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.

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.

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.

Extraction of a vascular function for a fully automated dynamic contrast-enhanced magnetic resonance brain image processing pipeline.

PURPOSE: To develop a deep-learning model that leverages the spatial and temporal information from dynamic contrast-enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmacokinetic (PK) modeling. METHODS: Patients with glioblastoma multiforme were scanned post-resection approximately every 2 months using a high spatial and temporal resolution DCE MR imaging sequence ( ≈5 s and ≈2 cm3 ). A region over the transverse sinus was manually drawn in the dynamic T1-weighted images to provide a ground truth VF. The manual regions and their resulting VF curves were used to train a deep-learning model based on a 3D U-net architecture. The model concurrently utilized the spatial and temporal information in DCE MR images to predict the VF. In order to analyze the contribution of the spatial and temporal terms, different weighted combinations were examined. The manual and deep-learning predicted regions and VF curves were compared. RESULTS: Forty-three patients were enrolled in this study and 155 DCE MR scans were processed. The 3D U-net was trained using a loss function that combined the spatial and temporal information with different weightings. The best VF curves were obtained when both spatial and temporal information were considered. The predicted VF curve was similar to the manual ground truth VF curves. CONCLUSION: The use of spatial and temporal information improved VF curve prediction relative to when only the spatial information is used. The method generalized well for unseen data and can be used to automatically estimate a VF curve suitable for quantitative PK modeling. This method allows for a more efficient clinical pipeline and may improve automation of permeability mapping.

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.

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.

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.

Aberrant limbic brain structures in young individuals at risk for mental illness.

AIM: Alterations in limbic structures may be present before the onset of serious mental illness, but whether subfield-specific limbic brain changes parallel stages in clinical risk is unknown. To address this gap, we compared the hippocampus, amygdala, and thalamus subfield-specific volumes in adolescents at various stages of risk for mental illness. METHODS: MRI scans were obtained from 182 participants (aged 12-25 years) from the Canadian Psychiatric Risk and Outcome study. The sample comprised of four groups: asymptomatic youth at risk due to family history of mental illness (Stage 0, n = 32); youth with early symptoms of distress (Stage 1a, n = 41); youth with subthreshold psychotic symptoms (Stage 1b, n = 72); and healthy comparison participants with no family history of serious mental illness (n = 37). Analyses included between-group comparisons of brain measurements and correlational analyses that aimed to identify significant associations between neuroimaging and clinical measurements. A machine-learning technique examined the discriminative properties of the clinical staging model. RESULTS: Subfield-specific limbic volume deficits were detected at every stage of risk for mental illness. A machine-learning classifier identified volume deficits within the body of the hippocampus, left amygdala nuclei, and medial-lateral nuclei of the thalamus that were most informative in differentiating between risk stages. CONCLUSION: Aberrant subfield-specific changes within the limbic system may serve as biological evidence to support transdiagnostic clinical staging in mental illness. Differential patterns of volume deficits characterize those at risk for mental illness and may be indicative of a risk-stage progression.

Nursing case management for people with hypertension in primary health care: A randomized controlled trial.

INTRODUCTION: Hypertension is a chronic disease that requires continuous and long-term care to prevent or delay the development of associated complications. Although various interventions for hypertension exist, case management in Brazil's primary healthcare is understudied. We examined nursing case management effectiveness for controlling blood pressure among Brazilian adults with hypertension in the public healthcare system. METHOD: A randomized controlled trial with a 12-month follow-up was conducted at a primary healthcare clinic in southern Brazil. Adult patients with hypertension were randomly allocated to intervention (n = 47) and usual care groups (n = 47). The nursing case management model includes nursing consultations, telephone contact, home visits, health education, and appropriate referrals. Patient outcomes (blood pressure, body mass index, waist circumference, quality of life, treatment adherence) were assessed at baseline and 6- and 12-month follow-up for the intervention group and at baseline and 12-month follow-up for the usual care group. Data were collected from only the intervention group at T6 to avoid contact between the researcher and the usual care group, and to check the care plan and modify it if necessary. RESULTS: After the intervention, the intervention group's blood pressure decreased significantly compared to the usual care group. The differences in systolic and diastolic blood pressure between the groups was -8.3 (intervention)/1.1 (usual care) mmHg (p = .004) and -7.4/-0.6 mmHg (p = .007), respectively. The intervention group had significantly greater improvement in waist circumference (-2.0/1.2 cm), body mass index (- 0.4/0.3), and treatment adherence (4.8/-1.1) than the usual care group (all p < .05). CONCLUSION: Nursing case management in primary healthcare may be effective for improving outcomes among patients with hypertension.

Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.

Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets. Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues - Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.

