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Abstract In this work, we report on the application of the polarization modulated spectroscopic ellipsometry-based surface plasmon resonance method for sensitive detection of microorganisms in Kretschmann configuration. So far, rotating analyzer and single wavelength polarization modulation methods have widely been investigated for phase sensitive surface plasmon resonance measurement. In this study, a much simpler optical setup relying on fast electro-optic phase modulator crystals is introduced for bacteria detection. A beta barium borate crystal connected to a function generator is adapted for generating phase shifts in the millisecond regime to extract the ellipsometric angles ( $$\Psi$$ Ψ and $$\Delta$$ Δ ) under the surface plasmon resonance condition. For detection, the gold surface was functionalized with anti-Escherichia coli antibodies, and E. coli K12 was attached to them. We show that polarization modulated spectroscopic ellipsometry achieves a refractive index resolution in the order of $$10^{-5}$$ 10 - 5 RIU, and a limit of detection of $$10^{2}$$ 10 2 CFU/mL for E. coli K12 which is compatible with other surface plasmon resonance based phase sensitive methods with more complex detection concepts. As a follow-up step, an optical model can be developed to enhance this biosensor’s performance, and applications for sorting and detecting other biological targets will be investigated.

Immunity of human populations against viruses of the genus Orthopoxvirus, to which monkeypox virus (MPXV), variola virus, and vaccinia virus belong, has been questioned recently because of the emergence of [...]

To determine a demographic overview of orthopoxvirus seroprevalence, we tested blood samples collected during 2003–2019 from France (n = 4,876), Bolivia (n = 601), Laos (n = 657), and Mali (n = 255) for neutralizing antibodies against vaccinia virus. In addition, we tested 4,448 of the 4,876 samples from France for neutralizing antibodies against cowpox virus. We confirmed extensive cross-immunity between the 2 viruses. Seroprevalence of antibodies was

BackgroundHuman T-cell leukemia virus type 1 (HTLV-1) is a retrovirus known to cause two major diseases: adult T-cell leukemia/lymphoma and a progressive neuromyelopathy—tropical spastic paraparesis. Many viruses may be involved in the pathogenesis of thyroiditis; however, few studies have focused on the role of HTLV-1. We aimed to investigate the association between HTLV-1 and biological thyroid dysfunction.MethodsWe included 357 patients with a positive HTLV-1 serology and thyroid-stimulating hormone assay data between 2012 and 2021 in a hospital in French Guiana; we compared the prevalence of hypothyroidism and hyperthyroidism in this group with that in an HTLV-1-negative control group (722 persons) matched for sex and age.ResultsThe prevalence of hypothyroidism and hyperthyroidism in patients with HTLV-1 infection was significantly higher than that in the control group (11% versus 3.2% and 11.3% versus 2.3%, respectively; p < 0.001).ConclusionOur study shows, for the first time, the association between HTLV-1 and dysthyroidism in a large sample, suggesting that thyroid function exploration should be systematically implemented in this population as this may have an impact on therapeutic management.

The determination of suitable habitats and roadkill hotspots allows for the prioritization of areas of conservation and the identification of road sections that require appropriate planning and development. Understanding the factors that influence the species' presence and those contributing to its road-related fatalities is crucial. This study demonstrates the use of MaxEnt modeling to map the potential distribution of the striped hyaena (Hyaena hyaena) in Batna province (East Algeria) using occurrence records and a set of environmental variables. The results indicated that the two primary environmental variables that influence the distribution of the species are the shrubland, and the slope with a contribution of 37.5 and 30.2 %, respectively, followed by built-up areas (12 %) and distance to roads (7.2 %). The species may occupy steep terrain with shrubland near anthropized areas. Road collisions represent a threat to the species, with 28 documented casualties from 2010 to 2020 in Batna. The kernel density estimation revealed an important roadkill hotspot along the national roads 3 and 28. This study represents a preliminary step for the use of wildlife ecological niche modeling and road management in Algeria. [ABSTRACT FROM AUTHOR]

The world of fluid mechanics is increasingly generating a large amount of data, thanks to the use of numerical simulation techniques. This offers interesting opportunities for incorporating machine learning methods to solve data-related problems such as model calibration. One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate a phenomenon. Indeed, the computational cost generated by some models of fluid mechanics pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. In this paper, we propose a framework which is a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. an approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e−09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

