Selection Method Research Articles

Personalized lung care: Bronchopulmonary dysplasia risk prediction tool tailored for neonates born in resource-limited settings.

Predicting bronchopulmonary dysplasia (BPD) to assess the risk-benefit of therapy is necessary considering the side effects of medications. We developed and validated an instrument for predicting BPD and compared it with an instrument currently used for neonates born in a Brazilian hospital. This was a retrospective cohort study of patients born between 2016 and 2020 with a gestational age (GA) between 23 and 30 weeks. Predictive equations were elaborated using methods of component variable selection collected on the 14th day of life; 70% of the sample was randomly selected for the construction of risk prediction equations and the remaining 30% for their validation, application, and comparison with the National Institute of Child Health and Human Development (NICHD) instrument. The sensitivity, specificity, and predictive values of the equations were calculated. The equation that used variables with p < 5% in Fisher's exact test presented the best results: specificity of 98% and positive predictive value of 93% and could be used for BPD prediction of all small-for-gestational-age (SGA) infants. The NICHD calculator applied to our population had a specificity of 93% and a positive predictive value of 75% and could not be applied to extremely SGA infants. Our tool can predict the risk of BPD on the 14th day of life, has higher specificity and positive predictive value to our population than the NICHD instrument, and can be suitable for SGA infants. The results must be confirmed by applying it to other populations to validate our tool.

Engineered Phenylalanine Ammonia-Lyases for the Enantioselective Synthesis of Aspartic Acid Derivatives.

Biocatalytic hydroamination of alkenes is an efficient and selective method to synthesize natural and unnatural amino acids. Phenylalanine ammonia-lyases (PALs) have been previously engineered to access a range of substituted phenylalanines and heteroarylalanines, but their substrate scope remains limited, typically including only arylacrylic acids. Moreover, the enantioselectivity in the hydroamination of electron-deficient substrates is often poor. Here, we report the structure-based engineering of PAL from Planctomycesbrasiliensis (PbPAL), enabling preparative-scale enantioselective hydroaminations of previously inaccessible yet synthetically useful substrates, such as amide- and ester-containing fumaric acid derivatives. Through the elucidation of cryo-electron microscopy (cryo-EM) PbPAL structure and screening of the structure-based mutagenesis library, we identified the key active site residue L205 as pivotal for dramatically enhancing the enantioselectivity of hydroamination reactions involving electron-deficient substrates. Our engineered PALs demonstrated exclusive a-regioselectivity, high enantioselectivity, and broad substrate scope. The potential utility of the developed biocatalysts was further demonstrated by a preparative-scale hydroamination yielding tert-butyl protected l-aspartic acid, widely used as intermediate in peptide solid-phase synthesis.

DSFE: Decoding EEG-based finger motor imagery using feature-dependent frequency, feature fusion and ensemble learning.

Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of featuredependent frequency band selection, feature fusion, and ensemble learning (DSFE) for finger motor imagery. First, a feature-dependent frequency band selection method based on correlation coefficient (FDCC) was proposed to select feature-specific effective bands. Second, a feature fusion method was proposed to fuse different types of candidate features to produce multiple refined sets of decoding features. Finally, an ensemble model using the weighted voting strategy was proposed to make full use of these diverse sets of final features. The results on a public EEG dataset of five fingers motor imagery showed that the DSFE method is effective and achieves the highest decoding accuracy of 50.64%, which is 7.64% higher than existing studies using exactly the same data. The experiments further revealed that both the effective frequency bands of different subjects and the effective frequency bands of different types of features are different in finger motor imagery. Furthermore, compared with two-hand motor imagery, the effective decoding information of finger motor imagery is transferred to the lower frequency. The idea and findings in this paper provide a valuable perspective for understanding fine motor imagery in-depth.

Advances in SARMs anti-doping analysis.

Selective androgen receptor modulators (SARMs) are performance-enhancing drugs (PEDs) that stimulate anabolism, increase muscle mass and strength and promote recovery from exercise. The use of SARMs in sports is considered doping and is strictly prohibited by the World Anti-Doping Agency (WADA) and the International Federation of Horseracing Authorities (IFHA). To monitor the abuse of SARMs in sports, it is essential to develop advanced, selective and sensitive analytical methods that provide reliable results. This review evaluates the advances in this area, with a focus on the identification of target analytes related to SARMs, such as SARMs, their metabolites or markers. The aim is to identify targets that could extend the detection windows of SARMs, provide scientific support for results management and/or offer an indirect biomarker-based approach to doping control. This review also aims to evaluate the current liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) methods developed for the monitoring of SARMs in different biological matrices, including traditional matrices such as urine and serum/plasma samples, as well as alternative matrices such as dried blood spots, hair and nail samples.