Clinical validity and intrarater and test-retest reliability of the Structured Clinical Interview for DSM-5 - Clinician Version (SCID-5-CV).

AIM: The Structured Clinical Interview for the DSM is one of the most used diagnostic instruments in clinical research worldwide. The current Clinician Version of the instrument (SCID-5-CV) has not yet been assessed in respect to its psychometric qualities. We aimed to assess the clinical validity and different reliability indicators (interrater test-retest, joint interview, face-to-face vs telephone application) of the SCID-5-CV in a large sample of 180 non-prototypical and psychiatric patients based on interviews conducted by raters with different levels of clinical experience. METHODS: The SCID-5-CV was administered face-to-face and by telephone by 12 psychiatrists/psychologists who took turns as raters and observers. Clinical diagnoses were established according to DSM-5 criteria and the longitudinal, expert, all data (LEAD) procedure. We calculated the percentage of agreement, diagnostic sensitivity and specificity, and the level of agreement (kappa) for diagnostic categories and specific diagnoses. RESULTS: The percentage of positive agreement between the interview and clinical diagnoses ranged between 73% and 97% and the diagnostic sensitivity/specificity were >0.70. In the joint interview, the levels of positive agreement were high (>75%) and kappa levels were >0.70 for most diagnoses. The values were less expressive, but still adequate, for interrater test-retest interviews. CONCLUSION: The SCID-5-CV presented excellent reliability and high specificity as assessed with different methods. The clinical validity of the instrument was also confirmed, which supports its use in daily clinical practice. We highlight the adequacy of the instrument to be used via telephone and the need for careful use by professionals with little experience in psychiatric clinical practice.

Analysis of behavior of the wear coefficient in different layers of acrylic resin teeth.

STATEMENT OF PROBLEM: Analysis of the wear coefficient (k) of the superficial and deep layers of acrylic resin teeth can help predict denture durability, but little has been published on the wear coefficient of denture teeth. PURPOSE: The purpose of this in vitro study was to determine the k value for the superficial and deep layers of the acrylic resin teeth of 6 different brands by using the fixed-ball microabrasive wear method. MATERIAL AND METHODS: Six artificial tooth specimens of 4 commercial brands were tested: Artiplus IPN (Ar), Biotone IPN (Bi), Magister (Ma), Premium (Pr), Trilux (Tr), and SR Vivodent (Vi). Two specimens from each brand were created, one for the superficial layer and the other for the deep layer. The test was performed on fixed-ball microabrasive wear equipment set to operate at a constant normal force of 0.5 N and a rotation speed of 100 rpm. The test time periods were 5.00, 8.33, and 11.66 minutes. The characteristics of the wear craters were measured by using an optical microscope at a magnification of ×50 and Leica Microsystems software. Wear coefficient (k) values were deduced by using the Archard equation for abrasive wear, Q=k·N, and were analyzed by using 1-way analysis of variance, complemented by the Tukey HSD test (α=.05). A different analysis was used for each layer. RESULTS: The analysis of variance of the wear coefficient revealed significant differences among the groups regarding the superficial layers (P=.009). The Tukey HSD test showed that the k values for the superficial layers of Artiplus specimens were significantly lower than those of the Vivodent and Magister specimens. CONCLUSIONS: One brand (Ar) presented significantly lower wear coefficient value for the surface layer. No difference in wear coefficient values was found among the tooth brands for the deep layer.

Depressive symptoms and factors associated with depression and suicidal behavior in substances user in treatment: Focus on suicidal behavior and psychological problems.

AIM: To investigate the prevalence of depressive symptoms, psychological problems, suicidal behaviour and their associations in substance users in treatment. METHODS: A cross-sectional study, with 307 substance users in an out-patient treatment facility, was undertaken. Socio-demographic data, psychoactive substances used, depressive symptoms, and suicide information were obtained. RESULTS: 70% of participants were depressed; of those, 8.1% were either under the influence of drugs or in withdrawal. Suicidal ideation was found to be present in those who had anxiety, were nervous, had depressive symptoms, or were under drug influence or in withdrawal. CONCLUSION: It is important to identify potential suicidal risk factors and implement the management of these conditions in substance users.

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p-value<0.001) and magnetic field strength (p-value<0.001) have statistically significant impacts on skull stripping results.

Virulence-related genes, adhesion and invasion of some Yersinia enterocolitica-like strains suggests its pathogenic potential.

