Cross-shore parallel tidal channel systems formed by alongshore currents.

Parallel tidal channel systems, characterized by commonly cross-shore orientation and regular spacing, represent a distinct class of tidal channel networks in coastal environments worldwide. Intriguingly, these cross-shore oriented channel systems can develop in environments dominated by alongshore tidal currents, for which the mechanisms remain elusive. Here, we combine remote sensing imagery analysis and morphodynamic simulations to demonstrate that the deflection of alongshore tidal currents at transitions in bed elevation determines the characteristic orientation of the parallel tidal channels. Numerical results reveal that sharp changes in bed elevation lead to nearly 90-degree intersection angles, while smoother transitions in bed profiles result in less perpendicular channel alignments. These findings shed light on the potential manipulation of tidal channel patterns in coastal wetlands, thus equipping coastal managers with a broader range of strategies for the sustainable management of these vital ecosystems in the face of climate change and sea level rise.

De novo engineering riboflavin production Bacillus subtilis by overexpressing the downstream genes in the purine biosynthesis pathway.

BACKGROUND: Bacillus subtilis is widely used in industrial-scale riboflavin production. Previous studies have shown that targeted mutagenesis of the ribulose 5-phosphate 3-epimerase in B. subtilis can significantly enhance riboflavin production. This modification also leads to an increase in purine intermediate concentrations in the medium. Interestingly, B. subtilis exhibits remarkable efficiency in purine nucleoside synthesis, often exceeding riboflavin yields. These observations highlight the importance of the conversion steps from inosine-5'-monophosphate (IMP) to 2,5-diamino-6-ribosylamino-4(3 H)-pyrimidinone-5'-phosphate (DARPP) in riboflavin production by B. subtilis. However, research elucidating the specific impact of these reactions on riboflavin production remains limited. RESULT: We expressed the genes encoding enzymes involved in these reactions (guaB, guaA, gmk, ndk, ribA) using a synthetic operon. Introduction of the plasmid carrying this synthetic operon led to a 3.09-fold increase in riboflavin production compared to the control strain. Exclusion of gmk from the synthetic operon resulted in a 36% decrease in riboflavin production, which was further reduced when guaB and guaA were not co-expressed. By integrating the synthetic operon into the genome and employing additional engineering strategies, we achieved riboflavin production levels of 2702 mg/L. Medium optimization further increased production to 3477 mg/L, with a yield of 0.0869 g riboflavin per g of sucrose. CONCLUSION: The conversion steps from IMP to DARPP play a critical role in riboflavin production by B. subtilis. Our overexpression strategies have demonstrated their effectiveness in overcoming these limiting factors and enhancing riboflavin production.

Effects of Mindfulness-Based Cancer Recovery training on anxiety, depression, post-traumatic stress disorder, and cancer-related fatigue in breast neoplasm patients undergoing chemotherapy.

RATIONALE: Mindfulness-Based Cancer Recovery (MBCR) program is a group course training for cancer patients that combines cancer knowledge and psychological knowledge, emphasizing focusing on the psychosomatic symptoms of cancer patients. Currently, the application value of Mindfulness-Based Cancer Recovery in improving psychosomatic health of cancer patients has been confirmed, however, its intervention effect on breast neoplasm patients has not yet been widely studied in China. PATIENT CONCERNS AND DIAGNOSES: This study introduced the Mindfulness-Based Cancer Recovery protocol into the rehabilitation process of breast cancer patients, aiming to elucidate the effects of Mindfulness-Based Cancer Recovery on anxiety, depression, post-traumatic stress disorder, and cancer-related fatigue in breast neoplasm patients, to provide a practical basis for improving the physical and mental health of breast cancer patients. INTERVENTION: In this study, 80 patients with chemotherapy-stage breast neoplasm attending the oncology department of a tertiary-level hospital from January 2022 to December 2022 were selected, 40 patients attending from January 2022 to June 2022 were included in the study group, and 40 patients attending from July 2022 to December 2022 were included in the control group. The control group was administered conventional care, and the study group was administered Mindfulness-Based Cancer Recovery based on conventional care in the control group for 8 weeks. After the intervention, hospital anxiety and depression scale, impact of event scale-revised, and cancer fatigue scale were used for evaluation. OUTCOMES: After the intervention, hospital anxiety and depression scale scores decreased in both groups compared with pre-intervention, with the study group scoring lower than the control group (P < .05). After the intervention, the impact of event scale-revised scores of the 2 groups decreased from the preintervention period, with the study group scoring lower than the control group (P < .05). After the intervention, cancer fatigue scale scores decreased in the 2 groups compared with the preintervention period, with the study group scoring lower than the control group (P < .05). LESSONS: Mindfulness-Based Cancer Recovery can effectively reduce the levels of anxiety, depression and post-traumatic stress disorder in breast neoplasm patients undergoing chemotherapy, reduce the levels of cancer-related fatigue, and promote the physical and mental health of patients.

