Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 115
Filter
1.
NeuroQuantology ; 20(10):4658-4667, 2022.
Article in English | EMBASE | ID: covidwho-2033481

ABSTRACT

The emergency use authorization for the coronavirus disease 2019(COVID-19) vaccine has risen up expectations and concerns. Post market surveillance plays key role in assessing the benefit and risks. Our study aimed to estimate the death rate and analyze the disproportionality of anaphylactic reactions after receiving the COVID-19 vaccine.Data was gathered from the Vaccine Adverse Event Reporting system (VAERS) public data releases.To determine the mortality rate and anaphylactic reactions linked to COVID-19 vaccine provided between December 20, 2020 and June 24, 2022, a thorough study was conducted. Individuals who were 18 years of age or older and had received the COVID vaccines compared with other vaccine of same age group were included. Age, gender, onset of interval and days in hospital has been considered in assessing death rate. By comparing reports of anaphylactic reactions following the COVID-19 vaccines to all other vaccines using the Evans criteria, a disproportional analysis is performed using Proportional Reporting Ratio (PRR).However, ongoing surveillance of older adults who have received vaccinations is necessary. With regard to an anaphylactic reaction linked to the COVID-19 vaccination, no potential signal was observed.To systematically validate the information provided by VAERS, additional epidemiologic investigations are required.

2.
Anais Da Academia Brasileira de Ciencias ; 94(4):e20210202, 2022.
Article in English | MEDLINE | ID: covidwho-2029823

ABSTRACT

BACKGROUND: Role of Convalescent plasma (COPLA) to treat severe COVID-19 is under investigation. We compared efficacy and safety of COPLA with fresh frozen plasma (FFP) in severe COVID-19 patients. METHODS: One group received COPLA with standard medical care (n = 14), and another group received random donor FFP, as control with standard medical care (n = 15) in severe COVID-19 disease. RESULTS: The proportion of patients free of ventilation at day seven were 78.5% in COPLA group, and 93.3 % in control group were not significant (p= 0.258). However, improved respiratory rate, O2 saturation, SOFA score, and Ct value were observed in the COPLA group. No serious adverse events were noticed by plasma transfusion in both groups.

3.
Asian Pacific Journal of Tropical Medicine ; 15(7):287-289, 2022.
Article in English | Scopus | ID: covidwho-2024694
4.
Qjm ; 02:02, 2022.
Article in English | MEDLINE | ID: covidwho-2018083

ABSTRACT

OBJECTIVES: This study aims to describe the demographic and clinical profile and ascertain the determinants of outcome among hospitalised COVID-19 adult patients enrolled in the National Clinical Registry for COVID-19 (NCRC). METHODS: NCRC is an on-going data collection platform operational in 42 hospitals across India. Data of hospitalized COVID-19 patients enrolled in NCRC between 1st September 2020 to 26th October 2021 were examined. RESULTS: Analysis of 29,509 hospitalised, adult COVID-19 patients [mean (SD) age: 51.1 (16.2) year;male: 18752 (63.6%)] showed that 15678 (53.1%) had at least one comorbidity. Among 25715 (87.1%) symptomatic patients, fever was the commonest symptom (72.3%) followed by shortness of breath (48.9%) and dry cough (45.5%). In-hospital mortality was 14.5% (n = 3957). Adjusted odds of dying were significantly higher in age-group >=60 years, males, with diabetes, chronic kidney diseases, chronic liver disease, malignancy, and tuberculosis, presenting with dyspnea and neurological symptoms. WHO ordinal scale 4 or above at admission carried the highest odds of dying [5.6 (95% CI: 4.6, 7.0)]. Patients receiving one [OR: 0.5 (95% CI: 0.4, 0.7)] or two doses of anti-SARS CoV-2 vaccine [OR: 0.4 (95% CI: 0.3, 0.7)] were protected from in-hospital mortality. CONCLUSIONS: WHO ordinal scale at admission is the most important independent predictor for in-hospital death in COVID-19 patients. Anti-SARS-CoV2 vaccination provides significant protection against mortality.

5.
1st International Conference on Expert Clouds and Applications, ICOECA 2022 ; 444:517-527, 2022.
Article in English | Scopus | ID: covidwho-2014046

ABSTRACT

Fake news has been in our society for a long time but with the introduction of social media, Internet and mobile phones the spread of fake news has severely increased. Social media sites are being used to effectively spread misinformation and hoaxes around the world which not only causes people to change their thinking but also manipulates their opinions and decisions. In today’s world it has become nearly impossible to detect if the given news is fake or real. With the arrival of the novel coronavirus-19 pandemic the propagation of fake news is now more than ever. In this time there is a need for something which can classify if a given news is real or not. In this article we aim to develop a model which, using some algorithms, determines if the given news is fake or not. Machine Learning is a form of Artificial Intelligence which utilizes various strategies applying them on data and algorithms to do the same way humans learn. Previous data is used as input by machine learning algorithms to predict new output values. Fake news has the ability to hurt both individuals and society if it is widely spread such as riots, violence and hatred against a community. Understanding the truth of new information and its message can have a positive impact on society when used in conjunction with news detection. We created four prediction models using machine learning that have an accuracy of above 90% which predicts if the given news is either fact or capped (fake). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

6.
International Journal of Early Childhood Special Education ; 14(5):1460-1467, 2022.
Article in English | Web of Science | ID: covidwho-2006509

ABSTRACT

Convolutional neural networks (CNNs) in particular have achieved successful outcomes in the categorization and analysis of medical image data using artificial intelligence (AI) approaches. This research proposes a deep CNN architecture for the classification of chest X-ray images in the diagnosis of COVID-19.An efficient and precise CNN classification was difficult since there was no chest X-ray picture dataset of a size and quality that was enough.The dataset has been preprocessed in different stages using different techniques to achieve an effective training dataset for the proposed CNN model to achieve its best performance. To deal with these complexities, such as the availability of a very-small-sized, imbalanced dataset with image-quality issues, the preprocessing stages of the datasets performed in this study include dataset balancing, medical experts' image analysis, and data augmentation.Experimental findings revealed an overall accuracy of up to 99.5%, showing the proposed CNN model's strong suit in the current application domain.) Two different scenarios were used to evaluate the CNN model.In the first case, the model was tested using 100 X-ray pictures from the original, properly processed dataset, and it was 100% accurate.The model has been tested in the second scenario using an independent dataset of COVID-19 X-ray pictures.Up to 99.5 percent of the test scenario's performance was achieved.An examination of the suggested model's performance in comparison to other models has been conducted using several machine learning methods.)When the proposed model was tested using an independent testing set, it outperformed all other models both generally and specifically.

