Multimodal ChatGPT-4V for Electrocardiogram Interpretation: Promise and Limitations.

This study evaluated the capabilities of the newly released ChatGPT-4V, a large language model with visual recognition abilities, in interpreting electrocardiogram waveforms and answering related multiple-choice questions for assisting with cardiovascular care.

Relationship between ATOH1 and tumor microenvironment in colon adenocarcinoma patients with different microsatellite instability status.

BACKGROUND: Colon adenocarcinoma (COAD) is one of the major varieties of malignant tumors threatening human health today. Immune checkpoint inhibitors (ICIs) have recently begun to emerge as an effective option for the treatment of COAD patients, but not all patients can benefit from ICI treatment. Previous studies have suggested that ICIs boast significant clinical effects on patients with microsatellite instability-high (MSI-H), while conversely patients with microsatellite-stable/microsatellite instability-low (MSS/MSI-L) have shown limited response. METHODS: We used ATAC-seq, RNA-seq, and mutation data from The Cancer Genome Atlas Colon adenocarcinoma (TCGA-COAD) cohort to perform multi-omics differential analysis on COAD samples with different MSI statuses, then further screened genes by additionally combining these results with survival analysis. We analyzed the effects of the screened genes on the tumor microenvironment and immunogenicity of COAD patients, and subsequently determined their influence on the efficacy of ICIs in COAD patients using a series of predictive indexes. RESULTS: Twelve genes were screened in the TCGA-COAD cohort, and after the combined survival analysis, we identified ATOH1 as having significant effects. ATOH1 is characterized by high chromatin accessibility, high expression, and high mutation in COAD patients in the MSI-H group. COAD patients with high ATOH1 expression are associated with a better prognosis, unique immune microenvironment, and higher efficacy in ICI treatment. Enrichment analysis showed that COAD patients with high ATOH1 expression displayed significant upregulation in their humoral immunity and other related pathways. CONCLUSIONS: We speculate that ATOH1 may influence the efficacy of ICIs therapy in patients with COAD by affecting the immune microenvironment and immunogenicity of the tumor.

Can DALL-E 3 Reliably Generate 12-Lead ECGs and Teaching Illustrations?

The recent integration of the latest image generation model DALL-E 3 into ChatGPT allows text prompts to easily generate the corresponding images, enabling multimodal output from ChatGPT. We explored the feasibility of DALL-E 3 for drawing a 12-lead ECG and found that it can draw rudimentary 12-lead electrocardiograms (ECG) displaying some of the parameters, although the details are not completely accurate. We also explored DALL-E 3's capacity to create vivid illustrations for teaching resuscitation-related medical knowledge. DALL-E 3 produced accurate CPR illustrations emphasizing proper hand placement and technique. For ECG principles, it produced creative heart-shaped waveforms tying ECGs to the heart. With further training, DALL-E 3 shows promise to expand easy-to-understand visual medical teaching materials and ECG simulations for different disease states. In conclusion, DALL-E 3 has the potential to generate realistic 12-lead ECGs and teaching schematics, but expert validation is still needed.

Role of body mass index and weight change in the risk of cancer: A systematic review and meta-analysis of 66 cohort studies.

Background: This study was designed to evaluate the effects of body mass index (BMI) and weight change on the risk of developing cancer overall and cancer at different sites. Methods: We searched PubMed and other databases up to July 2023 using the keywords related to 'risk', 'cancer', 'weight', 'overweight', and 'obesity'. We identified eligible studies, and the inclusion criteria encompassed cohort studies in English that focused on cancer diagnosis and included BMI or weight change as an exposure factor. Multiple authors performed data extraction and quality assessment, and statistical analyses were carried out using RevMan and R software. We used random- or fixed-effects models to calculate the pooled relative risk (RR) or hazard ratio along with 95% confidence intervals (CIs). We used the Newcastle-Ottawa Scale to assess study quality. Results: Analysis included 66 cohort studies. Compared to underweight or normal weight, overweight or obesity was associated with an increased risk of endometrial cancer, kidney cancer, and liver cancer but a decreased risk of prostate cancer and lung cancer. Being underweight was associated with an increased risk of gastric cancer and lung cancer but not that of postmenopausal breast cancer or female reproductive cancer. In addition, weight loss of more than five kg was protective against overall cancer risk. Conclusions: Overweight and obesity increase the risk of most cancers, and weight loss of >5 kg reduces overall cancer risk. These findings provide insights for cancer prevention and help to elucidate the mechanisms underlying cancer development. Registration: Reviewregistry1786.

