Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic.

Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.

Impact of easing COVID-19 safety measures on trauma computed tomography imaging volumes.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to substantial disruptions in healthcare staffing and operations. Stay-at-home (SAH) orders and limitations in social gathering implemented in spring 2020 were followed by initial decreases in healthcare and imaging utilization. This study aims to evaluate the impact of subsequent easing of SAH on trauma volumes, demand for, and turnaround times for trauma computed tomography (CT) exams, hypothesizing that after initial decreases, trauma volumes have increased as COVID safety measures have been reduced. METHODS: Patient characteristics, CT imaging volumes, and turnaround time were analyzed for all adult activated emergency department trauma patients requiring CT imaging at a single Level-I trauma center (1/2018-2/2022) located in the sixth most populous county in the USA. Based on COVID safety measures in place in the state of California, three time periods were compared: baseline (PRE, 1/1/2018-3/19/2020), COVID safety measures (COVID, 3/20/2020-1/25/2021), and POST (1/26/2021-2/28/2022). RESULTS: There were 16,984 trauma patients across the study (PRE = 8289, COVID = 3139, POST = 5556). The average daily trauma patient volumes increased significantly in the POST period compared to the PRE and COVID periods (13.9 vs. 10.3 vs. 10.1, p < 0.001), with increases in both blunt (p < 0.001) and penetrating (p = 0.002) trauma. The average daily number of trauma CT examinations performed increased significantly in the POST period compared to the PRE and COVID periods (56.7 vs. 48.3 vs. 47.6, p < 0.001), with significant increases in average turnaround time (47 min vs. 31 and 37, p < 0.001). CONCLUSION: After initial decreases in trauma radiology volumes following stay-at-home orders, subsequent easing of safety measures has coincided with increases in trauma imaging volumes above pre-pandemic levels and longer exam turnaround times.

Point-of-Care Brain MRI: Preliminary Results from a Single-Center Retrospective Study.

Background Point-of-care (POC) MRI is a bedside imaging technology with fewer than five units in clinical use in the United States and a paucity of scientific studies on clinical applications. Purpose To evaluate the clinical and operational impacts of deploying POC MRI in emergency department (ED) and intensive care unit (ICU) patient settings for bedside neuroimaging, including the turnaround time. Materials and Methods In this preliminary retrospective study, all patients in the ED and ICU at a single academic medical center who underwent noncontrast brain MRI from January 2021 to June 2021 were investigated to determine the number of patients who underwent bedside POC MRI. Turnaround time, examination limitations, relevant findings, and potential CT and fixed MRI findings were recorded for patients who underwent POC MRI. Descriptive statistics were used to describe clinical variables. The Mann-Whitney U test was used to compare the turnaround time between POC MRI and fixed MRI examinations. Results Of 638 noncontrast brain MRI examinations, 36 POC MRI examinations were performed in 35 patients (median age, 66 years [IQR, 57-77 years]; 21 women), with one patient undergoing two POC MRI examinations. Of the 36 POC MRI examinations, 13 (36%) occurred in the ED and 23 (64%) in the ICU. There were 12 of 36 (33%) POC MRI examinations interpreted as negative, 14 of 36 (39%) with clinically significant imaging findings, and 10 of 36 (28%) deemed nondiagnostic for reasons such as patient motion. Of 23 diagnostic POC MRI examinations with comparison CT available, three (13%) demonstrated acute infarctions not apparent on CT scans. Of seven diagnostic POC MRI examinations with subsequent fixed MRI examinations, two (29%) demonstrated missed versus interval subcentimeter infarctions, while the remaining demonstrated no change. The median turnaround time of POC MRI was 3.4 hours in the ED and 5.3 hours in the ICU. Conclusion Point-of-care (POC) MRI was performed rapidly in the emergency department and intensive care unit. A few POC MRI examinations demonstrated acute infarctions not apparent at standard-of-care CT examinations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Anzai and Moy in this issue.

Reliability of a three-dimensional spinal proprioception assessment for patients with adolescent idiopathic scoliosis.

