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The absence of standardized protocols for integrating end-stage renal disease patient data into AI models has constrained the potential of AI in enhancing patient care. Here, we present a protocol for processing electronic medical records from 1,336 peritoneal dialysis patients with more than 10,000 follow-up records. We describe steps for environment setup and transforming records into analyzable formats. We then detail procedures for developing a directly usable dataset for training AI models to predict one-year all-cause mortality risk. For complete details on the use and execution of this protocol, please refer to Ma et al.1.

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Background: Overexpression of monopolar spindle 1 (MPS1) and histone deacetylase 8 (HDAC8) is associated with the proliferation of liver cancer cells, so simultaneous inhibition of both MPS1 and HDAC8 could offer a promising therapeutic approach for the treatment of liver cancer. Dual-targeted MPS1/HDAC8 inhibitors have not been reported. Methods: A combined approach of pharmacophore modeling and molecular docking was used to identify potent dual-target inhibitors of MPS1 and HDAC8. Enzyme inhibition assays were performed to evaluate the optimal compound with the strongest inhibitory activity against MPS1 and HDAC8. The selectivity of MPH-5 for MPS1 and HDAC8 was assessed on a panel of 68 kinases and other histone deacetylases. Subsequently, molecular dynamics (MD) simulation verified the binding stability of the optimal compound to MPS1 and HDAC8. Ultimately, in vitro cellular assays and in vivo antitumor assays evaluated the antitumor efficacy of the most promising compound for the treatment of hepatocellular carcinoma. Results: Six dual-target compounds (MPHs 1-6) of both MPS1 and HDAC8 were identified from the database using a combined virtual screening protocol. Notably, MPH-5 showed nanomolar inhibitory effect on both MPS1 (IC50 = 4.52 ± 0.21 nM) and HDAC8 (IC50 = 6.07 ± 0.37 nM). MD simulation indicated that MPH-5 stably binds to both MPS1 and HDAC8. Importantly, cellular assays revealed that MPH-5 exhibited significant antiproliferative activity against human liver cancer cells, especially HepG2 cells. Moreover, MPH-5 exhibited low toxicity and high efficacy against tumor cells, and it overcomes drug resistance to some extent. In addition, MPH-5 may exert its antitumor effects by downregulating MPS1-driven phosphorylation of histone H3 and upregulating HDAC8-mediated K62 acetylation of PKM2. Furthermore, MPH-5 showed potent inhibition of HepG2 xenograft tumor growth in mice with no apparent toxicity and presented favorable pharmacokinetics. Conclusion: The study suggests that MPH-5 is a potent, selective, high-efficacy, and low-toxicity antitumor candidate for the treatment of hepatocellular carcinoma.

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Background: An intra-aortic balloon pump (IABP) is a mechanical circulatory device frequently used in patients undergoing coronary artery bypass grafting (CABG). As a treatment for perioperative haemodynamic instability, IABP insertion often implicates an adverse outcome. This study aimed to investigate the age- and sex-related disparity in risk factors for perioperative IABP insertion in CABG patients. Methods: A total of 2,460 CABG patients were included and divided into subgroups by age (elderly subgroup, ≥65 years; young subgroup, <65 years) and sex. Basic characteristics were compared between IABP and non-IABP patients in the overall patient group and the subgroups. Multivariate logistic analysis was used to investigate the significant risk factors for perioperative IABP application, and interaction effects among the potential risk factors were analysed. Combined receiver operating characteristic analysis was used to evaluate the prediction value of combined risk factors. Results: The overall patient group had a mean age of 61.5 years. The application rate of perioperative IABP was 8.0%. A left ventricular ejection fraction (LVEF) <50% significantly correlated with perioperative IABP application in the overall patient group and the subgroups. Traditional factors such as myocardial infarction history, atrial fibrillation history, and intraoperative estimated blood loss were significant risk factors in certain subgroups. Small dense low-density lipoprotein levels were significantly associated with IABP insertion in the male subgroup and young subgroup. The area under the curve of combined risk factors was significantly higher than that of LVEF <50% alone in the overall patient group and subgroups. Conclusion: Age- and sex-related differences were present in the risk factor distribution for perioperative IABP insertion in CABG patients.

