Sensitivity of PCR in conjunctival swab samples for the diagnosis of canine visceral leishmaniasis: a systematic review and meta-analysis.

To compare the sensitivity of conjunctival swab (CS) and conventional samples (blood, spleen, liver, lymphoid and cutaneous tissue) in the diagnosis of canine visceral leishmaniasis (CVL) by polymerase chain reaction (PCR), a systematic review and meta-analysis was carried out using PubMed, Science Direct, Scopus, Web of Science, VHL/BVS (Virtual Health Library), CAPES, and Scielo databases. Articles published from 2002 to 2022 were considered and the review was updated in Jul 2023. From the total of 371 identified studies, 8 met all the eligibility criteria and were included in this review. Data from 658 CVL-positive dogs and 2541 PCR results were considered. Using a random effect model, data on the sensitivity of the test was compared between intervention (CS samples) and comparison (all the other samples) groups. Overall, the use of CS in the PCR diagnosis of CVL produced 12% higher sensitivity (p=0.013) in the test than all the other samples in combination. The animals' clinical condition did not influence (p>0.142) this overall result. However, when CS was individually compared to each of the conventional samples, the consistent result was observed (p=0.012) only in the CS versus bone marrow comparison. Given their rapid acquisition, minimal invasiveness, and lower cost relative to conventional samples, CS samples present a promising alternative for the molecular diagnosis of CVL.

Evaluation of a novel phantom for the quality assurance of a six-degree-of-freedom couch 3D-printed at multiple centres.

This study aimed to validate a bespoke 3D-printed phantom for use in quality assurance (QA) of a 6 degrees-of-freedom (6DoF) treatment couch. A novel phantom design comprising a main body with internal cube structures, was fabricated at five centres using Polylactic Acid (PLA) material, with an additional phantom produced incorporating a PLA-stone hybrid material. Correctional setup shifts were determined using image registration by 3D-3D matching of high HU cube structures between obtained cone-beam computer tomography (CBCT) images to reference CTs, containing cubes with fabricated rotational offsets of 3.5°, 1.5° and -2.5° in rotation, pitch, and roll, respectively. Average rotational setup shifts were obtained for each phantom. The reproducibility of 3D-printing was probed by comparing the internal cube size as well as Hounsfield Units between each of the uniquely produced phantoms. For the five PLA phantoms, the average rot, pitch and roll correctional differences from the fabricated offsets were -0.3 ± 0.2°, -0.2 ± 0.5° and 0.2 ± 0.3° respectively, and for the PLA hybrid these differences were -0.09 ± 0.14°, 0.30 ± 0.00° and 0.03 ± 0.10°. There was found to be no statistically significant difference in average cube size between the five PLA printed phantoms, with the significant difference (P < 0.05) in HU of one phantom compared to the others attributed to setup choice and material density. This work demonstrated the capability producing a novel 3D-printed 6DoF couch QA phantom design, at multiple centres, with each unique model capable of sub-degree couch correction.

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations.

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets.

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed "batch effects") is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.

Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging.

Motion artifacts on magnetic resonance (MR) images degrade image quality and thus negatively affect clinical and research scanning. Considering the difficulty in preventing patient motion during MR examinations, the identification of motion artifact has attracted significant attention from researchers. We propose an automatic method for the evaluation of motion corrupted images using a deep convolutional neural network (CNN). Deep CNNs has been used widely in image classification tasks. While such methods require a significant amount of annotated training data, a scarce resource in medical imaging, the transfer learning and fine-tuning approaches allow us to use a smaller amount of data. Here we selected four renowned architectures, initially trained on Imagenet contest dataset, to fine-tune. The models were fine-tuned using patches from an annotated dataset composed of 68 T1-weighted volumetric acquisitions from healthy volunteers. For training and validation 48 images were used, while the remaining 20 images were used for testing. Each architecture was fine-tuned for each MR axis, detecting the motion artifact per patches from the three orthogonal MR acquisition axes. The overall average accuracy for the twelve models (three axes for each of four architecture) was 86.3%. As our goal was to detect fine-grained corruption in the image, we performed an extensive search on lower layers from each of the four architectures, since they filter small regions in the original input. Experiments showed that architectures with fewer layers than the original ones reported the better results for image patches with an overall average accuracy of 90.4%. The accuracies per architecture were similar so we decided to explore all four architectures performing a result consensus. Also, to determine the probability of motion artifacts presence on the whole acquisition a combination of the three axes were performed. The final architecture consists of an artificial neural network (ANN) classifier combining all models from the four shallower architectures, which overall acquisition-based accuracy was 100.0%. The proposed method generalization was tested using three different MR data: (1) MR image acquired in epilepsy patients (93 acquisitions); (2) MR image presenting susceptibility artifact (22 acquisitions); and (3) MR image acquired from different scanner vendor (20 acquisitions). The achieved acquisition-based accuracy on generalization tests (1) 90.3%, (2) 63.6%, and (3) 75.0%) suggests that domain adaptation is necessary. Our proposed method can be rapidly applied to large amounts of image data, providing a motion probability p∈[0,1] per acquisition. This method output can be used as a scale to identify the motion corrupted images from the dataset, thus minimizing the time spent on visual quality control.