Ucf-101 alleviates Ischaemia/Reperfusion induced retinal inflammation and injury via suppressing oxidative damage.

The Omi/HtrA2 inhibitor 5-[5-(2-nitrophenyl) furfuryliodine]-1,3-diphenyl-2-thiobarbituric acid (Ucf-101) has shown neuroprotective effects in the central nervous system. However, whether Ucf-101 can protect retinal ganglion cells (RGCs) after retinal ischemia/reperfusion (IR) has not been investigated. We aimed to investigate the effects of Ucf-101 on RGCs apoptosis and inflammation after IR-induced retinal injury in mice. We injected Ucf-101 into the mouse vitreous body immediately after IR injury. After 7 days, hematoxylin and eosin staining was conducted to assess retinal tissue damage. Next, retrograde labeling with FluoroGold, counting of RGCs and TUNEL staining were conducted to evaluate apoptosis. Immunohistochemistry, immunofluorescence staining, and western blotting were conducted to analyze protein levels. IR injury-induced retinal tissue damage could be prevented by Ucf-101 treatment. The number of TUNEL-positive RGCs was reduced by Ucf-101 treatment in mice with IR injury. Ucf-101 treatment inhibited the upregulation of Bax, cleaved caspase-3 and cleaved caspase-9 and activated the JNK/ERK/P38 signaling pathway. Furthermore, Ucf-101 treatment inhibited the upregulation of glial fibrillary acidic protein (GFAP), vimentin, Iba1 and CD68 in mice with IR injury. Ucf-101 prevents retinal tissue damage, improves the survival of RGCs, and suppresses microglial overactivation after IR injury. Ucf-101 might be a potential target to prevent RGCs apoptosis and inflammation in neurodegenerative eye diseases.

Glucoconjugated monoterpene indole alkaloids with xanthine oxidase inhibitory activity from Ophiorrhiza japonica.

Continued interest in the bioactive alkaloids led to the isolation of five undescribed alkaloids (1-5), ophiorglucidines A-E, and seven known analogues (6-12) from the water-soluble fraction of Ophiorrhiza japonica. The structures were elucidated based on spectroscopic data and quantum calculations as well as X-ray crystallographic analysis. The structure of 1 was characterized as a hexacyclic skeleton including a double bridge linking the indole and the monoterpene moieties, which is the first report of a single crystal with this type of structure. Moreover, the inhibitory effect of zwitterionic indole alkaloid glycosides on xanthine oxidase was found for the first time. The alkaloids 2 and 3, both of which have a pentacyclic zwitterionic system, were more active than the reference inhibitor, allopurinol (IC50 = 11.1 µM) with IC50 values of 1.0 µM, and 2.5 µM, respectively. Structure-activity relationships analyses confirmed that the carbonyl group at C-14 was a key functional group responsible for the inhibitory effects of these alkaloids.

Spatiotemporal transcriptomic landscape of rice embryonic cells during seed germination.