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Pancreatic cancer is an aggressive and challenging malignancy with limited treatment options, largely attributed to the dense tumor stroma and intrinsic drug resistance. Here, we introduce a novel iron-containing nanoparticle formulation termed PTFE, loaded with the ferroptosis inducer Erastin, to overcome these obstacles and enhance pancreatic cancer therapy. The PTFE nanoparticles were prepared through a one-step assembly process, consisting of an Erastin-loaded PLGA core stabilized by a MOF shell formed by coordination between Fe3+ and tannic acid. PTFE demonstrated a unique capability to repolarize tumor-associated macrophages (TAMs) into the M1 phenotype, leading to the regulation of dense tumor stroma by modulating the activation of tumor-associated fibroblasts (TAFs) and reducing collagen deposition. This resulted in enhanced nanoparticle accumulation and deep penetration, as confirmed by in vitro multicellular tumor spheroids and in vivo mesenchymal-rich subcutaneous pancreatic tumor models. Moreover, PTFE effectively combated tumor resistance by synergistically employing the Fe3+-induced Fenton reaction and Erastin-induced ferroptosis, thereby disrupting the redox balance. As a result, significant tumor growth inhibition was achieved in mice-bearing tumor model. Comprehensive safety evaluations demonstrated PTFE's favorable biocompatibility, highlighting its potential as a promising therapeutic platform to effectively address the formidable challenges in pancreatic cancer treatment.

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Transesophageal echocardiography (TEE) shows pericardial effusion and a gap between the left atrium and the aortic sinus by atrial septal defect occluder.

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BACKGROUND: Leaf feeders, such as Spodoptera frugiperda and Spodoptera litura, and stem borers Ostrinia furnacalis and Chilo suppressalis, occupy two different niches and are well adapted to their particular environments. Borer larvae burrow and inhabit the interior of stems, which are relatively dark. By contrast, the larvae of leaf feeders are exposed to sunlight during feeding. We therefore designed series of experiments to evaluate the effect of light intensity (0, 2000, and 10 000 lx) on these pests with different feeding modes. RESULTS: The development of all four pests was significantly delayed at 0 lx. Importantly, light intensity affected the development of both male and female larvae of borers, but only significantly affected male larvae of leaf feeders. Furthermore, the proportion of female offspring of leaf feeders increased with increasing light intensity (S. frugiperda: 33.89%, 42.26%, 57.41%; S. litura: 38.90%, 51.75%, 65.08%), but no significant differences were found in stem borers. This research also revealed that the survival rate of female leaf feeders did not vary across light intensities, but that of males decreased with increasing light intensity (S. frugiperda: 97.78%, 85.86%, 61.21%; S. litura: 95.83%, 73.54%, 58.99%). CONCLUSION: These results improve our understanding of how light intensity affects sex differences in important lepidopteran pests occupying different feeding niches and their ecological interactions with abiotic factors in agroecosystems. © 2024 Society of Chemical Industry.

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The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks-outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care-which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling.

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The study aims to develop AICare, an interpretable mortality prediction model, using electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) patients. AICare includes a multichannel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AICare outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships and variations in feature importance and provides reference values. An AI-doctor interaction system is developed for visualizing patients' health trajectories and risk indicators.

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Objective: Arterial stiffness is an important tissue biomarker of the progression of atherosclerotic diseases. Brachial-ankle pulse wave velocity (ba-PWV) is a gold standard of arterial stiffness measurement widely used in Asia. Changes in vascular wall shear stress (WSS) lead to artery wall remodeling, which could give rise to an increase in arterial stiffness. The study aimed to explore the association between ba-PWV and common carotid artery (CCA) WSS measured by a newly invented vascular vector flow mapping (VFM) technique. Methods: We included 94 subjects free of apparent cardiovascular disease (CVD) and divided them into a subclinical atherosclerosis (SA) group (N = 47) and non subclinical atherosclerosis (NSA) group (N = 47). CCA WSS was measured using the VFM technique. Bivariate correlations between CCA WSS and other factors were assessed with Pearson's, Spearman's, or Kendall's coefficient of correlation, as appropriate. Partial correlation analysis was conducted to examine the influence of age and sex. Multiple linear stepwise regression was used for the analysis of independent determinants of CCA WSS. Receiver operating characteristic (ROC) analysis was performed to find the association between CCA WSS and 10-year CVD risk. Results: The overall subjects had a mean age of 47.9 ± 11.2 years, and males accounted for 52.1%. Average systolic CCA WSS was significantly correlated with ba-PWV (r = -0.618, p < 0.001) in the SA group. Multiple linear stepwise regression analysis confirmed that ba-PWV was an independent determinant of average systolic CCA WSS (ß = -0.361, p = 0.003). The area under the curve (AUC) of average systolic CCA WSS for 10-year CVD risk ≥10% was 0.848 (p < 0.001) in the SA group. Conclusions: Average systolic CCA WSS was significantly correlated with ba-PWV and was associated with 10-year CVD risk ≥10% in the SA group. Therefore, CCA WSS measured by the VFM technique could be used for monitoring and screening subjects with potential CVD risks.

