Cyberbullying is often limited to the analysis of static and individual messages, an approach that is less effective at detecting the situational nature of online bullying and its escalation. The proposed study presents another theory Emotion Trajectory Modeling, which assesses the temporal dynamics of emotional states in a whole conversation. To perform this analysis, a hybrid deep learning model, which combines Transformer network and Convolutional Neural Network (CNN), is suggested. Transformer component to infer long-range contextual dependencies across the conversational sequence is used to model the emotional arc over time. At the same time, the CNN derives strong local features and hierarchical patterns of individual messages, distinguishable textual identifiers of abusive language. Such a two-architecture system will allow the system to detect sudden, negative changes in the emotional tone of a conversation, as they are one of the key indicators of bullying. The Multi-Label Twitter Dataset on Online Abuse was used to train and test the model. The classification accuracy is proven by experimental validation to be 96%. Such a good performance confirms the hypothesis that a dynamic analysis based on trajectories is better than the sole inspection of messages. The combination of global context awareness (Transformer) with local feature extraction (CNN) introduces a more accurate and reliable system of identifying complex types of cyberbullying.
Background: The best treatment modality for replacement of teeth is dental implant therapy. Success of dental implant is based on many factors. Objectives: The present study was conducted to assess failure rate of dental implant in medically compromised patients. Materials and Methods: This study consists of 70 medically impaired patients of both genders who undergone dental implants 5 years ago (Group I). A control group of an equal number of healthy individuals was selected (Group II). Failures were defined as bone loss surrounding the implant of more than 1 mm in the first year and more than 0.3 mm in each subsequent year. Results: Groups I and II had a total of 70 subjects, and each group had 80 implants. Diabetes was more common among medically challenged individuals, followed by hypothyroidism, organ transplantation, cardiovascular disease, and osteoporosis. When compared to the healthy group, the medically challenged group had the highest rates of implant failure and bone loss. Conclusion: Among medically compromised conditions, higher failure rate was found in diabetes.
Abstract— Lung cancer is a leading cause of death and early identification of lung cancer is crucial for improved treatment and survival. The traditional diagnostic methods are mostly imaging and clinical assessment that can sometimes have drawbacks including late diagnosis, subjectivity, and a lack of predictive value. A comprehensive lung cancer risk dataset is used and preprocessed in this study, using normalisation methods (StandardScaler and MinMaxScaler) and feature selection (Recursive Feature Elimination with Support Vector Machines). The SMOTE was used to overcome class imbalance. RandomizedSearchCV with KFold cross validation was used to train and optimize various ML regression and classification models such as LR, KNN, Gaussian NB, Multinomial NB, DT, SVC, RF, XGBoost, GB and MLP. Implemented a Voting Classifier which is a combination of Random Forest, Decision Tree and Extra Trees. Improved the interpretability of the model using used explainable AI methods like LIME and SHAP. Deployed using Flask framework. The evaluation using accuracy, precision, recall, f1 score and confusion matrix indicated that VC achieved 98.1% on all the criteria compared to all the classifiers considered individually. Combining ensemble learning and explainable AI enhances diagnostic reliability, transparency, and interpretability, greatly boosting the accuracy of lung cancer detection compared to traditional methods.
Introduction: Madhumalinivasanta rasa is a well acclaimed herbo-mineral compound containing mineral, herbal and animal products. The reference of text Rasachandanshu is taken for its design using Lakucha swarasa for final bhavana. This study aims to design Madhumalinivasanta rasa formulation as per classical guidelines and its characterisation using modern gadgets. Materials and Method: The collection of literary data, collection and authentication of raw materials, further pharmaceutical study and then analytical study was carried out with instrumental exploration. Results: The final product was reddish orange coloured smooth powder having aromatic odour. Analytical study marks presence of Alkaloid, Flavonoid and Phenolic Contents. The ICPMS, SEM EDX, FTIR and XRD shows microcrystalline aggregates structure, marks presence of Sodium, Magnesium, Calcium, Manganese, Iron, Copper, Zinc, Oxygen, Carbon, Mercury etc having nitro group "N-O", Free -OH groups etc. Conclusion: The data generated using high end sophisticated instruments can be used as a hallmark in standardization of Madhumalinivasanta rasa
Fluorouracil (5-FU), despite common use in management of colorectal cancer, shows a narrow therapeutic range, with neurological toxicity. A 75-year-old man with metastatic rectal cancer developed 10–12 episodes of persistent vomiting, followed by left-sided hemiparesis and dysarthria, two days after his third FOLFIRI cycle. Initial assessment and non-contrast CT suggested an acute ischemic stroke in the right middle cerebral artery territory. However, MRI subsequently demonstrated bilateral supratentorial hyperintensities with restricted diffusion, indicating acute toxic leukoencephalopathy related to chemotherapy. DPYD genotyping revealed a wild-type status, with no detectable dihydropyrimidine dehydrogenase deficiency. The patient was treated conservatively with intravenous fluids and supportive measures, resulting in significant neurological recovery and mRS score of 1 at discharge. This case demonstrates that 5-FU-associated neurotoxicity may occur despite the absence of DPYD mutations and highlights the potential importance of combining pharmacogenomic testing with phenotypic DPD assessment to improve fluoropyrimidine treatment safety.
