Recent Progress in Lipid Nanoparticles for Cancer Theranostics: Opportunity and Challenges

Cancer is one of the major leading causes of mortality in the world. The implication of nanotherapeutics in cancer has garnered splendid attention owing to their capability to efficiently address various difficulties associated with conventional drug delivery systems such as non-specific biodistribution, poor efficacy, and the possibility of occurrence of multi-drug resistance. Amongst a plethora of nanocarriers for drugs, this review emphasized lipidic nanocarrier systems for delivering anticancer therapeutics because of their biocompatibility, safety, high drug loading and capability to simultaneously carrying imaging agent and ligands as well. Furthermore, to date, the lack of interaction between diagnosis and treatment has hampered the efforts of the nanotherapeutic approach alone to deal with cancer effectively. Therefore, a novel paradigm with concomitant imaging (with contrasting agents), targeting (with biomarkers), and anticancer agent being delivered in one lipidic nanocarrier system (as cancer theranostics) seems to be very promising in overcoming various hurdles in effective cancer treatment. The major obstacles that are supposed to be addressed by employing lipidic theranostic nanomedicine include nanomedicine reach to tumor cells, drug internalization in cancer cells for therapeutic intervention, off-site drug distribution, and uptake via the host immune system. A comprehensive account of recent research updates in the field of lipidic nanocarrier loaded with therapeutic and diagnostic agents is covered in the present article. Nevertheless, there are notable hurdles in the clinical translation of the lipidic theranostic nanomedicines, which are also highlighted in the present review along with plausible countermeasures.Peer reviewedFinal Published versio

Pesticide free coating for papaya (Carica papaya 'Eksotika II')

Mature green stage (Index 2) papaya (Carica papaya L. ‘Eksotika II’) fruits were treated either with 2.5% calcium chloride infiltration, 0.75% chitosan coating, calcium infiltration at 2.5% then subsequently chitosan coating at 0.75% or with distilled water as the control. The fruits were then stored at 13±1°C for up to five weeks. Calcium infiltration was effective in maintaining the firmness and weight loss of the fruits. Firmness was 2.7 fold higher than the control and water loss was about 3% less. However, the chitosan coating had less effect on maintaining firmness (only 1.7 fold higher firmness) but had more effect in preventing weight loss resulting in 5.6% less weight loss. The chitosan coating treatment markedly slowed the ripening of papaya as shown by their reduced weight loss, delayed changes in their external colour (which is normally closely correlated with the internal colour) and other quality aspects. However, when calcium infiltration was combined with chitosan coating, this treatment further extended the storage life up to five weeks with better retention of fruits firmness and water loss control compared to the single treatments

Offshore structural reliability assessment by probabilistic procedures—a review

Offshore installations must be built to resist fatigue as well as extreme forces caused by severe environmental conditions. The structural reliability analysis is the popular practise to assess a variety of natural waves determined by the long‐term probability distribution of wave heights and corresponding periods on the site. In truth, however, these structures are subjected to arbitrary wave‐induced forces in the open ocean. Hence, it is much more reasonable to account for the changed loading characteristics by determining the probabilistic characteristics of the random loads and outcomes responses. The key challenges are uncertainties and the non‐linearity of Morison’s drag element, which results in non‐Gaussian loading and response distributions. This study would analyze advances achieved to date in a comprehensive probabilistic review of offshore fixed jacket-type platforms

Current practice of early leak detection methods for underground storage tanks

This article aims to provide general review on current practice of leak detection methods of underground storage tanks (UST). Fuel (i.e. gasoline and diesel oil) leakage from UST can contaminate groundwater and drinking water with various hydrocarbon contaminants. These leaks create ponds of fuel that spill into the land and aquifers, polluting and seriously destroying habitats. Numerous efforts have been focused on the development of leak detection to the tanks. However, without the opportunity to conduct fault intensity calibration and estimate a product's lifetime, there is a lack of information provided to consider the condition of previous underlying leakage. As a result, it is too late whether the harm has already been done. There are methods of detection that have been studied for the past ten years. Many approaches have been practised to detect leakage. Specific sensing devices will combine with additional applications that analyse and interpret the data to detect storage tank leaks. Various methods will provide different results depending on the feature chosen. Some approaches will use machine learning to analyse the provided data and provide the best leak detection result. This paper will explore the best leak detection techniques to improve underground tanks' structural integrity. At the end, this paper will give some overview on current practice early detection methods on underground storage tanks for future research

