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Abstract Background Shortcoming of traditional Nuss operation on adults is gradually found in the clinical practice. A new kind of introducer-bar complex was introduced. However, there is limited evidence regarding its safety and efficacy. Therefore, a single center, retrospective study was conducted to address this issue. Methods Patients with pectus excavatum who underwent surgery between January 2015 and June 2017 were consecutively enrolled in this study. In all, 52 patients underwent the modified procedure using the introducer-bar complex (new procedure group), whereas 48 underwent the traditional anti-Nuss procedure (traditional procedure group). Outcomes analysis of balanced baseline was performed to compare the intraoperative and postoperative short-term outcomes. Results All patients in the new procedure group had shorter operation duration (51.54 ± 20.32 vs. 79.45 ± 13.88 min, p = 0.017), postoperative hospitalizations (4.77 ± 1.62 vs. 6.86 ± 2.18 days, p = 0.028), plate removal surgery durations (39.30 ± 8.97 vs. 60.30 ± 10.49 min, p < 0.001), and less blood loss during operation (6.25 ± 4.88 vs. 10.90 ± 5.75 ml, p = 0.003) than patients in the traditional procedure group. There was no significant difference in the length of incision, postoperative Haller index, cost, number of steel bars, postoperative surgical outcome and incidence of complications between the two groups. Conclusion Through the main clinical outcome were similar, our results shown that modified procedure may have the shorter operation time, postoperative hospital stay, and operation time for plate removal and less blood loss, which may bring potential clinical benefits to patients.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

AbstractOne challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

Cancer classification is one of the main objectives for analyzing big biological datasets. Machine learning algorithms (MLAs) have been extensively used to accomplish this task. Several popular MLAs are available in the literature to classify new samples into normal or cancer populations. Nevertheless, most of them often yield lower accuracies in the presence of outliers, which leads to incorrect classification of samples. Hence, in this study, we present a robust approach for the efficient and precise classification of samples using noisy GEDs. We examine the performance of the proposed procedure in a comparison of the five popular traditional MLAs (SVM, LDA, KNN, Naïve Bayes, Random forest) using both simulated and real gene expression data analysis. We also considered several rates of outliers (10%, 20%, and 50%). The results obtained from simulated data confirm that the traditional MLAs produce better results through our proposed procedure in the presence of outliers using the proposed modified datasets. The further transcriptome analysis found the significant involvement of these extra features in cancer diseases. The results indicated the performance improvement of the traditional MLAs with our proposed procedure. Hence, we propose to apply the proposed procedure instead of the traditional procedure for cancer classification.

The study of CAD (computer aided design) modeling, design and manufacturing techniques has undergone a rapid growth over the past decades. In medicine, this development mainly concerned the dental and maxillofacial sectors. Significant progress has also been made in orthopedics with pre-operative CAD simulations, printing of bone models and production of patient-specific instruments. However, the traditional procedure that formulates the surgical plan based exclusively on two-dimensional images and interventions performed without the aid of specific instruments for the patient and is currently the most used surgical technique. The production of custom-made tools for the patient, in fact, is often expensive and its use is limited to a few hospitals. The purpose of this study is to show an innovative and cost-effective procedure aimed at prototyping a custom-made surgical guide for address the cubitus varus deformity on a pediatric patient. The cutting guides were obtained through an additive manufacturing process that starts from the 3D digital model of the patient’s bone and allows to design specific models using Creo Parametric. The result is a tool that adheres perfectly to the patient’s bone and guides the surgeon during the osteotomy procedure. The low cost of the methodology described makes it worth noticing by any health institution.

Abstract The aim of the study was to develop an algorithm for the interaction between an audiologist and speech-language therapist in initial amplification for hearing impaired children. Methods. 219 children aged 2 months to 16 years with hearing impairments were divided into 2 groups. The experimental group consisted of children who got a special course of medical, psychological and pedagogical rehabilitation at the stage of initial fitting, based on the new algorithm of interdisciplinary interaction between an audiologist and a speech-language therapist. The control group included children with traditional procedure of hearing aids fitting, in which the audiologist didnt interact with the speech therapist. Results. An algorithm for the interaction of an audiologist and speech-language therapist included 3 stages of hearing aids fitting (preparation, initial fitting, adaptation and fine tuning of hearing aids), the tasks of each specialist at these stages were identified, including the parents involvement in the process of fitting, a development of the protocol for the exchange of information between specialists. The algorithm was implemented in a special course of medical-psychological-pedagogical rehabilitation in the Center of Pediatric Audiology. It was found that in 1 month after the primary fitting among children who underwent this course, significantly more children used hearing aids for more than 8 hours a day and reached the integral criterion of effective hearing aids fitting compared with the control group. These children also took less time to adjust their hearing aid settings during repeated fitting sessions. Conclusion. A structured interdisciplinary interaction between an audiologist and speech-language therapist at the stage of initial amplification significantly increases the effectiveness of medical technology for restoring hearing function in children.

