Impact of MLL::AF9 Gene Rearrangement on Survival of Acute Myeloid Leukaemia Patients: An Insight into Pakistani Population.

OBJECTIVE: To ascertain the frequency of the MLL::AF9 gene rearrangement and its association with survival in Pakistani patients suffering from acute myeloid leukaemia (AML). STUDY DESIGN: Analytical study. Place and Duration of the Study: Department of Haematology, National Institute of Blood Diseases and Bone Marrow Transplantation, Karachi, Pakistan, from 2015 to 2020. METHODOLOGY: Patients without a history of past AML chemotherapy, aged from 10 to 75 years, were included. Individuals with metastatic cancer, chronic myeloid leukaemia, or other haematological conditions were excluded. Identifying the MLL::AF9 gene involved RNA extraction, cDNA synthesis, and Real-time PCR amplification. The Chi-square test was used to examine the relationship between survival and the MLL::AF9 mutation. A Welch two-sample t-test was used to evaluate survival days depending on the MLL::AF9 gene rearrangement, while ANOVA was used to analyse survival days across various death statuses. RESULTS: The mean age of 130 patients was 36.65 ± 13.01 years, with 64.62% being males. The most common leukaemia type was AML-M2 (n = 32, 24.62%). During the study follow-up, 22.31% were still alive, 40.77% died, and the status of 36.92% were unknown. MLL::AF9 gene rearrangement was present in 11.54%. The group with MLL::AF9 gene rearrangement had significantly longer mean 'survival days' (1,542.33 ± 926.07) compared to the group without the gene rearrangement (206.42 ± 359.57, p <0.001). CONCLUSION: MLL-AF9 mutation was present in 11.54%. Age and MLL::AF9 gene rearrangement were significant predictors of survival in leukaemia patients. KEY WORDS: Acute myeloid leukaemia, MLL::AF9, Gene rearrangement, Survival.

Exploring the Interactions between Obesity and Diabetes: Implications for Understanding Metabolic Dysregulation in a Saudi Arabian Adult Population.

The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants (n = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.99 kg/m2) with (n = 57) and without diabetes (n = 58) or overweight/obese (BMI > 24.99 kg/m2) with (n = 102) and without diabetes (n = 72). Distinct plasma metabolic phenotypes associated with high BMI and diabetes were identified using nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography mass spectrometry. Increased plasma glucose and dysregulated lipoproteins were characteristics of obesity in individuals with and without diabetes, but the obesity-associated lipoprotein phenotype was partially masked in individuals with diabetes. Although there was little difference between diabetics and nondiabetics in the global plasma LDL cholesterol and phospholipid concentration, the distribution of lipoprotein particles was altered in diabetics with a shift toward denser and more atherogenic LDL5 and LDL6 particles, which was amplified in the presence of obesity. Further investigation is warranted in larger Middle Eastern populations to explore the dysregulation of metabolism driven by interactions between obesity and diabetes in young adults.

Antimicrobial resistance and machine learning: past, present, and future.

Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.

Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things.

Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). However, challenges arise when dealing with sensitive data due to the dependence on large datasets. To address this issue, we propose an approach that combines different magnification factors of histopathological images using a residual network and information fusion in Federated Learning (FL). FL is employed to preserve the privacy of patient data, while enabling the creation of a global model. Using the BreakHis dataset, we compare the performance of FL with centralized learning (CL). We also performed visualizations for explainable AI. The final models obtained become available for deployment on internal IoMT systems in healthcare institutions for timely diagnosis and treatment. Our results demonstrate that the proposed approach outperforms existing works in the literature on multiple metrics.

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model.

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress.

Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers' attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

AI-based production and application of English multimode online reading using multi-criteria decision support system.

