Endoscopic ultrasonography (EUS) has progressed beyond diagnostic imaging to include EUS-guided tissue acquisition and EUS-directed therapies. This review provides an update on EUS-guided drainage and anastomotic procedures, and other therapeutic procedures. Today, EUS-guided drainage of symptomatic walled-off pancreatic fluid collections is the norm, with endoscopic necrosectomy as an adjunct. For high-risk surgical patients unsuitable for cholecystectomy, EUS-guided gallbladder drainage of acute cholecystitis is an option. Additionally, EUS-guided drainage of obstructed biliary and pancreatic ductal system can be performed as salvage procedures after unsuccessful endoscopic retrograde cholangiopancreatography (ERCP). Bariatric procedures such as Roux-en-Y gastric bypass alter the gastric anatomy, hindering access to the major papilla. This can be overcome by creating a conduit through the excluded stomach using EUS-directed transgastric ERCP. Gastric outlet obstruction and afferent loop syndrome can be treated using EUS-guided gastrojejunostomy. These therapeutic interventions are a major advancement in the field of interventional EUS, achieving significant clinical impact.
Humans ; Endosonography/methods* ; Drainage/methods* ; Cholangiopancreatography, Endoscopic Retrograde/methods* ; Ultrasonography, Interventional/methods* ; Anastomosis, Surgical/methods*

The clinical burden of colorectal cancer (CRC) is high. Population-based screening and early detection are essential to improve the long-term clinical outcome. Nonetheless, a significant proportion of patients still present at an advanced stage, including with acute large bowel obstruction. Image-enhanced endoscopy and artificial intelligence can improve the detection and diagnosis of colonic adenomas and early cancer. Endoscopic resection is regarded as the preferred curative treatment option for colonic adenoma and T0 and T1 CRC limited to the superficial submucosa. Emergency colonic stenting as bridge to interval curative surgery is increasingly accepted as a first-line option when technically feasible. Minimally invasive resection techniques such as laparoscopic colectomy and robot-assisted colorectal surgery have also come of age. These techniques reduce post-treatment morbidity, shorten the recovery process and can be cost-effective while maintaining long-term oncological cure. These outcome measures are relevant to our patients; therefore, minimally invasive approaches to curative resection should be embraced.
Humans ; Colorectal Neoplasms/surgery* ; Minimally Invasive Surgical Procedures/methods* ; Laparoscopy/methods* ; Colectomy/methods* ; Robotic Surgical Procedures/methods* ; Treatment Outcome ; Colonoscopy/methods*

Environmental damage affects many aspects of healthcare, from extreme weather events to evolving population disease. Singapore's healthcare sector has the world's second highest healthcare emissions per capita, hampering the nation's pledge to reduce emissions by 2030 and achieve net zero emissions by 2050. In this review, we provide an overview of the impact environmental damage has on healthcare, including facilities, supply chain and human health, and examine measures to address healthcare's impact on the environment. Utilising the 'R's of sustainability - rethinking, reducing/refusing, reusing/repurposing/reprocessing, repairing, recycling and research - we have summarised the opportunities and challenges across medical disciplines. Awareness and advocacy to adopt strategies at institutional and individual levels is needed to revolutionise our environmental footprint and improve healthcare sustainability. By leveraging evidence from ongoing trials and integrating sustainable practices, our healthcare system can remain resilient against environment-driven challenges and evolving healthcare demands while minimising further impacts of environmental destruction.
Humans ; Climate Change ; Delivery of Health Care ; Singapore ; Conservation of Natural Resources ; Sustainable Development ; Environment

The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.