An early force prediction control scheme using multimodal sensing of electromyography and digit force signals.

Different grasping gestures result in the change of muscular activity of the forearm muscles. Similarly, the muscular activity changes with a change in grip force while grasping the object. This change in muscular activity, measured by a technique called Electromyography (EMG) is used in the upper limb bionic devices to select the grasping gesture. Previous research studies have shown gesture classification using pattern recognition control schemes. However, the use of EMG signals for force manipulation is less focused, especially during precision grasping. In this study, an early predictive control scheme is designed for the efficient determination of grip force using EMG signals from forearm muscles and digit force signals. The optimal pattern recognition (PR) control schemes are investigated using three different inputs of two signals: EMG signals, digit force signals and a combination of EMG and digit force signals. The features extracted from EMG signals included Slope Sign Change, Willison Amplitude, Auto Regressive Coefficient and Waveform Length. The classifiers used to predict force levels are Random Forest, Gradient Boosting, Linear Discriminant Analysis, Support Vector Machines, k-nearest Neighbors and Decision Tree. The two-fold objectives of early prediction and high classification accuracy of grip force level were obtained using EMG signals and digit force signals as inputs and Random Forest as a classifier. The earliest prediction was possible at 1000 ms from the onset of the gripping of the object with a mean classification accuracy of 90 % for different grasping gestures. Using this approach to study, an early prediction will result in the determination of force level before the object is lifted from the surface. This approach will also result in better biomimetic regulation of the grip force during precision grasp, especially for a population facing vision deficiency.

Predicting mechanical neck pain intensity in computer professionals using machine learning: identification and correlation of key features.

Introduction: Mechanical neck pain has become prevalent among computer professionals possibly because of prolonged computer use. This study aimed to investigate the relationship between neck pain intensity, anthropometric metrics, cervical range of motion, and related disabilities using advanced machine learning techniques. Method: This study involved 75 computer professionals, comprising 27 men and 48 women, aged between 25 and 44 years, all of whom reported neck pain following extended computer sessions. The study utilized various tools, including the visual analog scale (VAS) for pain measurement, anthropometric tools for body metrics, a Universal Goniometer for cervical ROM, and the Neck Disability Index (NDI). For data analysis, the study employed SPSS (v16.0) for basic statistics and a suite of machine-learning algorithms to discern feature importance. The capability of the kNN algorithm is evaluated using its confusion matrix. Results: The "NDI Score (%)" consistently emerged as the most significant feature across various algorithms, while metrics like age and computer usage hours varied in their rankings. Anthropometric results, such as BMI and body circumference, did not maintain consistent ranks across algorithms. The confusion matrix notably demonstrated its classification process for different VAS scores (mild, moderate, and severe). The findings indicated that 56% of the pain intensity, as measured by the VAS, could be accurately predicted by the dataset. Discussion: Machine learning clarifies the system dynamics of neck pain among computer professionals and highlights the need for different algorithms to gain a comprehensive understanding. Such insights pave the way for creating tailored ergonomic solutions and health campaigns for this population.

Machine Learning Analytic-Based Two-Staged Data Management Framework for Internet of Things.

In applications of the Internet of Things (IoT), where many devices are connected for a specific purpose, data is continuously collected, communicated, processed, and stored between the nodes. However, all connected nodes have strict constraints, such as battery usage, communication throughput, processing power, processing business, and storage limitations. The high number of constraints and nodes makes the standard methods to regulate them useless. Hence, using machine learning approaches to manage them better is attractive. In this study, a new framework for data management of IoT applications is designed and implemented. The framework is called MLADCF (Machine Learning Analytics-based Data Classification Framework). It is a two-stage framework that combines a regression model and a Hybrid Resource Constrained KNN (HRCKNN). It learns from the analytics of real scenarios of the IoT application. The description of the Framework parameters, the training procedure, and the application in real scenarios are detailed. MLADCF has shown proven efficiency by testing on four different datasets compared to existing approaches. Moreover, it reduced the global energy consumption of the network, leading to an extended battery life of the connected nodes.

Reference genomes for conservation.

High-quality reference genomes for non-model species can benefit conservation.