Emphysematous hepatitis is a rare infectious disease, which corresponds to the presence of gas in the liver, without collection and after exclusion of vascular origin. This entity belongs to the group of emphysematous infections, whose physiopathology seems to be linked to the presence of unbalanced diabetes and to bacterial fermentation, leading to the production of gas within the liver parenchyma. Very few cases of emphysematous hepatitis have been described in the literature, and most of them had a rapidly fatal course. In this manuscript, we report the case of a 59-year-old man with emphysematous hepatitis due to wild-type Klebsiella pneumoniae that was successfully managed by surgery, and we perform a review of the literature to describe the clinical and biopathological aspects of this rare hepatic disease. Our manuscript underlines the need to perform biological and histopathological sampling to better understand the pathophysiology of this rare entity. The causes and mechanisms of emphysematous hepatitis, which seem to be multiple, lead us to believe that it is a syndrome rather than a simple infectious disease.

This study aimed to uncover the preventive capability of Zingiber officinale against myelotoxicity, leukaemia, and hepatotoxicity. For the most part, this work depended on the subcutaneous injection of cyclohexatriene in rabbits to cause the illness by a synthetic strategy. In parallel, another group of rabbits was exposed to the injection of cyclohexatriene under similar conditions with the feeding Zingiber officinale, where it was discovered that the cyclohexatriene-induced myelotoxicity was counteracted. The histological examination additionally uncovered the hepato-defensive intensity of Zingiber officinale. The most pharmacologically active molecules of Zingiber officinale were recognised by gas chromatography–mass spectrometry (GC-MS).

In this paper, we present a new approach to use machine learning (ML) for the calibration of a physical model allowing the reproduction of the vibratory behavior of an overhead line conductor. This physical model known as Strip Theory (ST) has the advantage of being very precise but very complicated and cumbersome in its software operations and manipulations. A second model known as the Wake Oscillator (WO) has been implemented in order to meet the limitations of the ST model. In order to be able to use the WO model instead of the ST model, very heavy manual adjustments are required, which makes its use complicated. Precisely, the WO must be able to generate a time series similar to a time series generated by the ST model. In order to respond to this limitation, a machine learning model known as ENS has been proposed. The machine learning model will therefore take as input the data from the WO model and output the data from the ST model. A series of Machine learning models have been implemented and tested. The ENS algorithm was retained with a best Pearson’s linear coefficient of determination (R2 Score) of almost 0.7 and a Root mean square deviation (RMSE) of 7.57e-09. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

BackgroundInborn errors of immunity (IEI) predispose patients to various infectious and non-infectious complications. Thanks to the development and expanding use of flow cytometry and increased awareness, the diagnostic rate of IEI has markedly increased in Algeria the last decade.AimThis study aimed to describe a large cohort of Algerian patients with probable IEI and to determine their clinical characteristics and outcomes.MethodsWe collected and analyzed retrospectively the demographic data, clinical manifestations, immunologic, genetic data, and outcome of Algerian IEI patients - diagnosed in the department of medical immunology of Beni Messous university hospital center, Algiers, from 2008 to 2021.ResultsEight hundred and seven patients with IEI (482 males and 325 females) were enrolled, 9.7% of whom were adults. Consanguinity was reported in 50.3% of the cases and a positive family history in 32.34%. The medium age at disease onset was 8 months and at diagnosis was 36 months. The median delay in diagnosis was 16 months. Combined immunodeficiencies were the most frequent (33.8%), followed by antibody deficiencies (24.5%) and well-defined syndromes with immunodeficiency (24%). Among 287 patients tested for genetic disorders, 129 patients carried pathogenic mutations; 102 having biallelic variants mostly in a homozygous state (autosomal recessive disorders). The highest mortality rate was observed in patients with combined immunodeficiency (70.1%), especially in patients with severe combined immunodeficiency (SCID), Omenn syndrome, or Major Histocompatibility Complex (MHC) class II deficiency.ConclusionThe spectrum of IEI in Algeria is similar to that seen in most countries of the Middle East and North Africa (MENA) region, notably regarding the frequency of autosomal recessive and/or combined immunodeficiencies.