Search Results (7)

Purpose: Speech-language pathologists (SLPs) are essential in evaluating tongue structure and function. Due to limited psychometrically validated assessment tools, evidence-based practitioners often rely on clinical expertise to inform their assessment and clinical decision-making. This study aimed to explore how SLPs assess tongue structure and function in children aged 1 to 10 years suspected of having a tongue-tie by examining global practice patterns. Methods: A total of 194 practicing, English-speaking SLPs participated in a global online survey. The survey gathered information on participant demographics, classification tools used, and methods for assessing tongue structure and function, oral motor function and speech production in children with suspected tongue-tie. Results: Participants reported using various measures, including case history, oral examination, and clinical assessment. These measures encompassed evaluation of tongue structure, oral motor tasks and functional measures, including observation of speech, feeding, and swallowing. Notably, 40% of participants indicated they did not use any published assessment tool. While over 90% of participants evaluated feeding skills through parent questionnaires, only 55% observed feeding during mealtimes. Additionally, SLPs in the United States reported using different classification tools for tongue-tie compared to their counterparts in Australia, the United Kingdom and other countries. Conclusion: There is a global trend of limited use of published tongue-tie assessment tools in clinical practice. Most clinicians rely on various measures to evaluate tongue structure and function in children with suspected tongue-tie. These findings highlight the need for a specialized assessment tool that is designed and validated for evaluating tongue structure and function in children beyond infancy. Full article

Introduction: Early detection of type 2 diabetes is essential for preventing long-term complications. However, screening the entire population for diabetes is not cost-effective, so identifying individuals at high risk for this disease is crucial. The aim of this study was to compare the performance of five diverse machine learning (ML) models in classifying undiagnosed diabetes using large heterogeneous datasets. Methods: We used machine learning data from several years of the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 to identify people with undiagnosed diabetes. The dataset included 45,431 participants, and biochemical confirmation of glucose control (HbA1c) were used to identify undiagnosed diabetes. The predictors were based on simple and clinically obtainable variables, which could be feasible for prescreening for diabetes. We included five ML models for comparison: random forest, AdaBoost, RUSBoost, LogitBoost, and a neural network. Results: The prevalence of undiagnosed diabetes was 4%. For the classification of undiagnosed diabetes, the area under the ROC curve (AUC) values were between 0.776 and 0.806. The positive predictive values (PPVs) were between 0.083 and 0.091, the negative predictive values (NPVs) were between 0.984 and 0.99, and the sensitivities were between 0.742 and 0.871. Conclusion: We have demonstrated that several types of classification models can accurately classify undiagnosed diabetes from simple and clinically obtainable variables. These results suggest that the use of machine learning for prescreening for undiagnosed diabetes could be a useful tool in clinical practice. Full article

Banking malware are malicious programs that attempt to steal confidential information, such as banking authentication credentials, from users. Zeus is one of the most widespread banking malware variants ever discovered. Since the Zeus source code was leaked, many other variants of Zeus have emerged, and tools such as anti-malware programs exist that can detect Zeus; however, these have limitations. Anti-malware programs need to be regularly updated to recognise Zeus, and the signatures or patterns can only be made available when the malware has been seen. This limits the capability of these anti-malware products because they are unable to detect unseen malware variants, and furthermore, malicious users are developing malware that seeks to evade signature-based anti-malware programs. In this paper, a methodology is proposed for detecting Zeus malware network traffic flows by using machine learning (ML) binary classification algorithms. This research explores and compares several ML algorithms to determine the algorithm best suited for this problem and then uses these algorithms to conduct further experiments to determine the minimum number of features that could be used for detecting the Zeus malware. This research also explores the suitability of these features when used to detect both older and newer versions of Zeus as well as when used to detect additional variants of the Zeus malware. This will help researchers understand which network flow features could be used for detecting Zeus and whether these features will work across multiple versions and variants of the Zeus malware. Full article

