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Hemophilia is an inherited X-linked coagulopathy defined by a deficiency or abnormality in the clotting function of factor VIII (hemophilia A) or factor IX (hemophilia B). The continuous improvement of the treatment has made it possible to monitor the patient through their life cycle with the inherent transition of care, initially by caregivers in childhood and later by the patient himself. Alterations associated with age added to chronic diseases are a constant challenge in the comprehensive treatment of the patient. The inhibitors are IgG alloantibodies directed to exogenous clotting factors, factor VIII or factor IX. The likelihood of developing inhibitors varies from one person with hemophilia to another and depends on the interaction between multiple genetic and environmental factors. This review offers a better understanding of the physiological alterations that allow a comprehensive assessment of the patient with hemophilia.

Hemophilia is an inherited X-linked coagulopathy defined by a deficiency or abnormality in the clotting function of factor VIII (Hemophilia A) or factor IX (Hemophilia B). Prophylaxis – the regular administration of therapeutic products to maintain hemostasis and prevent bleeding – is the mainstream of treatment. Addressing the development and scientific evidence for administrating prophylaxis is the goal of this review. Prophylaxis is the therapeutic modality of choice for people with severe hemophilia, being considered, in principle, a lifelong treatment. It should have an early onset, ideally as a primary, or at least secondary. Even lifelong tertiary prophylaxis seems to offer benefit, although further studies are still lacking. Individualized strategies should lead to an optimization of the dilemma between better joint outcomes versus involved costs.

The history of hemophilia is ancient, with descriptions dated to the 2nd century AD. The first modern narratives appeared in 1800s, when total blood transfusion was the only available treatment and life expectancy was remarkably low. Advances occurred with the use of plasma and cryoprecipitate, but only the discovered of factor concentrates revolutionized the treatment. The implantation of prophylaxis allowed hemophilic patients to prevent bleeding and the development of chronic arthropathy, although with a significant burdensome with the regular infusions. In the past 20 years, this field has witnessed major improvements, including the development of gene therapy and other pharmacological approaches.

Although deep learning has historically deep roots, with regard to the vast area of​ artificial intelligence and, more specifically, to the study of machine learning and artificial neural networks, it is only recently that this line of investigation has developed fruits with great commercial value, starting to have thus a significant impact on society. It is precisely because of the wide applicability of this technology nowadays that we must be alert, in order to be able to foresee the negative implications of its indiscriminate uses. Of fundamental importance, in this context, are the risks associated with collecting large amounts of data for training neural networks (and for other purposes too), the dilemma of the strong opacity of these systems, and issues related to the misuse of already trained neural networks, as exemplified by the recent proliferation of deepfakes. This text introduces and discusses these issues with a pedagogical bias, thus aiming to make the topic accessible to new researchers interested in this area of​ application of scientific models.

Introduction: Epidemiological studies on hemophilia in the Brazilian population are historically scarce. Despite the continuous effort made by the National Program of Inherited Bleeding Disorders to map this condition, little information is available, especially on the period prior to program conception. Therefore, the present study aims to assess the epidemiological, serological, and clinical characteristics of patients with hemophilia in the state of Rio Grande do Sul, Brazil. Methods: A total of 455 patients had their medical records reviewed from January 1, 2003 to December 31, 2007. Results: We observed a remarkable prevalence of hepatitis C virus (HCV) infection in patients with both hemophilia A and B, and this prevalence significantly increased along with age (p < 0.001). No positive anti-HCV results were observed among children younger than 5 years old. There was a significant correlation between the severity of hemophilia and the number of arthropathies in all age categories. Considering the presence of inhibitors, a significant difference was observed between age groups, as older patients had higher inhibitor titers. There was a significant correlation between mean coagulation factor consumption and the number of arthropathies in patients over 5 years old. Conclusions: This profile analysis of patients with hemophilia reflects a gradual improvement in treatment safety and efficiency, as well as the need for continued investment in this population.

Isosorbide-containing copolycarbonate (Bio-PC) is a partly biobased alternative to conventional bisphenol A (BPA) based polycarbonate (PC). Conventional PC is widely used in polymer processing technologies including thermoplastic foaming such as foam injection molding. At present, no detailed data is available concerning the foam injection molding behavior and foam properties of Bio-PC. This contribution provides first results on injection-molded foams based on isosorbide-containing PC. The structural foams were produced by using an endothermic chemical blowing agent (CBA) masterbatch and the low pressure foam injection molding method. The influence of weight reduction and blowing agent concentration on general foam properties such as density, morphology, and mechanical properties was studied. The test specimens consist of a foam core in the center and compact symmetrical shell layers on the sides. The thickness of the foam core increases with increasing weight reduction irrespective of the CBA concentration. The specific (mechanical) bending properties are significantly improved and the specific tensile properties can almost be maintained while reducing the density of the injection-molded parts.

