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Background: At present, clinical use of MRI in Alzheimer's disease (AD) is mostly focused on the assessment of brain atrophy, namely in the hippocampal region. Despite this, multiple biomarkers reflecting structural and functional brain connectivity changes have shown promising results in the assessment of AD. To help identify the most relevant ones that may stand a chance of being used in clinical practice, we compared multiple biomarker in terms of their value to discriminate AD from healthy controls and analyzed their age dependency. Methods: 20 AD patients and 20 matched controls underwent MRI-scanning (3T GE), including T1-weighted, diffusion-MRI, and resting-state-fMRI (rsfMRI). Whole brain, white matter, gray matter, cortical gray matter and hippocampi volumes were measured using icobrain. rsfMRI between regions of the default-mode-network (DMN) was assessed using group independent-component-analysis. Median diffusivity and kurtosis were determined in gray and white-matter. DTI data was used to evaluate pairwise structural connectivity between lobar regions and the hippocampi.Logistic-Regression and Random-Forest models were trained to classify AD-status based on, respectively different isolated features and age, and feature-groups combined with age. Results: Hippocampal features, features reflecting the functional connectivity between the medial-Pre-Frontal-Cortex (mPFC) and the posterior regions of the DMN, and structural interhemispheric frontal connectivity showed the strongest differences between AD-patients and controls. Structural interhemispheric parietal connectivity, structural connectivity between the parietal lobe and hippocampus in the right hemisphere, and mPFC-DMN-features, showed only an association with AD-status (p < 0.05) but not with age. Hippocampi volumes showed an association both with age and AD-status (p < 0.05).Smallest-hippocampus-volume was the most discriminative feature. The best performance (accuracy:0.74, sensitivity:0.74, specificity:0.74) was obtained with an RF-model combining the best feature from each feature-group (smallest hippocampus volume, WM volume, median GM MD, lTPJ-mPFC connectivity and structural interhemispheric frontal connectivity) and age. Conclusions: Brain connectivity changes caused by AD are reflected in multiple MRI-biomarkers. Decline in both the functional DMN-connectivity and the parietal interhemispheric structural connectivity may assist sepparating healthy-aging driven changes from AD, complementing hippocampal volumes which are affected by both aging and AD.

Acentuando a necessidade da continuação das pesquisas terapêuticas em pacientes com paracoccidioidose (blastomicose sul-americana), os autores apresentam 23 pacientes submetidos a diferentes esquemas terapêuticos, sendo 14 virgens de tratamento e 9 com uso prévio de uma ou mais drogas. A associação sulfametoxazol + trimetoprim ( SMZ + TMP) foi empregada em 5 pacientes virgens de tratamento e 9 sulfa-resistentes. Outros sete foram submetidos ao esquema clássico com sulfadoxina. Os pacientes que não responderam aos dois esquemas anteriores, com exceção de dois casos inicialmente graves, receberam anfotericina B. A avaliação clínica, radiológica, micológica e sorológica a longo prazo não demonstrou vantagens no emprego de SMZ + TMP em substituição aos sulfamídicos, nos pacientes virgens de tratamento. Entretanto, a associação SMZ + TMP parece ser uma opção válida nos casos sulfa-resistentes, onde teria primazia, considerando-se a toxicidade e necessidade de controle em regime hospitalar da anfotericina B. Ressaltam ainda a boa tolerância clínica e laboratorial da associação SMZ + TMP em cursos terapêuticos prolongados de até 2 anos, quando empregadas em baixas doses de manutenção.Three different therapeutic schedules for paracoccidioidomycosis were studied. Twenty three thoroughly investigated patients were submitted to long terrn treatment courses using either sulfadoxine, or sulfamethoxazol + thrimethoprim (SMZ + TMP) or Anfotericin B. Clinicai, roentgenographic, mycological and serologic evaluations were extensively performed. Particular attention to possible hematopoietic abnormalities due to long term, low-losage SMZ + TMP therapy was given. Data presented suggest that there seems to be no advantage in using SMZ + TMP in previously untreated patients, as compared to sulphadoxine alone. Otherwise sulpha-resistant patients may be successfully treated with the association SMZ + TMP. No side effects of clinicai significance were observed. Emphasis in given to good tolerance observed in patients submitted to as much as two years low-dosage therapeutic courses with the association SMZ + TMP.

