Death cases due to gas weapons

1995 ◽  
pp. 283-285
Author(s):  
M. A. Rothschild ◽  
V. Schneider
Keyword(s):  
Death Cases

Anticonvulsant Use Elevated In Sudden Death Cases

2010 ◽  
Vol 40 (2) ◽  
pp. 12
Author(s):  
MITCHEL L. ZOLER
Keyword(s):  
Sudden Death ◽  
Death Cases

Insights Studies for Certain Natural FDA Approved Polyphenolics and Repurposing for COVID-19

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1390-1395
Author(s):  
Arafa Musa ◽  
Ehab M. Mostafa ◽  
Mohammad M. Al-Sanea ◽  
Shaimaa R. Ahmed ◽  
Gomaa Mostafa-Hedeab ◽  
...  
Keyword(s):  
Docking Studies ◽  
Human Populations ◽  
Energy Score ◽  
Protease Enzyme ◽  
Human Disorders ◽  
Variable Chemical ◽  
Pancreatic Disorders ◽  
New Treatment ◽  
New Applications ◽  
Death Cases

Natural polyphenolic drugs were approved for treatment of various diseases. Diosmin, rutin, quercetin, aesculin, genistein, hesperidin and silybin are known for their safety and have been applied for several human disorders including cancer, cardiovascular disorders, atherosclerosis, oxidative stress, capillary fragility, liver and pancreatic disorders and others. As the structures of the selected polyphenolic compounds possess variable chemical moieties with diverse chemical and electronic characters, these properties have been employed for further insights studies to predict new applications concerning the newly occurred pandemic. COVID-19 is a terrible disease that attacked millions of human populations at the end of year 2019. As the number of death cases has increased to exceed one million of humans, the early discovery of new treatment from previously approved and safe drugs is the main target of this study. Employing the putative docking studies for the selected polyphenolic drugs were done and the results were promising, all tested drugs exhibited high affinity with the selected five proteins of protease enzyme, the docking score ranged from -8.4461 to -26.1691 kcal/mol with 3-7 variable interaction bonds. Among the tested drugs, diosmin appeared as the most promising drug as it exhibited the highest energy score and interaction bonds with the most of proteins.

A Note on Estimating the Case Fatality Ratio for Novel Corona Virus Disease (Covid-19)

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Prafulla Kumar Swain
Keyword(s):  
Confidence Interval ◽  
Graphical Representation ◽  
Virus Disease ◽  
Case Fatality ◽  
Case Fatality Ratio ◽  
Healthcare Planning ◽  
Robust Estimates ◽  
University Of Oxford ◽  
Pros And Cons ◽  
Death Cases

Background: In this paper an attempt has been made to estimate the Case Fatality Ratio (CFR) for coronavirus disease of India and few selected countries. and Also, highlighted the pros and cons of obtaining crude and adjusted CFR of COVID-19 pandemic. Material and Methods: Data extracted from WHO situation report and University of Oxford website have been used for this analysis. The CFR and its 95% confidence interval were computed, trend and bar plot was used for graphical representation. Results: The worldwide crude CFR stands 6.73% (95% CI 6.69 to 6.76) based on 21, 83, 877 confirmed and 1,46,872 death cases(as on 17th April,2020). Belgium was highest CFR 13.95% as compared to others. However, India’s CFR was found to be around 3.26% (as on 17th April, 2020). Conclusion: In conclusion, the estimation and interpretation of CFR is critical in response to ongoing COVID-19. The initial CFR estimates are subject to change, still it is useful for healthcare planning over the coming months. Moreover, the precise and robust estimates of CFR will be available only at the end of the epidemic.

Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release (Preprint)

2020 ◽  
Author(s):  
Samir Bandyopadhyay Sr ◽  
SHAWNI DUTTA ◽  
SHAWNI DUTTA ◽  
SHAWNI DUTTA
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Health Workers ◽  
Original Data ◽  
High Rate ◽  
Learning Approach ◽  
Good Decision ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Death Cases