A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations.

Radiomics extracts hundreds of features from medical images to quantitively characterize a region of interest (ROI). When applying radiomics, imbalanced or small dataset issues are commonly addressed using under or over-sampling, the latter being applied directly to the extracted features. Aim of this study is to propose a novel balancing and data augmentation technique by applying perturbations (erosion, dilation, contour randomization) to the ROI in cardiac computed tomography images. From the perturbed ROIs, radiomic features are extracted, thus creating additional samples. This approach was tested addressing the clinical problem of distinguishing cardiac amyloidosis (CA) from aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM). Twenty-one CA, thirty-two AS and twenty-one HCM patients were included in the study. From each original and perturbed ROI, 107 radiomic features were extracted. The CA-AS dataset was balanced using the perturbation-based method along with random over-sampling, adaptive synthetic (ADASYN) and the synthetic minority oversampling technique (SMOTE). The same methods were tested to perform data augmentation dealing with CA and HCM. Features were submitted to robustness, redundancy, and relevance analysis testing five feature selection methods (p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA). Support vector machine performed the classification tasks, and its performance were evaluated by means of a 10-fold cross-validation. The perturbation-based approach provided the best performances in terms of f1 score and balanced accuracy in both CA-AS (f1 score: 80%, AUC: 0.91) and CA-HCM (f1 score: 86%, AUC: 0.92) classifications. These results suggest that ROI perturbations represent a powerful approach to address both data balancing and augmentation issues.

Comparison of Feature Selection Methods—Modelling COPD Outcomes

Selecting features associated with patient-centered outcomes is of major relevance yet the importance given depends on the method. We aimed to compare stepwise selection, least absolute shrinkage and selection operator, random forest, Boruta, extreme gradient boosting and generalized maximum entropy estimation and suggest an aggregated evaluation. We also aimed to describe outcomes in people with chronic obstructive pulmonary disease (COPD). Data from 42 patients were collected at baseline and at 5 months. Acute exacerbations were the aggregated most important feature in predicting the difference in the handgrip muscle strength (dHMS) and the COVID-19 lockdown group had an increased dHMS of 3.08 kg (CI95 ≈ [0.04, 6.11]). Pack-years achieved the highest importance in predicting the difference in the one-minute sit-to-stand test and no clinical change during lockdown was detected. Charlson comorbidity index was the most important feature in predicting the difference in the COPD assessment test (dCAT) and participants with severe values are expected to have a decreased dCAT of 6.51 points (CI95 ≈ [2.52, 10.50]). Feature selection methods yield inconsistent results, particularly extreme gradient boosting and random forest with the remaining. Models with features ordered by median importance had a meaningful clinical interpretation. Lockdown seem to have had a negative impact in the upper-limb muscle strength.

Novel autoantibodies help diagnose anti-SSA antibody negative Sjögren disease and predict abnormal labial salivary gland pathology

ObjectivesSjögren disease (SjD) diagnosis often requires either positive anti-SSA antibodies or a labial salivary gland biopsy with a positive focus score (FS). One-third of patients with SjD lack anti-SSA antibodies...

Insights from an updated personnel selection meta-analytic matrix: Revisiting general mental ability tests' role in the validity-diversity trade-off.

General mental ability (GMA) tests have long been at the heart of the validity-diversity trade-off, with conventional wisdom being that reducing their weight in personnel selection can improve adverse impact, but that this results in steep costs to criterion-related validity. However, Sackett et al. (2022) revealed that the criterion-related validity of GMA tests has been considerably overestimated due to inappropriate range restriction corrections. Thus, we revisit the role of GMA tests in the validity-diversity trade-off using an updated meta-analytic correlation matrix of the relationships six selection methods (biodata, GMA tests, conscientiousness tests, structured interviews, integrity tests, and situational judgment tests) have with job performance, along with their Black-White mean differences. Our results lead to the conclusion that excluding GMA tests generally has little to no effect on validity, but substantially decreases adverse impact. Contrary to popular belief, GMA tests are not a driving factor in the validity-diversity trade-off. This does not fully resolve the validity-diversity trade-off, though: Our results show there is still some validity reduction required to get to an adverse impact ratio of .80, although the validity reduction is less than previously thought. Instead, it shows that the validity-diversity trade-off conversation should shift from the role of GMA tests to that of other selection methods. The present study also addresses which selection methods now emerge as most valid and whether composites of selection methods can result in validities similar to those expected prior to Sackett et al. (2022). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract PO3-24-08: Establishment of a selective culture method for breast cancer stem cells derived from patient tissue and a subgroup analysis of cancer stem cells