7.
Indian Journal of Critical Care Medicine ; 26:S52-S54, 2022.
Article in English | EMBASE | ID: covidwho-2006348

ABSTRACT

Aim and background: The prevalence of acute kidney injury (AKI) among COVID-19 patients admitted to ICU was 46%. There is a paucity of data on renal recovery in a cohort of patients with AKI. Since COVID-19 is considered a public health issue, the estimates from this study might help in prognostication and health resource management. Objective: To evaluate the predictors and dynamics of renal recovery in critically ill COVID-19 patients with AKI. To study the duration and magnitude of AKI, the proportion of patients dependent on dialysis at hospital discharge, and mortality among COVID-AKI patients. Materials and methods: A single-centre, observational study was conducted in a mixed adult ICU from March 1, 2020, to February 1, 2021. COVID-19 patients who presented with or developed AKI as per KDIGO criteria within 7 days of ICU admission were included. Baseline characteristics, hemodynamic parameters, and renal recovery kinetics were captured till the discharge of the patient. Patients were followed up till 90 days post-discharge. Logistic regression with best subset selection was performed with renal recovery as an outcome (recovery is defined as attaining AKI stage 0 by KDIGO definition or 33% reduction of serum creatine from baseline) and APACHE II, rapidity of onset and progression of AKI, the magnitude of AKI, inflammatory markers, comorbidities, and P/F ratios as predictor variables. There were no multicollinearities, influential observations. Penalized-likelihood criteria (AIC and BIC models) were applied and a model with the lowest AIC or BIC was considered as the best fit to predict nonrecovery from AKI. Results: 200 patients' data were analysed, of which 67 patients recovered from AKI. Of the 67 patients, 16, 9, and 10 patients had transient AKI (<48 hours), persistent AKI (2-7 days), and AKD (7-90 days), respectively. Dialysis was required for 136 patients. The average duration for recovery from AKI was 7.4 days. The best fit model with the lowest BIC that predicted nonrecovery from AKI were: the combination of APACHE II, day onset of AKI, and magnitude of AKI. Results of logistic regression showed admission APACHE II, day onset of AKI, and magnitude of AKI were statistically significant in predicting non-recovery from AKI [OR 1.1 (p < 0.001;95% CI 1.06-1.16), OR 1.6 (p = 0.001;1.24-2.24), and OR 2.9 (p < 0.001;2.03-4.36), respectively]. This model had sufficient discrimination with AUC 0.86 and was well calibrated [Hosmer-Lemeshow (HL) chi2, p = 0.06]. Overall mortality among COVID-AKI patients was 84%. Two patients were dependent on dialysis at hospital discharge. Upon follow-up of 31 survivors for 90 days, four deaths were recorded. Conclusion: In our study, a higher APACHE II score at admission, the longer time interval between admission to the onset of AKI and the higher magnitude of AKI during ICU stay predicted poor renal recovery. A significant proportion of our patients require dialysis support and this poses a challenge on hospital resources and financial burden to the family. We observed higher mortality among COVID-19 patients with AKI compared to those with AKI not associated with COVID-19.

8.
Indian Journal of Psychiatry ; 64, 2022.
Article in English | Web of Science | ID: covidwho-2003424
9.
European Journal of Molecular and Clinical Medicine ; 9(4):1921-1927, 2022.
Article in English | EMBASE | ID: covidwho-2003368

ABSTRACT

Introduction: Myopia is a major health issue in our society. There is a large number of proportion remain undiagnosed. High myopia can be associated with multiple consequences as myopic retinopathy, myopic macular degeneration, retinal detachment and amblyopia. The aim of our study to focus on magnitude of childhood myopia, increase awareness for myopia in our society so that we can reduce vision threatening sequelae in children. Methods: Study was conducted in ophthalmology department and ENT department government medical college Saharanpur, medicine department Uttar Pradesh medical sciences, Saifai and pediatric department GTB medical college New Delhi. Children between 7 to 16 years with ametropia included in the study to find out magnitude of myopia among them. Result: A total of 1460 children between the age of 7-16 years with complains of eyeache, headache, heaviness of head diminution of vision, and with other asthenopic symptoms included in the study. Among these 320 children were myopic. It shows high magnitude of myopia in children. Out of these myopic children mild grade myopic children were in high proportion (35.9%). Conclusion: The study showed the pattern of myopia in children in Indian population. Screening in schools and early diagnosis of refractive error affect the learning and performances of children. In our study we showed the pattern of severity of myopia in children. Study also showed the effect of digital screen time of children with myopia.

10.
Food Bioscience ; : 101977, 2022.
Article in English | ScienceDirect | ID: covidwho-2004071

ABSTRACT

The novel enveloped β-coronavirus SARS-CoV-2 (COVID-19) has offered a surprising health challenge all over the world. It develops severe pneumonia leading to acute respiratory distress syndrome (ARDS). Like SARS-COV-2, other encapsulated viruses like HIV, HSV, and influenza have also offered a similar challenge in the past. In this regard, many antiviral drugs are being explored with varying degrees of success to combat the associated pathological conditions. Therefore, upon scientific validation & development, these antiviral phytochemicals can attain a futuristic nutraceutical prospect in managing different encapsulated viruses. Houttuynia cordata (HC) is widely reported for activities such as antioxidant, anti-inflammatory, and antiviral properties. The major antiviral bioactive components of HC include essential oils (methyl n-nonyl ketone, lauryl aldehyde, capryl aldehyde), flavonoids (quercetin, rutin, hyperin, quercitrin, isoquercitrin), and alkaloids (norcepharadione B) & polysaccharides. HC can further be explored as a potential nutraceutical agent in the therapy of encapsulated viruses like HIV, HSV, and influenza. The review listed various conventional and green technologies that are being employed to extract potent phytochemicals with diverse activities from the HC. It was indicated that HC also inhibited molecular targets like 3C-like protease (3CLPRO) and RNA-dependent RNA polymerase (RdRp) of COVID-19 by blocking viral RNA synthesis and replication. Antioxidant and hepatoprotective effects of HC have been evident in impeding complications from marketed drugs during antiviral therapies. The use of HC as a nutraceutical is localized within some parts of Southeast Asia. Further technological advances can establish it as a nutraceutical-based functional food against pathogenic enveloped viruses like COVID 19.

11.
NeuroQuantology ; 20(8):4904-4912, 2022.
Article in English | EMBASE | ID: covidwho-1998067

ABSTRACT

In COVID-19 pandemic situations or in critical/emergency conditions patients experience a great problem because of the non-availability of doctors. A person in a medical emergency needs an immediate doctor to monitor and diagnose his/her medical problem and prescribe the medicine. In these types of critical conditions, there is a demand for some smart portable intensive care unit that can transmit the patient's vital parameters and provide this information to the doctor so that the patient gets lifesaving drugs as per the doctor’s prescription. This work proposed a low-cost and Internet of Things (IoT) based smart-portable intensive care unit (S-PICU) that is capable of transmitting the vital parameters of a patient to doctors’ mobile applications when the patient is in a remote location or transit because of a medical emergency. Patients in intensive care units (ICUs) with severe or life-threatening illnesses and injuries demand round-the-clock treatment, so this portable unit fulfils the medical requirement during emergency conditions. The android based mobile application fetches the patient’s data in real-time from the IoT cloud database system. A doctor can receive the patient’s data in digital and analog form on mobile and can easily set the drug infusion flow from their location. The proposed device helps the patient in critical/emergency conditions save a life because it can easily communicate with doctors through a smart mobile application and get prescribed drugs remotely. This device reduces doctors’ movement and better utilization of medical diagnoses. The automation feature of this device improves patient safety and features of this device improve the safety of patients and also the quality of medical treatment.

