From basic sciences and engineering to epileptology: A translational approach.

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.

Vector Symbolic Architectures as a Computing Framework for Emerging Hardware.

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.

The effect of different hip rotation angles on electromyography activity of the quadriceps muscle during closed kinetic chain tasks in healthy females.

[Purpose] The purpose of this study was to investigate electromyographic activity of the quadriceps muscle in different positions of hip rotation (while standing on the toes) in healthy females. [Participants and Methods] The study was conducted on 35 healthy females. Electromyographic activity of the rectus femoris, vastus medialis oblique, and vastus lateralis muscles were recorded with the hip in a neutral position as well as different angles of internal and external rotation (15, 30, and 45 degrees (°)) while the participants were standing on their toes. [Results] There was a significant difference of 15° in external rotation, a neutral position, 15° of internal rotation, and at the end of an internal rotation position. Paired comparison between muscle activity using Bonferroni showed that vastus lateralis activity in 15° of external rotation, a neutral position, 15° of internal rotation, and at the end of a position of internal rotation was more than that of rectus femoris muscle. [Conclusion] The present study showed that average vastus lateralis activity was the highest in all positions. However, the positions studied in the current study did not lead to an increase in muscle activity of the vastus medialis oblique as a medial dynamic stabilizer compared to vastus lateralis.

The Reliability of Standing Sagittal Measurements of Spinal Curvature and Range of Motion in Older Women With and Without Hyperkyphosis Using a Skin-Surface Device.

OBJECTIVE: The purpose of this study was to investigate the intrarater reliability of a skin-surface instrument (Spinal Mouse, Idiag, Voletswil, Switzerland) in measuring standing sagittal curvature and global mobility of the spine in older women with and without hyperkyphosis. METHODS: Measurements were made in 19 women with hyperkyphosis (thoracic kyphosis angle ≥50°), mean age 67 ± 5 years, and 14 women without hyperkyphosis (thoracic kyphosis angle <50°), mean age 63 ± 6 years. Sagittal thoracic and lumbar curvature and mobility of the spine were assessed with the Spinal Mouse during neutral standing, full spinal flexion, and full spinal extension. Tests were performed by the same examiner on 2 days with a 72-hour interval. The intrarater reliability of the measurements was analyzed using the intraclass correlation coefficient, standard error of measurement and minimal detectable change. RESULTS: Intraclass correlation coefficients ranged from 0.89 to 0.99 in both groups. The standard errors of measurement ranged from 1.02° to 2.06° in the hyperkyphosis group and from 1.15° to 2.22° in the normal group. The minimal detectable change ranged from 2.85° to 5.73° in the hyperkyphosis group and from 3.20° to 6.17° in the normal group. CONCLUSIONS: Our results indicated that the Spinal Mouse has excellent intrarater reliability for the measurement of sagittal thoracic and lumbar curvature and mobility of the spine in older women.

Perception of weight and health status among women working at health centres of Tehran.

Perception of body-weight status is an important determinant of weight-related behaviours and may affect the burden of weight disturbances as a public-health problem. No study has assessed self-perception of the weight status regarding body-fat distribution among health workers to date. The aim of this study was to evaluate the association of the perception of weight and health status among 542 women working at health centres of Tehran. We assessed their perceived body-weight and health status and measured waist- and hip-circumference, weight, and height to calculate waist-to-hip ratio (WHR) as a measure of fat distribution and body mass index (BMI, kg/m2). Women reported their sociodemographic information, and the perceived weight and health status were compared with their actual fatness status, defined based on WHR and BMI, to determine misperception of weight status. Multivariate logistic regression models were performed to assess the predictive effects of various sociodemographic factors and actual fatness on the perception of weight and health status. The results showed that more than 40% of women with normal BMI overestimated their body-weight status while only 15.8% of these women had central obesity. BMI was the most important variable associated with misperceived weight status as normal-weight women had significantly more misperception (OR 8.16, 95% CI 4.82-13.82) than overweight/obese women. WHR did not show any significant relationships with perceived weight status. In addition, perception of health status was not associated with actual fatness indices. It is concluded, BMI was the main predictor of the perception of weight status in female employees. The importance of using body-fat distribution in the perceptions of weight and health status should be emphasized.