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.

ChatGPT's ability to generate realistic experimental images poses a new challenge to academic integrity.

The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT's writing capabilities, recent updates have integrated DALL-E 3's image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT's nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding "invisible watermarks" to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

ChatGPT can pass the AHA exams: Open-ended questions outperform multiple-choice format.

The study by Fijacko et al. tested ChatGPT's ability to pass the BLS and ACLS exams of AHA, but found that ChatGPT failed both exams. A limitation of their study was using ChatGPT to generate only one response, which may have introduced bias. When generating three responses per question, ChatGPT can pass BLS exam with an overall accuracy of 84%. When incorrectly answered questions were rewritten as open-ended questions, ChatGPT's accuracy rate increased to 96% and 92.1% for the BLS and ACLS exams, respectively, allowing ChatGPT to pass both exams with outstanding results.

Mutations in the DNA polymerase binding pathway affect the immune microenvironment of patients with small-cell lung cancer and enhance the efficacy of platinum-based chemotherapy.

Background: Small-cell lung cancer (SCLC) is characterized by its high malignancy and is associated with a poor prognosis. In the early stages of the disease, platinum-based chemotherapy is the recommended first-line treatment and has demonstrated efficacy. However, SCLC is prone to recurrence and is generally resistant to chemotherapy in its later stages. Methods: Here, we collected samples from SCLC patients who received platinum-based chemotherapy, performed genomic and transcriptomic analyses, and validated our results with publicly available data. Results: SCLC patients with DNA polymerase binding pathway mutations had an improved prognosis after platinum chemotherapy compared with patients without such mutations. Patients in the mutant (MT) group had higher infiltration of T cells, B cells, and M1 macrophages compared with patients without DNA polymerase binding pathway mutations. Conclusions: DNA polymerase binding pathway mutations can be used as prognostic markers for platinum-based chemotherapy in SCLC.

Application of ATAC-seq in tumor-specific T cell exhaustion.

Researches show that chronic viral infection and persistent antigen and/or inflammatory signal exposure in cancer causes the functional status of T cells to be altered, mainly by major changes in the epigenetic and metabolic environment, which then leads to T cell exhaustion. The discovery of the immune checkpoint pathway is an important milestone in understanding and reversing T cell exhaustion. Antibodies targeting these pathways have shown superior ability to reverse T cell exhaustion. However, there are still some limitations in immune checkpoint blocking therapy, such as the short-term nature of therapeutic effects and high individual heterogeneity. Assay for transposase-accessible chromatin with sequencing(ATAC-seq) is a method used to analyze the accessibility of whole-genome chromatin. It uses hyperactive Tn5 transposase to assess chromatin accessibility. Recently, a growing number of studies have reported that ATAC-seq can be used to characterize the dynamic changes of epigenetics in the process of T cell exhaustion. It has been determined that immune checkpoint blocking can only temporarily restore the function of exhausted T cells because of an irreversible change in the epigenetics of exhausted T cells. In this study, we review the latest developments, which provide a clearer molecular understanding of T cell exhaustion, reveal potential new therapeutic targets for persistent viral infection and cancer, and provide new insights for designing effective immunotherapy for treating cancer and chronic infection.

Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: an Asian multi-center study.

BACKGROUND: The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa. PATIENTS AND METHODS: A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses. RESULTS: Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively. CONCLUSIONS: The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.