Abstract In the present study, 5-Hydroxy-2-(Oleoyloxymethyl) -4H-pyran-4-one (KMO 3), and their chelated with Cu(II) and Fe(III) ions were synthesized to explore their inhibitory activity against tyrosinase and cytotoxicity. To this end, the structures of the obtained compounds were confirmed by ATR/FT-IR, 13C and 1H-NMR, and UV-vis techniques. The results show that chelating fatty ester presents the bands at 1567m, 1511w cm-1 attributed to the coordinated carbonyl (Cu(II)←[O=C]2), and the bands at 1540m, 1519m cm−1 which were attributed to the coordinated carbonyl (Fe(III)←[O=C]3). The inhibitory effect of chelating Oleic acid 2 (inhibition 68.3% ± 4.5) showed a factor of 19 times higher than free fatty acid (3.6% ± 3.2). IC50 Anti-tyrosinase activity of the Kojic acid 1 and KMO 3 compounds were 62.8 ± 6.6 µM and 77.6 ± 4.3 µM. The IC50 and IC90 values for tyrosinase inhibitory activity for chelating fatty ester and their complexes are values > 400 µM. Finally, the assay with the series showed no hemolytic activity (EC50> 250 μg mL-1), and not cytotoxic to B16F10, ACP-02, and human dermal fibroblast cells at 100 µM and showed no hemolytic potential at the concentration of IC50 250 µM.

Molybdenum- or tungsten-dependent formate dehydrogenases have emerged as significant catalysts for the chemical reduction of CO 2 to formate, with biotechnological applications envisaged in climate-change mitigation. The role of Met405 in the active site of Desulfovibrio vulgaris formate dehydrogenase AB (DvFdhAB) has remained elusive. However, its proximity to the metal site and the conformational change that it undergoes between the resting and active forms suggests a functional role. In this work, the M405S variant was engineered, which allowed the active-site geometry in the absence of methionine S δ interactions with the metal site to be revealed and the role of Met405 in catalysis to be probed. This variant displayed reduced activity in both formate oxidation and CO 2 reduction, together with an increased sensitivity to oxygen inactivation., (open access.)

Metal-dependent formate dehydrogenases reduce CO 2 with high efficiency and selectivity, but are usually very oxygen sensitive. An exception is Desulfovibrio vulgaris W/Sec-FdhAB, which can be handled aerobically, but the basis for this oxygen tolerance was unknown. Here we show that FdhAB activity is controlled by a redox switch based on an allosteric disulfide bond. When this bond is closed, the enzyme is in an oxygen-tolerant resting state presenting almost no catalytic activity and very low formate affinity. Opening this bond triggers large conformational changes that propagate to the active site, resulting in high activity and high formate affinity, but also higher oxygen sensitivity. We present the structure of activated FdhAB and show that activity loss is associated with partial loss of the metal sulfido ligand. The redox switch mechanism is reversible in vivo and prevents enzyme reduction by physiological formate levels, conferring a fitness advantage during O 2 exposure., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Disease outbreaks are a common problem in aquaculture, with serious economic consequences to the sector. Some of the most important bacterial diseases affecting aquaculture are caused by Gram-negative bacteria including Vibrio spp. (vibriosis), Photobacterium damselae (photobacteriosis), Aeromonas spp. (furunculosis; haemorrhagic septicaemia) or Tenacibaculum maritimum (tenacibaculosis). Lipopolysaccharides (LPS) are important components of the outer membrane of Gram-negative bacteria and have been linked to strong immunogenic responses in terrestrial vertebrates, playing a role in disease development. To evaluate LPS effects in fish, we used a hot-phenol procedure to extract LPS from common fish pathogens. A. hydrophila , V. harveyi , T. maritimum and P. damselae purified LPS were tested at different concentrations (50, 100, 250 and 500 µg mL -1 ) at 3 days post-fertilisation (dpf) Danio rerio larvae, for 5 days. While P. damselae LPS did not cause any mortality under all concentrations tested, A. hydrophila LPS induced 15.5% and V. harveyi LPS induced 58.3% of zebrafish larvae mortality at 500 µg mL -1 . LPS from T. maritimum was revealed to be the deadliest, with a zebrafish larvae mortality percentage of 80.6%. Analysis of LPS separated by gel electrophoresis revealed differences in the overall LPS structure between the bacterial species analysed that might be the basis for the different mortalities observed.