Characterizing cellular features during seed germination is crucial for understanding the complex biological functions of different embryonic cells in regulating seed vigor and seedling establishment. We performed spatially enhanced resolution omics sequencing (Stereo-seq) and single-cell RNA sequencing (scRNA-seq) to capture spatially resolved single-cell transcriptomes of germinating rice embryos. An automated cell-segmentation model, employing deep learning, was developed to accommodate the analysis requirements. The spatial transcriptomes of 6, 24, 36, and 48 h after imbibition unveiled both known and previously unreported embryo cell types, including two unreported scutellum cell types, corroborated by in situ hybridization and functional exploration of marker genes. Temporal transcriptomic profiling delineated gene expression dynamics in distinct embryonic cell types during seed germination, highlighting key genes involved in nutrient metabolism, biosynthesis, and signaling of phytohormones, reprogrammed in a cell-type-specific manner. Our study provides a detailed spatiotemporal transcriptome of rice embryo and presents a previously undescribed methodology for exploring the roles of different embryonic cells in seed germination.

Exploring the interplay between angiosperm chlorophyll metabolism and environmental factors.

MAIN CONCLUSION: In this review, we summarize how chlorophyll metabolism in angiosperm is affected by the environmental factors: light, temperature, metal ions, water, oxygen, and altitude. The significance of chlorophyll (Chl) in plant leaf morphogenesis and photosynthesis cannot be overstated. Over time, researchers have made significant advancements in comprehending the biosynthetic pathway of Chl in angiosperms, along with the pivotal enzymes and genes involved in this process, particularly those related to heme synthesis and light-responsive mechanisms. Various environmental factors influence the stability of Chl content in angiosperms by modulating Chl metabolic pathways. Understanding the interplay between plants Chl metabolism and environmental factors has been a prominent research topic. This review mainly focuses on angiosperms, provides an overview of the regulatory mechanisms governing Chl metabolism, and the impact of environmental factors such as light, temperature, metal ions (iron and magnesium), water, oxygen, and altitude on Chl metabolism. Understanding these effects is crucial for comprehending and preserving the homeostasis of Chl metabolism.

Neoadjuvant toripalimab combined with axitinib in patients with locally advanced clear cell renal cell carcinoma: a single-arm, phase II trial.

BACKGROUND: A combination of axitinib and immune checkpoint inhibitors (ICIs) demonstrated promising efficacy in the treatment of advanced renal cell carcinoma (RCC). This study aims to prospectively evaluate the safety, efficacy, and biomarkers of neoadjuvant toripalimab plus axitinib in non-metastatic clear cell RCC. METHODS: This is a single-institution, single-arm phase II clinical trial. Patients with non-metastatic biopsy-proven clear cell RCC (T2-T3N0-1M0) are enrolled. Patients will receive axitinib 5 mg twice daily combined with toripalimab 240 mg every 3 weeks (three cycles) for up to 12 weeks. Patients then will receive partial (PN) or radical nephrectomy (RN) after neoadjuvant therapy. The primary endpoint is objective response rate (ORR). Secondary endpoints include disease-free survival, safety, and perioperative complication rate. Predictive biomarkers are involved in exploratory analysis. RESULTS: A total of 20 patients were enrolled in the study, with 19 of them undergoing surgery. One patient declined surgery. The primary endpoint ORR was 45%. The posterior distribution of πORR had a mean of 0.44 (95% credible intervals: 0.24-0.64), meeting the predefined primary endpoint with an ORR of 32%. Tumor shrinkage was observed in 95% of patients prior to nephrectomy. Furthermore, four patients achieved a pathological complete response. Grade ≥3 adverse events occurred in 25% of patients, including hypertension, hyperglycemia, glutamic pyruvic transaminase/glutamic oxaloacetic transaminase (ALT/AST) increase, and proteinuria. Postoperatively, one grade 4a and eight grade 1-2 complications were noted. In comparison to patients with stable disease, responders exhibited significant differences in immune factors such as Arginase 1(ARG1), Melanoma antigen (MAGEs), Dendritic Cell (DC), TNF Superfamily Member 13 (TNFSF13), Apelin Receptor (APLNR), and C-C Motif Chemokine Ligand 3 Like 1 (CCL3-L1). The limitation of this trial was the small sample size. CONCLUSION: Neoadjuvant toripalimab combined with axitinib shows encouraging activity and acceptable toxicity in locally advanced clear cell RCC and warrants further study. TRIAL REGISTRATION NUMBER: clinicaltrials.gov, NCT04118855.

