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Scientific Reports Jul 2024Plyometric training is characterized by high-intensity exercise which is performed in short term efforts divided into sets. The purpose of the present study was twofold:... (Randomized Controlled Trial)
Randomized Controlled Trial
Plyometric training is characterized by high-intensity exercise which is performed in short term efforts divided into sets. The purpose of the present study was twofold: first, to investigate the effects of three distinct plyometric exercise protocols, each with varying work-to-rest ratios, on muscle fatigue and recovery using an incline-plane training machine; and second, to assess the relationship between changes in lower limb muscle strength and power and the biochemical response to the three exercise variants employed. Forty-five adult males were randomly divided into 3 groups (n = 15) performing an exercise of 60 rebounds on an incline-plane training machine. The G0 group performed continuous exercise, while the G45 and G90 groups completed 4 sets of 15 repetitions, each set lasting 45 s with 45 s rest in G45 (work-to-rest ratio of 1:1) and 90 s rest in G90 (1:2 ratio). Changes in muscle torques of knee extensors and flexors, as well as blood lactate (LA) and ammonia levels, were assessed before and every 5 min for 30 min after completing the workout. The results showed significantly higher (p < 0.001) average power across all jumps generated during intermittent compared to continuous exercise. The greatest decrease in knee extensor strength immediately post-exercise was recorded in group G0 and the least in G90. The post-exercise time course of LA changes followed a similar pattern in all groups, while the longer the interval between sets, the faster LA returned to baseline. Intermittent exercise had a more favourable effect on muscle energy metabolism and recovery than continuous exercise, and the work-to-rest ratio of 1:2 in plyometric exercises was sufficient rest time to allow the continuation of exercise in subsequent sets at similar intensity.
Topics: Humans; Male; Rest; Muscle Fatigue; Adult; Muscle Strength; Plyometric Exercise; Young Adult; Muscle, Skeletal; Lactic Acid; Ammonia; Exercise
PubMed: 38956280
DOI: 10.1038/s41598-024-66146-2 -
Communications Medicine Jul 2024Human carcinoembryonic antigen cell adhesion molecule 1 (CEACAM1) is an inhibitory cell surface protein that functions through homophilic and heterophilic ligand...
BACKGROUND
Human carcinoembryonic antigen cell adhesion molecule 1 (CEACAM1) is an inhibitory cell surface protein that functions through homophilic and heterophilic ligand binding. Its expression on immune cells in human tumors is poorly understood.
METHODS
An antibody that distinguishes human CEACAM1 from other highly related CEACAM family members was labeled with Tb and inserted into a panel of antibodies that included specificity for programmed cell death protein 1 (PD1) and PD-L1, which are targets of immunotherapy, to gain a data-driven immune cell atlas using cytometry by time-of-flight (CyTOF). A detailed inventory of CEACAM1, PD1, and PD-L1 expression on immune cells in metastatic lesions to lymph node or soft tissues and peripheral blood samples from patients with treatment-naive and -resistant melanoma as well as peripheral blood samples from healthy controls was performed.
RESULTS
CEACAM1 is absent or at low levels on healthy circulating immune cells but is increased on immune cells in peripheral blood and tumors of melanoma patients. The majority of circulating PD1-positive NK cells, innate T cells, B cells, monocytic cells, dendritic cells, and CD4 T cells in the peripheral circulation of treatment-resistant disease co-express CEACAM1 and are demonstrable as discrete populations. CEACAM1 is present on distinct types of cells that are unique to the tumor microenvironment and exhibit expression levels that are highest in treatment resistance; this includes tumor-infiltrating CD8 T cells.
CONCLUSIONS
To the best of our knowledge, this work represents the first comprehensive atlas of CEACAM1 expression on immune cells in a human tumor and reveals an important correlation with treatment-resistant disease. These studies suggest that agents targeting CEACAM1 may represent appropriate partners for PD1-related pathway therapies.
PubMed: 38956268
DOI: 10.1038/s43856-024-00525-8 -
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 2024A complex Polytopic fuzzy set (CPoFS) extends a Polytopic fuzzy set (PoFS) by handling vagueness with degrees that range from real numbers to complex numbers within the...
