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Scientific Reports Jul 2024The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that...
The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.
Topics: Electrocardiography; Humans; Neural Networks, Computer; Signal Processing, Computer-Assisted; Algorithms; Wavelet Analysis; Machine Learning
PubMed: 38956261
DOI: 10.1038/s41598-024-65849-w -
Scientific Reports Jul 2024The relationship between bone mineral density and type 2 diabetes is still controversial. The aim of this study is to investigate the relationship between type 2...
The relationship between bone mineral density and type 2 diabetes is still controversial. The aim of this study is to investigate the relationship between type 2 diabetes mellitus (T2DM) and bone mineral density (BMD) in elderly men and postmenopausal women. The participants in this study included 692 postmenopausal women and older men aged ≥ 50 years, who were divided into the T2DM group and non-T2DM control group according to whether or not they had T2DM. The data of participants in the two groups were collected from the inpatient medical record system and physical examination center systems, respectively, of the Tertiary Class A Hospital. All data analysis is performed in SPSS Software. Compared with all T2DM group, the BMD and T scores of lumbar spines 1-4 (L1-L4), left femoral neck (LFN) and all left hip joints (LHJ) in the non-T2DM group were significantly lower than those in the T2DM group (P < 0.05), and the probability of major osteoporotic fracture in the next 10 years (PMOF) was significantly higher than that in T2DM group (P < 0.001). However, with the prolongation of the course of T2DM, the BMD significantly decreased, while fracture risk and the prevalence of osteoporosis significantly increased (P < 0.05). We also found that the BMD of L1-4, LFN and LHJ were negatively correlated with homeostatic model assessment-insulin resistance (HOMA-IR) (P = 0.028, P = 0.01 and P = 0.047, respectively). The results also showed that the BMD of LHJ was positively correlated with indirect bilirubin (IBIL) (P = 0.018). Although the BMD was lower in the non-T2DM group than in the T2DM group, the prolongation of the course of T2DM associated with the lower BMD. And the higher prevalence of osteoporosis and fracture risk significantly associated with the prolongation of the course of T2DM. In addition, BMD was significantly associated with insulin resistance (IR) and bilirubin levels in T2DM patients.Registration number: China Clinical Trials Registry: MR-51-23-051741; https://www.medicalresearch.org.cn/search/research/researchView?id=c0e5f868-eca9-4c68-af58-d73460c34028 .
Topics: Humans; Diabetes Mellitus, Type 2; Bone Density; Female; Male; Aged; Middle Aged; Postmenopause; Lumbar Vertebrae; Osteoporosis; Femur Neck; Risk Factors; Osteoporotic Fractures; Prevalence
PubMed: 38956260
DOI: 10.1038/s41598-024-65571-7 -
Scientific Reports Jul 2024The monocyte distribution width (MDW) has emerged as a promising biomarker for accurate and early identification of patients with potentially life-threatening...
Understanding the value of monocyte distribution width (MDW) in acutely ill medical patients presenting to the emergency department: a prospective single center evaluation.
The monocyte distribution width (MDW) has emerged as a promising biomarker for accurate and early identification of patients with potentially life-threatening infections. Here we tested the diagnostic performance of MDW in adult patients requiring hospital admission for community-acquired infections and sepsis, evaluated sources of heterogeneity in the estimates of diagnostic accuracy, and assessed the meaning of MDW in a patient population presenting to the emergency department (ED) for acute non-infectious conditions. 1925 consecutive patients were categorized into three groups: non-infection (n = 1507), infection (n = 316), and sepsis/septic shock (n = 102). Diagnostic performance for infection or sepsis of MDW alone or in combination with components of SOFA was tested using AUC of ROC curves, sensitivity, and specificity. The relationship between MDW and different pathogens as well as the impact of non-infectious conditions on MDW values were explored. For the prediction of infection, the AUC/ROC of MDW (0.84) was nearly overlapping that of procalcitonin (0.83), and C-reactive protein (0.89). Statistical optimal cut-off value for MDW was 21 for predicting infection (sensitivity 73%, specificity 82%) and 22 for predicting sepsis (sensitivity 79%, specificity 83%). The best threshold to rule out infection was MDW ≤ 17 (NPV 96.9, 95% CI 88.3-100.0), and ≤ 18 (NPV 99.5, 95% CI 98.3-100.0) to rule out sepsis. The combination of MDW with markers of organ dysfunction (creatinine, bilirubin, platelets) substantially improved the AUC (0.96 (95% CI 0.94-0.97); specificity and sensitivity of 88% and 94%, respectively). In conclusion, MDW has a good diagnostic performance in diagnosing infection and sepsis in patients presenting in ED. Its use as an infection marker even increases when combined with other markers of organ dysfunction. Understanding the impact of interactions of non-infectious conditions and comorbidities on MDW and its diagnostic accuracy requires further elucidation.