BACKGROUND In recent days, Covid-19 coronavirus has been an immense impact on social, economic fields in the world. The objective of this study determines if it is feasible to use machine learning method to evaluate how much prediction results are close to original data related to Confirmed-Negative-Released-Death cases of Covid-19. For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset and the prediction results are tally with the results predicted by clinical doctors. The prediction results are validated against the original data based on some predefined metric. The experimental results showcase that the proposed approach is useful in generating suitable results based on the critical disease outbreak. It also helps doctors to recheck further verification of virus by the proposed method. The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients with in a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors. It could be a promising supplementary confirmation method for frontline clinical doctors. The proposed method has a high prediction rate and works fast for probable accurate identification of the disease. The performance analysis shows that a high rate of accuracy is obtained by the proposed method. OBJECTIVE Validation of COVID-19 disease METHODS Machine Learning RESULTS 90% CONCLUSIONS The combined LSTM-GRU based RNN model provides a comparatively better results in terms of prediction of confirmed, released, negative, death cases on the data. This paper presented a novel method that could recheck occurred cases of COVID-19 automatically. The data driven RNN based model is capable of providing automated tool for confirming, estimating the current position of this pandemic, assessing the severity, and assisting government and health workers to act for good decision making policy. It could be a promising supplementary rechecking method for frontline clinical doctors. It is now essential for improving the accuracy of detection process. CLINICALTRIAL 2020-04-03 3:22:36 PM

ARIMA model for forecasting COVID-19 Death cases in India (Preprint)

2020 ◽  
Author(s):  
Jeya Sutha M
Keyword(s):  
Arima Model ◽  
Public Health Emergency ◽  
World Health ◽  
The People ◽  
The World ◽  
The Status ◽  
The Government ◽  
Health Organization ◽  
Spread Of Infection ◽  
Death Cases

UNSTRUCTURED COVID-19, the disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly contagious disease. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of July 25, 2020; 15,947,292 laboratory-confirmed and 642,814 deaths have been reported globally. India has reported 1,338,928 confirmed cases and 31,412 deaths till date. This paper presents different aspects of COVID-19, visualization of the spread of infection and presents the ARIMA model for forecasting the status of COVID-19 death cases in the next 50 days in order to take necessary precaution by the Government to save the people.

Homicide or Suicide: How Nudity Factors into This Determination

2021 ◽  
pp. 108876792110130
Author(s):  
Sarah W. Craun ◽  
Leah Tanner ◽  
Victoria Clausen ◽  
Melissa A. Merola ◽  
Leonard Opanashuk ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Disease Control ◽  
Life Stressors ◽  
Time Of Death ◽  
Female Victim ◽  
Strong Indicator ◽  
Centers For Disease Control ◽  
Death Cases

Anecdotal reports of deceased celebrities being found nude abound, yet research is lacking regarding the frequency of nudity at death. Moreover, it is unknown if nudity at the time of death is a useful investigative clue or a distracting non-factor in equivocal death cases. This study used data from 119,145 homicides and suicides reported to the Centers for Disease Control to explore victim nudity, prior life stressors, and demographics on the likelihood of a death being a homicide or a suicide. Logistic regression results indicate that a female victim being found nude is a strong indicator of homicide.

scholarly journals Trends and Clinico-Epidemiological Features of Human Rabies Cases in Bangladesh, 2006–2018

2020 ◽  
Vol 41 (S1) ◽  
pp. s416-s416
Author(s):  
Sumon Ghosh ◽  
Md. Sohel Rana ◽  
Sukanta Chowdhury
Keyword(s):  
Effective Means ◽  
Scale Up ◽  
Traditional Healers ◽  
Human Rabies ◽  
Epidemiological Analysis ◽  
Increasing Trend ◽  
The Mean ◽  
Death Cases ◽  
Death Data

Background: Vaccinating dogs against rabies is an effective means of reducing human rabies. Methods: We analyzed 1,327 clinically diagnosed human rabies deaths and mass dog vaccination (MDV) data during 2006–2018 to quantify the impacts of MDV on human rabies incidence in Bangladesh and a subset of rabies death data (n = 422) for clinico-epidemiological analysis. Results: We found a positive and increasing trend of dog population vaccination (P = .01 and τ = 0.71) and a negative and declining trend (P < .001 and τ = −0.88) of human rabies cases (correlation coefficient, −0.82). Among 422 human rabies death cases, most victims (78%) sought treatment from traditional healers, and 12% received postexposure prophylaxis (PEP). The mean incubation period of rabies cases with exposure sites on the head and neck (35 days) was shorter than the upper limb (mean, 64 days; P = .02) and lower limb (mean, 89 days; P < .01). MDV is effective for reducing human rabies cases in Bangladesh. Conclusions: Creating awareness among the animal bite victims to stop relying on traditional healers rather seeking PEP, addressing the role of traditional healers through an awareness education program in respect to the treatment of dog bites, ensuring availability of PEP, and continuing to scale up MDV can help prevent human rabies deaths.Funding: NoneDisclosures: None

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