Search Results (67)

The increasingly observable financial vulnerability of households in emerging market countries makes it imperative to investigate the factors influencing it. Considering that China stands as a representative of emerging market economies, analyzing the factors influencing household financial vulnerability in China presents great reference significance for the sustainable development of households in emerging market countries. Using data from the China Household Finance Survey (CHFS) household samples, this paper presents the regional distribution of households with financial vulnerability in China. Utilizing machine learning (ML), this research examines the factors that influence household financial vulnerability in China and determines the most significant ones. The results reveal that households with financial vulnerability in China takes up a proportion of more than 63%, and household financial vulnerability is lower in economically developed coastal regions than in medium and small-sized cities in the central and western parts of China. The analysis results of the SHAP method show that the debt leverage ratio of a household is the most significant feature variable in predicting financial vulnerability. The ALE plots demonstrate that, in a household, the debt leverage ratio, the age of household head, health condition, economic development and literacy level are significantly nonlinearly related to financial vulnerability. Heterogeneity analysis reveals that, except for household debt leverage and insurance participation, the key characteristic variables exerting the most pronounced effect on financial fragility differ between urban and rural households: household head age for urban families and physical health status for rural families. Furthermore, digital financial inclusion and social security exert distinct impacts on financial vulnerability, showing significantly stronger effects in high per capita GDP regions and low per capita GDP regions, respectively. These findings offer valuable insights for policymakers in emerging economies to formulate targeted financial risk mitigation strategies—such as developing household debt relief and prevention mechanisms and strengthening rural health security systems—and optimize policies for household financial health. Full article

Background: The aging process is accompanied by changes in the immunological status of a person. Immunosenescence is considered a significant cause of the development of cardiovascular diseases (CVD) in elderly people. However, to date, the relationship between immune/inflammatory processes and diseases associated with age is considered quite complex and is not fully understood. Immunophenotyping and the intracellular production of cytokines involved in the processes of inflammatory aging will allow us to identify biomarkers that are associated with cardiovascular diseases in the elderly. Objectives: To identify immunological markers associated with the process of inflammatory aging in older individuals with cardiovascular diseases. Methods: CD-phenotyping and intracellular cytokine analysis of peripheral blood using the flow cytometry method were conducted in 52 people over 60 years of age (group 1 had CVD and group 2 did not). Blood samples were stained with monoclonal antibodies (mAb) using Becton Dickinson (BD) reagents for the staining and binding of surface receptors CD4+, CD8+, CD14+, CD19+, CD16+, CD56+, CD59+, CD95+, and HLA DR+ and intracellular receptors TNF, IL-10, GM-CSF, VEGFR-2, IGF, and perforin. In addition, the following parameters were studied: questionnaire data (gender, age, alcohol consumption, smoking, physical activity, and marital status), clinical data (blood pressure (BP), heart rate (HR), body mass index (BMI)), comorbid conditions, and cardiovascular diseases (coronary heart disease (CHD), chronic heart failure (CHF), arterial hypertension (AH), previous myocardial infarction (PICS), diabetes mellitus (DM), atrial fibrillation (AF), and stroke). Results: The older patients with cardiovascular pathology had high levels of monocytes CD14+ (p = 0.014), low levels of CD8+ lymphocytes (p = 0.046), and low intracellular production of GM-CSF (p = 0.013) compared to the older people without CVD. Conclusions: The revealed differences in the expression of CD14+ monocytes indicate their role in the development of cardiovascular pathology associated with age-related changes. A decrease in cytotoxic CD8+ lymphocytes and intracellular GM-CSF production leads to an increased risk of developing cardiovascular diseases in older individuals. These observed changes with age will not only expand existing knowledge about the aging of the regulatory link of the immune system but also help to obtain data to predict CVD in older people. Thus, the obtained results support the use of these immunological markers to identify the risk of circulatory disease and a personalized approach in geriatric practice. Full article

