Potential Aid Research Articles

Exploring Saudi Teacher’s Readiness to Use Augmented Reality (AR) Technology to Teach English Language in Schools

This study seeks to investigate the readiness of Saudi teachers to use augmented reality (AR) technology to enhance English language instruction in secondary schools in Riyad, Saudi Arabia. With the increasing integration of technology into educational settings, AR technology has garnered considerable attention as a potential aid for engaging and motivating language learners. Throughout this research, ten Saudi English language teachers from selected secondary schools in Riyad were interviewed to collect data. The findings consistently indicate a resoundingly positive response to AR technology, with teachers keen to incorporate it into English language classes. In addition, the study revealed that Saudi teachers recognised several significant advantages of AR technology in English language teaching. These results suggest that AR has the potential to revolutionize traditional teaching methods and improve the educational experience for both teachers and students. English language teachers in the study area are eager to adopt AR technology as an effective instrument for teaching English in secondary schools. This study catalyzes future research and collaboration to effectively integrate AR technology into the English language curriculum, thereby transforming the way English is taught and learned in Saudi Arabian secondary schools.

Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks.

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety. To predict interactions between drugs used to treat these two diseases, we propose a method named Multi-Drug Features Learning with Drug Relation Regularization (MDFLDRR). First, we map feature vectors representing drugs in different feature spaces to the same. Second, we propose drug relation regularization to determine drug pair relationships in the interaction space. Experimental results demonstrate that MDFLDRR can be effectively applied to two DDI prediction goals: predicting unobserved interactions among drugs within the drug network and predicting interactions between drugs inside and outside the network. Publicly available evidence confirms that MDFLDRR can accurately identify DDIs between cardiovascular drugs and antidepressants. Lastly, by utilizing drug structure calculations, we ascertained the severity of newly discovered DDIs to mine the potential co-medication risks and aid in the smart management of pharmaceuticals.

HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration.

Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods for image analysis make them a potential aid in digital pathology. However, A significant challenge in developing computer-aided diagnostic systems for pathology is the lack of intuitive, open-source web applications for data annotation. This paper proposes a web service that efficiently provides a tool to visualize and annotate digitized histological images, integrating AI-driven predictive insights. While the tool is capable of handling various image formats, its primary use case is for Whole Slide Imaging (WSI) in the TIFF format, specifically tailored for histopathology applications. This innovative integration not only revolutionizes accessibility but also democratizes the utilization of complex deep-learning models for pathologists unfamiliar with such tools. Moreover, to demonstrate the effectiveness of this approach, we present a use case centered on the diagnosis of spindle cell skin neoplasm involving multiple annotators. Additionally, we conduct a usability study, showing the feasibility of the developed tool.

Vascularized lymph node transfer (VLNT) versus lymphaticovenous anastomosis (LVA) for chronic breast cancer-related lymphedema (BCRL): a retrospective cohort study of effectiveness over time.

Microsurgical reconstruction, including vascularized lymph node transfer (VLNT) and lymphaticovenous anastomosis (LVA), have emerged as promising treatment options for chronic breast cancer-related lymphedema (BCRL). Despite their clinical relevance, the precise timelines for patient improvement following these interventions remain rather unexplored. Therefore, the goal of this study was to compare the long-term outcomes and improvement patterns over time of VLNT versus LVA to lay open potential differences and aid in personalized counseling of future patients. A prospectively maintained, encrypted database was analyzed for patients with chronic BCRL treated with either VLNT or LVA with a minimum follow-up of one year. Patient-specific variables, such as body weight and circumferential arm measurements at distinct locations on both arms were documented preoperatively and on regular postoperative outpatient follow-ups. This study comprised 112 patients, of which 107 patients fully completed the one-year follow-up period. Both VLNT and LVA achieved significant arm size reductions. LVA showed an early peak in effectiveness within the first three months, followed by a subsequent decrease and eventual stabilization. Contrarily, VLNT exhibited a distinct pattern with two significant peaks at three and eighteen months. VLNT and LVA are both effective in long-term lymphedema management, yet they demonstrate marked differences in the timing of improvement. VLNT shows a delayed but more durable response, in contrast to the greater but shorter-lasting surge in effectiveness achieved by LVA. Interestingly, VLNT demonstrates an earlier onset of therapeutic impact than previously understood.

Identifying potential medical aid beneficiaries using machine learning: A Korean Nationwide cohort study.

