Unknotting tech neck by breaking the cycle of pain and disability: Comparing the impact of instrument assisted soft tissue mobilization on specific muscles and superficial back arm line.

BACKGROUND: Computer professionals often develop a forward head posture due to prolonged hours of computer use, leading to neck pain. Instrument-assisted soft tissue mobilization (IASTM), an advanced technique for treating myofascial trigger points, has become increasingly popular for addressing these musculoskeletal issues. OBJECTIVES: The study aimed to compare the effectiveness of IASTM mobilization on SBAL (superficial back arm line) and SM(specific muscles-upper trapezius, levator scapulae, and sternocleidomastoid) in managing chronic neck pain among computer professionals. PARTICIPANTS & METHODS: The study involved 62 computer professionals, randomly divided into two groups. Group A received IASTM on SBAL and group B received IASTM on SM for neck pain each receiving three sessions weekly for four weeks. Outcome variables like Neck Disability Index (NDI), NPRS(Neck Pain Rating Scale), Craniovertebral angle (CVA), and range of motion (ROM) for flexion, and side flexion (right & left side) were evaluated at baseline, 2 weeks and 4 weeks. RESULTS: Significant improvement in NPRS were observed in both the SBAL and SM groups after 2 weeks of IASTM, wth the SBAL group demonstrating greater improvement. At 4 weeks, IASTM on SBAL showed significantly higher improvements in NPRS, CVA, NDI, and flexion compared to the SM group. The repeated measures ANOVA indicated a significant main effect of both time and group, along with a significant interaction between time and group for all outcome variables, except for CVA. CONCLUSION: The study indicates that IASTM on SBAL may offer a more effective treatment for chronic neck pain in computer professionals compared to targeting specific muscles.

Association of digital device usage and dry eye disease in school children.

PURPOSE: To investigate the influence of digital device use (computers, laptops, tablets, smartphones) on dry eye disease (DED) in a pediatric population. SETTINGS AND DESIGN: This was a cross-sectional study. School children studying in grades 5-9 at two private schools in the city of Ahmedabad, the capital city of Gujarat, India were invited to participate in the study. METHODS: In this study, 462 children underwent ocular examination including tear film breakup time (TBUT) and Schirmer's test. Questionnaires were administered for collecting information on the type and duration of digital device usage separately for academic and leisure activities and the Ocular Surface Disease Index (OSDI) score. RESULTS: The mean age of participants was 11.2 + 1.4 years, and 63% were boys. The mean OSDI score was 37.2 + 11.8, and 90.5% had symptoms of DED. Children with moderate to severe DED (n = 88, 19%) had longer daily duration of device use and lower Schirmer's test and TBUT values compared to children with mild DED (P = 0.001). A cumulative exposure time of more than 3-3.5 h per day had a significantly increased risk of DED. Multivariable logistic regression analysis showed that increment in computer usage (odds ratio [OR] 1.94 for every half an hour increase, 95% confidence interval [CI] = 1.2-3.1) and children studying in higher grades (OR 1.30, 95% CI = 1.1-1.6) had a higher risk of moderate to severe dry eye. CONCLUSION: Cumulative device exposure time of more than 3-3.5 h per day had a significantly increased risk of pediatric DED. Children with an increment in computer usage by half an hour per day had a higher chance of experiencing moderate to severe dry eye. Policymakers should aim to restrict the screen time below 3 h on a daily basis.

Effects of 1-hour computer use on ulnar and median nerve conduction velocity and muscle activity in office workers.

OBJECTIVES: To compare the effects of 1-hour computer use on ulnar and median nerve conduction velocity and muscle activity in office workers with symptomatic neck pain and asymptomatic office workers. METHODS: A total of 40 participants, both male and female office workers, with symptomatic neck pain (n = 20) and asymptomatic (n = 20), were recruited. Pain intensity, ulnar nerve conduction velocity, median nerve conduction velocity, and muscle activity were determined before and after 1 hour of computer use. RESULTS: There was a significant increase in pain intensity in the neck area in both groups (P < .001). The symptomatic neck pain group revealed a significant decrease in the sensory nerve conduction velocity of the ulnar nerve (P = .008), whereas there was no difference in the median nerve conduction velocity (P > .05). Comparing before and after computer use, the symptomatic neck pain group had less activity of the semispinalis muscles and higher activity of the anterior scalene muscle than the asymptomatic group (P < .05). The trapezius and wrist extensor muscles showed no significant differences in either group (P > .05). CONCLUSIONS: This study found signs of neuromuscular deficit of the ulnar nerve, semispinalis muscle, and anterior scalene muscle after 1 hour of computer use among office workers with symptomatic neck pain, which may indicate the risk of neuromuscular impairment of the upper extremities. The recommendation of resting, and encouraging function and flexibility of the neuromuscular system after 1 hour of computer use should be considered.