A longitudinal study of the brain structure network changes in HIV patients with ANI: combined VBM with SCN.

Background: Despite the widespread adoption of combination antiretroviral therapy (cART) in managing HIV, the virus's impact on the brain structure of patients remains significant. This study aims to longitudinally explore the persistent effects of HIV on brain structure, focusing on changes in gray matter volume (GMV) and structural covariance network (SCN) among patients at the Asymptomatic Neurocognitive Impairment (ANI) stage. Methods: This research involved 45 HIV patients diagnosed with ANI and 45 demographically matched healthy controls (HCs). The participants were observed over a 1.5-year period. Differences in GMV between groups were analyzed using voxel-based morphometry (VBM), while the graph theory model facilitated the establishment of topological metrics for assessing network indices. These differences were evaluated using two-sample t-tests and paired-sample t-tests, applying the network-based statistics method. Additionally, the study examined correlations between GMV and cognitive performance, as well as clinical variables. Results: Compared with HCs, HIV patients demonstrated reduced GMV in the right middle temporal gyrus and left middle frontal gyrus (FWE, p < 0.05), along with decreased betweenness centrality (BC) in the left anterior cingulate and paracingulate cortex. Conversely, an increase in the clustering coefficient (Cp) was observed (FDR, p < 0.05). During the follow-up period, a decline in GMV in the right fusiform gyrus (FWE, p < 0.05) and a reduction in node efficiency (Ne) in the triangular part of the inferior frontal gyrus were noted compared with baseline measurements (FDR, p < 0.05). The SCN of HIV patients exhibited small-world properties across most sparsity levels (Sigma >1), and area under the curve (AUC) analysis revealed no significant statistical differences between groups. Conclusion: The findings suggest that despite the administration of combination antiretroviral therapy (cART), HIV continues to exert slow and sustained damage on brain structures. However, when compared to HCs, the small-world properties of the patients' SCNs did not significantly differ, and the clustering coefficient, indicative of the overall information-processing capacity of the brain network, was slightly elevated in HIV patients. This elevation may relate to compensatory effects of brain area functions, the impact of cART, functional reorganization, or inflammatory responses.

Effects of plyometric training on health-related physical fitness in untrained participants: a systematic review and meta-analysis.

Plyometric training (PT) is an effective training method for improving physical fitness among trained individuals; however, its impact on health-related physical fitness in untrained participants remains ambiguous. Therefore, this meta-analysis aimed to evaluate the effects of PT on health-related physical fitness among untrained participants. Six electronic databases (PubMed, CINAHL Plus, MEDLINE Complete, Web of Science Core Collection, SCOPUS, and SPORTDiscus) were systematically searched until March 2024. We included controlled trials that examined the effects of PT on health-related physical fitness indices in untrained participants. Twenty-one studies were eligible, including a total of 1263 participants. Our analyses revealed small to moderate effects of PT on body mass index, muscular strength, cardiorespiratory fitness, and flexibility (ES = 0.27-0.61; all p > 0.05). However, no significant effects were detected for body fat percentage and lean mass (ES = 0.21-0.41; all p > 0.05). In conclusion, the findings suggest that PT may be potentially effective in improving health-related physical fitness indices (i.e., body mass index, muscular strength, cardiorespiratory fitness, and flexibility) in untrained participants. However, the results should be interpreted cautiously due to data limitations in some fitness variables.

Air trap and removal on a pressure driven PDMS-based microfluidic device.