PURPOSE: Although it is evident that some patients with adolescent idiopathic scoliosis (AIS) have proprioceptive deficit in peripheral joints, knowledge on the proprioceptive function of the deformed spine is limited. Nonetheless, spinal proprioception in AIS may be affected three-dimensionally, prior studies only focussed on evaluating peripheral proprioception in single plane. Therefore, this study aimed to develop a novel spinal proprioception assessment using three-dimensional motion analysis in patients with AIS. METHODS: Participants were included if they had a primary diagnosis of AIS who did not receive or failed conservative treatments. Three trunk repositioning tests involving flexion-extension, lateral-flexion, and axial-rotation were conducted. A three-dimensional kinematics of the trunk was used as the outcome measures. The proprioceptive acuity was quantified by the repositioning error. The intra-examiner and test-retest reliability were analysed by the intraclass correlation coefficient (ICC). RESULTS: Fifty-nine patients with AIS were recruited. Regarding the trunk flexion-extension test, the single measure ICC showed moderate reliability (0.46) and the average measures ICC demonstrated good reliability (0.72). As for the trunk lateral-flexion test, the reliability of single measure and average measures ICC was moderate (0.44) and good (0.70) reliability, respectively. For the trunk axial-rotation test, the single measure ICC indicated fair reliability (0.32), while the average measures ICC showed moderate reliability (0.59). CONCLUSION: This is the first study to evaluate the reliability of novel three-dimensional spinal proprioception assessments in patients with AIS. The trunk flexion-extension repositioning test may be preferable clinical test given its highest reliability.

Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers.

OBJECTIVES: To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI. METHODS: A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1). RESULTS: In the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%. CONCLUSIONS: The recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy. KEY POINTS: • Deep learning can be applied to differentiate breast cancer molecular subtypes. • The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. • For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy.

A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.

OBJECTIVE: Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI. MATERIALS AND METHODS: This retrospective study included patients who underwent prostate MRI and ultrasound-MRI fusion transrectal biopsy between September 2014 and December 2016. Axial T2-weighted images were manually segmented by two abdominal radiologists, which served as ground truth. These manually segmented images were used for training on a customized hybrid 3D-2D U-Net CNN architecture in a fivefold cross-validation paradigm for neural network training and validation. The Dice score, a measure of overlap between manually segmented and automatically derived segmentations, and Pearson linear correlation coefficient of prostate volume were used for statistical evaluation. RESULTS: The CNN was trained on 299 MRI examinations (total number of MR images = 7774) of 287 patients. The customized hybrid 3D-2D U-Net had a mean Dice score of 0.898 (range, 0.890-0.908) and a Pearson correlation coefficient for prostate volume of 0.974. CONCLUSION: A deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.

Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor.

Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R2 values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor.

Neuroimaging Considerations in Patients with Chronic Kidney Disease.

Chronic Kidney Disease is a common medical condition that frequently overlaps with neurologic disease. Neuroimaging can be a useful tool to aid in the diagnoses of neurologic illness, including those that result from renal impairment. Some neuroimaging studies also have the potential to lead to adverse effects on the kidneys necessitating a thoughtful approach to selection of imaging modalities. In particular, multimodal imaging is becoming increasingly common in patients presenting with symptoms of acute stroke, a population that may be at higher risk for renal complications. This article will summarize the neuroimaging manifestations of conditions with shared renal and neurologic involvement and highlight considerations regarding the use of contrast media, nephrogenic systemic fibrosis, and metformin-associated lactic acidosis.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. PURPOSE: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration. STUDY TYPE: Retrospective. POPULATION: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). FIELD STRENGTH/SEQUENCE: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. ASSESSMENT: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. STATISTICAL TESTS: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. RESULTS: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. DATA CONCLUSION: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2.

Comparison of Predictions Between an EMG-Assisted Approach and Two Optimization-Driven Approaches for Lumbar Spine Loading During Walking With Backpack Loads.

OBJECTIVE: The efficacy of two optimization-driven biomechanical modeling approaches has been compared with an electromyography-assisted optimization (EMGAO) approach to predict lumbar spine loading while walking with backpack loads. BACKGROUND: The EMGAO approach adopts more variables in the optimization process and is complex in data collection and processing, whereas optimization-driven approaches are simple and include the fewest possible variables. However, few studies have been conducted on the efficacy of using the optimization-driven approach to predict lumbar spine loading while walking with backpack loads. METHOD: Anthropometric information of 10 healthy male adults as well as their kinematic, kinetic, and electromyographic data acquired while they walked with various backpack loads (no-load, 5%, 10%, 15%, and 20% of body weight) served as inputs into the model for predicting lumbosacral joint compression forces. The efficacy of two optimization-driven models, namely double linear optimization with constraints on muscle intensity and single linear optimization without any constraints, was investigated by comparing the resulting force profile with that provided by a current EMGAO approach. RESULTS: The double and single linear optimization approaches predicted mean deviations in peak force of -5.1%, and -19.2% as well as root-mean-square differences in force profile of 16.2%, and 25.4%, respectively. CONCLUSION: The double linear optimization approach was a relatively comparable estimator to the EMGAO approach in terms of its consistency, slight bias, and efficiency for predicting peak lumbosacral joint compression forces. APPLICATION: The double linear optimization approach is a useful biomechanical model for estimating peak lumbar compression forces while walking with backpack loads.

