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PloS One 2024Many newborn screening programs worldwide have introduced screening for diseases using DNA extracted from dried blood spots (DBS). In Germany, DNA-based assays are...
BACKGROUND
Many newborn screening programs worldwide have introduced screening for diseases using DNA extracted from dried blood spots (DBS). In Germany, DNA-based assays are currently used to screen for severe combined immunodeficiency (SCID), spinal muscular atrophy (SMA), and sickle cell disease (SCD).
METHODS
This study analysed the impact of pre-analytic DNA carry-over in sample preparation on the outcome of DNA-based newborn screening for SCID and SMA and compared the efficacy of rapid extraction versus automated protocols. Additionally, the distribution of T cell receptor excision circles (TREC) on DBS cards, commonly used for routine newborn screening, was determined.
RESULTS
Contaminations from the punching procedure were detected in the SCID and SMA assays in all experimental setups tested. However, a careful evaluation of a cut-off allowed for a clear separation of true positive polymerase chain reaction (PCR) amplifications. Our rapid in-house extraction protocol produced similar amounts compared to automated commercial systems. Therefore, it can be used for reliable DNA-based screening. Additionally, the amount of extracted DNA significantly differs depending on the location of punching within a DBS.
CONCLUSIONS
Newborn screening for SMA and SCID can be performed reliably. It is crucial to ensure that affected newborns are not overlooked. Therefore a carefully consideration of potential contaminating factors and the definition of appropriate cut-offs to minimise the risk of false results are of special concern. It is also important to note that the location of punching plays a pivotal role, and therefore an exact quantification of TREC numbers per μl may not be reliable and should therefore be avoided.
Topics: Humans; Neonatal Screening; Infant, Newborn; Muscular Atrophy, Spinal; Severe Combined Immunodeficiency; DNA; Dried Blood Spot Testing; High-Throughput Screening Assays; Polymerase Chain Reaction
PubMed: 38941330
DOI: 10.1371/journal.pone.0306329 -
PloS One 2024To improve the accuracy of modal analysis for a four-stage centrifugal-pump rotor system with a balancing disc based on the concentrated-mass analytical method, a...
To improve the accuracy of modal analysis for a four-stage centrifugal-pump rotor system with a balancing disc based on the concentrated-mass analytical method, a simplified concentrated mass mathematical model and an ANSYS simulation model are established. The results from these two models are compared to determine factors that cause significant differences in the mode shapes. Subsequently, an optimized mathematical model based on the corrected mass moment of an inertia matrix and stiffness correction coefficients is proposed, and the effectiveness of this optimized mathematical model is validated using a four-stage centrifugal pump with back blades. The results show that the natural frequencies obtained from the ANSYS simulations are consistently higher than those obtained using the analytical method. The simplification of the moment of inertia at the impeller and balancing disc contributes primarily to the calculated errors. The optimized mathematical model reduces the errors in the natural frequencies from 12.96%, 12.13%, 9.96%, 5.85%, and 8.74% to 2.45%, 1.56%, 0.65%, 5.34%, and 2.28%, respectively. The optimization of natural frequencies offers better performance at lower-order modes, whereas its effects on higher-order modes are less significant. The optimization method is applicable to centrifugal pumps with back blades and reduces the error in theoretical calculations, based on reductions in the concentrated mass from 13.11%, 12.85%, 9.91%, and 7.2% to 3.7%, 3.86%, 0.57%, and 2.87%, respectively, thus further confirming the feasibility of the optimized model design.
Topics: Centrifugation; Models, Theoretical; Computer Simulation; Equipment Design
PubMed: 38941321
DOI: 10.1371/journal.pone.0306061 -
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 2024Drought stress following climate change is likely a scenario that will have to face crop growers in tropical regions. In mitigating this constraint, the best option...
Identifying critical growth stage and resilient genotypes in cowpea under drought stress contributes to enhancing crop tolerance for improvement and adaptation in Cameroon.