12.
Journal of General Internal Medicine ; 37:S304, 2022.
Article in English | EMBASE | ID: covidwho-1995704

ABSTRACT

BACKGROUND: COVID-19 has increased awareness of fungal infections among hospitalized patients. With the use of multiple immune-modulating drugs in COVID-19 along with COVID-19 related immune suppression, the risk of fungal infections is high. We studied fungal infections in COVID-19 to identify patterns to aid in preventive measures. METHODS: We included all COVID-19 positive adult patients (≥18 years) hospitalized between March 1, 2020, to October 1, 2021. Fungal infections were deemed positive if they developed fevers, leukocytosis along with positive cultures (blood, respiratory or urine). Candida albicans was considered to be causative if either blood cultures were positive or positive cultures from 2 sites and antifungals were administered. Outcomes studied were rates, organisms involved, and in-hospital mortality. We used multivariable logistic regression models to examine characteristics associated with the development of fungal infections. Variables used in the model included patient demographics (age, gender, race), comorbidities (congestive heart failure, diabetes mellitus, chronic obstructive pulmonary disease, end- stage renal disease, cirrhosis, and cancer), medications used to treat COVID-19 (ivermectin, hydroxychloroquine, steroids, tocilizumab, baricitinib), the severity of disease (4C score, use of invasive mechanical ventilation (IMV), acute kidney injury (AKI) requiring hemodialysis), and presence of central venous catheters. RESULTS: Of 7508 admissions with COVID-19, 82 (1.1%) acquired fungal infections. Fungal infections developed in 61(3.7%) of the 1642 intensive care unit (ICU) admissions and 21 (0.4%) of the 5866 non-ICU admissions. Among the fungal infections - 33 were Candida albicans, 28 were non-candida albicans, 19 were molds and 2 were cryptococcus. Fungal infections were associated with the use of IMV (Odds Ratio (OR) 13.3, 95% confidence interval (CI) 6.7-26.3, p<0.001), steroids (OR 2.4, 95%CI 1.6- 3.6, p<0.001), and AKI requiring hemodialysis (OR 2.2, 95%CI 1.2-4.1, p=0.01). Of the 5866 non-ICU admissions, in-hospital mortality was significantly higher in those with fungal infections (65% vs 7.3%, p< 0.001). Similarly, among the 1642 ICU admissions, in-hospitalmortality was significantly higher in those with fungal infections (64% vs 37%, p<0.001). On logistic regression analysis, fungal infections were associated with higher in-hospital mortality (OR 2.0;95%CI 1.1- 3.6, p=0.03). Of the fungal infections, molds were associated with higher in-hospital mortality (OR 4.4, 95%CI 1.2- 16.4) while Candida albicans (OR 2.4, 95%CI 0.9-6.4, p=0.08) and non-albicans candida (OR 1.2, 95%CI 0.5-3.3, p=0.66) did not reach significance. CONCLUSIONS: Fungal infections are rare in hospitalized COVID-19 patients but ten times more common in ICU admission. Fungal infections were associated with IMV, steroids, and AKI requiring hemodialysis. Molds were associated with higher in-hospital mortality.