Oral vaccines are highly demanded by the aquaculture sector, to allow mass delivery of antigens without using the expensive and labor-intensive injectable vaccines. These later require individual handling of fish, provoking stress-related mortalities. One possible strategy to create injection-free vaccine delivery vehicles is the use of bacterial spores, extremely resistant structures with wide biotechnological applications, including as probiotics, display systems, or adjuvants. Bacterial spores, in particular those of Bacillus subtilis , have been shown to behave as mucosal vaccine adjuvants in mice models. However, such technology has not been extensively explored against fish bacterial disease. In this study, we used a laboratory strain of B. subtilis , for which a variety of genetic manipulation tools are available, to display at its spores surface either a Vibrio antigenic protein, OmpK, or the green fluorescence protein, GFP. When previously vaccinated by immersion with the OmpK- carrying spores, zebrafish survival upon a bacterial challenge with V. anguillarum and V. parahaemolyticus , increased up to 50 - 90% depending on the pathogen targeted. Further, we were able to detect anti-GFP-antibodies in the serum of European seabass juveniles fed diets containing the GFP-carrying spores and anti- V. anguillarum antibodies in the serum of European seabass juveniles fed the OmpK-carrying spores containing diet. More important, seabass survival was increased from 60 to 86% when previously orally vaccinated with in-feed OmpK- carrying spores. Our results indicate that B. subtilis spores can effectively be used as antigen-carriers for oral vaccine delivery in fish., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gonçalves, Santos, Coutinho, Pedrosa, Curado, Machado, Costas, Bonneville, Serrano, Carvalho, Díaz-Rosales, Oliva-Teles, Couto and Serra.)

Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, scanner- and center-specific factors. Most publicly available brain MRI datasets originate from the same center and are homogeneous in terms of scanner and used protocol. As such, devising robust methods that generalize to multi-scanner and multi-center data is crucial for transferring these techniques into clinical practice. We propose a novel data augmentation approach based on Gaussian Mixture Models (GMM-DA) with the goal of increasing the variability of a given dataset in terms of intensities and contrasts. The approach allows to augment the training dataset such that the variability in the training set compares to what is seen in real world clinical data, while preserving anatomical information. We compare the performance of a state-of-the-art U-Net model trained for segmenting brain structures with and without the addition of GMM-DA. The models are trained and evaluated on single- and multi-scanner datasets. Additionally, we verify the consistency of test-retest results on same-patient images (same and different scanners). Finally, we investigate how the presence of bias field influences the performance of a model trained with GMM-DA. We found that the addition of the GMM-DA improves the generalization capability of the DL model to other scanners not present in the training data, even when the train set is already multi-scanner. Besides, the consistency between same-patient segmentation predictions is improved, both for same-scanner and different-scanner repetitions. We conclude that GMM-DA could increase the transferability of DL models into clinical scenarios., Competing Interests: MM, ER, NP, RP, and DS are employed by icometrix. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Meyer, de la Rosa, Pedrosa de Barros, Paolella, Van Leemput and Sima.)

Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structures that are relevant for differential diagnosis of dementia, such as the hippocampi and cerebral lobes. Experiments were conducted in comparison to the widely used FreeSurfer software. The hippocampus segmentations were compared against manual segmentations, with significantly higher Dice coefficients obtained with icobrain dm (25-75th quantiles: 0.86-0.88) than with FreeSurfer (25-75th quantiles: 0.80-0.83). Other brain structures were also compared against manual delineations, with icobrain dm showing lower volumetric errors overall. Test-retest experiments show that the precision of all measurements is higher for icobrain dm than for FreeSurfer except for the parietal cortex volume. Finally, when comparing volumes obtained from Alzheimer's disease patients against age-matched healthy controls, all measures achieved high diagnostic performance levels when discriminating patients from cognitively healthy controls, with the temporal cortex volume measured by icobrain dm reaching the highest diagnostic performance level (area under the receiver operating characteristic curve = 0.99) in this dataset., Competing Interests: Declaration of Competing Interest The following authors are employed (or have been employed at the time of performing the work relevant for this paper) by icometrix: Hanne Struyfs, Diana M. Sima, Annemie Ribbens, Nuno Pedrosa de Barros, Thanh Vân Phan, Lene Claes, Maria Ines Ferraz Meyer, Wim Van Hecke, Dirk Smeets. Melissa Wittens and Ellis Niemantsverdriet have no competing interests. Sebastiaan Engelborghs has received unrestricted research grants from Janssen Pharmaceutica NV and ADx Neurosciences (paid to institution)., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Clinical use of MRSI is limited by the level of experience required to properly translate MRSI examinations into relevant clinical information. To solve this, several methods have been proposed to automatically recognize a predefined set of reference metabolic patterns. Given the variety of metabolic patterns seen in glioma patients, the decision on the optimal number of patterns that need to be used to describe the data is not trivial. In this paper, we propose a novel framework to (1) separate healthy from abnormal metabolic patterns and (2) retrieve an optimal number of reference patterns describing the most important types of abnormality. Using 41 MRSI examinations (1.5 T, PRESS, T E 135 ms) from 22 glioma patients, four different patterns describing different types of abnormality were detected: edema, healthy without Glx, active tumor and necrosis. The identified patterns were then evaluated on 17 MRSI examinations from nine different glioma patients. The results were compared against BraTumIA, an automatic segmentation method trained to identify different tumor compartments on structural MRI data. Finally, the ability to predict future contrast enhancement using the proposed approach was also evaluated., (© 2019 John Wiley & Sons, Ltd.)

