Understanding of the role of serum creatinine in a subset of the Brazilian population / Conhecimento de uma amostragem da população brasileira sobre a função da creatinina sérica

Abstract Introduction: Chronic kidney disease is usually asymptomatic, and its diagnosis depends on laboratory tests, with emphasis on serum creatinine and proteinuria. Objective: To assess knowledge on the role of serum creatinine as a biomarker of kidney function in a sample of the Brazilian population. Method: Cross-sectional observational study conducted in São Paulo (SP, Brazil), in which a random adult population was interviewed. Results: A total of 1138 subjects were interviewed, with a median age of 36 years old (27-52); 55.1% were female. Regarding the "creatinine" biomarker, 40.6% stated they had never performed such a test. When asked about their knowledge on the usefulness of this exam, only 19.6% knew its function. The other responses were "I don't know" (71.6%), evaluating heart function (0.9%) and liver function (7.8%). Of those who reported they had already taken a creatinine test, only 29.4% correctly identified the role of creatinine. When dividing the groups into "knows" and "does not know" the function of creatinine, a statistically significant difference (p < 0.05) was observed regarding level of education, female sex, being a healthcare student/worker, having ever measured creatinine, knowing someone with kidney disease and older age. In the multivariate analysis, the main variable related to knowing the creatinine role was having previously taken the test (OR 5.16; 95% CI 3.16-8.43, p < 0.001). Conclusion: There is a significant lack of knowledge about creatinine and its use in checkups. The results indicate that greater efforts are needed from healthcare professionals to raise awareness on the role of serum creatinine.

Resumo Introdução: A doença renal crônica costuma ser assintomática e seu diagnóstico depende da realização de exames laboratoriais, com destaque para a creatinina sérica e pesquisa de proteinúria. Objetivo: Avaliar em uma amostra da população brasileira o conhecimento sobre o papel da creatinina sérica como marcador de função renal. Método: Estudo observacional transversal realizado na cidade de São Paulo (SP, Brasil), em que foi entrevistada uma população adulta aleatória. Resultados: Foram entrevistados 1138 indivíduos, com idade mediana de 36 anos (27-52); 55,1% do sexo feminino. Com relação ao marcador "creatinina", 40,6% afirmaram que nunca realizaram tal dosagem. Quando questionados quanto ao conhecimento sobre a utilidade desse exame, somente 19,6% sabiam a sua função. As outras respostas foram "não sei" (71,6%), avaliar o funcionamento do coração (0,9%) e fígado (7,8%). Dos que afirmaram já terem realizado o exame de creatinina, somente 29,4% acertaram a função da creatinina. Ao dividir os grupos em "sabe" e "não sabe" a função da creatinina, percebeu-se diferença estatisticamente significante (p < 0,05) em relação ao grau de escolaridade, sexo feminino, ser aluno/trabalhador da saúde, ter dosado creatinina alguma vez, conhecer alguém com doença renal e maior idade. Na análise multivariada, a principal variável relacionada com conhecer a função da creatinina foi ter realizado o exame anteriormente (OR 5,16; IC 95% 3,16-8,43, p < 0,001). Conclusão: Há grande desconhecimento sobre a creatinina e seu uso em check-ups. Os resultados indicam que é necessário maior esforço por parte dos profissionais de saúde para divulgar o papel da creatinina sérica.

Frequency and phase correction of GABA-edited magnetic resonance spectroscopy using complex-valued convolutional neural networks.

PURPOSE: To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in convolutional neural networks (CNNs) for frequency and phase correction (FPC) of GABA-edited magnetic resonance spectroscopy (MRS) data. METHODS: An ablation study using simulated data was performed to determine the most effective input (real or complex) and convolution type (real or complex) to predict frequency and phase shifts in GABA-edited MEGA-PRESS data using CNNs. The best CNN model was subsequently compared using both simulated and in vivo data to two recently proposed deep learning (DL) methods for FPC of GABA-edited MRS. All methods were trained using the same experimental setup and evaluated using the signal-to-noise ratio (SNR) and linewidth of the GABA peak, choline artifact, and by visually assessing the reconstructed final difference spectrum. Statistical significance was assessed using the Wilcoxon signed rank test. RESULTS: The ablation study showed that using complex values for the input represented by real and imaginary channels in our model input tensor, with complex convolutions was most effective for FPC. Overall, in the comparative study using simulated data, our CC-CNN model (that received complex-valued inputs with complex convolutions) outperformed the other models as evaluated by the mean absolute error. CONCLUSION: Our results indicate that the optimal CNN configuration for GABA-edited MRS FPC uses a complex-valued input and complex convolutions. Overall, this model outperformed existing DL models.