Calcium and vitamin D plasma concentration and nutritional intake status in patients with chronic spinal cord injury: A referral center report.

BACKGROUND: Nutritional status influences bone health spinal cord injury (SCI). This study evaluates serum levels of 25-hydroxy-vitamin-D and calcium along with dietary intakes in patients with chronic SCI. MATERIALS AND METHODS: Total of 160 patients participated in this investigation. Dietary intakes were assessed by semi-quantitative food-frequency questionnaire. Serum calcium, phosphorus and 25(OH)-vitamin-D level were measured. RESULTS: Mean of serum calcium and 25(OH)-vitamin-D were 9.54 ± 0.64 mg/dl (standard error of the mean [SE]: 0.05) and 13.6 ± 10.99 µg/dl (SE: 0.9), respectively. Dairy intake was below recommended amount (1.8 ± 0.74 per serving (SE: 0.06), recommended: 4). A high prevalence (53.1%) of Vitamin D deficiency (25(OH) Vitamin D <13 ng/ml) was found. CONCLUSION: This study shows below adequate intake of calcium and Vitamin D in Iranian patients with SCI. These results insist on the importance of dietary modifications among these patients.

Sensitivity and specificity of body mass index in determining obesity in children.

BACKGROUND: The purpose of this study is to determine sensitivity and specificity of body mass index (BMI) based on Center for Disease Control 2000 (CDC) percentiles compared to fat mass index (FMI) as an indicator of being really obese in children. Obesity was compared based on these two indexes among children under study. MATERIALS AND METHODS: This cross-sectional study was conducted on 410 primary school girls aged 8-10 years, in the city of Esfahan. Weight and height were measured and BMI was calculated based on weight divided by height squared. Fat mass was measured by body composition analyzer (BCA) and FMI was calculated by dividing fat mass by height squared. FMI at or above the 90(th) percentile and FMI less than 90(th) percentile of reference data were considered as criterion for defining real obesity and normal adiposity, respectively. Receiver operating characteristic (ROC) curve was used to assess the performance of BMI in detecting obesity on the basis of FMI. Furthermore, the rate of agreement between two indices was calculated using Kappa coefficient P number. RESULTS: Mean and standard deviation of FMI and BMI in all children were 6 ± 2.1 (kg/m(2)) and 19.4 ± 3 (kg/m(2)), respectively. The area under the ROC curve for obesity was 0.75. The cutoff point, sensitivity, and specificity of BMI to classify children as obese compared to FMI were 21.2 kg/m(2), 79%, and 73%, respectively. In this cutoff point for BMI (21.2 kg/m(2)), the agreement rate between BMI and FMI for determining obesity status was 0.5 (P < 0.001). CONCLUSION: Our results indicated 79% of children who were recognized as obese based on FMI, were also classified as obese according to BMI. Twenty-seven percent of children, who were non-obese, were identified as obese based on BMI. It appears that FMI compared to BMI is more accurate in determining obesity, but further studies are required.

Evaluating the effective pressure applied by a valgus knee orthosis in individuals with medial knee osteoarthritis based on the dose-response relationship.