Topics: Humans; Emergency Service, Hospital; Male; Female; Middle Aged; Prospective Studies; Aged; Sepsis; Monocytes; Biomarkers; Adult; ROC Curve; Acute Disease; Aged, 80 and over; Community-Acquired Infections; Sensitivity and Specificity
PubMed: 38956252
DOI: 10.1038/s41598-024-65883-8 -
Scientific Reports Jul 2024Fibrinogen, a biomarker of thrombosis and inflammation, is related to a high risk for cardiovascular diseases. However, studies on the prognostic value of blood...
Fibrinogen, a biomarker of thrombosis and inflammation, is related to a high risk for cardiovascular diseases. However, studies on the prognostic value of blood fibrinogen concentrations for heart failure (HF) patients are few and controversial. We performed a retrospective analysis among acute or deteriorating chronic HF patients admitted to a hospital in Sichuan, China, between 2016 and 2019, integrating electronic health care records and external outcome data (N = 1532). During 6 months of follow-up, 579 HF patients were readmitted within 6 months, and 46 of them died. Surprisingly, we found an inverted U-shaped association of blood fibrinogen levels with risk of readmission within 6 months but not with risk of death within 6 months. It was found that HF patients had the highest risk for readmission within 6 months after reaching the turning point for blood fibrinogen (2.4 g/L). In HF patients with low fibrinogen levels < 2.4 g/L, elevated fibrinogen concentrations were still significantly associated with a higher risk for readmission within 6 months [OR = 2.3, 95% CI (1.2, 4.6); P = 0.014] after controlling for relevant covariates. There was no significant association between blood fibrinogen and readmission within 6 months [(OR = 1.0, 95% CI (0.9, 1.1); P = 0.675] in HF patients with high fibrinogen (> 2.4 g/L). The effect difference for the two subgroups was significant (P = 0.014). However, we did not observe any association between blood fibrinogen and death within 6 months stratified by the turning point, and the effect difference for the stratification was not significant (P = 0.380). We observed an inverted U-shaped association between blood fibrinogen and rehospitalization risk in HF patients for the first time. Additionally, our results did not support that elevated blood fibrinogen was related to increased death risk after discharge.
Topics: Humans; Fibrinogen; Heart Failure; Female; Male; Patient Readmission; Aged; Middle Aged; Retrospective Studies; Biomarkers; China; Risk Factors; Prognosis; Aged, 80 and over
PubMed: 38956249
DOI: 10.1038/s41598-024-66002-3 -
British Journal of Cancer Jul 2024Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and...
BACKGROUND
Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection.
METHODS
The study encompassed three phases: discovery, verification, and validation, comprising a total of 1309 individuals. Serum autoantibodies were profiled using the HuProt human proteome microarray, and autoantibody levels were measured using the enzyme-linked immunosorbent assay (ELISA). Twelve machine learning algorithms were employed to construct diagnostic models, and evaluated using the area under the receiver operating characteristic curve (AUC). The model application was facilitated through R Shiny, providing a graphical interface.
RESULTS
Thirteen autoantibodies targeting TAAs (CAST, FAM131A, GABPA, HDAC1, HDGFL1, HSF1, ISM2, PTMS, RNF219, SMARCE1, SNAP25, SRPK2, and ZPR1) were identified in the discovery phase. Subsequent verification and validation phases identified five TAAbs (anti-CAST, anti-HDAC1, anti-HSF1, anti-PTMS, and anti-ZPR1) that exhibited significant differences between ESCC and control subjects (P < 0.05). The support vector machine (SVM) model demonstrated robust performance, with AUCs of 0.86 (95% CI: 0.82-0.89) in the training set and 0.83 (95% CI: 0.78-0.88) in the test set. For early-stage ESCC, the SVM model achieved AUCs of 0.83 (95% CI: 0.79-0.88) in the training set and 0.83 (95% CI: 0.77-0.90) in the test set. Notably, promising results were observed for high-grade intraepithelial neoplasia, with an AUC of 0.87 (95% CI: 0.77-0.98). The web-based implementation of the early ESCC diagnostic tool is publicly accessible at https://litdong.shinyapps.io/ESCCPred/ .
CONCLUSION
This study provides a promising and easy-to-use diagnostic prediction model for early ESCC detection. It holds promise for improving early detection strategies and has potential implications for public health.
PubMed: 38956246
DOI: 10.1038/s41416-024-02781-w -
Scientific Reports Jul 2024With the aging world population, the incidence of soft tissue sarcoma (STS) in the elderly gradually increases and the prognosis is poor. The primary goal of this...
With the aging world population, the incidence of soft tissue sarcoma (STS) in the elderly gradually increases and the prognosis is poor. The primary goal of this research was to analyze the relevant risk factors affecting the postoperative overall survival in elderly STS patients and to provide some guidance and assistance in clinical treatment. The study included 2,353 elderly STS patients from the Surveillance, Epidemiology, and End Results database. To find independent predictive variables, we employed the Cox proportional risk regression model. R software was used to develop and validate the nomogram model to predict postoperative overall survival. The performance and practical value of the nomogram were evaluated using calibration curves, the area under the curve, and decision curve analysis. Age, tumor primary site, disease stage, tumor size, tumor grade, N stage, and marital status, are the risk variables of postoperative overall survival, and the prognostic model was constructed on this basis. In the two sets, both calibration curves and receiver operating characteristic curves showed that the nomogram had high predictive accuracy and discriminative power, while decision curve analysis demonstrated that the model had good clinical usefulness. A predictive nomogram was designed and tested to evaluate postoperative overall survival in elderly STS patients. The nomogram allows clinical practitioners to more accurately evaluate the prognosis of individual patients, facilitates the progress of individualized treatment, and provides clinical guidance.