Effect of MR head coil geometry on deep-learning-based MR image reconstruction.

PURPOSE: To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method. THEORY AND METHODS: Traditional and DL-based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods solve a system of equations derived from the image data whereas DL methods use data/target pairs to learn a generalizable reconstruction model. Two sets of head coil profiles were evaluated: (1) 8-channel and (2) 32-channel geometries. A DL model was compared to conjugate gradient SENSE (CG-SENSE) and L1-wavelet compressed sensing (CS) through quantitative metrics and visual assessment as coil overlap was increased. RESULTS: Results were generally consistent between experiments. As coil overlap increased, there was a significant (p < 0.001) decrease in performance in most cases for all methods. The decrease was most pronounced for CG-SENSE, and the DL models significantly outperformed (p < 0.001) their non-DL counterparts in all scenarios. CS showed improved robustness to coil overlap and signal-to-noise ratio (SNR) versus CG-SENSE, but had quantitatively and visually poorer reconstructions characterized by blurriness as compared to DL. DL showed virtually no change in performance across SNR and very small changes across coil overlap. CONCLUSION: The DL image reconstruction method produced images that were robust to coil overlap and of higher quality than CG-SENSE and CS. This suggests that geometric coil design constraints can be relaxed when using DL reconstruction methods.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. METHODS: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. RESULTS: Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. CONCLUSION: DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

Hyperaemia during dynamic handgrip exercise is preserved in healthy young subjects after recovery from COVID-19.

We sought to investigate possible impaired hyperaemia during dynamic handgrip exercise (HGE) in young healthy individuals who had recovered from COVID-19. We tested the vascular function in individuals recovered from COVID-19 using a nitric oxide donor (i.e., sodium nitroprusside; SNP), which could revert a possible impaired endothelial function during HGE. Further, we tested whether individuals who recovered from COVID-19 would present exaggerated brachial vascular resistance under an adrenergic agonist (i.e., phenylephrine; PHE) stimuli during HGE. Participants were distributed into two groups: healthy controls (Control; men: n = 6, 30 ± 3 years, 26 ± 1 kg/m2; and women: n = 5, 25 ± 1 years, 25 ± 1 kg/m2) and subjects recovered from COVID-19 (post-COVID; men: n = 6, 29 ± 3 years, 25 ± 1 kg/m2; and women: n = 10, 32 ± 4 years, 22 ± 1 kg/m2). Participants in the post-COVID group tested positive (RT-PCR) 12-14 weeks before the protocol. Heart rate (HR), brachial blood pressure (BP), brachial blood flow (BBF) and vascular conductance (BVC) at rest were not different between groups. The HGE increased HR (Control: Δ9 ± 0.4 bpm; and post-COVID: Δ11 ± 0.4 bpm) and BP (Control: Δ6 ± 1 mmHg; and post-COVID: Δ12 ± 0.6 mmHg) in both groups. Likewise, BBF (Control: Δ632 ± 38 ml/min; and post-COVID: Δ620 ± 27 ml/min) and BVC (Control: Δ6.6 ± 0.4 ml/min/mmHg; and post-COVID: Δ6.1 ± 0.3 ml/min/mmHg) increased during HGE. SNP did not change HGE-induced hyperaemia but did decrease BP, which induced a reflex-related increase in HR. PHE infusion also did not change the HGE-induced hyperaemia but raised BP and reduced HR. In conclusion, exercise-induced hyperaemia is preserved in healthy young subjects 12-14 weeks after recovery from COVID-19 infection.

Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage.

Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting in enhanced predictive precision, but are laborious and time intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. For comparison, a manual FE model was developed through manual segmentation (i.e., the de-facto standard approach). Both FE models were subjected to gait loading and the predicted mechanical response was compared. The semi-automated segmentation achieved a Dice similarity coefficient (DSC) of over 98% for both the femur and tibia. Hausdorff distance (mm) between the semi-automated and manual segmentation was 1.4 mm. The mechanical results (max principal stress and strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. We have made our semi-automated models publicly accessible to support and facilitate biomechanical modeling and medical image segmentation efforts ( https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).

Understanding of the role of serum creatinine in a subset of the Brazilian population.

INTRODUCTION: Chronic kidney disease is usually asymptomatic, and its diagnosis depends on laboratory tests, with emphasis on serum creatinine and proteinuria. OBJECTIVE: To assess knowledge on the role of serum creatinine as a biomarker of kidney function in a sample of the Brazilian population. METHOD: Cross-sectional observational study conducted in São Paulo (SP, Brazil), in which a random adult population was interviewed. RESULTS: A total of 1138 subjects were interviewed, with a median age of 36 years old (27-52); 55.1% were female. Regarding the "creatinine" biomarker, 40.6% stated they had never performed such a test. When asked about their knowledge on the usefulness of this exam, only 19.6% knew its function. The other responses were "I don't know" (71.6%), evaluating heart function (0.9%) and liver function (7.8%). Of those who reported they had already taken a creatinine test, only 29.4% correctly identified the role of creatinine. When dividing the groups into "knows" and "does not know" the function of creatinine, a statistically significant difference (p < 0.05) was observed regarding level of education, female sex, being a healthcare student/worker, having ever measured creatinine, knowing someone with kidney disease and older age. In the multivariate analysis, the main variable related to knowing the creatinine role was having previously taken the test (OR 5.16; 95% CI 3.16-8.43, p < 0.001). CONCLUSION: There is a significant lack of knowledge about creatinine and its use in checkups. The results indicate that greater efforts are needed from healthcare professionals to raise awareness on the role of serum creatinine.

An empirical analysis of structural neuroimaging profiles in a staging model of depression.

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions. Integrated clinical and neuroimaging data from three large Canadian studies were pooled (total n = 622 participants, aged 12-66 years). Four clinical profiles were used in the classification of a clinical staging model: healthy comparison individuals with no history of depression (HC, n = 240), individuals at high risk for serious mental illness due to the presence of subclinical symptoms (SC, n = 80), first-episode depression (FD, n = 82), and participants with recurrent MDD in a current major depressive episode (RD, n = 220). Whole-brain volumetric measurements were extracted with FreeSurfer 7.1 and examined using three different types of analyses. Hippocampal volume decrease and cortico-limbic thinning were the most informative features for the RD vs HC comparisons. FD vs HC revealed that FD participants were characterized by a focal decrease in cortical thickness and global enlargement in amygdala volumes. Greater total amygdala volumes were significantly associated with earlier onset of illness in the FD but not the RD group. We did not confirm the construct validity of a tested clinical staging model, as a differential pattern of brain alterations was identified across the three diagnostic groups that did not parallel a stepwise clinical staging approach. The pathological processes during early stages of the illness may fundamentally differ from those that occur at later stages with clinical progression.

Fully automatic segmentation of brain lacunas resulting from resective surgery using a 3D deep learning model.

The rapid and constant development of deep learning (DL) strategies is pushing forward the quality of object segmentation in images from diverse fields of interest. In particular, these algorithms can be very helpful in delineating brain abnormalities (lesions, tumors, lacunas, etc), enabling the extraction of information such as volume and location, that can inform doctors or feed predictive models. In this study, we describe ResectVol DL, a fully automatic tool developed to segment resective lacunas in brain images of patients with epilepsy. ResectVol DL relies on the nnU-Net framework that leverages the 3D U-Net deep learning architecture. T1-weighted MRI datasets from 120 patients (57 women; 31.5 ± 15.9 years old at surgery) were used to train (n=78) and test (n=48) our tool. Manual segmentations were carried out by five different raters and were considered as ground truth for performance assessment. We compared ResectVol DL with two other fully automatic methods: ResectVol 1.1.2 and DeepResection, using the Dice similarity coefficient (DSC), Pearson's correlation coefficient, and relative difference to manual segmentation. ResectVol DL presented the highest median DSC (0.92 vs. 0.78 and 0.90), the highest correlation coefficient (0.99 vs. 0.63 and 0.94) and the lowest median relative difference (9 vs. 44 and 12 %). Overall, we demonstrate that ResectVol DL accurately segments brain lacunas, which has the potential to assist in the development of predictive models for postoperative cognitive and seizure outcomes.

