Statistical Significance Tests Research Articles

Critical analysis of Parkinson’s disease detection using EEG sub-bands and gated recurrent unit

Parkinson’s disease, the second most common neurodegenerative disorder in the world. A progressive disease, which can worsen over time and lead to complications like mild cognitive impairments and dementia. The accurate diagnosis of Parkinson’s Disease (PD) remains a critical challenge in medical engineering. This study explores the potential of brain wave patterns for PD detection in healthy and unhealthy patients. The Power Spectral Density (PSD) and proposed PD detection model based on the Gated Recurrent Unit (GRU) is used to analyze brain activities. It was found that the detection of PD in patients was improved by classifying the RAW EEG in conjunction with the sub-bands using PSD as a feature and GRU as a classifier. The performance matrices including accuracy, precision, recall, and F1-score fall within a range of 90% to 98% for alpha, beta, and gamma sub-bands, while the area under the curve in the case of receiver operating characteristics curve achieved the maximum value of 1.00. To assess the differences between the groups with Parkinson’s disease and the healthy group, a statistical significance test was performed. The power spectral density of the two groups differed statistically significantly, according to the results, indicating that they could be useful as biomarkers for the identification of Parkinson’s disease. The results are compared and validated with the standard performance measures.

Detecting Differential Item Functioning Using Response Time

This study investigated uniform differential item functioning (DIF) detection in response times. We proposed a regression analysis approach with both the working speed and the group membership as independent variables, and logarithm transformed response times as the dependent variable. Effect size measures such as Δ[Formula: see text] and percentage change in regression coefficients in conjunction with the statistical significance tests were used to flag DIF items. A simulation study was conducted to assess the performance of three DIF detection criteria: (a) significance test, (b) significance test with Δ[Formula: see text], and (c) significance test with the percentage change in regression coefficients. The simulation study considered factors such as sample sizes, proportion of the focal group in relation to total sample size, number of DIF items, and the amount of DIF. The results showed that the significance test alone was too strict; using the percentage change in regression coefficients as an effect size measure reduced the flagging rate when the sample size was large, but the effect was inconsistent across different conditions; using Δ R2 with significance test reduced the flagging rate and was fairly consistent. The PISA 2018 data were used to illustrate the performance of the proposed method in a real dataset. Furthermore, we provide guidelines for conducting DIF studies with response time.

Generalization of neural network models for complex network dynamics

Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph. Data-driven approximations of differential equations present a promising alternative to traditional methods for uncovering a model of dynamical systems, especially in complex systems that lack explicit first principles. A recently employed machine learning tool for studying dynamics is neural networks, which can be used for solution finding or discovery of differential equations. However, deploying deep learning models in unfamiliar settings-such as predicting dynamics in unobserved state space regions or on novel graphs-can lead to spurious results. Focusing on complex systems whose dynamics are described with a system of first-order differential equations coupled through a graph, we study generalization of neural network predictions in settings where statistical properties of test data and training data are different. We find that neural networks can accurately predict dynamics beyond the immediate training setting within the domain of the training data. To identify when a model is unable to generalize to novel settings, we propose a statistical significance test.

Examining Long-Term Influences of Frailty on Outcomes for Adults Undergoing Left Ventricular Assist Device Therapy.

Frailty is common in adults with end-stage heart failure receiving a left ventricular assist device (LVAD). Short-term studies show frailty reversal post-LVAD. Little is known about long-term frailty and how frailty relates to key LVAD outcomes, including depression, quality of life (QoL), and cognition beyond 6 months. The purpose of this study was to examine physical frailty, depression, QoL, and cognition from pre-LVAD implantation to 3, 6, and 12 months post-LVAD implantation. Clinical data and measures of physical frailty, depression, QoL, and cognition pre- and post-LVAD were extracted from an existing institutional database. Descriptive statistics and tests for statistical significance were used to describe and compare changes over time. Frailty trajectories were identified to describe physical frailty from pre-LVAD to 12 months post-LVAD. The sample (n = 46) was predominantly male (76%), with a mean age of 64.7 ± 11 years, and over half (n = 25) were physically frail. Physical frailty was reduced by nearly half at 12 months. Frailty, depression, and QoL significantly improved at all time points (P < .001). Improvements in cognition did not reach statistical significance. Recipients of LVAD without improvement in frailty exhibited worse depression, QoL, and cognition scores at 12 months compared with those not frail pre-LVAD or no longer frail post-LVAD implantation. Some recipients of LVADs experienced physical frailty reversal with benefits sustained long-term. Those without improvement recorded worse long-term patient-reported outcomes. Identifying factors that predict physical frailty response to LVAD implantation is a key area of future research.