Celery (Apium graveolens L.) is a well- known plant and at the basis of the culinary tradition of different populations. In Italy, several celery ecotypes, presenting unique peculiarities, are grown by small local producers, and they need to be characterized, in order to be protected and safeguarded. The present work aims at developing a fast and non-destructive method for the discrimination of a common celery (the "Elne" celery) from a typical celery of Abruzzo (Central Italy). The proposed strategy is based on the use of an e-eye tool which allows the collection of images used to infer colorgrams. Initially, a principal component analysis model was used to investigate the trends and outliers in the data. Then, the classification between the common celery (Elne class) and celery from Torricella Peligna (Torricella class) was achieved by a discriminant analysis, conducted by sequential preprocessing through orthogonalization (SPORT) and sequential and orthogonalized covariance selection (SO-CovSel) and by a class-modelling method called soft independent modelling of class analogies (SIMCAs). Among these, the highest accuracy was provided by the strategies, based on the discriminant classifiers, both of which provided a total accuracy of 82% in the external validation. Full article

New psychoactive substances (NPSs) have concerned authorities worldwide, and monitoring them has become increasingly complex. In addition to the frequent emergence of new chemical structures, the composition of adulterants has changed rapidly. Reliable reference data on NPS are not always available, and identifying them has become an operational problem. In this study, we evaluated the infrared spectral data of 68 seized samples suspected of containing a synthetic cathinone (N-ethylpentylone). We used quantum chemistry tools to simulate infrared spectra as a benchmark and obtained infrared spectra for different cathinones, structurally analogous amphetamines, and possible adulterants. We employed these in silico data to construct different chemometric models and investigated the internal and external validation and classification requirements of the models. We applied the best models to predict the classification of the experimental data, which showed that the seized samples did not have a well-defined profile. Infrared spectra alone did not allow N-ethylpentylone to be distinguished from other substances. This study enabled us to evaluate whether experimental, in silico, and applied statistical techniques help to promote forensic analysis for decision-making. The seized samples required in-depth treatment and evaluation so that they could be correctly analyzed for forensic purposes. Full article

Seed shape in Silene species is often described by means of adjectives such as reniform, globose, and orbicular, but the application of seed shape for species classification requires quantification. A method for the description and quantification of seed shape consists in the comparison with geometric models. Geometric models based on mathematical equations were applied to characterize the general morphology of the seeds in 21 species of Silene. In addition to the previously described four models (M1 is the cardioid, and M2 to M4 are figures derived from it), we present four new geometric models (model 5–8). Models 5 and 6 are open cardioids that resemble M3, quite different from the flat models, M2 and M4. Models 7 and 8 were applied to those species not covered by models 2 to 6. Morphological measures were obtained to describe and characterize the dorsal view of the seeds. The analyses done on dorsal views revealed a notable morphological diversity and four groups were identified. A correlation was found between roundness of dorsal view and the geometric models based on lateral views, such that some of the groups defined by seed roundness are also characterized by the similarity to particular models. The usefulness of new morphological tools of seed morphology to taxonomy is discussed. Full article

Introduction: One important concern during the management of COVID-19 pneumonia patients with acute hypoxemic respiratory failure is early anticipation of the need for intubation. ROX is an index that can help in identification of patients with low and those with high risk of intubation. So, this study was planned to validate the diagnostic accuracy of the ROX index for prediction of COVID-19 pneumonia outcome (the need for intubation) and, in addition, to underline the significant association of the ROX index with clinical, radiological, demographic data. Material and methods: Sixty-nine RT-PCR positive COVID-19 patients were enrolled. The following data were collected: medical history, clinical classification of COVID-19 infection, the ROX index measured daily and the outcome assessment. Results: All patients with severe COVID-19 infection (100%) were intubated (50% of them on the 3rd day of admission), but only 38% of patients with moderate COVID-19 infection required intubation (all of them on the 3rd day of admission). The ROX index on the 1st day of admission was significantly associated with the presence of comorbidities, COVID-19 clinical classification, CT findings and intubation (p ≤ 0.001 for each of them). Regression analysis showed that sex and ROX.1 are the only significant independent predictors of intubation [AOR (95% CI): 16.9 (2.4– 117), 0.77 (0.69–0.86)], respectively. Cut-off point of the ROX index on the 1st day of admission was ≤ 25.26 (90.2% of sensitivity and 75% of specificity). Conclusions: ROX is a simple noninvasive promising tool for predicting discontinuation of high-flow oxygen therapy and could be used in the assessment of progress and the risk of intubation in COVID-19 patients with pneumonia. Full article