Fibroblast mediated dynamics in diffusively uncoupled myocytes: a simulation study using 2-cell motifs.

In healthy hearts myocytes are typically coupled to nearest neighbours through gap junctions. Under pathological conditions such as fibrosis, or in scar tissue, or across ablation lines myocytes can uncouple from their neighbours. Electrical conduction may still occur via fibroblasts that not only couple proximal myocytes but can also couple otherwise unconnected regions. We hypothesise that such coupling can alter conduction between myocytes via introduction of delays or by initiation of premature stimuli that can potentially result in reentry or conduction blocks. To test this hypothesis we have developed several 2-cell motifs and investigated the effect of fibroblast mediated electrical coupling between uncoupled myocytes. We have identified various regimes of myocyte behaviour that depend on the strength of gap-junctional conductance, connection topology, and parameters of the myocyte and fibroblast models. These motifs are useful in developing a mechanistic understanding of long-distance coupling on myocyte dynamics and enable the characterisation of interaction between different features such as myocyte and fibroblast properties, coupling strengths and pacing period. They are computationally inexpensive and allow for incorporation of spatial effects such as conduction velocity. They provide a framework for constructing scar tissue boundaries and enable linking of cellular level interactions with scar induced arrhythmia.

Cross-hemicord spinal fiber reorganization associates with cortical sensory and motor network expansion in the rat model of hemicontusion cervical spinal cord injury.

Magnetic resonance imaging plays an important role in characterizing microstructural changes and reorganization after traumatic injuries to the nervous system. In this study, we tested the feasibility of ex-vivo spinal cord diffusion tensor imaging (DTI) in combination with in vivo brain functional MRI to characterize spinal reorganization and its supraspinal association after a hemicontusion cervical spinal cord injury (SCI). DTI parameters (fractional anisotropy [FA], mean diffusion [MD]) and fiber orientation changes related to reorganization in the contused cervical spinal cord were compared to sham specimens. Altered fiber density and fiber directions occurred across the ipsilateral and contralateral hemicords but with only ipsilateral FA and MD changes. The hemicontusion SCI resulted in ipsilateral fiber breaks, voids and vivid fiber reorientations along the injury epicenter. Fiber directional changes below the injury level were primarily inter-hemispheric, indicating prominent below-level cross-hemispheric reorganization. In vivo resting state functional connectivity of the brain from the respective rats before obtaining the spinal cord samples indicated spatial expansion and increased connectivity strength across both the sensory and motor networks after SCI. The consistency of the neuroplastic changes along the neuraxis (both brain and spinal cord) at the single-subject level, indicates that distinctive reorganizational relationships exist between the spinal cord and the brain post-SCI.

Inhibiting Inducible Nitric Oxide Synthase with 1400W Reduces Soman (GD)-Induced Ferroptosis in Long-Term Epilepsy-Associated Neuropathology: Structural and Functional Magnetic Resonance Imaging Correlations with Neurobehavior and Brain Pathology.

Organophosphate (OP) nerve agent (OPNA) intoxication leads to long-term brain dysfunctions. The ineffectiveness of current treatments for OPNA intoxication prompts a quest for the investigation of the mechanism and an alternative effective therapeutic approach. Our previous studies on 1400W, a highly selective inducible nitric oxide synthase (iNOS) inhibitor, showed improvement in epilepsy and seizure-induced brain pathology in rat models of kainate and OP intoxication. In this study, magnetic resonance imaging (MRI) modalities, behavioral outcomes, and biomarkers were comprehensively investigated for brain abnormalities following soman (GD) intoxication in a rat model. T1 and T2 MRI robustly identified pathologic microchanges in brain structures associated with GD toxicity, and 1400W suppressed those aberrant alterations. Moreover, functional network reduction was evident in the cortex, hippocampus, and thalamus after GD exposure, and 1400W rescued the losses except in the thalamus. Behavioral tests showed protection by 1400W against GD-induced memory dysfunction, which also correlated with the extent of brain pathology observed in structural and functional MRIs. GD exposure upregulated iron-laden glial cells and ferritin levels in the brain and serum, 1400W decreased ferritin levels in the epileptic foci in the brain but not in the serum. The levels of brain ferritin also correlated with MRI parameters. Further, 1400W mitigated the overproduction of nitroxidative markers after GD exposure. Overall, this study provides direct evidence for the relationships of structural and functional MRI modalities with behavioral and molecular abnormalities following GD exposure and the neuroprotective effect of an iNOS inhibitor, 1400W. SIGNIFICANT STATEMENT: Our studies demonstrate the MRI microchanges in the brain following GD toxicity, which strongly correlate with neurobehavioral performances and iron homeostasis. The inhibition of iNOS with 1400W mitigates GD-induced cognitive decline, iron dysregulation, and aberrant brain MRI findings.