Reading and writing English have greater significance in learning oral English and comprehensive skills. Artificial Intelligence (AI) is important in many aspects of our lives, including education, healthcare, business, and so on. AI has allowed for significant advancements in the educational system. It has quickly risen to the top of the list of the most rapidly expanding educational technology disciplines. Through its creation, AI has contributed to the creation of new educational and knowledge techniques that are currently being researched across a wide range of fields. Chatbots, Robots' Assistant, Vidreader, Seeing AI, Classcraft, 3D holograms, and other AI-based programmes were developed to assist both teaching staff and students in using and improving the educational system. In the sphere of education, AI is focusing on sentimentalized artificial learning aids and smart instruction systems. The primary goal and objective of the education business is to construct an intelligent education system, which is now possible thanks to the development of teaching assistant robots, smart classrooms based on AI, and English teaching assistance, among other things. Artificial Intelligence techniques may now be employed at all stages of learning to improve the educational system. During the COVID-19 illness, students and teachers took their education and instruction online in a variety of ways. Learning can be done digitally so that folks do not fall behind in their education. The proposed study has considered multi-criteria decision support systems (MCDM) for AI-enabled production and application of English multimode online reading. This study has offered the application of the super decision tool to facilitate the experimental work. As a result of this, researchers will be able to find and design new solutions to the subject.

Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme.

Purpose: The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to improve medical diagnosis platforms. This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance. Methods: As the existing CT image analysis models use standard procedures to detect hemorrhages, the need for DL-based data analysis is essential to provide more accurate results. Generally, the existing techniques are limited with image training efficiency, image filtering procedures, and runtime system tuning modules. On the scope, this work develops a DL-based automated analysis of CT scan slices to find various levels of brain hemorrhages. Notably, this proposed system integrates Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architectures as Integrated Generative Adversarial-Convolutional Imaging Model (IGACM) for extracting the CT image features for detecting brain hemorrhages. Results: This system produces good results and takes lesser training time than existing techniques. This proposed system effectively works over CT images and classifies the abnormalities with more accuracy than current techniques. The experiments and results deliver the optimal detection of hemorrhages with better accuracy. It shows that the proposed system works with 5% to 10% of the better performance compared to other diagnostic techniques. Conclusion: The complex nature of CT images leads to noncorrelated feature complexities in diagnosis models. Considering the issue, the proposed system used GAN-based effective sampling techniques for enriching complex image samples into CNN training phases. This concludes the effective contribution of the proposed IGACM technique for detecting brain hemorrhages than the existing diagnosis models.

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction.

Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.

Integrated IoT-Based Secure and Efficient Key Management Framework Using Hashgraphs for Autonomous Vehicles to Ensure Road Safety.

Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner. This centralized network is vulnerable to cyber-attacks which can cause data loss, resulting in road accidents. Thus, to prevent the vehicular network from being attacked and to prevent the privacy of the data, key management is used. However, key management alone over a centralized network is not effective in ensuring data integrity in a vehicular network. To resolve this issue, various studies have introduced a blockchain-based approach and enabled key management over a decentralized network. This technique is also found effective in ensuring the privacy of all the stakeholders involved in a vehicular network. Furthermore, a blockchain-based key management system can also help in storing a large amount of data over a distributed network, which can encourage a faster exchange of information between vehicles in a network. However, there are certain limitations of blockchain technology that may affect the efficient working of autonomous vehicles. Most of the existing blockchain-based systems are implemented over Ethereum or Bitcoin. The transaction-processing capability of these blockchains is in the range of 5 to 20 transactions per second, whereas hashgraphs are capable of processing thousands of transactions per second as the data are processed exponentially. Furthermore, a hashgraph prevents the user from altering the order of the transactions being processed, and they do not need high computational powers to operate, which may help in reducing the overall cost of the system. Due to the advantages offered by a hashgraph, an advanced key management framework based on a hashgraph for secure communication between the vehicles is suggested in this paper. The framework is developed using the concept of Leaving of Vehicles based on a Logical Key Hierarchy (LKH) and Batch Rekeying. The system is tested and compared with other closely related systems on the basis of the transaction compilation time and change in traffic rates.