With the development of microfluidic technology, microfluidic chips have played a positive role in applications such as cell culture, microfluidic PCR, and nanopore gene sequencing. However, the presence of bubbles interferes with fluid flow and has a significant impact on experimental results. There are many reasons for the generation of bubbles in microfluidic chips, such as pressure changes inside the chip, air vibration inside the chip, and the open chip guiding air into the chip when driving fluid. This study designed and prepared a microfluidic device based on polydimethylsiloxane. First, air was actively introduced into the microfluidic chip, and bubbles were captured through the microfluidic device to simulate the presence of bubbles inside the chip in biological experiments. To remove bubbles trapped in the microfluidic chip, distilled water, distilled water containing surfactants, and mineral oil were pumped into the microfluidic chip. We compared and discussed the bubble removal efficiency under different driving fluids, driving pressures, and open/closed channel configurations. This study helps to understand the mechanism of bubble formation and removal in microfluidic devices, optimize chip structure design and experimental reagent selection, prevent or eliminate bubbles, and reduce the impact of bubbles on experiments.

MRI-based radiomic models for the preoperative prediction of extramural venous invasion in rectal cancer: A systematic review and meta-analysis.

AIM: To estimate the diagnostic value of magnetic resonance imaging (MRI)-based radiomic models in detecting the extramural venous invasion (EMVI) of rectal cancer. MATERIALS AND METHODS: Appropriate studies in multiple electronic databases were systematically retrieved. The Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score (RQS) were used to evaluate the eligible studies' methodology quality. Summary accuracy metrics were calculated, and the publication bias was detected using Deek's funnel plot. The sensitivity and meta-regression analysis were performed to investigate the causes of heterogeneity. RESULTS: For the seven eligible studies, which included 1175 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.80 (95 % CI, 0.70-0.88), 0.89 (95 % CI, 0.84-0.92), 7.0 (95 % CI, 4.7, 10.4), 0.22 (95 % CI, 0.14, 0.34), and 32 (95 % CI, 16, 65), respectively. The area under the receiver operating characteristic curve (AUC) was 0.91 (95 % CI, 0.88, 0.93). Moderate heterogeneity was found due to I2 values of 38.63 % and 32.29 % in sensitivity and specificity, respectively. Meta-regression analysis suggested that the patient enrollment, number of patients, segmentation method, and RQS score were the source of the heterogeneity. The head-to-head analysis suggested that radiomics model had a higher sensitivity for detection of EMVI than subjective evaluation by radiologist (0.47 vs. 0.73, p ≤ 0.001). CONCLUSION: Our study suggests that MRI-based radiomic models have good diagnostic value in detecting EMVI for rectal cancer patients. Nevertheless, more prospective and high-quality studies with larger sample sizes are needed in the future to validate these results.

An innovative prognostic auxiliary for colon adenocarcinoma based on zinc finger protein genes.

Background: The carcinogenesis and progression of colon adenocarcinoma (COAD) are intensively related to the abnormal expression of the zinc finger (ZNF) protein genes. We aimed to employ these genes to provide a reliable prognosis and treatment stratification tool for COAD patients. Methods: Cox and the least absolute shrinkage and selection operator (LASSO) regression analysis were applied, utilizing The Cancer Genome Atlas (TCGA) metadata, to build a ZNF protein gene-based prognostic model. Using this model, patients in the training cohort and testing cohort (GSE17537) were labelled as either high or low risk. Kaplan-Meier (KM) survival analysis and time-dependent receiver operating characteristic (ROC) curve analysis were performed in the patients with opposite risk status to assess the predictive ability in each cohort. The potentiality of the mechanism was explored by the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the degrees of expression of model genes were validated by immunohistochemistry (IHC). Results: The prognostic model consisting of INSM1, PHF21B, RNF138, SYTL4, WRNIP1, ZNF585B, and ZNF514, classified patients into opposite risk statuses. Patients in the high-risk subset had a considerably lower chance of surviving compared to those in the low-risk subset. There is a high probability that these model genes were attached to immune-related biological processes, which can be confirmed by the results of the above mechanistic methods. Moreover, patients in the low-risk subset also significantly outperformed the patients in the high-risk subset when calculating immune cells and function scores. Drug sensitivity and tumor immune dysfunction and exclusion (TIDE) analyses showed a clear difference in the immunological and chemotherapeutic efficacy predictions within the two risk groups. Additionally, the degrees of expression of model genes in high-risk and low-risk subsets presented great discrepancies. Conclusions: The signature may be applied as a predictive classifier to shepherd special medication for COAD patients.