Regional tissue oxygenation in asymptomatic neonates at high risk for neonatal abstinence syndrome and impact of non-pharmacologic interventions: A case report.

BACKGROUND: Improving neonatal abstinence syndrome (NAS) management is an important concern, and objective measures of its physiologic impact remain elusive. We sought to determine whether near-infrared spectroscopy (NIRS)-derived tissue oxygenation (rSO2) and fractional tissue oxygen extraction (FTOE) demonstrated physiologically plausible changes correlating with standard NAS scoring. METHODS: Thirty subjects (mean 39 weeks' GA and 3 127 g BW) underwent cerebral and peripheral muscle NIRS monitoring on Days of Life (DOL) Three, Five, and Seven. We examined correlations between NAS scores and FTOE and assessed the impact of non-pharmacologic swaddling and cuddling. RESULTS: No statistically significant correlations between NAS scores and FTOE were observed; however, plausible trends were demonstrated between NAS scores and cerebral measurements. Buprenorphine-exposed babies (57%) showed significantly lower FTOE when swaddled (DOL7). CONCLUSIONS: Tissue oxygenation monitoring demonstrates potential to provide objective, clinically relevant physiologic information on infants at risk for NAS. Further study is required to determine whether NIRS-derived measures could assist in individualizing NAS care.

Efficacy and Safety of a Fixed-Dose Combination of Vildagliptin and Pioglitazone in Indian Patients With Type 2 Diabetes Mellitus: A Randomized, Open-Label, Comparative, Phase III Study.

Background Type 2 diabetes mellitus (T2DM) arises due to a range of pathological abnormalities, necessitating a combination therapy to achieve optimal glycemic control. Vildagliptin, an effective and selective DPP-4 inhibitor, and pioglitazone, an insulin sensitizer, offer distinct mechanisms of action. Hence, the integration of these medications represents a logical and justified therapeutic strategy Objective To compare the efficacy, safety, and tolerability of vildagliptin and pioglitazone 50 mg/15 mg fixed-dose combination (FDC) tablets with individual monotherapy vildagliptin 50 mg and pioglitazone 15 mg tablets in Indian T2DM patients who were inadequately controlled on metformin monotherapy. Methods This was a randomized, open-label, comparative, multicenter, phase III study involving 195 T2DM patients with inadequate glycemic control on metformin ≥ 1000 mg/day. Patients were randomly assigned in a 1:1:1 ratio to the test product group (n=65) (vildagliptin 50 mg + pioglitazone 15 mg FDC tablets), reference product group 1 (n=65) (vildagliptin 50 mg tablet), or reference product group 2 (n=65) (pioglitazone 15 mg tablet reference product). The primary endpoint was the mean change in HbA1c levels from baseline to end of the study visit (12 weeks (84 days ±2)). The secondary endpoints were the mean change in fasting plasma glucose (FPG) and 2-hr postprandial plasma glucose (2-hr PPG) levels. Safety parameters were assessed till the end of the study. Results A total of 178 patients completed the study. At 12 weeks, the mean HbA1c level in the test group reduced to 6.85 ± 1.27%, in the reference product 1 group to 7.56 ± 1.72%, and in the reference product 2 groups to 7.37 ± 1.59%. The mean change in Hb1Ac from baseline in the test group was statistically significant compared to the reference groups (p=0.037). Similarly, the mean changes in the FPG and 2hr-PPG with the test product were statistically significant compared to reference products (p=0.041). The adverse events were comparable across all the treatment groups. Conclusion In Indian T2DM patients inadequately controlled on a daily maximum dose of metformin, treatment with vildagliptin and pioglitazone FDC showed better glycemic control than either vildagliptin or pioglitazone along with a good tolerability profile.

Circulating tumor DNA association with residual cancer burden after neoadjuvant chemotherapy in triple-negative breast cancer in TBCRC 030.

