Pattern and management of penetrating and nonpenetrating thoracic injuries

Background: Chest trauma constitutes a major public health problem which  includes the injuries to chest wall, pleura, tracheobronchial tree, lungs, diaphragm, oesophagus, heart and great vessels. It consist of more than ten percent of all traumas and twenty five percent of death due to trauma occurs because of chest injury. Chest trauma is increasing in frequency in urban hospitals. Penetrating and nonpenetrating thoracic injuries the most serious injuries leading to significant morbidity and mortality.Methods: This study was prospective observational study of 220 patients of thoracic trauma both penetrating and non-penetrating. These patients admitted in general surgical units from August 2017 to May 2018  of Pandit Bhagwat Dayal  Sharma,  PGIMS  Rohtak Haryana India. The study was pertaining to both penetrating  and non-penetrating chest trauma.Results: Out of 220 chest injury patients who were studied during the said period, Males were 203 and females 17 by a ratio of 12:1 and age ranged from lowest 18 years to 85 years of age. Majority of the patients (90.45%) sustained blunt injuries. RTA was the common mechanism of blunt injury affecting (50.45%) of patients. Multiple Rib fractures was the commonest type of chest injury (21.36%) followed by head injury (17.27%). Head injury was the commonest associated injury seen in our patients. Conclusions: Chest trauma resulting from road traffic accident remains a major mechanism of chest injury. The  measures to decrease the trauma are, educating people about traffic rules and regulations and strictly implementing them is necessary to reduce incidence of chest injuries

The Anointed Son, The Hired Gun, and the Chai Wala: Enemies and Insults in Politicians’ Tweets in the Run-Up to the 2019 Indian General Elections

This study seeks to assess the prevalence, style, and impact of antagonistic messaging on Twitter in the two years preceding the 2019 Indian General Elections. Focusing on the leadership of the two key parties – the ruling BJP, with Prime Minister Narendra Modi and party president Amit Shah, and the opposition INC’s president Rahul Gandhi, we attempt to understand how the politicians sought to portray each other on Twitter, and how their followers reacted to these characterizations, through the lens of Murray Edelman’s work on the ‘Political Enemy’. By thematically coding tweets and quantitatively analyzing their retweets, we find that negative tweets by and large are significantly more popular for all three politicians, and that the opposition leader allocated a significantly larger proportion of his tweets to attacks. We conclude that while leaders in power and those in opposition may take different stances with messaging, Twitter as a social networking site can perpetuate the online reward for attacking behavior

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems

Objective:We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems.Methods:A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death.Results:The final population comprised 743 patients (mean age 65  ±  17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores.Conclusion:Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time.Advances in knowledge:Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice

Particle-in-cell simulation study of PCE-gun for different hollow cathode aperture sizes

Pseudospark (PS) discharge is promising source for high brightness and high intensity electron beam pulses. In the present paper, an effort has been made to analyse the temporal behaviour of discharge current, applied voltage, plasma density in the PS discharge based PCE-Gun at different hollow cathode aperture sizes using 3-D particle-in-cell (PIC) simulation code “VORPAL”. The peak discharge current in the PS discharge is a function of hollow cathode dimensions. The plasma generation process by self ionization discharge is examined at different operating conditions. Argon is taken as the background neutral gas. It has been observed that the decrease in the aperture size from 8 mm to 3 mm increases the discharge current, the electron confinement time and the plasma density

Particle-in-cell simulation study of PCE-gun for different hollow cathode aperture sizes

225-229Pseudospark (PS) discharge is promising source for high brightness and high intensity electron beam pulses. In the present paper, an effort has been made to analyse the temporal behaviour of discharge current, applied voltage, plasma density in the PS discharge based PCE-Gun at different hollow cathode aperture sizes using 3-D particle-in-cell (PIC) simulation code “VORPAL”. The peak discharge current in the PS discharge is a function of hollow cathode dimensions. The plasma generation process by self ionization discharge is examined at different operating conditions. Argon is taken as the background neutral gas. It has been observed that the decrease in the aperture size from 8 mm to 3 mm increases the discharge current, the electron confinement time and the plasma density

Electrical characterization of argon and nitrogen based cold plasma jet

In this paper, a dielectric barrier discharge plasma based atmospheric pressure plasma jet has been generated in a floating helix and floating end ring electrode configuration using mixture of argon and nitrogen gases (50:50 ratio). This configuration is subjected to a range of supply frequencies (10–25 kHz) and supply voltages (6.5–9.5 kV) at a fixed rate of gas flow rate (i.e., 1 l/min). The electrical characterization of the plasma jet has been carried out using a high voltage probe and current transformer. The current–voltage characteristics have been analyzed, and the power consumed by the device has been estimated at different applied combinations of supply frequency and voltages for optimum power consumption and maximum jet length. A comparative analysis of the results of the above experiments has shown that maximum power consumed by the device in helix electrode configuration with end ring is 19 W for (Ar+N2) mixture as compared to only 12 mW and 7.7 mW for Ar and He gas respectively (With end ring), this may be due to the main ionization mechanisms which are different depending on the working gas. Furthermore, maximum jet length of 42 mm has been obtained for He gas at 6 kV/25 kHz due to penning ionization process in comparison to jet lengths of only 32 mm for Ar gas and jet length of only 26 mm for Ar+N2 mixture. The obtained average power consumed and maximum jet length for mixture of (Ar+N2) gases are 6.5 W and 26 mm