Assessment of the Diagnostic Reliability of Modified Alvarado Scores and Abdominal Ultrasonography in Acute Appendicitis.

Introduction Acute appendicitis is a common surgical emergency. Clinical assessment plays a major role; however, subtle clinical features in early stages and atypical presentation makes diagnosis challenging. Ultrasonography (USG) of the abdomen is a usual investigation that aids in diagnosis, however, it is operator dependent. A contrast-enhanced computed tomography (CECT) of the abdomen is more accurate; however, it exposes the patient to hazardous radiation. The study aimed to combine clinical assessment and USG abdomen in the reliable diagnosis of acute appendicitis. Objectives The objective of this study was to assess the diagnostic reliability of the Modified Alvarado Score and ultrasonography of the abdomen in acute appendicitis. Material and methods All patients with right iliac fossa pain, clinically suspected of having acute appendicitis, admitted to the department of general surgery, Kalinga Institute of Medical Sciences (KIMS), Bhubaneswar, between January 2019 and July 2020, who gave consent were included. Clinically, Modified Alvarado Score (MAS) was calculated, after which patients were subjected to USG abdomen, where findings were noted and a sonologic score was calculated. The study group was the patients who needed appendicectomy (n=138). Operative findings were noted. Histopathological diagnosis of acute appendicitis was deemed as confirmatory in these cases and was correlated with MAS and USG scores to determine diagnostic accuracy. Results A combined clinicoradiological (MAS + USG) score of seven showed a sensitivity of 81.8% and a specificity of 100%. The specificity of score seven or above was 100%; however, the sensitivity at 81.8%. The diagnostic accuracy of the clinicoradiological was 87.5%. The negative appendicectomy rate was 4.34%, with a diagnosis of acute appendicitis being confirmed for 95.7% of patients upon histopathological examination. Conclusion The MAS and USG of the abdomen, which is an affordable and non-invasive tool, showed increased diagnostic reliability, and hence it can help reduce the use of CECT abdomen, as CECT abdomen is considered as a gold standard for confirmation or exclusion of diagnosis of acute appendicitis. Use of the combined scoring system of MAS and USG abdomen can be used as a cost-effective alternative.

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization.

OBJECTIVE: Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features. METHODS: After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation. RESULTS: Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%. CONCLUSION: Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features.Supplemental data for this article is available online at at www.tandfonline.com/ijas .

Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way?

Wine research has as its core components the disciplines of sensory analysis, viticulture, and oenology. Wine quality is an important concept for each of these disciplines, as well as for both wine producers and consumers. Any technique that could help producers to understand the nature of wine quality and how consumers perceive it, will help them to design even more effective marketing strategies. However, predicting a wine's quality presents wine science modelling with a real challenge. We used sample data from Pinot noir wines from different regions of New Zealand to develop a mathematical model that can predict wine quality, and applied dimensional analysis with the Buckingham Pi theorem to determine the mathematical relationship among different chemical and physiochemical compounds. This mathematical model used perceived wine quality indices investigated by wine experts and industry professionals. Afterwards, machine learning algorithms are applied to validate the relevant sensory and chemical concepts. Judgments of wine intrinsic attributes, including overall quality, were made by wine professionals to two sets of 18 Pinot noir wines from New Zealand. This study develops a conceptual and mathematical framework to predict wine quality, and then validated these using a large dataset with machine learning approaches. It is worth noting that the predicted wine quality indices are in good agreement with the wine experts' perceived quality ratings.

Evaluating the detectability of methane point sources from satellite observing systems using microscale modeling.

This study evaluates the efficacy of current satellite observing systems to detect methane point sources from typical oil and gas production (O&G) facilities using a novel very high-resolution methane concentration dataset generated using a microscale model. Transport and dispersion of typical methane emissions from seven well pads were simulated and the column enhancements for pseudo satellite pixel sizes of 3, 1, and 0.05 km were examined every second of the 2-h simulations (7200 realizations). The detectability of plumes increased with a pixel resolution, but two orders of magnitude change in emission rates at the surface results only in about 0.4%, 1.6%, and 47.8% enhancement in the pseudo-satellite retrieved methane column at 3, 1, and 0.05 km, respectively. Average methane emission rates estimated by employing the integrated mass enhancement (IME) method to column enhancements at 0.05 km showed an underestimation of the mean emissions by 0.2-6.4%. We show that IME derived satellite-based inversions of methane emissions work well for large persistent emission sources (e.g., super emitters), however, the method is ill-suited to resolve short-term emission fluctuations (< 20 min) in typical well site emissions due to the limitations in satellite detection limits, precision, overpass timing, and pixel resolution.

Prognosis of sexual dimorphism with unfused hyoid bone: Artificial intelligence informed decision making with discriminant analysis.

