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PloS One 2024Weld defect inspection is an essential aspect of testing in industries field. From a human viewpoint, a manual inspection can make appropriate justification more...
Weld defect inspection is an essential aspect of testing in industries field. From a human viewpoint, a manual inspection can make appropriate justification more difficult and lead to incorrect identification during weld defect detection. Weld defect inspection uses X-radiography testing, which is now mostly outdated. Recently, numerous researchers have utilized X-radiography digital images to inspect the defect. As a result, for error-free inspection, an autonomous weld detection and classification system are required. One of the most difficult issues in the field of image processing, particularly for enhancing image quality, is the issue of contrast variation and luminosity. Enhancement is carried out by adjusting the brightness of the dark or bright intensity to boost segmentation performance and image quality. To equalize contrast variation and luminosity, many different approaches have recently been put forth. In this research, a novel approach called Hybrid Statistical Enhancement (HSE), which is based on a direct strategy using statistical data, is proposed. The HSE method divided each pixel into three groups, the foreground, border, and problematic region, using the mean and standard deviation of a global and local neighborhood (luminosity and contrast). To illustrate the impact of the HSE method on the segmentation or detection stage, the datasets, specifically the weld defect image, were used. Bernsen and Otsu's methods are the two segmentation techniques utilized. The findings from the objective and visual elements demonstrated that the HSE approach might automatically improve segmentation output while effectively enhancing contrast variation and normalizing luminosity. In comparison to the Homomorphic Filter (HF) and Difference of Gaussian (DoG) approaches, the segmentation results for HSE images had the lowest result according to Misclassification Error (ME). After being applied to the HSE images during the segmentation stage, every quantitative result showed an increase. For example, accuracy increased from 64.171 to 84.964. In summary, the application of the HSE method has resulted in an effective and efficient outcome for background correction as well as improving the quality of images.
Topics: Humans; Algorithms; Image Processing, Computer-Assisted; Radiographic Image Enhancement
PubMed: 38941319
DOI: 10.1371/journal.pone.0306010 -
PloS One 2024To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the...
To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model's superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.
Topics: Deep Learning; Internet of Things; Databases, Factual; Neural Networks, Computer; Humans; Information Storage and Retrieval
PubMed: 38941309
DOI: 10.1371/journal.pone.0306291 -
PloS One 2024Art v4.01 is a well-known profilin protein belonging to the pan-allergens group and is commonly involved in triggering allergic asthma, polyallergy, and...
Art v4.01 is a well-known profilin protein belonging to the pan-allergens group and is commonly involved in triggering allergic asthma, polyallergy, and cross-sensitization. It is also referred to as Wormwood due to its origin. Crude wormwood extracts are applied for allergen-specific immunotherapy (AIT). Whether the recombinant Art v4.01 (rArt v4.01) can produce in vivo immunological tolerance by subcutaneous immunotherapy (SCIT) remains elusive. In this study, to investigate the in vivo immunological response of rArt v4.01, Th2, Th1, Treg, Th17 type-related cytokines and phenotypes of immune cells were tested, facilitating the exploration of the underlying mechanisms. The expression and purification of Art v4.01 were carried out using recombinant techniques. Allergic asthma female BALB/c mice were induced by subcutaneous sensitization of wormwood pollen extract and intranasal challenges. SCIT without adjuvant was performed using the rArt v4.01 and wormwood pollen extract for 2 weeks. Following exposure to challenges, the levels of immunoglobulin E (IgE), cytokines, and inflammatory cells were assessed through enzyme-linked immunosorbent assay (ELISA) and histological examination of sera, bronchoalveolar lavage fluid (BALF), and lung tissue. These parameters were subsequently compared between treatment groups receiving rArt v4.01 and wormwood pollen extract. The rArt v4.01 protein was expressed, which had a high purity (>90%) and an allergenic potency. Compared with the pollen extract, rArt v4.01 was superior in terms of reducing the number of white blood cells (WBCs), total nucleated cells (TNCs), and monocytes (MNs) in BALF and the degree of lung inflammation (1.77±0.99 vs. 2.31±0.80, P > 0.05). Compared with the model group, only rArt v4.01 reduced serum IgE level (1.19±0.25 vs. 1.61±0.17 μg/ml, P = 0.062), as well as the levels of Th2 type-related cytokines (interleukin-4 (IL-4) (107.18±16.17 vs. 132.47±20.85 pg/ml, P < 0.05) and IL-2 (19.52±1.19 vs. 24.02±2.14 pg/ml, P < 0.05)). The study suggested that rArt v4.01 was superior to pollen extract in reducing the number of inflammatory cells in BALF, pneumonitis, levels of pro-inflammatory cytokines, and serum IgE level. These findings confirmed that Art v4.01 could be a potential candidate protein for allergen-specific immunotherapy.
