The incidence of penetrating and lacerated neck injuries has been rising in recent decades largely because of urban violence. Injury to the neck frequently results in multiple regional injuries and in addition poses serious threat to vital structures in the neck.From 1999 to 2005, forty-two cases of penetrating neck injuries which were treated in our hospital were included in this study. Thirty one (73.8%) injuries were due to homicide, six cases (14.2%) were due to suicide attempt and five (11.9%) were accidental injuries. Surgical management included tracheostomy neck exploration and wound repair. All the patients were followed up for a minimum period of six months. Six patients (14.2%) had unilateral vocal cord paralysis. Two patients (4.7%) developed tracheal stenosis.A proper evaluation, rapid air way intervention and proper surgical repair are essential for a successful outcome.

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Ultrasound guidance for steroid injection in de Quervain disease is useful in identifying the presence of subcompartments and effectively injecting the drug into tendon sheath. We prospectively studied 50 patients with features of de Quervain disease to determine the effectiveness of ultrasound in positioning of needle for steroid injection and effectiveness of single versus multiple injections in the presence of subcompartments. Scalp vein set was inserted into the tendon sheath under ultrasound guidance and sterile conditions. Mixture containing 1 mL of methylprednisolone 40?mg with 1 mL of 2% lignocaine was injected and the patient followed for 6 months. In patients having subcompartments, improvement was better when two separate injections into each subcompartment were given compared with single. Ultrasound guidance is helpful in identifying the existence of subcompartment and injecting the subcompartments separately. Scalp vein set may be very effective in ultrasound-guided injection. This is a level III study.

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Aim of the study:The aim of study is to analyse the functional outcomes of patients with comminuted intertrochanteric fracture treated by primary bipolar hemiarthroplasty and it's complications. Methodology: Thirty patients (17 males and 13 females-all were 60 years old or above) who underwent bipolar hemiarthroplasty for unstable intertrochanteric fractures were prospectively evaluated. Results: In our study, mean age of the patient was 74.4 yrs,24 cases were of type 2 fractures, 4cases were type 3 fracture and 2 cases were type 4 fracture. 11patients had limb shortening of less than 2 cms, whereas 2 patient had limb shortening of more than 2cms.5 patients had abductor weakness. At 12 months follow up in 21 cases (70%), 3 cases (10%) had poor results. Conclusion:Our study, we concluded that bipolar hemiarthroplasty for unstable intertrochanteric fracture in elderly patients reduce the prolonged immoblisation, deformity, pain and provides better early ambulation and improved quality of life.

A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.