In recent decades, there has been an increase in the prevalence of overweight and obesity. Obesity has become an underestimated pandemic and a public health threat around the world. Adipose tissue is positioned as an endocrine organ that secretes a wide range of pro-inflammatory cytokines and adipokines, inducing a state of chronic subinflammation. The results of epidemiological studies over the past 30 years have also shown that visceral adipose tissue is an independent risk factor for the development of atherosclerosis, cardiometabolic diseases and chronic kidney disease. We performed a systematic review to summarize important aspects of the state of chronic subinflammation in the context of its effect on the decrease in glomerular filtration rate and the development of chronic kidney disease. The review deals with the etiology and pathogenesis of obesity, the hormonal profile of adipose tissue, the molecular mechanisms of the effect of pro-inflammatory cytokines and adipokines on the kidneys, and the pathophysiology of renal diseases. Information on the topic from publications based on the Pubmed database has been used.
When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals.
Introduction. Coal mining is the main source of anthropogenic impact on the landscapes of the Kemerovo Region – Kuzbass. The current mine reclamation rate lags far behind the annual increase in disturbed lands. A reclamation fund can be a perfect solution to this relevant issue. The present research objective was to analyze and structure the available data on the anthropogenic impact of coal mining in Kuzbass. The article reviews new effic ient methods of reclamation and resoiling.
Study objects and methods. The study featured ten years of research publications that were registered in the PubMed database of the National Center for Biotechnology Information (USA), Elsevier (Scopus, ScienceDirect), the Web of Science, and the Russian Electronic Library (eLibrary.ru).
Results and discussion. The research revealed the following Kuzbass districts that experience the greatest mining impact: Novokuznetsk, Prokopyevsk, Kemerovo, Belovo, and Leninsk-Kuznetskiy. The authors also identified the most common pollutants associated with coal mining. Polycyclic aromatic hydrocarbons (PAHs) appeared to be the most dangerous pollutants: as waste coal burns, these substances cover considerable distances with the wind. Biodegradation seems to be the optimal solution because PAHs are known to be carcinogenic, and most mine tips are located near settlements. The article also features mine reclamation laws and introduces a list of plants with a high absorption capacity recommended for biological reclamation, as well as microorganisms and their consortia used for bioremediation.
Conclusion. The authors identified the most promising methods of mine reclamation in the Kemerovo region, i.e. bioremediation with pollutant-binding microbial consortia and plants.
Eyelid sebaceous gland carcinoma (SGC) is a rare but life-threatening condi-tion. However, there is limited computational research associated with un-derlying protein interactions specific to eyelid sebaceous gland carcinoma. The aim of our study is to identify and analyse the genes associated with eyelid sebaceous gland carcinoma using text mining and to develop a protein-protein interaction network to predict significant biological pathways using bioinformatics tool. Genes associated with eyelid sebaceous gland carcinoma were retrieved from the PubMed database using text mining with key terms ‘eyelid’, ‘sebaceous gland carcinoma’ and excluding the genes for ‘Muir-Torre Syndrome’. The interaction partners were identified using STRING. Cytoscape was used for visualization and analysis of the PPI network. Molec-ular complexes in the network were predicted using MCODE plug-in and ana-lyzed for gene ontology terms using DAVID. PubMed retrieval process identi-fied 79 genes related to eyelid sebaceous gland carcinoma. The PPI network associated with eyelid sebaceous gland carcinoma produced 79 nodes, 1768 edges. Network analysis using Cytoscape identified nine key genes and two molecular complexes to be enriched in the protein-protein interaction net-work. GO enrichment analysis identified biological processes cell fate com-mitment, Wnt signalling pathway, retinoic acid signalling and response to cytokines to be enriched in our network. Genes identified in the study might play a pivotal role in understanding the underlying molecular pathways in-volved in the development and progression of eyelid sebaceous gland carci-noma. Furthermore, it may aid in the identification of candidate biomarkers and therapeutic targets in the treatment of eyelid sebaceous gland carcino-ma.
Idiopathic inflammatory myopathies are a group of rare connective tissue diseases with a well-documented association with malignancy. The mechanisms underlying the increased risk of neoplasms in the course of myositis are not fully understood. The Pubmed database has been thoroughly screened for articles concerning cancer-associated myositis (CAM). The article summarizes the current state of knowledge on the epidemiology and pathogenesis of CAM. Furthermore, it analyses potential risk and protective factors for developing CAM, with particular emphasis on the association with distinct serological profiles. The review summarizes recommendations proposed so far for the management of CAM and presents a novel scheme for cancer screening proposed by the authors. Moreover, promising areas requiring further research were indicated.
Worldwide rising trend in infertility has been observed in the past few years with male infertility arising as a major problem. One main reason for the rise in male infertility cases is declining semen quality. It was found that any factor that affects semen quality can affect male fertility. There are several modifiable factors affecting semen quality including air pollution, use of pesticides and harmful chemicals, exposure to excessive heat, and can lead to decreased male fertility.