A Video-Based Classification System for Assessing Locomotor Skills in Children.

The Test of Gross Motor Development 2 (TGMD-2) is currently the standard approach for assessing fundamental movement skills (FMS), including locomotor and object control skills. However, its extensive application is restricted by its low efficiency and requirement of expert training for large-scale evaluations. This study evaluated the accuracy of a newly-developed video-based classification system (VCS) with a marker-less sensor to assess children's locomotor skills. A total of 203 typically-developing children aged three to eight years executed six locomotor skills, following the TGMD-2 guidelines. A Kinect v2 sensor was used to capture their activities, and videos were recorded for further evaluation by a trained rater. A series of computational-kinematic-based algorithms was developed for instant performance rating. The VCS exhibited moderate-to-very good levels of agreement with the rater, ranging from 66.1% to 87.5%, for each skill, and 72.4% for descriptive ratings. Paired t-test revealed that there were no significant differences, but significant positive correlation, between the standard scores determined by the two approaches. Tukey mean difference plot suggested there was no bias, with a mean difference (SD) of -0.16 (1.8) and respective 95% confidence interval of 3.5. The kappa agreement for the descriptive ratings between the two approaches was found to be moderate (k = 0.54, p < 0.01). Overall, the results suggest the VCS could potentially be an alternative to the conventional TGMD-2 assessment approach for assessing children's locomotor skills without the necessity of the presence of an experienced rater for the administration.

Acute Effect of Brief Mindfulness-Based Intervention Coupled with Fluid Intake on Athletes' Cognitive Function.

During the half time of intermittent team sports, substantial physiological changes relating to acid-base balance and glycemic response affect the second-half performance. Refuel and rehydrate strategy is therefore necessary to be investigated. This field experiment assessed the acute effect of a brief mindfulness-based intervention (MBI) coupled with fluid intake on players' cognitive function in a simulated soccer game. In a single-blinded, randomized, cross-over experiment, 14 male players received three treatments [Control: noncarbohydrate solution + traveling introduction audio; CHO: Carbohydrate (CHO)-electrolyte solution + traveling introduction audio; and CHO-M: CHO-electrolyte solution + MBI] during a simulated half-time break of a soccer game. Participants' mindfulness level, blood glucose and lactate, rating of perceived exertion, and cognitive function performance assessed by the Stroop effect task (ST), Corsi block-tapping test (CBT), and rapid visual information processing task (RVIPT) were immediately measured before, during, and after the trial. Repeated measure ANOVA was used for statistical analysis. The results revealed that: (1) in ST, the CHO_M group performed better than the Control group and marginally better than the CHO group; (2) in CBT, both the Control group and CHO-M group responded faster in the posttest than in the pretest; however, the performance of the CHO group remained the same; (3) the CHO group spent less time on missing numbers in post RVIPT compared with the other two groups. In conclusion, findings of this study provided a preliminary evidence of the positive effect of MBI coupled with CHO intake on athletes' cognitive function, with both positive and negative effect of CHO ingestion.

Differences in lower-limb coordination and coordination variability between novice and experienced runners during a prolonged treadmill run at anaerobic threshold speed.

This study investigated differences in lower-limb coordination and coordination variability between experienced and novice runners during a prolonged run. Thirty-four participants were categorised as either experienced (n = 17) or novice runners (n = 17). All participants performed a 31-min treadmill run at their individual anaerobic threshold speed, and lower-limb kinematic data were acquired in the sagittal plane at the beginning, middle, and end of the run. Lower-limb coordination and variability during the stance phase were quantified using a vector coding technique for hip-knee, knee-ankle, pelvis-thigh, thigh-shank, and shank-foot couplings. Repeated-measure analysis of covariance revealed that running experience and time had significant interactions on the coordination patterns for hip-knee and pelvis-thigh couplings. During the midstance, experienced runners exhibited a higher percentage of in-phase motion for pelvis-thigh and knee-ankle couplings while novice runners displayed a higher percentage of distal motion for pelvis-thigh coupling and anti-phase motion for hip-knee coupling. Experienced runners displayed more variability in hip-knee and shank-foot couplings, and novice runners had more variability in hip, knee, and thigh motion. Experienced and novice runners adapted to progressive fatigue through different lower-limb coordination patterns. Throughout the prolonged run, experienced runners demonstrated greater coordination variability and novice runners displayed greater joint and segment variability.