Purpose: To improve the detection of peritumoral changes in GBM patients by exploring the relation between MRSI information and the distance to the solid tumor volume (STV) defined using structural MRI (sMRI)., Methods: Twenty-three MRSI studies (PRESS, TE 135 ms) acquired from different patients with untreated GBM were used in this study. For each MRSI examination, the STV was identified by segmenting the corresponding sMRI images using BraTumIA, an automatic segmentation method. The relation between different metabolite ratios and the distance to STV was analyzed. A regression forest was trained to predict the distance from each voxel to STV based on 14 metabolite ratios. Then, the trained model was used to determine the expected distance to tumor (EDT) for each voxel of the MRSI test data. EDT maps were compared against sMRI segmentation., Results: The features showing abnormal values at the longest distances to the tumor were: %NAA, Glx/NAA, Cho/NAA, and Cho/Cr. These four features were also the most important for the prediction of the distances to STV. Each EDT value was associated with a specific metabolic pattern, ranging from normal brain tissue to actively proliferating tumor and necrosis. Low EDT values were highly associated with malignant features such as elevated Cho/NAA and Cho/Cr., Conclusion: The proposed method enables the automatic detection of metabolic patterns associated with different distances to the STV border and may assist tumor delineation of infiltrative brain tumors such as GBM., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors., Methods: A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment., Results: AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system., Conclusion: Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Purpose: To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data., Methods: 28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling., Results: The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance., Conclusion: Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

The quality of MR-Spectroscopy data can easily be affected in in vivo applications. Several factors may produce signal artefacts, and often these are not easily detected, not even by experienced spectroscopists. Reliable and reproducible in vivo MRS-data requires the definition of quality requirements and goals, implementation of measures to guarantee quality standards, regular control of data quality, and a continuous search for quality improvement. The first part of this review includes a general introduction to different aspects of quality management in MRS. It is followed by the description of a series of tests and phantoms that can be used to assure the quality of the MR system. In the third part, several methods and strategies used for quality control of the spectroscopy data are presented. This review concludes with a reference to a few interesting techniques and aspects that may help to further improve the quality of in vivo MR-spectra., (Copyright © 2017 Elsevier Inc. All rights reserved.)

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1)H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin., (Copyright © 2016 John Wiley & Sons, Ltd.)

The use of electrospun nanofibers for tissue engineering and regenerative medicine applications is a growing trend as they provide improved support for cell proliferation and survival due, in part, to their morphology mimicking that of the extracellular matrix. Sterilization is a critical step in the fabrication process of implantable biomaterial scaffolds for clinical use, but many of the existing methods used to date can negatively affect scaffold properties and performance. Poly(lactic-co-glycolic acid) (PLGA) has been widely used as a biodegradable polymer for 3D scaffolds and can be significantly affected by current sterilization techniques. The aim of this study was to investigate pulsed ozone gas as an alternative method for sterilizing PLGA nanofibers. The morphology, mechanical properties, physicochemical properties, and response of cells to PLGA nanofiber scaffolds were assessed following different degrees of ozone gas sterilization. This treatment killed Geobacillus stearothermophilus spores, the most common biological indicator used for validation of sterilization processes. In addition, the method preserved all of the characteristics of nonsterilized PLGA nanofibers at all degrees of sterilization tested. These findings suggest that ozone gas can be applied as an alternative method for sterilizing electrospun PLGA nanofiber scaffolds without detrimental effects.