Polymeric Nanoparticles for the Treatment of Prostate Cancer- Technological Prospecting and Critical Analysis.

BACKGROUND: Polymeric nanoparticles have a wide diversity, and due to their toxicity and biodegradability, they have been widely used in the health area. Its use allows stability of some compounds, targeted delivery, and increased half-life, in this context, making some treatment proposals more effective. Prostate cancer, in turn, is among the types of cancer with the highest mortality, and the lack of effective treatment causes several strategies to meet this need. OBJECTIVE: The objective of this work was to verify patents that use polymeric nanoparticles for the treatment of prostate cancer. METHODS: For that, specific keywords to direct the search were applied in Patent Scope. After obtaining the patents, one was selected for the development of critical analysis in relation to its chemistry and biology. RESULTS: A total of five patents were found and, of these, an invention that used PCL-PLGA-PEGCOOH polymeric nanoparticles with two natural compounds, resveratrol and celastrol, providing an alternative method to traditional monotherapies. CONCLUSION: The prospective analysis serves to direct us in relation to the technologies currently used in certain fields. Based on several cases of cancer and specifically the countless cases of prostate cancer, five recent patents were found using polymeric nanoparticles. However, only one brought a different aspect of all the treatments used lately. It brought an invention containing two natural compounds being carried with polymeric nanoparticles with promising results.

Biosurfactant Production from Pineapple Waste and Application of Experimental Design and Statistical Analysis.

The use of non-conventional carbon sources for biosurfactant-producing microorganisms is a promising alternative in fermentation to substitute costly substrates. So, the current research used pineapple peel as a cost-effective and renewable substrate because of its rich composition in minerals and sugars and high availability. Following a 22 full factorial design, a bacterial strain of Bacillus subtilis produced biosurfactants in fermentative media containing different concentrations of glucose and concentrated pineapple peel juice (CPPJ). The influence of these two independent variables was evaluated according to three different responses: surface tension reduction rate (STRR), emulsification index (EI24), and concentration of semi-purified biosurfactant (SPB). The maximum value for STRR (57.63%) was obtained in media containing 0.58% glucose (w/v) and 5.82% CPPJ (v/v), while the highest EI24 response (58.60%) was observed at 2% glucose (w/v) and 20% CPPJ (v/v) and maximum SPB (1.28 g/L) at 3.42% glucose (w/v) and 34.18% CPPJ (v/v). Statistical analysis indicated that the CPPJ variable mostly influenced the STRR and SPB responses, whereas the EI24 was significantly influenced by pineapple peel juice and glucose contents.

Fatores estressores de estudantes de enfermagem em diferentes cenários simulados

Objetivo: Comparar a percepção de fatores estressores de estudantes de enfermagem antes e após sua experiência em cinco cenários diferentes de simulação. Material e Método: Pesquisa de intervenção, longitudinal e quantitativa, realizado com uma amostra por conveniência de 35 estudantes de enfermagem de uma universidade do sul do Brasil. O critério de inclusão foi estar matriculado no curso Saúde do Adulto e do Idoso do sexto semestre do programa de Enfermagem, que tem duração de 10 semestres. O critério de exclusão foi ter formação prévia em um programa com competências em exame clínico. O critério de descontinuidade foi a ausência de pelo menos uma experiência simulada. O questionário KEZKAK, validado em português e adaptado com 31 itens para estudos de simulação, foi utilizado para avaliar a percepção de fatores estressores durante cinco cenários simulados sobre suporte básico e avançado de vida. Resultados: Após experiência dos estudantes em cinco diferentes cenários de simulação, observou-se uma redução significativa na percepção dos seguintes estressores: falta de competência (p= 0,0329); contato com o sofrimento (p= 0,0279); relacionamento com tutores e companheiros (p= 0,4699 / p= 0,0135 respectivamente); e ser magoado no relacionamento com o paciente (p= 0,0110). Conclusões: A execução de cenários variados e com nível progressivo de complexidade, pode ajudar a reduzir os fatores de estresse percebidos pelos estudantes de enfermagem durante as atividades de simulação clínica.

