New strategies for a sustainable 99mTc supply to meet increasing medical demands: Promising solutions for current problems.

The continuing rapid expansion of 99mTc diagnostic agents always calls for scaling up 99mTc production to cover increasing clinical demand. Nevertheless, 99mTc availability depends mainly on the fission-produced 99Mo supply. This supply is seriously influenced during renewed emergency periods, such as the past 99Mo production crisis or the current COVID-19 pandemic. Consequently, these interruptions have promoted the need for 99mTc production through alternative strategies capable of providing clinical-grade 99mTc with high purity. In the light of this context, this review illustrates diverse production routes that either have commercially been used or new strategies that offer potential solutions to promote a rapid production growth of 99mTc. These techniques have been selected, highlighted, and evaluated to imply their impact on developing 99mTc production. Furthermore, their advantages and limitations, current situation, and long-term perspective were also discussed. It appears that, on the one hand, careful attention needs to be devoted to enhancing the 99Mo economy. It can be achieved by utilizing 98Mo neutron activation in commercial nuclear power reactors and using accelerator-based 99Mo production, especially the photonuclear transmutation strategy. On the other hand, more research efforts should be devoted to widening the utility of 99Mo/99mTc generators, which incorporate nanomaterial-based sorbents and promote their development, validation, and full automization in the near future. These strategies are expected to play a vital role in providing sufficient clinical-grade 99mTc, resulting in a reasonable cost per patient dose

RNA-Seq Analysis Reveals a Six-Gene SoxR Regulon in Streptomyces coelicolor

The redox-regulated transcription factor SoxR is conserved in diverse bacteria, but emerging studies suggest that this protein plays distinct physiological roles in different bacteria. SoxR regulates a global oxidative stress response (involving \u3e100 genes) against exogenous redox-cycling drugs in Escherichia coli and related enterics. In the antibiotic producers Streptomyces coelicolor and Pseudomonas aeruginosa, however, SoxR regulates a smaller number of genes that encode membrane transporters and proteins with homology to antibiotic-tailoring enzymes. In both S. coelicolor and P. aeruginosa, SoxR-regulated genes are expressed in stationary phase during the production of endogenously-produced redox-active antibiotics. These observations suggest that SoxR evolved to sense endogenous secondary metabolites and activate machinery to process and transport them in antibiotic-producing bacteria. Previous bioinformatics analysis that searched the genome for SoxR-binding sites in putative promoters defined a five-gene SoxR regulon in S. coelicolor including an ABC transporter, two oxidoreductases, a monooxygenase and an epimerase/dehydratase. Since this in silico screen may have missed potential SoxR-targets, we conducted a whole genome transcriptome comparison of wild type S. coelicolor and a soxR-deficient mutant in stationary phase using RNA-Seq. Our analysis revealed a sixth SoxR-regulated gene in S. coelicolor that encodes a putative quinone oxidoreductase. Knowledge of the full complement of genes regulated by SoxR will facilitate studies to elucidate the function of this regulatory molecule in antibiotic producers

Comparing predictive performance of near infrared spectroscopy at a field, regional, national and continental scales by using spiking and data mining techniques

The development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties is a crucial step for variable rate application in precision agriculture. The objective of the present study was to compare the prediction performance of vis-NIR spectroscopy at local, regional, national and continental scales using data mining techniques including spiking. Fresh soil samples collected from farms in the UK, Czech Republic, Germany, Denmark and the Netherlands were scanned with a fibre-type vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305-2200 nm. After dividing spectra into calibration (75%) and validation (25%) sets, spectra in the calibration set were subjected to three multivariate calibration models. The partial least squares regression (PLSR), multivariate adaptive regression splines (MARS) and support vector machines (SVM), with leave-one-out cross-validation were used to establish calibration models of total nitrogen (TN), total carbon (TC) and soil moisture content (MC). The results showed the lowest model performance to be obtained when the single field (local scale) data were used in the calibration models. The effect of spiking was significant and the best model performance was obtained when local samples collected from two fields in the UK were spiked with European soil samples (continental), followed by when the same samples were spiked with UK samples (national). Therefore, these results suggest that continental and national vis-NIR calibration models can be successfully used to predict TN, TC and MC. Therefore, selection of the optimal soil samples with the appropriate data mining technique should be considered when developing vis-NIR calibration models for a non-standard soil to cover a wide variation range

Electrocardiogram as prognostic and diagnostic parameter in follow up of patients with heart failure

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An Evaluation of Machine Learning and Big Data Analytics Performance in Cloud Computing and Computer Vision