A complex Polytopic fuzzy set (CPoFS) extends a Polytopic fuzzy set (PoFS) by handling vagueness with degrees that range from real numbers to complex numbers within the unit disc. This extension allows for a more nuanced representation of uncertainty. In this research, we develop Complex Polytopic Fuzzy Sets (CPoFS) and establish basic operational laws of CPoFS. Leveraging these laws, we introduce new operators under a confidence level, including the confidence complex Polytopic fuzzy Einstein weighted geometric aggregation (CCPoFEWGA) operator, the confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (CCPoFEOWGA) operator, the confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (CCPoFEHGA) operator, the induced confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (I-CCPoFEOWGA) operator and the induced confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (I-CCPoFEHGA) operator, enhancing decision-making precision in uncertain environments. We also investigate key properties of these operators, including monotonicity, boundedness, and idempotency. With these operators, we create an algorithm designed to solve multiattribute decision-making problems in a Polytopic fuzzy environment. To demonstrate the effectiveness of our proposed method, we apply it to a numerical example and compare its flexibility with existing methods. This comparison will underscore the advantages and enhancements of our approach, showing its efficiency in managing complex decision-making scenarios. Through this, we aim to demonstrate how our method provides superior performance and adaptability across different situations.
PubMed: 38956256
DOI: 10.1038/s41598-024-65679-w -
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 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 2024Despite recent advancements in peripheral nerve regeneration, the creation of nerve conduits with chemical and physical cues to enhance glial cell function and support...
Despite recent advancements in peripheral nerve regeneration, the creation of nerve conduits with chemical and physical cues to enhance glial cell function and support axonal growth remains challenging. This study aimed to assess the impact of electrical stimulation (ES) using a conductive nerve conduit on sciatic nerve regeneration in a rat model with transection injury. The study involved the fabrication of conductive nerve conduits using silk fibroin and Au nanoparticles (AuNPs). Collagen hydrogel loaded with green fluorescent protein (GFP)-positive adipose-derived mesenchymal stem cells (ADSCs) served as the filling for the conduit. Both conductive and non-conductive conduits were applied with and without ES in rat models. Locomotor recovery was assessed using walking track analysis. Histological evaluations were performed using H&E, luxol fast blue staining and immunohistochemistry. Moreover, TEM analysis was conducted to distinguish various ultrastructural aspects of sciatic tissue. In the ES + conductive conduit group, higher S100 (p < 0.0001) and neurofilament (p < 0.001) expression was seen after 6 weeks. Ultrastructural evaluations showed that conductive scaffolds with ES minimized Wallerian degeneration. Furthermore, the conductive conduit with ES group demonstrated significantly increased myelin sheet thickness and decreased G. ratio compared to the autograft. Immunofluorescent images confirmed the presence of GFP-positive ADSCs by the 6th week. Locomotor recovery assessments revealed improved function in the conductive conduit with ES group compared to the control group and groups without ES. These results show that a Silk/AuNPs conduit filled with ADSC-seeded collagen hydrogel can function as a nerve conduit, aiding in the restoration of substantial gaps in the sciatic nerve with ES. Histological and locomotor evaluations indicated that ES had a greater impact on functional recovery compared to using a conductive conduit alone, although the use of conductive conduits did enhance the effects of ES.
Topics: Animals; Nerve Regeneration; Sciatic Nerve; Rats; Tissue Scaffolds; Gold; Rats, Sprague-Dawley; Silk; Mesenchymal Stem Cells; Electric Stimulation; Fibroins; Metal Nanoparticles; Male; Recovery of Function; Guided Tissue Regeneration; Hydrogels
PubMed: 38956215
DOI: 10.1038/s41598-024-65286-9 -
Scientific Reports Jul 2024The INSPIRE randomized clinical trial demonstrated that a high protein diet (HPRO) combined with neuromuscular electrical stimulation (NMES) attenuates muscle atrophy... (Randomized Controlled Trial)
Randomized Controlled Trial
Identification of metabolites associated with preserved muscle volume after aneurysmal subarachnoid hemorrhage due to high protein supplementation and neuromuscular electrical stimulation.