Depending on the metric and non-metric skeletal features of various bones, forensic experts proposed diverse sex identification methods. The main focus of the present study is to calculate sexual dimorphism in human unfused or disarticulated hyoid bone and compared it with studies conducted by different researchers. For this study, 293 unfused hyoid bones were accumulated and investigated from 173 male and 120 female cadavers of the northwest Indian population from the age of 15 to 80 years. Initially, discriminant analysis was performed on the dataset to predict sex and to get an idea for the crucial variables for sexual dimorphism. Later, significant variables predicted by the discriminant analysis were used for machine learning approaches to improve accuracy for sex determination. The standard scaler method is used for pre-processing of the data before machine learning analysis and to prevent overfitting and underfitting, 70 % of the whole dataset was utilized in the training of the model and the remaining data were used for testing the model. According to the discriminant analysis, body length (BL) and body height (BH) were found to be highly significant for the sex determination and predicted sex with 75.1 % accuracy. However, implementation of machine learning approaches such as the XG Boost classifier increased the accuracy to 83 % with sensitivity, and specificity scores of 0.81 and 0.84, respectively. Moreover, the ROC-AUC score achieved by the XG Boost classifier is 0.89; indicating machine learning investigation can improve the sex determination accuracy up to the appropriate standard.

Novel viscoelastic gelling agent with unique physico-chemical properties.

A novel innovative viscoelastic gelling agent (novel gel, NG) has been developed by combining citric acid (CA) and disodium 5-guanylate (DG). NG has the potential to replace other gelling agents such as gelatine, which has been commonly used in foods, dietary supplements, pharmaceutical and cosmetic products including ointments and sprays. NG has unique physico-chemical properties, including a wide range of concentration-dependent, temperature-sensitive gel strengths. Based on the rheological measurement results, NG depicted similar shear thinning behaviour to gelatine, within shear rates ranging from 25.8 to 129 (s-1). NG also significantly increased the shelf-life (by 21 days) of minced beef, as well as inhibited the growth of major spoilage pathogens, such as E. coli, S. aureus, Salmonella sp., Listeria sp., yeast and moulds, making it an ideal candidate for gelatine replacement.

Modeling protein-protein interactions in axon initial segment to understand their potential impact on action potential initiation.

The axon initial segment (AIS) region is crucial for action potential initiation due to the presence of high-density AIS protein voltage-gated sodium channels (Nav). Nav channels comprise several serine residues responsible for the recruitment of Nav channels into the structure of AIS through interactions with ankyrin-G (AnkG). In this study, a series of computational experiments are performed to understand the role of AIS proteins casein kinase 2 and AnkG on Nav channel recruitment into the AIS. The computational simulation results using Virtual cell software indicate that Nav channels with all serine sites available for phosphorylation bind to AnkG with strong affinity. At the low initial concentration of AnkG and casein kinase 2, the concentration of Nav channels reduces significantly, suggesting the importance of casein kinase 2 and AnkG in the recruitment of Nav channels.

Recent changes of trans-boundary air pollution over the Yellow Sea: Implications for future air quality in South Korea.

The influence of air pollutants originating from the Chinese region on air quality over South Korea has been a major concern for policymakers. To investigate the inter-annual trends of the long-distance transport of air pollutants from China to South Korea, multi-year trend analysis was carried out for Aerosol Optical Depth (AOD, as a proxy of particulate matter), and CO (a water-insoluble air pollutant) and SO2 (a partially water-soluble air pollutant), over three regions in Northeast Asia. Air pollutants are typically long-range transported from the highly polluted parts of China to South Korea through the Yellow Sea. Taking advantage of this geographical merit, we carried out the multi-year trend analysis with a special focus on the Yellow Sea region. Decreasing trends of about 5-10%, 13-17% and 55-61% during the last decade were observed in surface CO, AOD and tropospheric SO2 columns over the North China Plain (NCP), Yellow Sea (YS), and South Korea (SK), respectively. Such decreasing trends were also found consistently during the last three, five, and seven years, indicating that the changes in pollution levels are likely in response to recent policy measures taken by the Chinese and Korean governments to improve air quality over the regions. Due to these efforts, the amounts of air pollutants transported from China to South Korea are expected to decrease in future years, to the likely rates of 1.50 ppb yr-1, 0.05 DU yr-1, and 0.56 µg m-3 yr-1 over the YS region for CO, SO2, and PM2.5, respectively. Given the ambitious plans recently announced by the Chinese government for the 21st meeting of Conference of Parties (COP21) and its co-control effects, the suggested percentage rates may even be conservative numbers. This analysis is expected to provide South Korean policymakers with valuable information to establish new air pollution policies in South Korea.

Development and characterization of self emulsifying drug delivery system of a poorly water soluble drug using natural oil.

Objective of present study involves preparation and evaluation of self emulsifying drug delivery system (SEDDS) of ibuprofen using peanut oil. SEDDS were composed of varying concentrations of peanut oil (solvent), tween 80 (surfactant) and span 20 (co-surfactant). Influence of concentration of surfactant/co-surfactant and globule size on dissolution rate was investigated. Dissolution rate was studied in phosphate buffer pH 6.8 using dissolution apparatus II. The dissolution rate of self emulsifying capsule was found to be significantly faster than that from conventional tablet. The optimized SEDDS released approximately above 85% of ibuprofen within 30 min, while conventional ibuprofen tablet could released only 36% in 30 min. Therefore, these SEDDS could be a better alternative to conventional drug delivery system of ibuprofen.