To identify potential medical aid beneficiaries using demographic and medical history of individuals and analyzing important features qualitatively. This retrospective, national cohort, case-control study included data from the National Health Insurance Service (NHIS) in Korea between January 1, 2002 and December 31, 2019. Potential medical aid beneficiaries were classified using several machine learning models (linear models and tree-based models). Demographic data such as age, sex, region, insurance type, insurance fee, and medical history such as diagnosis, operation, statement, visits, and costs were collected. Those data were transformed into a one-dimensional vector for each individual, allowing machine learning models to learn. For feature importance calculation, we used the average gain across all splits for each feature. 274,635 individuals were finally included in the study population, and 62,501 were classified as potential medical aid beneficiaries. XGBoost successfully classified potential medical aid beneficiaries with an AUROC of around 0.891. Assuming predicting before two years, the performance was still significant with an AUROC of around 0.832. Economic variables, such as insurance fees and several costs, turned out to be the most important, but variables regarding medical status, such as the results of blood tests and history of chronic diseases, were also important. Machine learning-based models successfully screened potential medical aid beneficiaries. Qualitative analysis of important features well reflected prior knowledge regarding public health. These findings can contribute to the soundness of healthcare finance and the improvement of public health.

Beetroot Juice Supplementation for Enhanced Endurance Exercise Performance: A Systematic Review

Beetroot juice supplementation has emerged as a potential ergogenic aid for endurance athletes. This systematic review aimed to summarize the current evidence on the effects of beetroot juice supplementation on endurance exercise performance. A comprehensive search of electronic databases yielded 10 studies that met the inclusion criteria. The results showed that beetroot juice supplementation improved exercise performance, reduced oxygen consumption, and enhanced muscle oxygenation during exercise. Additionally, beetroot juice supplementation may also reduce exercise-induced muscle damage. The available evidence suggests that beetroot juice supplementation can be a useful adjunct to endurance training. However, further studies are needed to fully elucidate the effects of beetroot juice supplementation on exercise performance and to determine the optimal dosage and duration of supplementation.

The Role of Exogenous Ketones in Road Cycling: Evidence, Mechanisms, and Performance Claims

Background: Ketone body supplementation has emerged as a potential ergogenic aid in cycling. Exogenous ketones, primarily in the form of beta-hydroxybutyrate, offer an alternative fuel source, bypassing the need for strict ketogenic diets. However, the science surrounding their efficacy remains complex, with mixed field findings and unexplored mechanisms. Methods: A narrative review of the current literature was conducted, synthesizing studies on the metabolic and cognitive effects of ketone bodies in cycling. The review included an examination of human and mechanistic studies, along with emerging hypotheses on ketone bodies and their role in modulating red blood cell production and recovery processes. Results: Ketone body supplementation can theoretically spare glycogen, reduce muscle protein breakdown, enhance fat oxidation, and improve recovery by mitigating oxidative stress and inflammation. Additionally, ketone bodies may support cognitive function, reducing perceived mental fatigue. Preliminary evidence also suggests a potential role in modulating erythropoietin levels through histone acetylation, though further research is needed to establish its impact on oxygen delivery. Despite the theoretical potential, the practical assessment of field studies shows disappointing effects on performance from ketone body supplementation. Conclusions: While ketone bodies offer several potential benefits for cyclists, the variability in individual responses, lack of long-term data, and inconsistent findings in performance studies highlight the need for further research. Optimizing dosage, timing, and understanding the broader implications of ketone body supplementation will be crucial for their practical application in cycling.

From Smoking to Vaping: The Motivation for E-Cigarette Use at the Neurobiological Level - An fMRI Study.