BACKGROUND: There is evidence that valgus knee orthosis improves clinical and biomechanical outcomes in individuals with medial knee osteoarthritis (MKOA). It is unclear whether variations in pressure application by orthosis straps can affect the biomechanical outcomes. This study aimed to determine the dose-response relationship between different orthosis straps tensions and changes in knee adduction moment (KAM) parameters in individuals with MKOA. METHOD: Twenty-four individuals with symptomatic MKOA were enrolled in this quasi-experimental study. Five tension conditions in orthosis straps were tested in 20-mmHg increments, from 0 (no pressure) to 100 (maximal pressure) mmHg. Patients were asked to adjust the orthosis strap tension based on their perceived comfort. After each condition, a 3D gait analysis was performed, and KAM parameters were calculated. The participants also reported their satisfaction with knee orthosis adjustment for each pressure condition. RESULTS: With successive increases in strap tension from 40 to 80 mmHg, the first peak, second peak, and angular impulse of KAM decreased nonlinearly (from 6 % to 25 %). Increasing the orthosis strap tension to 100 mmHg significantly decreased (P < 0.05) the participants' satisfaction level. The effective dosages (IC50) of pressure for the first peak, second peak, and angular impulse of KAM as responses were 58, 65, and 69 mmHg, respectively. CONCLUSION: The KAM decline was not linear as the strap pressure increased. Patients were dissatisfied with orthosis adjustment when strap tension was above 80 mmHg. The optimum dosage of pressure on the knee joint's lateral side for adjusting an orthosis' strap tension is approximately 69 mmHg.

Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks.

Memory-augmented neural networks enhance a neural network with an external key-value (KV) memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized KV memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or remove redundancy to the key memory representation. In effect, it provides an additional degree of freedom to flexibly control the tradeoff between robustness and the resources required to store and compute the generalized KV memory. This is particularly useful for realizing the key memory on in-memory computing hardware where it exploits nonideal, but extremely efficient nonvolatile memory devices for dense storage and computation. Experimental results show that adapting this parameter on demand effectively mitigates up to 44% nonidealities, at equal accuracy and number of devices, without any need for neural network retraining.

In-memory factorization of holographic perceptual representations.

Disentangling the attributes of a sensory signal is central to sensory perception and cognition and hence is a critical task for future artificial intelligence systems. Here we present a compute engine capable of efficiently factorizing high-dimensional holographic representations of combinations of such attributes, by exploiting the computation-in-superposition capability of brain-inspired hyperdimensional computing, and the intrinsic stochasticity associated with analogue in-memory computing based on nanoscale memristive devices. Such an iterative in-memory factorizer is shown to solve at least five orders of magnitude larger problems that cannot be solved otherwise, as well as substantially lowering the computational time and space complexity. We present a large-scale experimental demonstration of the factorizer by employing two in-memory compute chips based on phase-change memristive devices. The dominant matrix-vector multiplication operations take a constant time, irrespective of the size of the matrix, thus reducing the computational time complexity to merely the number of iterations. Moreover, we experimentally demonstrate the ability to reliably and efficiently factorize visual perceptual representations.

A Primer on Hyperdimensional Computing for iEEG Seizure Detection.

A central challenge in today's care of epilepsy patients is that the disease dynamics are severely under-sampled in the currently typical setting with appointment-based clinical and electroencephalographic examinations. Implantable devices to monitor electrical brain signals and to detect epileptic seizures may significantly improve this situation and may inform personalized treatment on an unprecedented scale. These implantable devices should be optimized for energy efficiency and compact design. Energy efficiency will ease their maintenance by reducing the time of recharging, or by increasing the lifetime of their batteries. Biological nervous systems use an extremely small amount of energy for information processing. In recent years, a number of methods, often collectively referred to as brain-inspired computing, have also been developed to improve computation in non-biological hardware. Here, we give an overview of one of these methods, which has in particular been inspired by the very size of brains' circuits and termed hyperdimensional computing. Using a tutorial style, we set out to explain the key concepts of hyperdimensional computing including very high-dimensional binary vectors, the operations used to combine and manipulate these vectors, and the crucial characteristics of the mathematical space they inhabit. We then demonstrate step-by-step how hyperdimensional computing can be used to detect epileptic seizures from intracranial electroencephalogram (EEG) recordings with high energy efficiency, high specificity, and high sensitivity. We conclude by describing potential future clinical applications of hyperdimensional computing for the analysis of EEG and non-EEG digital biomarkers.