Although cloud computing is receiving a lot of attention, security remains a significant barrier to its general adoption. Cloud service users frequently worry about data loss, security risks, and availability issues. Because of the accessibility and openness of the huge volume of data amassed by sensors and the web throughout recent years, computer applications have seen a remarkable change from straightforward data processing to machine learning. Two widely used technologies, Big Data and Cloud computing, are the focus of worry in the IT industry. Enormous data sets are put away, handled, and broke down under the possibility of "Big Data." Then again, cloud computing centres around giving the framework to make such systems conceivable in a period and cash saving way. The objective of the review is to survey the Big Data Analytics and Machine learning ideal models for use in cloud computing and computer vision. The programmed data examination of enormous data sets and the production of models for the wide connections between data are the centre highlights of machine learning (ML). The usefulness of machine learning-based strategies for identifying threats in a cloud computing environment is surveyed and compared in this research

Induction of Proteases in Peritoneal Carcinomatosis, the Role of ICAM-1/CD43 Interaction

Introduction: The development of peritoneal metastases is a significant clinical issue in the treatment of abdominal cancers and is associated with poor prognosis. We have previously shown that ICAM-1-CD43 interaction plays a significant role in tumor adhesion. However, an invasive phenotype is critical to establish tumor progression via cell associated and secreted proteases including matrix metalloproteinases. High metalloproteinases level significantly enhanced metastasis phenotype on tumors, a detrimental effect on surgical outcome. We investigated the role of direct and indirect signaling between the mesothelium and the tumor cells in enhancing tumor invasion and possible therapeutic intervention.Methods: Mesothelial cells were enzymatically derived from human omental tissue and implanted in 24 wells plates. Colorectal cancer cells were then introduced and allowed a direct and an indirect contact with the mesothelial layer. Anti-ICAM antibodies, anti-CD43 antibodies, and heparin were used to block MMP production. Gelatin zymography was performed on the supernatant to detect MMPs activity.Results: MMP production was observed in mesothelial and tumor cells. Direct contact between cell types enhanced MMP9 and 2 (p < 0.05). Indirect contact also stimulate MMPs but at a lower degree. ICAM-1 blocking antibodies attenuated MMP production in direct contact to that observed in the indirect. Heparin introduction achieved a similar outcome. Conclusions: ICAM-1-CD43 interaction plays a vital role in tumor cells-peritoneum adhesion and invasion, which is manifested by the increased production of MMPs leading to tumor invasion and peritoneal loco-regional. Blocking this interaction with heparin can provide a new therapeutic option

Comparative study of bioethanol production from sugarcane molasses by using Zymomonas mobilis and Saccharomyces cerevisiae

The study was designed to compare the bioethanol production from Zymomonas mobilis and Saccharomyces cerevisiae using molasses as production medium. The focus was on the retention time at lab scale. Bioethanol and petroleum blend can be used in existing gasoline engines. Present study showed a more cost-effective procedure for production of ethanol from sugar-cane molasses by using bacterial strain "Z. mobilis". Laboratory scale unit was designed to perform the experiments through batch fermentation and to determine the impact of leading parameters, including fermentation temperature, pH, sugar concentration, and nutrients. S. cerevisiae produced 8.3% (v/v) bioethanol provided sugar concentration 14 g /100 ml with the fermentation efficiency of 92.5%. On the contrary, Z.mobilis produced 9.3% (v/v) bioethanol by utilizing 16 g/100 ml sugar with the fermentation efficiency of 90.5%. Effect of nutrients on fermentation was determined using molasses as feedstock. Thin layer chromatography was also performed to assess the possible impurities in molasses as compared to the pure sugar. The pH and fermentation temperature was optimized for the enhanced yield of bioethanol.Key words: Bioethanol, molasses, fermentation, Zymomonas mobilis, Saccharomyces cerevisiae

Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression

A number of studies have shown that visible and near infrared spectroscopy (VIS-NIRS) offers a rapid on-site measurement tool for the determination of total contaminant concentration of petroleum hydrocarbons compounds (PHC), heavy metals and metalloids (HM) in soil. However none of them have yet assessed the feasibility of using VIS-NIRS coupled to random forest (RF) regression for determining both the total and bioavailable concentrations of complex chemical mixtures. Results showed that the predictions of the total concentrations of polycyclic aromatic hydrocarbons (PAH), PHC, and alkanes (ALK) were very good, good and fair, and in contrast, the predictions of the bioavailable concentrations of the PAH and PHC were only fair, and poor for ALK. A large number of trace elements, mainly lead (Pb), aluminium (Al), nickel (Ni), chromium (Cr), cadmium (Cd), iron (Fe) and zinc (Zn) were predicted with very good or good accuracy. The prediction results of the total HMs were also better than those of the bioavailable concentrations. Overall, the results demonstrate that VIS-NIR DRS coupled to RF is a promising rapid measurement tool to inform both the distribution and bioavailability of complex chemical mixtures without the need of collecting soil samples and lengthy extraction for further analysis