13.
Tran, Khanh Bao, Lang, Justin J.; Compton, Kelly, Xu, Rixing, Acheson, Alistair R.; Henrikson, Hannah Jacqueline, Kocarnik, Jonathan M.; Penberthy, Louise, Aali, Amirali, Abbas, Qamar, Abbasi, Behzad, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abbastabar, Hedayat, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed Abdelwahab, Abdoli, Gholamreza, Abdulkadir, Hanan Abdulkadir, Abedi, Aidin, Abegaz, Kedir Hussein, Abidi, Hassan, Aboagye, Richard Gyan, Abolhassani, Hassan, Absalan, Abdorrahim, Abtew, Yonas Derso, Abubaker Ali, Hiwa, Abu-Gharbieh, Eman, Achappa, Basavaprabhu, Acuna, Juan Manuel, Addison, Daniel, Addo, Isaac Yeboah, Adegboye, Oyelola A.; Adesina, Miracle Ayomikun, Adnan, Mohammad, Adnani, Qorinah Estiningtyas Sakilah, Advani, Shailesh M.; Afrin, Sumia, Afzal, Muhammad Sohail, Aggarwal, Manik, Ahinkorah, Bright Opoku, Ahmad, Araz Ramazan, Ahmad, Rizwan, Ahmad, Sajjad, Ahmad, Sohail, Ahmadi, Sepideh, Ahmed, Haroon, Ahmed, Luai A.; Ahmed, Muktar Beshir, Ahmed Rashid, Tarik, Aiman, Wajeeha, Ajami, Marjan, Akalu, Gizachew Taddesse, Akbarzadeh-Khiavi, Mostafa, Aklilu, Addis, Akonde, Maxwell, Akunna, Chisom Joyqueenet, Al Hamad, Hanadi, Alahdab, Fares, Alanezi, Fahad Mashhour, Alanzi, Turki M.; Alessy, Saleh Ali, Algammal, Abdelazeem M.; Al-Hanawi, Mohammed Khaled, Alhassan, Robert Kaba, Ali, Beriwan Abdulqadir, Ali, Liaqat, Ali, Syed Shujait, Alimohamadi, Yousef, Alipour, Vahid, Aljunid, Syed Mohamed, Alkhayyat, Motasem, Al-Maweri, Sadeq Ali Ali, Almustanyir, Sami, Alonso, Nivaldo, Alqalyoobi, Shehabaldin, Al-Raddadi, Rajaa M.; Al-Rifai, Rami H. Hani, Al-Sabah, Salman Khalifah, Al-Tammemi, Ala'a B.; Altawalah, Haya, Alvis-Guzman, Nelson, Amare, Firehiwot, Ameyaw, Edward Kwabena, Aminian Dehkordi, Javad Javad, Amirzade-Iranaq, Mohammad Hosein, Amu, Hubert, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderson, Jason A.; Animut, Yaregal Animut, Anoushiravani, Amir, Anoushirvani, Ali Arash, Ansari-Moghaddam, Alireza, Ansha, Mustafa Geleto, Antony, Benny, Antwi, Maxwell Hubert, Anwar, Sumadi Lukman, Anwer, Razique, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Arab-Zozani, Morteza, Aremu, Olatunde, Argaw, Ayele Mamo, Ariffin, Hany, Aripov, Timur, Arshad, Muhammad, Artaman, Al, Arulappan, Judie, Aruleba, Raphael Taiwo, Aryannejad, Armin, Asaad, Malke, Asemahagn, Mulusew A.; Asemi, Zatollah, Asghari-Jafarabadi, Mohammad, Ashraf, Tahira, Assadi, Reza, Athar, Mohammad, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Attia, Sameh, Aujayeb, Avinash, Ausloos, Marcel, Avila-Burgos, Leticia, Awedew, Atalel Fentahun, Awoke, Mamaru Ayenew, Awoke, Tewachew, Ayala Quintanilla, Beatriz Paulina, Ayana, Tegegn Mulatu, Ayen, Solomon Shitu, Azadi, Davood, Azadnajafabad, Sina, Azami-Aghdash, Saber, Azanaw, Melkalem Mamuye, Azangou-Khyavy, Mohammadreza, Azari Jafari, Amirhossein, Azizi, Hosein, Azzam, Ahmed Y. Y.; Babajani, Amirhesam, Badar, Muhammad, Badiye, Ashish D.; Baghcheghi, Nayereh, Bagheri, Nader, Bagherieh, Sara, Bahadory, Saeed, Baig, Atif Amin, Baker, Jennifer L.; Bakhtiari, Ahad, Bakshi, Ravleen Kaur, Banach, Maciej, Banerjee, Indrajit, Bardhan, Mainak, Barone-Adesi, Francesco, Barra, Fabio, Barrow, Amadou, Bashir, Nasir Z.; Bashiri, Azadeh, Basu, Saurav, Batiha, Abdul-Monim Mohammad, Begum, Aeysha, Bekele, Alehegn Bekele, Belay, Alemayehu Sayih, Belete, Melaku Ashagrie, Belgaumi, Uzma Iqbal, Bell, Arielle Wilder, Belo, Luis, Benzian, Habib, Berhie, Alemshet Yirga, Bermudez, Amiel Nazer C.; Bernabe, Eduardo, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhandari, Bharti Bhandari, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhattacharyya, Krittika, Bhojaraja, Vijayalakshmi S.; Bhuyan, Soumitra S.; Bibi, Sadia, Bilchut, Awraris Hailu, Bintoro, Bagas Suryo, Biondi, Antonio, Birega, Mesfin Geremaw Birega, Birhan, Habitu Eshetu, Bjørge, Tone, Blyuss, Oleg, Bodicha, Belay Boda Abule, Bolla, Srinivasa Rao, Boloor, Archith, Bosetti, Cristina, Braithwaite, Dejana, Brauer, Michael, Brenner, Hermann, Briko, Andrey Nikolaevich, Briko, Nikolay Ivanovich, Buchanan, Christina Maree, Bulamu, Norma B.; Bustamante-Teixeira, Maria Teresa, Butt, Muhammad Hammad, Butt, Nadeem Shafique, Butt, Zahid A.; Caetano dos Santos, Florentino Luciano, Cámera, Luis Alberto, Cao, Chao, Cao, Yin, Carreras, Giulia, Carvalho, Márcia, Cembranel, Francieli, Cerin, Ester, Chakraborty, Promit Ananyo, Charalampous, Periklis, Chattu, Vijay Kumar, Chimed-Ochir, Odgerel, Chirinos-Caceres, Jesus Lorenzo, Cho, Daniel Youngwhan, Cho, William C. S.; Christopher, Devasahayam J.; Chu, Dinh-Toi, Chukwu, Isaac Sunday, Cohen, Aaron J.; Conde, Joao, Cortés, Sandra, Costa, Vera Marisa, Cruz-Martins, Natália, Culbreth, Garland T.; Dadras, Omid, Dagnaw, Fentaw Teshome, Dahlawi, Saad M. A.; Dai, Xiaochen, Dandona, Lalit, Dandona, Rakhi, Daneshpajouhnejad, Parnaz, Danielewicz, Anna, Dao, An Thi Minh, Darvishi Cheshmeh Soltani, Reza, Darwesh, Aso Mohammad, Das, Saswati, Davitoiu, Dragos Virgil, Davtalab Esmaeili, Elham, De la Hoz, Fernando Pio, Debela, Sisay Abebe, Dehghan, Azizallah, Demisse, Biniyam, Demisse, Fitsum Wolde, Denova-Gutiérrez, Edgar, Derakhshani, Afshin, Derbew