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems.

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data. We have incorporated transfer learning (TL) and data augmentation (DA) mechanisms to improve the CNN model's predictive outcomes. For the fine-tuning process, the CNN model was trained with breast histology image data. Furthermore, the held-out cross-validation method for best model selection and hyper-parameter tuning was incorporated. In addition, various performance evaluation metrics for model performance assessment were computed. The experimental results confirmed that the proposed model outperformed the baseline models across all evaluation metrics, achieving 99.04% accuracy. We recommend the proposed method for early recognition of BC in IoMT healthcare systems due to its high performance.

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare.

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

Human Gait Analysis and Prediction Using the Levenberg-Marquardt Method.

A high-accuracy gait data prediction model can be used to design prosthesis and orthosis for people having amputations or ailments of the lower limb. The objective of this study is to observe the gait data of different subjects and design a neural network to predict future gait angles for fixed speeds. The data were recorded via a Biometrics goniometer, while the subjects were walking on a treadmill for 20 seconds each at 2.4 kmph, 3.6 kmph, and 5.4 kmph. The data were then imported into Matlab, filtered to remove movement artifacts, and then used to design a neural network with 60% data for training, 20% for validation, and remaining 20% for testing using the LevenbergMarquardt method. The mean-squared error for all the cases was in the order of 10-3 or lower confirming that our method is correct. For further comparison, we randomly tested the neural network function with untrained data and compared the expected output with actual output of the neural network function using Pearson's correlation coefficient and correlation plots. We conclude that our framework can be successfully used to design prosthesis and orthosis for lower limb. It can also be used to validate gait data and compare it to expected data in rehabilitation engineering.

A Novel of New 7D Hyperchaotic System with Self-Excited Attractors and Its Hybrid Synchronization.

In this study, a novel 7D hyperchaotic model is constructed from the 6D Lorenz model via the nonlinear feedback control technique. The proposed model has an only unstable origin point. Thus, it is categorized as a model with self-excited attractors. And it has seven equations which include 19 terms, four of which are quadratic nonlinearities. Various important features of the novel model are analyzed, including equilibria points, stability, and Lyapunov exponents. The numerical simulation shows that the new class exhibits dynamical behaviors such as chaotic and hyperchaotic. This paper also presents the hybrid synchronization for a novel model via Lyapunov stability theory.

Nephroprotective property of Trifolium repens leaf extract against paracetamol-induced kidney damage in mice.

Paracetamol-induced toxicity is one of the major drawbacks in the treatment of inflammatory conditions. The present study was performed to find out the impacts of phenolic compounds in the Trifolium repens (white clover) leaves (TR) against the paracetamol-induced nephrotoxicity in mice. The extract was administered orally to mice in different doses (1, 2, and 3 mL having a dry mass of 11.0 mg/mL) along with paracetamol (300 mg/kg) alone or in combination. Kidney histology, serum renal function tests, serum electrolytes, reduced glutathione, and lipid peroxidation were evaluated. Paracetamol significantly affected kidney weight, renal profile biochemistry, serum electrolytes, reduced glutathione, and thiobarbituric acid reactive substances (as lipid peroxidation) in mice. The amount of total phenolic compounds identified in the extract was 8.89 mg/g, representing 80.8% of the extract. The simultaneous administration of the TR extract leads to the normalization of all those parameters, which were deviated due to paracetamol ingestion. Kidney histological examination showed that nephrotoxicity was induced due to paracetamol, while the extract rich in phenolic compounds acts as therapeutic agents. The administration of extract also normalized the reduced glutathione from 0.837 to 2.21 of the paracetamol and paracetamol with 3-mL extract, respectively. Lipid peroxidation in the kidney was significantly (p < 0.05) declined by the extract (0.435 µmol/g) as compared to PC (3.96 µmol/g). In conclusion, TR extract possesses active beneficial phenolic compounds with nephroprotective function.

