Recent studies of carbon oxidation by scanning tunneling microscopy indicate that measured rates of carbon oxidation can be affected by randomly distributed defects in the carbon structure, which ®ary in size. Ne®ertheless, the impact of this obser®ation on the analysis or modeling of the oxidation rate has not been critically assessed. This work focuses on the stochastic analysis of the dynamics of carbon clusters' con®ersions during the oxidation of a carbon sheet. According to the classic model of Nagle and Strick-( ) land-Constable NSC , two classes of carbon clusters are in®ol®ed in three types of reactions: gasification of basal-carbon clusters, gasification of edge-carbon clusters, and con®ersion of the edge-carbon clusters to the basal-carbon clusters due to thermal annealing. To accommodate the dilution of basal clusters, howe®er, the NSC model is modified for the later stage of oxidation in this work. Master equations go®erning the numbers of three classes of carbon clusters, basal, edge and gasified, are formulated from stochastic population balance. The stochastic pathways of the three different classes of carbon during oxidation, that is, their means and the fluctuations around these means, ha®e been numerically simulated independently by the algorithm deri®ed from the master equations, as well as by an e®ent-dri®en Monte Carlo algorithm. Both algorithms ha®e gi®en rise to identical results.

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Since the outbreak of the COVID-19 pandemic, increasing evidence suggests that infected patients present a high incidence of thrombotic complications. Besides affecting respiratory tract it also causes systemic inflammation which also leads to coagulopathy affecting major blood vessels in the body. This report describes a case of aortic, renal artery thrombosis in a patient admitted for evaluation of abdominal pain and detected to have high titer of SARS COV-2 IgG antibodies with no prior history suggestive of typical COVID-19 infection (COVID-19 RTPCR and antigen tested negative).

Pneumonia is one of the leading causes of death and morbidity, both in developing and developed countries and is the commonest cause of hospitalization in adults and children. In the assessment and management of Community Acquired Pneumonia [CAP], disease assessment is crucial, guiding therapeutic options. Knowledge of relevant prognostic factors might be useful for early identification of patients at high risk requiring intensive care treatment. AIMS AND OBJECTIVES: To study and compare Pneumonia Severity Index and CURB-65 in assessing the severity of Community Acquired Pneumonia. MATERIALS AND METHODS: 60 cases of Community Acquired Pneumonia admitted in the Department of General Medicine, Victoria hospital and Bowring and Lady Curzon hospital, BMCRI, Bangalore between the periods of October 2010 to September 2012 were included in the study. All the patients are assessed using Pneumonia Severity Index scoring and CURB65 scoring. STATISTICAL METHODS: Analysis of variance (ANOVA) has been used to find the significance of study parameters between three or more groups of patients, Chi-square/ Fisher Exact test has been used to find the significance of study parameters on categorical scale between two or more groups. RESULTS AND CONCLUSIONS: The comparison between mortality rates in different risk classes in our study and that of the previous studies showed that in all the studies mortality rates progressively increases with increasing risk scores in both PSI and CURB-65 risk classes. The comparison of PSI and CURB-65 with respect to sensitivity, specificity and predictive values has good specificity and NPV but sensitivity and PPV are less impressive. Specificity of CURB-65 was found to be better than PSI probably because a major limitation of the PSI is the unbalanced impact of age on the score, resulting in a potential underestimation of severe CAP particularly in younger otherwise healthy individuals. In predicting ICU admission, both PSI and CURB65 has good specificity and in predicting ventilation, PSI has better sensitivity than CURB65. By using the knowledge of these criteria, patients of CAP can be better prognosticated as regards severity of their illness with consequently better triaging of patients, utilisation of resources and appropriate treatment to improve the outcome in this disease