellent sensitivity, even when tested with asymptomatic patient samples, along with the mobile detection scheme and non-invasive sampling procedure, makes this biosensor design ideal for mass screening. [ABSTRACT FROM AUTHOR]

Objective: The objective was to describe the characteristics of the use of clinical ultrasound in two health centers (SC) of Health Area VII of the Region of Murcia (CS Murcia-Sur and CS Floridablanca)., Design: Observational, descriptive, longitudinal, prospective and multicenter study. SITE: CS Murcia-Sur and CS Floridablanca (Health Area VII of the Region of Murcia)., Participants: One hundred and thirty-five patients were included., Interventions: Performance of clinical ultrasound in the primary care (PC) consultation., Main Measurements: Demographic variables (age, sex), as well as clinical variables (reason for consultation, type of ultrasound, results, referrals to the second hospital level, degree of diagnostic agreement) were collected., Results: One hundred and thirty-five patients were included, more than 50% were female. The main reason for consultation was musculoskeletal and soft tissue symptoms (44.4%), followed by digestive symptoms (21.5%). 44.4% of the ultrasounds were classified as normal, while pathological findings were found in 55.6%. Confirmatory tests were requested in 43.7% and the findings were confirmed in 67% of the patients., Conclusions: The use of ultrasound in PC allows to show the high prevalence of pathological findings in the examination of patients. In a technique that helps the clinician in his diagnostic-therapeutic process. The integration of clinical ultrasound in the PC consultation can save complementary studies and referrals to a second level of care. Its implementation in PC requires proper training of professionals., (Copyright © 2023 The Authors. Publicado por Elsevier España S.L.U. All rights reserved.)

Background: Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was to introduce a new regulatory and clinical paradigm-a Neural-Net Artificial Pancreas (NAP)-an encoding of an AID algorithm into a neural network that approximates its action and assess NAP versus the original AID algorithm. Methods: The University of Virginia Model-Predictive Control (UMPC) algorithm was encoded into a neural network, creating its NAP approximation. Seventeen AID users with T1D were recruited and 15 participated in two consecutive 20-h hotel sessions, receiving in random order either NAP or UMPC. Their demographic characteristics were ages 22-68 years old, duration of diabetes 7-58 years, gender 10/5 female/male, White Non-Hispanic/Black 13/2, and baseline glycated hemoglobin 5.4%-8.1%. Results: The time-in-range (TIR) difference between NAP and UMPC, adjusted for entry glucose level, was 1 percentage point, with absolute TIR values of 86% (NAP) and 87% (UMPC). The two algorithms achieved similar times <70 mg/dL of 2.0% versus 1.8% and coefficients of variation of 29.3% (NAP) versus 29.1 (UMPC)%. Under identical inputs, the average absolute insulin-recommendation difference was 0.031 U/h. There were no serious adverse events on either controller. NAP had sixfold lower computational demands than UMPC. Conclusion: In a randomized crossover study, a neural-network encoding of a complex model-predictive control algorithm demonstrated similar performance, at a fraction of the computational demands. Regulatory and clinical doors are therefore open for contemporary machine-learning methods to enter the AID field. Clinical Trial Registration number: NCT05876273.

Hyperdegranulation of neutrophilic granulocytes is a common finding in sepsis that directly contributes to the heightened immune response leading to organ dysfunction. Currently, cell degranulation is detected by flow cytometry, which requires large infrastructure that is not always available at the point of care. Here, we propose a plasmonic assay for detecting the degranulation status of septic cells colorimetrically. It is based on triggering the aggregation of gold nanoparticles with cationic granule proteins. Cells from septic patients contain fewer granules and therefore release less cationic proteins than healthy cells. This results in red-colored assays than can be easily detected by eye. The assay can selectively detect cationic granule proteins even in the presence of an excess of unrelated proteins, which is key to detect degranulation with high specificity. Coupling this signal generation mechanism with a magnetic purification step enabled the identification of septic cells with the same performance as flow cytometry. This makes the proposed method a promising alternative for diagnosing sepsis in decentralized healthcare schemes.

In this paper, a novel predictive Extended State Observer (ESO)-based discrete controller with guaranteed input-to-state stability is developed. Predictive controllers based on ESOs are gaining acceptance for regulating MIMO systems with disturbances, uncertainties or actuator constrains. However, there is an important concern about this control structure regarding its closed-loop stability; a key property that is not strictly guaranteed with the most part of the previous formulations. This paper shows that -under the same assumptions that are normally taken in the ESO literature- a predictive ESO-based controller that is proved to be input-to-state stable can be easily constructed by adding two fixed terms in the cost-index definition. A simulation case study of the glucose control in patients with type-1 diabetes is additionally given in order to illustrate the main advantages of this control structure., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

Lung-secreted IgG and IgM antibodies are valuable biomarkers for monitoring the local immune response against respiratory infections. These biomarkers are found in lower airway secretions that need to be liquefied prior to analysis. Traditional methods for sample liquefaction rely on reducing disulfide bonds, which may damage the structure of the biomarkers and hamper their immunodetection. Here, we propose an alternative enzymatic method that uses O 2 bubbles generated by endogenous catalase enzymes in order to liquefy respiratory samples. The proposed method is more efficient for liquefying medium- and high-viscosity samples and does not fragment the antibodies. This prevents damage to antigen recognition domains and recognition sites for secondary antibodies that can decrease the signal of immunodetection techniques. The suitability of the enzymatic method for detecting antibodies in respiratory samples is demonstrated by detecting anti-SARS-CoV-2 IgG and IgM to viral N-protein with gold standard ELISA in bronchial aspirate specimens from a multicenter cohort of 44 COVID-19 patients. The enzymatic detection sharply increases the sensitivity toward IgG and IgM detection compared to the traditional approach based on liquefying samples with dithiothreitol. This improved performance could reveal new mechanisms of the early local immune response against respiratory infections that may have gone unnoticed with current sample treatment methods.

Alcohol abuse has been identified as a risk factor for contracting human immunodeficiency virus (HIV) and accelerating disease progression. Our study aims to determine alcohol consumption rates among Ecuadorian HIV positive (HIV+) patients prior to diagnosis to evaluate its impact as an independent risk factor for contracting HIV. Additionally, we will examine post-diagnosis consumption rates among the HIV+ population.We provided anonymous questionnaires to 300 HIV+ patients and 600 internal medicine patients at 3 hospitals in Quito, Ecuador. Questionnaires quantified alcohol usage prior to HIV diagnosis, at time of diagnosis, and post-diagnosis while accounting for other potential HIV risk factors. We then determined frequencies of alcohol consumption and confounding variables. Finally, we performed a multivariable logistic regression controlling for confounders to determine the statistical significance of alcohol consumption as an independent risk factor for HIV.Our results showed increased odds for contracting HIV among those who drank daily (OR 5.3, CI 2.0-14.0) and those who consumed 6 or more alcoholic beverages on days they drank (OR 5.0, CI 3.1-8.2). Through multivariable analysis, we found that abstaining from binge drinking was a protective factor with an OR 0.5 (0.3-0.96). The percentage of HIV+ patients abstaining from alcohol increased from 30% twelve months prior to diagnosis to 57% after diagnosis.Our results show that alcohol abuse significantly increases the risk of contracting HIV. We found that prior to diagnosis, HIV patients consistently drank more frequently and a greater amount than the control group. Alcohol use significantly decreased among HIV+ patients after diagnosis.