BACKGROUND: We aimed to examine circulating tumor DNA (ctDNA) and its association with residual cancer burden (RCB) using an ultrasensitive assay in patients with triple-negative breast cancer (TNBC) receiving neoadjuvant chemotherapy. PATIENTS AND METHODS: We identified responders (RCB 0/1) and matched non-responders (RCB 2/3) from the phase II TBCRC 030 prospective study of neoadjuvant paclitaxel versus cisplatin in TNBC. We collected plasma samples at baseline, 3 weeks and 12 weeks (end of therapy). We created personalized ctDNA assays utilizing MAESTRO mutation enrichment sequencing. We explored associations between ctDNA and RCB status and disease recurrence. RESULTS: Of 139 patients, 68 had complete samples and no additional neoadjuvant chemotherapy. Twenty-two were responders and 19 of those had sufficient tissue for whole-genome sequencing. We identified an additional 19 non-responders for a matched case-control analysis of 38 patients using a MAESTRO ctDNA assay tracking 319-1000 variants (median 1000 variants) to 114 plasma samples from 3 timepoints. Overall, ctDNA positivity was 100% at baseline, 79% at week 3 and 55% at week 12. Median tumor fraction (TFx) was 3.7 × 10-4 (range 7.9 × 10-7-4.9 × 10-1). TFx decreased 285-fold from baseline to week 3 in responders and 24-fold in non-responders. Week 12 ctDNA clearance correlated with RCB: clearance was observed in 10 of 11 patients with RCB 0, 3 of 8 with RCB 1, 4 of 15 with RCB 2 and 0 of 4 with RCB 3. Among six patients with known recurrence, five had persistent ctDNA at week 12. CONCLUSIONS: Neoadjuvant chemotherapy for TNBC reduced ctDNA TFx by 285-fold in responders and 24-fold in non-responders. In 58% (22/38) of patients, ctDNA TFx dropped below the detection level of a commercially available test, emphasizing the need for sensitive tests. Additional studies will determine whether ctDNA-guided approaches can improve outcomes.

A Rare Case of Septic Ovarian Thrombophlebitis Caused by Tissierella praeacuta.

We report a case of Tissierella praeacuta bacteremia and septic thrombophlebitis of the ovarian vein as a rare puerperal complication in a young patient. She was successfully managed with subcutaneous low molecular weight heparin (LMWH) and intravenous (IV) antibiotics before transitioning to a prolonged course of oral antibiotics at discharge.

Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

INTRODUCTION: Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images. In the proposed work, an Ideal Shallow Neural Network (ISNN) is utilized to improve the prediction accuracy, and the computational complexity is reduced by implementing an Artificial Jellyfish Optimization (AJO) algorithm for minimizing the feature dimensionality. METHOD: The proposed method utilizes MRI images for the verification process as they are more informative than the CT scan image. The BRATS and the Kaggle datasets are used in this work and a Gabor filtering technique is used for noise reduction and a histogram equalization is used for enhancing the tumor boundary regions. The classification results observed from the AJO-ISNN are further forwarded towards the segmentation process and which uses the Centroid Weighted Segmentation (WCS) along with a Grasshopper Optimization Algorithm (GOA) for improving the segmentation over the boundary regions of the brain tumor. RESULT: The experimental result indicates a classification accuracy of 95.14% on the proposed AJO-ISNN model and AJO-ISNN is comparatively better than the Convolutional Neural Network (CNN) model accuracy of 85.41% and VGG 19 model accuracy of 93.75% while implemented with the AJO optimization model. Similarly, the Dice Similarity Coefficient of the proposed CWS-GOA also reaches 93.15% when performed with both BRATS and Kaggle datasets. CONCLUSION: Apart from the accuracy attainments the proposed work classifies and segments the tumor region in around 65 seconds on average of 200 image verifications and that is comparatively better than the previous multi-cascaded CNN and the InceptionV3 models.

An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework.

Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using data cleaning and normalization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to improve interpretation of prediction results. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%.

Sex-based structural and functional MRI outcomes in the rat brain after soman (GD) exposure-induced status epilepticus.