Hemophilia is an inherited X-linked coagulopathy defined by a deficiency or abnormality in the clotting function of factor VIII (hemophilia A) or factor IX (hemophilia B). The continuous improvement of the treatment has made possible to monitor the patient through their life cycle with the inherent transition of care, initially by caregivers in childhood and later by the patient himself. Alterations associated with age added to chronic diseases are a constant challenge in the comprehensive treatment of the patient. One of the main results related to the use of factors VIII and IX is the development of inhibitors, which are IgG alloantibodies directed to exogenous coagulation factors. The likelihood of developing inhibitors varies from one person with hemophilia to another and depends on the interaction between genetic and environmental factors. This review offers a better understanding of the physiological alterations that allow a comprehensive assessment of the patient with hemophilia. [ABSTRACT FROM AUTHOR]

Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially increases computational cost. Although deep learning-based methods have been proposed for several cases of limited-data CT, few works in the literature have dealt with beam-hardening artifacts, and none have addressed the problems caused by randomly selected projections and a highly limited span. We propose the deep learning-based prior image constrained (PICDL) framework, a hybrid method used to yield CT images free from beam-hardening artifacts in different limited-data scenarios based on the combination of a modified version of the Prior Image Constrained Compressed Sensing (PICCS) algorithm that incorporates the L2 norm (L2-PICCS) with a prior image generated from a preliminary FDK reconstruction with a deep learning (DL) algorithm. The model is based on a modification of the U-Net architecture, incorporating ResNet-34 as a replacement of the original encoder. Evaluation with rodent head studies in a small-animal CT scanner showed that the proposed method was able to correct beam-hardening artifacts, recover patient contours, and compensate streak and deformation artifacts in scenarios with a limited span and a limited number of projections randomly selected. Hallucinations present in the prior image caused by the deep learning model were eliminated, while the target information was effectively recovered by the L2-PICCS algorithm.

Abstract Heart diseases are associated with changes in the biomechanical properties of the myocardial wall. However, there is no modality available to assess myocardial stiffness directly. Brillouin microspectroscopy (mBS) is a consolidated mechanical characterization technique, applied to the study of the viscoelastic and elastic behavior of biological samples and may be a valuable tool for assessing the viscoelastic properties of the cardiac tissue. In this work, viscosity and elasticity were assessed using mBS in heart samples obtained from healthy and unhealthy mice (n = 6 per group). Speckle-tracking echocardiography (STE) was performed to evaluate heart deformation. We found that mBS was able to detect changes in stiffness in the ventricles in healthy myocardium. The right ventricle showed reduced stiffness, in agreement with its increased compliance. mBS measurements correlated strongly with STE data, highlighting the association between displacement and stiffness in myocardial regions. This correlation was lost in pathological conditions studied. The scar region in the infarcted heart presented changes in stiffness when compared to the rest of the heart, and the hypertrophied left ventricle showed increased stiffness following aortic stenosis, compared to the right ventricle. We demonstrate that mBS can be applied to determine myocardial stiffness, that measurements correlate with functional parameters and that they change with disease.

Phytoplasmas are insect-vectored, difficult-to-culture bacterial pathogens that infect a wide variety of crop and non-crop plants, and are associated with diseases that can lead to significant yield losses in agricultural production worldwide. Phytoplasmas are currently grouped in the provisional genus ‘Candidatus Phytoplasma’, which includes 49 ‘Candidatus’ species. Further differentiation of phytoplasmas into ribosomal groups is based on the restriction fragment length polymorphism (RFLP) pattern of the 16S rRNA-encoding operon, with more than 36 ribosomal groups (16Sr) and over 100 subgroups reported. Since disease symptoms on plants are not associated with phytoplasma identity, accurate diagnostics is of critical importance to manage disease associated with these microorganisms. Phytoplasmas are typically detected from plant and insect tissue using PCR-based methods targeting universal taxonomic markers. Although these methods are relatively sensitive, specific and are widely used, they have limitations, since they provide limited resolution of phytoplasma strains, thus necessitating further assessment of biological properties and delaying implementation of mitigation measures. Moreover, the design of PCR primers that can target multiple loci from phytoplasmas that differ at the sequence level can be a significant challenge. To overcome these limitations, a PCR-independent, multilocus sequence typing (MLST) assay to characterize an array of phytoplasmas was developed. Hybridization probe s targeting cpn60, tuf, secA, secY, and nusA genes, as well as 16S and rp operons, were designed and used to enrich DNA extracts from phytoplasma-infected samples for DNA fragments corresponding to these markers prior to Illumina sequencing. This method was tested using different phytoplasmas including ‘Ca. P. asteris’ (16SrI-B), ‘Ca. P. pruni’ (16SrIII-A),‘Ca. P. prunorum’ (16SrX-B), ‘Ca. P. pyri’ (16SrX-C), ‘Ca. P. mali’ (16SrX-A), and ‘Ca. P. solani’ (16SrXII-A). Thousands of reads were obtained for each gene with multiple overlapping fragments, which were assembled to generate full-length (typically >2 kb), high-quality sequences. Phytoplasma groups and subgroups were accurately determined based on 16S ribosomal RNA and cpn60 gene sequences. Hybridization-based MLST facilitates the enrichment of target genes of phytoplasmas and allows the simultaneous determination of sequences corresponding to seven different markers. In this proof-of-concept study, hybridization-based MLST was demonstrated to be an efficient way to generate data regarding ‘Ca. Phytoplasma’ species/strain differentiation.