Pattern recognition of EMG signals for low level grip force classification.

Grasping of the objects is the most frequent activity performed by the human upper limb. The amputations of the upper limb results in the need for prosthetic devices. The myoelectric prosthetic devices use muscle signals and apply control techniques for identification of different levels of hand gesture and force levels. In this study; a different level force contraction experiment was performed in which Electromyography (EMGs) signals and fingertip force signals were acquired. Using this experimental data; a two-step feature selection process is applied for the designing of a pattern recognition algorithm for the classification of different force levels. The two step feature selection process consist of generalized feature ranking using ReliefF, followed by personalized feature selection using Neighborhood Component Analysis (NCA) from the shortlisted features by earlier technique. The classification algorithms applied in this study were Support Vector Machines (SVM) and Random Forest (RF). Besides feature selection; optimization of the number of muscles during classification of force levels was also performed using designed algorithm. Based on this algorithm; the maximum classification accuracy using SVM classifier and two muscle set was achieved as high as 99%. The optimal feature set consisted features such as Auto Regressive coefficients, Willison Amplitude and Slope Sign Change. The mean classification accuracy for different subjects, achieved using SVM and RF was 94.5% and 91.7% respectively.

The Digital Divide: A Retrospective Survey of Digital Rectal Examinations during the Workup of Rectal Cancers.

Background: Digital rectal examination (DRE) is considered an important part of the physical examination. However, it is unclear how many patients have a DRE performed at the primary care level in the work-up of rectal cancer, and if the absence of a DRE causes a delay to consultation with a specialist. Methods: A retrospective patient questionnaire was sent to 1000 consecutive patients with stage II or stage III rectal cancer. The questionnaire asked patients to recall if they had a DRE performed by their general practitioner (GP) when they first presented with symptoms or a positive FIT test. Demographic data, staging data, and time to consultation with a specialist were also collected. Results: A thousand surveys were mailed out, and a total of 262 patients responded. Of the respondents, 46.2% did not recall undergoing a digital rectal examination by their primary care provider. Women were less likely to undergo a DRE than men (28.6% vs. 44.3%, p = 0.019). While there was a trend towards longer times to specialist consultation in patients who did not undergo a DRE (27.0 vs. 12.2 weeks), this was not statistically significant (p = 0.121). Conclusion: A significant proportion of patients who are FIT positive or have symptomatic rectal bleeding do not recall having a DRE by their primary care provider. Barriers may include lack of comfort with performing DRE or lack of time. Clearer guidelines and more support for GP's may increase uptake of DRE.

Large BACs transfect more efficiently in circular topology.

The effect of DNA topology on transfection efficiency of mammalian cells has been widely tested on plasmids smaller than 10 kb, but little is known for larger DNA vectors carrying intact genomic DNA containing introns, exons, and regulatory regions. Here, we demonstrate that circular BACs transfect more efficiently than covalently closed linear BACs. We found up to 3.1- and 8.9- fold higher eGFP expression from circular 11 kb and 100 kb BACs, respectively, compared to linear BACs. These findings provide insights for improved vector development for gene delivery and expression studies of large intact transgenes in mammalian cells.

EMG based classification for pick and place task.

The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.

Elevated Soluble Galectin-3 as a Marker of Chemotherapy Efficacy in Breast Cancer Patients: A Prospective Study.

PURPOSE: Galectin-3 (Gal-3) is a glycan-binding lectin with a debated role in cancer progression due to its various functions and patterns of expression. The current study investigates the relationship between breast cancer prognosis and secreted Gal-3. METHODS: Breast cancer patients with first time cancer diagnosis and no prior treatment (n = 88) were placed in either adjuvant or neoadjuvant setting based on their treatment modality. Stromal and plasma Gal-3 levels were measured in each patient at the time of diagnosis and then throughout treatment using immunohistochemistry (IHC) and ELISA, respectively. Healthy women (>18 years of age, n = 63) were used to establish baseline levels of plasma Gal-3. Patients were followed for 84 months for disease-free survival analysis. RESULTS: Enhanced levels of plasma (adjuvant) and stromal (neoadjuvant) Gal-3 were found to be markers of chemotherapy efficacy. The patients with chemotherapy-induced increase in extracellular Gal-3 had longer disease-free interval and significantly lower rate of recurrence during 84-month follow-up compared to patients with unchanged or decreased secretion. CONCLUSION: The findings support the use of plasma Gal-3 as a marker for chemotherapy efficacy when no residual tumor is visible through imaging. Furthermore, stromal levels in any remaining tumors postchemotherapy can also be used to predict long-term prognosis in patients.