OBJECTIVE: Exposure to the nerve agent, soman (GD), induces status epilepticus (SE), epileptogenesis, and even death. Although rodent models studying the pathophysiological mechanisms show females to be more reactive to soman, no tangible sex differences in brains postexposure have been reported. In this study, we used multimodal imaging using MRI in adult rats to determine potential sex-based biomarkers of soman effects. METHODS: Male and female Sprague Dawley rats were challenged with 1.2 × LD50 soman followed by medical countermeasures. Ten weeks later, the brains were analyzed via structural and functional MRI. RESULTS: Despite no significant sex differences in the initial SE severity after soman exposure, long-term MRI-based structural and functional differences were evident in the brains of both sexes. While T2 MRI showed lesser soman-induced neurodegeneration, large areas of T1 enhancements occurred in females than in males, indicating a distinct pathophysiology unrelated to neurodegeneration. fMRI-based resting-state functional connectivity (RSFC), indicated greater reductions in soman-exposed females than in males, associating with the T1 enhancements (unrelated to neurodegeneration) rather than T2-hyperintensity or T1-hypointensity (representing neurodegeneration). The wider T1 enhancements associating with the decreased spontaneous neuronal activity in multiple resting-state networks in soman-exposed females than males suggest that neural changes unrelated to cellular atrophy impinge on brain function postexposure. Taken together with lower spontaneous neural activity in soman-exposed females, the results indicate some form of neuroprotective state that was not present in males. SIGNIFICANCE: The results indicate that endpoints other than neurodegeneration may need to be considered to translate sex-based nerve agent effects in humans.

The use of Vaccinia Immune Globulin in the Treatment of Severe Mpox. Virus Infection in Human Immunodeficiency Virus/AIDS.

We report a case of progressive, severe mpox virus (MPXV) infection in a patient with AIDS despite a standard course of tecovirimat. He significantly improved after administration of vaccinia immune globulin intravenous (VIGIV) highlighting its use as an adjunct for severe disease in immunocompromised hosts.

A longitudinal analysis of resting energy expenditure and body composition in people with spinal cord injury undergoing surgical repair of pressure injuries: a pilot study.

BACKGROUND: Data informing energy needs of people with spinal cord injury (SCI) and pressure injuries are scarce, the impact of surgical repair unknown, and the role of body composition in healing unexplored. The study aims were to investigate resting energy expenditure (REE) over the course of pressure injury surgical repair, compare with available energy prediction equations, and explore associations between body composition and wound healing. METHODS: Indirect calorimetry measured REE pre-surgery, post-surgery, at suture removal and hospital discharge. A clinically significant change was defined as +/-10% difference from pre-surgery. Eight SCI-specific energy prediction equations were compared to pre-surgery REE. Wound breakdown (Yes/No), weight, waist circumference (WC), and body composition (fat mass [FM], fat-free mass [FFM], bioimpedance spectroscopy) were measured. RESULTS: Twenty people underwent pressure injury surgical repair (95% male, mean age 56 ± 12 years, 70% paraplegia). Between pre-surgery and discharge, mean REE increased (+118 kcal/d, p = 0.005), but with <10% change at any timepoint. An energy prediction equation incorporating FFM showed greatest agreement (rc = 0.779, 95% CI: 0.437, 0.924). Those with wound breakdown (65%) had a higher weight (12.7 kg, 95% CI: -4.0, 29.3), WC (17.8 cm, 95% CI: -5.1, 40.7), and FM % (36.0% [IQR 31.8, 40.2] vs 26.0% [IQR 15.6, 41.3]) than those without wound breakdown, although statistical significance was not reached. CONCLUSION: The presence of pressure injuries and subsequent surgical repair did not impact REE and energy prediction equations incorporating FFM performed best. While not statistically significant, clinically important differences in body composition were observed in those with wound breakdown.

A Deep Learning Regression Model for Photonic Crystal Fiber Sensor With XAI Feature Selection and Analysis.

