Search

BACKGROUND: Few studies have examined the burden of postacute sequelae of coronavirus disease 2019 (COVID-19) (PASC) in low- and middle-income countries. We sought to characterize PASC with self-reported questionnaires and clinical examinations of end-organ function in Lima, Peru. METHODS: From January to July 2021, we recruited participants at least 8 weeks after COVID-19 diagnosis from a case registry in Lima, Peru. We evaluated participants for PASC with questionnaires, neuropsychiatric evaluations, chest X-ray, spirometry, electrocardiogram, and echocardiogram. We used multivariable models to identify risk factors for PASC. RESULTS: We assessed 989 participants for PASC at a median 4.7 months after diagnosis. Clinically significant respiratory symptoms were reported by 68.3% of participants, particularly those who had been severely ill during acute COVID-19, and were associated with cardiac findings of ventricular hypertrophy or dilation on echocardiogram. Neuropsychiatric questionnaires were consistent with depression in 20.7% and cognitive impairment in 8.0%. Female sex and older age were associated with increased risk of respiratory (adjusted odds ratio [aOR], 2.36 [95% confidence interval {CI}, 1.69-3.31] and aOR, 1.01 [95% CI, 1.00-1.03], respectively) and neuropsychiatric sequelae (aOR, 2.99 [95% CI, 2.16-4.18] and aOR, 1.02 [95% CI, 1.01-1.03], respectively). CONCLUSIONS: COVID-19 survivors in Lima, Peru, experienced frequent postacute respiratory symptoms and depression, particularly among older and female participants. Clinical examinations highlighted the need for cardiopulmonary rehabilitation among persons with severe COVID-19; psychosocial support may be required among all COVID-19 survivors.

Abstract Rapid urbanization has reformed the peripheries of China’s main cities during the last four decades. The rural areas have been constantly developed through infrastructure development in recent days which introduces a newly semi-urban region referred to as Desakota. The purpose of this research is to discover potential influencing factors of housing construction technology in desakotas of China which needs to be assessed for the implementation of Desakotas forming globally. In this study, a total of six key primary factors and twenty key secondary factors have been shortlisted, which were surveyed by 120 respondents. After the statistical analysis, it is seen that one primary factor, “Architectural and engineering mechanical factors” has been ranked top. Moreover, “Cost, financial and economic evaluation and Analysis of the impact of the natural environment on buildings” both ranked top among twenty secondary factors. This study adds to the body of knowledge by revealing the primary contributing elements for the first time that need to be taken into consideration in the rural and town communities of desakotas in China. The study results could help stakeholders, construction firms, academicians, researchers, and the government to focus their efforts and resources on the significant changes in the Desakotas in preparation for rapid urban development.

(Ni–Mn) co-doped ZnO nanoparticles were synthesized for different doping concentrations (1 %, 2 %, and 3 %) and compared with the purely synthesized ZnO nanoparticles. Green synthesis route was followed to fabricate both pure and co-doped ZnO nanoparticles and the orange peel extract was used as the reducing agent. The main objectives of this research were to reduce the use of toxic chemicals and develop an eco-friendly green route for both pure and doped ZnO nanoparticle synthesis. Additionally, the co-doping effect on the photocatalytic activity was also observed. The synthesized particles were extensively characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) analysis, Fourier Transform Infrared spectroscopy (FTIR), and Ultraviolet–visible (UV–vis) spectroscopy. XRD analysis confirmed the exitance of ZnO nanoparticles with wurtzite structure. No extra peaks were found for the co-doped sample confirming the successful doping. The crystallite size decreased after doping and the range varied from 36 nm to 35 nm with increasing doping concentration. SEM analysis revealed the average particle size ranging from 66.42 nm to 37.52 nm with increasing doping concentration, the particle was irregular in shape, and agglomeration was observed. The FTIR data unveiled the existence of Ni–O, Mn–O, and Zn–O bands in the synthesized materials. The band gap of the NPs was determined from the UV–Visible spectroscopy analysis which varied from 3.30 eV to 2.71 eV with increasing doping concentration. The photocatalytic activity significantly improved with the increment of the doping concentration. The 2 % and 3 % doped samples degraded almost 97 % of the methylene blue dye at 30 min.

BACKGROUND: Tuberculosis, like COVID-19, is most often a pulmonary disease. The COVID-19 pandemic has severely disrupted tuberculosis services in myriad ways: health facility closures, lockdowns, travel bans, overwhelmed healthcare systems, restricted export of antituberculous drugs, etc. The effects of the shared risk on outcomes of the two diseases is not known, particularly for the first year of the pandemic, during the period before COVID-19 vaccines became widely available. OBJECTIVE: We embarked on a systematic review to elucidate the consequences of tuberculosis on COVID-19 outcomes and of COVID-19 on tuberculosis outcomes during the pre-vaccination period of the pandemic. METHODS: The systematic review protocol is registered in PROSPERO. We conducted an initial search of PubMed, Embase, Web of Science, WHO coronavirus database, medRxiv, bioRxiv, preprints.org, and Google Scholar using terms relating to COVID-19 and tuberculosis. We selected cohort and case-control studies for extraction and assessed quality with the Newcastle-Ottawa scale. RESULTS AND CONCLUSION: We identified 2108 unique abstracts published between December 2019 and January 2021. We extracted data from 18 studies from 8 countries. A total of 650,317 persons had a diagnosis of COVID-19, and 4179 had a diagnosis of current or prior tuberculosis. We explored links between tuberculosis and COVID-19 incidence, mortality, and other adverse outcomes. Nine studies reported on mortality and 13 on other adverse outcomes; results on the association between tuberculosis and COVID-19 mortality/adverse outcomes were heterogenous. Tuberculosis outcomes were not fully available in any studies, due to short follow-up (maximum of 3 months after COVID-19 diagnosis), so the effects of COVID-19 on tuberculosis outcomes could not be assessed. Much of the rapid influx of literature on tuberculosis and COVID-19 during this period was published on preprint servers, and therefore not peer-reviewed. It offered limited examination of the effect of tuberculosis on COVID-19 outcomes and even less on the effect of COVID-19 on tuberculosis treatment outcomes.

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.

Summary: Despite the near ubiquitous presence of Ig‐based antibodies in vertebrates, IgE is unique to mammals. How and why it emerged remains mysterious. IgE expression is greatly constrained compared to other IgH isotypes. While other IgH isotypes are relatively abundant, soluble IgE has a truncated half‐life, and IgE plasma cells are mostly short‐lived. Despite its rarity, IgE is consequential and can trigger life‐threatening anaphylaxis. IgE production reflects a dynamic steady state with IgG memory B cells feeding short‐lived IgE production. Emerging evidence suggests that IgE may also potentially be produced in longer‐lived plasma cells as well, perhaps as an aberrancy stemming from its evolutionary roots from an antibody isotype that likely functioned more like IgG. As a late derivative of an ancient systemic antibody system, the benefits of IgE in mammals likely stems from the antibody system's adaptive recognition and response capability. However, the tendency for massive, systemic, and long‐lived production, common to IgH isotypes like IgG, were likely not a good fit for IgE. The evolutionary derivation of IgE from an antibody system that for millions of years was good at antigen de‐sensitization to now functioning as a highly specialized antigen‐sensitization function required heavy restrictions on antibody production—insufficiency of which may contribute to allergic disease. [ABSTRACT FROM AUTHOR]