Electronic cigarettes (e-cigarettes) are considered a potential aid for smoking cessation. This study investigates the neurobiological mechanisms underlying the motivational properties of e-cigarettes compared to tobacco cigarettes and provides insights into their addictive potential and implications for smoking cessation. The study included 47 daily e-cigarette users, the majority of whom were former smokers (n=43), and 28 nicotine-naïve participants as controls. Among the e-cigarette users, 32 individuals currently used e-cigarettes exclusively, while 15 also used tobacco cigarettes (dual users). Functional magnetic resonance imaging was used to examine anticipatory brain activation to reward-predictive stimuli for money, e-liquid, or tobacco cigarettes, as well as the instrumental response to obtain each reward. Exclusive e-cigarette users demonstrated increased activation in limbic regions, e.g., the nucleus accumbens, during the anticipation of e-liquid and money over tobacco cigarettes. Prefrontal regions showed greater activation during the anticipation of money compared to e-liquid or tobacco cigarettes. Dual users showed greater activation for money compared to tobacco cigarettes in the hippocampal limbic system. No significant differences were observed in the anticipation of e-liquid and tobacco cigarettes among dual users. E-cigarettes are perceived as equally rewarding as money, highlighting their addictive potential. Among exclusive e-cigarette users, money and e-liquid have stronger motivational properties than tobacco cigarettes (even among former smokers), suggesting that e-cigarettes may be an effective tool for tobacco cessation. The comparable appeal of e-liquid and tobacco cigarettes among dual users may suggest that a complete switch to e-cigarettes may be more effective for smoking cessation. It is important to understand the motivational properties of e-cigarettes, particularly compared to traditional tobacco products. E-cigarettes with little or no addictive potential might not be effective as a substitute for tobacco cigarettes; however, a high addictive potential would, conversely, pose a risk to non-smokers. Our data suggest that e-cigarettes have a sufficiently high addictive potential to make them an attractive alternative for smokers, especially if they switch completely from tobacco to e-cigarettes.

Augmented reality: A potential aid for preoxygenation in children

Augmented reality: A potential aid for preoxygenation in children

Innovation in physical education teaching based on biomechanics feedback: Design and evaluation of personalized training programs

Psychometric training is a learning and development process that is tied to the requirements and interests of individual learners. The effectiveness and the fun of workout practices can be greatly enhanced by using more personal methods in training. These programs entail tailoring workouts to the fitness level and aims of every participant, as well as their perception of the workout plans. The suggestion of this investigation is to develop and evaluate the effectiveness of individual physical training for teachers focused on biomechanics feedback. Proper body alignment is crucial during any exercise to avoid injuries and achieve maximum results; it is always difficult to sustain correct posture. For motion tracking and to give real-time biomechanics feedback to the students in this study, a refined convolutional neural network (RCNN) has been presented. Filming was done using high-speed cameras and motion capture systems to capture biomechanical responses, which included joint angle, muscle activation patterns, and body posture during activities. This study involved 158 participants drawn from different learning institutions. This approach offers rational and specific feedback on the postures of the body; it enables people to correct themselves and sustain motivation without engaging a trainer. In this study, participants with different levels of fitness engaged with the interactive system were compared to the traditional training method. The result indicated a positive shift in the delivery of personalized training with biomechanics feedback on the system’s potential teaching aid in physical education classes. The study, thus underscores the importance of technology supporting change in physical education programs to improve the student’s learning experiences and their performance in exercises.

Fast onset of thrombolytic effect of efficiently inhalable spray-dried rivaroxaban powder formulations

Pulmonary embolism is a critical medical condition and can lead to cardiovascular arrest or death if left untreated. Pulmonary delivery of an anti-coagulant therapeutic could provide a locally limited and efficient therapy, moreover combined with a potentially fast onset of the therapeutic effect that is demanded in emergency situations. Rivaroxaban (riva) was formulated as an inhalable dry powder that can be administered directly to the lungs in order to reach high local drug doses. Particles obtained spray drying dichloromethane-methanol mixtures yielded an aerodynamic diameter of about or smaller than 5 µm and were found to reach an emitted fine particle fraction (FPFEF) of at least 60 %. Pulmonary administration to rats using a dry powder insufflator revealed a nearly immediate therapeutic onset confirmed by the anticoagulant effects after 5 min in the plasma, followed by a plateau over the next 4 h. Such rivaroxaban particles were further formulated in binary blends using different lactose carriers (Inhalac® 70/251/400). These interactive blends resulted in high emitted fractions of at least 87 %, and furthermore, the highest fine particle fraction − total dose (FPFTD) (60 %) of all tested formulations was achieved by using Inhalac® 400 as a carrier. The data strongly suggest a significant therapeutic potential of a rivaroxaban dry powder formulation, combining fast onset and a plateau of plasma concentrations turning it into a potential first aid therapeutic.