The present review focuses on some of these environmental factors that affect semen quality and hence, can cause male infertility. The literature from 2000 till June 2021 was searched from various English peer-reviewed journals and WHO fact sheets using the USA National Library of Medicine (PubMed) database, the regional portal of Virtual Health Library, and Scientific Electronic Library Online. The search terms used were: “Air pollution and male fertility”, “Chemicals and male infertility”, “Heat exposure and infertility”, “heavy metals and male fertility”.
Adverse environmental factors have a significant impact on semen quality, leading to decreased sperm concentration, total sperm count, motility, viability, and increased abnormal sperm morphology, sperm DNA fragmentation, ultimately causing male infertility. However, all these factors are modifiable and reversible, and hence, by mere changing of lifestyle, many of these risk factors can be avoided.
Research output related to artificial intelligence (AI) in vascular diseases has been poorly investigated. The aim of this study was to evaluate scientific publications on AI in non-cardiac vascular diseases. A systematic literature search was conducted using the PubMed database and a combination of keywords and focused on three main vascular diseases (carotid, aortic and peripheral artery diseases). Original articles written in English and published between January 1995 and December 2020 were included. Data extracted included the date of publication, the journal, the identity, number, affiliated country of authors, the topics of research, and the fields of AI. Among 171 articles included, the three most productive countries were USA, China, and United Kingdom. The fields developed within AI included: machine learning (n = 90; 45.0%), vision (n = 45; 22.5%), robotics (n = 42; 21.0%), expert system (n = 15; 7.5%), and natural language processing (n = 8; 4.0%). The applications were mainly new tools for: the treatment (n = 52; 29.1%), prognosis (n = 45; 25.1%), the diagnosis and classification of vascular diseases (n = 38; 21.2%), and imaging segmentation (n = 38; 21.2%). By identifying the main techniques and applications, this study also pointed to the current limitations and may help to better foresee future applications for clinical practice.
Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.
We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.
Results and conclusion
The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter $$\approx 5700$$
(4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts.
Neck pain is one of the most common musculoskeletal disorders, having an age-standardised prevalence rate of 27.0 per 1000 population in 2019. This literature review describes the global epidemiology and trends associated with neck pain, before exploring the psychological and biological risk factors associated with the initiation and progression of neck pain.
The PubMed database and Google Scholar search engine were searched up to May 21, 2021. Studies were included that used human subjects and evaluated the effects of biological or psychological factors on the occurrence or progression of neck pain, or reported its epidemiology.
Psychological risk factors, such as long-term stress, lack of social support, anxiety, and depression are important risk factors for neck pain. In terms of the biological risks, neck pain might occur as a consequence of certain diseases, such as neuromusculoskeletal disorders or autoimmune diseases. There is also evidence that demographic characteristics, such as age and sex, can influence the prevalence and development of neck pain, although further research is needed.
The findings of the present study provide a comprehensive and informative overview that should be useful for the prevention, diagnosis, and management of neck pain.
Background: COVID-19 is a rapidly spreading communicable disease worldwide. It varies widely in its spectrum of manifestations, from being mild self-limiting disease, to fulminant disease, often leading to complications and death. Diabetes is an important co-morbidity linked to severity of infection by SARSCoV- 2, which predisposes them to severe pneumonia. Poor glycaemic control is associated with worse outcomes. The disease burden of COVID-19 is continuously increasing, and with a high prevalence of diabetes, it is all the more important to understand the vital aspects of COVID-19 infection in diabetic population. Hence, we try to provide close insights into its pathophysiology, clinical characteristics, recommendations on management and prevention and possible avenues for improving disease outcomes.
Methods: PubMed database and Google Scholar were searched using the key terms ‘COVID-19’, ‘SARS CoV- 2’, ‘Corona’ and ‘diabetes’. Full texts of the retrieved articles were accessed and referred. Three main mechanisms which influence COVID-19 disease manifestation in diabetics include: (a) Entry of virus via ACE-2 receptors (b) Action through Dipeptidyl-peptidase-4, and (c) Elevation of glucose concentration in airways by elevated blood glucose.ACE-2 is expressed in alveolar epithelial cells, heart, renal-tubular and intestinal epithelia and pancreas. S-Glycoprotein on the surface of SARS-CoV-2 binds to this ACE-2 and undergoes a conformational change. This allows its’ proteolytic digestion by host cell proteases TMPRSS2 and Furin, leading to internalization of virus. Viral entry into cells triggers an inflammatory response by T-helper-cells and at times, a ‘cytokine storm’, resulting in organ damage. Apart from diminishing neutrophil chemotaxis and reducing phagocytosis, by which diabetes predisposes individuals to infections, there are several specific factors with respect to SARS-CoV2: (i) Increased ACE-2 expression (ii) Raised Furin (iii) Diminished T-cell functioning, and (iv) Increased IL-6 levels. Movement restrictions, increased stress due to social isolation and lack of physical activity further complicates the issue. It is therefore, much essential to raise awareness among front-line workers. Finally, the current situation emphasizes the need for more clinical investigation and define best practices for optimum outcomes.
Bangladesh Journal of Medical Science Vol. 21(1) 2022 Page : 19-23