Lab Home Research Articles

Home Management of COVID-19 Patients: A Successful Model in Non-severe COVID-19 Patients in the Developing World.

BackgroundAround 80-85% of coronavirus disease 2019 (COVID-19) cases were reported to have mild disease and home treatment of such patients was proved to be effective without significant morbidity or mortality. Therefore, the aim of this study was to assess the outcome of home management of non-severe COVID-19 infection in healthcare providers in the developing world.MethodsThis observational cohort study was conducted at the National Institute of Cardiovascular Diseases from June 2020 till January 2021. It included health care workers who tested positive for COVID-19 with non-severe infection and received home treatment. The COVID-19 management team monitored their symptoms and oxygen saturation over the phone. Need-based lab tests, X-rays, home proning, steroids, and oxygen were administered along with the standard intuitional management strategies. Study outcomes included duration of recovery, need for hospitalization, and expiry.ResultsA total of 128 patients were included, out of which 98 (76.6%) were male, and the mean age was 32.9 ± 5.9 years. Fever was the most common symptom, seen in 89.8% of patients. Most of the patients (85.9%) had no pre-existing comorbidities. Five patients received home oxygen therapy, seven received steroid therapy, and one received home pruning. The average time of recovery was 13.8 ± 8.1 days with no mortality; however, 14 (10.9%) patients were hospitalized due to worsening of symptoms.ConclusionHome treatment for COVID-19 patients with mild to moderate disease after appropriate risk assessment can be a safe and effective option to preserve hospital capacities for more needy and severely ill patients.

V-Care Pathologist: A-Z Pathology Services

V-Care Pathologist, a mobile application which will provide a-z pathology services to the patients. Patient will be able to see all the test details according to category. Patient will able to book an appointment, he/she will able to book an appointment for lab visit or home visit. Patient will get their test reports online, only if he/she already paid for the test. Patient will able to see their medical history, for that patients will have to fill the form related to their medical history and their personal details. This application will also provide some test suggestions to the patients according to the patient's symptoms using disease prediction feature. This application will reduce the work load of the pathologist, as this application will automate their work. Admin will able to see the graphical representation of the disease, as different patients has different kind of disease, so this feature will help pathologist to get the accurate information about how many patients has particular kind of disease.

Molecular Biology Techniques Q&A

Molecular Biology Techniques Q&A

ALFRED: an allele frequency database for diverse populations and DNA polymorphisms--an update.

ALFRED (the ALelle FREquency Database) is designed to store and disseminate frequencies of alleles at human polymorphic sites for multiple populations, primarily for the population genetics and molecular anthropology communities. Currently ALFRED has information on over 180 polymorphic sites for more than 70 populations. Since our initial release of the database we have focussed on increasing the quantity and quality of data, making reciprocal links between ALFRED and other related databases, and providing useful tools to make the data more comprehensible to the end user. ALFRED is accessible from the Kidd Lab home page (http://info.med.yale. edu/genetics/kkidd/) or from ALFRED directly (http://alfred.med.yale. edu/alfred/index.asp).

Nuclear-labeling index analysis (NLIA), a software package used to perform accurate automation of cell nuclear-labeling index analysis on immunohistochemically stained rat liver samples

The nuclear labeling index (labeled nuclei/100 nuclei) and the apoptotic index (apoptotic cells/100 cells) are important parameters of cell growth and death. Automatic counting of labeled nuclei is desirable since manual counting is tedious, time-consuming, and with a greater potential for inaccuracies. A nuclear-labeling index analysis ( NLIA) software package was developed in this laboratory to perform the counting process automatically and accurately. This software package consists of an application program NLIA and a set of macros for obtaining nuclear data that is used in Scion Image. It is designed to work cooperatively with Scion Image, Adobe Photoshop, and Microsoft Office. NLIA has two basic functions: building nuclear data files and analyzing nuclear data. A color image captured from an immunohistochemically stained or autoradiographic sample is loaded into NLIA. Nuclear data can be entered into the program manually, automatically, or in combination. In the manual data entering mode, NLIA acts as an object-counting tool, while in the automatic mode it acts as a data picker: picking up the data generated by Scion Image into memory. A method to enter nuclear data (both labeled nuclei and unlabeled nuclei) in the automatic mode is described. The color image is processed in Adobe Photoshop, where the interested color ranges are selected and separated. These are then analyzed in Scion Image with the help of the macros for obtaining nuclear data. Since the advanced particle analysis function is used, the counting process is automatic and rapid. Data from thousands of nuclei can be obtained within seconds. To ensure the accuracy of the analysis, a nuclear data checking and edit feature is employed in NLIA: results of computer-generated counting can be compared with the original color image by overlaying the plot of counting results onto the original color image. In this way any computer counting mistakes can be easily discovered and corrected by the operator. Corrected nuclear data (including nuclear size, location, shape) are then stored in data files. These data files can be used in NLIA to obtain cell density and nuclear labeling indices. Because criteria for obtaining nuclear data (truncation diameter, shape factor) can be set by the operator in NLIA, nuclear size distribution and shape variation can be analyzed. This method provides a fast and accurate way to determine cell nuclear-labeling indices. Currently, Scion Image is a freeware on the internet, and NLIA software package is available from our lab home page. Methods presented here expand the Scion Image ability to analyze color images by using color separation techniques in a commercial graphic application. The instrumentation required can be relatively inexpensive, and the methods described may be useful in studies of cell kinetics, lesion growth, and tumor therapy.