Explore the vast range of titles available.

This current review study covers the applications of vetiver essential oil (VEO) in phytoremediation, emphasizing its remedial capabilities in the cleaning of environmental pollutants like pesticides, fertilizers, fungicides, herbicides, heavy metals, dyes, and other industrial wastes such as chemical, mining, pharmaceutical, and other radioactive wastes. The review also emphasizes the pharmacological potential of vetiver essential oil for different applications, such as antioxidant, anti-inflammatory, antifungal, antibacterial, antitubercular, antihyperglycemic, antidepressant, hepatoprotective, and nephroprotective uses. The commercial potential of vetiver essential oil in diverse sectors, including global perspectives, is also illustrated along with demand scenarios in different sectors like food, beverage, fragrance, cosmetic and aromatherapy, hygiene, and pharmaceutical sectors. The main constituents of vetiver oil, their relative proportion, and the key findings of pharmacological studies performed using VEOs or their constituents are also summarized in this review article, with special emphasis on activity against phytopathogens.

International studies of the health of Indigenous and tribal peoples provide important public health insights. Reliable data are required for the development of policy and health services. Previous studies document poorer outcomes for Indigenous peoples compared with benchmark populations, but have been restricted in their coverage of countries or the range of health indicators. Our objective is to describe the health and social status of Indigenous and tribal peoples relative to benchmark populations from a sample of countries. Collaborators with expertise in Indigenous health data systems were identified for each country. Data were obtained for population, life expectancy at birth, infant mortality, low and high birthweight, maternal mortality, nutritional status, educational attainment, and economic status. Data sources consisted of governmental data, data from non-governmental organisations such as UNICEF, and other research. Absolute and relative differences were calculated. Our data (23 countries, 28 populations) provide evidence of poorer health and social outcomes for Indigenous peoples than for non-Indigenous populations. However, this is not uniformly the case, and the size of the rate difference varies. We document poorer outcomes for Indigenous populations for: life expectancy at birth for 16 of 18 populations with a difference greater than 1 year in 15 populations; infant mortality rate for 18 of 19 populations with a rate difference greater than one per 1000 livebirths in 16 populations; maternal mortality in ten populations; low birthweight with the rate difference greater than 2% in three populations; high birthweight with the rate difference greater than 2% in one population; child malnutrition for ten of 16 populations with a difference greater than 10% in five populations; child obesity for eight of 12 populations with a difference greater than 5% in four populations; adult obesity for seven of 13 populations with a difference greater than 10% in four populations; educational attainment for 26 of 27 populations with a difference greater than 1% in 24 populations; and economic status for 15 of 18 populations with a difference greater than 1% in 14 populations. We systematically collated data across a broader sample of countries and indicators than done in previous studies. Taking into account the UN Sustainable Development Goals, we recommend that national governments develop targeted policy responses to Indigenous health, improving access to health services, and Indigenous data within national surveillance systems. The Lowitja Institute.

BackgroundThe use of colon capsule endoscopy (CCE) has surged following the COVID-19 pandemic, establishing itself as an alternative for non-urgent lower gastrointestinal investigations. However, bowel preparation remains a significant challenge in CCE, as unlike traditional colonoscopy, there is no mechanism for washing, suctioning, or positional changes to optimise mucosal exposure. While interobserver variability in bowel preparation scoring is well-documented in traditional colonoscopy due to its subjective nature, variability in whole-video bowel preparation assessment for CCE remains unexplored.ObjectiveThis sub-study of the CESCAIL study aims to achieve two primary objectives: 1. To assess the standard for agreement in bowel cleansing quality in CCE based on evaluations by expert readers. 2. To determine whether AI-assisted bowel preparation assessment improves interobserver variability and explore changes in intra-observer variability before and after AI implementation.MethodAs part of the CESCAIL study, 25 completed videos were randomly selected from a pool of 673 CCE recordings. Eight readers with varying levels of CCE experience assessed bowel cleansing quality using the Leighton Rex scale and the Colon Capsule CLEansing Assessment and Report (CC-CLEAR) score. Following a 6-month washout period, the same readers reassessed the videos using AI-assisted analyses to evaluate improvements in interobserver variability and changes in intra-observer variability between manual and AI-assisted readings. Interobserver variability was assessed using intraclass correlation coefficients (ICC) and bootstrapping with 1,000 iterations.ResultIn this interim analysis, the Leighton Rex scale demonstrated an overall intraclass correlation coefficient (ICC) of 0.55 (95% CI: 0.48–0.63), indicating moderate agreement, whereas the CC-CLEAR tool achieved an ICC of 0.89 (95% CI: 0.86–0.92), reflecting good agreement. CC-CLEAR showed significantly reduced interobserver variability compared to the Leighton Rex scale. Furthermore, regression showed no significant differences in interobserver variability amongst reader groups with varying levels of experience. ICC values were 0.91 (95% CI: 0.88–0.92) for readers with >500 CCE reads, 0.90 (95% CI: 0.87–0.92) for those with 100–500 reads, and 0.93 (95% CI: 0.91–0.94) for readers with <100 reads.ConclusionInterobserver agreement for the Leighton Rex scale was moderate, whereas CC-CLEAR achieved good agreement, irrespective of the readers’ experience levels. The AI-assisted reading phase of the study is ongoing, with results on interobserver and intra-observer variability anticipated in the next phase.

