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BackgroundThe clinical manifestations of Wilson disease (WD) are related to copper accumulation in the liver and the brain, but little is known about other tissue involvement regarding metabolic changes in WD. In vitro studies suggested that the loss of intestinal ATP7B affects metabolic dysregulation in WD. We tested this hypothesis by evaluating the gut microbiota and lipidome in 2 mouse models of WD and by characterizing a new mouse model with a targeted deletion of Atp7b in the intestine.MethodsCecal content 16S sequencing and untargeted hepatic and plasma lipidome analyses in the Jackson Laboratory toxic-milk and the Atp7b null global knockout mouse models of WD were profiled and integrated. Intestine-specific Atp7b knockout mice (Atp7bΔIEC) were generated and characterized using targeted lipidome analysis following a high-fat diet challenge.ResultsGut microbiota diversity was reduced in animal models of WD. Comparative prediction analysis revealed amino acid, carbohydrate, and lipid metabolism functions to be dysregulated in the WD gut microbial metagenome. Liver and plasma lipidomic profiles showed dysregulated triglyceride and diglyceride, phospholipid, and sphingolipid metabolism in WD models. However, Atp7bΔIEC mice did not show gut microbiome differences compared to wild type. When challenged with a high-fat diet, Atp7bΔIEC mice exhibited profound alterations to fatty acid desaturation and sphingolipid metabolism pathways as well as altered APOB48 distribution in intestinal epithelial cells.ConclusionsGut microbiome and lipidome underlie systemic metabolic manifestations in murine WD. Intestine-specific ATP7B deficiency affected both intestinal and systemic response to a high-fat challenge but not the microbiome profile, at least at early stages. WD is a systemic disease in which intestinal-specific ATP7B loss and diet influence the phenotype and the lipidome profile.

While atmospheric warming intensifies the global water cycle, regionalised effects of climate change on water loss, irrigation supply, and food security are highly variable. Here, we elucidate the impacts of the climate crisis on irrigation water availability and cropping area in Nepal's largest irrigation scheme, the Sunsari Morang Irrigation Scheme (SMIS), by accounting for the hydraulic capacity of existing canal systems, and potential changes realised under future climates. To capture variability implicit in climate change projections, we invoke multiple Representative Concentration Pathways (RCPs; 4.5 and 8.5) across three time horizons (2016–2045, 2036–2065, and 2071–2100). We reveal that although climate change increases water availability to agriculture from December through March, the designed discharge of 60 m3/s would not be available in February-March for both RCPs under all three time horizons. Weed growth, silt deposition, and poor maintenance have reduced the current canal capacity from the design capacity of 60 m3/s to 53 m3/s up to 10.7 km from the canal intake (representing a 12% reduction in the discharge capacity of the canal). Canal flow is further reduced to 35 m3/s at 13.8 km from canal intake, representing a 27% reduction in flow capacity relative to the original design standards. Based on climate projections, and assuming ceteris paribus irrigation infrastructure, total wheat cropping area could increase by 12–19%, 23–27%, and 12–35% by 2016–2045, 2036–2065, and 2071–2100, respectively, due to increased water availability borne by the changing climate. The case for further investment in irrigation infrastructure via water diversion, or installation of efficient pumps at irrigation canal intakes is compelling. Such investment would catalyse a step-change in the agricultural economy that is urgently needed to sustain the Nepalese economy, and thus evoke beneficial cascading implications for global food security. [ABSTRACT FROM AUTHOR]

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems. [ABSTRACT FROM AUTHOR]

Background: In current residency programs, surgical skill acquisition relies on observation and incremental practice, often due to limited surgical opportunities, potentially lowering learner confidence and impacting patient outcomes. To counter this, mandatory wet lab training has been introduced to enhance surgical exposure. Aim: To evaluate the role of wet laboratory training for residents of Ophthalmology. Material and Methods: Thirty-two postgraduate residents participated in three supervised sessions, practicing small incision cataract surgery (SICS) on goat eyes. Faculty evaluated their performance using the SICS Ophthalmic Simulated Surgical Competency Assessment Rubric (Sim-OSSCAR:SICS) scale. Residents provided feedback through a pretested questionnaire. Results: Significant improvement in mean scores across sessions (16.094 ± 5.449 after exposure 1, 22.188 ± 4.948 after exposure 2, and 29.094 ± 5.384 after exposure 3, p < 0.001) was seen. Qualitative analysis indicated high relevance of wet lab training (71.9%), with residents acknowledging its usefulness (68.7% strongly agreed, 31.3% agreed) and confidence boost (75% strongly agreed, 25% agreed). Most residents found ease in performing different (93.7%) and subsequent (96.9%) steps during wet lab training. Conclusion: This study emphasized the importance of wet lab training is crucial, as it significantly contributes to enhancing patient care quality by allowing residents to refine surgical skills in a safe environment. [ABSTRACT FROM AUTHOR]