Topics: Animals; Female; Asthma; Mice; Mice, Inbred BALB C; Disease Models, Animal; Immune Tolerance; Recombinant Proteins; Cytokines; Immunoglobulin E; Pollen; Desensitization, Immunologic; Allergens; Profilins; Bronchoalveolar Lavage Fluid; Injections, Subcutaneous
PubMed: 38941291
DOI: 10.1371/journal.pone.0280418 -
PloS One 2024In this study, we employed various machine learning models to predict metabolic phenotypes, focusing on thyroid function, using a dataset from the National Health and...
In this study, we employed various machine learning models to predict metabolic phenotypes, focusing on thyroid function, using a dataset from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012. Our analysis utilized laboratory parameters relevant to thyroid function or metabolic dysregulation in addition to demographic features, aiming to uncover potential associations between thyroid function and metabolic phenotypes by various machine learning methods. Multinomial Logistic Regression performed best to identify the relationship between thyroid function and metabolic phenotypes, achieving an area under receiver operating characteristic curve (AUROC) of 0.818, followed closely by Neural Network (AUROC: 0.814). Following the above, the performance of Random Forest, Boosted Trees, and K Nearest Neighbors was inferior to the first two methods (AUROC 0.811, 0.811, and 0.786, respectively). In Random Forest, homeostatic model assessment for insulin resistance, serum uric acid, serum albumin, gamma glutamyl transferase, and triiodothyronine/thyroxine ratio were positioned in the upper ranks of variable importance. These results highlight the potential of machine learning in understanding complex relationships in health data. However, it's important to note that model performance may vary depending on data characteristics and specific requirements. Furthermore, we emphasize the significance of accounting for sampling weights in complex survey data analysis and the potential benefits of incorporating additional variables to enhance model accuracy and insights. Future research can explore advanced methodologies combining machine learning, sample weights, and expanded variable sets to further advance survey data analysis.
Topics: Humans; Machine Learning; Thyroid Gland; Male; Female; Phenotype; Middle Aged; Adult; Nutrition Surveys; Thyroid Function Tests; ROC Curve; Neural Networks, Computer
PubMed: 38941283
DOI: 10.1371/journal.pone.0304785 -
PloS One 2024Personal care for body organs is a well-known practice of human beings, especially those organs that need regular care to improve function or hygiene. The ear is a...
BACKGROUND
Personal care for body organs is a well-known practice of human beings, especially those organs that need regular care to improve function or hygiene. The ear is a unique sense organ with a specific anatomical shape to perform the function of hearing and balance.
OBJECTIVES
To determine healthcare practitioners' current knowledge, behavior, and attitude regarding ear care.
SUBJECTS AND METHODS
This cross-sectional study was conducted among healthcare practitioners at different hospitals in Najran City, Saudi Arabia, from 25th June to 30th August 2022. A self-administered questionnaire was distributed among healthcare practitioners using an online survey. The questionnaire includes basic demographic characteristics (i.e. gender, speciality, and religion). It assesses the knowledge, behavior, and attitude toward ear care, and the use of mobile headphones and earrings that affect ear health. All statistical data were analyzed using SPSS version 26.
RESULTS
Of the 209 healthcare practitioners involved, 60.8% were females, and 46.9% were physicians. The prevalence of self-ear cleaning was 97.6%. Of them, 33% were cleaning their ears every week. Cotton buds were the most preferred method for self-ear cleaning. The proportion of participants who injured their ears while cleaning was 8.6%. The most common treatment method to relieve ear pain was visiting a doctor (44.4%) and utilizing a painkiller (29.7%). Interestingly, respondents who injured their ears during cleaning and those who experienced wax accumulation were significantly more common among physicians.
CONCLUSION
Self-ear cleaning practices are widely prevalent in this study which could be related to the lack of knowledge about ear care among healthcare practitioners. Physicians who experienced wax accumulation tend to use other methods for self-ear cleaning rather than cotton buds. Further research is needed to determine the knowledge, attitude, and practices of the population who are working in healthcare institutions.
