MATERIALS AND METHODS: This cross-sectional study consisted of 45 subjects who were divided into 3 groups based on the severity of root resorption using radiographs: normal (RO), mild (RM), and severe (RS). DSPP in GCF samples was analyzed using both methods. Questionnaires were distributed to 30 orthodontists to evaluate future user acceptance.
RESULTS: The sensitivity and specificity of the kit were 0.98 and 0.8 respectively. The DSPP concentrations measured using ELISA were the highest in the RS group (6.33 ± 0.85 ng/mL) followed by RM group (3.77 ± 0.36 ng/mL) and the RO group had the lowest concentration (2.23 ± 0.55 ng/mL). The new kit portrayed similar results as the ELISA, the optical density (OD) values were the highest in the RS group (0.62 ± 0.10) followed by RM group (0.33 ± 0.03) and the RO group (0.19 ± 0.06). The differences among all the groups were statistically significant (p
METHODS: Urine samples were collected from pesticide applicators in Malaysia, Uganda, and the UK during mixing/application days (and also during non-application days in Uganda). Samples were collected pre- and post-activity on the same day and analysed for biomarkers of active ingredients (AIs), including synthetic pyrethroids (via the metabolite 3-phenoxybenzoic acid [3-PBA]) and glyphosate, as well as creatinine. We performed multilevel Tobit regression models for each study to assess the relationship between exposure modifying factors (e.g., mixing/application of AI, duration of activity, personal protective equipment [PPE]) and urinary biomarkers of exposure.
RESULTS: From the Malaysia, Uganda, and UK studies, 81, 84, and 106 study participants provided 162, 384 and 212 urine samples, respectively. Pyrethroid use on the sampling day was most common in Malaysia (n = 38; 47%), and glyphosate use was most prevalent in the UK (n = 93; 88%). Median pre- and post-activity 3-PBA concentrations were similar, with higher median concentrations post-compared to pre-activity for glyphosate samples in the UK (1.7 to 0.5 μg/L) and Uganda (7.6 to 0.8 μg/L) (glyphosate was not used in the Malaysia study). There was evidence from individual studies that higher urinary biomarker concentrations were associated with mixing/application of the AI on the day of urine sampling, longer duration of mixing/application, lower PPE protection, and less education/literacy, but no factor was consistently associated with exposure across biomarkers in the three studies.
CONCLUSIONS: Our results suggest a need for AI-specific interpretation of exposure modifying factors as the relevance of exposure routes, levels of detection, and farming systems/practices may be very context and AI-specific.
METHODS: The expression of PXMP4 mRNA in HCC tissues and corresponding adjacent tissues was detected by Q-PCR, and the expression of PXMP4 protein was detected by Western blot and immunohistochemistry. The correlation of PXMP4 protein expression with clinicopathological features and prognosis of HCC was analyzed.
RESULTS: The expression levels of PXMP4 mRNA and protein in HCC tissues were significantly higher than those in adjacent tissues (P < 0.05), and its high expression was significantly correlated with tumor differentiation, lymph node metastasis, depth of invasion and TNM stage (P < 0.05). Patients with high expression of PXMP4 had a poor prognosis (P < 0.05).
CONCLUSION: The high expression of PXMP4 may promote the occurrence and development of HCC, and inhibition of PXMP4 may be one of the potential molecular targets for targeted therapy of HCC.
MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.
MATERIALS AND METHODS: A systematic search of PubMed and Scopus databases was done to identify the articles that are relevant to the topic including systematic reviews and original articles.
RESULTS: Several studies showed that both serum and urine Gd-IgA1 differentiate IgA nephropathy patients from healthy people and other glomerulonephropathies. Thus, it is useful as a less invasive diagnostic biomarker, although detection methods varied between studies with different sensitivities. There are various reports of its use as a prognostic parameter. Evidence is emerging for its use as a monitoring parameter for treatment.
CONCLUSION: Galactose deficient IgA1 is a promising biomarker in the management of IgA nephropathy, although a more robust and standardised means of estimation is required.