Sildenafil-induced spinal cord infarction: a case report.

Spinal cord infarction is a rare condition, accounting for only a small percentage of strokes. It can be classified into cervical and thoracolumbar infarctions, with various factors contributing to its occurrence. Sildenafil, a phosphodiesterase type 5 inhibitor commonly used for erectile dysfunction, has been associated with cardiovascular side effects, including transient hypotension. In this case report, we present the unusual occurrence of spinal cord infarction in a 65-year-old man who had self-administered high doses of sildenafil without a doctor's prescription. The patient experienced severe radicular pain in the lumbar region and subsequent weakness in the lower limbs. Evaluation revealed an anterior spinal cord infarction in the thoracic region, confirmed by MRI imaging. After excluding other potential causes, it was concluded that the intake of sildenafil likely led to systemic hypotension, resulting in spinal cord infarction. This case highlights the importance of considering sildenafil as a possible contributor to spinal cord infarction, particularly when used at high doses. Further studies are needed to better understand the relationship between sildenafil and vascular complications, including spinal cord infarction.

Automatic labeling of Parkinson's Disease gait videos with weak supervision.

Motor dysfunction in Parkinson's Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-consuming, expensive, semi-subjective, and may potentially result in conflicting labels across different raters. To address this problem, we propose an automatic, objective, and weakly-supervised method for labeling PD patients' gait videos. The proposed method accepts videos of patients and classifies their gait scores as normal (Gait score in MDS-UPDRS = 0) or PD (MDS-UPDRS ≥ 1). Unlike previous work, the proposed method does not require a priori MDS-UPDRS ratings for training, utilizing only domain-specific knowledge obtained from neurologists. We propose several labeling functions that classify patients' gait and use a generative model to learn the accuracy of each labeling function in a self-supervised manner. Since results depended upon the estimated values of the patients' 3D poses, and existing pre-trained 3D pose estimators did not yield accurate results, we propose a weakly-supervised 3D human pose estimation method for fine-tuning pre-trained models in a clinical setting. Using leave-one-out evaluations, the proposed method obtains an accuracy of 89% on a dataset of 29 PD subjects - a significant improvement compared to previous work by 7%-10% depending upon the dataset. The method obtained state-of-the-art results on the Human3.6M dataset. Our results suggest that the use of labeling functions may provide a robust means to interpret and classify patient-oriented videos involving motor tasks.

Strategic Fast Induction Heating to Combat Hysteresis Barriers in a Flexible MOF for Rapid CO2 Desorption in Biogas Upgrading.

A major challenge in Cyclic Swing Separation using flexible adsorbents that have high equilibrium CO2  adsorption capacity is their very low-pressure hysteresis that hinders efficient desorption. Mg-Gallate MOF is such a flexible adsorbent that only begins to release CO2 at its pore closing pressure at 0.08 bar and 30 °C, showing very slow and inefficient desorption in pressure or temperature swing. Therefore, a novel strategy is presented that combines state of art technique Magnetic Induction Heating with a vacuum swing for fast and efficient CO2 desorption from flexible adsorbents at a moderately elevated temperature (70 °C).

Effects of transcranial direct current stimulation on cognitive dysfunction in multiple sclerosis.

BACKGROUND: Around 40%-70% of patients with multiple sclerosis (MS) may experience cognitive impairments during the course of their disease with detrimental effects on social and occupational activities. Transcranial direct current stimulation (tDCS has been investigated in pain, fatigue, and mood disorders related to MS, but to date, few studies have examined effects of tDCS on cognitive performance in MS. OBJECTIVE: The current study aimed to investigate the effects of a multi-session tDCS protocol on cognitive performance and resting-state brain electrical activities in patients with MS. METHODS: Twenty-four eligible MS patients were randomly assigned to real (anodal) or sham tDCS groups. Before and after 8 consecutive daily tDCS sessions over the left dorsolateral prefrontal cortex (DLPFC), patients' cognitive performance was assessed using the Cambridge Brain Sciences-Cognitive Platform (CBS-CP). Cortical electrical activity was also evaluated using quantitative electroencephalography (QEEG) analysis at baseline and after the intervention. RESULTS: Compared to the sham condition, significant improvement in reasoning and executive functions of the patients in the real tDCS group was observed. Attention was also improved considerably but not statistically significantly following real tDCS. However, no significant changes in resting-state brain activities were observed after stimulation in either group. CONCLUSION: Anodal tDCS over the left DLPFC appears to be a promising therapeutic option for cognitive dysfunction in patients with MS. Larger studies are required to confirm these findings and to investigate underlying neuronal mechanisms.

