Incidence, Patterns, and Characteristics of Patients With Acute Kidney Injury Requiring Renal Replacement Therapy in Cancer Settings.

Background Cancer patients are at increased risk of multi-organ failure due to either the primary disease burden or certain non-cancer-related risk factors. Among the most common complications is acute kidney injury (AKI), which is frequently seen in cancer settings. Among patients with cancer, the incidence of renal injury reaches up to 12.5%. However, critical care units have a much higher incidence, up to 50%. This study aimed to describe the characteristics of Asian populations with AKI with a background of malignancy, along with risk factors and outcomes. Materials and methods A retrospective tertiary-care single-center study was conducted in the intensive care unit (ICU). It included 182 cancer patients with AKI who were followed over a 36-month period. Results Our results revealed a mortality rate of 50.5% among cancer patients with AKI, with the highest mortality rate being among those with solid and hematologic malignancies. Common predisposing factors were sepsis (28%), dehydration (18.1%), and hypotension (9.9%). Several drugs were found to be among the most toxic agents, including vancomycin, colistin, nonsteroidal anti-inflammatory drugs, angiotensin receptor blockers, amphotericin, and certain chemotherapeutic drugs (especially cisplatin). A strong association was found between the length of ICU stay and mortality (p=<0.05), indicating a reduced survival rate with prolonged hospital stay even in critical care settings. Conclusion In summary, AKI in cancer patients increases their mortality due to a variety of risk factors. However, the early involvement of onconephrology and a multidisciplinary approach will result in better outcomes.

A Comprehensive Survey of ChatGPT: Advancements, Applications, Prospects, and Challenges.

Large Language Models (LLMs) especially when combined with Generative Pre-trained Transformers (GPT) represent a groundbreaking in natural language processing. In particular, ChatGPT, a state-of-the-art conversational language model with a user-friendly interface, has garnered substantial attention owing to its remarkable capability for generating human-like responses across a variety of conversational scenarios. This survey offers an overview of ChatGPT, delving into its inception, evolution, and key technology. We summarize the fundamental principles that underpin ChatGPT, encompassing its introduction in conjunction with GPT and LLMs. We also highlight the specific characteristics of GPT models with details of their impressive language understanding and generation capabilities. We then summarize applications of ChatGPT in a few representative domains. In parallel to the many advantages that ChatGPT can provide, we discuss the limitations and challenges along with potential mitigation strategies. Despite various controversial arguments and ethical concerns, ChatGPT has drawn significant attention from research industries and academia in a very short period. The survey concludes with an envision of promising avenues for future research in the field of ChatGPT. It is worth noting that knowing and addressing the challenges faced by ChatGPT will mount the way for more reliable and trustworthy conversational agents in the years to come.

Zavegepant nasal spray for the acute treatment of migraine: A meta analysis.

BACKGROUND: Zavegepant nasal spray is a novel CGRP receptor antagonist that has been developed for the acute treatment of migraine - a prevalent disease leading to disability and economic burden. The meta-analysis aims to quantify the efficacy of Zavegepant compared to standard care or placebo in achieving pain freedom, freedom from most bothersome symptoms (MBS), sustained pain freedom, and pain relapse at 2 to 48 hours. METHODS: Databases and registers were systematically searched to identify relevant clinical trials. Two independent reviewers used a standardized data extraction form to collect relevant data on primary and secondary outcomes. Statistical analysis was performed in RevMan 5.4 software. The efficacy of Zavegepant was compared to placebo using odds ratios (OR) with 95% confidence intervals (CI). Heterogeneity was assessed using the I2 statistic, chi-square test, Z value, and P value. Cochrane ROB-2 and ROBINS-I tools were used to assess the biases (osf.io/b32ne). RESULTS: Of 36 identified studies, 3 were included in this meta-analysis. Zavegepant was more effective in achieving pain freedom (OR: 1.6, P < .00001), and freedom from MBS at 2 hours (OR = 1.4, P < .00001). The intervention group demonstrated a higher likelihood of sustained pain freedom between 2 and 48 hours (OR = 1.74, P < .00001). Although there was a trend towards reduced pain relapse between 2 and 48 hours in the intervention group, the difference was insignificant (OR = 0.67, P = .11). CONCLUSION: This meta-analysis confirms the effectiveness of Zavegepant nasal spray in treating acute migraine, with significant improvements in pain and symptom relief. Further research is needed to determine the effect on pain relapse and overall safety.

ChatGPT-based Biological and Psychological Data Imputation.

Missing data are a common problem for large cohort or longitudinal research and have been handled through data imputation. Based on simplified models such as linear or nonlinear interpolations, current imputation methods may not be accurate for real-life data such as biological and behavioral data. The purpose of this work was to explore the capability of ChatGPT, a powerful Large Language Model (LLM) developed by OpenAI, for biological and psychological data imputation. We tested the feasibility using data from the Human Connectome Project. Performance was evaluated by comparing the imputed data against known ground truth (GT) and measured with metrics like Pearson correlation coefficient (r), relative accuracy (MP), and mean absolute error (MAE). Comparative analyses with traditional imputation techniques are also conducted to demonstrate the superior efficacy of the ChatGPT as a data imputer. In summary, through customized data-to-text prompting engineering, ChatGPT can successfully capture intricate patterns and dependencies within biological data, resulting in precise imputations. Fine-tuning ChatGPT with domain-specific biological vocabulary with human in-loop as an interpreter enhances the accuracy and relevance of the imputations.

ECSU-Net: An Embedded Clustering Sliced U-Net Coupled With Fusing Strategy for Efficient Intervertebral Disc Segmentation and Classification.