Topics: Humans; Saudi Arabia; Female; Male; Adult; Health Knowledge, Attitudes, Practice; Cross-Sectional Studies; Surveys and Questionnaires; Middle Aged; Health Personnel; Attitude of Health Personnel; Ear
PubMed: 38941273
DOI: 10.1371/journal.pone.0303761 -
PloS One 2024In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units,...
In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units, OTUs). Several methods taking the compositional nature of the sequencing data into account exist. However, these methods cannot infer correlations between OTU abundances and other variables. In this paper we introduce the novel methods SparCEV (Sparse Correlations with External Variables) and SparXCC (Sparse Cross-Correlations between Compositional data) for quantifying correlations between OTU abundances and either continuous phenotypic variables or components of other compositional datasets, such as transcriptomic data. SparCEV and SparXCC both assume that the average correlation in the dataset is zero. Iterative versions of SparCEV and SparXCC are proposed to alleviate bias resulting from deviations from this assumption. We compare these new methods to empirical Pearson cross-correlations after applying naive transformations of the data (log and log-TSS). Additionally, we test the centered log ratio transformation (CLR) and the variance stabilising transformation (VST). We find that CLR and VST outperform naive transformations, except when the correlation matrix is dense. SparCEV and SparXCC outperform CLR and VST when the number of OTUs is small and perform similarly to CLR and VST for large numbers of OTUs. Adding the iterative procedure increases accuracy for SparCEV and SparXCC for all cases, except when the average correlation in the dataset is close to zero or the correlation matrix is dense. These results are consistent with our theoretical considerations.
Topics: Microbiota; Algorithms; Humans
PubMed: 38941272
DOI: 10.1371/journal.pone.0305032 -
JMIR Research Protocols Jun 2024Artificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have...
BACKGROUND
Artificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have shown potential for a range of clinical tasks such as diagnostics, prognostics, and therapeutic decision-making such as drug dosing. There is, however, an urgent need to ensure that these technologies remain safe for all populations. Recent literature demonstrates the need for rigorous performance error analysis to identify issues such as algorithmic encoding of spurious correlations (eg, protected characteristics) or specific failure modes that may lead to patient harm. Guidelines for reporting on studies that evaluate AI medical devices require the mention of performance error analysis; however, there is still a lack of understanding around how performance errors should be analyzed in clinical studies, and what harms authors should aim to detect and report.
OBJECTIVE
This systematic review will assess the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) investigating AI medical devices as interventions in clinical settings. The review will also explore how performance errors are analyzed including whether the analysis includes the investigation of subgroup-level outcomes.
METHODS
This systematic review will identify and select RCTs assessing AI medical devices. Search strategies will be deployed in MEDLINE (Ovid), Embase (Ovid), Cochrane CENTRAL, and clinical trial registries to identify relevant papers. RCTs identified in bibliographic databases will be cross-referenced with clinical trial registries. The primary outcomes of interest are the frequency and severity of AI errors, patient harms, and reported AEs. Quality assessment of RCTs will be based on version 2 of the Cochrane risk-of-bias tool (RoB2). Data analysis will include a comparison of error rates and patient harms between study arms, and a meta-analysis of the rates of patient harm in control versus intervention arms will be conducted if appropriate.
RESULTS
The project was registered on PROSPERO in February 2023. Preliminary searches have been completed and the search strategy has been designed in consultation with an information specialist and methodologist. Title and abstract screening started in September 2023. Full-text screening is ongoing and data collection and analysis began in April 2024.
CONCLUSIONS
Evaluations of AI medical devices have shown promising results; however, reporting of studies has been variable. Detection, analysis, and reporting of performance errors and patient harms is vital to robustly assess the safety of AI medical devices in RCTs. Scoping searches have illustrated that the reporting of harms is variable, often with no mention of AEs. The findings of this systematic review will identify the frequency and severity of AI performance errors and patient harms and generate insights into how errors should be analyzed to account for both overall and subgroup performance.
TRIAL REGISTRATION
PROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
PRR1-10.2196/51614.
Topics: Humans; Randomized Controlled Trials as Topic; Artificial Intelligence; Algorithms; Systematic Reviews as Topic; Patient Harm; Equipment and Supplies; Research Design
PubMed: 38941147
DOI: 10.2196/51614 -
JMIR Research Protocols Jun 2024The lack of regular physical activity (PA) in individuals with spinal cord injury (SCI) in the United States is an ongoing health crisis. Regular PA and exercise-based... (Randomized Controlled Trial)
Randomized Controlled Trial
mHealth-Based Just-in-Time Adaptive Intervention to Improve the Physical Activity Levels of Individuals With Spinal Cord Injury: Protocol for a Randomized Controlled Trial.
