Construction of indolizine scaffolds from α,ω-alkynoic acids and α,ω-vinylamines via sequential-relay catalysis in "one pot".

A simple and efficient method has been developed for the synthesis of a diverse range of aryl-fused indolizin-3-ones through sequential Au(I)-catalyzed hydrocarboxylation, aminolysis, and cyclization, followed by ruthenium-catalyzed ring-closing metathesis. Moderate to good yields were observed with satisfactory substrate scope and functional group tolerance. The developed protocol represents a practical strategy for the construction of bioactive aryl-fused indolizin-3-ones.

"One pot" synthesis of quinazolinone-[2,3]-fused polycyclic scaffolds in a three-step reaction sequence.

Diverse quinazolinone-[2,3]-fused polycyclic skeletons occupy a prominent position in drug discovery. Even with currently available methods there still remain unmet needs for flexible access to such structures. Herein, we have explored a mild "one pot" procedure for the construction of various quinazolinone-[2,3]-fused polycycles. The procedure involves Pd-catalyzed carbonylation of N-(2-iodophenyl)acetamides, release of the masked terminal amine, and two sequential and spontaneous cyclizations. This generally applicable approach features easy assembly of precursors from readily available starting materials, mild reaction conditions, non-cumbersome operation, and polycyclic diversity.

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.

Synthesis, biological evaluation, and molecular modeling of ORM-10921 and its analogs as α2C -adrenoceptor antagonists.

The α2C -adrenoceptor (α2C -AR) is regarded as one of the potential targets for antipsychotics. A few of structurally diverse α2C -AR antagonists have been reported, among which ORM-10921, containing one rigid tetracyclic framework with two neighboring chiral centers, has exhibited remarkable antipsychotic-like effects and pro-cognitive properties in different animal models. Yet the binding mode of ORM-10921 remains elusive. In this study, all of its four stereoisomers and a set of its analogs were synthesized and in vitro evaluated for their α2C -AR antagonist activities. The molecular docking study and hydration site analysis gave a rational explanation for the biological results, which might provide helpful insights into the binding mode and future optimization.

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction.

Accurate hyperspectral remote sensing information is essential for feature identification and detection. Nevertheless, the hyperspectral imaging mechanism poses challenges in balancing the trade-off between spatial and spectral resolution. Hardware improvements are cost-intensive and depend on strict environmental conditions and extra equipment. Recent spectral imaging methods have attempted to directly reconstruct hyperspectral information from widely available multispectral images. However, fixed mapping approaches used in previous spectral reconstruction models limit their reconstruction quality and generalizability, especially dealing with missing or contaminated bands. Moreover, data-hungry issues plague increasingly complex data-driven spectral reconstruction methods. This paper proposes SpectralMAE, a novel spectral reconstruction model that can take arbitrary combinations of bands as input and improve the utilization of data sources. In contrast to previous spectral reconstruction techniques, SpectralMAE explores the application of a self-supervised learning paradigm and proposes a masked autoencoder architecture for spectral dimensions. To further enhance the performance for specific sensor inputs, we propose a training strategy by combining random masking pre-training and fixed masking fine-tuning. Empirical evaluations on five remote sensing datasets demonstrate that SpectralMAE outperforms state-of-the-art methods in both qualitative and quantitative metrics.

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.

[Analysis of signal peptides and secreted proteins in the whole proteome of Pichia pastoris].

The signal peptide is a key factor that affects the efficiency of protein secretion in Pichia pastoris. Currently, the most used signal peptide is the α-mating factor (MFα) pre-pro leader from Saccharomyces cerevisiae. This exogenous signal peptide has been successfully utilized to express and secret many heterologous proteins. However, MFα is not suitable for the secretory expression of all heterologous proteins. Many typical signal peptides are present in the secretory proteins of P. pastoris, which provides more options besides MFα. Therefore, it is necessary to analyze and identify more efficient endogenous signal peptides that can guide the secretion of heterologous proteins in P. pastoris. In this study, we employed bioinformatics tools such as SignalP, TMHMM, Phobius, WoLF PSORT, and NetGPI to predict endogenous signal peptides from the entire proteome of P. pastoris GS115 (ATCC 20864). Moreover, we analyzed the distribution, length, amino acid composition, and conservation of these signal peptides. Additionally, we screened 69 secreted proteins and their signal peptides, and through secretome validation, we identified 10 endogenous signal peptides that have potential to be used for exogenous protein expression. The endogenous signal peptides obtained in this study may serve as new valuable tools for the expression and secretion of heterologous proteins in P. pastoris.

Multimodal Approach in the Diagnosis of Urologic Malignancies: Critical Assessment of ChatGPT-4V's Image-Reading Capabilities.

ChatGPT-4V model with image interpretation tested for distinguishing kidney & prostate tumors from normal tissue.

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.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets.

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

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.

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.

Step into the era of large multimodal models: a pilot study on ChatGPT-4V(ision)'s ability to interpret radiological images.

BACKGROUND: The introduction of ChatGPT-4V's 'Chat with images' feature represents the beginning of the era of large multimodal models (LMMs), which allows ChatGPT to process and answer questions based on uploaded images. This advancement has the potential to transform how surgical teams utilize radiographic data, as radiological interpretation is crucial for surgical planning and postoperative care. However, a comprehensive evaluation of ChatGPT-4V's capabilities in interpret radiological images and formulating treatment plans remains to be explored. PATIENTS AND METHODS: Three types of questions were collected: (1) 87 USMLE-style questions, submitting only the question stems and images without providing options to assess ChatGPT's diagnostic capability. For questions involving treatment plan formulations, a five-point Likert scale was used to assess ChatGPT's proposed treatment plan. The 87 questions were then adapted by removing detailed patient history to assess its contribution to diagnosis. The diagnostic performance of ChatGPT-4V was also tested when only medical history was provided. (2) We randomly selected 100 chest radiography from the ChestX-ray8 database to test the ability of ChatGPT-4V to identify abnormal chest radiography. (3) Cases from the 'Diagnose Please' section in the Radiology journal were collected to evaluate the performance of ChatGPT-4V in diagnosing complex cases. Three responses were collected for each question. RESULTS: ChatGPT-4V achieved a diagnostic accuracy of 77.01% for USMLE-style questions. The average score of ChatGPT-4V's treatment plans was 3.97 (Interquartile Range: 3.33-4.67). Removing detailed patient history dropped the diagnostic accuracy to 19.54% (P<0.0001). ChatGPT-4V achieved an AUC of 0.768 (95% CI: 0.684-0.851) in detecting abnormalities in chest radiography, but could not specify the exact disease due to the lack of detailed patient history. For cases from 'Diagnose Please' ChatGPT provided diagnoses consistent with or very similar to the reference answers. CONCLUSION: ChatGPT-4V demonstrated an impressive ability to combine patient history with radiological images to make diagnoses and directly design treatment plans based on images, suggesting its potential for future application in clinical practice.

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.