Selection of Features and Classifiers for EMG-EEG-Based Upper Limb Assistive Devices-A Review.

Bio-signals are distinctive factors in the design of human-machine interface, essentially useful for prosthesis, orthosis, and exoskeletons. Despite the progress in the analysis of pattern recognition based devices; the acceptance of these devices is still questionable. One reason is the lack of information to identify the possible combinations of features and classifiers. Besides; there is also a need for optimal selection of various sensors for sensations such as touch, force, texture, along with EMGs/EEGs. This article reviews the two bio-signal techniques, named as electromyography and electroencephalography. The details of the features and the classifiers used in the data processing for upper limb assist devices are summarised here. Various features and their sets are surveyed and different classifiers for feature sets are discussed on the basis of the classification rate. The review was carried out on the basis of the last 10-12 years of published research in this area. This article also outlines the influence of modality of EMGs and EEGs with other sensors on classifications. Also, other bio-signals used in upper limb devices and future aspects are considered.

Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG.

Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.

Role of 1.5 mm microplates in treatment of symphyseal fracture of mandible: A stress analysis based comparative study.

AIMS AND OBJECTIVES: To perform comparative finite element analysis (FEA)for 2.0 mm standard miniplate and 1.5 mm microplate in isolated symphyseal mandibular fractures. MATERIAL AND METHOD: Two FE models of mandible were developed and symphyseal fracture was created in both of them. Each fractured model was reduced and fixed using two mini plates and two microplates, respectively according Champ's principle. Stresses thus developed in the plates after application of masticatory load were observed in the models. RESULTS: Results of the study indicated there was no significant difference in the stresses developed in either of the bone plating system. CONCLUSION: 1.5 mm Microplates can be used in place of 2.0 mm miniplates in isolated symphyseal fractures of the mandible.

Correction: Generalized Tetanus Initially Presenting with Dysmasesis.

[This corrects the article DOI: 10.7759/cureus.644.].

Generalized Tetanus Initially Presenting with Dysmasesis.

A 58-year-old male farmer, with no significant history of any chronic morbidity, was admitted via the Emergency Department of SMHS Hospital, Srinagar, with a history of an injury to the hand 20 days earlier followed by a three-day history of dysmasesis (difficulty chewing), progressing to trismus and generalized stiffness interfering with his daily activities. The patient was clinically managed as tetanus on grounds of high clinical suspicion. The patient was treated for a week and discharged without any sequelae to follow-up in the Neurology Outpatient Department of the SMHS Hospital and is currently doing well. After a week of successful management, we received the blood and wound culture reports of the patient that had been sent at the time of his admission to the hospital, which overwhelmingly tested positive for Clostridium tetani. Tetanus is a disease to be suspected post-trauma in patients, especially in developing countries like India. Despite active and passive immunization, it continues to be a significant public health problem in developing countries and should be readily suspected and treated. Although prevention is important for tetanus, the outcomes can be improved by early clinical diagnosis and treatment.

New therapeutic targets in alcoholic hepatitis.

Alcoholic liver disease (ALD) is a leading cause of liver-related morbidity and mortality worldwide. ALD encompasses a spectrum of disorders including asymptomatic steatosis, steatohepatitis, fibrosis, cirrhosis and its related complications, and the acute-on-chronic state of alcoholic hepatitis. While multidisciplinary efforts continue to be aimed at curbing progression of this spectrum of disorders, there is an urgent need to focus our efforts on effective therapeutic interventions for alcoholic hepatitis (AH), the most severe form of ALD. AH is characterized by an abrupt development of jaundice and complications related to liver insufficiency and portal hypertension in patients with heavy alcohol intake. The mortality of patients with severe AH is very high (20-50 % at 3 months). The current therapeutic regimens are limited. The development of new therapies requires translational studies in human samples and suitable animal models that reproduce clinical and histological features of human AH. This review article summarizes the clinical syndrome, pre-clinical translational tools, and pathogenesis of AH at a molecular and cellular level, with the aim of identifying new targets of potential therapeutic intervention.