The efficacy of intramuscular electrical stimulation in the management of patients with myofascial pain syndrome: a systematic review.

INTRODUCTION: Myofascial pain syndrome (MPS) is one of the most common disorders causing chronic muscle pain. Almost one-third of patients with musculoskeletal complaints meet the MPS criteria. The aim of this study is to evaluate the effectiveness of intramuscular electrical stimulation (IMES) in patients with MPS through a systematic review method. METHODS: PubMed, Scopus, Embase, ProQuest, PEDro, Web of Science, and CINAHL were systematically searched to find out the eligible articles without language limitations from 1990 to December 30, 2020. All relevant randomized controlled trials that compared the effectiveness of IMES with sham-IMES, dry needling, or exercise therapy in patients with MPS were included. Full texts of the selected studies were critically appraised using Revised Cochrane risk-of-bias tool for randomized trials (RoB2). RESULTS: Six studies (out of 397) had met our inclusion criteria (involving 158 patients) and were entered to the systematic review. Outcome measures examined in these studies included pain, range of motion, pressure pain threshold, biochemical factors, disability, and amount of analgesic use. In the most studies, it has been shown that IMES is more effective than the control group in improving some outcome measurements such as pain. CONCLUSION: There is preliminary evidence from a few small trials suggesting the efficacy of IMES for the care of myofascial pain syndrome. The data support the conduct of larger trials investigating the efficacy of IMES.

Near-channel classifier: symbiotic communication and classification in high-dimensional space.

Brain-inspired high-dimensional (HD) computing represents and manipulates data using very long, random vectors with dimensionality in the thousands. This representation provides great robustness for various classification tasks where classifiers operate at low signal-to-noise ratio (SNR) conditions. Similarly, hyperdimensional modulation (HDM) leverages the robustness of complex-valued HD representations to reliably transmit information over a wireless channel, achieving a similar SNR gain compared to state-of-the-art codes. Here, we first propose methods to improve HDM in two ways: (1) reducing the complexity of encoding and decoding operations by generating, manipulating, and transmitting bipolar or integer vectors instead of complex vectors; (2) increasing the SNR gain by 0.2 dB using a new soft-feedback decoder; it can also increase the additive superposition capacity of HD vectors up to 1.7[Formula: see text] in noise-free cases. Secondly, we propose to combine encoding/decoding aspects of communication with classification into a single framework by relying on multifaceted HD representations. This leads to a near-channel classification (NCC) approach that avoids transformations between different representations and the overhead of multiple layers of encoding/decoding, hence reducing latency and complexity of a wireless smart distributed system while providing robustness against noise and interference from other nodes. We provide a use-case for wearable hand gesture recognition with 5 classes from 64 EMG sensors, where the encoded vectors are transmitted to a remote node for either performing NCC, or reconstruction of the encoded data. In NCC mode, the original classification accuracy of 94% is maintained, even in the channel at SNR of 0 dB, by transmitting 10,000-bit vectors. We remove the redundancy by reducing the vector dimensionality to 2048-bit that still exhibits a graceful degradation: less than 6% accuracy loss is occurred in the channel at - 5 dB, and with the interference from 6 nodes that simultaneously transmit their encoded vectors. In the reconstruction mode, it improves the mean-squared error by up to 20 dB, compared to standard decoding, when transmitting 2048-dimensional vectors.

An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection.

We propose a new algorithm for detecting epileptic seizures. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states are constructed. These vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes. This flexibility allows our algorithm to identify the electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using k-fold cross-validation and all electrodes, our algorithm surpasses state-of-the-art algorithms yielding significantly shorter latency (8.81 s vs. 11.57 s) in seizure onset detection, and higher specificity (97.31% vs. 94.84%) and accuracy (96.85% vs. 95.42%). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2% specificity loss). Using only the top 10% of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller.

