Investigation of the mechanisms of Genkwa Flos hepatotoxicity by a cell metabolomics strategy combined with serum pharmacology in HL-7702 liver cells

1. To investigate Genkwa Flos hepatotoxicity, a cell metabolomics strategy combined with serum pharmacology was performed on human HL-7702 liver cells in this study.

2. Firstly, cell viability and biochemical indicators were determined and the cell morphology was observed to confirm the cell injury and develop a cell hepatotoxicity model. Then, with the help of cell metabolomics based on UPLC-MS, the Genkwa Flos group samples were completely separated from the blank group samples in the score plots and seven upregulated as well as two down-regulated putative biomarkers in the loading plot were identified and confirmed. Besides, two signal molecules and four enzymes involved in biosynthesis pathway of lysophosphatidylcholine and the sphingosine kinase/sphingosine-1-phosphate pathway were determined to investigate the relationship between Genkwa Flos hepatotoxicity and these two classic pathways. Finally, the metabolic pathways related to specific biomarkers and two classic metabolic pathways were analyzed to explain the possible mechanism of Genkwa Flos hepatotoxicity.

3. Based on the results, lipid peroxidation and oxidative stress, phospholipase A₂/lysophosphatidylcholine pathway, the disturbance of sphingosine-1-phosphate metabolic profile centered on sphingosine kinase/sphingosine-1-phosphate pathway and fatty acid metabolism might be critical participators in the progression of liver injury induced by Genkwa Flos.     

Development of a core outcome set for venous leg ulceration (CoreVen) research evaluations (protocol)

BackgroundA venous leg ulcer is a chronic leg wound caused by poor venous blood circulation in the lower limbs. It is a recurring condition causing pain, malodour, reduced mobility, and depression. Randomised controlled trials evaluating treatments for venous leg ulcers provide important evidence to inform clinical decision-making. However, for findings to be useful, outcomes need to be clinically meaningful, consistently reported across trials, and fully reported. Research has identified the large number of outcomes reported in venous leg ulcer trials, impacting both synthesis of results, and clinical decision-making. To address this, a core outcome set will be developed. A core outcome set is an agreed standardised set of outcomes which should be, as a minimum, measured and reported in all trials which evaluate treatment effectiveness for a given indication. A core outcome set has the potential to reduce research waste, improve the utility of RCTs, reduce reporting bias, facilitate treatment comparisons across different sources of evidence and expedite the production of systematic reviews, meta-analyses and evidence-based clinical guidelines.AimThe aim of this project is to develop a core outcome set for research evaluating the effectiveness of interventions for treating venous leg ulceration.MethodsThrough a scoping review of the literature on venous leg ulceration, we will firstly identify a list of candidate outcome domains (broad categories in relation to what is being measured) from randomised controlled trials and qualitative research, and outcomes (specific methods in relation to what is being measured). In two further stages, we will use the resulting lists of outcome domains and outcomes to design two online surveys. A range of stakeholders will be invited to participate in the surveys and they will be asked to indicate which outcome domains and outcomes are most important and should be considered as core in future research reports.     

Germline gain-of-function MMP11 variant results in an aggressive form of colorectal cancer

Matrix metalloproteinase-11 (MMP11) is an enzyme with proteolytic activity against matrix and nonmatrix proteins. Although most MMPs are secreted as inactive proenzymes and are later activated extracellularly, MMP11 is activated intracellularly by furin within the constitutive secretory pathway. It is a key factor in physiological tissue remodeling and its alteration may play an important role in the progression of epithelial malignancies and other diseases. TCGA colon and colorectal adenocarcinoma data showed that upregulation of MMP11 expression correlates with tumorigenesis and malignancy. Here, we provide evidence that a germline variant in the MMP11 gene (NM_005940: c.232C>T; p.(Pro78Ser)), identified by whole exome sequencing, can increase the tumorigenic properties of colorectal cancer (CRC) cells. P78S is located in the prodomain region, which is responsible for blocking MMP11's protease activity. This variant was detected in the proband and all the cancer-affected family members analyzed, while it was not detected in healthy relatives. In silico analyses predict that P78S could have an impact on the activation of the enzyme. Furthermore, our in vitro analyses show that the expression of P78S in HCT116 cells increases tumor cell invasion and proliferation. In summary, our results show that this variant could modify the structure of the MMP11 prodomain, producing a premature or uncontrolled activation of the enzyme that may contribute to an early CRC onset in these patients. The study of this gene in other CRC cases will provide further information about its role in CRC development, which might improve patient treatment in the future.     

Esophageal Surveillance Practices in Esophageal Atresia Patients: A Survey by the Eastern Pediatric Surgery Network

IntroductionEndoscopic surveillance guidelines for patients with repaired esophageal atresia (EA) rely primarily on expert opinion. Prior to embarking on a prospective EA surveillance registry, we sought to understand EA surveillance practices within the Eastern Pediatric Surgery Network (EPSN).MethodsAn anonymous, 23-question Qualtrics survey was emailed to 181 physicians (surgeons and gastroenterologists) at 19 member institutions. Likert scale questions gauged agreement with international EA surveillance guideline-derived statements. Multiple-choice questions assessed individual and institutional practices.ResultsThe response rate was 77%. Most respondents (80%) strongly agree or agree that EA surveillance endoscopy should follow a set schedule, while only 36% claimed to perform routine upper GI endoscopy regardless of symptoms. Many institutions (77%) have an aerodigestive clinic, even if some lack a multi-disciplinary EA team. Most physicians (72%) expressed strong interest in helping develop evidence-based guidelines.ConclusionsOur survey reveals physician agreement with current guidelines but weak adherence. Surveillance methods vary greatly, underscoring the lack of evidence-based data to guide EA care. Aerodigestive clinics may help implement surveillance schedules. Respondents support evidence-based protocols, which bodes well for care standardization. Results will inform the first multi-institutional EA databases in the United States (US), which will be essential for evidence-based care.Level of EvidenceThis is a prognosis study with level 4 evidence.     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.