Iron deficiency anaemia (IDA) often prompts gastrointestinal investigations because of malignancy concerns. However, the diagnostic yield for significant gastric or colorectal pathologies is low, frequently resulting in repeated upper gastrointestinal (GI) endoscopy (OGD) and colonoscopy before small bowel evaluation in recurrent cases. Panenteric capsule endoscopy (PCE)/colon capsule endoscopy (CCE) provides a single-procedure solution for assessing the small bowel and colon, potentially streamlining diagnostics in a one-stop clinic. The Scotcap study found that 59% of CCE patients required follow-up procedures for therapeutics or completion, raising costs. The faecal immunochemical test (FIT) was introduced as a triage tool for identifying high-risk patients for direct colonoscopy, but its role in the IDA pathway with PCE remains insufficiently studied. This multicentre retrospective study evaluated the prevalence of significant polyps in IDA patients. It used NHS England CCE follow-up colonoscopy criteria to estimate the proportion requiring urgent follow-up colonoscopy if similar findings were observed with CCE. It also aimed to identify an optimal FIT threshold to minimise unnecessary reinvestigations. 1,335 patients who underwent both OGD and colonoscopy between September 2023 and September 2024 from 3 UK centres were included. Data collected included haemoglobin, age, endoscopic findings, bowel preparation quality, repeat procedures and small bowel capsule use. NHS England CCE criteria were applied to predict urgent CCE-to-colonoscopy conversion (CCC) rates if CCE were the index test. Logistic regression, decision curve analysis and cost-benefit analysis were conducted to predict optimal FIT thresholds for PCE based on colonoscopy findings. 671 (50.3%) patients had IDA, with 89% undergoing FIT before their procedures. Significant findings included 39 colorectal cancers (5.8%), 202 patients with polyps (30.1%), 10 cases of inflammatory bowel disease (1.5%), and 15 failed colonoscopies requiring computed tomography (CT) scans (2.2%). Only 12 patients (1.8%) required repeat tests or small bowel capsules. Extrapolating colonic findings to predict the CCC rate, FIT demonstrated an area under the curve (AUC) of 0.62. FIT thresholds of 10, 55 and 100 were associated with significant increases in CCC rates. Logistic regression revealed no difference in CCC rates between FIT thresholds of ≤10 and 11–55, but showed significant differences in CCC rates for FIT 56–100 (p=0.024) and FIT >100 (p=0.0044). The cost–benefit analysis identified FIT 10 as the threshold where net benefit shifted from positive to negative because of CCC. At this threshold, the CCC rate was 7.9%, based solely on identified pathologies, excluding poor bowel preparation and incomplete procedures. Significant pathologies in the IDA pathway were uncommon. A FIT threshold in the range of 10–55 was identified as a desirable target for CCE to minimise CCC rates, with full results pending.

