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Freshwater bodies are a vital resource for humans, yet they are threatened by numerous factors, many of which remain unidentified. In this study, we assessed the concentrations of seven heavy metals (HMs) in forty-eight water samples and analyzed their correlation with zooplankton populations and human health risks at four sub-divisional sites in West Bengal, India. Sampling was conducted throughout the year. We used indices based on HMs and zooplankton to evaluate human health risks. The results revealed that the total taxonomic richness of zooplankton (Rotifera, Copepoda, and Branchiopods) was 72 species, with Rotifera richness 25, Copepoda 37, and Branchiopods 10 species. These results demonstrated a negative correlation between zooplankton abundance and HMs concentration, highlighting zooplankton’s potential as significant indicator species. Pearson’s correlation analysis revealed a significant relationship between HMs concentration and zooplankton abundance ( P  < 0.05). Among the four sub-divisional areas studied, Haldia exhibited the highest level of heavy metal pollution, with 40.47% of heavy metals posing potential health risks (HQ > 1) to both adults and children due to industrial effluent compared to the other study sites. Overall, the Metal Index and Nemerow Pollution Index indicated that 83.34% and 97.92% of water samples, respectively, were contaminated by heavy metals. Among the heavy metals, Nickel (Ni) and Zinc (Zn) were identified as having the highest health risk potential for both groups. The Health Hazard Index (HI) values suggested that children are at higher risk (HI > 1) compared to adults. Specifically, the HQ-ingestion rate was higher in children, while the HQ-dermal (HQd) contamination was higher in adults. Throughout the assessment period, both children and adults were found to be at potential health risk from heavy metals. These findings provide baseline information crucial for addressing environmental health risks and promoting sustainable practices.

This study evaluates the performance and growth patterns of Micro-Small and Medium enterprises (MSMEs) within industrial infrastructures in Assam, focusing on the Kamrup and Kamrup Metro districts. Through a detailed analysis of investment, employment, profitability, and sales trends from 2017 to 2022, the research identifies both opportunities and challenges faced by these enterprises. The findings reveal a decline in employment, particularly among male and unskilled workers, reflecting structural issues in the workforce. Investment in capital assets such as plant and machinery has decreased, while working capital investments remain stable, indicating a focus on liquidity. Profitability shows stable gross profits but shrinking net margins, pointing to rising operational costs. While domestic sales have grown steadily, the absence of international sales highlights missed export opportunities. The role of industrial infrastructure in supporting MSME growth is evident through access to essential services, proximity to markets, and logistical advantages. However, maintenance issues, policy inefficiencies, and limited technological support hinder long-term growth. The study offers policy recommendations aimed at improving infrastructure quality, expanding market access, and promoting sustainable business practices, contributing to the success and sustainability of MSMEs.

The Triple Bottom Line (TBL) concept, introduced over two decades ago by John Elkington, was once seen as a breakthrough in redefining business success by including social and environmental responsibilities alongside profit. However, in recent years, even its creator has raised concerns about its practical impact and widespread misapplication. This study presents a systematic review of existing literature on the relationship between corporate sustainability and the TBL framework, with a particular focus on Environmental, Social, and Governance (ESG) criteria. Using stakeholder and agency theory as the conceptual lens, the paper explores how ESG integration has influenced corporate sustainability performance across different contexts. Following the PRISMA guidelines, the review process involved careful selection and screening of relevant studies from major databases. Bibliometric tools, including VOSviewer, were employed to visualize research trends, co-occurrence patterns, and thematic clusters. A total of 65 scholarly articles were analyzed, highlighting both the growing interest in sustainability discourse and the persistent challenges in translating TBL principles into measurable strategies. Key obstacles include inconsistent ESG frameworks, stakeholder conflicts, and difficulties in evaluation. The study contributes to the ongoing conversation by mapping critical research developments and identifying gaps that must be addressed to advance sustainable business practices.

The clinical features of thyroid eye disease are dictated by the orbit’s compartmentalisation; particularly, the muscle cone, which is delimited by the rectus muscles, their inter-muscular septa and the posterior sclera. The cone is anchored to the orbit apex and contains the posterior globe, the muscle bellies, a fat pad, and the blood circulation, optic nerve, and CSF sheath. It is surrounded by mobile extraconal fat, retained by the orbital septum.Thyroid eye disease is caused by expansion of muscle bellies and fat within the cone. Mechanical properties of the cone determine that the disease partitions into three phases: circumferential expansion, with forward displacement of extraconal fat; axial elongation, with increasing cone pressure; impedance of posterior venous outflow, with cone oedema and venous flow reversal.Venous flow reversal can be observed in the conjunctival circulation. It is initially transient, accompanying rises in cone pressure caused by eye movements, but later becomes permanent. It is a useful clinical sign that locates diseased muscles and anticipates venous compressive crises.Strabismus arises when inflamed rectus muscles, swollen by hydrated glycosaminoglycans, lose contractility and compliance. The incomitance is moderated by increasing stiffness affecting all the rectus muscles, as they are stretched during cone expansion.Immunomodulation, which rapidly reduces cone volume, relieving muscle elongation and stiffness, may paradoxically unmask strabismus. However, ciclosporin A suppresses late post-inflammatory fibrosis and only 4 of 71 patients so-treated required strabismus surgery.The cone model also accounts for the variety of clinical presentations of thyroid eye disease.

Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC −0.32% vs −0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS.

IntroductionA non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice.Methods and analysisA retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.Ethics and disseminationThe study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.Trial registration numberNCT06018545.

Imaging in thyroid eye disease (TED) is used to exclude other diagnoses, assess for apical crowding and plan surgery. But to quantify TED activity objectively, subjective clinical scoring assessments remain the norm. Magnetic resonance imaging (MRI) T2-relaxation times correlate with extra-ocular muscle (EOM) inflammation, but are confounded by signal from fat. We investigated whether T2-relaxation mapping in combination with fat fraction (FF) measurements could quantify disease activity in EOMs objectively. Sixty-two TED patients and six controls were enroled for coronal short tau inversion recovery (STIR), T2 multi-echo fast-spin echo and multi-echo fast-gradient echo MRI of the orbits. STIR signal intensity ratios (SIRs), T2-relaxation times and percentage FF were derived for inferior, lateral, superior and medial recti bilaterally. Twelve patients were re-scanned following immunosuppressive treatment. The results found a positive correlation for all subjects between T2 and SIR (p < 0.001), but only mean T2 differed significantly between patients and controls (p < 0.001). We measured FF in EOMs for the first time and found it greater in TED (p < 0.001). There was also a significant reduction in mean T2 after treatment, with a corresponding reduction in the clinical activity score (CAS) in almost all patients. We show that T2-relaxation times differentiate between normal and inflamed EOMs and are responsive to treatment. Combined, uniquely, with FF measurement in EOMs, an objective, quantitative marker of inflammation in TED-affected muscles could be derived. T2-relaxation times mirrored improvements in CAS after treatment, occasionally preceding them. Rarely, they diverged, suggesting limitations in the CAS as a disease burden marker.

Aims and ObjectivesA non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the Emergency Department (ED), and several Artificial Intelligence (AI) tools have been developed to detect abnormalities on NCCTH, however there is currently little real-world evidence to support adoption. The AI-REACT study (IRAS 310995, NCT05427838) evaluated the impact of AI algorithm on the diagnostic performance of ED clinicians, radiologists and radiographers.Method and DesignA retrospective dataset of 150 NCCTH was compiled, including 63 normal control cases and 97 abnormal cases containing intracranial haemorrhage (ICH), infarct, midline shift, mass effect, or skull fracture. 30 readers of varying experience were recruited across four NHS trusts including 10 general radiologists, 15 Emergency Medicine clinicians, and five CT radiographers. Readers interpreted each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using an arbitrated consensus opinion of 2 neuroradiologists as ground truth, the stand-alone performance of qER was assessed, and its impact on the readers’ diagnostic performance analysed.Results and ConclusionPooled analyses demonstrated a significant increase in reader sensitivity for abnormal scans (0.828 to 0.897, +0.069, 95%CI +0.106 to +0.0136, p >0.001) and ICH (0.846 to 0.916, +0.07 95%CI 0.108 to 0.0321 p = >0.001). ED clinicians with AI assistance demonstrated a sensitivity of 0.879 (abnormality) and 0.948 (ICH) compared to unaided radiologist sensitivity 0.890 (abnormality) and 0.939 (ICH), with no statistically significant changes in specificity.Use of AI-assisted image interpretation led to a significant increase in the ability of ED clinicians to accurately identify abnormality and ICH on CT Head scans, to a level comparable to that of radiologists. These important findings should be fully explored prospectively. Further analysis of the effects on pathology and reader subgroups will help to identify potential strengths and use cases for this application.

Abstract The size and anatomical complexity make giant intracranial aneurysms challenging surgical lesions. There is limited literature available for those arising from distal branches. The cases that have been reported in the literature have all presented with symptoms from a rupture leading to an intracranial haemorrhage. In this case report, the authors present a case of a giant aneurysm arising from a cortical branch of the middle cerebral artery presenting as an extra-axial tumour. A 76-year-old gentleman presented with a 2-day history of subjective left arm numbness. Imaging revealed a large conical right-sided parietal lesion. Intraoperatively, it was found that the lesion was being supplied by a single vascular pedicle. Histology was consistent with an aneurysm. In this case, that patient did not have any evidence of a rupture unlike all reported cases of cortical giant aneurysms. This case highlights the myriad location and presentation of giant intracranial aneurysms.