Molecular mechanisms of autoimmunity triggered by microbial infection

Autoimmunity can be triggered by microbial infection. In this context, the discovery of Toll-like receptors (TLRs) provides new insights and research perspectives. TLRs induce innate and adaptive antimicrobial immune responses upon exposure to common pathogen-associated molecules, including lipopeptides, lipopolysaccharides, and nucleic acids. They also have the potential, however, to trigger autoimmune disease, as has been revealed by an increasing number of experimental reports. This review summarizes important facts about TLR biology, available data on their role in autoimmunity, and potential consequences for the management of patients with autoimmune disease

Inflammatory response following neutrophil recovery postchemotherapy in acute myeloid leukemia cases without evidence of infection: role of homing of neutrophils

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Neutropenic sepsis is a common clinical entity occurring in postchemotherapy patients. Infection may not be the cause of fever in such patients after neutrophil-count recovery. Herein, we present two patients who developed fever during the neutropenic phase of induction chemotherapy and were treated with broad-spectrum antibiotics until they were no longer febrile and had recovered their neutrophil count. Being off antibiotics, they redeveloped fever within 48–72 hours. These fevers seemed to be secondary to postinfectious inflammatory response and not infection, supported by the fact that adequate antibiotic treatment was given and the collected fluid contained neutrophils but the cultures were negative. We hypothesize an explanation for this phenomenon based on the “homing of neutrophils” to bone marrow, which involves chemoattraction of CXC chemokine receptor (CXCR)-4 expressed on neutrophils towards the chemokine stromal cell-derived factor (SDF)-1 (CXCL12) expressed constitutively by bone marrow. Literature has shown that elevation of SDF-1 levels at injured/inflamed sites might create a similar gradient. This gradient results in the migration of neutrophils to the sites of previous injury/inflammation, leading to the formation of sterile abscesses. Based on our cases, we also conclude that antibiotics do not prevent the formation or treat such sterile “abscesses”; however, the drainage of these “abscesses” and treatment with anti-inflammatory agents are useful in such cases

Survey on Dynamic Query Forms for Database Queries

The databases used in today’s scientific research and web handle huge volumes of data. Such databases contain hundreds or even thousands of complex relations and attributes. The proposed system that implements dynamic query forms for non-relational data. The DQF captures a user’s preference and ranks query form components which assists the user in making decisions. Query form generation is an iterative process and initially requires user guidance in the form of feedback .The system automatically generates ranking list of form components, at each iteration and the desired form components are added by the user in Query forms. The ranking of form components depend on the captured user preference. The query results can be viewed at each iteration by the user after filling and submitting the query form. In order to measure the quality of the results generated by the Query form, a probabilistic model has been developed

FORMULATION AND DEVELOPMENT OF SUSTAINED RELEASE MATRIX TABLETS OF LORNOXICAM

Lornoxicam is a NSAID having oxicam class mainly prescribed in the treatment of osteoarthritis and rheumatoid arthritis. NSAID have the potential to relieve the pain and inflammation without the immunosuppressive and metabolic side effects associated with corticosteroids. Generally the classification of NSAID is applied to drugs that inhibit one or more steps in the metabolism of Arachidonic Acid (AA). In general, NSAID do not inhibit lipoxygenase formation or the formation of other inflammatory mediators. Due to its more biological half-life i.e. 3-5 hrs. in India, the dosage form is available in 8-16 mg, it can be increased upto 24 mg/day if necessary. The main objectives of present investigation are to confirm the drug by various analytical techniques, to study the drug excipients compatibility, to avoid the dose as well as the frequency of the dosage form and to perform the stability. The tablet can be developed with the combination of HPMC K 100M and Ethyl Cellulose as a matrix former. Lornoxicam is NSAID that has numerous functions in the body. It can be absorbed rapidly and completely from gastrointestinal track after the oral administration. Absolute bioavailability of Lornoxicam is 90-100%. No first pass effect is observed. It is found in the plasma in the unchanged form and as its hydroxylated metabolite. The hydroxylated metabolite exhibits no pharmacological activity. CYP2C3 has been shown to be the primary enzyme responsible for the biotransformation of Lornoxicam. Approximately 2/3 part of Lornoxicam is eliminated via the liver and 1/3 via the kidneys as inactive substance. Lornoxicam inhibits the production of prostaglandins by inhibiting the action of cyclooxygenase, which regulates the conversion of Arachidonic Acid to Prostaglandins. Lornoxicam mainly prescribed in the treatment of osteoarthritis and rheumatoid arthritis, and also in the management of ankylosing spondylitis, acute sciatica and low back pain.Keywords: Lornoxicam, Sustained release, matrix

Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached$8. 8 trillion (95% uncertainty interval UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this,$13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and$1.4 billion was repurposed from existing health projects. $3.1 billion (22.4$2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7$1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Performance Evalution of Multistage Offline Marathi Script Recognition System

Handwriting is the most effective way by which civilized people speaks. Devanagari is the basic Script widely used all over India. Many Indian languages like Hindi, Marathi, Rajasthani are based on Devanagari Script. Devanagari Scripts Hindi language is the third common language used all over the word. In the proposed work an artificial neural network based classifier and statistical and structural method based feature extraction approach is used for the recognition of the script. Optical isolated Marathi Characters are taken as an input image from the scanner. An input image is preprocessed and segmented. Features are extracted in terms of various structural and statistical features like End points, middle bar, loop, end bar, aspect ratio etc. Feature vector is applied to Self organizing map (SOM) which is one of the classifier of an artificial neural Network.SOM is trained for such 5000 different characters collected from 500 persons. The characters are classified into three different classes. The proposed classifier attains 93 % accuracy. General Terms Classification algorithm used is Self Organizing map and Feature Extraction Technique used are Structural and Statistical feature extraction techniques.