In this study, we aimed to examine and analyze liver abnormalities among patients with systemic lupus erythematosus (SLE), including both newly diagnosed patients and those being followed up, as well as the prevalence of lupus hepatitis. MethodsThis was a prospective observational study. Clinical data, liver function tests (LFTs), and the findings from the ultrasonography of the abdomen among the patients were prospectively recorded and evaluated. ResultsOverall, 28 of the total 135 (20.7%) patients had liver abnormalities, including biochemical and those detected via ultrasonography. Ten patients had transaminitis, defined as aspartate aminotransferase (AST) or alanine aminotransferase (ALT) levels >2 times the upper limit of normal (ULN). Nine patients had elevated alkaline phosphatase (ALP) or gamma-glutamyl transferase (GGT) of >2 times ULN. In three patients, transaminitis was due to anti-tubercular therapy (ATT)-induced hepatitis; in seven (5.2%), no specific cause for transaminitis could be identified, and hence they were classified as cases of lupus hepatitis. On comparing clinical features between patients with (n=7) and without lupus hepatitis (n=128), the condition was more prevalent in newly diagnosed SLE patients compared to those who had been on followup [six (85.7%) vs. 30 (23.6%), p=0.002]. All seven patients with lupus hepatitis had complete resolution of the transaminitis on follow-ups. However, one patient who had received ATT (isoniazid, rifampicin, ethambutol, and pyrazinamide) died. Ultrasonography showed fatty liver in seven patients and chronic liver disease in one patient. ConclusionIn this study, transaminitis due to lupus hepatitis was seen in newly diagnosed lupus patients and was not associated with disease activity. Before diagnosing lupus hepatitis, drug-induced liver disease has to be ruled out, and if persistent LFT abnormalities are present, further workup is suggested to rule out overlap with primary biliary cirrhosis and/or autoimmune hepatitis.
In the present day computer era cloud computing is emerging into a huge environment which requires security services incorporated into cloud servers or data centers or even in cloud data storages available in different locations equipped with a very high speed networks. Due to increase in number cloud users and more are showing interest to use the environment, providing security is becoming a major concern because unless the cloud environment is secure no user will show interest and will be keen to invest or migrate from distributed environment to cloud computing environment. In this paper we designed a security framework which is based on Multi Agent Based System (MABS) architecture that tends to provide security at various levels and providing the authenticated and accurate information to the cloud data seeker, our architecture mainly focuses on dual aspects such as Agent Layer and the Cloud Data Storage Layer and we have implemented the proposed framework in WEKA tool and used JDK 1.5 and generated results pertaining all the agents identified in this paper.
In today’s technology-driven and Internet-obsessed society, it can be challenging to go through huge amounts of information and find relevant knowledge for various educational contexts. Simple, fast, and adaptable machine learning algorithms make such tasks easier to complete. K-means is the most effective unsupervised learning technique for classifying data into meaningful groups. K-means groups data by shared characteristics. K-means clusters are determined by k. Unfortunately, standard k-means requires a lot of math. Scholars have suggested strategies to improve k-means grouping. This work recommends computing initial centroids and establishing a distance between data points that are unlikely to change their cluster in subsequent iterations and those that are extremely likely to do so to lessen the load of k-means clustering for very large data sets. This piece will find information digits whose cluster is statistically likely to alter in the following few cycles. After processing several datasets, it is compared to other K-Means methods
The proposed project aims to address the escalating concern of cyberbullying on social media platforms by utilizing machine learning classification algorithms, such as Support Vector Machine, to mitigate its effects. To improve the accuracy of the solution, the project will leverage the Natural Language Toolkit to extract features including bigrams, trigrams, n-grams, and unigrams. To evaluate the efficacy of this approach in detecting cyberbullying in tweets, a comparison will be conducted against baseline features and alternative machine learning algorithms. However, it is crucial to consider ethical implications when employing machine learning algorithms to minimize false positives and safeguard innocent individuals from potential harm. In essence, this project holds promise in advancing the development of robust tools for identifying and preventing cyberbullying on social media platforms, a pressing issue in our modern digital era.
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