Ocular injury and its associated factors among patients admitted to a hospital in Selangor

Background: There are various factors associated with ocular injury such as age, gender, nationality, ethnicity, work related factor and alcohol intake. The aim of this study is to determine the proportion of ocular injury and its associated factors among patients admitted to a hospital in Selangor. Materials and Methods: A hospital-based cross-sectional study design and universal sampling method were used. Data collection was conducted in a hospital in Selangor by retrospective review of secondary data for last six months from data collection period. The data were transferred into a proforma. The data analysis was done using Statistical Package for Social Sciences (SPSS) version 20. Chi square test was done to determine the relationship between the associated factors of ocular injury. Result: There were 11 8 proforma collected. There were higher proportion of ocular injury in age group of 20-39 (59.3%), male (92.4%), Malay (47.5%) and Malaysian (65.3%). Cases of unilateral eye involvement were highest (88.1%) and occurred unintentionally (94.9%). Mostly (80.5%), patients sought immediate treatment after ocular injury. The commonest place of incident and source of ocular injury were industrial premises (33.1%) and blunt object (28.0%) respectively. 52.5% of ocular injury were of non penetrating diagnosis. There was high proportion of ocular injury due to work related factor (44.9%). Besides, there were significant relationship between types of nationality with work related factor (p=0.001) and types with sources of injury (p=0.001). There was no relationship between period of time taken before treatment with types of injury (p=0.118). Conclusion: In short, ocular injury is more common in age group of 20-39 years old, male, Malaysian and Malay. There is high proportion of ocular injury due to work related factor. There are also significant relationship between types of nationality with work related factor and types of ocular injury with sources of injury among patients with ocular injury

Environmental impacts of utilization of ageing fixed offshore platform for ocean thermal energy conversion

Most Malaysian jacket platforms have outlived their design life. As these old platforms have outlived their design life, other alternatives must be considered. As several offshore oil and gas extraction installations approach the end of their operational life, many options such as decommissioning and the development of a new source of energy such as wind farms are introduced. The objective of this paper is to investigate the environmental impacts of utilising ageing fixed offshore platform as a source for Ocean Thermal Energy Conversion (OTEC). The environmental impact of utilising an ageing fixed offshore platform as an OTEC source is discussed. OTEC produces energy by taking advantage of temperature variations between the ocean surface water and the colder deep water through cold-water intake piping, which requires a seawater depth of 700 metres. The output of this study shows that OTEC is envisioned to preserve marine life, becoming a new and reliable source of energy, assist clean water production, and reduce the negative impact of climate change. OTEC platforms utilising ageing platforms may lead to 44 % of fish catch in the ocean, remove 13 GW of surface ocean heat for every GW of electricity production per year, generate 1.3105 tonnes of hydrogen per year for each GW of electricity generated. In addition, OTEC platforms can reduce approximately 5106 tonnes of carbon dioxide from the environment for 1 GW of electricity generated per year, and supply 2 million litres of water per day for a 1 MW platform. Since Malaysia's seawater profile allows for installing a fixed offshore platform as an OTEC power plant, Malaysia has many potentials to profit from the OTEC process

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)–VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition in gene function pathways from nuclear to cytoplasmic to extracellular, corresponding with Pediatric Logistic Organ Dysfunction score (PELOD) readings at 0, 24, and 48 h. ANN was the most accurate of the six ML models applied for survival prediction. This study successfully correlated PELOD with transcriptomic data, mapping enriched GE modules in acute sepsis. By integrating network analysis methods to identify key gene modules and using machine learning for sepsis prognosis, this study offers valuable insights for precision-based treatment strategies in future research. The observed temporal-spatial pattern of cellular recovery in sepsis could prove useful in guiding clinical management and therapeutic interventions

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London