Unsupervised low-dose CT denoising using bidirectional contrastive network.

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) scans significantly reduce radiation exposure, but introduce higher levels of noise and artifacts that compromise image quality and diagnostic accuracy. Supervised learning methods have proven effective in denoising LDCT images, but are hampered by the need for large, paired datasets, which pose significant challenges in data acquisition. This study aims to develop a robust unsupervised LDCT denoising method that overcomes the reliance on paired LDCT and normal-dose CT (NDCT) samples, paving the way for more accessible and practical denoising techniques. METHODS: We propose a novel unsupervised network model, Bidirectional Contrastive Unsupervised Denoising (BCUD), for LDCT denoising. This model innovatively combines a bidirectional network structure with contrastive learning theory to map the precise mutual correspondence between the noisy LDCT image domain and the clean NDCT image domain. Specifically, we employ dual encoders and discriminators for domain-specific data generation, and use unique projection heads for each domain to adaptively learn customized embedded representations. We then align corresponding features across domains within the learned embedding spaces to achieve effective noise reduction. This approach fundamentally improves the model's ability to match features in latent space, thereby improving noise reduction while preserving fine image detail. RESULTS: Through extensive experimental validation on the AAPM-Mayo public dataset and real-world clinical datasets, the proposed BCUD method demonstrated superior performance. It achieved a peak signal-to-noise ratio (PSNR) of 31.387 dB, a structural similarity index measure (SSIM) of 0.886, an information fidelity criterion (IFC) of 2.305, and a visual information fidelity (VIF) of 0.373. Notably, subjective evaluation by radiologists resulted in a mean score of 4.23, highlighting its advantages over existing methods in terms of clinical applicability. CONCLUSIONS: This paper presents an innovative unsupervised LDCT denoising method using a bidirectional contrastive network, which greatly improves clinical applicability by eliminating the need for perfectly matched image pairs. The method sets a new benchmark in unsupervised LDCT image denoising, excelling in noise reduction and preservation of fine structural details.

A maize seed variety identification method based on improving deep residual convolutional network.

Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network's learning process and enabled more efficient information propagation across the network layers. Meanwhile, in the later residual blocks of the model, we introduced both the efficient channel attention (ECA) mechanism and depthwise separable (DS) convolution, which reduced the model's parameter cost and enabled the capturing of more precise and detailed features. Finally, a Swish-PReLU mixed activation function was introduced globally to improve the overall predictive power of the model. The results showed that our model achieved an impressive accuracy of 91.23% in corn seed classification, surpassing other related models. Compared with the original model, our model improved the accuracy by 7.07%, reduced the loss value by 0.19, and decreased the number of parameters by 40%. The research suggested that this method can efficiently classify corn seeds, holding significant value in seed variety identification.

All-visible-light-driven stiff-stilbene photoswitches.

Molecular photoswitches are potent tools to construct dynamic functional systems and responsive materials that can be controlled in a non-invasive manner. As P-type photoswitches, stiff-stilbenes attract increasing interest, owing to their superiority in quantum yield, significant geometric differences between isomers, excellent thermostability and robust switching behavior. Nevertheless, the UV-light-triggered photoisomerization of stiff-stilbenes has been a main drawback for decades as UV light is potentially harmful and has low penetration depth. Here, we provided a series of para-formylated stiff-stilbenes by Rieche ortho-formylation to achieve all-visible-light-responsiveness. Additional phenolic groups provide access to late-stage chemical modification facilitating design of molecules responsive to visible light. Remarkably, the photoisomerization of aldehyde-appended stiff-stilbenes could be fully manipulated using visible light, accompanied by a high photostationary state (PSS) distribution. These features render them excellent candidates for future visible-light-controllable smart materials and dynamic systems.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. OBJECTIVE: This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. METHODS: In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. RESULTS: A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. CONCLUSIONS: The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.