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Abstract Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human–machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human–machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human–machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human–machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Abstract Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data—a task at which the benchmarked single-omics methods proved inadequate.

Abstract To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients’ readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.

Engineers used TCAD tools for semiconductor devices modeling. However, it is computationally expensive and time-consuming for advanced devices with smaller dimensions. Therefore, this work proposes a machine learning-based device modeling algorithm to capture the complex nonlinear relationship between parameters and electrical characteristics of gate-all-around (GAA) nanowire field-effect transistors (NWFETs) from technology computer-aided design (TCAD) simulation results. This method utilizes a densely connected deep neural networks (DenseDNN), which establishes direct connections between layers in the neural networks, provides stronger feature extraction and information transmission capabilities. By incorporating cost-sensitive learning methods, the proposed model focus more on the critical data that determines device characteristics, leading to accurate prediction of key device characteristics under various parameters. Experimental results on a test dataset of 116 NWFETs demonstrate the effectiveness of this method. The DenseDNN model with cost-sensitive learning exhibits better performance than traditional deep neural networks (DNN) with various widths and depths, with a prediction error below 1.62%. Moreover, compared to TCAD simulation results, the model can speedup $10^{6}\times$ .

Abstract Psychotic symptoms are reported as one of the most common complications of Alzheimer's disease (AD), in whom they are associated with more rapid deterioration and increased mortality. Empiric treatments, namely first and second‐generation antipsychotics, confer modest efficacy in patients with AD and with psychosis (AD+P) and themselves increase mortality. Recent studies suggested the use and beneficial effects of antidepressants among patients with AD+P. This motivates our rationale for exploring their potential as a novel combination therapy option among these patients. We included electronic medical records of 10,260 patients with AD in our study. Survival analysis was performed to assess the effects of the combination of antipsychotics and antidepressants on the mortality of these patients. A protein–protein interaction network representing AD+P was built, and network analysis methods were used to quantify the efficacy of these drugs on AD+P. A combined score was developed to measure the potential synergetic effect against AD+P. Our survival analyses showed that the co‐administration of antidepressants with antipsychotics have a significant beneficial effect in reducing mortality. Our network analysis showed that the targets of antipsychotics and antidepressants are well‐separated, and antipsychotics and antidepressants have similar Signed Jaccard Index (SJI) scores to AD+P. Eight drug pairs, including some popular recommendations like aripiprazole/sertraline, showed higher than average scores which suggest their potential in treating AD+P via strong synergetic effects. Our proposed combinations of antipsychotic and antidepressant therapy showed a strong superiority over current antipsychotics treatment for AD+P. The observed beneficial effects can be further strengthened by optimizing drug‐pair selection based on our systems pharmacology analysis.

BackgroundPrevious studies on the management of chronic subdural hematoma (cSDH) mainly focused on the risk of postoperative recurrence and measures to prevent it. In this study, we propose the use of a non-invasive postoperative treatment method, the modified Valsalva maneuver (MVM), as a means of reducing the recurrence of cSDH. This study aims to clarify the effects of MVM on functional outcomes and recurrence rates.MethodsA prospective study was conducted at the Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from November 2016 to December 2020. The study included 285 adult patients who underwent burr-hole drainage for the treatment of cSDH and received subdural drains. These patients were divided into two groups: the MVM group (n = 117) and the control group (n = 98). In the MVM group, patients received treatment with a customized MVM device for at least 10 times per hour, 12 h per day. The study's primary endpoint was the recurrence rate of SDH, while functional outcomes and morbidity 3 months after surgery were the secondary outcomes.ResultsIn the current study, 9 out of 117 patients (7.7%) in the MVM group experienced a recurrence of SDH, while 19 out of 98 patients (19.4%, p < 0.05) in the HC group experienced a recurrence of SDH. Additionally, the infection rate of diseases such as pneumonia (1.7%) was significantly lower in the MVM group compared to the HC group (9.2%, p < 0.001, odds ratio (OR = 0.1). After 3 months of the surgery, 109 out of 117 patients (93.2%) in the MVM group achieved a favorable prognosis, compared to 80 out of 98 patients (81.6%) in the HC group (p = 0.008, with an OR of 2.9). Additionally, infection rate (with an OR of 0.2) and age (with an OR of 0.9) are independent predictors of a favorable prognosis at the follow-up stage.ConclusionsThe use of MVM in the postoperative management of cSDHs has been shown to be safe and effective, resulting in reduced rates of cSDH recurrence and infection following burr-hole drainage. These findings suggest that MVM treatment may lead to a more favorable prognosis at the follow-up stage.

