INTRODUCTION

Acute coronary syndrome (ACS) and end-stage renal disease (ESRD) are both prevalent globally. The diagnosis and management of ACS in ESRD is difficult because the interplay of cardiovascular and renal disease is complicated. The guidelines for ACS may not be applicable to the ESRD population because the trials from which these are drawn mostly excluded ESRD patients.

To determine the clinical profile and outcomes of CKD patients on dialysis admitted for ACS in the Philippine General Hospital (PGH).

We did a retrospective cohort study and employed a retrospective review of electronic medical records among ESRD patients presenting with ACS in PGH from May 2021 to November 2023. The collected data was analyzed using univariate and bivariate statistics using PRISM software.

A total of 48 patients with ESRD were admitted for ACS in this study – 8 with STEMI and 40 with NSTEMI. The mean age was 61 years old and 33 (68.8%) were male. Among those with STEMI, six (75%) presented with Kilip II or more. While among those with NSTEMI, 17 (42.5%) had a GRACE score >140 and 27 (67.5%) had an NSTEMI TIMI risk score >2. On average, the patients were on hemodialysis for 31 months prior to admission. The most common comorbidities were hypertension (91.7%) and heart failure (83.3%). On admission, 18 (37.5%) presented with SBP >160, 7 (14.6%) patients presented with shock, and 4 (8.3%) patients presented with cardiac arrest. 38 (79.2%) patients had anemia on admission. 21 (43.8%) patients had left ventricular hypertrophy on electrocardiogram while 34 (70.8%) patients had cardiomegaly on chest radiography. The average left ventricular ejection fraction on echocardiogram was 46% and 27 (90%) patients had segmental wall motion abnormalities. The most common angiographic finding was 3-vessel coronary artery disease seen in 50% of patients. Almost all patients received dualantiplatelet therapy, high dose statin, and beta-blocker. The mortality rate was high at 43.8% with cardiovascular causes being the most common cause of death.

This study demonstrates the high mortality rate among patients with ESRD presenting with ACS. Our study portrays that patients with ESRD present with higher risk features including abnormalities in vital signs, laboratories, imaging, high prognostications score, and high in-hospital morbidity.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Molecular knowledge of human gastric corpus epithelium remains incomplete. Here, by integrated analyses using single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) techniques, we uncovered the spatially resolved expression landscape and gene-regulatory network of human gastric corpus epithelium. Specifically, we identified a stem/progenitor cell population in the isthmus of human gastric corpus, where EGF and WNT signaling pathways were activated. Meanwhile, LGR4, but not LGR5, was responsible for the activation of WNT signaling pathway. Importantly, FABP5 and NME1 were identified and validated as crucial for both normal gastric stem/progenitor cells and gastric cancer cells. Finally, we explored the epigenetic regulation of critical genes for gastric corpus epithelium at chromatin state level, and identified several important cell-type-specific transcription factors. In summary, our work provides novel insights to systematically understand the cellular diversity and homeostasis of human gastric corpus epithelium in vivo.
Humans ; Epigenesis, Genetic ; Gastric Mucosa/metabolism* ; Chromatin/metabolism* ; Stem Cells ; Epithelium/metabolism* ; Fatty Acid-Binding Proteins/metabolism*