Abstract Purpose Cancer stem cells are considered to cause recurrence and metastasis due to their resistance to drug and radiation therapy. Breast cancer stem cells are therefore the true target for the treatment of breast cancer. In this study, we established a method to selectively culture breast cancer stem cells from patient-derived breast cancer tissue, and we analyzed their characteristics using immunostaining to identify gene expression. Method Breast cancer tissues from ER+, HER2- and drug-naive patients were fragmented and cultured using low-adherent plates (spheroid culture). We investigated the stemness of the breast cancer cells obtained by the spheroid culture by immunostaining the breast cancer stem cell markers and examining their proliferation and differentiation in a mouse transplantation model. The diversity of the cells was further examined by means of the expression of the EMT markers and genetic analysis. Results In 48 cases, the proportion of CD44+/CD24- breast cancer cells increased from 13.8% to 61.6% in the spheroid culture. In addition, even small numbers of cells obtained by the spheroid culture were transplanted into mice, and the transplanted cells subsequently differentiated in the mice, demonstrating their stemness. ER expression was negatively changed in 52.1% of cases, and the expression of EMT markers, Twist, Snail, and Vimentin, was increased from 43.8% to 75.0%, 12.9% to 58.1%, and 7.71% to 37.7%, respectively. A DNA microarray with 14 cases showed that breast cancer stem cells obtained from ER+ and HER2- breast cancer tissue were classified into two groups. Conclusion We demonstrated that breast cancer stem cells were selectively cultured from patient-derived breast cancer tissue in a spheroid culture. Furthermore, breast cancer stem cells have different characteristics from breast cancer cells in primary tumors and are diverse at an EMT level, and in genetic analyses. Table. Expression of primary tumors and spheroids Citation Format: Satoshi Sueoka, Takayuki Kadoya, Kai Azusa, Shinsuke Sasada, Akiko Emi, Morihito Okada, Yukino Kobayashi, Koh Nakayama. Establishment of a selective culture method for breast cancer stem cells derived from patient tissue and a subgroup analysis of cancer stem cells [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-24-08.

Optimal Parking Space Selection and Vehicle Driving Decision for Autonomous Parking System Based on Multi-Attribute Decision

Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision modules. This paper proposes a method for optimal parking space selection and vehicle driving decisions. In terms of selecting the optimal parking space, a multi-attribute decision method is designed considering the type of parking space, walking distance, and other factors. In terms of vehicle driving decisions, we first predict the behavior and trajectory of the target vehicle in a specific scenario, and then use a combination of rule-based and learning-based decision methods for safe and comfortable vehicle driving behavior decisions. Simulation results show that the proposed methods can find the optimal parking space according to the parking lot map and improve the efficiency and smoothness of vehicle driving while ensuring driving safety.

Developing and validating multi-omics prediction models for late patient-reported dysphagia in head and neck radiotherapy.

To investigate models developed using radiomic and dosiomic (multi-omics) features from planning and treatment imaging for late patient-reported dysphagia in head and neck radiotherapy.&#xD;&#xD;Materials and methods: Training (n=64) and testing (n=23) cohorts of head and neck cancer patients treated with curative intent chemo-radiotherapy with a follow-up time greater than 12 months were retrospectively examined. Patients completed the MD Anderson Dysphagia Inventory and a composite score ≤60 was interpreted as patient-reported dysphagia. A chart review collected baseline dysphagia and clinical factors. Multi-omic features were extracted from planning and last synthetic CT images using the pharyngeal constrictor muscle contours as a region of interest. Late patient-reported dysphagia models were developed using a random forest backbone, with feature selection and up-sampling methods to account for the imbalanced data. Models were developed and validated for multi-omic feature combinations for both timepoints. &#xD;&#xD;Results: A clinical and radiomic feature model developed using the planning CT achieved good performance (validation: sensitivity=80±27% / balanced accuracy=71±23%, testing: sensitivity =80±10% / balanced accuracy=73±11%). The synthetic CT models did not show improvement over the plan CT multi-omics models, with poor reliability of the radiomic features on these images. Dosiomic features extracted from the synthetic CT showed promise in predicting late patient-reported dysphagia.&#xD;&#xD;Conclusion: Multi-omics models can predict late patient-reported dysphagia in head and neck radiotherapy patients. Synthetic CT dosiomic features show promise in developing successful models to account for changes in delivered dose distribution. Multi-center or prospective studies are required prior to clinical implementation of these models.&#xD.

