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G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer.
Azad, H, Akbar, MY, Sarfraz, J, Haider, W, Riaz, MN, Ali, GM, Ghazanfar, S
Journal of biomolecular structure & dynamics. 2024;:1-14
Abstract
Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era's massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git.Communicated by Ramaswamy H. Sarma.
2.
Effects of home confinement on mental health and lifestyle behaviours during the COVID-19 outbreak: insights from the ECLB-COVID19 multicentre study.
Ammar, A, Trabelsi, K, Brach, M, Chtourou, H, Boukhris, O, Masmoudi, L, Bouaziz, B, Bentlage, E, How, D, Ahmed, M, et al
Biology of sport. 2021;38(1):9-21
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Plain language summary
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the respiratory syndrome coronavirus 2 (SARS-CoV-2). To curb the spread of the 2020 pandemic, social distancing, self-isolation and nationwide lockdown measures were put in place. These measures along with hygiene care are recognized as the most effective ways to curb the spread of disease. However; the weakening of social contacts can result in anxiety, frustration, panic attacks, loss or sudden increase of appetite, insomnia, depression, mood swings, delusions, fear, sleep disorders, and suicidal/domestic violence. The purpose of the study is to provide scientific data to help identify risk factors for the psychosocial strain during the COVID-19 outbreak. The study is an international cross-disciplinary online survey and was circulated in April 2020. 1047 replies were analysed from this preliminary phase. The results show a significant difference in all tested parameters and therefore reveal a large burden for mental wellbeing combined with a tendency towards an unhealthy lifestyle during, compared to before, the confinement enforced by the COVID-19 pandemic. These results highlight the importance for policy makers to consider strategies to promote wellbeing during future confinements.
Abstract
Although recognised as effective measures to curb the spread of the COVID-19 outbreak, social distancing and self-isolation have been suggested to generate a burden throughout the population. To provide scientific data to help identify risk factors for the psychosocial strain during the COVID-19 outbreak, an international cross-disciplinary online survey was circulated in April 2020. This report outlines the mental, emotional and behavioural consequences of COVID-19 home confinement. The ECLB-COVID19 electronic survey was designed by a steering group of multidisciplinary scientists, following a structured review of the literature. The survey was uploaded and shared on the Google online survey platform and was promoted by thirty-five research organizations from Europe, North Africa, Western Asia and the Americas. Questions were presented in a differential format with questions related to responses "before" and "during" the confinement period. 1047 replies (54% women) from Western Asia (36%), North Africa (40%), Europe (21%) and other continents (3%) were analysed. The COVID-19 home confinement evoked a negative effect on mental wellbeing and emotional status (P < 0.001; 0.43 ≤ d ≤ 0.65) with a greater proportion of individuals experiencing psychosocial and emotional disorders (+10% to +16.5%). These psychosocial tolls were associated with unhealthy lifestyle behaviours with a greater proportion of individuals experiencing (i) physical (+15.2%) and social (+71.2%) inactivity, (ii) poor sleep quality (+12.8%), (iii) unhealthy diet behaviours (+10%), and (iv) unemployment (6%). Conversely, participants demonstrated a greater use (+15%) of technology during the confinement period. These findings elucidate the risk of psychosocial strain during the COVID-19 home confinement period and provide a clear remit for the urgent implementation of technology-based intervention to foster an Active and Healthy Confinement Lifestyle AHCL).
3.
Application of proteomics to investigate stress-induced proteins for improvement in crop protection.
Afroz, A, Ali, GM, Mir, A, Komatsu, S
Plant cell reports. 2011;(5):745-63
Abstract
Proteomics has contributed to defining the specific functions of genes and proteins involved in plant-pathogen interactions. Proteomic studies have led to the identification of many pathogenicity and defense-related genes and proteins expressed during phytopathogen infections, resulting in the collection of an enormous amount of data. However, the molecular basis of plant-pathogen interactions remains an intensely active area of investigation. In this review, the role of differential analysis of proteins expressed during fungal, bacterial, and viral infection is discussed, as well as the role of JA and SA in the production of stress related proteins. Resistance acquired upon induction of stress related proteins in intact plant leaves is mediated by potentiation of pathogens via signal elicitors. Stress related genes extensively used in biotechnology had been cited. Stress related proteins identified must be followed through for studying the molecular mechanism for plant defense against pathogens.