Venous thromboembolism (VTE) is a complication in children with acute lymphoblastic leukemia (ALL). The Chinese Children's Cancer Group-ALL-2015 protocol was carried out in China, and epidemiology, clinical characteristics, and risk factors associated with VTE were analyzed. We collected data on VTE in a multi-institutional clinical study of 7640 patients with ALL diagnosed in 20 hospitals from January 2015 to December 2019. First, VTE occurred in 159 (2.08%) patients, including 90 (56.6%) during induction therapy and 108 (67.92%) in the upper extremities. T-ALL had a 1.74-fold increased risk of VTE (95% CI 1.08-2.8, P = 0.022). Septicemia, as an adverse event of ALL treatment, can significantly promote the occurrence of VTE (P < 0.001). Catheter-related thrombosis (CRT) accounted for 75.47% (n = 120); and, symptomatic VTE, 58.49% (n = 93), which was more common in patients aged 12-18 years (P = 0.023), non-CRT patients (P < 0.001), or patients with cerebral thrombosis (P < 0.001). Of the patients with VTE treated with anticoagulation therapy (n = 147), 4.08% (n = 6) had bleeding. The VTE recurrence rate was 5.03% (n = 8). Patients with VTE treated by non-ultrasound-guided venous cannulation (P = 0.02), with residual thrombus (P = 0.006), or with short anticoagulation period (P = 0.026) had high recurrence rates. Thus, preventing repeated venous puncture and appropriately prolonged anticoagulation time can reduce the risk of VTE recurrence.
Humans ; Child ; Venous Thromboembolism/etiology* ; East Asian People ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/epidemiology* ; Risk Factors ; Thrombosis/chemically induced* ; China/epidemiology* ; Anticoagulants/adverse effects* ; Recurrence

Background/Aims: Non-alcoholic fatty liver disease (NAFLD) is associated with the development of cardiovascular disease. While existing studies have examined cardiac remodeling in NAFLD, there has been less emphasis on the development of carotid atherosclerosis and stroke. We sought to conduct a meta-analysis to quantify the prevalence, risk factors, and degree of risk increment of carotid atherosclerosis and stroke in NAFLD. Methods: Embase and Medline were searched for articles relating to NAFLD, carotid atherosclerosis, and stroke. Proportional data was analysed using a generalized linear mixed model. Pairwise meta-analysis was conducted to obtain odds ratio or weighted mean difference for comparison between patients with and without NAFLD. Results: From pooled analysis of 30 studies involving 7,951 patients with NAFLD, 35.02% (95% confidence interval [CI], 27.36–43.53%) had carotid atherosclerosis with an odds ratio of 3.20 (95% CI, 2.37–4.32; P<0.0001). Pooled analysis of 25,839 patients with NAFLD found the prevalence of stroke to be 5.04% (95% CI, 2.74–9.09%) with an odds ratio of 1.88 (95% CI, 1.23–2.88; P=0.02) compared to non-NAFLD. The degree of steatosis assessed by ultrasonography in NAFLD was closely associated with risk of carotid atherosclerosis and stroke. Older age significantly increased the risk of developing carotid atherosclerosis, but not stroke in NAFLD. Conclusions This meta-analysis shows that a stepwise increment of steatosis of NAFLD can significantly increase the risk of carotid atherosclerosis and stroke development in NAFLD. Patients more than a third sufferred from carotid atherosclerosis and routine assessment of carotid atherosclerosis is quintessential in NAFLD.

Methods: Relevant data from the National Readmission Database in 2015–2018 were analyzed, and the computer-assisted procedures of cervical and thoracolumbar spinal surgery were identified using International Classification of Diseases 9th and 10th revision codes. Patient demographics, surgical data, readmissions, and total charges were examined. Comorbidity burden was calculated using the Charlson and Elixhauser comorbidity index. Complication rates were determined on the basis of diagnosis codes. Results: A total of 48,116 cervical cases and 27,093 thoracolumbar cases were identified using computer-assisted navigation. No major differences in sex, age, or comorbidities over time were found. The utilization of computer-assisted navigation for cervical and thoracolumbar spinal fusion cases increased from 2015 to 2018 and normalized to their respective years’ total cases (Pearson correlation coefficient=0.756, p =0.049; Pearson correlation coefficient=0.9895, p =0.010). Total charges for cervical and thoracolumbar cases increased over time (Pearson correlation coefficient=0.758, p =0.242; Pearson correlation coefficient=0.766, p =0.234). Conclusions The use of computer-assisted navigation in spinal surgery increased significantly from 2015 to 2018. The average cost grossly increased from 2015 to 2018, and it was higher than the average cost of nonnavigated spinal surgery. With the increased utilization and standardization of computer-assisted navigation in spinal surgeries, the cost of care of more patients might potentially increase. As a result, further studies should be conducted to determine whether the use of computer-assisted navigation is efficient in terms of cost and improvement of care.