Mammographic density and breast cancer risk among Black American women.

High mammographic density is a well-established risk factor for breast cancer; however, data from Black women are limited. It is largely unknown how mammographic density is associated with breast cancer subtypes among Black women. We examined the association between percent mammographic density (PMD) and breast cancer risk among participants in the Black Women's Health Study. Digital screening mammograms were available for 363 cases and 5541 non-cases. Cumulus software was used to assess PMD. We used inverse probability of sampling weights and Cox proportional hazards models, adjusted for age and body mass index, to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) overall and by age at mammography and estrogen receptor (ER) status of the breast tumors. Multivariable models included additional breast cancer risk factors. Tests of statistical significance were 2-sided. In simple models, women in the highest quartile of PMD had 53% increased odds of breast cancer compared to those in the lowest quartile (HR 1.53; 95% CI: 1.11, 2.11). HRs were 1.37 (95% CI: 0.83, 2.24) among women <55 years of age and 1.68 (95% CI: 1.10, 2.56) among women aged ≥55 years. HRs were 1.49 (95% CI: 1.02, 2.16) for ER+ cancer and 1.45 (95% CI: 0.73, 2.87) for ER- cancer. Associations were largely unchanged in multivariable models. In this study of U.S. Black women, higher PMD was associated with ER+ and ER- breast cancer risk. Findings from this study reinforce the importance of breast density as a risk factor for breast cancer in Black women.

Sequence-to-Sequence Models and Their Evaluation for Spoken Language Normalization of Slovenian

Sequence-to-sequence models have been applied to many challenging problems, including those in text and speech technologies. Normalization is one of them. It refers to transforming non-standard language forms into their standard counterparts. Non-standard language forms come from different written and spoken sources. This paper deals with one such source, namely speech from the less-resourced highly inflected Slovenian language. The paper explores speech corpora recently collected in public and private environments. We analyze the efficiencies of three sequence-to-sequence models for automatic normalization from literal transcriptions to standard forms. Experiments were performed using words, subwords, and characters as basic units for normalization. In the article, we demonstrate that the superiority of the approach is linked to the choice of the basic modeling unit. Statistical models prefer words, while neural network-based models prefer characters. The experimental results show that the best results are obtained with neural architectures based on characters. Long short-term memory and transformer architectures gave comparable results. We also present a novel analysis tool, which we use for in-depth error analysis of results obtained by character-based models. This analysis showed that systems with similar overall results can differ in the performance for different types of errors. Errors obtained with the transformer architecture are easier to correct in the post-editing process. This is an important insight, as creating speech corpora is a time-consuming and costly process. The analysis tool also incorporates two statistical significance tests: approximate randomization and bootstrap resampling. Both statistical tests confirm the improved results of neural network-based models compared to statistical ones.

Leveraging Machine Learning for Sophisticated Rental Value Predictions: A Case Study from Munich, Germany

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R2 score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R2 and RMSE (p-values &lt; 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector.

Rice Leaf Diseases Classification Using Deep Learning Algorithms for Smartphone Application Development: An Empirical Study

Rice is the most widely consumed grain across the world. The rice plants often suffer from diseases. Early detection of such diseases and adopting remedial measures can help the farmers to avoid major losses and can produce best-quality crops in large quantities. However, the conventional rice leaf disease detection techniques are often not accurate, time-consuming and sometimes require laboratory testing. In this context, automatic rice leaf disease detection techniques are presented based on the various deep learning classifiers (namely MobileNetV2, ResNet50, VGG16 and Le-Net5) and an Android application is also developed in order to instantly determine the possible rice diseases from the uploaded rice leaf images captured by the smartphone. The developed models are tested using the publicly available benchmark rice leaf dataset containing three types of rice leaf diseases, namely bacterial leaf blight, leaf smut and brown spot. Experimental results show that MobileNetV2 model performed better compared to other models in terms of classification accuracy, recall and [Formula: see text]-score. The results of statistical significance test also confirmed the superiority of the MobileNetV2 model over other compared deep learning models.