Phytochemical profile and pharmacological properties of Trifolium repens.

Trifolium repens belongs to the family Leguminosae and has been used for therapeutic purposes as traditional medicine. The plant is widely used as fodder and leafy vegetables for human uses. However, there is a lack of a detailed review of its phytochemical profile and pharmacological properties. This review presents a comprehensive overview of the phytochemical profile and biological properties of T. repens. The plant is used as antioxidants and cholinesterase inhibitors and for anti-inflammatory, antiseptic, analgesic, antirheumatic ache, and antimicrobial purposes. This review has summarized the available updated useful information about the different bioactive compounds such as simple phenols, phenolic acids, flavones, flavonols, isoflavones, pterocarpans, cyanogenic glucosides, saponins, and condensed tannins present in T. repens. The pharmacological roles of these secondary metabolites present in T. repens have been presented. It has been revealed that T. repens contain important phytochemicals, which is the potential source of health-beneficial bioactive components for food and nutraceuticals industries.

Assessment of clinical characteristics and mortality-associated factors in COVID-19 Critical cases in Kuwait

PurposeTo assess the clinical characteristics and identify mortality predicting factors in intensive care unit (ICU)-admitted COVID-19 confirmed patients. MethodsWe recruited and analyzed COVID-19 infected adult patients (age[≥] 18 years) who were admitted to the ICU at Jaber AlAhmad Al Sabah Hospital,Kuwait, between 1stMarch, 2020and 30thApril 2020. The patients were followed up to 20th May, 2020. The risk factors associated with in-hospital mortality were assessed using multiple regression analysis. ResultsWe recruited a total of 103 ICU patients in this retrospective cohort. The median age of the patients was 53 years (Interquartile Range (IQR): 44-63 years). The fatality rate was 43.7%. Among the patients, majority were males (85.5%) and 38% patients had more than two comorbidities. Pre-existing hypertension (OR:3.2,95%CI: 1.2-8.9), moderate/severe ARDS (OR: 3.4, 95%CI: 1.1-10.8),lymphocyte counts <0.5 (OR: 6.1, 95%CI: 1.2-29.8)albumin < 22 (OR: 7.5, 95%CI: 2.1-26.2), procalcitonin >0.2 (OR: 3.8, 95%CI: 1.3-7.8), D-Dimer >1200 (OR: 5.1, 95%CI: 1.2-21.6), and the need forcontinuous renal replacement therapy (OR: 9.3, 95%CI: 2.4-36.2) weresignificantly associated with mortality. ConclusionThis study describes the clinical characteristics and predictors ofmortality among ICU patients. Early identification of risk factors to mortality might help in their better outcome.

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data.

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient's clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

Abstract Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health.

Rapid Intrahepatic Progression of Hepatocellular Carcinoma after Transarterial Chemoembolization: A Case Report.

The prevalence of hepatocellular carcinoma (HCC) is increasing worldwide. Multiple strategies are available for its management including surgical removal, chemotherapeutic drugs, and ablative and chemoembolization procedures. Transarterial chemoembolization (TACE) is currently being used for the unresectable intrahepatic tumor with no vascular invasion or metastasis to other organs. The common adverse effects associated with this technique involve self-limiting fever, vomiting, and abdominal pain. Liver failure is reported in a few cases. In this report, we present a case of 37-year-old HCC patient who experienced rapid progression of tumor following TACE. Our patient came to the Gastroenterology & Hepatology Department, Nishtar Hospital, Multan, with signs concerning acute liver failure within a few months following TACE. On triphasic computed tomography (CT) scan, there was evidence of multiple new infiltrating lesions in both lobes of the liver and portal vein thrombosis, which were not present before TACE procedure. Hence, we made a diagnosis of acute, chronic liver disease due to the rapid intrahepatic progression of HCC. This is a rare side effect of TACE procedure and highlights the significance of proper counseling of the patients undergoing this intervention.