Traditional versus dynamic sitting: Lumbar spine kinematics and pain during computer work and activity guided tasks.

Dynamic sitting may mitigate low back pain during prolonged seated work. The current study compared pelvis and lumbar spine kinematics, pain, and work productivity, in traditional and dynamic sitting. Sixteen participants completed three 20-min blocks of computer work and activity guided tasks in a traditional office chair or backless and multiaxial rotating seat pan while kinematics were measured from accelerometers on the low back. Pain ratings were recorded on a visual analogue scale every 10 min. Similar pelvis and lumbar kinematics emerged when performing computer work in traditional and dynamic sitting. Pelvis and lumbar sagittal and frontal plane shifts and fidgets were largest for dynamic sitting in the activity guided tasks. Buttocks pain was higher in dynamic sitting, but low back pain and work productivity were unaffected. Dynamic sitting increased spine movement during activity guided tasks, without negatively impacting lumbar kinematics, low back pain, or productivity during seated computer work.

A tree-based corpus annotated with Cyber-Syndrome, symptoms, and acupoints.

Prolonged and over-excessive interaction with cyberspace poses a threat to people's health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people's health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare.

Orbicularis oculi muscle activity during computer reading under different degrees of artificially-induced aniseikonia.

Background: Aniseikonia is a binocular vision disorder that has been associated with asthenopic symptoms. However, asthenopia has been evaluated with subjective tests that make difficult to determine the level of aniseikonia. This study aims to objectively evaluate the impact of induced aniseikonia at different levels on visual fatigue by measuring the orbicularis oculi muscle activity in the dominant and non-dominant eyes while performing a reading task. Methods: Twenty-four collegiate students (24.00 ± 3.86 years) participated in this study. Participants read a passage for 7 minutes under four degrees of aniseikonia (0%, 3%, 5% and 10%) at 50 cm. Orbicularis oculi muscle activity of the dominant and non-dominant eye was recorded by surface electromyography. In addition, visual discomfort was assessed after each task by completing a questionnaire. Results: Orbicularis oculi muscle activity increased under induced aniseikonia (i.e., greater values for the 10% condition in comparison to 0%, and 3% conditions (p = 0.034 and p = 0.023, respectively)). No statistically significant differences were observed in orbicularis oculi muscle activity for the time on task and between the dominant and non-dominant eyes. Additionally, higher levels of subjective visual discomfort were observed for lower degrees of induced aniseikonia. Conclusion: Induced aniseikonia increases visual fatigue at high aniseikonia degrees as measured by the orbicularis oculi muscle activity, and at low degrees as measured with subjective questionnaires. These findings may be of relevance to better understand the visual symptomatology of aniseikonia.

Using Computer Vision to Annotate Video-Recoded Direct Observation of Physical Behavior.

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18-59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images.

Extracorporeal shock wave lithotripsy: retrospective study on possible predictors of treatment success and revisiting the role of non-contrast-enhanced computer tomography in kidney and ureteral stone disease.

Extracorporeal shock wave lithotripsy (ESWL) is a safe and efficient treatment option for urinary stone disease. The overall stone-free rate (SFR) varies significantly. This study aimed to assess the influence of stone size, location, stone density, and skin-to-stone distance (SSD), on the outcome of ESWL. We assessed whether pre-treatment non-contrast-enhanced CT scan (NCCT) confers significant advantages compared to kidney-ureter-bladder film (KUB) only. We reviewed the medical records of 307 cases (165 men, 142 women) with renal and ureteral stones treated consecutively at our institution with ESWL between 2020 and 2023. 44 of these underwent a NCCT. The outcome of ESWL was defined in two ways: visible stone fragmentation on KUB, and the need for further treatment. Overall success of fragmentation was 85% (261 patients). 61% of patients (n = 184) didn't need any further treatment. Stone size and location correlated significantly with treatment outcomes regarding the need for further treatment (p = 0.004) and stone fragmentation (p = 0.016), respectively. Unlike mean SSD (p = 0.462), the mean attenuation value (MAV) significantly correlated with the need for retreatment (p = 0.016). MAV seems to be a better predictor of treatment success (AUC of the ROC curve: 0.729), compared to stone size (AUC: 0.613). The difference between groups (with and without NCCT) in both treatment outcomes did not reach statistical significance. During decision-making, information regarding SSD and MAV can be useful in more dubious scenarios. However, it appears that their inclusion doesn't provide substantial advantages when compared to relying solely on KUB.