A framework for quality control of corpus callosum segmentation in large-scale studies.

BACKGROUND: The corpus callosum (CC) is the largest white matter structure in the brain, responsible for the interconnection of the brain hemispheres. Its segmentation is a required preliminary step for any posterior analysis, such as parcellation, registration, and feature extraction. In this context, the quality control (QC) of CC segmentation allows studies on large datasets with no human interaction, and the proper usage of available automated and semi-automated algorithms. NEW METHOD: We propose a framework for QC of CC segmentation based on the shape signature, computed at 49 distinct resolutions. At each resolution, a support vector machine (SVM) classifier was trained, generating 49 individual classifiers. Then, a disagreement metric was used to cluster these individual classifiers. The final ensemble was constructed by selecting one representation from each cluster. RESULTS: The proposed framework achieved an area under the curve (AUC) metric of 98.25% on the test set (207 subjects) employing an ensemble composed of 12 components. This ensemble outperformed all individual classifiers. COMPARISON WITH EXISTING METHODS: To the best of our knowledge, this is the first approach to assess quality of CC segmentations on large datasets without the need for a ground-truth. CONCLUSIONS: The shape descriptor is robust and versatile, describing the segmentation at different resolutions. The selection of classifiers and the disagreement measure lead to an ensemble composed of high-quality and heterogeneous classifiers, ensuring an optimal trade-off between the ensemble size and high AUC.

Dual-domain cascade of U-nets for multi-channel magnetic resonance image reconstruction.

The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-channel magnetic resonance (MR) image reconstruction. The two-element network combinations were evaluated for the four possible image-k-space domain configurations: a) W-net II, b) W-net KK, c) W-net IK, and d) W-net KI. Selected four element (WW-nets) and six element (WWW-nets) networks were also examined. Two configurations of each network were compared: 1) each coil channel was processed independently, and 2) all channels were processed simultaneously. One hundred and eleven volumetric, T1-weighted, 12-channel coil k-space datasets were used in the experiments. Normalized root mean squared error, peak signal-to-noise ratio and visual information fidelity were used to assess the reconstructed images against the fully sampled reference images. Our results indicated that networks that operate solely in the image domain were better when independently processing individual channels of multi-channel data. Dual-domain methods were better when simultaneously reconstructing all channels of multi-channel data. In addition, the best cascade of U-nets performed better (p < 0.01) than the previously published, state-of-the-art Deep Cascade and Hybrid Cascade models in three out of four experiments.

Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set.

Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance brain images. The cohort includes 190 subjects with CA, white mater hyperintensites of presumed vascular origin or multiple sclerosis, as well as 211 presumed healthy subjects. We determined a set of handcrafted and convolutional discriminant features to perform this task. A support vector machine (SVM) was used to perform this four-class classification task. Our approach had an accuracy rate of 97.5% (higher than chance accuracy of 52.6% for guessing majority class), sensitivity of 96.4% and specificity of 97.9% in identifying subjects with CA, suggesting that the proposed combination of features may be used as an imaging biomarker for characterizing atherosclerotic disease on brain imaging.

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

The Valsalva maneuver revisited by wavelets.