Dogs with acute congestive heart failure (CHF) can develop acute kidney injury (AKI); the prevalence of this condition has not been defined. This study aimed to assess the occurrence of AKI (increase in serum creatinine (sCr) ≥ 0.3 mg/dL) within 48 h from admission in dogs with myxomatous mitral valve disease (MMVD) with acute CHF, and the role of urinary neutrophil gelatinase-associated lipocalin (uNGAL) as a predictive marker of AKI. This was a multicentric, prospective observational study. Thirty dogs were included. The types and dosages of the diuretics administered, as well as the serum and urinary chemistry, including uNGAL and uNGAL, to the urinary creatinine ratio (uNGALC), were determined at admission (T0) and after 24 (T24) and 48 (T48) hours of hospitalization. Nineteen dogs developed AKI. We found no statistically significant differences in sCr, uNGAL, uNGALC, diuretic dosage, or hours of hospitalization between dogs that developed AKI and those that did not. The urinary NGAL and uNGALC values were not statistically significantly different at any time point, while the sCr was higher at T24 and T48 than T0. Our findings suggest that AKI in MMVD dogs with CHF is primarily functional, driven by effective decongestion rather than severe tubular damage, with the benefits of decongestion outweighing transient increases in sCr. Full article

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution. Full article

Background: Glycocalyx disintegration is associated with adverse outcomes in patients with trauma or sepsis. As microvascular dysfunction has an impact on disease progression in chronic heart failure (CHF) patients, we hypothesized that changes in microcirculation might be associated with mortality. Methods: Fifty patients with ischemic and non-ischemic cardiomyopathy and conservative treatment with baseline measurements of the sublingual microcirculation (via Sidestream Darkfield videomicroscopy) were followed up for two years. Glycocalyx thickness was assessed indirectly by calculation of the perfused boundary region (PBR). Results: Loss of glycocalyx was pronounced in non-survivors after one, n = 10, and two years, n = 16; PBR: 2.05 μm (1.88–2.15 μm) vs. 1.87 μm (1.66–2.03 μm) and 2.04 (1.93–2.11) vs. 1.84 (1.62–1.97); p = 0.042 and p = 0.003, respectively. Area under the ROC curve for the analysis of the predictive value of PBR on two-year mortality was 0.77 (p = 0.003; SE: 0.07, CI (95%): 0.63–0.91). ROC curve analysis determined a PBR of 1.9 μm as the best predictor for two-year mortality (sensitivity: 0.81; specificity: 0.59). Moreover, multivariate regression analysis revealed PBR and functional capillary density as significant predictors of two-year mortality, p = 0.036 and p = 0.048, respectively. Conclusions: Glycocalyx disintegration is related to poor overall survival in CHF patients. Full article

Boiling heat transfer, utilizing latent heat during phase change, has widely been used due to its high thermal efficiency and plays an important role in existing and next-generation cooling technologies. The most critical parameter in boiling heat transfer is critical heat flux (CHF), which represents the maximum heat flux a heated surface can sustain during boiling. CHF is primarily influenced by the wicking performance, which governs liquid supply to the surface. This study experimentally and numerically analyzed the wicking performance of micropillar structures at various temperatures (20–95 °C) using distilled water as the working fluid to provide fundamental data for CHF prediction. Infrared (IR) visualization was used to extract the wicking coefficient, and the experimental data were compared with computational fluid dynamics (CFD) simulations for validation. At room temperature (20 °C), the wicking coefficient increased with larger pillar diameters (D) and smaller gaps (G). Specifically, the highest roughness factor sample (D04G10, r = 2.51) exhibited a 117% higher wicking coefficient than the lowest roughness factor sample (D04G20, r = 1.51), attributed to enhanced capillary pressure and improved liquid supply. Additionally, for the same surface roughness factor, the wicking coefficient increased with temperature, showing a 49% rise at 95 °C compared to 20 °C due to reduced viscous resistance. CFD simulations showed strong agreement with experiments, with error within ±10%. These results confirm that the proposed numerical methodology is a reliable tool for predicting wicking performance near boiling temperatures. Full article