Phytonutrient formulation using gum Arabic and Adansonia Digitata pulp: Lessons for boosting the human immune system - part 1

BackgroundThe human innate immune system (HIIs) defends the body against pathogens and relies on the energy obtained from food sources to increase its metabolic activity. AimIn this study, a new blended 3 in1 formula comprising A. senegal, A. seyal, and Adansonia digitata at a ratio of 5:3:2 was proposed as a potential aid in the management of Covid-19 patients with Metabolic Syndrome (MeT-S) disorders, with a specific focus on those presenting with impaired health. MethodsThe utilization of Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-MS) was employed to analyze 3 blended ingredient combinations, including the 3in1 formula, A. senegal, A. seyal, and A. digitata pulp, for the presence of elements by detecting the levels of moisture, ash, minerals, protein, amino acids, carbohydrates, sugars, prebiotic polysaccharides, energy, dietary fibers, and crude fibers. This advanced technique has replaced the titration method used in previous studies. ResultsThe mineral content of the blended 3in1 sample was found to be dominated by potassium (70.56±2.35), Calcium (68.54±3.12), Magnesium (16.60±4.80), Phosphorus (11.50±2.50), Sodium (31.4±4.80), Zinc (19.19±2.10), Iron (19 ± 0.14), Copper (15.12±1.81), and selenium (0.037 ± 0.005) g/100 g DW. The blended 3 in1 sample also had a higher hydroxyproline content (30.17 g/100 g and a higher total protein and carbohydrate content than the blended 2 in1 sample. Additionally, arabinose was identified as the major reducing sugar with up to 48.23 and 49.97g/100 gDW. The prebiotic polysaccharide content was 88.61±3.12g/100 gDW for the blended 3 in1 sample, which was significantly higher than that of the blended 2 in1 sample. Furthermore, the energy value of the blended 3 in1 sample was significantly higher at 350.12±5.210 kcal/100 g than the blended 2 in1 sample at 340.67±3.155 kWh. The Insoluble Dietary Fibers (IDFs) and Soluble Dietary Fibers (SDFs) of the blended 3 in1 sample were 12.6±2.10 g/100 gDW and 87±2.124 g/100 g DW, respectively, which were significantly higher than those of the blended 2 in1 sample. ConclusionThe discovery of distinct molecular compositions of A. senegal and A. seyal when combined with Adansonia Digitata Pulp (ADLP) in the 3 in1 blend demonstrated enhanced HIIs among Covid-19 patients with compromised MeT-S disorders. This may be considered a valuable natural antiviral agent for the treatment of such individuals.

Sleep Potentiating Effect of <i>Pleurotus squarrusolus</i> Ethanolic Extract on Diazepam-Induced Sleeping Time in Male Wistar Rats

This study examines the sleep-promoting properties of an ethanolic extract derived from <i>Pleurotus squarrosulus</i> in male Wistar rats, particularly in conjunction with the sleep-inducing effects of diazepam. The impetus for this research is driven by the rising global incidence of sleep disorders and the ongoing need for effective, natural adjuncts to current therapeutic strategies. The bioactive compounds identified in <i>Pleurotus squarrosulus</i> may exert mood-modulating effects, which could play a role in its overall therapeutic impact on sleep and well-being. The extract was subjected to analysis using High-Performance Liquid Chromatography (HPLC), which revealed the presence of gallic acid, p-coumaric acid, ferulic acid, catechin, rutin, apigenin, quercetin, and kaempferol. Among these compounds, gallic acid and quercetin exhibited the highest concentrations. Diazepam, a traditional sedative, was administered alone as well as in combination with two different doses of the extract, namely 250mg and 500mg. The results demonstrated a significant increase in sleeping time in both the Diazepam + Sample 250mg and Diazepam + Sample 500mg groups compared to the control group, indicating an enhancement in sleep quality. These findings are consistent with previous research, which highlighted the presence of bioactive compounds known for their diverse biological effects. This study introduces <i>Pleurotus squarrosulus </i>as a potential natural sleep aid, showcasing a promising synergy when combined with diazepam. Further investigation into the mechanisms and safety considerations of this combination offers an optimistic path for the development of integrated approaches to sleep management.

Exploring The Connection of Spirituality at Work in Enhancing Nurses’ Job Performance: Insight from Malaysian Hospitals

The performance of nurses can be seen as a reflection of healthcare organizations, as they constitute the most critical human resources within hospitals. However, challenges arise when issues such as staff shortages, high turnover rates, and burdensome workloads contribute to poor nurse performance. Conversely, the incorporation of spirituality in the workplace provides nurses with an opportunity to align their inner selves within a supportive community environment, fostering a deeper sense of purpose in their professional responsibilities. This study sought to determine the connection between spirituality at work and nurses’ job performance, surveying 350 nurses employed in public hospitals across Malaysia. Data analysis was conducted using Statistical Package for Social Sciences (SPSS) software version 27, revealing that all three aspects of spirituality at work, namely inner life, meaningful work, and sense of community, had a positive and significant influence on nurses’ job performance. This study provides potential aid to decision-makers in Malaysian healthcare organizations by offering significant insights for the development of strong spirituality at work values within their organizational setting. Additionally, this study presents recommendations for future research and proposes methods for the authorities to incorporate spirituality at work values into nurses’ jobs, ultimately enhancing the overall performance of healthcare services.