Topics: Humans; Aged; Female; Sarcoma; Male; Nomograms; Prognosis; Aged, 80 and over; SEER Program; Risk Factors; ROC Curve; Proportional Hazards Models
PubMed: 38956230
DOI: 10.1038/s41598-024-65657-2 -
Scientific Reports Jul 2024Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal...
Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.
Topics: Magnetic Resonance Imaging; Brain Neoplasms; Humans; Neural Networks, Computer; Image Processing, Computer-Assisted; Algorithms; Brain
PubMed: 38956224
DOI: 10.1038/s41598-024-65714-w -
Scientific Reports Jul 2024Recent studies have shown a growing interest in the so-called "aperiodic" component of the EEG power spectrum, which describes the overall trend of the whole spectrum...
Recent studies have shown a growing interest in the so-called "aperiodic" component of the EEG power spectrum, which describes the overall trend of the whole spectrum with a linear or exponential function. In the field of brain aging, this aperiodic component is associated both with age-related changes and performance on cognitive tasks. This study aims to elucidate the potential role of education in moderating the relationship between resting-state EEG features (including aperiodic component) and cognitive performance in aging. N = 179 healthy participants of the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) dataset were divided into three groups based on age and education. Older adults exhibited lower exponent, offset (i.e. measures of aperiodic component), and Individual Alpha Peak Frequency (IAPF) as compared to younger adults. Moreover, visual attention and working memory were differently associated with the aperiodic component depending on education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, while an opposite trend was found in those with low education. While further investigation is needed, this study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.
Topics: Humans; Electroencephalography; Cognition; Male; Female; Aged; Aging; Adult; Middle Aged; Memory, Short-Term; Young Adult; Brain; Educational Status; Attention; Aged, 80 and over
PubMed: 38956186
DOI: 10.1038/s41598-024-66049-2 -
Scientific Reports Jul 2024Birds maintain some of the highest body temperatures among endothermic animals. Often deemed a selective advantage for heat tolerance, high body temperatures also limits...
Birds maintain some of the highest body temperatures among endothermic animals. Often deemed a selective advantage for heat tolerance, high body temperatures also limits birds' thermal safety margin before reaching lethal levels. Recent modelling suggests that sustained effort in Arctic birds might be restricted at mild air temperatures, which may require reductions in activity to avoid overheating, with expected negative impacts on reproductive performance. We measured within-individual changes in body temperature in calm birds and then in response to an experimental increase in activity in an outdoor captive population of Arctic, cold-specialised snow buntings (Plectrophenax nivalis), exposed to naturally varying air temperatures (- 15 to 36 °C). Calm buntings exhibited a modal body temperature range from 39.9 to 42.6 °C. However, we detected a significant increase in body temperature within minutes of shifting calm birds to active flight, with strong evidence for a positive effect of air temperature on body temperature (slope = 0.04 °C/ °C). Importantly, by an ambient temperature of 9 °C, flying buntings were already generating body temperatures ≥ 45 °C, approaching the upper thermal limits of organismal performance (45-47 °C). With known limited evaporative heat dissipation capacities in these birds, our results support the recent prediction that free-living buntings operating at maximal sustainable rates will increasingly need to rely on behavioural thermoregulatory strategies to regulate body temperature, to the detriment of nestling growth and survival.
Topics: Animals; Arctic Regions; Songbirds; Cold Temperature; Body Temperature Regulation; Body Temperature; Breeding; Reproduction; Female; Male; Temperature
PubMed: 38956145
DOI: 10.1038/s41598-024-65208-9 -
Scientific Reports Jul 2024Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and...
Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of life of breast cancer survivors. This study aimed to assess the level of distress in breast cancer survivors and analyze the variables that significantly affect distress using machine learning techniques. A survey was conducted with 641 adult breast cancer patients using the National Comprehensive Cancer Network Distress Thermometer tool. Participants identified various factors that caused distress. Five machine learning models were used to predict the classification of patients into mild and severe distress groups. The survey results indicated that 57.7% of the participants experienced severe distress. The top-three best-performing models indicated that depression, dealing with a partner, housing, work/school, and fatigue are the primary indicators. Among the emotional problems, depression, fear, worry, loss of interest in regular activities, and nervousness were determined as significant predictive factors. Therefore, machine learning models can be effectively applied to determine various factors influencing distress in breast cancer patients who have completed primary treatment, thereby identifying breast cancer patients who are vulnerable to distress in clinical settings.
Topics: Humans; Breast Neoplasms; Female; Machine Learning; Cancer Survivors; Middle Aged; Adult; Psychological Distress; Quality of Life; Stress, Psychological; Aged; Depression; Surveys and Questionnaires
PubMed: 38956137
DOI: 10.1038/s41598-024-65132-y