Honey is a natural product and can be described by its botanical origin, determined by the plants from which the bees collect nectar. It significantly influences the taste of honey and is often used as a quality criterion. Unfortunately, this opens up the possibility of food fraud. Currently, various methods are used to check the authenticity of monofloral honey. The laborious, manual melissopalynology is considered an essential tool in the verification process. In this work, the volatile organic compounds obtained from the headspace of honey are used to prove their authenticity. The headspace of 58 honey samples was analyzed using a commercial easy-to-use gas chromatography-coupled ion mobility spectrometer with a headspace sampler (HS-GCxIMS). The honey samples were successfully differentiated by their six different botanical origins using specific markers with principal component analysis in combination with linear discriminant analysis. In addition, 15 honey-typical compounds were identified using measurements of reference compounds. Taking a previously published strategy, retention times of marker compounds were correlated with GC-coupled mass spectrometry (GC-MS) measurements to assist in the identification process.

Cutaneous melanoma is one of the most aggressive and deadliest cancers in human beings due to its invasiveness and other factors. Histopathological analysis is crucial for a proper diagnosis. Optical tissue clearing is a novel field that allows 3D image acquisition of large-scale biological tissues. Optical clearing and immunolabeling for 3D fluorescence imaging has yet to be extensively applied to melanoma. In the present manuscript, we establish, for the first time, an optical clearing and immunostaining procedure for human melanoma and human cell line-derived melanoma xenograft models using the CUBIC (clear, unobstructed brain imaging cocktails) technique. We have successfully cleared the samples and achieved 3D volumetric visualization of the tumor microenvironment, vasculature, and cell populations.

Summary: Three-dimensional (3D) optical imaging techniques can expand our knowledge about physiological and pathological processes that cannot be fully understood with 2D approaches. Standard diagnostic tests frequently are not sufficient to unequivocally determine the presence of a pathological condition. Whole-organ optical imaging requires tissue transparency, which can be achieved by using tissue clearing procedures enabling deeper image acquisition and therefore making possible the analysis of large-scale biological tissue samples. Here, we review currently available clearing agents, methods, and their application in imaging of physiological or pathological conditions in different animal and human organs. We also compare different optical tissue clearing methods discussing their advantages and disadvantages and review the use of different 3D imaging techniques for the visualization and image acquisition of cleared tissues. The use of optical tissue clearing resources for large-scale biological tissues 3D imaging paves the way for future applications in translational and clinical research.

A method of wet chemical synthesis suitable for high throughput and combinatorial applications has been developed for the synthesis of porous resistive thick-film gas sensors. This method is based on the robot-controlled application of unstable metal oxide suspensions on an array of 64 inter-digital electrodes positioned on an Al2O3 substrate. SnO2, WO3, ZrO2, TiO2, CeO2, In2O3 and Bi2O3 were chosen as base oxides, and were optimised by doping or mixed oxide formation. The parallel synthesis of mixed oxide sensors is illustrated by representative examples. The electrical characteristics and the sensor performance of the films were measured by high-throughput impedance spectroscopy while supplying various test gases (H2, CO, NO, NO2, propene). Data collection, data mining techniques applied and the best potential sensor materials discovered are presented.

The workflow of a high throughput screening setup for the rapid identification ofnew and improved sensor materials is presented. The polyol method was applied to preparenanoparticular metal oxides as base materials, which were functionalised by surface doping.Using multi-electrode substrates and high throughput impedance spectroscopy (HT-IS) awide range of materials could be screened in a short time. Applying HT-IS in search of newselective gas sensing materials a NO2-tolerant NO sensing material with reducedsensitivities towards other test gases was identified based on iridium doped zinc oxide.Analogous behaviour was observed for iridium doped indium oxide.