Anaphylaxis is a serious side effect experienced by mainly anaesthetists as compare to other physicians. Owing to simultaneous administration of many drugs in perioperative period, causality assessment of drug causing the adverse reactions is usually difficult. Here, were present a rare case of a propofol induced hypersensitivity reaction in a young lady who was posted for a robotic cystectomy under general anaesthesia for ovarian cyst. She was given Propofol in perioperative period along with other anaesthetics. She developed hypotension, tachycardia and facial flushing, angioedema and urticaria over forearms. The causal agent of this adverse event was confirmed by measurement of mast cell tryptase, multiple skin patch test and intradermal sensitivity test. Patch and intradermal sensitivity test were negative for all the drugs used in perioperative period except propofol. Clinical features, investigation and causality assessment suggest Propofol to be the causative drug for anaphylactic reaction. All patients with a suspected anaphylactic reaction during anaesthesia should be investigated to determine the allergic nature of the reaction and to identify the responsible drug.

No abstract

Epilepsy is a disease of grave concern these days due to the negligence in its treatment in many parts of the world. Its detection and diagnose requires high skill, large amount of time and money. Thus, due to lack of treatment, epilepsy which can be diagnosed with simple epileptic drugs turn refractory. This can be avoided if it is detected at an early stage. Also, the data received after a patient undergo EEG is quite complex. Visualizing that data in an effective way and knowing important timestamps in a recorded EEG signal can help one save time and increase accuracy of detection. An automated system utilizing conventional machine learning is thus proposed in this study that uses features extracted from EEG signals. We have used a seizure detection model and visualized data and the result using various python libraries. Seizure detection is a model which is able to identify the presence of abnormal activities in the brain. Seizure prediction is a model which is able to predict in advance if he/she is going to face seizures in coming time by just studying the EEG signals of present state of that patient. Supervised Machine learning (random forest classifier) was employed to analyze recorded EEG signals for epilepsy detection. Data in the datasets was visualized using matplotlib. Classifier was visualized using Graphviz and pydot. Random forest model predicted epilepsy with a good accuracy of 96.87%, Sensitivity came out to be 98.4% and Specificity was 90.7%.

No abstract

The work is about musculoskeletal disorders (MSDs). In today world musculoskeletal disorders are the major problem. It increased dramatically from the 1980 because workload have been increased. Most of them are suffering from arthritis, sprain, strain and there are a lot of diseases also through which peoples are suffering from MSDs. It is characterized into many ways like according to pain stress, mobility, bones muscles and ligaments. There are many surveys done to study musculoskeletal disorders across all over the world. Musculoskeletal disorders are happened because of poor health, increase in weight, fatigue, stress or drugs, smoke can also be the cause of MSDs. We see every day there is a discovery of new disease, new cause of happening a MSDs or new symptoms. In Mexico factories MSD is a major and increasing gradually year by year. Low back issue and shoulder pain is very common which happened due to machines and their work load management for example wrong design of machines, vibrations in machines and etc. Many machines and tests are available to diagnose musculoskeletal disorders in which most commonly used tests are blood test which comes under the category of laboratory test. Xray’s, MRI, CT scan are also used to test MSDs.