Molla, Meseret, Dereje, Diriba, Deribe, Kalkidan Solomon, Desai, Rupak, Desalegn, Markos Desalegn, Dessalegn, Fikadu Nugusu, Dessalegni, Samuel Abebe A.; Dessie, Gashaw, Desta, Abebaw Alemayehu, Dewan, Syed Masudur Rahman, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Dianatinasab, Mostafa, Diao, Nancy, Diaz, Daniel, Digesa, Lankamo Ena, Dixit, Shilpi Gupta, Doaei, Saeid, Doan, Linh Phuong, Doku, Paul Narh, Dongarwar, Deepa, dos Santos, Wendel Mombaque, Driscoll, Tim Robert, Dsouza, Haneil Larson, Durojaiye, Oyewole Christopher, Edalati, Sareh, Eghbalian, Fatemeh, Ehsani-Chimeh, Elham, Eini, Ebrahim, Ekholuenetale, Michael, Ekundayo, Temitope Cyrus, Ekwueme, Donatus U.; El Tantawi, Maha, Elbahnasawy, Mostafa Ahmed, Elbarazi, Iffat, Elghazaly, Hesham, Elhadi, Muhammed, El-Huneidi, Waseem, Emamian, Mohammad Hassan, Engelbert Bain, Luchuo, Enyew, Daniel Berhanie, Erkhembayar, Ryenchindorj, Eshetu, Tegegne, Eshrati, Babak, Eskandarieh, Sharareh, Espinosa-Montero, Juan, Etaee, Farshid, Etemadimanesh, Azin, Eyayu, Tahir, Ezeonwumelu, Ifeanyi Jude, Ezzikouri, Sayeh, Fagbamigbe, Adeniyi Francis, Fahimi, Saman, Fakhradiyev, Ildar Ravisovich, Faraon, Emerito Jose A.; Fares, Jawad, Farmany, Abbas, Farooque, Umar, Farrokhpour, Hossein, Fasanmi, Abidemi Omolara, Fatehizadeh, Ali, Fatima, Wafa, Fattahi, Hamed, Fekadu, Ginenus, Feleke, Berhanu Elfu, Ferrari, Allegra Allegra, Ferrero, Simone, Ferro Desideri, Lorenzo, Filip, Irina, Fischer, Florian, Foroumadi, Roham, Foroutan, Masoud, Fukumoto, Takeshi, Gaal, Peter Andras, Gad, Mohamed M.; Gadanya, Muktar A.; Gaipov, Abduzhappar, Galehdar, Nasrin, Gallus, Silvano, Garg, Tushar, Gaspar Fonseca, Mariana, Gebremariam, Yosef Haile, Gebremeskel, Teferi Gebru, Gebremichael, Mathewos Alemu, Geda, Yohannes Fikadu, Gela, Yibeltal Yismaw, Gemeda, Belete Negese Belete, Getachew, Melaku, Getachew, Motuma Erena, Ghaffari, Kazem, Ghafourifard, Mansour, Ghamari, Seyyed-Hadi, Ghasemi Nour, Mohammad, Ghassemi, Fariba, Ghimire, Ajnish, Ghith, Nermin, Gholamalizadeh, Maryam, Gholizadeh Navashenaq, Jamshid, Ghozy, Sherief, Gilani, Syed Amir, Gill, Paramjit Singh, Ginindza, Themba G.; Gizaw, Abraham Tamirat T.; Glasbey, James C.; Godos, Justyna, Goel, Amit, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Golitaleb, Mohamad, Gorini, Giuseppe, Goulart, Bárbara Niegia Garcia, Grosso, Giuseppe, Guadie, Habtamu Alganeh, Gubari, Mohammed Ibrahim Mohialdeen, Gudayu, Temesgen Worku, Guerra, Maximiliano Ribeiro, Gunawardane, Damitha Asanga, Gupta, Bhawna, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Gurara, Mekdes Kondale, Guta, Alemu, Habibzadeh, Parham, Haddadi Avval, Atlas, Hafezi-Nejad, Nima, Hajj Ali, Adel, Haj-Mirzaian, Arvin, Halboub, Esam S.; Halimi, Aram, Halwani, Rabih, Hamadeh, Randah R.; Hameed, Sajid, Hamidi, Samer, Hanif, Asif, Hariri, Sanam, Harlianto, Netanja I.; Haro, Josep Maria, Hartono, Risky Kusuma, Hasaballah, Ahmed I.; Hasan, S. M. Mahmudul, Hasani, Hamidreza, Hashemi, Seyedeh Melika, Hassan, Abbas M.; Hassanipour, Soheil, Hayat, Khezar, Heidari, Golnaz, Heidari, Mohammad, Heidarymeybodi, Zahra, Herrera-Serna, Brenda Yuliana, Herteliu, Claudiu, Hezam, Kamal, Hiraike, Yuta, Hlongwa, Mbuzeleni Mbuzeleni, Holla, Ramesh, Holm, Marianne, Horita, Nobuyuki, Hoseini, Mohammad, Hossain, Md Mahbub, Hossain, Mohammad Bellal Hossain, Hosseini, Mohammad-Salar, Hosseinzadeh, Ali, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Househ, Mowafa, Huang, Junjie, Hugo, Fernando N.; Humayun, Ayesha, Hussain, Salman, Hussein, Nawfal R.; Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Iftikhar, Pulwasha Maria, Ikuta, Kevin S.; Ilesanmi, Olayinka Stephen, Ilic, Irena M.; Ilic, Milena D.; Immurana, Mustapha, Innos, Kaire, Iranpour, Pooya, Irham, Lalu Muhammad, Islam, Md Shariful, Islam, Rakibul M.; Islami, Farhad, Ismail, Nahlah Elkudssiah, Isola, Gaetano, Iwagami, Masao, J, Linda Merin, Jaiswal, Abhishek, Jakovljevic, Mihajlo, Jalili, Mahsa, Jalilian, Shahram, Jamshidi, Elham, Jang, Sung-In, Jani, Chinmay T.; Javaheri, Tahereh, Jayarajah, Umesh Umesh, Jayaram, Shubha, Jazayeri, Seyed Behzad, Jebai, Rime, Jemal, Bedru, Jeong, Wonjeong, Jha, Ravi Prakash, Jindal, Har Ashish, John-Akinola, Yetunde O.; Jonas, Jost B.; Joo, Tamas, Joseph, Nitin, Joukar, Farahnaz, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kabir, Ali, Kacimi, Salah Eddine Oussama, Kadashetti, Vidya, Kahe, Farima, Kakodkar, Pradnya Vishal, Kalankesh, Laleh R.; Kalankesh, Leila R.; Kalhor, Rohollah, Kamal, Vineet Kumar, Kamangar, Farin, Kamath, Ashwin, Kanchan, Tanuj, Kandaswamy, Eswar, Kandel, Himal, Kang, HyeJung, Kanno, Girum Gebremeskel, Kapoor, Neeti, Kar, Sitanshu Sekhar, Karanth, Shama D.; Karaye, Ibraheem M.; Karch, André, Karimi, Amirali, Kassa, Bekalu Getnet, Katoto, Patrick D. M. C.; Kauppila, Joonas H.; Kaur, Harkiran, Kebede, Abinet Gebremickael, Keikavoosi-Arani, Leila, Kejela, Gemechu Gemechu, Kemp Bohan, Phillip M.; Keramati, Maryam, Keykhaei, Mohammad, Khajuria, Himanshu, Khan, Abbas, Khan, Abdul Aziz Khan, Khan, Ejaz Ahmad, Khan, Gulfaraz, Khan, Md Nuruzzaman, Khan, Moien A. B.; Khanali, Javad, Khatab, Khaled, Khatatbeh, Moawiah Mohammad, Khatib, Mahalaqua Nazli, Khayamzadeh, Maryam, Khayat Kashani, Hamid Reza, Khazeei Tabari, Mohammad Amin, Khezeli, Mehdi, Khodadost, Mahmoud, Kim, Min Seo, Kim, Yun Jin, Kisa, Adnan, Kisa, Sezer, Klugar, Miloslav, Klugarová, Jitka, Kolahi, Ali-Asghar, Kolkhir, Pavel, Kompani, Farzad, Koul, Parvaiz A.; Koulmane Laxminarayana, Sindhura Lakshmi, Koyanagi, Ai, Krishan, Kewal, Krishnamoorthy, Yuvaraj, Kucuk Bicer, Burcu, Kugbey, Nuworza, Kulimbet, Mukhtar, Kumar, Akshay, Kumar, G. Anil, Kumar, Narinder, Kurmi, Om P.; Kuttikkattu, Ambily, La Vecchia, Carlo, Lahiri, Arista, Lal, Dharmesh Kumar, Lám, Judit, Lan, Qing, Landires, Iván, Larijani, Bagher, Lasrado, Savita, Lau, Jerrald, Lauriola, Paolo, Ledda, Caterina, Lee, Sang-woong, Lee, Shaun Wen Huey, Lee, Wei-Chen, Lee, Yeong Yeh, Lee, Yo Han, Legesse, Samson Mideksa, Leigh, James, Leong, Elvynna, Li, Ming-Chieh, Lim, Stephen S.; Liu, Gang, Liu, Jue, Lo, Chun-Han, Lohiya, Ayush, Lopukhov, Platon D.; Lorenzovici, László, Lotfi, Mojgan, Loureiro, Joana A.; Lunevicius, Raimundas, Madadizadeh, Farzan, Mafi, Ahmad R.; Magdeldin, Sameh, Mahjoub, Soleiman, Mahmoodpoor, Ata, Mahmoudi, Morteza, Mahmoudimanesh, Marzieh, Mahumud, Rashidul Alam, Majeed, Azeem, Majidpoor, Jamal, Makki, Alaa, Makris, Konstantinos Christos, Malakan Rad, Elaheh, Malekpour, Mohammad-Reza, Malekzadeh, Reza, Malik, Ahmad Azam, Mallhi, Tauqeer Hussain, Mallya, Sneha Deepak, Mamun, Mohammed A.; Manda, Ana Laura, Mansour-Ghanaei, Fariborz, Mansouri, Borhan, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Martini, Santi, Martorell, Miquel, Masoudi, Sahar, Masoumi, Seyedeh Zahra, Matei, Clara N.; Mathews, Elezebeth, Mathur, Manu Raj, Mathur, Vasundhara, McKee, Martin, Meena, Jitendra Kumar, Mehmood, Khalid, Mehrabi Nasab, Entezar, Mehrotra, Ravi, Melese, Addisu, Mendoza, Walter, Menezes, Ritesh G.; Mengesha, SIsay Derso, Mensah, Laverne G.; Mentis, Alexios-Fotios A.; Mera-Mamián, Andry Yasmid Mera, Meretoja, Tuomo J.; Merid, Mehari Woldemariam, Mersha, Amanual Getnet, Meselu, Belsity Temesgen, Meshkat, Mahboobeh, Mestrovic, Tomislav, Miao Jonasson, Junmei, Miazgowski, Tomasz, Michalek, Irmina Maria, Mijena, Gelana Fekadu Worku, Miller, Ted R.; Mir, Shabir Ahmad, Mirinezhad, Seyed Kazem, Mirmoeeni, Seyyedmohammadsadeq, Mirza-Aghazadeh-Attari, Mohammad, Mirzaei, Hamed, Mirzaei, Hamid Reza, Misganaw, Abay Sisay, Misra, Sanjeev, Mohammad, Karzan Abdulmuhsin, Mohammadi, Esmaeil, Mohammadi, Mokhtar, Mohammadian-Hafshejani, Abdollah, Mohammadpourhodki, Reza, Mohammed, Arif, Mohammed, Shafiu, Mohan, Syam, Mohseni, Mohammad, Moka, Nagabhishek, Mokdad, Ali H.; Molassiotis, Alex, Molokhia, Mariam, Momenzadeh, Kaveh, Momtazmanesh, Sara, Monasta, Lorenzo, Mons, Ute, Montasir, Ahmed Al, Montazeri, Fateme, Montero, Arnulfo, Moosavi, Mohammad Amin, Moradi, Abdolvahab, Moradi, Yousef, Moradi Sarabi, Mostafa, Moraga, Paula, Morawska, Lidia, Morrison, Shane Douglas, Morze, Jakub, Mosapour, Abbas, Mostafavi, Ebrahim, Mousavi, Seyyed Meysam, Mousavi Isfahani, Haleh, Mousavi Khaneghah, Amin, Mpundu-Kaambwa, Christine, Mubarik, Sumaira, Mulita, Francesk, Munblit, Daniel, Munro, Sandra B.; Murillo-Zamora, Efrén, Musa, Jonah, Nabhan, Ashraf F.; Nagarajan, Ahamarshan Jayaraman, Nagaraju, Shankar Prasad, Nagel, Gabriele, Naghipour, Mohammadreza, Naimzada, Mukhammad David, Nair, Tapas Sadasivan, Naqvi, Atta Abbas, Narasimha Swamy, Sreenivas, Narayana, Aparna Ichalangod, Nassereldine, Hasan, Natto, Zuhair S.; Nayak, Biswa Prakash, Ndejjo, Rawlance, Nduaguba, Sabina Onyinye, Negash, Wogene Wogene, Nejadghaderi, Seyed Aria, Nejati, Kazem, Neupane Kandel, Sandhya, Nguyen, Huy Van Nguyen, Niazi, Robina Khan, Noor, Nurulamin M.; Noori, Maryam, Noroozi, Nafise, Nouraei, Hasti, Nowroozi, Ali, Nuñez-Samudio, Virginia, Nzoputam, Chimezie Igwegbe, Nzoputam, Ogochukwu Janet, Oancea, Bogdan, Odukoya, Oluwakemi Ololade, Oghenetega, Onome Bright, Ogunsakin, Ropo Ebenezer, Oguntade, Ayodipupo Sikiru, Oh, In-Hwan, Okati-Aliabad, Hassan, Okekunle, Akinkunmi Paul, Olagunju, Andrew T.; Olagunju, Tinuke O.; Olakunde, Babayemi Oluwaseun, Olufadewa, Isaac Iyinoluwa, Omer, Emad, Omonisi, Abidemi E. Emmanuel, Ong, Sokking, Onwujekwe, Obinna E.; Orru, Hans, Otstavnov, Stanislav S.; Oulhaj, Abderrahim, Oumer, Bilcha, Owopetu, Oluwatomi Funbi, Oyinloye, Babatunji Emmanuel, P A, Mahesh, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Pakbin, Babak, Pakshir, Keyvan, Pakzad, Reza, Palicz, Tamás, Pana, Adrian, Pandey, Anamika, Pandey, Ashok, Pant, Suman, Pardhan, Shahina, Park, Eun-Cheol, Park, Eun-Kee, Park, Seoyeon, Patel, Jay, Pati, Siddhartha, Paudel, Rajan, Paudel, Uttam, Paun, Mihaela, Pazoki Toroudi, Hamidreza, Peng, Minjin, Pereira, Jeevan, Pereira, Renato B.; Perna, Simone, Perumalsamy, Navaraj, Pestell, Richard G.; Pezzani, Raffaele, Piccinelli, Cristiano, Pillay, Julian David, Piracha, Zahra Zahid, Pischon, Tobias, Postma, Maarten J.; Pourabhari Langroudi, Ashkan, Pourshams, Akram, Pourtaheri, Naeimeh, Prashant, Akila, Qadir, Mirza Muhammad Fahd, Quazi Syed, Zahiruddin, Rabiee, Mohammad, Rabiee, Navid, Radfar, Amir, Radhakrishnan, Raghu Anekal, Radhakrishnan, Venkatraman, Raeisi, Mojtaba, Rafiee, Ata, Rafiei, Alireza, Raheem, Nasiru, Rahim, Fakher, Rahman, Md Obaidur, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rahmani, Shayan, Rahmanian, Vahid, Rajai, Nazanin, Rajesh, Aashish, Ram, Pradhum, Ramezanzadeh, Kiana, Rana, Juwel, Ranabhat, Kamal, Ranasinghe, Priyanga, Rao, Chythra R.; Rao, Sowmya J.; Rashedi, Sina, Rashidi, Amirfarzan, Rashidi, Mahsa, Rashidi, Mohammad-Mahdi, Ratan, Zubair Ahmed, Rawaf, David Laith, Rawaf, Salman, Rawal, Lal, Rawassizadeh, Reza, Razeghinia, Mohammad Sadegh, Rehman, Ashfaq Ur, Rehman, Inayat ur, Reitsma, Marissa B.; Renzaho, Andre M. N.; Rezaei, Maryam, Rezaei, Nazila, Rezaei, Negar, Rezaei, Nima, Rezaei, Saeid, Rezaeian, Mohsen, Rezapour, Aziz, Riad, Abanoub, Rikhtegar, Reza, Rios-Blancas, Maria, Roberts, Thomas J.; Rohloff, Peter, Romero-Rodríguez, Esperanza, Roshandel, Gholamreza, Rwegerera, Godfrey M.; S, Manjula, Saber-Ayad, Maha Mohamed, Saberzadeh-Ardestani, Bahar, Sabour, Siamak, Saddik, Basema, Sadeghi, Erfan, Saeb, Mohammad Reza, Saeed, Umar, Safaei, Mohsen, Safary, Azam, Sahebazzamani, Maryam, Sahebkar, Amirhossein, Sahoo, Harihar, Sajid, Mirza Rizwan, Salari, Hedayat, Salehi, Sana, Salem, Marwa Rashad, Salimzadeh, Hamideh, Samodra, Yoseph Leonardo, Samy, Abdallah M.; Sanabria, Juan, Sankararaman, Senthilkumar, Sanmarchi, Francesco, Santric-Milicevic, Milena M.; Saqib, Muhammad Arif Nadeem, Sarveazad, Arash, Sarvi, Fatemeh, Sathian, Brijesh, Satpathy, Maheswar, Sayegh, Nicolas, Schneider, Ione Jayce Ceola, Schwarzinger, Michaël, Šekerija, Mario, Senthilkumaran, Subramanian, Sepanlou, Sadaf G.; Seylani, Allen, Seyoum, Kenbon, Sha, Feng, Shafaat, Omid, Shah, Pritik A.; Shahabi, Saeed, Shahid, Izza, Shahrbaf, Mohammad Amin, Shahsavari, Hamid R.; Shaikh, Masood Ali, Shaka, Mohammed Feyisso, Shaker, Elaheh, Shannawaz, Mohammed, Sharew, Mequannent Melaku Sharew, Sharifi, Azam, Sharifi-Rad, Javad, Sharma, Purva, Shashamo, Bereket Beyene, Sheikh, Aziz, Sheikh, Mahdi, Sheikhbahaei, Sara, Sheikhi, Rahim Ali, Sheikhy, Ali, Shepherd, Peter Robin, Shetty, Adithi, Shetty, Jeevan K.; Shetty, Ranjitha S.; Shibuya, Kenji, Shirkoohi, Reza, Shirzad-Aski, Hesamaddin, Shivakumar, K. M.; Shivalli, Siddharudha, Shivarov, Velizar, Shobeiri, Parnian, Shokri Varniab, Zahra, Shorofi, Seyed Afshin, Shrestha, Sunil, Sibhat, Migbar Mekonnen, Siddappa Malleshappa, Sudeep K.; Sidemo, Negussie Boti, Silva, Diego Augusto Santos, Silva, Luís Manuel Lopes Rodrigues, Silva Julian, Guilherme, Silvestris, Nicola, Simegn, Wudneh, Singh, Achintya Dinesh, Singh, Ambrish, Singh, Garima, Singh, Harpreet, Singh, Jasvinder A.; Singh, Jitendra Kumar, Singh, Paramdeep, Singh, Surjit, Sinha, Dhirendra Narain, Sinke, Abiy H.; Siraj, Md Shahjahan, Sitas, Freddy, Siwal, Samarjeet Singh, Skryabin, Valentin Yurievich, Skryabina, Anna Aleksandrovna, Socea, Bogdan, Soeberg, Matthew J.; Sofi-Mahmudi, Ahmad, Solomon, Yonatan, Soltani-Zangbar, Mohammad Sadegh, Song, Suhang, Song, Yimeng, Sorensen, Reed J. D.; Soshnikov, Sergey, Sotoudeh, Houman, Sowe, Alieu, Sufiyan, Mu'awiyyah Babale, Suk, Ryan, Suleman, Muhammad, Suliankatchi Abdulkader, Rizwan, Sultana, Saima, Sur, Daniel, Szócska, Miklós, Tabaeian, Seidamir Pasha, Tabarés-Seisdedos, Rafael, Tabatabaei, Seyyed Mohammad, Tabuchi, Takahiro, Tadbiri, Hooman, Taheri, Ensiyeh, Taheri, Majid, Taheri Soodejani, Moslem, Takahashi, Ken, Talaat, Iman M.; Tampa, Mircea, Tan, Ker-Kan, Tat, Nathan Y.; Tat, Vivian Y.; Tavakoli, Ahmad, Tavakoli, Arash, Tehrani-Banihashemi, Arash, Tekalegn, Yohannes, Tesfay, Fisaha Haile, Thapar, Rekha, Thavamani, Aravind, Thoguluva Chandrasekar, Viveksandeep, Thomas, Nihal, Thomas, Nikhil Kenny, Ticoalu, Jansje Henny Vera, Tiyuri, Amir, Tollosa, Daniel Nigusse, Topor-Madry, Roman, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Traini, Eugenio, Tran, Mai Thi Ngoc, Tripathy, Jaya Prasad, Ukke, Gebresilasea Gendisha, Ullah, Irfan, Ullah, Saif, Ullah, Sana, Unnikrishnan, Bhaskaran, Vacante, Marco, Vaezi, Maryam, Valadan Tahbaz, Sahel, Valdez, Pascual R.; Vardavas, Constantine, Varthya, Shoban Babu, Vaziri, Siavash, Velazquez, Diana Zuleika, Veroux, Massimiliano, Villeneuve, Paul J.; Violante, Francesco S.; Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vo, Bay, Vu, Linh Gia, Wadood, Abdul Wadood, Waheed, Yasir, Walde, Mandaras Tariku, Wamai, Richard G.; Wang, Cong, Wang, Fang, Wang, Ning, Wang, Yu, Ward, Paul, Waris, Abdul, Westerman, Ronny, Wickramasinghe, Nuwan Darshana, Woldemariam, Melat, Woldu, Berhanu, Xiao, Hong, Xu, Suowen, Xu, Xiaoyue, Yadav, Lalit, Yahyazadeh Jabbari, Seyed Hossein, Yang, Lin, Yazdanpanah, Fereshteh, Yeshaw, Yigizie, Yismaw, Yazachew, Yonemoto, Naohiro, Younis, Mustafa Z.; Yousefi, Zabihollah, Yousefian, Fatemeh, Yu, Chuanhua, Yu, Yong, Yunusa, Ismaeel, Zahir, Mazyar, Zaki, Nazar, Zaman, Burhan Abdullah, Zangiabadian, Moein, Zare, Fariba, Zare, Iman, Zareshahrabadi, Zahra, Zarrintan, Armin, Zastrozhin, Mikhail Sergeevich, Zeineddine, Mohammad A.; Zhang, Dongyu, Zhang, Jianrong, Zhang, Yunquan, Zhang, Zhi-Jiang, Zhou, Linghui, Zodpey, Sanjay, Zoladl, Mohammad, Vos, Theo, Hay, Simon I.; Force, Lisa M.; Murray, Christopher J. L..
The Lancet ; 400(10352):563-591, 2022.
Article in English | ProQuest Central | ID: covidwho-1991370

ABSTRACT

Summary Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Funding Bill & Melinda Gates Foundation.

14.
International Journal of Pharmaceutical and Clinical Research ; 14(7):595-599, 2022.
Article in English | EMBASE | ID: covidwho-1976136

ABSTRACT

Aim: To evaluate the usefulness of D-Dimer levels in blood to correlate with disease severity in COVID 19 patients. Material & Methods: The present retrospective study includes 50 patients hospitalized for COVID-19 during a period of 6 months in Government Medical College, Bettiah, Bihar, India. D-dimer evaluation was performed using an immunoturbidimetric assay on Erba Mannheim ECL 105 machine. Results: The study was conducted on 50 COVID 19 positive patients admitted to the hospital. Of the total patients, 34 were male and 16 were female. In mild cases D Dimer varies from 52 ng/ml to 192 ng/ml with mean 98.3 and median 99. In moderate cases D Dimer varies from 262 ng/ml to 998 ng/ml with mean 664.8 and median 812. Conclusion: D dimer helps in identifying severe disease and can be used as an essential biomarker in developing the management protocol for COVID 19 patients.

15.
COVID-19 and the Sustainable Development Goals ; : 259-284, 2022.
Article in English | PMC | ID: covidwho-1966242
16.
Proc Natl Acad Sci U S A ; 119(31): e2200592119, 2022 Aug 02.
Article in English | MEDLINE | ID: covidwho-1960616

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant contains extensive sequence changes relative to the earlier-arising B.1, B.1.1, and Delta SARS-CoV-2 variants that have unknown effects on viral infectivity and response to existing vaccines. Using SARS-CoV-2 virus-like particles (VLPs), we examined mutations in all four structural proteins and found that Omicron and Delta showed 4.6-fold higher luciferase delivery overall relative to the ancestral B.1 lineage, a property conferred mostly by enhancements in the S and N proteins, while mutations in M and E were mostly detrimental to assembly. Thirty-eight antisera samples from individuals vaccinated with Pfizer/BioNTech, Moderna, or Johnson & Johnson vaccines and convalescent sera from unvaccinated COVID-19 survivors had 15-fold lower efficacy to prevent cell transduction by VLPs containing the Omicron mutations relative to the ancestral B.1 spike protein. A third dose of Pfizer vaccine elicited substantially higher neutralization titers against Omicron, resulting in detectable neutralizing antibodies in eight out of eight subjects compared to one out of eight preboosting. Furthermore, the monoclonal antibody therapeutics casirivimab and imdevimab had robust neutralization activity against B.1 and Delta VLPs but no detectable neutralization of Omicron VLPs, while newly authorized bebtelovimab maintained robust neutralization across variants. Our results suggest that Omicron has similar assembly efficiency and cell entry compared to Delta and that its rapid spread is due mostly to reduced neutralization in sera from previously vaccinated subjects. In addition, most currently available monoclonal antibodies will not be useful in treating Omicron-infected patients with the exception of bebtelovimab.


Subject(s)
Antibodies, Monoclonal, Humanized , Antibodies, Neutralizing , Antibodies, Viral , COVID-19 , SARS-CoV-2 , Spike Glycoprotein, Coronavirus , Antibodies, Monoclonal, Humanized/therapeutic use , Antibodies, Neutralizing/immunology , Antibodies, Neutralizing/therapeutic use , Antibodies, Viral/therapeutic use , COVID-19/therapy , COVID-19/virology , Humans , Mutation , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Spike Glycoprotein, Coronavirus/genetics
17.
J Am Chem Soc ; 144(30): 13663-13672, 2022 Aug 03.
Article in English | MEDLINE | ID: covidwho-1960253

ABSTRACT

Many existing protein detection strategies depend on highly functionalized antibody reagents. A simpler and easier to produce class of detection reagent is highly desirable. We designed a single-component, recombinant, luminescent biosensor that can be expressed in laboratory strains of Escherichia coli and Saccharomyces cerevisiae. This biosensor is deployed in multiple homogeneous and immobilized assay formats to detect recombinant SARS-CoV-2 spike antigen and cultured virus. The chemiluminescent signal generated facilitates detection by an unaugmented cell phone camera. Binding-activated tandem split-enzyme (BAT) biosensors may serve as a useful template for diagnostics and reagents that detect SARS-CoV-2 antigens and other proteins of interest.


Subject(s)
Biosensing Techniques , COVID-19 , Humans , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/metabolism
18.
Proc Natl Acad Sci U S A ; 119(30): e2122236119, 2022 Jul 26.
Article in English | MEDLINE | ID: covidwho-1947759

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) readily infects a variety of cell types impacting the function of vital organ systems, with particularly severe impact on respiratory function. Neurological symptoms, which range in severity, accompany as many as one-third of COVID-19 cases, indicating a potential vulnerability of neural cell types. To assess whether human cortical cells can be directly infected by SARS-CoV-2, we utilized stem-cell-derived cortical organoids as well as primary human cortical tissue, both from developmental and adult stages. We find significant and predominant infection in cortical astrocytes in both primary tissue and organoid cultures, with minimal infection of other cortical populations. Infected and bystander astrocytes have a corresponding increase in inflammatory gene expression, reactivity characteristics, increased cytokine and growth factor signaling, and cellular stress. Although human cortical cells, particularly astrocytes, have no observable ACE2 expression, we find high levels of coronavirus coreceptors in infected astrocytes, including CD147 and DPP4. Decreasing coreceptor abundance and activity reduces overall infection rate, and increasing expression is sufficient to promote infection. Thus, we find tropism of SARS-CoV-2 for human astrocytes resulting in inflammatory gliosis-type injury that is dependent on coronavirus coreceptors.


Subject(s)
Astrocytes , Cerebral Cortex , SARS-CoV-2 , Viral Tropism , Angiotensin-Converting Enzyme 2/metabolism , Astrocytes/enzymology , Astrocytes/virology , Cerebral Cortex/virology , Humans , Organoids/virology , Primary Cell Culture , SARS-CoV-2/physiology
19.
Journal of education and health promotion ; 11, 2022.
Article in English | EuropePMC | ID: covidwho-1940073

ABSTRACT

BACKGROUND: Conducting online classes and assessment during the COVID-19 pandemic is not without challenges. The world of medical education is adapting online training and assessment because of COVID-19 pandemic restrictions. The present study was conducted to assess the students' perception regarding the process, difficulties encountered and perceived effectiveness of online assessment. MATERIALS AND METHODS: Online viva-voce (theory and visual based) was conducted in a government medical college in Karwar, Karnataka, India using videoconferencing application (Google Meet) to 149 second MBBS students as a formative assessment in 2020 over 3 months. Ten students per day joined Google Meet, 10 questions were asked to each student and assessed using a tutor marking system (on-spot). A feedback questionnaire (Google Form) was administered to students who attended online Viva-Voce. Data was analysed using descriptive and inferential statistics (Student's t-test). RESULTS: Out of 149 students, 132 participated and responded to a feedback questionnaire. Majority of the participants (91%) agreed that questions covered all topics kept for viva, 82% of them felt it would be helpful for performance in final examinations. Thirty percent of students faced network issues at their places, 45% felt nervous while facing viva in the presence of other students and 35% of participants preferred online methods over traditional viva voce. Online viva voce can be transparent (90%) and less biased (88%) if done in structured format. CONCLUSION: Online viva-voce may become relevant and effective in medical education assessment with transparent marking system for students' performance.

20.
Chem ; 2022 Jul 18.
Article in English | MEDLINE | ID: covidwho-1936147

ABSTRACT

The long-lasting COVID-19 pandemic and increasing SARS-CoV-2 variants demand effective drugs for prophylactics and treatment. Protein-based biologics offer high specificity yet their noncovalent interactions often lead to drug dissociation and incomplete inhibition. Here we developed covalent nanobodies capable of binding with SARS-CoV-2 irreversibly via proximity-enabled reactive therapeutic (PERx) mechanism. A latent bioreactive amino acid FFY was designed and genetically encoded into nanobodies to accelerate PERx reaction rate. In comparison with the noncovalent wildtype nanobody, the FFY-incorporated covalent nanobodies neutralized both wildtype SARS-CoV-2 and its Alpha, Delta, Epsilon, Lambda, and Omicron variants with potency drastically increased. This PERx-enabled covalent nanobody strategy and insights on potency increase can be valuable to developing effective therapeutics for various viral infections.

SELECTION OF CITATIONS
SEARCH DETAIL