Robust high-dimensional memory-augmented neural networks.

Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networks with an explicit memory to overcome these issues. Access to this explicit memory, however, occurs via soft read and write operations involving every individual memory entry, resulting in a bottleneck when implemented using the conventional von Neumann computer architecture. To overcome this bottleneck, we propose a robust architecture that employs a computational memory unit as the explicit memory performing analog in-memory computation on high-dimensional (HD) vectors, while closely matching 32-bit software-equivalent accuracy. This is achieved by a content-based attention mechanism that represents unrelated items in the computational memory with uncorrelated HD vectors, whose real-valued components can be readily approximated by binary, or bipolar components. Experimental results demonstrate the efficacy of our approach on few-shot image classification tasks on the Omniglot dataset using more than 256,000 phase-change memory devices. Our approach effectively merges the richness of deep neural network representations with HD computing that paves the way for robust vector-symbolic manipulations applicable in reasoning, fusion, and compression.

Thoracic and Lumbar Sagittal Spinal Curvature Adaptations between Elite Iranian Road and Speed Cyclists.

BACKGROUND: Despite existing some research on the effects of professional cycling on cyclist's spinal curvature angles, no research is available concentrating on possible differences in various types of professional cycling. Road (outdoor) and speed (indoor) cyclists have different anthropometric and predominant postures during cycling. The current study aims to investigate if cyclists with different types of cycling may have dissimilar spinal curvature adaptations. OBJECTIVE: Forty-eight male subjects, including 16 elite road cyclists, 16 elite sprint cyclists and 16 non-athletes as the control group were recruited in this non-experimental study. MATERIAL AND METHODS: In this cross-sectional study, a spinal mouse was used to measure the thoracic and lumbar curvature angles in standing position. RESULTS: The mean values of thoracic kyphosis and lumbar lordosis angles were found as follows 48.3±7.2º & -20.3±7.2º for elite road cyclists; 46.6±8.1º & -22.5±7.7º for elite sprint cyclists; and 37.5±4.1º & -19±6.3º for the control group. The results confirmed that both the road and sprint cyclists showed significantly more hyper-kyphosis posture in their thoracic region when compared to the control group (p<0.05). CONCLUSION: All road cyclists showed no significantly higher degrees of thoracic angle relative to the sprint cyclists (p>0.05). However, all the cyclists and the control groups showed a normal range of lumbar lordosis angle. As a conclusion, the results of this study confirmed that an elite cyclist may predispose these subjects to the risk of hyper-kyphotic posture. The road cycling may result in more hyper-kyphosis due to the longer time, spent in flexed position.

A Pediatric Parathyroid Carcinoma: An Unusual Clinical Presentation and Mini-review.

INTRODUCTION: Primary hyperparathyroidism (PHPT) is a rare condition in the pediatric population. Parathyroid carcinoma (PC) is a very uncommon cause of PHPT, accounting for < 1% of pediatric PHPT cases. It is challenging to distinguish between parathyroid adenoma (PA), the most common cause of PHPT, and PC. In this report, we described a young female who presented with a history of progressive limping and was finally diagnosed with PC. CASE PRESENTATION: A 15-year-old girl presented with progressive limping and bone pain for 8 years. She was referred by an orthopedic surgeon because of elevated intact parathyroid hormone (iPTH) for further evaluation. Physical examination revealed a large, firm, and non-tender neck mass, left hip tenderness, and limited range of motion. The initial biochemistry tests showed a borderline high calcium level of 10.8 mg/dl, an elevated iPTH level of 2876 pg/mL, and a decreased phosphorus level of 2.4 mg/dL. The 99mTechnetium (Tc) sestamibi scan displayed early intense activity in the right thyroid lobe persisting in the three-hour repeat scan, compatible with a parathyroid lesion. The patient underwent right-sided neck exploration and parathyroidectomy. Intraoperative and pathology findings confirmed the diagnosis of PC. Immunohistochemistry (IHC) staining revealed creatine kinase (CK) and CD31 in endothelial cells of the tumor. Ki67 staining was also positive in 2% - 3% of tumor cells. The whole exome sequencing (WES) study was negative for cell division cycle 73 (CDC73) and multiple endocrine neoplasia 1 (MEN1) genes. CONCLUSIONS: PC should be considered as a differential diagnosis of PHPT in the pediatric population, even in the presence of mild hypercalcemia.

Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings.

OBJECTIVE: We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). METHODS: Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. RESULTS: We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36-100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9 s versus 14.7 s). Moreover, the algorithm can reliably identify (with a p-value ) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. CONCLUSION AND SIGNIFICANCE: Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch.

The effects of chromium and vitamin D3 co-supplementation on insulin resistance and tumor necrosis factor-alpha in type 2 diabetes: a randomized placebo-controlled trial.

The current study was conducted to assess the effects of simultaneous usage with vitamin D3 and chromium picolinate (CrPic) supplementations on homeostasis model assessment of insulin resistance (HOMA-IR), fasting blood glucose (FBS), hemoglobin A1c (HbA1c), tumor necrosis factor-α (TNF-α), and lipid profile in type 2 diabetes mellitus (T2DM). Ninety-two patients with T2DM were randomly allocated to the following 4 groups for 4 months: (I) placebo of vitamin D3 (n = 23); (II) vitamin D3 supplement at a dose of 50 000 IU/week (n = 23); (III) CrPic supplement at a dose of 500 µg/day (n = 23); and (IV) both vitamin D3 at a dose of 50 000 IU/week and CrPic at a dose of 500 µg/day (n = 23). HOMA-IR levels increased significantly in groups I and II after the intervention. However, this increase in group I was significantly higher than that in group II after the treatment. HOMA-IR levels were controlled in groups III and IV during the intervention. TNF-α decreased significantly in groups II, III, and IV after the intervention. FBS, HbA1c, and lipid profile did not change significantly in total groups after the intervention. It seems that chromium and vitamin D3 co-supplementation are probably effective in controlling HOMA-IR by decreasing TNF-α in T2DM. Novelty Chromium alone and/or in simultaneous pretreatment with vitamin D3 is more effective than vitamin D3 in controlling HOMA-IR in T2DM. Chromium and vitamin D3 alone and/or in simultaneous pretreatment decrease TNF-α in T2DM.

Studying the correlation between balance assessment by Biodex Stability System and Berg Scale in stroke individuals.

INTRODUCTION: Balance disorders are considered to be a serious clinical manifestation after stroke. Therefore, to assess stroke patients' balance performance, use of a quantitative method appears essential. A fundamental step would be the approval of the efficiency of the measurement instruments. The current study aimed to investigate correlations between balance assessment as examined by Biodex Stability System (BSS) and the clinical Berg Balance Scale (BBS) in post-stroke hemiparesis. METHODS: Twenty-five stroke survivors and 25 healthy age-sex matched subjects were recruited. The subjects were assessed using BSS during 3 days, with a 24-h interval. The high interclass correlation coefficient (ICC) values showed that the system was reliable enough to continue the study. The clinical evaluation was performed by the standard BBS. RESULTS: There was a significant moderate negative correlation between the Biodex overall indices and BBS scores in the stroke groups (ravg = -0.68) and in the healthy cohort (ravg = -0.55). Also, a significant moderate negative correlation was found between the Biodex antero-posterior stability indices and BBS scores in the stroke groups (ravg = -0.67) and in healthy cohort (ravg = -0.55). The correlation between the Biodex mediolateral stability indices and BBS scores was moderate to low in the stroke and healthy groups (ravg = -0.67 and -0.39 respectively). DISCUSSION AND CONCLUSION: Moderate negative correlation between the stability indices of the Biodex Stability System and BBS scores indicates that dynamic balance status of the participants partially reflects their functional balance status.