The Valsalva maneuver is an autonomic test that evokes short sharp cardiovascular fluctuations mediated by the autonomic nervous system. Numerous spectral analysis methods have been proposed to analyze biological signals. When applied to heart rate (HR) variability, two major bands related to autonomic influence have been defined: LF (mainly sympathetic) and HF (parasympathetic). However, conventional spectral approaches are based on the assumption of stationarity, and most require at least five minutes of recording. These two requirements cannot be fulfilled when analysis of dynamic processes such as the regulatory action of the autonomic nervous system is required. Wavelet transform is a mathematical tool that, by determining the temporal localization of the changes, the frequencies involved and their contribution to the entire signal, overcomes the limitations imposed by conventional methods. In the present work, we use wavelets to evaluate autonomic influence through the LF and HF band powers on acute changes in systolic blood pressure (sBP) and RR intervals (RRI) during the Valsalva maneuver. Eighteen healthy volunteers performed the maneuver by blowing, after a deep inspiration and with a closed glottis, against a pressure of 40 mmHg for 15 seconds. Data were analyzed in three different periods: 1) the last minute just prior to the test (CTR); 2) the 15 seconds of the Valsalva maneuver (VM); 3) during the next 35 seconds after the maneuver (aVM). We observed that LF power increased in sBP and RRI in both VM and ower only increased after Valsalva. The data showed a marked increase in sympathetic activity during and after the maneuver and an increase in parasympathetic outflow after aVM. In conclusion, the ability of wavelets to analyze short non-stationary signals makes wavelet transform a promising tool to evaluate physiological and pathological autonomic conditions.

Ozonized solutions favor the repair of experimentally induced skin wounds in rats / Soluções ozonizadas favorecem o reparo de feridas cutâneas experimentalmente induzidas em ratos

This study aimed to evaluate and compare the effects of ozonized solutions on tissue wound repair in rats. Treatments consisted of ozonized water (GA), 0.9% sodium chloride (GCL), ozonized oil (GO), and 0.2% allantoin cream (GAL). The morphometric evaluation showed that wounds of the GA group presented a higher degree of retraction (p<0.05) at three and eight days of treatment (37.96 and 84.81%, respectively). Picrosirius red staining showed that groups GA and GO presented higher deposition (p<0.05) of type I collagen at 15 and 22 days of treatment, respectively. The neovascularization was higher in wounds of group GO on days 3, 8, and 15 (p<0.05), with higher VEGF immunostaining. (p<0.05). Thus, ozonized water enhances wound retraction and assists in the maturation and remodeling phase, while ozonized oil promotes higher neovascularization during tissue repair and higher deposition of type I collagen from the third week of treatment.(AU)

O objetivo deste estudo foi avaliar e comparar os efeitos de soluções ozonizadas sobre o reparo tecidual de feridas em ratos. Foram realizados os tratamentos: água ozonizada (GA), cloreto de sódio 0,9% (GCL), óleo ozonizado (GO) e creme de alantoína 0,2% (GAL). À avaliação morfométrica, as feridas do grupo GA apresentaram maior grau de retração (p<0,05) aos três e oito dias de tratamento (37,96% e 84,81%, respectivamente). A coloração de picrosirius red mostrou que os grupos GA e GO apresentaram maior deposição (p<0,05) de colágeno do tipo I aos 15 e aos 22 dias de tratamento, respectivamente. Já a variável neovascularização foi maior (p<0,05) nas feridas do grupo GO nos dias três, oito e 15, o que fora ratificado à imunoistoquímica, com maior imunomarcação de VEGF nas feridas do grupo GO (p<0,05). Conclui-se que a água ozonizada potencializa a retração da ferida e auxilia na fase de maturação e remodelamento, enquanto o óleo ozonizado promove maior neovascularização durante o reparo tecidual e maior deposição de colágeno do tipo I a partir da terceira semana de tratamento.(AU)

Hereditary sensory neuropathy type 1 in a Portuguese family-electrodiagnostic and autonomic nervous system studies.

Hereditary sensory and autonomic neuropathy type 1 (HSAN 1) is a dominantly inherited disorder; its gene locus is mapped on chromosome 9q22. Three different missense mutations (C133Y, C133W and V144D) have been described in 11 families from Australia, England and Austria. Common clinical features have been found in these families. We report the clinical and electrophysiological features of three members of a large Portuguese family with HSAN 1 and the C133Y missense mutation. The affected members showed typical clinical features. Electrophysiological findings were consistent with a distal axonal predominantly sensory neuropathy with motor involvement, in three different severity stages. No autonomic involvement was detected in sudomotor and cardiovascular tests. This report documents the lesion of the motor nerve fibers in this disease, as well as the preservation of the autonomic nervous system function, therefore suggesting that HSNA is an inappropriate name for this disorder.