A model study of teaching method reform of computer laboratory course integrating internet of things technology.

The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.

Comparison of condylar position after free fibular flap mandibular reconstruction using computer-assisted and traditional techniques.

OBJECTIVES: To compare the changes in condylar position after mandibular reconstruction with free fibular flap(FFF) and the differences between computer-assisted techniques and traditional methods on CT images. METHODS: Thirty-four patients who underwent mandibular reconstruction with free fibular flap were selected according to the inclusion and exclusion criteria. In the 3D group, virtual surgical planning (VSP) with osteotomy cutting plate and placement guiding plate were used, while the traditional group underwent freehand reconstruction. The CT data of 68 temporomandibular joints (TMJs) were recorded before and immediately after surgery. The condylar position was evaluated by measuring the anterior space (AS), posterior space (PS) and superior space (SS), and the ln (PS/AS) was calculated according to the method proposed by Pullinger and Hollender. RESULTS: In the patients included in the 3D group, the condyle on the ipsilateral side moved slightly backward; however, in the patients in the traditional group, the ipsilateral side moved considerably anteroinferior. No obvious changes on the contralateral side were noted. In the 3D group, 33% of ipsilateral condyles were in the posterior position postoperatively when compared with the preoperative position (13%). In the traditional group, the number of ipsilateral condyles in the anterior position increased from 4 to 10, accounting for 53% postoperatively. Contrary to the traditional group, the 3D group presented less condylar displacement on the ipsilateral side postoperatively. CONCLUSIONS: This study showed a decreased percentage of change in condylar position postoperatively when VSP was used. Virtual surgical planning improved the accuracy of FFF mandibular reconstruction and made the condylar position more stable.

MaRGE: A graphical environment for MaRCoS.

The open-source console MaRCoS, which stands for "Magnetic Resonance Control System", combines hardware, firmware and software elements for integral control of Magnetic Resonance Imaging (MRI) scanners. Previous developments have focused on making the system robust and reliable, rather than on users, who have been somewhat overlooked. This work describes a Graphical User Interface (GUI) designed for intuitive control of MaRCoS, as well as compatibility with clinical environments. The GUI is based on an arrangement of tabs and a renewed Application Program Interface (API). Compared to the previous versions, the MaRGE package ("MaRCoS Graphical Environment") includes new functionalities such as the possibility to export images to standard DICOM formats, create and manage clinical protocols, or display and process image reconstructions, among other features conceived to simplify the operation of MRI scanners. All prototypes in our facilities are commanded by MaRCoS and operated with the new GUI. Here we report on its performance on an experimental 0.2 T scanner designed for hard-tissue, as well as a 72 mT portable scanner presently installed in the radiology department of a large hospital. The possibility to customize, adapt and streamline processes has substantially improved our workflows and overall experience.

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

BACKGROUND: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. METHODS: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). RESULTS: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). CONCLUSIONS: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