BACKGROUND
The lack of regular physical activity (PA) in individuals with spinal cord injury (SCI) in the United States is an ongoing health crisis. Regular PA and exercise-based interventions have been linked with improved outcomes and healthier lifestyles among those with SCI. Providing people with an accurate estimate of their everyday PA level can promote PA. Furthermore, PA tracking can be combined with mobile health technology such as smartphones and smartwatches to provide a just-in-time adaptive intervention (JITAI) for individuals with SCI as they go about everyday life. A JITAI can prompt an individual to set a PA goal or provide feedback about their PA levels.
OBJECTIVE
The primary aim of this study is to investigate whether minutes of moderate-intensity PA among individuals with SCI can be increased by integrating a JITAI with a web-based PA intervention (WI) program. The WI program is a 14-week web-based PA program widely recommended for individuals with disabilities. A secondary aim is to investigate the benefit of a JITAI on proximal PA, defined as minutes of moderate-intensity PA within 120 minutes of a PA feedback prompt.
METHODS
Individuals with SCI (N=196) will be randomized to a WI arm or a WI+JITAI arm. Within the WI+JITAI arm, a microrandomized trial will be used to randomize participants several times a day to different tailored feedback and PA recommendations. Participants will take part in the 24-week study from their home environment in the community. The study has three phases: (1) baseline, (2) WI program with or without JITAI, and (3) PA sustainability. Participants will provide survey-based information at the initial meeting and at the end of weeks 2, 8, 16, and 24. Participants will be asked to wear a smartwatch every day for ≥12 hours for the duration of the study.
RESULTS
Recruitment and enrollment began in May 2023. Data analysis is expected to be completed within 6 months of finishing participant data collection.
CONCLUSIONS
The JITAI has the potential to achieve long-term PA performance by delivering tailored, just-in-time feedback based on the person's actual PA behavior rather than a generic PA recommendation. New insights from this study may guide intervention designers to develop engaging PA interventions for individuals with disability.
TRIAL REGISTRATION
ClinicalTrials.gov NCT05317832; https://clinicaltrials.gov/study/NCT05317832.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
DERR1-10.2196/57699.
Topics: Humans; Spinal Cord Injuries; Exercise; Telemedicine; Male; Female; Adult; Middle Aged; Exercise Therapy; Randomized Controlled Trials as Topic
PubMed: 38941145
DOI: 10.2196/57699 -
JMIR Research Protocols Jun 2024Pulmonary rehabilitation is widely recommended to improve functional status and as secondary and tertiary prevention in individuals with chronic pulmonary diseases....
Assessing Functional Capacity in Directly and Remotely Monitored Home-Based Settings in Individuals With Chronic Respiratory Diseases: Protocol for a Multinational Validation Study.
BACKGROUND
Pulmonary rehabilitation is widely recommended to improve functional status and as secondary and tertiary prevention in individuals with chronic pulmonary diseases. Unfortunately, access to timely and appropriate rehabilitation remains limited. To help close this inaccessibility gap, telerehabilitation has been proposed. However, exercise testing is necessary for effective and safe exercise prescription. Current gold-standard tests, such as maximal cardiopulmonary exercise testing (CPET) and the 6-minute walk test (6MWT), are poorly adapted to home-based or telerehabilitation settings. This was an obstacle to the continuity of services during the COVID-19 pandemic. It is essential to validate tests adapted to these new realities, such as the 6-minute stepper test (6MST). This test, strongly inspired by 6MWT, consists of taking as many steps as possible on a "stepper" for 6 minutes.
OBJECTIVE
This study aims to evaluate the metrological qualities of 6MST by (1) establishing concurrent validity and agreement between the 6MST and CPET, as well as with the 6MWT; (2) determining test-retest reliability in a home-based setting with direct and remote (videoconferencing) monitoring; and (3) documenting adverse events and participant perspectives when performing the 6MST in home-based settings.