Introduction: The anti-Nuss procedure has gradually been found to have several shortcomings in clinical practice. Accordingly, our department previously designed and introduced a new steel plate. However, there is limited evidence regarding its safety and efficacy. Thus, we aim to compare the efficacy and safety of the conventional anti-Nuss operation with those of a modified anti-Nuss operation using a flexible plate.Methods: Patients with pectus carinatum who underwent surgery between January 2014 and August 2019 were consecutively enrolled in this single-center, retrospective study. In all, 53 patients underwent the modified procedure using the new steel plate (new procedure group), whereas 43 underwent the conventional anti-Nuss procedure (traditional procedure group). Outcome analysis was performed using SPSS to compare the intraoperative and postoperative short-term outcomes.Results: All patients in the new procedure group had shorter operation duration (75.23 ± 11.90 vs. 82.45 ± 9.30 min, p = 0.008), postoperative hospitalizations (3.42 ± 0.95 vs. 4.64 ± 1.53 days, p = 0.039), and plate removal surgery durations (40.60 ± 3.47 vs. 60.30 ± 9.75 min, p = 0.041) than patients in the traditional procedure group. There were no significant differences in the length of incision, postoperative Haller index, cost, postoperative surgical outcome, and incidence of complications between the two groups.Conclusion: Our data reveal that the main clinical outcomes were similar for after anti-Nuss operation and modified anti-Nuss operation. However, the modified procedure for pectus carinatum had a shorter operation duration, postoperative hospitalization, and plate removal surgery duration.

Cellulose is a natural polymer formed by chains of polysaccharide carbohydrates. Traditional processes for extraction of cellulose are expensive and require solvents with high ionic strength and stringent conditions. In this paper, we propose the extraction of cellulose from Bougainvillea spectabilis by using a traditional procedure, such as alkaline and acidic hydrolysis extraction, as well as the Vieira extraction method with the aid of two ionic liquids (ILs), with the purpose of using a solvent that allows reducing the harmful impact on the environment and human health. The obtained fibrous materials were analyzed by FTIR spectroscopy and the gravimetric method. According to gravimetric measurements, the results show an efficiency of 99.3% for the proposed extraction method and of 42.97% for acid hydrolysis. These results show that the proposed method allows adequate extraction using ionic solvents.

The traditional procedure of predicting the settling velocity of a spherical particle is inconvenient as it involves iterations, complex correlations, and an unpredictable degree of uncertainty. The limitations can be addressed efficiently with artificial intelligence-based machine-learning algorithms (MLAs). The limited number of isolated studies conducted to date were constricted to specific fluid rheology, a particular MLA, and insufficient data. In the current study, the generalized application of ML was comprehensively investigated for Newtonian and three varieties of non-Newtonian fluids such as Power-law, Bingham, and Herschel Bulkley. A diverse set of nine MLAs were trained and tested using a large dataset of 967 samples. The ranges of generalized particle Reynolds number (ReG) and drag coefficient (CD) for the dataset were 10−3 < ReG (-) < 104 and 10−1 < CD (-) < 105, respectively. The performances of the models were statistically evaluated using an evaluation metric of the coefficient-of-determination (R2), root-mean-square-error (RMSE), mean-squared-error (MSE), and mean-absolute-error (MAE). The support vector regression with polynomial kernel demonstrated the optimum performance with R2 = 0.92, RMSE = 0.066, MSE = 0.0044, and MAE = 0.044. Its generalization capability was validated using the ten-fold-cross-validation technique, leave-one-feature-out experiment, and leave-one-data-set-out validation. The outcome of the current investigation was a generalized approach to modeling the settling velocity.

The activity described in this chapter is centered on learning dance and uses YouTube as a primary resource. YouTube can help supplement a teacher’s instruction to more effectively communicate a wide array of styles. The activity builds on a relatively traditional procedure of modeling, except that it gives learners more flexibility in terms of time usage, scheduling, as well as an opportunity to experiment with various ideas. It also gives learners a level of authority over what, when, and how they learn after the teacher’s initial introduction. The lessons outlined were born from a desire to better represent the diversity of styles that exist in various regions of Kenya. By using YouTube, lessons are not limited to the knowledge of the teacher and as a result, students can have more freedom in what they learn.