Water security has become a critical issue worldwide in the 21st century due to rapid population growth, urbanization, and climate change. These factors have affected water availability, particularly for domestic consumption, leading to a potential water crisis. To better understand the factors affecting water security among various income groups and explore water consumption patterns, a study was conducted using a multiple regression model. The dependent variable was water security, and the independent variables were based on socioeconomic characteristics. A total of 571 residents from Malaysia's higher-income, middle-income, and lowest-income groups participated in the study. The study found that several factors significantly influenced water security, including race, household size, type of house, education, and income. In Johor, household size, type of house, and income explained 65% of the variance in water security for the overall income group. In Terengganu, race, type of house, education, and income explained 96% of the variance in water security for the overall income group. The variable income had a significant relationship with water security, with approximately 0.01% and 0.05% in Johor and 0.10% in Terengganu. These results indicate that socioeconomic factors play a crucial role in water security for both indoor and outdoor activities. Access to water is a basic need that affects the quality of life, especially for drinking water. Therefore, these findings are essential for water operators to manage and educate households through campaigns on sustainable water use and conservation. The lack of availability and access to basic water impedes individuals and communities from achieving a greater quality of life. [ABSTRACT FROM AUTHOR]

Introduction: Glaucoma is one of the leading cause of irreversible blindness worldwide. Among the affected ones, approximately 50% are not aware of it, because of its asymptomatic nature, especially in the early stages. Ripasudil Hydrochloride Hydrate is a novel drug, being the Rho-associated coiled-coil- containing protein kinase (ROCK) inhibitor eye drop that increases conventional aqueous outflow through the trabecular meshwork and Schlemm’s canal and thus lowers the intraocular pressure. Material and Methods: This prospective study included the first 150 consecutive eyes in patients of uncontrolled glaucoma with IOP > 21 mmHg despite maximal topical anti-glaucoma medications in at least one eye and aged eighteen or above. The patients with neovascular glaucoma, closed or barely open anterior chamber angle or with history of ocular surgery including refractive surgery were excluded. The purpose and nature of the study were explained in detail, and informed consent was obtained from all patients. Results: This study included 115 patients with 150 affected eyes. The mean age of patients in this series was 43.6 years with a range of 24-73 years. Right eye was affected in 47, left in 33 patients and the remaining 35 patients had bilateral involvement. The mean pachymetry was 521.13 μm in right eye and 523.53 μm in left eye and the mean cup disc ratio was 0.8 and 0.7 in right and left eye respectively. The mean baseline intraocular pressure in our series was 18.60 mmHg +- 7.60 mmHg. The mean IOP at six month follow up visit (final follow-up) was 11.83mmHg +- 4.45, which was statistically significant. The mean reductions in IOP at one month was 12.64mmHg, at 3 months follow up 12.07mmHg. Conclusion: The results of our study implies that ripasudil is a favourable anti glaucoma drug that can lower intraocular pressure. The additive pressure lowering effects of ripasudil in addition to existing maximal therapy can be because of different mechanism of action, it can delay or evade surgery in glaucoma. Transient conjunctival hyperaemia, not necessitating discontinuation was the most common complication in this series. [ABSTRACT FROM AUTHOR]

The Urban Heat Island (UHI) effect is a significant concern in today's rapidly urbanising cities, with exacerbating heatwaves' impact, urban livelihood, and environmental well-being. This study aims to assess the cooling effect of blue-green spaces in Bhubaneswar, India, and explore their implications for mitigating UHI effects. Satellite images were processed with Google Earth Engine (GEE) to produce information on the blue-green spaces' land surface temperatures (LST). The Normalised Difference Vegetation Index (NDVI) and Modified Normalised Difference Water Index (MNDWI) were employed to quantify the presence and characteristics of these blue-green spaces. The findings revealed significant spatial variations in the LST, with higher temperatures observed in bare land and built-up areas and lower temperatures in proximity to the blue-green spaces. In addition, a correlation analysis indicated the strong influence of the built-up index (NDBI) on the LST, emphasising the impact of urbanisation on local climate dynamics. The analysis demonstrated the potential of blue-green spaces in reducing surface temperatures and mitigating UHI effects. Based on these results, strategic interventions were proposed, such as increasing the coverage of green spaces, optimising access to water bodies, and integrating water-sensitive design principles into urban planning to enhance the cooling effects and foster a more sustainable and resilient urban environment. This study highlighted the importance of leveraging remote sensing and GEE for urban UHI analyses. It provides valuable insights for policymakers and urban planners to prioritise nature-based solutions for heat mitigation in Bhubaneswar and other similar cities. Future research could delve deeper into a quantitative assessment of the cooling benefits of specific blue-green infrastructure interventions and explore their socio-economic impacts on urban communities. [ABSTRACT FROM AUTHOR]

Aim: To determine the corneal changes after phacoemulsification on corneal topography. Material and Methods: The present hospital-based prospective, longitudinal, interventional study conducted on 90 patients who underwent uncomplicated phacoemulsification in the Upgraded Department of Ophthalmology Government Medical College, Jammu from 1st November 2020 to 31st October 2021. Results: In this study the mean K1 value pre operatively was 42.30 ± 1.36D and at 3 months was 42.22 ± 1.32D. The mean K2 value pre operatively was 42.98 ± 1.38D and at 3 months was 42.73 ± 1.31D. The mean astigmatism pre operatively was 0.68 ± 0.45D and at 3 months was 0.45 ± 0.29D Conclusion: It concludes that the average astigmatism decreased gradually with increasing postoperative period and was statistically significant at 3 months with p<0.001. [ABSTRACT FROM AUTHOR]