Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant global health issue, affecting over 30% of the population worldwide due to the rising prevalence of metabolic risk factors such as obesity and type 2 diabetes mellitus. This spectrum of liver disease ranges from isolated steatosis to more severe forms such as steatohepatitis, fibrosis, and cirrhosis. Recent studies highlight the role of gut microbiota in MASLD pathogenesis, showing that dysbiosis significantly impacts metabolic health and the progression of liver disease. This review critically evaluates current microbiome-centered therapies in MASLD management, including prebiotics, probiotics, synbiotics, fecal microbiota transplantation, and emerging therapies such as engineered bacteria and bacteriophage therapy. We explore the scientific rationale, clinical evidence, and potential mechanisms by which these interventions influence MASLD. The gut-liver axis is crucial in MASLD, with notable changes in microbiome composition linked to disease progression. For instance, specific microbial profiles and reduced alpha diversity are associated with MASLD severity. Therapeutic strategies targeting the microbiome could modulate disease progression by improving gut permeability, reducing endotoxin-producing bacteria, and altering bile acid metabolism. Although promising, these therapies require further research to fully understand their mechanisms and optimize their efficacy. This review integrates findings from clinical trials and experimental studies, providing a comprehensive overview of microbiome-centered therapies’ potential in managing MASLD. Future research should focus on personalized strategies, utilizing microbiome features, blood metabolites, and customized dietary interventions to enhance the effectiveness of these therapies.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant global health issue, affecting over 30% of the population worldwide due to the rising prevalence of metabolic risk factors such as obesity and type 2 diabetes mellitus. This spectrum of liver disease ranges from isolated steatosis to more severe forms such as steatohepatitis, fibrosis, and cirrhosis. Recent studies highlight the role of gut microbiota in MASLD pathogenesis, showing that dysbiosis significantly impacts metabolic health and the progression of liver disease. This review critically evaluates current microbiome-centered therapies in MASLD management, including prebiotics, probiotics, synbiotics, fecal microbiota transplantation, and emerging therapies such as engineered bacteria and bacteriophage therapy. We explore the scientific rationale, clinical evidence, and potential mechanisms by which these interventions influence MASLD. The gut-liver axis is crucial in MASLD, with notable changes in microbiome composition linked to disease progression. For instance, specific microbial profiles and reduced alpha diversity are associated with MASLD severity. Therapeutic strategies targeting the microbiome could modulate disease progression by improving gut permeability, reducing endotoxin-producing bacteria, and altering bile acid metabolism. Although promising, these therapies require further research to fully understand their mechanisms and optimize their efficacy. This review integrates findings from clinical trials and experimental studies, providing a comprehensive overview of microbiome-centered therapies’ potential in managing MASLD. Future research should focus on personalized strategies, utilizing microbiome features, blood metabolites, and customized dietary interventions to enhance the effectiveness of these therapies.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant global health issue, affecting over 30% of the population worldwide due to the rising prevalence of metabolic risk factors such as obesity and type 2 diabetes mellitus. This spectrum of liver disease ranges from isolated steatosis to more severe forms such as steatohepatitis, fibrosis, and cirrhosis. Recent studies highlight the role of gut microbiota in MASLD pathogenesis, showing that dysbiosis significantly impacts metabolic health and the progression of liver disease. This review critically evaluates current microbiome-centered therapies in MASLD management, including prebiotics, probiotics, synbiotics, fecal microbiota transplantation, and emerging therapies such as engineered bacteria and bacteriophage therapy. We explore the scientific rationale, clinical evidence, and potential mechanisms by which these interventions influence MASLD. The gut-liver axis is crucial in MASLD, with notable changes in microbiome composition linked to disease progression. For instance, specific microbial profiles and reduced alpha diversity are associated with MASLD severity. Therapeutic strategies targeting the microbiome could modulate disease progression by improving gut permeability, reducing endotoxin-producing bacteria, and altering bile acid metabolism. Although promising, these therapies require further research to fully understand their mechanisms and optimize their efficacy. This review integrates findings from clinical trials and experimental studies, providing a comprehensive overview of microbiome-centered therapies’ potential in managing MASLD. Future research should focus on personalized strategies, utilizing microbiome features, blood metabolites, and customized dietary interventions to enhance the effectiveness of these therapies.

