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The Interprofesional collaborative practice (IPCP) is the need of the hour for improved patient care. The procedure of tracheotomy is a life saving procedure and the implementation of the Interprofessional collaborative practice module for the same comprising of the ENT surgeon, Physiotherapist, Nursing staff, OT and Trauma technician decreases the number of complications. This study was carried out to develop and evaluate the Interprofessional collaborative practice module for Tracheostomy. The project has been carried out as a prospective before and after study with the departments of ENT, nursing and Allied health sciences. The facilitators were from the above departments.They were sensitized and developed the Interprofessional education (IPE team),which then collaborated to develop the IPCP module.This IPE team after faculty meetings developed the module with learning objectives, teaching learning methods and methods of assessment. Standardized Readiness scale for Interprofessional Learning Scale (RIPLS), was adopted for the module. The questionnaires for assessment and the module were structured and validated.The template of reflection was compiled for the execution of the module. The students training comprised of the demonstration session, baseline Team OSCE, practice sessions and the final Team OSCE. The baseline and final Team OSCE scores,reflections and RIPLS scores were compared. Team OSCE scores baseline vs Final for IPCP competencies i.e. Competency 1-Values and Ethics for Interprofessional Practice, Competency 2-Roles and Responsibilities, Competency 3-Interprofessional Communication, Competency 4-Teams and Teamwork during Pretracheostomy (PreT),Tracheostomy(T) and PostTracheostomy (PostT) were calculated. Faculty observations: TOSCE scores (pre T/T/postT) significantly improved for all the four IPCP competencies (p < 0.001). Self evaluation did not get any significant improvements in PreT and T but significant improvement (P < 0.001) in competency 2 for Post T. Peer evaluation there was significant improvement for the competencies 1 & 2 and overall as well (p < 0.001) during preT, competency 2 during T and competency 2, 3, 4 during PostT. The reflections had a highly significant change from baseline to final (p < 0.001).On final evaluation for the Readiness scale for Interprofessional learning the faculties and students had significant changes in opinions in all the items of the readiness scale (p < 0.05). The project was able to achieve a motivated IPE team which could successfully structure and effectively conduct the IPCP module for the procedure of tracheostomy.
RESUMEN
The pandemic was announced by the world health organization coronavirus (COVID-19) universal health dilemma. Any scientific appliance which contributes expeditious detection of coronavirus with a huge recognition rate may be excessively fruitful to doctors. In this environment, innovative automation like deep learning, machine learning, image processing and medical image like chest radiography (CXR), computed tomography (CT) has been refined promising solution contrary to COVID-19. Currently, a reverse transcription-polymerase chain reaction (RT-PCR) test has been used to detect the coronavirus. Due to the moratorium period is high on results tested and huge false negative estimates, substitute solutions are desired. Thus, an automated machine learning-based algorithm is proposed for the detection of COVID-19 and the grading of nine different datasets. This research impacts the grant of image processing and machine learning to expeditious and definite coronavirus detection using CXR and CT medical imaging. This results in early detection, diagnosis, and cure for the accomplishment of COVID-19 as early as possible. Firstly, images are preprocessed by normalization to enhance the quality of the image and removing of noise. Secondly, segmentation of images is done by fuzzy c-means clustering. Then various features namely, statistical, textural, histogram of gradients, and discrete wavelet transform are extracted (92) and selected from the feature vector by principle component analysis. Lastly, k-NN, SRC, ANN, and SVM are used to make decisions for normal, pneumonia, COVID-19 positive patients. The performance of the system has been validated by the k (5) fold cross-validation technique. The proposed algorithm achieves 91.70% (k-Nearest Neighbor), 94.40% (Sparse Representation Classifier), 96.16% (Artificial Neural Network), and 99.14% (Support Vector Machine) for COVID detection. The proposed results show feature combination and selection improves the performance in 14.34 s with machine learning and image processing techniques. Among k-NN, SRC, ANN, and SVM classifiers, SVM shows more efficient results that are promising and comparable with the literature. The proposed approach results in an improved recognition rate as compared to the literature review. Therefore, the algorithm proposed shows immense potential to benefit the radiologist for their findings. Also, fruitful in prior virus diagnosis and discriminate pneumonia between COVID-19 and other pandemics.