Renal angiomyolipoma selective arterial embolization: Australian tertiary centre experience over 10 years.

The purpose of this study is to evaluate the patient selection methods, treatment outcomes, complications, clinical and radiological follow-up after renal angiomyolipoma (AML) treatment with selective arterial embolization (SAE) in an Australian metropolitan tertiary centre. This study presents a retrospective single-centre review of patients' medical records who underwent SAE for renal AML during the period of 1st January 2012 and 1st January 2023. A total of 32 SAE procedures for renal AML occurred during the study period. Three episodes were classified as emergency cases [9.38%] and the remaining 29 were treated electively. Mean AML size pre-treatment was 69.45 mm (range = 33-177; SD = 31.69). All AMLs demonstrated hyper-vascularity on contrast-enhanced cross-sectional imaging (arterial-phase enhancement characteristics and/or prominent tortuous feeding vessels) [n = 32; 100%] or an intralesional aneurysm or pseudoaneurysm [n = 12; 42.85%]. Periprocedural complications [n = 3; 9.38%] included: one intralesional haemorrhage after embolization, one vascular access site complication, and one lipiduria-associated urinary tract infection. No patients suffered a life-threatening complication, non-target embolization, deterioration in renal function or death following SAE. Re-treatment with SAE was performed in only three patients [10.71%]. Hospital mean length of stay was 1.58 days. Median durations of clinical and radiological follow-up post-treatment were 493 days (range = 104-1645) and 501 days (range = 35-1774), respectively. Follow-up imaging revealed AML total size reduction in all cases [mean = -17.17 mm; -26.51%] and 50% had obliteration of lesion hyper-vascularity after one episode of SAE. Outpatient clinical follow-up signifies that none of the patients included in the study have re-presented with lesion haemorrhage after successful SAE. In this study, renal AMLs were treated safely with a high degree of success by using SAE, and there were very low rates of periprocedural complications. Follow-up of patients after SAE treatment of renal AML should include both radiological (assessment for reduction in lesion vascularity and size) and clinical review in an outpatient clinic setting (either by an interventional radiologist or urologist).

Solution of engineering design and truss topology problems with improved forensic-based investigation algorithm based on dynamic oppositional based learning

AbstractThe forensic-based investigation (FBI) is a metaheuristic algorithm inspired by the criminal investigation process. The collaborative efforts of the investigation and pursuit teams demonstrate the FBI’s involvement during the exploitation and exploration phases. When choosing the promising population, the FBI algorithm’s population selection technique focuses on the same region. This research aims to propose a dynamic population selection method for the original FBI and thereby enhance its convergence performance. To achieve this objective, the FBI may employ dynamic oppositional learning (DOL), a dynamic version of the oppositional learning methodology, to dynamically navigate to local minima in various locations. Therefore, the proposed advanced method is named DOLFBI. The performance of DOLFBI on the CEC2019 and CEC2022 benchmark functions is evaluated by comparing it with several other popular metaheuristics in the literature. As a result, DOLFBI yielded the lowest fitness value in 18 of 22 benchmark problems. Furthermore, DOLFBI has shown promising results in solving real-world engineering problems. It can be argued that DOLFBI exhibits the best convergence performance in cantilever beam design, speed reducer, and tension/compression problems. DOLFBI is often utilized in truss engineering difficulties to determine the minimal weight. Its success is comparable to other competitive MAs in the literature. The Wilcoxon signed-rank and Friedman rank tests further confirmed the study’s stability. Convergence and trajectory analyses validate the superior convergence concept of the proposed method. When the proposed study is compared to essential and enhanced MAs, the results show that DOLFBI has a competitive framework for addressing complex optimization problems due to its robust convergence ability compared to other optimization techniques. As a result, DOLFBI is expected to achieve significant success in various optimization challenges, feature selection, and other complex engineering or real-world problems.

The posterior selection method for hyperparameters in regularized least squares method

The posterior selection method for hyperparameters in regularized least squares method

Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso

ABSTRACT With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale streamflow and explores the application of the Lasso feature selection method alongside three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction. Through comparative experiments, we found that the Lasso method significantly enhances the model's performance, with a respective increase in the generalization capabilities of the three models by 21, 12, and 14%. Among the selected features, lagged streamflow and precipitation play dominant roles, with streamflow closest to the prediction date consistently being the most crucial feature. In comparison to the TTS and RF models, the LSTM model demonstrates superior performance and generalization capabilities in streamflow prediction for 1–7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions. Overall, this study deepens our understanding of feature selection and machine learning models in hydrology, providing valuable insights for hydrological simulations under the influence of complex human activities.