The INSPIRE randomized clinical trial demonstrated that a high protein diet (HPRO) combined with neuromuscular electrical stimulation (NMES) attenuates muscle atrophy and may improve outcomes after aneurysmal subarachnoid hemorrhage We sought to identify specific metabolites mediating these effects. Blood samples were collected from subjects on admission prior to randomization to either standard of care (SOC; N = 12) or HPRO + NMES (N = 12) and at 7 days. Untargeted metabolomics were performed for each plasma sample. Sparse partial least squared discriminant analysis identified metabolites differentiating each group. Correlation coefficients were calculated between each metabolite and total protein per day and muscle volume. Multivariable models determined associations between metabolites and muscle volume. Unique metabolites (18) were identified differentiating SOC from HPRO + NMES. Of these, 9 had significant positive correlations with protein intake. In multivariable models, N-acetylleucine was significantly associated with preserved temporalis [OR 1.08 (95% CI 1.01, 1.16)] and quadricep [OR 1.08 (95% CI 1.02, 1.15)] muscle volume. Quinolinate was also significantly associated with preserved temporalis [OR 1.05 (95% CI 1.01, 1.09)] and quadricep [OR 1.04 (95% CI 1.00, 1.07)] muscle volume. N-acetylserine and β-hydroxyisovaleroylcarnitine were associated with preserved temporalis or quadricep volume. Metabolites defining HPRO + NMES had strong correlations with protein intake and were associated with preserved muscle volume.
Topics: Humans; Male; Female; Middle Aged; Subarachnoid Hemorrhage; Diet, High-Protein; Muscle, Skeletal; Metabolomics; Muscular Atrophy; Electric Stimulation Therapy; Aged; Metabolome; Dietary Supplements
PubMed: 38956192
DOI: 10.1038/s41598-024-64666-5 -
Diabetes, Obesity & Metabolism Jul 2024To perform a meta-analysis to investigate the effects of intermittent fasting (IF), as compared with either a control diet (CON) and/or calorie restriction (CR), on body... (Review)
Review
AIM
To perform a meta-analysis to investigate the effects of intermittent fasting (IF), as compared with either a control diet (CON) and/or calorie restriction (CR), on body composition and cardiometabolic health in individuals with prediabetes and type 2 diabetes (T2D).
METHODS
PubMed, Web of Science, and Scopus were searched from their inception to March 2024 to identify original randomized trials with parallel or crossover designs that studied the effects of IF on body composition and cardiometabolic health. Weighted mean differences (WMDs) or standardized mean differences with 95% confidence intervals (CIs) were calculated using random-effects models.
RESULTS
Overall, 14 studies involving 1101 adults with prediabetes or T2D were included in the meta-analysis. IF decreased body weight (WMD -4.56 kg [95% CI -6.23 to -2.83]; p = 0.001), body mass index (BMI; WMD -1.99 kg.m [95% CI -2.74 to -1.23]; p = 0.001), glycated haemoglobin (HbA1c; WMD -0.81% [95% CI -1.24 to -0.38]; p = 0.001), fasting glucose (WMD -0.36 mmol/L [95% CI -0.63 to -0.09]; p = 0.008), total cholesterol (WMD -0.31 mmol/L [95% CI -0.60 to -0.02]; p = 0.03) and triglycerides (WMD -0.14 mmol/L [95% CI -0.27 to -0.01]; p = 0.02), but did not significantly decrease fat mass, insulin, low-densitiy lipoprotein, high-density lipoprotein, or blood pressure as compared with CON. Furthermore, IF decreased body weight (WMD -1.14 kg [95% CI -1.69 to -0.60]; p = 0.001) and BMI (WMD -0.43 kg.m [95% CI -0.58 to -0.27]; p = 0.001), but did not significantly affect fat mass, lean body mass, visceral fat, insulin, HbA1c, lipid profiles or blood pressure.
CONCLUSION
Intermittent fasting is effective for weight loss and specific cardiometabolic health markers in individuals with prediabetes or T2D. Additionally, IF is associated with a reduction in body weight and BMI compared to CR, without effects on glycaemic markers, lipid profiles or blood pressure.
PubMed: 38956175
DOI: 10.1111/dom.15730 -
Scientific Reports Jul 2024Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In...
Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.
PubMed: 38956157
DOI: 10.1038/s41598-024-66007-y