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Metabolic syndrome is considered to be a triggering factor for deterioration of health related quality of life. In present study we assessed hearing loss consequent to metabolic syndrome. A total of 100 patients diagnosed for metabolic syndrome (IDF criteria) were included in the study. All the patients underwent pure tone audiometry and impedance audiometry. All the patients underwent anthropometric measurements, lipid profile, blood sugar and blood pressure assessments. Data was analyzed using SPSS 21.0 software. A total of 62% patients had sensorineural hearing loss. Maximum (35%) had mild hearing loss, followed by moderate hearing loss (23%). Only 4 (4%) cases had severe hearing loss. Older age, wider waist circumference, higher fasting blood glucose levels and lower blood pressure were found to be significantly associated with sensorineural hearing loss and its severity on univariate analysis. However, on multivariate assessment only age and waist circumference showed a significant association with hearing loss.
RESUMEN
The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using image processing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.
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There has been an escalation of patients presenting with symptoms of Laryngopharyngeal reflux disease (LPRD) in the otorhinolaryngology clinics due to life style and dietary changes. This study was undertaken to evaluate the effect of various proton pump inhibitors in the treatment of LPRD using Reflux symptom index (RSI) and Reflux finding score (RFS). This was a prospective study conducted from June 2016 to February 2017 with a total of 240 patients with symptoms and signs of LPR. The patients were divided into 5 groups. Each group was subjected to particular proton pump inhibitor. There were 124 males 116 females with a mean age 34.3 and rural to urban ratio being 11. After 3 months, RFS and RSI score within each group, improved significantly with Proton pump inhibitor therapy. In our study patients who were treated with omeprazole 20 mg twice daily had the highest improvement in laryngeal symptoms and laryngeal findings. We conclude emphasizing the effectiveness of proton pump inhibitors with incorporation of lifestyle modification in the successful management of LPRD.
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BACKGROUND: Controversy surrounds regarding the role of human papillomavirus (HPV) in oral precancerous and cancerous lesions in India where smokeless, tobacco consumption is rampant. AIMS: The present study was carried out with an aim to investigate the presence and type of HPV infection in oropharyngeal leukoplakia and to determine the association of HPV positivity with various patient and lesion characteristics. SETTINGS AND DESIGN: Prospective case series. MATERIALS AND METHODS: A total of fifty confirmed cases of oral leukoplakia (OL), aged 18-55 years were enrolled in the study. Specimens were obtained from the site of lesion. All the specimens were subjected to histopathological analysis and HPV-16, 18 detection was done using real-time polymerase chain reaction technique. RESULTS: Mean age of patients was 34.00 ± 8.85. Majority were males (62%), from middle social class (78%) and were educated up to graduation or above (54%). All the patients had one or more adverse habits (betel, chewing tobacco, smoking and alcohol) with mean duration of disease was 3.53 ± 3.05 years. Five patients (10%) had diabetes. Burning sensation (86%) and trismus (36%) were the reported complaints. Maximum were Stage II (50%), sized 2-4 cm2 (60%), homogenous (70%). Floor of mouth and tongue were most commonly involved sites (50%). Majority had mild dysplasia (58%). Acanthosis (66%), hyperplasia (62%), koliocytosis (40%), and keratosis (98%) were quite common. HPV (16/18) could not be detected in any specimen. CONCLUSIONS: The present study highlighted the fact that association of HPV with oral leukoplakia seems to be overestimated and needs to be reexamined with consensus Human papilloma virus (HPV) primers to detect HPV types with more valid empirical relationships.