Intracranial aneurysms (IAs) are highly prevalent in the population, and their rupture poses a significant risk of death or disability. However, the treatment of aneurysms, whether through interventional embolization or craniotomy clipping surgery, is not always safe and carries a certain proportion of morbidity and mortality. Therefore, early detection and prompt intervention of IAs with a high risk of rupture is of notable clinical significance. Moreover, accurately predicting aneurysms that are likely to remain stable can help avoid the risks and costs of over-intervention, which also has considerable social significance. Recent advances in artificial intelligence (AI) technology offer promising strategies to assist clinical trials. This review will discuss the state-of-the-art AI applications for assessing the rupture risk of IAs, with a focus on achievements, challenges, and potential opportunities.

Early brain injury (EBI) subsequent to subarachnoid hemorrhage (SAH) is strongly associated with delayed cerebral ischemia and poor patient prognosis. Based on investigations into the molecular mechanisms underlying EBI, neurovascular dysfunction resulting from SAH can be attributed to a range of pathological processes, such as microvascular alterations in brain tissue, ionic imbalances, blood–brain barrier disruption, immune–inflammatory responses, oxidative stress, and activation of cell death pathways. Research progress presents a variety of promising therapeutic approaches for the preservation of neurological function following SAH, including calcium channel antagonists, endothelin-1 receptor blockers, antiplatelet agents, anti-inflammatory agents, and anti-oxidative stress agents. EBI can be mitigated following SAH through neuroprotective measures. To enhance our comprehension of the relevant molecular pathways involved in brain injury, including brain ischemia–hypoxic injury, neuroimmune inflammation activation, and the activation of various cell-signaling pathways, following SAH, it is essential to investigate the evolution of these multifaceted pathophysiological processes. Facilitating neural repair following a brain injury is critical for improving patient survival rates and quality of life.

Ischemic stroke is a common type of stroke that significantly affects human well-being and quality of life. In order to further characterize the pathophysiology of ischemic stroke and develop new treatment strategies, ischemic stroke models with controllable and consistent response to potential clinical treatments are urgently needed. The middle cerebral artery occlusion (MCAO) model is currently the most widely used animal model of ischemic stroke. This review discusses various methods for constructing the MCAO model and compares their advantages and disadvantages in order to provide better approaches for studying ischemic stroke.

Aim: Since tissue inhibitors of matrix metalloproteinase 3 (TIMP-3) was reported to be a potential risk factor of atherosclerosis, aneurysm, hypertension, and post-ischaemic neuronal injury, it may also be a candidate risk factor of stress. Therefore, this study was designed to explore the causal role of TIMP-3 in the risk of ischaemic stroke (IS) and intracerebral haemorrhage (ICH), which are the two main causes of stress via this Mendelian Randomisation (MR) study.Methods: The summarised data of TIMP-3 level in circulation was acquired from the Cooperative Health Research in the Region of Augsburg public database and the outcome of IS and ICH was obtained from genome-wide association studies conducted by MEGASTROKE and the International Stroke Genetics Consortium, respectively. Five statistical methods including inverse-variance weighting, weighted-median analysis, MR-Egger regression, MR Pleiotropy RESidual Sum and Outlier test, and MR-Robust Adjusted Profile Score were applied to evaluate the causal role of TIMP-3 in the occurrence of IS and ICH. Inverse-variance weighting was applied for assessing causality. Furthermore, heterogeneity and pleiotropic tests were utilised to confirm the reliability of this study.Results: We found that TIMP-3 could be a positively causal relationship with the incidence of IS (OR = 1.026, 95% CI: 1.007–1.046, p = 0.0067), especially for the occurrence of small vessel stroke (SVS; OR = 1.045, 95% CI: 1.016–1.076, p = 0.0024). However, the causal effects of TIMP-3 on another IS subtype cardioembolic stroke (CES; OR = 1.049, 95% CI: 1.006–1.094, p = 0.024), large artery stroke (LAS; OR = 1.0027, 95% CI: 0.9755–1.0306, p = 0.849) and ICH (OR = 0.9900, 95% CI: 0.9403–1.0423, p = 0.701), as well as ICH subtypes were not observed after Bonferroni corrections (p = 0.00714).Conclusion: Our results revealed that high levels of circulating TIMP-3 causally increased the risk of developing IS and SVS, but not CES, LAS, ICH, and all ICH subtypes. Further investigation is required to elucidate the underlying mechanism.