Prevalence of Internet Use among Adolescents of 15 to 18 Years of Age in A Village under RHTC of A Medical College: Cross-Sectional Study

Background: The environment in which today’s adolescents are growing has changed drastically and affected their access to internet as well. The study was conducted with the aim of estimating the prevalence of internet use and pattern of internet use among adolescents of a village area. Methods: About 114 adolescents of a village in Western Maharashtra belonging to age group 15-18 years participated in the study. They were selected based on the inclusion criteria of age group and willingness to participate in the study via universal sampling method and were interviewed with the help of semi-structured questionnaire, which included questions regarding socio-demography, internet use and its interrelationship with other variables. Chi-square test was used as a test of statistical significance to study the association between the predictor variable and the outcome with (p&lt;0.05) as a level of significance. Results: The prevalence of internet use was 99.12%. Where 74% of adolescents used internet for an average of 1-2 hours in a day and 26% used internet for an about 2-4 hours in a day. Taking p &lt; 0.05 as a level of significance we found gender was significantly associated with duration of internet use (p = 0.01) and Purpose of internet use (p = 0.004). Conclusion: Almost all adolescents have access to internet and are more inclined towards its use for non-educational purpose which can be detrimental for their educational progress and long-term career goals. Hence, monitoring of internet use by adolescents has to be done by guardians/teachers. Further studies should be conducted on cause-effect relationship to study the either way of association.

Emergency physicians perspectives of state continuing medical education requirements for medical licensure.

This study aims to better understand the perspectives of emergency medicine physicians' on the role that state-mandated, topic-specific continuing medical education (CME) plays in addressing knowledge gaps, its relevance to current emergency practice, its reported burden and costs of CME activities to emergency physicians, and its perceived improvement in patient care. A cross-sectional survey was designed by the Coalition of Board-Certified Emergency Physicians (COBCEP) and distributed in February 2023 to all American Board of Emergency Medicine (ABEM)-certified physicians. Statistical tests of significance (Pearson's chi-square and Fisher's exact test) assessed the cost and time spent on CME as well as the perceived value placed on CME by ABEM-certified physicians to improve patient care. Data were summarized using descriptive statistics. There were 5562 (13.0%) responses from the 43656 physicians who received the survey-5506 responses were included for analysis. Over half of the physicians (53.0%) had more than 15 years of post-residency practice experience. Most physicians (57.3%) spent less than $5,000 per year on obtaining CME. Most physicians practicing in states with state-mandated, topic-specific CME requirements believed that participation in ABEM continuing certification could be used to reduce the need for state-mandated, topic-specific CME requirements (83.6%) and state-mandated, topic-specific requirements were believed to be unlikely to improve patient care (70.8%). Although well-intended, state CME requirements may lack relevancy and can, at times, place an undue burden on emergency physicians. Tailoring CME requirements to increase relevance to their patient populations and reduce barriers to completing CME could enhance knowledge translation and improve patient outcomes.

The role of effect size and significance test in research design and analysis

Effect size is a straightforward method for measuring the difference between two groups and plays a critical role in research design and significance testing. This paper addresses the concept of effect size, explaining what it is, how it is calculated, and how it can be interpreted. It also explores related topics, such as statistical significance, meta-analysis, and power analysis. Effect sizes can be understood in the context of confidence intervals, which provide similar information to statistical significance tests but focus more on the magnitude of the effect rather than the size of the sample.

ANALYTICAL STUDY ON FACTORS AFFECTING THE ATTITUDE AND PERSPECTIVES OF FEMALE PATIENTS ON THE USE OF ARTIFICIAL INTELLIGENCE IN THE ASSESSMENT OF SCREENING MAMMOGRAMS

Background: Breast cancer is one of the most common cancers among Indian women, with an incidence of 25.8 per 100,000 women according to the Ministry of Health and Family Welfare. Late detection is responsible for poor quality of life (QOL), and it is the leading cause of death. Studies have shown that people are positive towards AI performing assessment tasks in healthcare in general and in mammography screening. Moreover, it is challenging to implement strategies based on self-breast inspection or do mammography in rural regions or low and middle income nations for a variety of reasons. Objective: This study was conducted to assess the perception and attitude of various female patients reporting to the radiology unit of a tertiary care centre regarding the use of Artificial Intelligence in routine mammogram screening. Study Design: A cross sectional questionnaire based study was conducted female patients attending the radiology unit of a tertiary Care Centre in Central Kerala. Methods: After obtaining, informed written consent was taken from the study participants. A predesigned, pretested, validated checklist was used to collect the required data. The Knowledge, attitude and practice was ascertained through Likerts scale and scoring done accordingly. Statistical tests of significance was employed to assess the possible associations between various variables with the knowledge, attitude and perceptions regarding Artifical Intelligence based Mammogram Results: Among the 170 study subjects analysed, 68.6% had a satisfactory knowledge about Artificial intelligence. Among the 170 study subjects analysed, 86% had a good attitude about being screened through AI enabled mammogram techniques. 92% of the total study subjects registered for mammogram had done it based on self choice. The association between age and willingness to get screened was found to be statistically significant. It was also seen that there was a statistically very high significance between Knowledge regarding AI and the independent decision to get screened. There was also a statistically significant association between age and scores &gt;60% regarding knowledge and attitude regarding the vaccination among the study subjects. Conclusions: Majority of the study population was having a satisfactory knowledge regarding AI usage in healthcare.