A Deep Learning Multi-output regression model is employed to correctly model the relationships between optical design parameters of an asymmetric Twin Elliptical Core Photonic Crystal Fiber (TEC-PCF) and its sensing performances. TEC-PCF acts as a biosensor to detect the blood glucose level taking hemoglobin components into account. Since asymmetric TEC-PCF uses a dual elliptical core, four super modes have to be evaluated to analyze the sensing performance in terms of effective index difference, transmission spectrum, coupling length, and sensor sensitivity. The dataset used in this work is of the optical design parameters of the sensor and Finite Element Method (FEM) results with effective indices of four super modes obtained from the COMSOL Multiphysics by varying hemoglobin concentration to 120 g/L, 140 g/L, and 160 g/L. Gretel.ai's free open-source synthetic data library is used to augment the dataset to make the training more efficient. Explainable AI (XAI) feature analysis using Shapley Additive Explanations (SHAP) framework is used for two purposes: feature selection and to know the feature's effect on prediction. The former led to the development of an optimal model with much fewer computational demands and the latter made the model interpretable. The proposed model can predict the accurate super modes when given input specifications with wavelength ranging from 1.27- [Formula: see text] and for various glucose concentrations under the influence of hemoglobin much faster compared to the other numerical simulations which are computationally expensive. Computation time taken by the proposed Artificial Neural Network (ANN) model, the proposed model with XAI and FEM is also being compared.

Lateralized Supraspinal Functional Connectivity Correlate with Pain and Motor Dysfunction in Rat Hemicontusion Cervical Spinal Cord Injury.

Afferent nociceptive activity in the reorganizing spinal cord after SCI influences supraspinal regions to establish pain. Clinical evidence of poor motor functional recovery in SCI patients with pain, led us to hypothesize that sensory-motor integration transforms into sensory-motor interference to manifest pain. This was tested by investigating supraspinal changes in a rat model of hemicontusion cervical SCI. Animals displayed ipsilateral forelimb motor dysfunction and pain, which persisted at 6 weeks after SCI. Using resting state fMRI at 8 weeks after SCI, RSFC across 14 ROIs involved in nociception, indicated lateral differences with a relatively weaker right-right connectivity (deafferented-contralateral) compared to left-left (unaffected-ipsilateral). However, the sensory (S1) and motor (M1/M2) networks showed greater RSFC using right hemisphere ROI seeds when compared to left. Voxel seeds from the somatosensory forelimb (S1FL) and M1/M2 representations reproduced the SCI-induced sensory and motor RSFC enhancements observed using the ROI seeds. Larger local connectivity occurred in the right sensory and motor networks amidst a decreasing overall local connectivity. This maladaptive reorganization of the right (deafferented) hemisphere localized the sensory component of pain emerging from the ipsilateral forepaw. A significant expansion of the sensory and motor network s overlap occurred globally after SCI when compared to sham, supporting the hypothesis that sensory and motor interference manifests pain. Voxel-seed based analysis revealed greater sensory and motor network overlap in the left hemisphere when compared to the right. This left predominance of the overlap suggested relatively larger pain processing in the unaffected hemisphere, when compared to the deafferented side.

MRI based composite parameter of multiple tissue types for improved patient-level hemispheric and regional level lateralization in pediatric epilepsy.

Although voxel-based morphometry (VBM) of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) changes aid in epileptic seizure lateralization, type of T1 pulse sequence, preprocessing steps and tissue segmentation methods lead to variation in tissue classification. Here, we test the prediction accuracy of individual MRI based tissue types and a novel composite ratio parameter [(GM + WM)/CSF], sensitive to parenchymal changes and independent of tissue classification variations. Pediatric patients with partial seizures (both simple and complex), but normal and lesion-free MRI were considered (33 patients; unilateral EEG; 17 female / 16 male; age mean ± SD = 11.5 ± 5 years). MRI based seizure lateralization was performed for each patient and verified with EEG findings alone or in combination with seizure semiology. T1 weighted MRI from patients and normal control subjects was spatially transformed to the Talairach atlas and automatically segmented into GM, WM and CSF tissue types. 41 age matched normal controls (11 female / 30 male; age mean ± SD = 14.6 ± 3 years) served as the null distribution to test tissue type deviations across each epilepsy patient. When verified with the patient EEG prediction, WM, GM and CSF had a hemispheric match of 76%, 70% and 55% respectively, while the composite ratio [(GM + WM)/CSF)] showed the highest accuracy of 85%. When EEG findings and seizure semiology were combined, MRI predictions using the composite ratio improved further to 88%. To further localize the epileptic focus, regional level (frontal, temporal, parietal and occipital) MRI predictions were obtained. The composite ratio performed at 88-91% accuracy, revealing regional MRI changes, not predictable with EEG. The results show inconsistent changes in GM and WM in majority of the pediatric epilepsy patients and demonstrate the applicability of the composite ratio [(GM + WM)/CSF)] as a superior predictor, independent of tissue classification variations. Clinical EEG findings combined with seizure semiology, can overcome scalp EEG's limitations and lean towards the MRI lateralization in specific cases.