Activation of the TGF-ß Pathway Enhances the Efficacy of Platinum-Based Chemotherapy in Small Cell Lung Cancer Patients.

Background: Platinum-based chemotherapy is the first choice of treatment for patients diagnosed with small lung cell cancer (SCLC). However, many patients exhibit resistance to it. Therefore, it is imperative to further investigate a prognostic biomarker indicating sensitivity to this therapy. Methods: We collected and performed RNA sequencing on 45 SCLC samples from the Zhujiang Hospital (Local-SCLC). In addition, we used a public cohort from George et al. as a validation cohort (George-SCLC). The transforming growth factor ß signaling pathway (TGFB) activation status was determined according to the related ssGSEA score. We analyzed immune cell ratios, pathway activation scores, and immune-related genes in SCLC patients to further elucidate the potential mechanisms. Results: A high activation status of the TGFB pathway was associated with improved prognosis in SCLC patients receiving platinum-based chemotherapy (Local-SCLC: HR = 0.0238, (95% CI, 0.13-0.84), p = 0.0238; George-SCLC: HR = 0.0315, (95% CI, 0.28-0.98), p = 0.0315). Immune infiltration analysis showed that the TGFB-HIGH group had more M1 macrophages and Th1 cells, whilst fewer M2 macrophages, Th2 cells, and Treg cells were found in the Local-SCLC cohort. Mechanistic analysis showed that the TGBF-HIGH group was upregulated in STING-mediated immunity, apoptosis, and cell cycle arrest, as well as being downregulated in the process of DNA damage repair. Conclusions: SCLC patients exhibiting a high activation status of the TGFB pathway demonstrate an improved prognosis with platinum-based chemotherapy. The potential underlying mechanism may be related to antitumor immune enhancement and DNA damage repair inhibition.

Prognosis prediction model for a special entity of gastric cancer, linitis plastica.

BACKGROUND: Gastric linitis plastica (GLP) is characteristic by its poor prognosis and highly aggressive characteristics compared with other types of gastric cancer (GC). However, the guidelines have not yet been distinguished between GLP and non-GLP. METHODS: A total of 342 eligible patients with GLP identified in the Surveillance, Epidemiology, and End Results (SEER) dataset were randomly divided into training set (n=298) and validation set (n=153). A nomogram would be developed with the constructed predicting model based on the training cohort's data, and the validation cohort would be used to validate the model. Principal component analysis (PCA) was used to evaluate the differences between groups. Cox regression and LASSO (least absolute shrinkage and selection operator) were used to construct the models. Calibration curve, time-dependent receiver operating characteristic (ROC) curve, concordance index (C-index) and decision curve analysis (DCA) were used to evaluate the predicting performance. Restricted mean survival time (RMST) was used to analyze the curative effect of adjuvant therapy. RESULTS: For patients in training cohort, univariable and multivariable Cox analyses showed that age, examined lymph nodes (LN.E), positive lymph nodes (LN.P), lesion size, combined resection, and radiotherapy are independent prognostic factors for overall survival (OS), while chemotherapy can not meet the proportional hazards (PHs) assumption; age, race, lesion size, LN.E, LN.P, combined resection and marital status are independent prognostic factors for cancer-specific survival (CSS). The C-index of the nomogram was 0.678 [95% confidence interval (CI), 0.660-0.696] and 0.673 (95% CI, 0.630-0.716) in the training and validation cohort, respectively. Meanwhile, the C-index of the CSS nomogram was 0.671 (95% CI, 0.653-0.699) and 0.650 (95% CI, 0.601-0.691) in the training and validation cohort for CSS, respectively. Furthermore, the nomogram was well calibrated with satisfactory consistency. RMST analysis further determined that chemotherapy and radiotherapy might be beneficial for improving 1- and 3-year OS and CSS, but not the 5-year CSS. CONCLUSIONS: We developed nomograms to help predict individualized prognosis for GLP patients. The new model might help guide treatment strategies for patients with GLP.

