No abstract

The global problem at the moment is COVID-19 caused by corona-virus which led to the worldwide lockdown. However there are still people who are not taking proper precautions and maintaining social-distancing while going out for emergency situations. To ensure this the government is trying to monitor the actions of each and every citizen. It is not realistically possible to monitor the actions of each and every citizen. This problem can be possibly solved by the use of machine learning. In this paper we have presented face detection based technique to combat with COVID-19. At first this technique captures the image and detects the face using viola zones method. Then the detected face images are classified to detect the mask. It actually detects whether every person have used mask or not. If there present anyone without mask then the system starts alarm to inform it to the responsible authority and to alert the public. Here Principal Component Analysis (PCA) has been used to extract the feature from the detected face images and for the detection of mask K-Nearest Neighbor (KNN) classifier has been used.

Background Treatment of rifampin-resistant and/or multidrug-resistant tuberculosis (RR/MDR-TB) requires multiple drugs and outcomes remain suboptimal. Some drugs are associated with improved outcome, however whether particular pharmacokinetic-pharmacodynamic relationships predict outcome is unknown. Methods Adults with pulmonary RR/MDR-TB in Tanzania, Bangladesh and Russian Federation receiving therapy with local regimens were enrolled from June, 2016 to July, 2018. Serum was collected after two, four, and eight weeks for each drug’s area under the concentration-time curve (AUC0-24) and quantitative susceptibility of the Mycobacterium tuberculosis isolate measured by minimum inhibitory concentrations (MIC). Individual drug AUC0-24/MIC targets were assessed by adjusted odds ratios (OR) for association with favorable treatment outcome and hazard ratios (HR) for time to sputum culture conversion. K-means clustering algorithm separated the cohort of the most common multidrug regimen into four clusters by AUC0-24/MIC exposures. Results Among 290 patients, 62 (21%) experienced treatment failure, including 30 deaths. Moxifloxacin AUC0-24/MIC target of 58 was associated with favorable treatment outcome [OR 3.75 (1.21, 11.56), p = 0·022], while levofloxacin AUC0-24/MIC of 118.3, clofazimine AUC0-24/MIC of 50.5, and pyrazinamide AUC0-24 of 379 mg*h/L were associated with faster culture conversion [HR > 1·0, p < 0.05]. Other individual drug exposures were not predictive. Clustering by AUC0-24/MIC revealed those with lowest multidrug exposures had slowest culture conversion. Conclusion Amidst multidrug regimens for RR/MDR-TB, serum pharmacokinetics and M. tuberculosis MICs were variable, yet defined parameters to certain drugs – fluoroquinolones, pyrazinamide, clofazimine – were predictive and should be optimized to improve clinical outcome.

Because of the large number of infected individuals, an estimate of the future burdens of the long-term consequences of SARS-CoV-2 infection is needed. This systematic review examined associations between SARS-CoV-2 infection and incidence of categories of and selected chronic conditions, by age and severity of infection (inpatient vs. outpatient/mixed care). MEDLINE and EMBASE were searched (Jan 1, 2020 to Oct 4, 2022) and reference lists scanned. We included observational studies from high-income OECD countries with a control group adjusting for sex and comorbidities. Identified records underwent a two-stage screening process. Two reviewers screened 50% of titles/abstracts, after which DistillerAI acted as second reviewer. Two reviewers then screened the full texts of stage one selections. One reviewer extracted data and assessed risk of bias; results were verified by another. Random-effects meta-analysis estimated pooled hazard ratios (HR). GRADE assessed certainty of the evidence. Twenty-five studies were included. Among the outpatient/mixed SARS-CoV-2 care group, there is high certainty of a small-to-moderate increase (i.e., HR 1.26 to 1.99) among adults ≥65 years of any cardiovascular condition, and of little-to-no difference (i.e., HR 0.75 to 1.25) in anxiety disorders for individuals <18, 18-64, and ≥65 years old. Among 18-64 and ≥65 year-olds receiving outpatient/mixed care there are probably (moderate certainty) large increases (i.e., HR ≥2.0) in encephalopathy, interstitial lung disease, and respiratory failure. After SARS-CoV-2 infection, there is probably an increased risk of diagnoses for some chronic conditions; whether the magnitude of risk will remain stable into the future is uncertain.

Because of the large number of infected individuals, an estimate of the future burdens of the long-term consequences of SARS-CoV-2 infection is needed. This systematic review examined associations between SARS-CoV-2 infection and incidence of categories of and selected chronic conditions, by age and severity of infection (inpatient vs. outpatient/mixed care). MEDLINE and EMBASE were searched (1 January 2020 to 4 October 2022) and reference lists scanned. We included observational studies from high-income OECD countries with a control group adjusting for sex and comorbidities. Identified records underwent a two-stage screening process. Two reviewers screened 50% of titles/abstracts, after which DistillerAI acted as second reviewer. Two reviewers then screened the full texts of stage one selections. One reviewer extracted data and assessed risk of bias; results were verified by another. Random-effects meta-analysis estimated pooled hazard ratios (HR). GRADE assessed certainty of the evidence. Twenty-five studies were included. Among the outpatient/mixed SARS-CoV-2 care group, there is high certainty of a small-to-moderate increase (i.e. HR 1.26–1.99) among adults ≥65 years of any cardiovascular condition, and of little-to-no difference (i.e. HR 0.75–1.25) in anxiety disorders for individuals <18, 18–64, and ≥65 years old. Among 18–64 and ≥65 year-olds receiving outpatient/mixed care there are probably (moderate certainty) large increases (i.e. HR ≥2.0) in encephalopathy, interstitial lung disease, and respiratory failure. After SARS-CoV-2 infection, there is probably an increased risk of diagnoses for some chronic conditions; whether the magnitude of risk will remain stable into the future is uncertain.