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors.

Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.

Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach.

Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes.

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks.

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

Application-Based Production and Testing of a Core-Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles.

Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core-sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the development of a smart sleeveless shirt for measuring the kinematic angles of the trunk relative to the pelvis in complicated three-dimensional movements. The sensor's piezoresistive properties and characteristics were studied with respect to the type of core material used. Sensor performance was optimized by straining above the intended working region to increase the consistency and accuracy of the piezoresistive sensor. The accuracy of the sensor when tracking random movements was tested using a rigorous 4-h random wave pattern to mimic what would be required for satisfactory use in prototype devices. By processing the raw signal with a machine learning algorithm, we were able to track a strain of random wave patterns to a normalized root mean square error of 1.6%, highlighting the consistency and reproducible behavior of the relatively simple sensor. Then, we evaluated the performance of these sensors in a prototype motion capture shirt, in a study with 12 participants performing a set of eight different types of uniaxial and multiaxial movements. A machine learning random forest regressor model estimated the trunk flexion, lateral bending, and rotation angles with errors of 4.26°, 3.53°, and 3.44° respectively. These results demonstrate the feasibility of using smart textiles for capturing complicated movements and a solution for the real-time monitoring of daily activities.

Estimation of Ankle Joint Power during Walking Using Two Inertial Sensors.

(1) Background: Ankle joint power, as an indicator of the ability to control lower limbs, is of great relevance for clinical diagnosis of gait impairment and control of lower limb prosthesis. However, the majority of available techniques for estimating joint power are based on inverse dynamics methods, which require performing a biomechanical analysis of the foot and using a highly instrumented environment to tune the parameters of the resulting biomechanical model. Such techniques are not generally applicable to real-world scenarios in which gait monitoring outside of the clinical setting is desired. This paper proposes a viable alternative to such techniques by using machine learning algorithms to estimate ankle joint power from data collected by two miniature inertial measurement units (IMUs) on the foot and shank, (2) Methods: Nine participants walked on a force-plate-instrumented treadmill wearing two IMUs. The data from the IMUs were processed to train and test a random forest model to estimate ankle joint power. The performance of the model was then evaluated by comparing the estimated power values to the reference values provided by the motion tracking system and the force-plate-instrumented treadmill. (3) Results: The proposed method achieved a high accuracy with the correlation coefficient, root mean square error, and normalized root mean square error of 0.98, 0.06 w/kg, and 1.05% in the intra-subject test, and 0.92, 0.13 w/kg, and 2.37% in inter-subject test, respectively. The difference between the predicted and true peak power values was 0.01 w/kg and 0.14 w/kg with a delay of 0.4% and 0.4% of gait cycle duration for the intra- and inter-subject testing, respectively. (4) Conclusions: The results of this study demonstrate the feasibility of using only two IMUs to estimate ankle joint power. The proposed technique provides a basis for developing a portable and compact gait monitoring system that can potentially offer monitoring and reporting on ankle joint power in real-time during activities of daily living.

Effects of Root Debridement With Hand Curettes and Er:YAG Laser on Chemical Properties and Ultrastructure of Periodontally-Diseased Root Surfaces Using Spectroscopy and Scanning Electron Microscopy.