Ranking co-change candidates suggested by FLeCCS using programmer sensitivity

Identifying co-change candidates when making changes to a particular program entity is an important software engineering task which is known as change impact analysis. A number of techniques for detecting co-change candidates currently exist. Most of these techniques work considering file level, class level, or method level granularity. FLeCCS is a recently introduced technique that suggests co-change candidates considering even fragment level granularity. FLeCCS ranks its co-change candidates considering file proximity. However, programmer sensitivity might be an important factor while ranking co-change candidates. Intuitively, it is more comfortable for a programmer to change her own code rather than changing someone else's code. Considering this fact, we investigate ranking co-change candidates suggested by FLeCCS considering programmer sensitivity.In our research, we have two main contributions: (1) proposing a composite ranking mechanism considering three things - file proximity, code similarity, and programmer sensitivity and (2) an empirical evaluation of our proposed mechanism by comparing it with file proximity ranking and similarity extent ranking mechanisms on thousands of revisions of five subject systems. We compare the ranking mechanisms in ranking the co-change candidates suggested by FLeCCS. According to our investigation incorporating four measures (Precision At K, Recall At K, Mean Average Precision and Mean Reciprocal Rank), composite ranking mechanism significantly outperforms the other two existing ranking mechanisms. Our findings are supported by statistical significance tests. We believe that our Proposed Composite Ranking mechanism is not only applicable to FLeCCS but also to other co-change suggestion techniques.

Computer vision based distributed denial of service attack detection for resource-limited devices

The growing adoption of Internet of Things (IoT) has rendered them a desirable target for cyber-attacks. One of the biggest threats to these systems is the distributed denial of service (DDoS) attack, which is a botnet-based attack. The reason for the increasing usage of machine learning and deep learning-based intrusion detection systems in IoT network security is their ability to recognize DDoS attack. Recent studies, however, shows how susceptible IoT networks are to these kinds of attacks and detection accuracy can be greatly lowered. While a majority of studies has concentrated on DDoS attack detection for deep learning, little attention has been paid to computer vision, especially image-based artificial intelligence technologies like convolutional neural network (CNN). In this study, we use an image-based dataset to evaluate the effectiveness of CNN, an effective computer vision approach, for DDoS attack detection in IoT contexts. Owing to the small size of the selected dataset and in order to improve the CNN model’s detection efficiency, we implement various data augmentation techniques prior to the model’s training, including scaling, rotation, and vertical and horizontal flipping. Next, we introduce an efficient CNN-based method for detection of DDoS attacks in IoT settings. Ultimately, we came to the conclusion that the statistical significance testing showed that there is a significance difference among the five models employed during the study, and the VGG19 which has higher accuracy (99.74%) and less computing cost (6020.80 s), which enables IoT devices to perform DDoS attack detection with cost-effectiveness.