Background/Objectives: This study aimed to investigate the prognostic value of the red cell distribution width-to-estimated glomerular filtration rate (RGR) ratio in patients hospitalized with chronic heart failure (CHF) and its potential interaction with NT-proBNP levels. By integrating anemia and renal dysfunction markers, the RGR may provide enhanced predictive insights regarding extended length of hospital stay (ELOS) > 7 days, in-hospital mortality, and 6-month all-cause mortality across specific CHF phenotypes. Methods: In this retrospective, single-center pilot observational study, 627 CHF admissions (January 2022–August 2024) were analyzed. Patients were classified according to the ESC guidelines into heart failure with reduced (HFrEF), mildly reduced (HFmrEF), or preserved ejection fraction (HFpEF). The RGR was calculated as red cell distribution width standard deviation (RDW-SD) divided by estimated glomerular filtration rate (eGFR). Predictive accuracy was evaluated using logistic regression, receiver operating characteristic (ROC) analyses, and stepwise Cox proportional hazard regression. Results: RGR was significantly higher in HFrEF than in HFpEF (p = 0.042) and predicted ELOS only in HFpEF (AUC = 0.619). In contrast, for in-hospital mortality, RGR achieved excellent discrimination in HFrEF (AUC = 0.945), outperforming RDW and NT-proBNP. In HFmrEF, RDW exhibited the highest predictive power (AUC = 0.826), whereas in HFpEF, NT-proBNP was the strongest predictor (AUC = 0.958), although RGR preserved good discrimination (AUC = 0.746). Across the entire cohort and HF phenotypes, RGR consistently emerged as a significant predictor in univariable analysis. In multivariable models, it improved the significance prognosis especially alongside NT-proBNP in the entire cohort and HFrEF. For 6-month all-cause mortality, RGR surpassed RDW in prediction in all HF phenotypes. Conclusions: The RGR independently predicts prolonged hospitalization, in-hospital, and 6-month mortality in CHF—often outperforming RDW and eGFR and being comparable to NT-proBNP, especially in HFrEF. These findings suggest that RGR may serve as a valuable risk stratification tool in CHF management. Full article

Background/Objectives: The objective of this study was to investigate the association between congestive heart failure (CHF) and T1 mapping in both liver lobes using cardiac MRI. Methods: This retrospective study included patients who underwent cardiac MRI with T1 mapping sequences on a 1.5 T scanner. The liver T1 values were measured in four hepatic regions, utilizing cardiac short axis and four-chamber views. Echocardiographic and laboratory data were collected within 90 days of the cardiac MRI. Comparisons of the liver T1 values and echocardiographic parameters between patients with and without elevated NT-proBNP levels (>125 pg/mL) were conducted using the Mann–Whitney U test. Logistic regression models were employed to adjust for confounding factors. Results: A total of 397 patients were included (with a median age of 56 years; 127 females), of whom 35% (n = 138) exhibited elevated NT-proBNP levels. The patients with elevated NT-proBNP levels showed a larger end-diastolic volume (EDV: 92 vs. 81 mL/m², p < 0.001) and a lower LVEF level (50% vs. 60%, p < 0.001). The liver T1 was significantly higher in the right liver lobe (670 vs. 596 ms, p < 0.001) and the caudate lobe (664 vs. 598 ms, p < 0.001), but not in the left lobe (571 vs. 568 ms, p = 0.068) or the dome (590 vs. 560 ms, p = 0.1). T1 mapping in the caudate (OR 1.013, 95% CI 1.004–1.023, p = 0.005) and right liver lobes (OR 1.012, 95% CI 1.003–1.021, p = 0.009) remained independently predictive in the logistic regression analysis. Conclusions: Elevated T1 values in the caudate and right liver lobes assessed by cardiac MRI were independently associated with CHF and outperformed T1 measurements in the left liver lobe in predicting disease. Full article

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology to detect the presence of respiratory conditions, with a focus on COPD. Methods: The samples of human serum from 41 patients with respiratory diseases (11 patients with COPD, 20 with bronchial asthma (BA), and 10 with asthma–COPD overlap syndrome) and 103 patients with ischemic heart disease, complicated by chronic heart failure (CHF), were analyzed using SERS. A multivariate analysis of the SERS characteristics of human serum was performed using Partial Least Squares Discriminant Analysis (PLS-DA) to classify the following groups: (1) all respiratory disease patients versus the pathological referent group, which included CHF patients, and (2) patients with COPD versus those with BA. Results: We found that a combination of SERS characteristics at 638 and 1051 cm⁻¹ could help to identify respiratory diseases. The PLS-DA model achieved a mean predictive accuracy of 0.92 for classifying respiratory diseases and the pathological referent group (0.85 sensitivity, 0.97 specificity). However, in the case of differentiating between COPD and BA, the mean predictive accuracy was only 0.61. Conclusions: Therefore, the metabolic and proteomic composition of human serum shows significant differences in respiratory disease patients compared to the pathological referent group, but the differences between patients with COPD and BA are less significant, suggesting a similarity in the serum and general pathogenetic mechanisms of these two conditions. Full article