Computer-aided cognitive training combined with tDCS can improve post-stroke cognitive impairment and cerebral vasomotor function: a randomized controlled trial.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is the focus and difficulty of poststroke rehabilitation intervention with an incidence of up to 61%, which may be related to the deterioration of cerebrovascular function. Computer-aided cognitive training (CACT) can improve cognitive function through scientific training targeting activated brain regions, becoming a popular training method in recent years. Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique, can regulate the cerebral vascular nerve function, and has an effect on the rehabilitation of cognitive dysfunction after stroke. This study examined the effectiveness of both CACT and tDCS on cognitive and cerebrovascular function after stroke, and explored whether CACT combined with tDCS was more effective. METHODS: A total of 72 patients with PSCI were randomly divided into the conventional cognitive training (CCT) group (n = 18), tDCS group (n = 18), CACT group (n = 18), and CACT combined with tDCS group (n = 18). Patients in each group received corresponding 20-minute treatment 15 times a week for 3 consecutive weeks. Montreal Cognitive Assessment (MoCA) and the Instrumental Activities of Daily Living Scale (IADL) were used to assess patients' cognitive function and the activities of daily living ability. Transcranial Doppler ultrasound (TCD) was used to assess cerebrovascular function, including cerebral blood flow velocity (CBFV), pulse index (PI), and breath holding index (BHI). These outcome measures were measured before and after treatment. RESULTS: Compared with those at baseline, both the MoCA and IADL scores significantly increased after treatment (P < 0.01) in each group. There was no significantly difference in efficacy among CCT, CACT and tDCS groups. The CACT combined with tDCS group showed greater improvement in MoCA scores compared with the other three groups (P < 0.05), especially in the terms of visuospatial and executive. BHI significantly improved only in CACT combined with tDCS group after treatment (p ≤ 0.05) but not in the other groups. Besides, no significant difference in CBFV or PI was found before and after the treatments in all groups. CONCLUSION: Both CACT and tDCS could be used as an alternative to CCT therapy to improve cognitive function and activities of daily living ability after stroke. CACT combined with tDCS may be more effective improving cognitive function and activities of daily living ability in PSCI patients, especially visuospatial and executive abilities, which may be related to improved cerebral vasomotor function reflected by the BHI. TRIAL REGISTRATION NUMBER: The study was registered in the Chinese Registry of Clinical Trials (ChiCTR2100054063). Registration date: 12/08/2021.

Neuromorphic one-shot learning utilizing a phase-transition material.

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

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.

A novel activation function based on DNA enzyme-free hybridization reaction and its implementation on nonlinear molecular learning systems.

With the advent of the post-Moore's Law era, the development of traditional silicon-based computers has reached its limit, and there is an urgent need to develop new computing technologies to meet the needs of science, technology, and daily life. Due to its super-strong parallel computing capability and outstanding data storage capacity, DNA computing has become an important branch and hot research topic of new computer technology. DNA enzyme-free hybridization reaction technology is widely used in DNA computing, showing excellent performance in computing power and information processing. Studies have shown that DNA molecules not only have the computing function of electronic devices, but also exhibit certain human brain-like functions. In the field of artificial intelligence, activation functions play an important role as they enable artificial intelligence systems to fit and predict non-linear and complex variable relationships. Due to the difficulty of implementing activation functions in DNA computing, DNA circuits cannot easily achieve all the functions of artificial intelligence. DNA circuits need to rely on electronic computers to complete the training and learning process. Based on the parallel computing characteristics of DNA computing and the kinetic features of DNA molecule displacement reactions, this paper proposes a new activation function. This activation function can not only be easily implemented by DNA enzyme-free hybridization reaction reactions, but also has good nesting properties in DNA circuits, and can be cascaded with other DNA reactions to form a complete DNA circuit. This paper not only provides the mathematical analysis of the proposed activation function, but also provides a detailed analysis of its kinetic features. The activation function is then nested into a nonlinear neural network for DNA computing. This system is capable of fitting and predicting a certain nonlinear function.

A novel algorithm for maximum power point tracking using computer vision (CVMPPT).

The behavior of an illuminated solar module can be characterized by its power-voltage curve. Tracking the peak of this curve is essential to harvest the maximum power by the module. The position of the peak varies with temperature and irradiance and needs to be traced. Under partial shading conditions, the number of peaks increases and makes it more difficult to find the global maximum power point (MPP). Various methods are used for maximum power point tracking (MPPT) that are based on iterations. These methods are time-consuming and fail to work satisfactorily under rapidly changing environmental conditions. In this paper, a novel algorithm is proposed that for the first time, utilizes computer vision to find the global maximum power point. This algorithm, which is implemented in Matlab/Simulink, is free of voltage iterations and gives the real-time data for the maximum power point. The proposed algorithm increases the speed and the reliability of the MPP tracking via replacing analogue electronics calculations by digital means. The validity of the algorithm is experimentally verified.

Integrated Portable and Stationary Health Impact-Monitoring System for Firefighters.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.