An Unusual/Rare Case of Toothbrush Impalement Injury in Floor of Mouth.

Foreign bodies of the aero-digestive tract are commonly seen emergencies in ENT practice. Young children often present with accidental ingestion of foreign bodies like coins and battery cells. However penetrative foreign bodies of the oral cavity and the aerodigestive tract are relatively rare. They can mostly me managed conservatively, however, they may develop life threatening complications like deep neck space infections and major arterial injury in few cases. After doing extensive literature search, we could find only 1 similar case with impaled toothbrush in the floor of mouth. Therefore, we are reporting only the second case of a child who presented with impalement of toothbrush in the floor of mouth. The toothbrush was surgically removed under general anaesthesia and post-operative period was uneventful. Take home message is to not forcefully pull out the foreign body and seek otorhinolaryngological intervention at the earliest.

Clinico-Etiological Evaluation of Epistaxis.

Epistaxis or bleeding from nose, a commonly confronted condition in the department of otorhinolaryngology can be a disquietening experience and sometimes a life threatening emergency for the patient. The aim of this study is to study the clinical profile and aetiology in epistaxis patients. An observational prospective study carried out over a period of 12 months in the Department of Otorhinolaryngology, Head and Neck Surgery, Swami Rama Himalayan University, Swami Ram Nagar, Dehradun, Uttarakhand. A total of 104 patients of all age groups and gender presenting with epistaxis were included in the study. Males formed majority of the patients (68.27%) as compared to female patients (31.73%). Most of the patients were in the age group of 51-70 years with majority being farmers (30.77%). The finding of variation with age was statistically significant (p < 0.05) with most patients in the age group of 51-60 years presenting in winter season. Local causes were observed to be more common (50.96%) among which trauma was the predominant cause (23.08%). Systemic causes formed 37.58% of cases, out of which hypertension was the commonest cause. In our study, non-surgical measures were most commonly employed treatment modality (85.58%) among which medical management was done in most patients. Trauma and hypertension contributed to the majority of patients presenting with epistaxis in our study with cold, dry winter months associated with increased incidence of epistaxis.

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data. This paper proposes a novel method to predict the net radiation for five crop surfaces using global solar radiation and canopy temperature. This contribution includes the generation of real-time data for five crops grown in West Bengal state of India. After manual analysis and data preprocessing, data normalization has been done before applying machine learning approaches for training a robust model. We have presented the comparison in various machine learning algorithm such as ridge and spline regression, random forest, ensemble and deep neural networks. The result shows that the gradient boosting regression and ridge regression are outperforming other ML approaches. The estimated predictors enable to reduce the number of resources in terms of time, cost and manpower for proper net radiation estimation. Thus, the problem of predicting net radiation over various crop surfaces can be sorted out through ML algorithm.

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets.

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

A new clustering method for the diagnosis of CoVID19 using medical images.

With the spread of COVID-19, there is an urgent need for a fast and reliable diagnostic aid. For the same, literature has witnessed that medical imaging plays a vital role, and tools using supervised methods have promising results. However, the limited size of medical images for diagnosis of CoVID19 may impact the generalization of such supervised methods. To alleviate this, a new clustering method is presented. In this method, a novel variant of a gravitational search algorithm is employed for obtaining optimal clusters. To validate the performance of the proposed variant, a comparative analysis among recent metaheuristic algorithms is conducted. The experimental study includes two sets of benchmark functions, namely standard functions and CEC2013 functions, belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value, Friedman test, and box-plot. Further, the presented clustering method tested against three different types of publicly available CoVID19 medical images, namely X-ray, CT scan, and Ultrasound images. Experiments demonstrate that the proposed method is comparatively outperforming in terms of accuracy, precision, sensitivity, specificity, and F1-score.

A Review on the Development of Earthquake Warning System Using Low-Cost Sensors in Taiwan.