Colon capsule endoscopy (CCE) is an established non-invasive alternative to colonoscopy for selected low-risk patients. However, its diagnostic limitations, including incomplete transit, and an inability to biopsy or perform polypectomy, often lead to conversion to conventional colonoscopy (CCC). Understanding the factors contributing to CCC is critical to optimise patient selection, procedural efficiency and cost-effectiveness. This study aimed to identify and characterise the pre- and intra-procedural factors predicting CCC, including issues with bowel preparation, capsule excretion, pathology detection (polyp number and size) and other clinical or technical variables that influence the need for follow-up investigations. This prospective analysis is nested within the CESCAIL study, involving patients who underwent CCE between November 2021 and June 2024. Data were analysed from both symptomatic and post-polypectomy surveillance cohorts. Variables included demographics, comorbidities, medications and laboratory values. Statistical modelling employed LASSO selection, followed by multivariate logistic and linear regression. Sensitivity analyses were conducted using both complete case analysis (CCA) and multiple imputation by chained equations (MICE). A total of 603 participants were included. The CCC rate was 54.1% (326/603), with colonoscopy accounting for 32.9% and flexible sigmoidoscopy for 21.2% of follow-up procedures. Among intra-procedural contributors, pathology detection during CCE alone led to 145 CCC cases (24%), making it the leading cause. Inadequate bowel preparation and capsule battery depletion also contributed to CCC. Pre-procedural predictors of CCC included elevated log-transformed faecal haemoglobin (f-Hb) levels (OR=1.48, 95% CI: 1.18–1.86, p<0.001) and current smoking status (OR=1.44, 95% CI: 1.01–2.11, p=0.047). The adjusted predictive accuracy of f-Hb for CCC was modest (AuROC=0.62), reflecting the low-risk nature of this cohort, selected using an f-Hb threshold of ≤100 µg/g. Men had significantly higher capsule excretion rates (OR=2.22, 95% CI: 1.10–4.58, p=0.024), but this did not translate to lower CCC, likely because of their higher rate of advanced polyp detection. While male sex was associated with better bowel cleansing on univariate analysis (OR=1.46, p=0.023), this was not significant in multivariable analysis (p=0.084). Bowel preparation quality was significantly poorer in patients with type 2 diabetes mellitus (OR=0.40, 95% CI: 0.18–0.87, p=0.022), consistent with previous colonoscopy literature. Factors associated with pathology detection included alcohol consumption (p=0.023), smoking (p=0.025), psychological conditions (p=0.013) and haemoglobin level (p=0.046) for polyp number; and antidepressant (p=0.028) and beta-blocker use (p=0.008) for polyp size. Larger polyps were more commonly observed in antidepressant users, although the underlying mechanism remains unclear. Conversely, beta-blocker use was associated with smaller polyps and lower CCC rates, aligning with some prior findings suggesting a potential tumour-suppressive role. Non-smokers with lower f-Hb levels are less likely to need CCC. Patient selection criteria are key to minimising the colonoscopy conversion rate. Our findings would benefit from validation in different populations to develop a robust CCE Conversion Scoring System (CECS) and ultimately improve the cost-effectiveness of treatment.

Background Colon capsule endoscopy (CCE) has become an alternative to traditional colonoscopy for low-risk patients. However, CCE's low completion rate and inability to take biopsies or remove polyps often result in a CCE-to-conventional colonoscopy conversion (CCC). Objective(s) The aim is to identify the factors that predict issues with bowel cleansing, capsule excretion rates, pathology detection, and the need for CCC. Methods This prospective study analysed data from patients who underwent CCE as part of the CESCAIL study from Nov 2021 till June 2024. Predictive factors were examined for their association with CCC, including patient demographics, comorbidities, medications, and laboratory results from symptomatic and surveillance groups. Statistical methods such as LASSO, linear, and logistic regression were applied. Results Six hundred and three participants were analysed. Elevated f-Hb levels (OR = 1.48, 95% CI:1.18–1.86, p  = 0.0002) and smoking (OR = 1.44, 95% CI: 1.01–2.11, p  = 0.047) were significantly associated with CCC. The area under the curve (AUC) of elevated f-Hb for predicting CCC was 0.62 after adjusting for confounders. Diabetes was linked to poor bowel preparation (OR = 0.40, 95%CI:0.18–0.87, p  = 0.022). Alcohol ( p  = 0.004), smoking ( p  = 0.003), psychological conditions ( p  = 0.001), and haemoglobin levels ( p  = 0.046) were significantly associated with the number of polyps, whilst antidepressants ( p  = 0.003) and beta-blockers ( p  = 0.001) were linked to the size of polyps. Conclusion Non-smokers with lower f-Hb levels are less likely to need conventional colonoscopy (CCC). Patient selection criteria are key to minimising the colonoscopy conversion rate. Our findings would benefit from validation in different populations to develop a robust CCE Conversion Scoring System (CECS) and ultimately improve the cost-effectiveness.