TMS and tDCS as potential tools for the treatment of cognitive deficits in Parkinson's disease: a meta-analysis.

Cognitive deficits are common nonmotor symptoms in Parkinson's disease (PD). Non-Invasive Brain Stimulation (NIBS) could be a potential aid to prevent or delay dementia progression in this clinical population. However, previous studies reported controversial results concerning their efficacy on cognitive symptoms of PD. Hence, the present meta-analysis aims to systematically examine the effects of NIBS as possible treatments for PD cognitive impairments. Understanding NIBS' impact on these symptoms may be of outstanding importance to implement new therapeutic strategies and improve the patients' quality of life. EMBASE, Scopus, and PubMed databases were systematically searched for consecutive studies published from 2000 to March 2023 describing Randomized Controlled Trials studies evaluating the effect of NIBS on PD cognitive symptoms. From the included studies, data concerning neuropsychological tests were extracted and grouped into six cognitive domains, separately analyzed. Hedge's method was computed as the effect size measure of the extracted data; heterogeneity among studies and publication bias were also assessed. The Cochrane's RoB2 tool was used to evaluate the risk of bias for each of the included studies. After database searching and screening of texts, sixteen studies met the inclusion criteria. No significant results emerged from any investigated cognitive domain when comparing NIBS and sham treatments. Several factors may have contributed to the lack of effects; among these, methodological choices, the small sample of studies, the high heterogeneity of data and stimulation protocols pose the need for more controlled studies to highlight the potentiality of NIBS as a future treatment for PD cognitive impairments.

EFEITOS DO SUCO DE UVA TINTO INTEGRAL NA PRESSÃO ARTERIAL DE REPOUSO DE MULHERES CLIMATÉRICAS

Systemic Arterial Hypertension (SAH) is a disease that, in most cases, involves genetic factors and lifestyle that affects approximately 21.4% of the Brazilian population, with 55% of this number represented by women and individuals over 75 years, accounting for 22.6% of deaths in Brazil. In women, the increased risk of developing SAH occurs in the period corresponding to the climacteric. Considered as a moment of transition between a woman's reproductive and non-reproductive period, the climacteric occurs between 40 and 60 years and involves the following phases: premenopause, perimenopause and postmenopause. It is known that treatment for SAH is more effective when it involves nutritional and lifestyle strategies combined with drug treatment. Among the alternatives, studies indicate the existence of a hypotensive effect in substances derived from grapes. Thus, this study aimed to observe the effect of ingestion of whole red grape juice on resting blood pressure in climacteric and hypertensive women in the interior of São Paulo. This is a randomized controlled, exploratory study, which included women aged 40 to 60 years with more than three months of amenorrhea and at least two climacteric symptoms. They were randomly divided into 2 groups: Control Group (CG) and Intervention Group (IG) to ingest 190 ml of whole grape juice for 28 days. The volunteers answered self-administered questionnaires and underwent physical examinations and measurement of resting blood pressure data during the first and last visit to compare the effect of the intervention. After the experimental protocol, it was observed that the protocol and dose used were able to cause a decrease in diastolic blood pressure in the intervention group, without causing significant changes in other parameters. Despite the need for further studies with greater control in the organization of groups, in addition to more doses and treatment times, this work corroborates the data that whole grape juice is a potential aid in controlling blood pressure in postmenopausal women.

Carbon monoxide inhalation as a potential aid to enhance aerobic endurance.

Carbon monoxide inhalation as a potential aid to enhance aerobic endurance.