Background/Objectives: Chronic heart failure (CHF) remains a significant global health burden, with high morbidity, prolonged hospitalizations, and increased mortality. Traditional biomarkers such as NT-proBNP provide prognostic value; however, novel biomarker ratios may enhance risk stratification. This study evaluated the predictive utility of the NT-proBNP-to-albumin ratio (NTAR), red cell distribution width-to-eGFR ratio (RGR), and red cell distribution width-to-fibrinogen ratio (RFR) for hospital length of stay (LOS), extended hospitalization (ELOS), in-hospital mortality, and 6-month all-cause mortality. Methods: A retrospective observational pilot study was conducted on 382 CHF admissions (2022–2024) with comprehensive laboratory assessment. Biomarker performance was assessed through uni- and multivariate logistic regression, receiver operating characteristic curve, and Cox proportional hazards stepwise methods of analyses for refining predictive models. Results: NTAR and RGR emerged as significant predictors of hospitalization outcomes. NTAR demonstrated a moderate correlation with prolonged LOS (r = 0.45, p < 0.001) and was an independent predictor of ELOS (AUC = 0.697, OR = 2.438, p < 0.001), outperforming NT-proBNP. Additionally, NTAR significantly predicted in-hospital mortality (AUC = 0.768, OR = 4.461, p < 0.001) and 6-month all-cause mortality (AUC = 0.766, OR = 4.185, p < 0.001). RGR was the strongest predictor of in-hospital mortality (AUC = 0.785, HR = 2.18, p = 0.005), highlighting its role in renal dysfunction and erythropoietic alterations in CHF. The RFR observed prognostic value was minimal. Conclusions: In our study, NTAR and RGR offered valuable prognostic value underscoring the interplay of cardiac stress, nutritional status, and renal function in CHF prognosis. Further multicenter validation is warranted for these biomarkers. Full article

This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH). Our approach enables hyperparameter tuning for deep learning models with two hidden layers and multiple types of hyperparameters, enhancing the model’s capacity to work with complex optimization problems. The primary goal is to improve hyperparameter tuning performance in deep learning models compared to conventional methods such as Bayesian Optimization and Random Search. Furthermore, CASOH is evaluated alongside the state-of-the-art hyperparameter reinforcement learning (Hyp-RL) framework to ensure a comprehensive assessment. The CASOH framework integrates the Metropolis-Hastings algorithm with a uniform random sampling approach, increasing the likelihood of identifying promising hyperparameter configurations. Specifically, we developed a correlation between the objective function and samples, allowing subsequent samples to be strongly correlated with the current sample by applying an acceptance probability in our sampling algorithm. The effectiveness of our proposed method was examined using regression datasets such as Boston Housing, Critical heat flux (CHF), Concrete compressive strength, Combined Cycle Power Plant, Gas Turbine CO, and NOx Emission, as well as an ‘in-house’ dataset of lattice-physics parameters generated from a Monte Carlo code for nuclear fuel assembly simulation. One of the primary goals of this study is to construct an optimized deep-learning model capable of accurately predicting lattice-physics parameters for future applications of machine learning in nuclear reactor analysis. Our results indicate that this framework achieves competitive accuracy compared to conventional random search and Bayesian optimization methods. The most significant enhancement was observed in the lattice-physics dataset, achieving a 56.6% improvement in prediction accuracy, compared to improvements of 53.2% by Hyp-RL, 44.9% by Bayesian optimization, and 38.8% by random search relative to the nominal prediction. While the results are promising, further empirical validation across a broader range of datasets would be helpful to better assess the framework’s suitability for optimizing hyperparameters in complex problems involving high-dimensional parameters, highly non-linear systems, and multi-objective optimization tasks. Full article