A comparative study on evaluation of role of 1.5 mm microplates and 2.0 mm standard miniplates in management of mandibular fractures using bite force as indicator of recommendation.

AIMS AND OBJECTIVES: To compare the maximum voluntary bite force generated at different periods during mandibular fracture healing using miniplates and microplates as means of rigid internal fixation. MATERIALS AND METHODS: Maximum voluntary bite force was recorded in healthy young individuals of different age group from either gender. Patients suffering from symphyseal and parasymphyseal and body fractures were selected and randomly treated using miniplate and microplate osteosynthesis by open reduction and rigid internal fixation. Postoperative bite forces at intervals of 1st, 2nd, 4th, and 6th week were recorded and compared with control group. OBSERVATIONS AND RESULTS: It was noticed that bite forces were significantly reduced in the study groups in comparison to control group and at different intervals of treatment. There was a progressive improvement in the bite force with passage of time. There was no statistical significance in the observed bite force in both the study groups at different intervals of assessment. CONCLUSION: 1.5 mm microplates provide adequate stability comparable to miniplates for the treatment of fractured mandible and should be preferred over miniplates. We further suggest that bite forces should be considered for the assessment of clinical union of bone as well as studies pertaining to selection of hardware for rigid internal fixation.

Extra pontine osmotic demyelination syndrome.

The osmotic demyelination syndrome (ODS) has been identified as a complication of the rapid correction of hyponatremia for decades. However, in recent years, a variety of other medical conditions have been associated with the development of ODS, independent of changes in serum sodium which cause a rapid changes in osmolality of the interstitial (extracellular) compartment of the brain leading to dehydration of energy-depleted cells with subsequent axonal damage that occurs in characteristic areas. Slow correction of the serum sodium concentration and additional administration of corticosteroids seems to be a major prevention step in ODS patients. In the current report we aimed to share a rare case which we observed in our hospital. A 65 year old female admitted as altered sensorium with history of vomiting, diarrhea was managed with intravenous fluids for 2 days at a peripheral health centre. Patient was referred to our centre with encephalopathy, evaluated and found to have hyponatremia and hypokalemia rest of biochemical parameters and septic profile were normal. Patient's electrolyte disturbances were managed as per guidelines but encephalopathy persisted. Supportive treatment was continued and patient was discharged after 2 wks of stay in hospital after gaining full sensorium and neurological functions.

Non-Hyperammonemic valproate encephalopathy.

A 21-year-old male known case of primary hypothyroidism, Seizure disorder sequelae of an old trauma receiving sodium valproate, clobazam and phenobarbitone for control of Generalized tonic clonic seizures reported to neurology OPD with history of altered sensorium and gait unsteadiness for 1 week with history of hike in valproate dose 2 weeks before. On examination he was drowsy. Neurological examination was unremarkable except for gait unsteadiness and ataxia. Patient was admitted and evaluated for acute worsening. All (the) biochemical parameters including complete blood count, liver function tests, kidney function tests, routine urine examination, arterial blood gas analysis, blood and urine culture tests were normal. CSF analysis was also normal. Repeat MRI brain was also done which depicted all old changes with no fresh changes which will account for worsening of his sensorium. EEG was suggestive of diffuse encephalopathy. Thyroid function tests were also normal. Valproate encephalopathy was suspected and Valproate was empirically stopped and he was put on levetiracetam and phenytoin. His sensorium improved rapidly after stoppage of valproate with normalization of EEG. Serum valproate Levels were high with serum ammonia levels were in the normal range. We made the inference of nonhyperammoneamic valproate encephalopathy. This case highlights the existence of non-hyperammonemic valproate induced encephalopathy, suggesting mechanisms other than hyperammonemia responsible for this encephalopathy.