Objective: To compare the perception of nursing students before and after their experience in five different simulation scenarios. Materials and Methods: Interventional, longitudinal and quantitative research, conducted with a convenience sample of 35 nursing students from a university in the south of Brazil. The inclusion criterion was to be enrolled in the course Adult and Elderly Health of the sixth semester of the graduate Nursing program, which has a duration of 10 semesters. The exclusion criterion was having previous training in a program with competencies in clinical examination. The discontinuity criterion was the absence of at least one simulated experience. To assess the perception of stressors during simulated scenarios in basic and advanced life support, the KEZKAK questionnaire, validated in Portuguese and adapted with 31 items for simulation studies, was used. Results: After experiencing five different simulation scenarios, there was a significant reduction in the perception of the following stressors: lack of competence (p= 0.0329); contact with suffering (p= 0.0279); relationship with tutors and peers (p= 0.4699 / p= 0.0135 respectively); and hurt feelings in the relationship with the patient (p= 0.0110). Conclusions: Performing varied scenarios with increasing levels of complexity may help to reduce the stressors perceived by nursing students during clinical simulation activities.

Objetivo: Comparar la percepción de factores estresantes en estudiantes de enfermería antes y después de su experiencia en cinco escenarios diferentes de simulación. Material y Método: Investigación de intervención, longitudinal y cuantitativa, realizada con una muestra por conveniencia de 35 estudiantes de enfermería de una Universidad del Sur de Brasil. El criterio de inclusión fue estar matriculado en la asignatura de Salud del Adulto y Adulto Mayor del sexto semestre de la carrera de Enfermería que tiene un total de 10 semestres de formación. El criterio de exclusión fue tener formación previa em una carrera com competencias en examen clínico. El criterio de discontinuidad fue la ausencia en al menos una experiencia simulada. Para evaluar la percepción de estresores durante los escenarios simulados en soporte vital básico y avanzado, se utilizó el cuestionario KEZKAK validado en lengua portuguesa, adaptado com 31 ítems para estudios en el ámbito de la simulación. Resultados: Después de la experiencia en cinco escenarios diferentes de simulación, hubo una reducción significativa en la percepción de los siguientes factores estresantes: falta de competencia (p= 0,0329); contacto con el sufrimiento (p= 0,0279); relación con tutores y compañeros (p= 0,4699 / p=0,0135); y sentirse herido en la relación con el paciente (p= 0,0110). Conclusiones: La ejecución de escenarios variados y con un nivel de complejidad progresivo, puede ayudar a reducir los estresores que perciben los estudiantes de enfermería durante las actividades de simulación clínica.

A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation.

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice.

Brain connectomes in youth at risk for serious mental illness: an exploratory analysis.

BACKGROUND: Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI. METHODS: Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data. RESULTS: Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections. CONCLUSIONS: Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.

Improving Fast MRI Reconstructions with Pretext Learning in Low-Data Regime.

Supervised deep learning methods have shown great promise for making magnetic resonance (MR) imaging scans faster. However, these supervised deep learning models need large volumes of labelled data to learn valuable representations and produce high-fidelity MR image reconstructions. The data used to train these models are often fully-sampled raw MR data, retrospectively under-sampled to simulate different MR acquisition acceleration factors. Obtaining high-quality, fully sampled raw MR data is costly and time-consuming. In this paper, we exploit the self supervision based learning by introducing a pretext method to boost feature learning using the more commonly available under-sampled MR data. Our experiments using different deep-learning-based reconstruction models in a low data regime demonstrate that self-supervision ensures stable training and improves MR image reconstruction.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

High Retention and Purification of Bromelain Enzyme (Ananas comosus L. Merrill) from Pineapple Juice Using Plain and Hollow Polymeric Membranes Techniques.