Abstract A013: Advanced Gene Signatures for the Diagnosis and Personalized Treatment of Pancreatic Ductal Adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDA) is among the most lethal tumors, with limited treatment options. The absence of reliable screening and diagnostic tools (biomarkers or procedures) significantly contributes to its poor prognosis. Biomarkers that can diagnose PDA, select optimal therapy combinations, and predict the risk of occult metastases are crucial for enhancing treatment outcomes. Previously, we identified methylation signatures capable of predicting outcomes and diagnosing PDA, focusing on genes/proteins with preclinical evidence of influencing key drugs (gemcitabine [Gem], 5-fluorouracil [5FU], oxaliplatin, cisplatin, paclitaxel, and irinotecan) used in treatment. To make these findings more clinically applicable and propose a novel approach for personalized treatment selection between Gem-based (Gem/nab-paclitaxel) and 5FU-based (FOLFIRINOX) combinations, we explored the diagnostic value of genes associated specifically with Gem and 5FU. The objective was to develop a gene signature for diagnosing PDA and aiding in treatment selection. Based on recent literature, we updated our previous gene panels (from 55 to 93 for Gem, and from 20 to 28 for 5FU). We analyzed RNA and gene expression differences between normal (NT) and PDA tissues using TNMplot.com, a web tool that leverages data from the Gene Expression Omnibus (NCBI-GEO), Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories, employing Mann-Whitney or Kruskal-Wallis tests for statistical significance. For the Gem-specific gene panel, the combined RNA expression of 90 out of 93 genes was significantly higher in PDA compared to NT (2.59, p=6.48e-61), while the combined gene expression of 80 out of 93 genes was also significantly higher in PDA tissues (1.62, p=3.48e-29). For the 5FU-specific gene panel, the combined RNA expression of 27 out of 28 genes was significantly elevated in PDA (2.00, p=5.44e-57), and the combined gene expression of 23 out of 28 genes was significantly higher in PDA tissues (1.21, p=1.44e-7). Our preliminary findings suggest that this approach has the potential to serve as an effective tool for diagnosing PDA and guiding treatment selection. However, these results must be validated through larger prospective studies. Additionally, this application could serve as a screening tool for high-risk populations, such as individuals with potentially malignant lesions like intraductal papillary mucinous neoplasms, first-degree relatives of PDA patients, or those with concerning genetic risks (e.g., BRCA1 or BRCA2 mutations). Citation Format: Harshitha Puram, Deepak Sherpally, Sravan Jeepalyam, Ashish Manne. Advanced Gene Signatures for the Diagnosis and Personalized Treatment of Pancreatic Ductal Adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A013.

Correction to: A statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences

Correction to: A statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences

A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction

Accurate wind power forecasting is pivotal in facilitating the efficient integration of renewable energy sources into existing power grids. This study proposes a novel hybrid framework, termed BWO-BiLSTM-ATT, which synergistically combines the strengths of a bidirectional long short-term memory (BiLSTM) network, a self-attention mechanism, and the Beluga Whale Optimization (BWO) algorithm. The BiLSTM architecture, with its unique ability to capture bidirectional temporal dependencies, effectively models the intricate dynamics present in wind power time series data. Integrating the self-attention mechanism further enhances the model's performance by discerning and emphasizing the most salient features within the input data. Furthermore, employing the BWO algorithm optimizes the model's hyperparameters, ensuring optimal configuration and enhancing its predictive accuracy and generalization capabilities. The proposed BWO-BiLSTM-ATT framework was rigorously evaluated using real-world data from an offshore wind farm in Yangjiang City, China. Comparative analyses were conducted against several baseline algorithms, including persistence, ARIMA, SVR, and vanilla LSTM models. The results demonstrate the superior performance of the BWO-BiLSTM-ATT framework, achieving higher R-squared and explained variance scores, coupled with lower error metrics such as MSE, RMSE, MedAE, and MAE. Statistical significance tests further corroborated the substantial performance improvements offered by the proposed framework. The combination of advanced deep learning techniques and metaheuristic optimization algorithms embedded in the BWO-BiLSTM-ATT framework provides a robust and accurate solution for wind power forecasting, enabling efficient management and seamless integration of renewable energy resources into existing power grids.

Prevalence and Pattern of Ear Disease Among Primary School Students in Samoa: Preliminary Results from ENT Clinic Outreach to Urban, Rural and Remote Settings.

Objectives: To assess the prevalence and pattern of ear disease among students attending primary school in urban, rural, and remote regions of Samoa in order to develop an evidence-based national school ear and hearing health program. Methods: Retrospective clinical data review of all primary school students seen by the Ear, Nose & Throat Clinic of Samoa during school-based outreach visits from March 2022 to April 2024. A purposefully designed spreadsheet was created to extract the following information from the clinical records: school location (urban, rural, remote), gender (male, female), age (years), age category (0-4, 5-9, 10-14, 15+ years), right ear diagnosis, left ear diagnosis. Data were entered into the SPSS statistical software package for descriptive statistical analysis and association tests of significance. Results: A total of 1491 primary school students were seen for ear health examinations (51.1% male, 48.9% female), with a mean age of 9.1 years (SD = 2.3). Five primary schools were included, representing urban (n = 865), rural (n = 258), and remote (n = 368) settings. Overall, 682 (45.7%) students had normal ear examination results for both ears, 237 (15.9%) had an ear pathology in 1 ear only, and 568 (38.1%) had ear pathology in both ears. Bilateral ear pathology was significantly higher among students who were in the 5 to 9 age group (P < .00001), female (P = .035), and from rural settings (P = .0001). The most common pathology was impacted cerumen (36.8%), followed by otitis media with effusion (7.5%). Overall prevalence of chronic suppurative otitis media (CSOM) was 2.6% (n = 39 students). Following the school visits, no further intervention was required for 1180 students (79%), mostly due to successful cerumen and foreign body removals for 468 students (31.4%). Conclusion: Impacted cerumen is a significant issue among Samoan school-aged students, and future school-based ear health programs should include staff members trained in cerumen removal. CSOM prevalence suggests an avoidable burden of disease that must be addressed.