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OBJECTIVES: Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons. MATERIALS AND METHODS: This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features. RESULTS: We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05. CONCLUSION: Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

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Tetracycline (TC) is a widely used antibiotic that adversely affects ecosystems and, therefore, must be removed from the environment. Owing to their strong ability to oxidise pollutants, including antibiotics, and selectivity for these pollutants, an improved oxidation method based on sulphate radicals (SO4·-) has gained considerable interest. In this study, a novel technique for removing TC was developed by activating peroxymonosulphate (PMS) using a ZnFe2O4 catalyst. Using the co-precipitation method, a ZnFe2O4 catalyst was prepared by doping zinc into iron-based materials, which increased the redox cycle, while PMS was active and facilitated the production of free radicals. According to electron paramagnetic resonance spectroscopy results, a ZnFe2O4 catalyst may activate PMS and generate SO4·-, HO·, O2·-, and 1O2 to eliminate TC. This research offers a new method for creating highly effective heterogeneous catalysts that can activate PMS and destroy antibiotics. The study proposes the following degradation pathways: hydroxylation and ring-opening of TC based on the products identified using ultra-performance liquid chromatography-mass spectrometry. These results illustrated that the prepared ZnFe2O4 catalyst effectively removed TC and exhibited excellent catalytic performance.

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BACKGROUND: In recent years, computer-assisted intervention and robot-assisted surgery are receiving increasing attention. The need for real-time identification and tracking of surgical tools and tool tips is constantly demanding. A series of researches focusing on surgical tool tracking and identification have been performed. However, the size of dataset, the sensitivity/precision, and the response time of these studies were limited. In this work, we developed and utilized an automated method based on Convolutional Neural Network (CNN) and You Only Look Once (YOLO) v3 algorithm to locate and identify surgical tools and tool tips covering five different surgical scenarios. MATERIALS AND METHODS: An algorithm of object detection was applied to identify and locate the surgical tools and tool tips. DarkNet-19 was used as Backbone Network and YOLOv3 was modified and applied for the detection. We included a series of 181 endoscopy videos covering 5 different surgical scenarios: pancreatic surgery, thyroid surgery, colon surgery, gastric surgery, and external scenes. A total amount of 25,333 images containing 94,463 targets were collected. Training and test sets were divided in a proportion of 2.5:1. The data sets were openly stored at the Kaggle database. RESULTS: Under an Intersection over Union threshold of 0.5, the overall sensitivity and precision rate of the model were 93.02% and 89.61% for tool recognition and 87.05% and 83.57% for tool tip recognition, respectively. The model demonstrated the highest tool and tool tip recognition sensitivity and precision rate under external scenes. Among the four different internal surgical scenes, the network had better performances in pancreatic and colon surgeries and poorer performances in gastric and thyroid surgeries. CONCLUSION: We developed a surgical tool and tool tip recognition model based on CNN and YOLOv3. Validation of our model demonstrated satisfactory precision, accuracy, and robustness across different surgical scenes.

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In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6 [Formula: see text] and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.

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Both receptor-binding domain in spike protein (S-RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human neuropilin-1 (NRP1) are important in the virus entry, and their concomitant inhibition may become a potential strategy against the SARS-CoV-2 infection. Herein, five novel dual S-RBD/NRP1-targeting peptides with nanomolar binding affinities were identified by structure-based virtual screening. Particularly, RN-4 was found to be the most promising peptide targeting S-RBD (Kd = 7.4 ± 0.5 nM) and NRP1-BD (the b1 domain of NRP1) (Kd = 16.1 ± 1.1 nM) proteins. Further evidence in the pseudovirus infection assay showed that RN-4 can significantly inhibit the SARS-CoV-2 pseudovirus entry into 293 T cells (EC50 = 0.39 ± 0.09 µM) without detectable side effects. These results suggest that RN-4, a novel dual S-RBD/NRP1-targeting agent, holds potential as an effective therapeutic to combat the SARS-CoV-2 infection.