Automatic vertebra segmentation from computed tomography (CT) image is the very first and a decisive stage in vertebra analysis for computer-based spinal diagnosis and therapy support system. However, automatic segmentation of vertebra remains challenging due to several reasons, including anatomic complexity of spine, unclear boundaries of the vertebrae associated with spongy and soft bones. Based on 2D U-Net, we have proposed an Embedded Clustering Sliced U-Net (ECSU-Net). ECSU-Net comprises of three modules named segmentation, intervertebral disc extraction (IDE) and fusion. The segmentation module follows an instance embedding clustering approach, where our three sliced sub-nets use axis of CT images to generate a coarse 2D segmentation along with embedding space with the same size of the input slices. Our IDE module is designed to classify vertebra and find the inter-space between two slices of segmented spine. Our fusion module takes the coarse segmentation (2D) and outputs the refined 3D results of vertebra. A novel adaptive discriminative loss (ADL) function is introduced to train the embedding space for clustering. In the fusion strategy, three modules are integrated via a learnable weight control component, which adaptively sets their contribution. We have evaluated classical and deep learning methods on Spineweb dataset-2. ECSU-Net has provided comparable performance to previous neural network based algorithms achieving the best segmentation dice score of 95.60% and classification accuracy of 96.20%, while taking less time and computation resources.

Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation With Cascade Incremental Learning.

Visual understanding of liver vessels anatomy between the living donor-recipient (LDR) pair can assist surgeons to optimize transplant planning by avoiding non-targeted arteries which can cause severe complications. We propose to visually analyze the anatomical variants of the liver vessels anatomy to maximize similarity for finding a suitable Living Donor-Recipient (LDR) pair. Liver vessels are segmented from computed tomography angiography (CTA) volumes by employing a cascade incremental learning (CIL) model. Our CIL architecture is able to find optimal solutions, which we use to update the model with liver vessel CTA images. A novel ternary tree based algorithm is proposed to map all the possible liver vessel variants into their respective tree topologies. The tree topologies of the recipient's and donor's liver vessels are then used for an appropriate matching. The proposed algorithm utilizes a set of defined vessel tree variants which are updated to maintain the maximum matching options by leveraging the accurate segmentation results of the vessels derived from the incremental learning ability of the CIL. We introduce a novel concept of in-order digital string based comparison to match the geometry of two anatomically varied trees. Experiments through visual illustrations and quantitative analysis demonstrated the effectiveness of our approach compared to state-of-the-art.

SPST-CNN: Spatial pyramid based searching and tagging of liver's intraoperative live views via CNN for minimal invasive surgery.

Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflations and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The navigation ability in terms of searching and tagging within liver views has not been characterized, and current object detection methods do not account for the mechanics of how these features could be applied to the liver images. In this research, we have proposed spatial pyramid based searching and tagging of liver's intraoperative views using convolution neural network (SPST-CNN). By exploiting a hybrid combination of an image pyramid at input and spatial pyramid pooling layer at deeper stages of SPST-CNN, we reveal the gains of full-image representations for searching and tagging variable scaled liver live views. SPST-CNN provides pinpoint searching and tagging of intraoperative liver views to obtain up-to-date information about the location and shape of the area of interest. Downsampling input using image pyramid enables SPST-CNN framework to deploy input images with a diversity of resolutions for achieving scale-invariance feature. We have compared the proposed approach to the four recent state-of-the-art approaches and our method achieved better mAP up to 85.9%.

Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images.

An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT examination, low-dose CT (LDCT) is an effective solution for medical imaging. However, inhomogeneous appearances and indistinct boundaries due to additional noise and streaks artifacts in LDCT images often make it a challenging task. This study aims to extract a liver model from LDCT images for facilitating medical expert in surgical planning and post-operative assessment along with low radiation risk to the patient. Our method carried out liver extraction by employing residual convolutional neural networks (LER-CN), which is further refined by noise removal and structure preservation components. After patch-based training, our LER-CN shows a competitive performance relative to state-of-the-art methods for both clinical and publicly available MICCAI Sliver07 datasets. We have proposed training and learning algorithms for LER-CN based on back propagation gradient descent. We have evaluated our method on 150 abdominal CT scans for liver extraction. LER-CN achieves dice similarity coefficient up to 96.5[Formula: see text], decreased volumetric overlap error up to 4.30[Formula: see text], and average symmetric surface distance less than 1.4 [Formula: see text]. These findings have shown that LER-CN is a favorable method for medical applications with high efficiency allowing low radiation risk to patients.

Image-Aligned Dynamic Liver Reconstruction Using Intra-Operative Field of Views for Minimal Invasive Surgery.

During hepatic minimal invasive surgery (MIS), 3-D reconstruction of a liver surface by interpreting the geometry of its soft tissues is achieving attractions. One of the major issues to be addressed in MIS is liver deformation. Moreover, it severely inhibits free sight and dexterity of tissue manipulation, which causes its intra-operative morphology and soft tissue motion altered as compared to its pre-operative shape. While many applications focus on 3-D reconstruction of rigid or semi-rigid scenes, the techniques applied in hepatic MIS must be able to cope with a dynamic and deformable environment. We propose an efficient technique for liver surface reconstruction based on the structure from motion to handle liver deformation. The reconstructed liver will assist surgeons to visualize liver surface more efficiently with better depth perception. We use the intra-operative field of views to generate 3-D template mesh from a dense keypoint cloud. We estimate liver deformation by finding best correspondence between 3-D templates and reconstruct a liver image to calculate translation and rotational motions. Our technique then finely tunes deformed surface by adding smoothness using shading cues. Up till now, this technique is not used for solving the human liver deformation problem. Our approach is tested and validated with synthetic as well as real in vivo data, which reveal that the reconstruction accuracy can be enhanced using our approach even in challenging laparoscopic environments.