Application of 3D Printing in Training Health Care Providers; the Development of Diverse Facial Overlays for Simulation-Based Medical Training.

The medical simulation manikins used by healthcare learners provide the training of numerous clinical skills but often lack diversity with respect to race, ethnicity, age, and sex. Having a diverse medical education environment is imperative for exposing learners to the diverse population of patients they may encounter when in practice. In this technical report, the development of diverse and cost-effective facial overlays produced using 3D scanning, 3D printing, and silicone to be used on top of the current medical manikins at Lakeridge Health Hospital (Oshawa, Ontario, Canada) is described. To obtain consistent feedback throughout the development process, an advisory committee was consulted monthly at Lakeridge Health Hospital. The process began by determining that two facial overlays would be developed based on the two groups that represent the highest percentage of visible minorities in the Durham Region (Ontario, Canada). Facial overlays representing the South Asian (31.8%) and Black (29.6%) races were chosen. To prevent the generalizability of the facial features of these two races, volunteers who identified as specific ethnicities (East Indian and Jamaican) within each race were selected. To add variation in age for the facial overlays, the East Indian facial overlay was edited to represent an adolescent teenager (15 to 17 years old) and the Jamaican overlay was edited to represent an elderly citizen (over 60 years old). The facial overlays were developed from the 3D scans of the two volunteers and were used to create the design of 3D printed molds, in which silicone was poured in. Pigments were added to the silicone to match the skin tones of the two volunteers, and these specific tones were used as the base color for each facial overlay. Details, such as wrinkles, eyebrows, and lip color, were painted on top of the base using additional pigmented silicone. Additionally, neck overlays were created to provide continuity of the skin tone of the facial overlay. To retain the functionality of the medical manikins, the eyes of the facial overlays were cut out, and the mouth was cut open to allow for intubation training. For stability purposes, Velcro attachments were added to the facial and neck overlays so that they could be secured onto the medical manikins. Overall, the costs to manufacture both facial overlays resulted in CAD 235.65, including local taxes. Once manufactured, both facial overlays were tested by medical students (n=18) during two separate advanced cardiovascular life support (ACLS) training sessions in the local, hospital-based simulation laboratory at Lakeridge Health Hospital. The feedback obtained suggested a need to improve the functionality of the facial overlays by making the mouths bigger and less stiff for easier intubation. However, the overlays were accepted overall as a means to add diversity to the current medical manikins. In the end, cost-effective and diverse facial overlays were created to be used on top of the medical manikins that are currently being used by healthcare learners at Lakeridge Health Hospital.

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms.

The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K's optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.

A Torn ACL Mapping in Knee MRI Images Using Deep Convolution Neural Network with Inception-v3.

The anterior cruciate ligaments (ACL) are the fundamental structures in preserving the common biomechanics of the knees and most frequently damaged knee ligaments. An ACL injury is a tear or sprain of the ACL, one of the fundamental ligaments in the knee. ACL damage most generally happens during sports, for example, soccer, ball, football, and downhill skiing, which include sudden stops or changes in direction, jumping, and landings. Magnetic resonance imaging (MRI) has a major role in the field of diagnosis these days. Specifically, it is effective for diagnosing the cruciate ligaments and any related meniscal tears. The primary objective of this research is to detect the ACL tear from MRI knee images, which can be useful to determine the knee abnormality. In this research, a Deep Convolution Neural Network (DCNN) based Inception-v3 deep transfer learning (DTL) model was proposed for classifying the ACL tear MRI images. Preprocessing, feature extraction, and classification are the main processes performed in this research. The dataset utilized in this work was collected from the MRNet database. A total of 1,370 knee MRI images are used for evaluation. 70% of data (959 images) are used for training and testing, and 30% of data (411 images) are used in this model for performance analysis. The proposed DCNN with the Inception-v3 DTL model is evaluated and compared with existing deep learning models like VGG16, VGG19, Xception, and Inception ResNet-v28. The performance metrics like accuracy, precision, recall, specificity, and F-measure are evaluated to estimate the performance analysis of the model. The model has obtained 99.04% training accuracy and 95.42% testing accuracy in performance analysis.