Effects of backpack and double pack loads on postural stability.

Measurement of postural stability is crucial for identifying predictors of performance, determining the efficacy of physical training and rehabilitation techniques and evaluating and preventing injuries, particularly for heavy load carriage in hikers, mountain search and rescue personnel and soldiers. This study investigated the effect of load distribution on postural stability in an upright stance using backpack and double pack loads under conflicting or impaired somatosensory, visual and vestibular conditions. The sensory organisation tests were conducted on 20 young adults before and after a 10-min level walking exercise. Young adults' ability to use inputs from somatosensory and visual systems to maintain postural stability was significantly reduced following a 10-min walking exercise with a heavy backpack (30% of body weight), whereas no significant changes were observed for double pack carriage. Thus, the distribution of heavy loads to the front and back provides superior balance control compared with back-only loading. Practitioner summary: This study investigated the effects of heavy (30% of body weight) load distribution on postural stability after a 10-min walking exercise. Backpack carriage significantly reduced postural stability, whereas there was no significant effect under double pack loads. Distribution of heavy loads on the front-and-back is desirable for superior balance control.

Muscle activity during low-speed rear impact.

PURPOSE: Whiplash associated disorders remain a major health problem in terms of impact on health care and on societal costs. Aetiology remains controversial including the old supposition that the cervical muscles do not play a significant role. This study examined the muscle activity from relevant muscles during rear-end impacts in an effort to gauge their influence on the aetiology of whiplash associated disorders. METHODS: Volunteers were subjected to a sub-injury level of rear impact. Surface electromyography (EMG) was used to record cervical muscle activity before, during and after impact. Muscle response time and EMG signal amplitude were analysed. Head, pelvis, and T1 acceleration data were recorded. RESULTS: The activities of the cervical muscles were found to be significant. The sternocleidomastoideus, trapezius and erector spinae were activated on average 59 ms, 73 ms and 84 ms after the impact stimulus, respectively, prior to peak head acceleration (113 ms). CONCLUSION: The cervical muscles reacted prior to peak head acceleration, thus in time to influence whiplash biomechanics and possibly injury mechanisms. It is recommended therefore, that muscular influences be incorporated into the development of the new rear-impact crash test dummy in order to make the dummy as biofidelic as possible.

Extent of BOLD Vascular Dysregulation Is Greater in Diffuse Gliomas without Isocitrate Dehydrogenase 1 R132H Mutation.

Purpose To determine the effect that R132H mutation status of diffuse glioma has on extent of vascular dysregulation and extent of residual blood oxygen level-dependent (BOLD) abnormality after surgical resection. Materials and Methods This study was an institutional review board-approved retrospective analysis of an institutional database of patients, and informed consent was waived. From 2010 to 2017, 39 treatment-naïve patients with diffuse glioma underwent preoperative echo-planar imaging and BOLD functional magnetic resonance imaging. BOLD vascular dysregulation maps were made by identifying voxels with time series similar to tumor and dissimilar to healthy brain. The spatial overlap between tumor and vascular dysregulation was characterized by using the Dice coefficient, and areas of BOLD abnormality outside the tumor margins were quantified as BOLD-only fraction (BOF). Linear regression was used to assess effects of R132H status on the Dice coefficient, BOF, and residual BOLD abnormality after surgical resection. Results When compared with R132H wild-type (R132H-) gliomas, R132H-mutated (R132H+) gliomas showed greater spatial overlap between BOLD abnormality and tumor (mean Dice coefficient, 0.659 ± 0.02 [standard error] for R132H+ and 0.327 ± 0.04 for R132H-; P < .001), less BOLD abnormality beyond the tumor margin (mean BOF, 0.255 ± 0.03 for R132H+ and 0.728 ± 0.04 for R132H-; P < .001), and less postoperative BOLD abnormality (residual fraction, 0.046 ± 0.0047 for R132H+ and 0.397 ± 0.045 for R132H-; P < .001). Receiver operating characteristic curve analysis showed high sensitivity and specificity in the discrimination of R132H+ tumors from R132H- tumors with calculation of both Dice coefficient and BOF (area under the receiver operating characteristic curve, 0.967 and 0.977, respectively). Conclusion R132H mutation status is an important variable affecting the extent of tumor-associated vascular dysregulation and the residual vascular dysregulation after surgical resection. © RSNA, 2018 Online supplemental material is available for this article.