Activation of the DDR Pathway Leads to the Down-Regulation of the TGFß Pathway and a Better Response to ICIs in Patients With Metastatic Urothelial Carcinoma.

Immune checkpoint inhibitors (ICIs) have changed the treatment paradigm of metastatic urothelial carcinoma (mUC), a dominant type of bladder cancer (BC). Previous studies have shown an association between gene mutations in the DNA damage response (DDR) pathway and the immunotherapy response in mUC but have neglected the effect of the activation level of the DDR pathway on the ICI response in mUC. A published immunotherapy cohort with genome, transcriptome and survival data for 348 mUC patients was used. An external cohort (The Cancer Genome Atlas Bladder Cancer) and the GSE78220 cohort were used for validation. The activation level of the DDR pathway was quantified using single-sample gene set enrichment analysis (ssGSEA). Further analysis on the genome, immunogenicity, and the immune microenvironment was conducted using the DDR ssGSEA enrichment score-high (DSSH) group and the DDR ssGSEA enrichment score-low (DSSL) group. In the mUC cohorts, the DSSH group was associated with longer overall survival times (P=0.026; Hazard ratio=0.67; 95%CI: 0.46-0.95). The DSSH group was also associated with higher tumor mutation burden, neoantigen load, immune-activated cell patterns, and immune-related gene expression levels. The GSEA results indicated an immune activation state in DSSH group, which correlated with a down-regulation in the transforming growth factor ß receptor signaling pathway. Our study suggests that the activation level of the DDR pathway may be a novel predictive marker for immunotherapy efficacy in patients with mUC.

Distinguish the Role of Radiotherapy From Chemoradiotherapy for Gastric Cancer With Behavior of Metastasis-Indolent in Lymph Node.

BACKGROUND: Although the landmark INT-0116 trial and National Comprehensive Cancer Network (NCCN) guidelines recommended pT3-4Nx gastric cancer (GC) patients to receive chemoradiotherapy, the role of radiotherapy has not been distinguished from chemoradiotherapy. METHODS: GC with behavior of metastasis-indolent in lymph node (MILN) being confirmed with more than 15 examined LNs after gastrectomy were identified using the Surveillance, Epidemiology and End Result (SEER) database. The cancer-specific survival (CSS) of subgroups for radiotherapy, chemotherapy, chemoradiotherapy and non-adjuvant-treatment were compared. Propensity score matching (PSM) was performed between radiotherapy and non-radiotherapy subgroups to further distinguish the role of radiotherapy from chemoradiotherapy. Cox regression was performed to identify whether radiotherapy or chemotherapy could independently improve prognosis. RESULTS: We identified 690 MILN GC patients in SEER database. 5-year CSS was 71.9% in radiotherapy subgroup and 75.1% in non-radiotherapy subgroup(HR = 1.013, 95% CI = 0.714-1.438, p = 0.940), 75.6% in chemotherapy subgroup and 68.5% in non-chemotherapy subgroup(HR = 0.616, 95% CI = 0.430-0.884, p = 0.008), 52.5% in radiotherapy-alone subgroup and 71.9% in non-adjuvant treatment group (HR = 1.604, 95% CI = 0.575-4.471, p = 0.360), 72.9% in chemoradiotherapy subgroup and 79.5% in chemotherapy-alone subgroup (HR = 1.365, 95% CI = 0.859-2.172, p = 0.185), respectively. Further, PSM markedly improved balance of variables between radiotherapy subgroup and non-radiotherapy subgroup. After PSM, the role of the variables of radiotherapy and chemotherapy in contributing to improving CSS are consistent with that before PSM. Cox regression showed chemotherapy, tumor size, tumor invasiveness and Lauren classification were independent prognostic factors, but not including radiotherapy. CONCLUSIONS: Chemoradiotherapy confers superior prognosis to MILN GC patients compared with surgery alone might only be attributed to chemotherapy rather than radiotherapy.