B-lymphoblastic leukaemia presenting as intrahepatic cholestasis

Acute cholangitis is a critical medical condition requiring prompt intervention. This case report explores the complexities and uncertainties encountered in clinical decision-making when faced with a patient presenting with symptoms suggestive of acute cholangitis. We emphasise the importance of considering individual circumstances and factors in the diagnostic process. A 38-year-old woman with a history of Crohn’s colitis presented with abdominal pain, jaundice and leukocytosis. Initial evaluation raised suspicions of acute cholangitis, but unexpected findings of blast cells in the peripheral smear led to a diagnosis of B-lymphoblastic leukaemia with BCR-ABL1 fusion. Treatment with steroids and chemotherapy resulted in the resolution of liver abnormalities. This case underscores the necessity of comprehensive assessments for obstructive jaundice and highlights the potential diagnostic challenges posed by underlying haematologic malignancies. It also raises awareness about drug-induced liver injury, and emphasises the importance of complete blood counts and differentials in the initial workup. Healthcare providers should be vigilant in considering alternative diagnoses when faced with obstructive jaundice, as misdiagnosis can lead to invasive procedures with potential adverse events

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Predict Beam Normal Irradiation and Global Horizontal Irradiation using Deep learning and Time series Algorithms

Solar forecasting is one of important use case in field of data analytics that has grown exponentially in past couple of decades. Advent of neural network with improvement in computation systems has radically improved solar forecasts and enabled more accurate prediction. In recent years, a lot of emphasize is given to not only predict solar forecast but also improve existing results by applying various models. This research focus on hybrid approach of combining time series and neural network to improve solar forecasts results and take up existing challenges in the area of solar energy. Hybrid model forecast produced results with decent evaluation metrics, i.e. RMSE of 38.34 W/m2 , MAE of 27.771 W/m2 for GHI while RMSE of 97.7 W/m2 and MAE of 78.46 W/m2 for BNI respectively. Also, Time series and deep neural networks are implemented to compare metrics with hybrid model metrics and comparison is done between metrics in current literature review and those obtained from all model implementation

A machine learning model for Alzheimer’s disease prediction

Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. In this paper, a methodology SMOTE-RF is proposed for AD prediction. Alzheimer’s is predicted using machine learning (ML) algorithms. Performance of three algorithms decision tree (DT), extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies (OASIS) longitudinal dataset available on Kaggle is used for experiments. Dataset is balanced using synthetic minority oversampling technique (SMOTE). Experiments are done on both imbalanced and balanced datasets. DT obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum 87.84% accuracy on the imbalanced dataset. DT obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. Maximum accuracy of 95.03% is achieved with SMOTE-R

A machine learning model for Alzheimer’s disease prediction

Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. In this paper, a methodology SMOTE-RF is proposed for AD prediction. Alzheimer’s is predicted using machine learning (ML) algorithms. Performance of three algorithms decision tree (DT), extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies (OASIS) longitudinal dataset available on Kaggle is used for experiments. Dataset is balanced using synthetic minority oversampling technique (SMOTE). Experiments are done on both imbalanced and balanced datasets. DT obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum 87.84% accuracy on the imbalanced dataset. DT obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. Maximum accuracy of 95.03% is achieved with SMOTE-R

Hepatoprotective effect of trimethylgallic acid esters against carbon tetrachloride-induced liver injury in rats

803-809Gallic acid and its derivatives are potential therapeutic agents for treating various oxidative stress mediated disorders. In the present study, we investigated the hepatoprotective effects of newly synthesized conjugated trimethylgallic acid (TMGA) esters against carbon tetrachloride (CCl4)-induced hepatotoxicity in rats. Animals were pre-treated with TMGA esters at their respective doses for 7 days against CCl4-induced hepatotoxicity. The histopathological changes were evaluated to find out degenerative fatty changes including vacuole formation, inflammation and tissue necrosis. Various biomarkers of oxidative stress (lipid peroxidation, glutathione levels, and endogenous antioxidant enzyme activities), liver enzymes (AST and ALT), triacylglycerol and cholesterol were evaluated. Pre-treatment with TMGA esters (MRG, MGG, MSG, and MUG at the dose of 28.71, 30.03, 31.35, 33.62 mg/kg/day), respectively reversed the CCl4-induced liver injury scores (reduced vacuole formation, inflammation and necrosis), biochemical parameters of plasma (increased AST, ALT, TG, and cholesterol), antioxidant enzymes (increased lipid peroxidation and nitrite levels; decreased glutathione levels, superoxide dismutase and catalase activities) in liver tissues and inflammatory surge (serum TNF-α) significantly. The study revealed that TMGA esters exerted hepatoprotective effects in CCl4-induced rats, specifically by modulating oxidative-nitrosative stress and inflammation