The Cartesian medical model is leading to overmedicalization all over the world. Modern palliative care, derived from a reflection on the Cartesian medical model, aims to relieve the suffering of critically ill patients and their caregivers. It also focuses on the unnecessary harm of medical technology itself. This paper presents some issues from a case and inspires further thinking and profound understanding of China's palliative care by reviewing the history of relieving suffering, the views and shortcomings of the Cartesian medical model, and the application of life-sustaining treatment and good death.

A two-photon near infrared(NIR)fluorescence turn-on sensor with high selectivity and sensitivity for Zn2+detection has been developed.This sensor exhibits a large Stokes'shift(-300 nm)and can be excited from 900 to 1000 nm,with an emission wavelength of-785 nm,making it ideal for imaging in biological tissues.The sensor's high selectivity for Zn2+over other structurally similar cations,such as Cd2+,makes it a promising tool for monitoring zinc ion levels in biological systems.Given the high concentration of zinc in thrombi,this sensor could provide a useful tool for in vivo thrombus imaging.

Artificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge. This review examines key sources of uncertainty—out-of-distribution, aleatoric, and model uncertainties—and highlights the importance of independent confidence metrics and explainable AI for safe integration. Independent confidence metrics assess the reliability of AI predictions, while explainable AI provides transparency, enhancing collaboration between AI and radiologists.The development of zero-error tolerance models, designed to minimize errors, sets new standards for safety. Addressing these challenges will enable AI to become a trusted partner in radiology, advancing care standards and patient outcomes.

We report a case of a 24-year-old patient who presented after a head trauma with a traumatic occlusion of his left internal carotid artery. He underwent diagnostic cerebral angiogram and was found to have a direct left carotid-cavernous fistula (CCF) with retrograde filling from the posterior circulation across the posterior communicating artery. Because of the severe injury to the left internal carotid artery (ICA), reconstructive repair of the ICA was not possible. The patient underwent deconstructive repair of the CCF by coil embolization using a posterior retrograde approach. Coils were successfully placed in the cavernous sinus and back into the left ICA with complete cure of the CCF and restoration of cerebral perfusion distal to the treated CCF. We review the types of CCFs, their clinical presentation, and their endovascular treatments. Retrograde access of a direct CCF is rarely reported in the literature, and we believe this approach offers a viable alternative in appropriately selected patients.

We report a case of a 24-year-old patient who presented after a head trauma with a traumatic occlusion of his left internal carotid artery. He underwent diagnostic cerebral angiogram and was found to have a direct left carotid-cavernous fistula (CCF) with retrograde filling from the posterior circulation across the posterior communicating artery. Because of the severe injury to the left internal carotid artery (ICA), reconstructive repair of the ICA was not possible. The patient underwent deconstructive repair of the CCF by coil embolization using a posterior retrograde approach. Coils were successfully placed in the cavernous sinus and back into the left ICA with complete cure of the CCF and restoration of cerebral perfusion distal to the treated CCF. We review the types of CCFs, their clinical presentation, and their endovascular treatments. Retrograde access of a direct CCF is rarely reported in the literature, and we believe this approach offers a viable alternative in appropriately selected patients.

Artificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge. This review examines key sources of uncertainty—out-of-distribution, aleatoric, and model uncertainties—and highlights the importance of independent confidence metrics and explainable AI for safe integration. Independent confidence metrics assess the reliability of AI predictions, while explainable AI provides transparency, enhancing collaboration between AI and radiologists.The development of zero-error tolerance models, designed to minimize errors, sets new standards for safety. Addressing these challenges will enable AI to become a trusted partner in radiology, advancing care standards and patient outcomes.

We report a case of a 24-year-old patient who presented after a head trauma with a traumatic occlusion of his left internal carotid artery. He underwent diagnostic cerebral angiogram and was found to have a direct left carotid-cavernous fistula (CCF) with retrograde filling from the posterior circulation across the posterior communicating artery. Because of the severe injury to the left internal carotid artery (ICA), reconstructive repair of the ICA was not possible. The patient underwent deconstructive repair of the CCF by coil embolization using a posterior retrograde approach. Coils were successfully placed in the cavernous sinus and back into the left ICA with complete cure of the CCF and restoration of cerebral perfusion distal to the treated CCF. We review the types of CCFs, their clinical presentation, and their endovascular treatments. Retrograde access of a direct CCF is rarely reported in the literature, and we believe this approach offers a viable alternative in appropriately selected patients.

Artificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge. This review examines key sources of uncertainty—out-of-distribution, aleatoric, and model uncertainties—and highlights the importance of independent confidence metrics and explainable AI for safe integration. Independent confidence metrics assess the reliability of AI predictions, while explainable AI provides transparency, enhancing collaboration between AI and radiologists.The development of zero-error tolerance models, designed to minimize errors, sets new standards for safety. Addressing these challenges will enable AI to become a trusted partner in radiology, advancing care standards and patient outcomes.

Inflammatory bowel diseases comprising Crohn’s disease and ulcerative colitis have emerged as global diseases. Multiple distinct therapeutic mechanisms have allowed us to increase our rates of achieving remission and reducing permanent disease-related morbidity. However, there is limited data to inform relative positioning of different therapies. This review will summarize existing literature on use of clinical decision models to inform relative efficacy of one therapeutic mechanism compared to the other given individual patient characteristics. It will also demonstrate the value of serologic, transcriptomic (from biopsies), and microbiome-based biomarkers in identifying which therapy is most likely to work for a given patient. We will review the existing gaps in the literature in this field and suggest a path forward for future studies to better inform patient care, incorporating the principles of precision medicine in the management of inflammatory bowel disease.

Perianeurysmal cysts are a rare and poorly understood finding in patients both with treated and untreated aneurysms. While the prior literature suggests that a minority of perianeurysmal cysts develop 1-4 years following endovascular aneurysm treatment, this updated review demonstrates that nearly half of perianeurysmal cysts were diagnosed following aneurysm coiling, with the other half diagnosed concurrently with an associated aneurysm prior to treatment. 64% of perianeurysmal cysts were surgically decompressed, with a 39% rate of recurrence requiring re-operation. We report a case of a 71-year-old woman who presented with vertigo and nausea and was found to have a 3.4 cm perianeurysmal cyst 20 years after initial endovascular coiling of a ruptured giant ophthalmic aneurysm. The cyst was treated with endoscopic fenestration followed by open fenestration upon recurrence. The case represents the longest latency from initial aneurysm treatment to cyst diagnosis reported in the literature and indicates that the diagnosis of perianeurysmal cyst should remain on the differential even decades after treatment. Based on a case discussion and updated literature review, this report highlights proposed etiologies of development and management strategies for a challenging lesion.