Imaging Genetic Heterogeneity in Glioblastoma and Other Glial Tumors: Review of Current Methods and Future Directions.

OBJECTIVE: The purpose of this review is to summarize advances in the molecular analysis of gliomas, the role genetics plays in MRI features, and how machine-learning approaches can be used to survey the tumoral environment. CONCLUSION: The genetic profile of gliomas influences the course of treatment and clinical outcomes. Though biopsy is the reference standard for determining tumor genetics, it can suffer diagnostic delays due to surgical planning and pathologic assessment. Radiogenomics may allow rapid, low-risk characterization of genetic heterogeneity.

Effects of kneeling posture on chest compression during cardiopulmonary resuscitation in males.

AIMS AND OBJECTIVES: To study the effects of kneeling posture on chest compression during cardiopulmonary resuscitation (CPR) in males. BACKGROUND: Efficiency of chest compression during CPR affected millions of victims over the world. There are still no clear guidelines on kneeling posture that a rescuer should adopt in performing CPR. DESIGN: A self-controlled repeated-measures design was applied in this study. The efficiency of chest compression on a mannequin when three kneeling postures were adopted (farthest, self-adjusted and nearest) was analysed. METHODS: Eighteen participants with qualified first-aid certificate were recruited. Each participant had to perform three sessions of CPR, using one of the three different kneeling postures (i.e., farthest, self-adjusted and nearest) in each. They were performed in a random order chosen by drawing lots. Each session consisted of five cycles of CPR in each kneeling posture. Each cycle consisted of 30 strokes of chest compression performed within 18 s with a 4-s pause between consecutive cycles. Each session lasted for 2 min. The participants were allowed to rest for 10 min on a chair between sessions. Efficiency of chest compression was quantified by compression force, joint angle, heart rate and energy expenditure. After each session of CPR, the participants were surveyed about their rate of perceived exertion. RESULTS: Efficiency of chest compression in self-adjusted and nearest kneeling postures was significantly better than that of the farthest one. While the self-adjusted and nearest postures had the similar effect, most of the participants preferred self-adjusted kneeling posture because of lower rate of perceived exertion. CONCLUSION: The use of the self-adjusted and nearest kneeling postures during CPR in males resulted in more effective chest compression with lower perceived exertion, compared with the farthest kneeling posture. Both these positions can be objectively recommended to enhance the efficiency of chest compression and thereby increase the cardiac arrest survival rate. RELEVANCE TO CLINICAL PRACTICE: More consistent force and higher endurance could be achieved by performing CPR at self-adjusted kneeling posture.

Gender Differences in Energy Expenditure During Walking With Backpack and Double-Pack Loads.

OBJECTIVE: To investigate gender differences in energy expenditure during walking with backpack and double-pack loads. BACKGROUND: Studies have reported that energy expenditure during walking with double-pack loads is lower compared with backpack carriage. However, the effect of gender on energy expenditure while walking with these two load distribution systems has not been investigated. METHOD: Thirty healthy young adults (15 female and 15 male participants) walked on a treadmill with backpack and double-pack loads weighing 30% of their body weight at a speed of 0.89 m/s for 10 min. The energy expenditure in terms of oxygen consumption (VO2) and respiratory exchange ratio (RER) were continuously monitored using a portable gas analyzer throughout each walking exercise. A mixed-design analysis of variance model was adopted to test the effects of gender, pack, and time on VO2 and RER. RESULTS: No time effect was observed on VO2. However, significant gender, pack, and interaction effects were observed. The lowest VO2 was found in female participants under double-pack carriage. No significant gender or pack differences existed in RER. However, RER significantly and incrementally increased in time from the 4th through 6th, 8th, and 10th min. CONCLUSION: This study revealed that heavy double-pack load carriage for healthy young female participants had significantly lower energy expenditure (normalized by the entire system weight, i.e., the participant's weight plus the weight of the pack) than that of the male participants in a 10-min walking exercise. APPLICATION: The findings of this study indicated that healthy young female participants carried a heavy double-pack with less energy cost (normalized by the entire system weight, i.e., the participant's weight plus the weight of the pack) compared with their male counterparts during a 10-min walking exercise.