Analysis of genetic biomarkers, polymorphisms in ADME-related genes and their impact on pharmacotherapy for prostate cancer.

Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa.

Expression analysis of tumour necrosis factor alpha (TNF-α) and alkaline phosphatase in occupational workers exposed to low dose of X-radiation: A case-control study.

OBJECTIVES: To evaluate liver and inflammatory biomarkers in occupationally exposed radiology workers. METHODS: The descriptive study was conducted at Mufti Mehmood Memorial Teaching Hospital and Gomal Centre of Biochemistry and Biotechnology, Gomal University, Dera Ismail Khan, Pakistan, from September 2017 to May 2018, and comprised X-ray technicians working 48-72 hours per week, and a group of age- and gender-matched unexposed healthy controls. The exposed group was divided into three sub-groups based on their radiation work duration. Liver health status involved estimation of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, gamma-glutamyl transferase GGT and bilirubin through automated chemistry analyser, while serum tumour necrosis factor-alpha and interleukin- 6 levels through enzyme-linked immunosorbent assay technique. Relative gene expression analysis of tumour necrosis factor-alpha and alkaline phosphatase was performed through reverse transcription-polymerase chain reaction. Data was analysed using SPSS 20. RESULTS: Of the 70 subjects, 50(71.4%) were cases with a mean age of 36.98±8.07 years and 20(28.6%) were controls with a mean age of 36.80±7.78 years. Serum alanine aminotransferase and alkaline phosphatase levels showed significant elevation in the cases compared to the controls (p<0.0001), although alanine aminotransferase levels were within the normal range. The difference in aspartate aminotransferase, gamma-glutamyl transferase and bilirubin levels was not significant (p>0.05). Tumour necrosis factor-alpha concentration was significantly high in the cases compared to the controls (p<0.0001). In contrast with proteomic analysis, relative gene expression analysis revealed reduced level of alkaline phosphatase and tumour necrosis factor-alpha in the cases compared to the controls (p<0.05). CONCLUSIONS: Serum proteomic analysis of X-ray technicians indicated acute inflammatory conditions, while genomic analysis exhibited down-regulation of alkaline phosphatase and tumour necrosis factor-alpha genes.

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

Ecological Aerobic Ammonia and Methane Oxidation Involved Key Metal Compounds, Fe and Cu.

Interactions between metals and microbes are critical in geomicrobiology and vital in microbial ecophysiological processes. Methane-oxidizing bacteria (MOB) and ammonia-oxidizing microorganisms (AOM) are key members in aerobic environments to start the C and N cycles. Ammonia and methane are firstly oxidized by copper-binding metalloproteins, monooxygenases, and diverse iron and copper-containing enzymes that contribute to electron transportation in the energy gain pathway, which is evolutionally connected between MOB and AOM. In this review, we summarized recently updated insight into the diverse physiological pathway of aerobic ammonia and methane oxidation of different MOB and AOM groups and compared the metabolic diversity mediated by different metalloenzymes. The elevation of iron and copper concentrations in ecosystems would be critical in the activity and growth of MOB and AOM, the outcome of which can eventually influence the global C and N cycles. Therefore, we also described the impact of various concentrations of metal compounds on the physiology of MOB and AOM. This review study could give a fundamental strategy to control MOB and AOM in diverse ecosystems because they are significantly related to climate change, eutrophication, and the remediation of contaminated sites for detoxifying pollutants.

Chemical mechanism of the high solubility pathway for the carbon dioxide free production of iron.

We determine the fundamental iron oxide high solubility mechanism that drives a new electrolytic pathway to iron production, and eliminates a major CO(2) emission source, for example it is produced using wind and solar energy, in a molten carbonate electrolyte, at a high rate and a low electrolysis energy.