Background: Colon capsule endoscopy (CCE) has seen increased adoption since the COVID-19 pandemic, offering a non-invasive alternative for lower gastrointestinal investigations. However, inadequate bowel preparation remains a key limitation, often leading to higher conversion rates to colonoscopy. Manual assessment of bowel cleanliness is inherently subjective and marked by high interobserver variability. Recent advances in artificial intelligence (AI) have enabled automated cleansing scores that not only standardise assessment and reduce variability but also align with the emerging semi-automated AI reading workflow, which highlights only clinically significant frames. As full video review becomes less routine, reliable, and consistent, cleansing evaluation is essential, positioning bowel preparation AI as a critical enabler of diagnostic accuracy and scalable CCE deployment. Objective: This CESCAIL sub-study aimed to (1) evaluate interobserver agreement in CCE bowel cleansing assessment using two established scoring systems, and (2) determine the impact of AI-assisted scoring, specifically a TransUNet-based segmentation model with a custom Patch Loss function, on both interobserver and intraobserver agreement compared to manual assessment. Methods: As part of the CESCAIL study, twenty-five CCE videos were randomly selected from 673 participants. Nine readers with varying CCE experience scored bowel cleanliness using the Leighton–Rex and CC-CLEAR scales. After a minimum 8-week washout, the same readers reassessed the videos using AI-assisted CC-CLEAR scores. Interobserver variability was evaluated using bootstrapped intraclass correlation coefficients (ICC) and Fleiss’ Kappa; intraobserver variability was assessed with weighted Cohen’s Kappa, paired t-tests, and Two One-Sided Tests (TOSTs). Results: Leighton–Rex showed poor to fair agreement (Fleiss = 0.14; ICC = 0.55), while CC-CLEAR demonstrated fair to excellent agreement (Fleiss = 0.27; ICC = 0.90). AI-assisted CC-CLEAR achieved only moderate agreement overall (Fleiss = 0.27; ICC = 0.69), with weaker performance among less experienced readers (Fleiss = 0.15; ICC = 0.56). Intraobserver agreement was excellent (ICC > 0.75) for experienced readers but variable in others (ICC 0.03–0.80). AI-assisted scores were significantly lower than manual reads by 1.46 points (p < 0.001), potentially increasing conversion to colonoscopy. Conclusions: AI-assisted scoring did not improve interobserver agreement and may even reduce consistency amongst less experienced readers. The maintained agreement observed in experienced readers highlights its current value in experienced hands only. Further refinement, including spatial analysis integration, is needed for robust overall AI implementation in CCE.

BackgroundArtificial Intelligence (AI) assisted reading in Small Bowel Capsule Endoscopy (SBCE) has recently been shown to achieve comparable and potentially superior accuracy compared to standard clinician reading. In Colon Capsule Endoscopy (CCE), AI algorithms have also demonstrated some promising results.1 However, the extent of AI-assisted reading’s advantage remains unclear, particularly regarding its performance across different polyp sizes, morphologies, locations, and non-polyp-related factors. Understanding this is essential for optimising AI performance and clinical integration.Objective(s)This CESCAIL sub-analysis evaluates the per-polyp diagnostic accuracy of AI-assisted versus standard clinician reads (pathways) and identifies key factors influencing AI-assisted accuracy using AiSPEEDTM.MethodsA total of 1,803 polyps from 673 patients were analysed at the per-polyp level to assess diagnostic accuracy in terms of sensitivity and PPV, as well as the factors influencing the improved accuracy of AI-assisted readings compared to standard clinician readings. Factors examined included polyp size, morphology, location, patient demographics (age and sex), bowel preparation quality, capsule excretion rates, comorbidities, medications, reading time, and video duration. Statistical methods included, McNemar’s test, superiority and non-inferiority analyses, Generalised Estimating Equations, and generalized linear models with interaction terms, were employed to identify key predictors of enhanced diagnostic accuracy in both AI-assisted and standard readings.ResultsAI-assisted reading demonstrated significantly higher sensitivity with clear superiority for smaller polyps (<10 mm) compared to larger ones (≥10 mm) (OR 2.27 vs 0.88, p<0.0001). While there was no observed difference in diagnostic accuracy between pathways for polyps ≥10 mm, non-inferiority was established. AI accuracy remained consistent between polyps measuring 6–9 mm and ≤5 mm (p=0.64). The most notable improvement was observed with hyperplastic polyps (OR 5.4, p<0.0001), particularly in the rectal region (OR 5.7, p<0.0001). No significant differences were identified for pedunculated, subpedunculated, LST, or SSL polyps. Furthermore, AI-assisted readings were significantly more accurate for left-sided polyps compared to right-sided ones (OR 2.36 vs 1.66, p<0.0001), although AI-assisted reads outperformed standard reads in both locations.Abstract O73 Figure 1Comparison of per polyp diagnostic yields: ai-assisted vs standard pathway[Figure omitted. See PDF]ConclusionThis study highlights the strengths of AI-assisted reading, particularly for detecting smaller adenomas and hyperplastic polyps, with notable accuracy in the left colon. Next-generation AI should focus on distinguishing significant from diminutive polyps and enhancing polyp characterisation, especially for right-sided lesions.ReferenceMoen S, Vuik FER, Kuipers EJ, Spaander MCW. Artificial intelligence in colon capsule endoscopy-a systematic review. Diagnostics (Basel) 2022;12(8).