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Domestically and internationally, the effect of fracture flowing water and transferring heat on the temperature field of surrounding rock in high-level radioactive waste repositories is a popular research area. Compared with straight fracture flowing water and transferring heat, there are few relevant literatures about the heat transfer of curved fracture water flow. Based on the conceptive model of flowing water and transferring heat in curved fractured rock mass, the influence of flowing water and transferring heat in "I", "L", , and shaped fractures on the temperature field of rock mass is calculated by using discrete element program. The findings indicate that: When the model goes into a stable state under four working conditions, the rock on the x = 0-2 m mostly forms a heat transfer path from left to right; the x = 2-4 m primarily forms a heat transfer path from bottom to top, and the temperature gradient reveals that the isotherm of 40-45 °C is highly similar to the shape of four different fractures, indicating that flowing water and transferring heat in the fracture configuration dominate the temperature field of the right side rock mass. The direction of the flowing water and transferring heat of the fracture exerts a dominant effect on the temperature of the rock mass than the length.

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Objective: Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant neoplasm with rising incidence worldwide. Gremlin 1 (GREM1), a regulator of bone morphogenetic protein (BMP) signaling, fine-tunes extensive biological processes, including organ morphology, cellular metabolism, and multiple pathological developments. The roles of GREM1 in PDAC remain unknown. Methods: Varieties of public databases and online software were employed to analyze the expressions at transcription and protein levels of GREM1 in multiple malignant neoplasms including PDAC, and in addition, its potential pro-tumoral functions in PDAC were further evaluated. A total of 340 serum samples of pancreatic disease, including PDAC, low-grade malignant pancreatic neoplasm, benign pancreatic neoplasm, pancreatitis, and 132 healthy controls, were collected to detect GREM1. The roles of serum GREM1 in the diagnosis and prediction of survival of PDAC after radical resection were also analyzed. Results: Bioinformatics analyses revealed that GREM1 was overexpressed in PDAC and predicted a poorer survival in PDAC. A higher protein level of GREM1 in PDAC correlated with stroma formation and immunosuppression by recruiting varieties of immunosuppressive cells, including T regulatory cells (Tregs), M2 macrophages, myeloid-derived suppressor cells (MDSCs), and exhaustion T cells into the tumor microenvironment. A higher level of serum GREM1 was observed in PDAC patients, compared to healthy control (p < 0.001). Serum GREM1 had a good diagnostic value (area under the curve (AUC) = 0.718, p < 0.001), and its combination with carbohydrate antigen 199 (CA199) achieved a better diagnostic efficacy (AUC = 0.914, p < 0.001), compared to CA199 alone. The cutoff value was calculated by receiver operating characteristic (ROC) analysis, and PDAC patients were divided into two groups of low and high GREM1. Logistic analyses showed serum GREM1 positively correlated with tumor size (hazard ratio (HR) = 7.097, p = 0.032) and histopathological grades (HR = 2.898, p = 0.014). High-level serum GREM1 (1,117.8 pg/ml) showed a shorter postoperative survival (p = 0.0394). Conclusion: Higher intra-tumoral expression of GREM1 in PDAC contributes to tumor stroma and immunosuppressive tumor microenvironment, presenting its therapeutic potential. High-level serum GREM1 predicts poorer survival after resection. A combination of serum CA199 and GREM1 shows a stronger diagnostic efficacy in PDAC.

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The COVID-19 pandemic has caused devastating economic and social disruption. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform the distribution of limited healthcare resources. To address this challenge, we propose a machine learning model, MedML, to conduct the hospitalization and severity prediction for the pediatric population using electronic health records. MedML extracts the most predictive features based on medical knowledge and propensity scores from over 6 million medical concepts and incorporates the inter-feature relationships in medical knowledge graphs via graph neural networks. We evaluate MedML on the National Cohort Collaborative (N3C) dataset. MedML achieves up to a 7% higher AUROC and 14% higher AUPRC compared to the best baseline machine learning models. MedML is a new machine learnig framework to incorporate clinical domain knowledge and is more predictive and explainable than current data-driven methods.