Effects of HTLV-1 on leukocyte trafficking and migration in ACs compared to healthy individuals

Human T-lymphotropic virus type 1 (HTLV-1) is a RNA virus belonging to Retroviridae family and is associated with the development of various diseases, including adult T-cell leukemia/lymphoma (ATLL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). Aside from HAM/TSP, HTLV-1 has been implicated in the development of several disorders that mimic auto-inflammation. T-cell migration is important topic in the context of HTLV-1 associated diseases progression. The primary objective of this case–control study was to assess the relationship between increased mRNA expression in virus migration following HTLV-1 infection. PBMCs from 20 asymptomatic patients and 20 healthy subjects were analyzed using real-time PCR to measure mRNA expression of LFA1, MLCK, RAC1, RAPL, ROCK1, VAV1 and CXCR4. Also, mRNA expression of Tax and HBZ were evaluated. Mean expression of Tax and HBZ in ACs (asymptomatic carriers) was 0.7218 and 0.6517 respectively. The results revealed a noteworthy upregulation of these genes involved in T-cell migration among ACs patients in comparison to healthy individuals. Considering the pivotal role of gene expression alterations associated with the progression into two major diseases (ATLL or HAM/TSP), analyzing the expression of these genes in the ACs group can offer probable potential diagnostic markers and aid in monitoring the condition of ACs.

Tissue Classification After Bone-Anchored Hearing Implant Surgery: A Machine Learning Approach to Monitoring Skin Response.

Here, we aim to 1) expand the available evidence for the use of machine learning techniques for soft tissue classification after BCD surgery and 2) discuss the implications of such approaches toward the development of classification applications to aid in tissue monitoring. The application of machine learning techniques in the soft tissue literature has become a large field of study. One of the most commonly reported outcomes after percutaneous bone-conduction device (BCD) surgery is soft tissue health. Unfortunately, the classification of tissue around the abutment as healthy versus not healthy is a subjective process, even though such decisions can have implications for treatment (i.e., topical steroid versus surgical revision) and resources (e.g., clinician time). We built and tested a convolutional neural network (CNN) model for the classification of tissues that were rated as "green" (i.e., healthy), "yellow" (i.e., unhealthy minor), and "red" (i.e., unhealthy severe). Representative image samples were gathered from a regional bone-conduction amplification site (N = 398; 181 samples of green; 144 samples of yellow; 73 samples of red). The image samples were cropped, zoomed, and normalized. Feature extraction was then implemented and used as the input to train an advanced CNN model. Accuracy of image classification for the healthy ("green") versus not healthy ("yellow" and "red") model was approximately 87%. Accuracy of image classification for the unhealthy ("yellow") versus unhealthy ("red") model was approximately 94%. Monitoring tissue health is an ongoing challenge for BCD users and their clinicians not trained in soft tissue management (e.g., audiologists). If machine learning can aid in the classification of tissue health, this would have significant implications for stakeholders. Here we discuss how machine learning can be applied to tissue classification as a potential technological aid in the coming years.

Physiological Biomarkers of Upper Motor Neuron Dysfunction in ALS.

Upper motor neuron (UMN) dysfunction is an important feature of amyotrophic lateral sclerosis (ALS) for the diagnosis and understanding of pathogenesis. The identification of UMN signs forms the basis of ALS diagnosis, although may be difficult to discern, especially in the setting of severe muscle weakness. Transcranial magnetic stimulation (TMS) techniques have yielded objective physiological biomarkers of UMN dysfunction in ALS, enabling the interrogation of cortical and subcortical neuronal networks with diagnostic, pathophysiological, and prognostic implications. Transcranial magnetic stimulation techniques have provided pertinent pathogenic insights and yielded novel diagnostic and prognostic biomarkers. Cortical hyperexcitability, as heralded by a reduction in short interval intracortical inhibition (SICI) and an increase in short interval intracortical facilitation (SICF), has been associated with lower motor neuron degeneration, patterns of disease evolution, as well as the development of specific ALS clinical features including the split hand phenomenon. Reduction in SICI has also emerged as a potential diagnostic aid in ALS. More recently, physiological distinct inhibitory and facilitatory cortical interneuronal circuits have been identified, which have been shown to contribute to ALS pathogenesis. The triple stimulation technique (TST) was shown to enhance the diagnostic utility of conventional TMS measures in detecting UMN dysfunction. Resting-state EEG is a novel neurophysiological technique developed for directly interrogating cortical neuronal networks in ALS, that have yielded potentially useful physiological biomarkers of UMN dysfunction. The present review discusses physiological biomarkers of UMN dysfunction in ALS, encompassing conventional and novel TMS techniques developed to interrogate the functional integrity of the corticomotoneuronal system, focusing on pathogenic, diagnostic, and prognostic utility.