Recently, experimental results have demonstrated that perpendicular magnetic tunnel junction (p-MTJ) with the antiferromagnetic(AFM)/ferromagnetic (FM)/oxide structure can achieve field-free spin-orbit torque (SOT) switching since the AFM metal strip can not only generate the SOT, but also provide an exchange bias (HEX), making it suitable for practical applications. However, owing to that the HEX is weak, such field-free SOT switching is incomplete, thus resulting in severe switching reliability. In addition, a large SOT switching current (ISOT) is also required, leading to high switching energy dissipation. In this paper, to address these issues, the voltage-controlled magnetic anisotropy (VCMA) is introduced to assist the SOT switching, and such novel switching method is referred as voltage-gated SOT (VGSOT). First, we develop a physics-based compact model for the three-terminal VGSOT-MTJ device, which includes three modules, i.e., the electrical module, the tunnel magnetoresistance module and the dynamic switching module. Then, the impact of the VCMA effect on the field-free SOT switching is investigated by solving a modified Landau-Lifshitz-Gilbert (LLG) equation with consideration of the VCMA, SOT and HEX. Simulation results show that thanks to the introduction of the VCMA effect, the critical ISOT can be reduced greatly, and the incomplete field-free SOT switching can be completed. With further analysis, we obtain a special switching condition, under which complete SOT field-free switching can be achieved with a shortest path and ultra-low power. Moreover, a novel write pulse scheme is proposed to achieve high speed and reliability.

It is evident that increasing the intensive-care-unit (ICU) capacity and giving priority to admitting and treating patients will reduce the number of COVID-19 deaths, but the quantitative assessment of these measures has remained inadequate. We develop a comprehensive, non-Markovian state transition model, which is validated through the accurate prediction of the daily death toll for two epicenters: Wuhan, China and Lombardy, Italy. The model enables prediction of COVID-19 deaths in various scenarios. For example, if appropriate treatment priorities had been used, the death toll in Wuhan and Lombardy would have been reduced by about 10% and 7%, respectively. The strategy depends on the epidemic scale and is more effective in countries with a younger population structure. Analyses of data from China, South Korea, Italy, and Spain suggest that countries with less per capita ICU medical resources should implement this strategy in the early stage of the pandemic to reduce mortalities. We emphasize that the results of this paper should be interpreted purely from a scientific and a quantitative-analysis point of view. No ethical implications are intended and meaningful.

Abstract In this paper, we investigate the dynamics of a nonlinear differential system, a mathematical model of the coupled hematopoiesis network. The asymptotic stability of a unique positive periodic solution of the system under certain conditions is proved theoretically. Furthermore, we propose a linear feedback control scheme to guarantee the asymptotic stability of the system when the above conditions do not hold. Finally, an example and some numerical simulations are displayed to support the obtained results.

Since Internet of Things devices should ideally be self-powered, there is great demand for devices with ultra-low operating voltages that can run on energy harvested from a noisy environment. In this paper, a novel all spin logic (ASL) device with voltage controlled magnetic anisotropy (VCMA) effect amplified by negative capacitance (NC) is proposed for the first time. This novel device can operate at a working voltage as low as 1 mV. A three-step pulse scheme is also proposed. Based on simulations of the VCMA effect and the NC effect coupled with the spin modular approach, the feasibility of the proposed ASL device with a 1-mV working voltage and its immunity to variations in device parameters and supply voltage are analyzed.

One crucial aspect of precision medicine is to allow physicians to recommend the most suitable treatment for their patients. This requires understanding the treatment heterogeneity from a patient-centric view, quantified by estimating the individualized treatment effect (ITE). With a large amount of genetics data and medical factors being collected, a complete picture of individuals' characteristics is forming, which provides more opportunities to accurately estimate ITE. Recent development using machine learning methods within the counterfactual outcome framework shows excellent potential in analyzing such data. In this research, we propose to extend meta-learning approaches to estimate individualized treatment effects with survival outcomes. Two meta-learning algorithms are considered, T-learner and X-learner, each combined with three types of machine learning methods: random survival forest, Bayesian accelerated failure time model and survival neural network. We examine the performance of the proposed methods and provide practical guidelines for their application in randomized clinical trials (RCTs). Moreover, we propose to use the Boruta algorithm to identify risk factors that contribute to treatment heterogeneity based on ITE estimates. The finite sample performances of these methods are compared through extensive simulations under different randomization designs. The proposed approach is applied to a large RCT of eye disease, namely, age-related macular degeneration (AMD), to estimate the ITE on delaying time-to-AMD progression and to make individualized treatment recommendations. [ABSTRACT FROM AUTHOR]