Seismic instrumentation for earthquake early warnings (EEWs) has improved significantly in the last few years, considering the station coverage, data quality, and the related applications. The official EEW system in Taiwan is operated by the Central Weather Bureau (CWB) and is responsible for issuing the regional warning for moderate-to-large earthquakes occurring in and around Taiwan. The low-cost micro-electro-mechanical system (MEMS)-based P-Alert EEW system is operational in Taiwan for on-site warnings and for producing shakemaps. Since 2010, this P-Alert system, installed by the National Taiwan University (NTU), has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an on-site warning system, it is particularly useful for on-site warning. Using real-time seismic signals, each P-Alert system can provide a 2-8 s-long warning time for the locations situated in the blind zone of the CWB regional warning system. The shakemaps plotted using this instrumentation help to assess the damage pattern and rupture directivity, a key feature in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Earlier, the network provided only peak ground acceleration (PGA) shakemaps, but has now been updated to include peak ground velocity (PGV), spectral acceleration (Sa) at different periods, and CWB intensity maps. The PGA and PGV shakemaps plotted using this network have proven helpful in establishing the fact that PGV is a better indicator of damage detection than PGA. This instrumentation is also useful in structural health-monitoring and estimating co-seismic deformations. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries.

Real-Time Production of PGA, PGV, Intensity, and Sa Shakemaps Using Dense MEMS-Based Sensors in Taiwan.

Using low-cost sensors to build a seismic network for earthquake early warning (EEW) and to generate shakemaps is a cost-effective way in the field of seismology. National Taiwan University (NTU) network employing 748 P-Alert sensors based on micro-electro-mechanical systems (MEMS) technology is operational for almost the last 10 years. This instrumentation is capable of recording the strong ground motions of up to ± 2g and is dense enough to record the near-field ground motion. It has proven effective in generating EEW warnings and delivering real-time shakemaps to the concerned disaster relief agencies to mitigate the earthquake-affected regions. Before 2020, this instrumentation was used to plot peak ground acceleration (PGA) shakemaps only; however, recently it has been upgraded to generate the peak ground velocity (PGV), Central Weather Bureau (CWB) Intensity scale, and spectral acceleration (Sa) shakemaps at different periods as value-added products. After upgradation, the performance of the network was observed using the latest recorded earthquakes in the country. The experimental results in the present work demonstrate that the new parameters shakemaps added in the current work provide promising outputs, and are comparable with the shakemaps given by the official agency CWB. These shakemaps are helpful to delineate the earthquake-hit regions which in turn is required to assist the needy well in time to mitigate the seismic risk.

Atypical Facial Chondroid Syringoma, A Rare Clinical Entity: Case Report with Review of Literature.

Chondroid syringoma (CS) is a rare mixed tumor of sweat gland origin with a reported incidence of less than 0.1%. The tumor is mostly benign and usually seen in males. It comprises of both epithelial and mesenchymal elements. The usual presentation and clinical course of this condition makes it liable to be confused with other commoner skin conditions. We hereby present a case of facial CS in a young female patient.

Influenza aerosols in UK hospitals during the H1N1 (2009) pandemic--the risk of aerosol generation during medical procedures.

BACKGROUND: Nosocomial infection of health-care workers (HCWs) during outbreaks of respiratory infections (e.g. Influenza A H1N1 (2009)) is a significant concern for public health policy makers. World Health Organization (WHO)-defined 'aerosol generating procedures' (AGPs) are thought to increase the risk of aerosol transmission to HCWs, but there are presently insufficient data to quantify risk accurately or establish a hierarchy of risk-prone procedures. METHODOLOGY/PRINCIPAL FINDINGS: This study measured the amount of H1N1 (2009) RNA in aerosols in the vicinity of H1N1 positive patients undergoing AGPs to help quantify the potential risk of transmission to HCWs. There were 99 sampling occasions (windows) producing a total of 198 May stages for analysis in the size ranges 0.86-7.3 µm. Considering stages 2 (4-7.3 µm) and 3 (0.86-4 µm) as comprising one sample, viral RNA was detected in 14 (14.1%) air samples from 10 (25.6%) patients. Twenty three air samples were collected while potential AGPs were being performed of which 6 (26.1%) contained viral RNA; in contrast, 76 May samples were collected when no WHO 2009 defined AGP was being performed of which 8 (10.5%) contained viral RNA (unadjusted OR = 2.84 (95% CI 1.11-7.24) adjusted OR = 4.31 (0.83-22.5)). CONCLUSIONS/SIGNIFICANCE: With our small sample size we found that AGPs do not significantly increase the probability of sampling an H1N1 (2009) positive aerosol (OR (95% CI) = 4.31 (0.83-22.5). Although the probability of detecting positive H1N1 (2009) positive aerosols when performing various AGPs on intensive care patients above the baseline rate (i.e. in the absence of AGPs) did not reach significance, there was a trend towards hierarchy of AGPs, placing bronchoscopy and respiratory and airway suctioning above baseline (background) values. Further, larger studies are required but these preliminary findings may be of benefit to infection control teams.