Background: Congestive heart failure (CHF) is a complex chronic disease, and it is associated with a second comorbid condition in more than half of cases. Self-management programs can be specific to CHF or generic for chronic diseases. Several tools have been validated for CHF. Presently, there are no established generic instruments that are validated for measuring self-management in CHF. Objective: This study aims to evaluate the internal reliability and construct validity (psychometric properties) of the Partners in Health (PIH) scale for patients with congestive heart failure, a generic chronic disease self-management tool. Methods: The study included 210 adult CHF patients [120 with heart failure with reduced ejection fraction (HfrEF), 90 with preserved ejection fraction (HfpEF)], from Community Cardiology Outpatients in West Melbourne, Australia, who were treated in community cardiology and were included between May 2022 and Jan 2024. The screened patient population were diagnosed with CHF and were eligible for an SGLT-2 inhibitor. Cohort analysis used the Bayesian confirmatory factor analysis to evaluate the a priori four-factor structure. Omega coefficients and 95% credible intervals (CI) were used to assess internal reliability. Results: In the CHF (HFrEF) and preserved ejection fraction (HFpEF) cohorts, participants’ mean [standard deviation (SD)] age was 66.8 (13.5) and 71.3 (9.76) years. Description of study sociodemographics highlighted that 88% and 52% of patients were male, there was a BMI > 50% in both cohorts, eGFR > 60 mL/min were 59% and 74%, and LVEF < 40% and > 50% were 99% and 100%, respectively. Model fit for the hypothesised model was adequate (posterior predictive p = 0.073) and all hypothesised factor loadings were substantial (>0.6) and significant (p < 0.001). Omega coefficients (95% CI) for the PIH subscales of Knowledge, Partnership, Management and Coping were 0.84 (0.79–0.88), 0.79 (0.73–0.84), 0.89 (0.85–0.91) and 0.84 (0.79–0.88), respectively. Conclusion: This study is original in confirming the dimensionality, known-group validity, and reliability of the PIH scale for measuring generic self-management in outpatients with CHF syndrome. Full article

For patients on ventilation without acute respiratory distress syndrome (ARDS), there are, as yet, limited data on ventilation strategies. We hypothesized that driving pressure (DP) and mechanical power (MP) may play key roles for the late development of ARDS in patients without initial ARDS. A post hoc analysis of a database from our previous cohort was performed. The mean DP/MP was computed from the data before ARDS development or until ventilator support was discontinued within 28 days. The association between DP/MP and late development of ARDS within 28 days was determined. One hundred and twelve patients were enrolled, among whom seven developed ARDS. Univariate Cox regression showed that congestive heart failure (CHF) history and higher levels of mean MP and DP were associated with ARDS development. Multivariate models revealed that the mean MP and mean DP were still factors independently associated with ARDS development at hazard ratios of 1.177 and 1.226 after adjusting for the CHF effect. Areas under the receiver operating characteristic curves for mean DP/MP in predicting ARDS development were 0.813 and 0.759, respectively. In conclusion, high mean DP and MP values may be key factors associated with late ARDS development. The mean DP had a better predicted value for the development of ARDS than the mean MP. Full article

Congestive heart failure (CHF) is a fatal disease with progressive severity and no cure; the heart’s inability to adequately pump blood leads to fluid accumulation and frequent hospital readmissions after initial treatments. Therefore, it is imperative to continuously monitor CHF patients during its early stages to slow its progression and enable timely medical interventions for optimal treatment. An increase in interstitial fluid pressure (IFP) is indicative of acute CHF exacerbation, making IFP a viable biomarker for predicting upcoming CHF if continuously monitored. In this paper, we present an inductor-capacitor (LC) sensor for subcutaneous wireless and continuous IFP monitoring. The sensor is composed of inexpensive planar copper coils defined by a simple craft cutter, which serves as both the inductor and capacitor. Because of its sensing mechanism, the sensor does not require batteries and can wirelessly transmit pressure information. The sensor has a low-profile form factor for subcutaneous implantation and can communicate with a readout device through 4 layers of skin (12.7 mm thick in total). With a soft silicone rubber as the dielectric material between the copper coils, the sensor demonstrates an average sensitivity as high as –8.03 MHz/mmHg during in vitro simulations. Full article

Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew’s correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision. Full article