Lung infection or inflammation-a puzzling case of MDA-5 associated juvenile dermatomyositis

Abstract Background Juvenile dermatomyositis (JDM) is an uncommon inflammatory myopathy predominantly affecting children under 18 years of age. Diagnosis relies on identifying specific clinical features, such as muscle weakness, skin rash, elevated muscle enzymes, and MRI and muscle biopsy findings. Autoantibodies associated with inflammatory myopathy offer valuable prognostic insights and can indicate the risk of internal organ involvement, though they are relatively rare in childhood myopathies. JDM can progress to interstitial lung disease (ILD) if associated with MDA5 antibodies, and immunosuppressive therapy constitutes the primary treatment approach. Case presentation We present a unique case of JDM complicated by disseminated histoplasmosis in a 12-year-old African American male cross-country runner with no prior medical history. He presented with unintentional weight loss and a rash on his hands, genitals, and fingertips, which persisted despite previous treatments. Diagnosis of JDM was confirmed through clinical and laboratory evaluations. Over time, the patient developed recurrent fevers, thrombocytopenia, and signs of ILD, leading to the identification of disseminated histoplasmosis as a complicating factor. Appropriate antifungal treatment resolved the infectious condition, while continued immunosuppression aided in managing JDM and ILD. Conclusions Juvenile dermatomyositis (JDM) remains a rare and intricate autoimmune disorder affecting young individuals. The presence of MDA5 antibodies in JDM patients can lead to severe complications like ILD, necessitating vigilant monitoring. Management includes immunosuppressive therapy, with glucocorticoids and mycophenolate mofetil proving effective, particularly in Clinically Amyopathic Dermatomyositis (CADM) cases. In cases of refractory disease, intravenous immunoglobulin (IVIG) plays a crucial role, offering a safe and beneficial adjunct to treatment. We emphasize the importance of recognizing atypical presentations of JDM, as it can lead to delays in diagnosis and treatment. Our case highlights the complexities of managing dual lung pathology, where a secondary infection exacerbated lung nodules and thrombocytopenia, while ILD was a consequence of atypical myopathy. Combining antifungal treatment with immunosuppression effectively managed both conditions and follow-up evaluations demonstrated improvement in ILD. Awareness of potential fungal infections in immunosuppressed JDM patients is crucial for successful treatment and patient outcomes

A Comparative Study on the Behavior of Islamic and Conventional Stocks in the Presence of Oil Price, Gold Price, and Financial Risk Factors: Evidence from Dow Jones Indices

The research aims to analyze the influence of the gold price, oil price and financial risk on Islamic and conventional securities on comparative as well as on individual bases. Monthly prices of oil and gold are extracted from the websites of West Texas Intermediate and World Gold Council, whereas time series data for financial risk is derived from the Volatility Index of S&P 500.  All these variables are found to be cointegrated at the first difference with both the Dow Jones indices, which means that gold, oil and financial risk have long term association with Islamic and conventional stocks. In order to find the direction and magnitude, this study applied the Newey-West HAC test, which also handles autocorrelation and heteroscedasticity issues in the time series data. The findings of the study suggest that gold prices are positively associated whereas oil prices and financial risk are negatively associated with both types of securities. Though the direction of the nexus is similar for Islamic and conventional stocks, but the magnitude differs especially in case of oil and financial risk. Nevertheless, it can be concluded that there is no diversification prospect between conventional and Islamic stocks under the influence of oil prices, financial risk, and gold prices

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students’ performance, thus mitigating the probability of student failures. Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students’ performance through school and home tutoring. Students’ educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students’ performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature