https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

Introduction and objective: Approximately 1 in 3 women will require antimicrobial
treatment for a Urinary Tract Infection (UTI) before age 24, and 40% to 50% of women will
have a UTI during their lifetime. UTIs in male patients are considered complicated.
Escherichia coli is the most common cause of UTIs.
Materials and methods: In the present study 7056 patients with clinical symptoms and
suspected to UTI were sampled. Clean-Catch midstream urine of the patients was collected.
Urine specimens were cultured for isolation of the microbial agents of UTI. The isolated
bacteria were identified using biochemical tests. Disk diffusion susceptibility test was used
to determine susceptibility of bacterial agents to antibiotics.
Results: In this study 553(8.7%) patients out of 7056 were shown to be urine culture
positive (68% females and 32% males). The most isolated bacterium was E. coli with
frequency rate of 59%. The other bacteria were Klebsiella spp. (11.6%), Enterobacter spp.
(9.8%), Pseudomonas spp. (7.2 %), Proteus spp. (2.9%), Acinetobacter spp. (2.7%),
Congolese positive Staphylococci (2.2%), Coagolase negative Staphylococci (2.3%),
Citrobacter spp. (1.3%) and Streptococci α hemolytic (1.1%). All Gram-negative bacteria
were more sensitive to amikacin (90.5-100%). The Gram-positive cocci isolated were more
sensitive to tobramycin, kanamycin and ciprofloxacin (100%).
Conclusion: It is concluded that Gram-negative bacilli were responsible for UTI infections
in our patients. The most common isolated bacteria from urinary tract infections were E. coli
and the most effective antimicrobial agents were amikacin, tobramicin and ciprofleoxacin
against Gram-negative bacilli and also the most effective antibiotics against Gram-positive
cocci were kanamicin, tobramicin and ciprofleoxacin.

Study of bacteria isolated from urinary tract infections and determinationof their susceptibility to antibiotics

Background: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity.
Objective: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter.
Methods: We collected 31,100 English tweets containing COVID-19 vaccine–related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia.
Results: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion.
Conclusions: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

/pdf/tweet-topics-and-sentiments-relating-to-covid-19-vaccination-4mbihf9d1v.pdf

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis.

Research background: The outbreak and spread of COVID-19 brought disastrous influences to the development of human society, especially the development of economy. Purpose of the article: Considering that knowing about the situations of the existing studies about COVID-19 and economy is not only helpful to understand the research progress and the connections between COVID-19 and economy, but also provides effective suggestions for fighting against COVID-19 and protecting economy, this paper analyzes the existing studies on COVID-19 and economy from the perspective of bibliometrics. Methods: Firstly, the discussion starts from the statistical analysis, in which the basic distributions of the studies on different countries/regions, different publication sources, different publication years, etc., are presented. Then, the paper shows the cooperation situations of the researchers from analyzing the related citation networks, co-citation networks and cooperation networks. Further, the theme analysis of the related studies is presented, in which the related co-occurrence networks are shown, and then the detailed analyses of the studies are introduced. Based on these analyses, the discussions about future research are presented, and finally we draw a conclusion. Findings & value added: The analyses not only present the basic situation on the research about COVID-19 and Economy, but also show the future research trends, which can provide meaningful research expectations. © 2021 Nicolaus Copernicus University.

/pdf/the-research-on-covid-19-and-economy-from-2019-to-2020-m9ogt8tlh5.pdf

The research on COVID-19 and economy from 2019 to 2020: analysis from the perspective of bibliometrics

Identification and prioritization of strategies to tackle COVID-19 outbreak: A group-BWM based MCDM approach.

The paper reviews the (a) existing knowledge on corona virus disease (b) policy responses to it, and (c) its impact on the people, economic activities, and environment of India. The pandemic has increased a sense of fear and insecurity among people due to probable job and pay loss. As well, the nationwide lockdown imposed by the government has increased cases of domestic violence and child abuse. The industrial sectors—tourism, aviation, agriculture, construction, retail, hotels, textile, gems/jewellery, fast moving consumer goods (FMCG), manufacturing, and start-ups are temporarily closed leading to a significant revenue loss. Consequently, existence of many sectors and employability of a sizable number of employees is at stake. Despite the economic slowdown, the lockdown has become a boon for the environment to revive due to less pollution and reduced discharge of effluents to water from factories. The policy formulators should consider this as wakeup call and thereby align the people, economy, and environment strategically.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pa.2372

COVID-19 pandemic! It's impact on people, economy, and environment

ABSTRACT India approved COVID-19 vaccine called Covaxin, developed by the Indian Council of Medical Research and Bharat Biotech Ltd. The primary objective of the study was to estimate the effectiveness of Covaxin in preventing breakthrough SARS-CoV-2 infection in healthcare workers (HCWs). A test-negative matched case-control study was conducted among HCWs of tertiary care hospital in Eastern India. Any HCW who tested positive for COVID-19 using RT-PCR during April and May 2021 was taken as the case. The HCWs who tested negative for COVID-19 by RT-PCR were considered as controls after matching with the date of testing and profession of the cases. Vaccination data were collected from the institution’s vaccine database and recall. In case of discrepancy, it was confirmed from the CoWIN portal (cowin.gov.in). The sample size was 670 participants (335 pairs). Conditional logistic regression models were used to calculate the adjusted odds ratio for breakthrough SARS-CoV-2 infection. Vaccine effectiveness was calculated using the following formula: VE = (1-aOR) × 100%. Sensitivity analysis was done for effectiveness of Covaxin, excluding Covishield vaccination. The mean age of participants was 29.1 years (SD = 7.1), and the majority were males (55.2%). Among the study participants, 60% were completely vaccinated, 18.51% were partially vaccinated, and 21.49% were unvaccinated. After adjusting for age, gender, type of household and past history of COVID-19 disease in conditional logistic models, the vaccine effectiveness was 22% (aOR 0.78, 95% CI: 0.52–1.17; p = .233). Sensitivity analysis with Covaxin showed an effectiveness of 29% (aOR 0.71, 95% CI: 0.47–1.08; p = .114) for preventing breakthrough SARS-CoV-2 infection.

https://doi.org/10.1080/21645515.2022.2034456

Effectiveness of COVID-19 vaccine (Covaxin) against breakthrough SARS-CoV-2 infection in India

The study aimed to assess the adverse events following COVID‐19 vaccine (Covaxin) immunization at a tertiary care institution and also assess the predictors of the adverse events following immunization (AEFI). The prospective observational study was conducted in a tertiary care institute among the Covaxin beneficiaries between June 28 and September 6, 2021. A total of 1826 participants were assessed for any local or systemic adverse events after seven days of vaccination. A telephonic interview was conducted, and the beneficiaries were assessed according to the adverse event grading. A total of 1826 participants were assessed for AEFI, and 544 (29.8%) reported at least one of the AEFI. No severe adverse events were reported, and about 1.6% had moderate AEFI. Pain at the injection site (14.6%), fever (9.7%), and myalgia (5.9%) were the common adverse events reported by the participants. AEFI incidence was higher in the first dose (38.1%) when compared to the second dose (26.4%), and this finding was significant with a p < 0.001. The major factors associated with AEFI were female sex, history of an allergic reaction, presence of comorbidities, acute infection in the past 3 months, and intake of chronic medications. Precaution needs to be taken while vaccinating individuals having allergies, comorbidities, acute infection in the last 3 months, and individuals on chronic medication.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jmv.27655

Adverse events following immunization of COVID‐19 (Covaxin) vaccine at a tertiary care center of India

Self-employment programmes in india: a journey from integrated rural development programme (irdp) to national rural livelihood mission (nrlm).

Government along with non-government organisations initiates various development programmes for the different strata of the society. The extent of those programmes is known after the evaluation only. Impact evaluation provides the causal effect of a programme on the outcome. Measurement of impact of any developmental programme is a difficult task. This paper provides a systematic review on various quantitative methods available for impact evaluation and suggests the use of combination of methods for enhancing the robustness of the impact.

Abhisek Mishra

Papers

COVID-19 pandemic! It's impact on people, economy, and environment

Effectiveness of COVID-19 vaccine (Covaxin) against breakthrough SARS-CoV-2 infection in India

Adverse events following immunization of COVID‐19 (Covaxin) vaccine at a tertiary care center of India

Self-employment programmes in india: a journey from integrated rural development programme (irdp) to national rural livelihood mission (nrlm).

Methods of Impact Evaluation: A Review