Thymoquinone (Tq) and gallic acid (GA) are known for counter-tumorigenic characteristics. GA inhibits cancer cell proliferation by interfering with many apoptotic signaling pathways, producing more reactive oxygen species (ROS), focusing on the cell cycle, and suppressing the expression of oncogenes and matrix metalloproteinases (MMPs). In this study, thymoquinone (after reducing to thymohydroquinone) and gallic acid are esterified to form thymohydroquinyl gallate (a prodrug). Thymohydroquinyl gallate (THQG) possesses enhanced antineoplastic efficacy and targeted delivery potential. The chemical and spectroscopic analysis confirms ester synthesis. Gold nanoparticles (AuNPs) are employed as nanocarriers due to their physicochemical and optical characteristics, biocompatibility, and low toxicity. As an efficient drug transporter, gold nanoparticles (AuNPs) shield conjugated drugs from enzymatic digestion. The prodrug acts as a reducing agent for Au metal atoms and is loaded onto it after reduction. The nano drug is radiolabeled with ^99mTc and ¹³¹I to monitor the drug biodistribution in animals using a gamma camera and single-photon emission computerized tomography (SPECT). ¹³¹I is an antineoplastic that helps enhance the drug's efficiency. Chromatographic results reveal promising radiolabeling percentages. In vitro, drug release shows sustained release at pH⁓5.8. In vitro 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) cytotoxicity assay reveals drug potency on CAL 27 and MCF 7 cell lines.

Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I 2 statistic, with I 2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs.40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.

Hepatic angiomyolipoma (HAML) is a rare, benign mesenchymal liver tumor encountered in Asia, primarily in females, and can be found within the right hepatic lobe, but also in other areas of the liver. Immunohistochemically, HAMLs are characteristically positive for human melanoma black-45 antigen (HMB-45) and can histochemically vary in the composition of angiomatous, lipomatous, and myomatous tissue, together with the presence of epithelioid cells. In this case report, we discuss a previously healthy patient presenting with bloating and previously documented concern of liver lesions, found to have HAML confirmed by surgical pathology. Surgery was decided, as HAMLs greater than 10 cm are at risk of rupture. This is one of the first documented cases of HAML resected through robot-assisted bisegmentectomy and cholecystectomy, and therefore, intraoperative images have been included to assist in the planning of future robotic cases.

Hepatic angiomyolipoma (HAML) is a rare, benign mesenchymal liver tumor encountered in Asia, primarily in females, and can be found within the right hepatic lobe, but also in other areas of the liver. Immunohistochemically, HAMLs are characteristically positive for human melanoma black-45 antigen (HMB-45) and can histochemically vary in the composition of angiomatous, lipomatous, and myomatous tissue, together with the presence of epithelioid cells. In this case report, we discuss a previously healthy patient presenting with bloating and previously documented concern of liver lesions, found to have HAML confirmed by surgical pathology. Surgery was decided, as HAMLs greater than 10 cm are at risk of rupture. This is one of the first documented cases of HAML resected through robot-assisted bisegmentectomy and cholecystectomy, and therefore, intraoperative images have been included to assist in the planning of future robotic cases.

Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I 2 statistic, with I 2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs.40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.

Hepatic angiomyolipoma (HAML) is a rare, benign mesenchymal liver tumor encountered in Asia, primarily in females, and can be found within the right hepatic lobe, but also in other areas of the liver. Immunohistochemically, HAMLs are characteristically positive for human melanoma black-45 antigen (HMB-45) and can histochemically vary in the composition of angiomatous, lipomatous, and myomatous tissue, together with the presence of epithelioid cells. In this case report, we discuss a previously healthy patient presenting with bloating and previously documented concern of liver lesions, found to have HAML confirmed by surgical pathology. Surgery was decided, as HAMLs greater than 10 cm are at risk of rupture. This is one of the first documented cases of HAML resected through robot-assisted bisegmentectomy and cholecystectomy, and therefore, intraoperative images have been included to assist in the planning of future robotic cases.

Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I 2 statistic, with I 2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs.40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.