Histopathological spectrum of skin lesions in the elderly: Experience from a tertiary hospital in Southeast Nigeria

Background: There are only a few epidemiological studies published on skin disorders in the elderly within the Nigerian context and none from the Southeast Region of the country. In addition, none of these studies has considered the pattern and frequency of histopathologically diagnosed geriatric skin lesions. Hence, we attempted to determine the frequency as well as the age and gender distributions of histologically diagnosed dermatological diseases in geriatric population from skin biopsies submitted to the histopathology department of a tertiary care hospital in Southeast Nigeria. Materials and Methods: This is a cross-sectional retrospective hospital-based study involving all skin biopsies of patients 60 years and above, received at the Department of Histopathology, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria, from January 2004 to December 2019. Results: During the study period, 751 skin biopsies were received in the histopathology department. Of these, 142 were from patients who were older than 60 years. Thus, the overall share of geriatric patients was 18.9%. The mean age at presentation was 71.1 ± 8.6 years. The M: F was 1:1, and most of the patients belonged to the age group of 60–69 years (69 cases, 48.6%). The mean age of the male patients was 72.1 ± 9.5 years. In the female patients, it was 70.1 ± 7.5 years. The most common disease category was neoplasms (91, 64.1%). Most neoplasms were malignant. There were 67/142 (47.2%) malignant lesions. The most common was Squamous cell carcinoma (SCC) (30 cases) which is 21.1% of all geriatric skin biopsies and 44.8% of malignant skin biopsies. This is closely followed by melanoma (29 cases). Conclusion: Malignant neoplasms, benign neoplasms, and papulosquamous disorders are the three most common histologically diagnosed skin lesions in our geriatric population. The most common skin malignancies in this group of patients are SCC and malignant melanoma

Energy and exergy analysis of solar dryer with triple air passage direction collector powered by wind generator

© 2022 Springer Nature Switzerland AG. Part of Springer Nature. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s40095-022-00502-8The objective of this study is to thermodynamically investigate the performance of solar dryers by delaying the airflow in the collector. For this reason, a triple air path on a single pass collector with the fan powered by a wind generator was developed and evaluated in a very humid climate. The evaluation parameters were drying efficiency, energy and exergy analysis, sustainability assessment, CO2 mitigation ability and effective moisture diffusivity of dried product. The results showed that the collector efficiency of triple air passage path collector designs improved the direct passage collector by 119 %. The overall collector and drying efficiencies were 8.43 % and 2.6 % higher than the direct flow path collector. The specific energy consumption was 1.1033 kWh/kg while the specific moisture extraction rate was obtained as 0.273 kg/kW, respectively. The average exergy efficiency ranged between 38.09 % and 63.81 % while the waste exergy ratio, improvement potential and sustainability index for the three dryers ranged from 0.00 to 1.13, 7.54 x 10-7 to 2.003 kW and 0.00 to 11.47, respectively. Using the solar dryers instead of the coal-powered dryer will mitigate more CO2 into the atmosphere in the range of 9741.334 to 21481. 476 tons of CO2 per year while using grid-based electricity will limit the least amount of CO2 in the range of 12.981 to 14.153351.50 tons of CO2 per year.Peer reviewe

Metadata record for: HIT-COVID, a global database tracking public health interventions to COVID-19

This dataset contains key characteristics about the data described in the Data Descriptor HIT-COVID, a global database tracking public health interventions to COVID-19. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON forma