Yersina enterocolitica-like species have not been extensively studied regarding its pathogenic potential. This work aimed to assess the pathogenic potential of some Y. enterocolitica-like strains by evaluating the presence of virulence-related genes by PCR and their ability to adhere to and invade Caco-2 and HEp-2 cells. A total of 50 Y. frederiksenii, 55 Y. intermedia and 13 Y. kristensenii strains were studied. The strains contained the following genes: Y. frederiksenii, fepA(44%), fes(44%) and ystB(18%); Y. intermedia, ail(53%), fepA (35%), fepD(2%), fes(97%), hreP(2%), ystB(2%) and tccC(35%); Y. kristensenii, ail(62%), ystB(23%), fepA(77%), fepD(54%), fes(54%) and hreP(77%). Generally, the Y. enterocolitica-like strains had a reduced ability to adhere to and invade mammalian cells compared to the highly pathogenic Y. enterocolitica 8081. However, Y. kristensenii FCF410 and Y. frederiksenii FCF461 presented high invasion potentials in Caco-2 cells after five days of pre-incubation increased by 45- and 7.2-fold compared to Y. enterocolitica 8081, respectively; but, the ail gene was not detected in these strains. The presence of virulence-related genes in some of the Y. enterocolitica-like strains indicated their possible pathogenic potential. Moreover, the results suggest the existence of alternative virulence mechanisms and that the pathogenicity of Y. kristensenii and Y. frederiksenii may be strain-dependent.

The use of asymmetric distributions in average bioequivalence.

Generic drugs have been commercialized in numerous countries. Most of these countries approve the commercialization of a generic drug when there is evidence of bioequivalence between the generic drug and the reference drug. Generally, the pharmaceutical industry is responsible for the bioequivalence test under the supervision of a regulatory agency. This procedure is concluded after a statistical data analysis. Several agencies adopt a standard statistical analysis based on procedures that were previously established. In practice, we face situations in which this standard model does not fit to some sets of bioequivalence data. In this study, we propose an evaluation of bioequivalence using univariate and bivariate models based on an extended generalized gamma distribution and a skew-t distribution, under a Bayesian perspective. We introduce a study of the empirical power of hypothesis tests for univariate models, showing advantages in the use of an extended generalized gamma distribution. Three sets of bioequivalence data were analyzed under these new procedures and compared with the standard model proposed by the majority of regulatory agencies. In order to verify that the asymmetrical distributions are usually better fitted for the data, when compared with the standard model, model discrimination methods were used, such as the Deviance Information Criterion (DIC) and quantile-quantile plots. The research concluded that, in general, the use of the extended generalized gamma distribution may be more appropriate to model bioequivalence data in the original scale. Copyright © 2016 John Wiley & Sons, Ltd.

Facts, presumptions, and myths on the solvent-free and catalyst-free Biginelli reaction. What is catalysis for?

The current manuscript describes the role and importance of catalysis and solvent effects for the Biginelli multicomponent reaction. The overwhelming number of new catalysts and conditions recently published for the Biginelli synthesis, including in some manuscripts entitled "catalyst-free" and/or "solvent-free" have incentivized controversies and hot debates regarding the importance of developing new catalysts and reaction conditions to perform this very important multicomponent reaction. These so-called "catalyst-free" reports have generated much confusion in the field, requiring urgent elucidations. In this manuscript, we exemplify, demystify, and discuss the crucial role of catalysis, solvent effects, mechanisms, kinetics, facts, presumptions, and myths associated with the Biginelli reaction aiming to avoid current and future confusion and to stimulate new approaches.

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.

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 ).

SimuVet: a preliminary study of the innovative development of a simulator for epidural anesthesia training in dogs.

Epidural anesthesia in dogs is a locoregional anesthesia technique used in veterinary medicine, becoming an important integrated application in the anesthetic protocol to provide safer and more effective analgesia to patients. For this, professionals must adhere to rigorous guidelines and possess technical skills. In this context, in veterinary education, the development of practical clinical skills represents a crucial aspect in the training of these professionals. However, traditional teaching methods have proven insufficient to ensure a consistent level of competence among recent graduates. The introduction of non-animal alternatives for educational purposes has contributed to the development of simulation-based teaching, an innovative and accessible field capable of enhancing pre-clinical proficiency in students and reducing the use of live animals and cadavers. Despite its application in various areas of veterinary education, there are no conclusive results regarding the development of accessible simulators capable of effectively enhancing training in epidural anesthesia in dogs. Therefore, this article represents a pioneering study aimed at sharing a method for creating SimuVet, a realistic simulator for training epidural anesthesia in dogs. The simulator was fully developed by veterinary researchers with limited experience in 3D printing and, after preliminary analysis, demonstrated excellent performance and ultrasonographic anatomy. Future work will focus on the formal validation of this simulator with the aim of improving the teaching and learning process for students and experts in performing epidural anesthesia in companion animals.