Drought stress following climate change is likely a scenario that will have to face crop growers in tropical regions. In mitigating this constraint, the best option should be the selection and use of resilient varieties that can withstand drought threats. Therefore, a pot experiment was conducted under greenhouse conditions at the Research and Teaching Farm of the Faculty of Agronomy and Agricultural Sciences of the University of Dschang. The objectives are to identify sensitive growth stage, to identify drought-tolerant genotypes with the help of yield-based selection indices and to identify suitable selection indices that are associated with yield under non-stress and stress circumstances. Eighty-eight cowpea genotypes from the sahelian and western regions of Cameroon were subjected to drought stress at vegetative (VDS) and flowering (FDS) stages by withholding water for 28 days, using a split plot design with two factors and three replications. Seed yields under stress (Ys) and non-stress (Yp) conditions were recorded. Fifteen drought indices were calculated for the two drought stress levels against the yield from non-stress plants. Drought Intensity Index (DII) under VDS and FDS were 0.71 and 0.84 respectively, indicating severe drought stress for both stages. However, flowering stage was significantly more sensitive to drought stress compared to vegetative stage. Based on PCA and correlation analysis, Stress Tolerance Index (STI), Relative Efficiency Index (REI), Geometric Mean Productivity (GMP), Mean Productivity (MP), Yield Index (YI) and Harmonic Mean (HM) correlated strongly with yield under stress and non-stress conditions and are therefore suitable to discriminate high-yielding and tolerant genotypes under both stress and non-stress conditions. Either under VDS and FDS, CP-016 exhibited an outstanding performance under drought stress and was revealed as the most drought tolerant genotype as shown by ranking, PCA and cluster analysis. Taking into account all indices, the top five genotypes namely CP-016, CP-021, MTA-22, CP-056 and CP-060 were identified as the most drought-tolerant genotypes under VDS. For stress activated at flowering stage (FDS), CP-016, CP-056, CP-021, CP-028 and MTA-22 were the top five most drought-tolerant genotypes. Several genotypes with insignificant Ys and irrelevant rank among which CP-037, NDT-001, CP-036, CP-034, NDT-002, CP-031, NDT-011 were identified as highly drought sensitive with low yield stability. This study identified the most sensitive stage and drought tolerant genotypes that are proposed for genetic improvement of cowpea.
Topics: Cameroon; Droughts; Genotype; Vigna; Adaptation, Physiological; Stress, Physiological; Crops, Agricultural; Seeds
PubMed: 38941312
DOI: 10.1371/journal.pone.0304674 -
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 2024This article represents a novel study of the design and analysis of a wind turbine system that includes a line-side permanent magnet synchronous generator (PMSG) with an...
This article represents a novel study of the design and analysis of a wind turbine system that includes a line-side permanent magnet synchronous generator (PMSG) with an ultra-step-up DC-DC converter for voltage regulation. Integrating renewable energy sources such as wind power into the grid requires efficient and reliable power conversion systems to handle fluctuating power and ensure a stable power supply. The wind turbine system utilizes a PMSG, which offers several advantages over traditional induction generators, including higher efficiency, reduced maintenance, and better power quality. The line-side configuration allows for increased control and flexibility, allowing the system to respond dynamically to grid conditions. This wind turbine system involves the integration of a grid-side PMSG-fed DC-DC converter between the PMSG and the grid. The converter enables a seamless flow of electricity between the wind turbine and the grid. By actively controlling the intermediate circuit voltage, the converter efficiently regulates the output voltage of the wind turbine and thus enables constant power generation regardless of fluctuating wind speeds. The simulation outcomes illustrate the efficacy of the proposed system in achieving voltage regulation and seamless integration with the grid. Performance is evaluated under various operating conditions and compared to conventional wind turbines.
Topics: Wind; Electric Power Supplies; Electricity; Renewable Energy; Equipment Design
PubMed: 38941302
DOI: 10.1371/journal.pone.0305272 -
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 2024To investigate associations between a swimmer's career progression and winning a medal at the Olympic Games (OG) or World Championships (WC). A total of 4631 swimming...
To investigate associations between a swimmer's career progression and winning a medal at the Olympic Games (OG) or World Championships (WC). A total of 4631 swimming performances of 1535 top swimmers (653 women, 882 men) from 105 nationalities since1973 were extracted from FINA rankings. A panel of 12 predictor variables including nationality, gender, competition, age, number and timing of competitions, pattern of progressions and regressions in performance, and medal outcomes was established. Linear logistic regression was used to study the association between winning a medal and predictor variables. Logistic regression coefficients were obtained by training on 80% of the database, and prediction accuracy evaluated on the remaining 20%. Using the training set, a selection of 9 most relevant features for prediction of winning a medal (target variable) was obtained through exhaustive feature selection and cross-validation: nationality, competition, number of competitions, number of annual career progressions (nb_prog), maximum annual career progression (max-progr), number of annual career regressions (nb_reg), age at maximum annual progression, P6 (the level of performance six months before the World Championships or Olympic Games), and P2 (the level of performance two months before the World Championships or Olympic Games). A logistic regression model was built and retrained on the entire training set achieved an area under the ROC curve of ~90% on the test set. The odds of winning a medal increased by 1.64 (95% CI, 1.39-1.91) and 1.44 (1.22-1.72) for each unit of increase in max-progr and n-prog, respectively. Odds of winning a medal decreased by 0.60 (0.49-0.72) for a unit increase in n-reg. In contrast, the odds increased by 1.70 (1.39-2.07) and 4.35 (3.48-5.42) for improvements in the 6 and 2 months before competition (P<0.001, for all variables). The likelihood of a swimmer winning an international medal is improved by ~40-90% with progressions from season-to-season, and reducing the number of regressions in performance. The chances of success are also improved 2- to 4-fold by substantial improvements in performance in the months before competition.
Topics: Humans; Swimming; Male; Female; Athletic Performance; Awards and Prizes; Adult; Logistic Models; Competitive Behavior; Athletes
PubMed: 38941281
DOI: 10.1371/journal.pone.0304444 -
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