The demand for bromelian and pineapple fruit has been increasing substantially in the world because of their benefits for the human health and use in diverse areas. In this context, this work aimed to study the capacity of higher retention (concentration); bromelain activity underwent ultrafiltration from pineapple juice (Ananas comusus L. Merrill). All assays were carried out at pH 7.0 and 7.5, and at 0.05 and 0.40 bar of transmembrane pressures. Results have shown that at the best operating conditions, between 85 and 87% of bromelain activity was recovered using the plain membrane separation process at 0.05 bar. The ultrafiltration has shown the capacity to retain 100% of proteolytic activity of the bromelain extracted. The samples have kept the same physics properties after ultrafiltration, and the result was verified via electrophoresis. The bromelain enzyme obtained was characterized, and pH 7 and between 30 and 40 °C were the best conditions. Therefore, this work shows that the use of both polymeric membranes has shown high efficiency, and can be used in the purification of bromelain enzymes.

Cerebello-limbic functional connectivity patterns in youth at clinical high risk for psychosis.

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.

Cellulase production by Aspergillus niger using urban lignocellulosic waste as substrate: Evaluation of different cultivation strategies.

Cellulases are used in various industries, acting efficiently and sustainably in the degradation of cellulose contained in different raw materials and recovering high value products. It is the third largest group of enzymes consumed industrially, as they are required in processes linked to the food, biofuel, textile, cleaning products, among others. However, the main disadvantage in the use of commercial cellulases is the high cost. In this context, the objective of this work was to determine conditions for obtaining more efficient and economical cellulases. For this, the efficiency in obtaining the extracellular cellulases endoglucanase (CMCase) and exoglucanase (FPase) by a fungus Aspergillus niger was investigated using an urban lignocellulosic waste as substrate characterized by tree leaves collected from squares and avenues in urban areas. As urban lignocellulosic waste is an innovative raw material, its chemical composition was determined. This substrate contains 20.36% cellulose and induced the production of cellulases in all fermentation methods, proving to be a promising and sustainable source. The influence of the nutrient medium on CMCase and FPase activities was evaluated for three different sequential fermentation (SF) configurations. Medium 2 provided an increase of up to 100 U/L of CMCase and FPase in relation to medium 1. The interactive effect of pH and moisture content on CMCase e FPase production under SF was studied in a central composite design (CCD). Also, different fermentation methods (solid state, submerged and sequential) were evaluated. The use of SF increased the enzymatic activities of both cellulases by 140% compared to other conventional methods and also stood out in the production of proteins (270.05 µg/mL) and reducing sugars (1.19 mg/mL). The desirability function determined the optimal activities of CMCase and FPase as 413.49 U/L and 230.68 U/L, respectively, obtained from the optimal variables of pH 5.5 and 75% moisture content under SF. The effect of pH and moisture content on the activity of each cellulase was analyzed using the Pareto chart and response surface methodology (RSM). These results revealed favorable strategies for cellulase production, such as the use of urban lignocellulosic waste, SF and ideal operational conditions.

Childhood trauma and amygdala nuclei volumes in youth at risk for mental illness.

BACKGROUND: Adults with significant childhood trauma and/or serious mental illness may exhibit persistent structural brain changes within limbic structures, including the amygdala. Little is known about the structure of the amygdala prior to the onset of SMI, despite the relatively high prevalence of trauma in at-risk youth. METHODS: Data were gathered from the Canadian Psychiatric Risk and Outcome study. A total of 182 youth with a mean age of 18.3 years completed T1-weighted MRI scans along with clinical assessments that included questionnaires on symptoms of depression and anxiety. Participants also completed the Childhood Trauma and Abuse Scale. We used a novel subfield-specific amygdala segmentation workflow as a part of FreeSurfer 6.0 to examine amygdala structure. RESULTS: Participants with higher trauma scores were more likely to have smaller amygdala volumes, particularly within the basal regions. Among various types of childhood trauma, sexual and physical abuse had the largest effects on amygdala subregions. Abuse-related differences in the right basal region mediated the severity of depression and anxiety symptoms, even though no participants met criteria for clinical diagnosis at the time of assessment. CONCLUSION: The experience of physical or sexual abuse may leave detectable structural alterations in key regions of the amygdala, potentially mediating the risk of psychopathology in trauma-exposed youth.