Introduction: The efficacy of erbium-doped yttrium aluminum garnet (Er:YAG) laser for root debridement in comparison with curettes has been the subject of many recent investigations. Considering the possibility of chemical and ultra-structural changes in root surfaces following laser irradiation, this study sought to assess the effects of scaling and root planing (SRP) with curettes and Er:YAG laser on chemical properties and ultrastructure of root surfaces using spectroscopy and scanning electron microscopy (SEM). Methods: In this in vitro experimental study, extracted sound human single-rooted teeth (n = 50) were randomly scaled using manual curettes alone or in conjunction with Er:YAG laser at 100 and 150 mJ/pulse output energies. The weight percentages of carbon, oxygen, phosphorous and calcium remaining on the root surfaces were calculated using spectroscopy and the surface morphology of specimens was assessed under SEM. Data were analyzed using one-way analysis of variance (ANOVA). Results: No significant differences (P > 0.05) were noted in the mean carbon, oxygen, phosphorous and calcium weight percentages on root surfaces following SRP using manual curettes with and without laser irradiation at both output energies. Laser irradiation after SRP with curettes yielded rougher surfaces compared to the use of curettes alone. Conclusion: Although laser irradiation yielded rougher surfaces, root surfaces were not significantly different in terms of chemical composition following SRP using manual curettes with and without Er:YAG laser irradiation. Er:YAG laser can be safely used as an adjunct to curettes for SRP.

A Comparison between Moderate- and High-dose Furazolidone in Triple Regimens for Helicobacterpylori Eradication in Iran.

BACKGROUND Furazolidone has been used as an alternative for clarithromycin or metronidazole in Helicobacterpylori (H.pylori) eradication regimens. In Iran, 14-day Furazolidone-containing quadruple regimens have shown promising eradication rates, but short-course, low dose therapies are always attractive. Therefore, we designed a study to compare the efficacy of two 10-day triple regimens containing moderate and high dose furazolidone for H.pylori eradication. METHODS Two hundred and ten patients with peptic ulcer disease who were naïve to H.pylori treatment were included. They were randomized into 2 groups: 105 patients received omeprazole 20mg, amoxicillin 1000mg, and furazolidone 200mg(OAF-400), all twice a day for ten days.And the remaining 105 patients received omeprazole 20mg twice a day, amoxicillin 1000mg twice a day and furazolidone 200mg three times a day for ten days(OAF-600). Urease breath test was performed 8 weeks after the treatment to confirm H. pylori eradication. RESULTS The intention-to-treat eradication rate was 76.19% in group OAF-400 and 80.95% in group OAF-600 (pp=0.38). Per protocol eradication rates were 81.63% and 89.47%, respectively (p= 0.11).Severe adverse effects were reported by 8.6% of the patients in group OAF-400 and 5.7% of the patient in group OAF-600 (p=0.1). However, the total side effects (including mild, moderate, and severe ones) were significantly more prevalent in the OAF-600 group (p=0.001). CONCLUSION None of our triple furazolidone-based regimens (moderate- and high-dose) could achieve the standard eradication rate, and therefore, cannot be considered as a suitable option for first-line treatment.

Study of Helicobacter pylori genotype status in cows, sheep, goats and human beings.

BACKGROUND: Helicobacter pylori is one of the most controversial bacteria in the world causing diverse gastrointestinal diseases. The transmission way of this bacterium still remains unknown. The possibility of zoonotic transmission of H. pylori has been suggested, but is not proven in nonprimate reservoirs. In the current survey, we investigate the presence of H. pylori in cow, sheep and goat stomach, determine the bacterium virulence factors and finally compare the human H. pylori virulence factors and animals in order to examine whether H. pylori might be transmitted from these animals to human beings. METHODS: This cross- sectional study was performed on 800 gastric biopsy specimens of cows, sheep, goats and human beings. The PCR assays was performed to detection of H. pylori, vacA and cagA genes. The PCR products of Ruminant's samples with positive H. pylori were subjected to DNA sequencing analysis. Statistical tests were applied for data analysis. RESULTS: Overall 6 (3%) cows, 32 (16%) sheep and 164 (82%) human beings specimens were confirmed to be H. pylori positive; however we were not able to detect this bacterium in all 200 goat samples. The vacA s1a/m1a was the predominant H. pylori genotype in all three kinds of studied population. There was 3.4-8.4% variability and 92.9-98.5% homology between sheep and human samples. CONCLUSIONS: Considering the high sequence homology among DNA of H. pylori isolated from sheep and human, our data suggest that sheep may act as a reservoir for H. pylori and in the some extent share the ancestral host for the bacteria with human.