Predicting types of human-related maritime accidents with explanations using selective ensemble learning and SHAP method

Maritime accidents frequently lead to severe property damage and casualties, and an accurate and reliable risk prediction model is necessary to help maritime stakeholders assess the current risk situation. Therefore, the present study proposes a hybrid methodology to develop an explainable prediction model for maritime accident types. Based on the advantages of selective ensemble learning method, this study pioneers to introduce a two-stage model selection method, aiming to enhance the predictive accuracy and stability of the model. Then, SHAP (Shapley Additive Explanations) method is integrated to identify effective mapping associations of seafarers’ unsafe acts and their risk factors with the prediction results. The results demonstrate that the model developed achieves good prediction performance with an accuracy of 87.50% and an F1-score of 84.98%, which benefits stakeholders in assessing the type of maritime accident in advance, so as to make proactive intervention measures.

Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data

Background:Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD). Method:We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo. Results:Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group. Conclusions:HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.

An Objective Space Constraint-Based Evolutionary Method for High-Dimensional Feature Selection [Research Frontier]

An Objective Space Constraint-Based Evolutionary Method for High-Dimensional Feature Selection [Research Frontier]

The Use of Weighted Euclidean Distance to Provide Assistance in the Selection of Safety Risk Prevention and Control Strategies for Major Railway Projects

A major railway project is a complex, giant system with multi-party participation, one characterized by complex geological conditions, long construction periods and large scale, which leads to an increased likelihood of safety risk events during construction. In order to solve the problem of scientific selection and formulation of safety risk prevention and control strategies for major railway projects, an auxiliary selection method of safety risk prevention and control strategies for major railway projects based on weighted Euclidean distance (WED) is proposed. The relevant ontology is used to conceptualize and formalize the knowledge of safety risks of major railroad projects, and combine the characteristics of major railroad projects; it refers to the prevention and control measures of historical safety risk events associated with major railroad projects, and then constructs the knowledge structure and case base around safety risks of major railroad projects and the circumstances of the case. In determining the comprehensive weights, the G1 method is used to determine the subjective weights, the anti-entropy weight method is used to determine the objective weights and game theory combines the subjective and objective weights. In comparing the array of safety risk prevention and control cases associated with major railway projects, the weighted Euclidean distance is used to calculate the similarity between these cases and the target case, which in turn assists project managers in determining the safety risk prevention and control strategies appropriate for major railway projects. This study takes Landslide No. 1 in the Tunnel A inlet planning area as an example. It utilizes the WED method to assist in selecting safety risk prevention and control strategies for major railway projects, which verifies the method’s feasibility. The proposed method enriches the method of the assisted selection of safety risk prevention and control strategies for major railway projects, makes strategy formulation more scientific, has specific reference significance for the formulation of safety risk prevention and control strategies for major railway projects, and promotes the improvement of safety risk prevention and risk control for participating units.

The burden and management competency of cardiomyopathies in China: a nationwide survey study

The public health burden of cardiomyopathies and competency in their management by health agencies in China are not well understood. This study adopted a multi-stage sampling method for hospital selection. In the first stage, nationwide tertiary hospital recruitment was performed. As a result, 88 hospitals with the consent of the director of cardiology and access to an established electronic medical records system, were recruited. In the second stage, we sampled 66 hospitals within each geographic-economic stratification through a random sampling process. Data on (1) the outpatient and inpatient visits for cardiomyopathies between 2017 and 2021 and (2) the competency in the management of patients with cardiomyopathies, were collected. The competency of a hospital to provide cardiomyopathy care was evaluated using a specifically devised scale. The outpatient and inpatient visits for cardiomyopathies increased between 2017 and 2021by 38.6% and 33.0%, respectively. Most hospitals had basic facilities for cardiomyopathy assessment. However, access to more complex procedures was limited, and the integrated management pathway needs improvement. Only 4 (6.1%) of the 66 participating hospitals met the criteria for being designated as a comprehensive cardiomyopathy center, and only 29 (43.9%) could be classified as a primary cardiomyopathy center. There were significant variations in competency between hospitals with different administrative and economic levels. The health burden of cardiomyopathies has increased significantly between 2017 and 2021 in China. Although most tertiary hospitals in China can offer basic cardiomyopathy care, more advanced facilities are not yet universally available. Moreover, inconsistencies in the management of cardiomyopathies across hospitals due to differing administrative and economic levels warrants a review of the nation allocation of medical resources. This work was supported by the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2023-I2M-1-001) and the National High Level Hospital Clinical Research Funding (2022-GSP-GG-17).