METHODS
Three centers (Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec in Québec, Groupement des Hôpitaux de l'Institut Catholique de Lille in France, and FormAction Santé in France) will be involved in this multinational project, which is divided into 2 studies. For study 1 (objective 1), 30 participants (Québec, n=15; France, n=15) will be recruited. Two laboratory visits will be performed to assess anthropometric data, pulmonary function, and the 3 exercise tolerance tests (CPET, 6MWT, and 6MST). Concurrent validity (paired sample t tests and Pearson correlations) and agreement (Bland-Altman plots with 95% agreement limits) will be evaluated. For study 2 (objectives 2 and 3), 52 participants (Québec, n=26; France, n=26) will be recruited. Following a familiarization trial (trial 1), the 6MST will be conducted on 2 separate occasions (trials 2 and 3), once under direct supervision and once under remote supervision, in a randomized order. Paired sample t test, Bland-Altman plots, and intraclass correlations will be used to compare trials 2 and 3. A semistructured interview will be conducted after the third trial to collect participants' perspectives.
RESULTS
Ethical approval was received for this project (October 12, 2023, in Québec and September 25, 2023, in France) and the first participant was recruited in February 2024.
CONCLUSIONS
This study innovates by validating a new clinical test necessary for the development and implementation of new models of rehabilitation adapted to home and telerehabilitation contexts. This study also aligns with the United Nations Sustainable Development Goals by contributing to augmenting health care service delivery (goal 3) and reducing health care access inequalities (goal 11).
TRIAL REGISTRATION
ClinicalTrials.gov NCT06447831; https://clinicaltrials.gov/study/NCT06447831.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
DERR1-10.2196/57404.
Topics: Humans; Chronic Disease; Exercise Test; Reproducibility of Results; COVID-19; Male; Female; Adult; Middle Aged; Telerehabilitation; Walk Test; Telemedicine
PubMed: 38941132
DOI: 10.2196/57404 -
JAMA Network Open Jun 2024The efficacy of a semirecumbent position (SRP) in reducing postoperative hypoxemia during anesthesia emergence is unclear despite its widespread use. (Randomized Controlled Trial)
Randomized Controlled Trial
IMPORTANCE
The efficacy of a semirecumbent position (SRP) in reducing postoperative hypoxemia during anesthesia emergence is unclear despite its widespread use.
OBJECTIVE
To determine the differences in postoperative hypoxemia between patients in an SRP and a supine position.
DESIGN, SETTING, AND PARTICIPANTS
This randomized clinical trial was performed at a tertiary hospital in China between March 20, 2021, and May 10, 2022. Patients scheduled to undergo laparoscopic upper abdominal surgery under general anesthesia were enrolled. Study recruitment and follow-up are complete.
INTERVENTIONS
Patients were randomized to 1 of the following positions at the end of the operation until leaving the postanesthesia care unit: supine (group S), 15° SRP (group F), or 30° SRP (group T).
MAIN OUTCOMES AND MEASURES
The primary outcome was the incidence of postoperative hypoxemia in the postanesthesia care unit. Severe hypoxemia was also evaluated.
RESULTS
Out of 700 patients (364 men [52.0%]; mean [SD] age, 47.8 [11.3] years), 233 were randomized to group S (126 men [54.1%]; mean [SD] age, 48.2 [10.9] years), 233 to group F (122 men [52.4%]; mean [SD] age, 48.1 [10.9] years), and 234 to group T (118 women [50.4%]; mean [SD] age, 47.2 [12.1] years). Postoperative hypoxemia differed significantly among the 3 groups (group S, 109 of 233 [46.8%]; group F, 105 of 233 [45.1%]; group T, 76 of 234 [32.5%]; P = .002). This difference was statistically significant for groups T vs S (risk ratio [RR], 0.69 [95% CI, 0.55-0.87]; P = .002) and groups T vs F (RR, 0.72 [95% CI, 0.57-0.91]; P = .007), but not for groups F vs S (RR, 0.96 [95% CI, 0.79-1.17]; P = .78). Severe hypoxemia also differed among the 3 groups (group S, 61 of 233 [26.2%]; group F, 53 of 233 [22.7%]; group T, 36 of 234 [15.4%]; P = .01). This difference was statistically significant for groups T vs S (RR, 0.59 [95% CI, 0.41-0.85]; P = .005).
CONCLUSIONS AND RELEVANCE
In this randomized clinical trial of SRP during anesthesia recovery in patients undergoing laparoscopic upper abdominal surgery, postoperative hypoxemia was significantly reduced in group T compared with group F or group S.
TRIAL REGISTRATION
Chinese Clinical Trial Registry Identifier: ChiCTR2100045087.
Topics: Humans; Male; Female; Middle Aged; Hypoxia; Postoperative Complications; Patient Positioning; Adult; Anesthesia Recovery Period; Anesthesia, General; China; Laparoscopy; Supine Position; Abdomen
PubMed: 38941098
DOI: 10.1001/jamanetworkopen.2024.16797