Prevalence and Predictors of Musculoskeletal Disorders among Bus Drivers Engaged in Long- Distance Transportation in Port Harcourt, Nigeria

Background An increasing number of occupational injuries occur due to work-related musculoskeletal disorders (WRMSDs) in both developing and industrialized countries. These disorders have an impact on workers' health as well as society at large, posing medical, occupational, and economic challenges.  Objectives This research assessed work-related musculoskeletal disorders and its risk factors among Bus drivers for private long-distance Transport Companies in Port Harcourt, Nigeria.  Materials and Methods This study employed a cross-sectional study design. The multi-stage sampling technique was used to sample 338 long-distance bus drivers in the city of Port Harcourt. Participants' demographic data and information regarding work related musculoskeletal disorders were collected using a modified version of the Standardized Nordic Musculoskeletal Survey questionnaire. Descriptive statistics determined prevalence and inferential statistics was used to determine associated risk factors of WRMSD. The p-value of the test of statistical significance was set at ≤0.05. The participants were all male.  Result 58.6% of the research sample had WRMSDs, according to the findings. After logistic regression analysis, only marital status (AOR=5.714, 95% CI for AOR=3.046- 10.721; p=0.001), prolonged sitting of ≥8 hours, (AOR=0.350; 95% CI for AOR=0.166-0.741; p=0.006) and previous history of pain in a previous occupation (AOR=5.714, 95% CI for AOR=3.046-10.721; p=0.001), were significantly associated with the prevalence of WRMSD.  Conclusion WRMSDs are prevalent in private long-distance bus drivers and are strongly correlated with marrital status, prolonged sitting times (more than eight hours), and a history of muscle or joint discomfort in a prior job.

FDA-approved PDE4 inhibitors alleviate the dominant toxicity of ALS-FTD-associated CHCHD10S59L by reducing the PINK1/Parkin pathway.

Mutations in coiled-coil-helix-coiled-coil-helix domain containing 10 (CHCHD10) have been identified as a genetic cause of amyotrophic lateral sclerosis and/or frontotemporal dementia(ALS-FTD). In our previous studies using in vivo Drosophila model expressing CHCHD10S59L, and human cell models expressing CHCHD10S59L, we have identified that the PINK1/Parkin pathway is activated and causes cellular toxicity. Furthermore, we demonstrated that pseudo-substrate inhibitors for PINK1 and mitofusin2 agonists mitigated the cellular toxicity of CHCHD10S59L. Evidences using in vitro, in vivo genetic, and chemical tools indicate that inhibiting PINK1 would be the most promising treatment for CHCHD10S59L-induced diseases. An in vivo human cell culture and in vivo Drosophila models expressing CHCHD10S59L mutant were utilized in this study to evaluate the effect of PDE4 inhibitors in PINK-parkin mediated cytotoxicity through immunohistochemical and seahorse assays. Data were analysed using one-way ANOVA and post-hoc Dunnett's test for statistical significance. We investigated cellular pathways that can modulate the PINK1/Parkin pathway and reduce CHCHD10S59L-induced cytotoxicity. Here, we report that FDA-approved PDE4 inhibitors reduced CHCHD10S59L-induced morphological and functional mitochondrial defects in human cells and an in vivo Drosophila model expressing C2C10HS81L. Multiple PDE4 inhibitors decreased PINK1 accumulation and downstream mitophagy induced by CHCHD10S59L. These findings suggest that PDE4 inhibitors currently available in the market may be repositioned to treat CHCHD10S59L-induced ALS-FTD and possibly other related diseases, and that disease treatment with PDE4 inhibitors should include careful consideration of the PINK1/Parkin pathway, as it is generally recognized as a protective pathway.