Biological Signals Research Articles

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

A biochemical feedback signal for hypothermia treatment for neonatal hypoxic–ischemic encephalopathy: focusing on central nervous system proteins in biofluids

Hypothermia has been widely used to treat moderate to severe neonatal hypoxic–ischemic encephalopathy (HIE), yet evaluating the effects of hypothermia relies on clinical neurology, neuroimaging, amplitude-integrated electroencephalography, and follow-up data on patient outcomes. Biomarkers of brain injury have been considered for estimating the effects of hypothermia. Proteins specific to the central nervous system (CNS) are components of nervous tissue, and once the CNS is damaged, these proteins are released into biofluids (cerebrospinal fluid, blood, urine, tears, saliva), and they can be used as markers of brain damage. Clinical reports have shown that CNS-specific marker proteins (CNSPs) were early expressed in biofluids after brain damage and formed unique biochemical profiles. As a result, these markers may serve as an indicator for screening brain injury in infants, monitoring disease progression, identifying damage region of brain, and assessing the efficacy of neuroprotective measures. In clinical work, we have found that there are few reports on using CNSPs as biological signals in hypothermia for neonatal HIE. The aim of this article is to review the classification, origin, biochemical composition, and physiological function of CNSPs with changes in their expression levels after hypothermia for neonatal HIE. Hopefully, this review will improve the awareness of CNSPs among pediatricians, and encourage future studies exploring the mechanisms behind the effects of hypothermia on these CNSPs, in order to reduce the adverse outcome of neonatal HIE.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data

BackgroundSurvival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction.MethodsWe introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application.ResultsThe proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer.ConclusionsThis study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

Engineering Interconnected Nanofluidic Channel in a Hydrogel Supernetwork toward K+ Ion Accelerating Transport and Efficient Sensing.

Ion transportation via the mixed mechanisms of hydrogels underpins ultrafast biological signal transmission in nature, and its application to the rapid and sensitive sensing detection of human specific ions is of great interest for the field of medical science. However, current research efforts are still unable to achieve transmission results that are comparable to those of bioelectric signals. Herein, 3D interconnected nanochannels based on poly(pyrrole-co-dopamine)/poly(vinyl alcohol) (P(Py-co-DA)/PVA) supernetwork conductive hydrogels are designed and fabricated as stimuli-responsive structures for K+ ions. Distinct from conventional configurations, which exhibit rapid electron transfer and permeability to biosubstrates, interconnected nanofluidic nanochannels collaborated with the P(Py-co-DA) conductive polymer in the supernetwork conductive hydrogel significantly improve conductivity (88.3 mS/cm), ion transport time (0.1 s), and ion sensitivity (74.6 mV/dec). The faster ion response time is attributed to the synergism of excellent conductivity originating from the P(Py-co-DA) polymer and the electronic effect in the interconnected nanofluidic channels. Furthermore, the supernetwork conductive hydrogel demonstrates K+ ion selectivity relative to other cations in biofluids such as Na+, Mg2+, and Ca2+. The DFT calculation indicates that the small solvation energy and low chemical transfer resistance are the main reasons for the excellent K+ ion selectivity. Finite element analysis (FEA) simulations further support these experimental results. Consequently, the P(Py-co-DA)/PVA supernetwork conductive hydrogels enriched with the 3D interconnected nanofluidic channels developed in this work possess excellent sensing of K+ ions. This strategy provides great insight into efficient ion sensing in traditional biomedical sensing that has not been explored by previous researchers.

A High-Adhesive and Sensitive Hydrogel with Interpenetrating and Interlocking Networked Structure for Efficient Biological Signal Monitoring

A High-Adhesive and Sensitive Hydrogel with Interpenetrating and Interlocking Networked Structure for Efficient Biological Signal Monitoring

IMAR in Frequency Domain for Separating Speech Signals by STFT

Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

Mechanosensing by Piezo1 and its implications in the kidney.

Piezo1 is an essential mechanosensitive transduction ion channel in mammals. Its unique structure makes it capable of converting mechanical cues into electrical and biological signals, modulating biological and (patho)physiological processes in a wide variety of cells. There is increasing evidence demonstrating that the piezo1 channel plays a vital role in renal physiology and disease conditions. This review summarizes the current evidence on the structure and properties of Piezo1, gating modulation, and pharmacological characteristics, with special focus on the distribution and (patho)physiological significance of Piezo1 in the kidney, which may provide insights into potential treatment targets for renal diseases involving this ion channel.

Tubastatin alleviates epidural fibrosis via negatively targeting TGFβ/PI3K/Akt pathway.

Epidural fibrosis (EF) is a chronic, progressive and severe disease. Histone deacetylase 6 (HDAC6) regulates biological signals and cell activities by deacetylating lysine residues and participates in TGF-β-induced epithelial-mesenchymal transition (EMT). Nevertheless, the effect and mechanism of HDAC6 in EF remain unclear. To investigate the effect and mechanism of HDAC6 inhibition on repressing epidural fibrosis. HDAC6 expression and α-smooth muscle actin (α-SMA) in normal human tissue and human EF tissue were assessed by quantitative real-time PCR (qRT-PCR) and western blotting. Human fibroblasts were treated with TGF-β ± HDAC6 inhibitors (Tubastatin) and fibrotic markers including collagen I, collagen III, α-SMA and fibronectin were assessed using western blotting. Then TGFβ1 receptor (TGFβ1-R), PI3K and Akt were analyzed using qRT-PCR and western blotting. Rats were undergone laminectomy± Tubastatin(intraperitoneally injection; daily for 7 days) and epidural scar extracellular matrix (ECM) expression was gauged using immunoblots. Increasing HDAC6 expression was associated with α-SMA enrichment. Tubastatin remarkably restrained TGF-β-induced level of collagen and ECM deposition in human fibroblasts, and the discovery was accompanied by decreased PI3K and Akt phosphorylation. Moreover, Tubastatin also inhibited TGF-β-mediated HIF-1α and VEGF expression. In the epidural fibrosis model, we found that Tubastatin weakened scar hyperplasia and collagen deposition, and effectively inhibited the process of epidural fibrosis. These results indicated that Tubastatin inhibited HDAC6 expression and decreased TGF-β/ PI3K/ Akt pathway that promotes collagen and ECM deposition and VEGF release, leading reduction of myofibroblast activation. Hence, Tubastatin ameliorated epidural fibrosis development.

ScLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Алгоритмы передачи информации в системах управления биологическими объектами

One of the problems of the theory of management of objects with a high degree of uncertainty of behavior is solved: the development of theoretical foundations of analysis, modeling methods and improvement of bio cybernetic systems, their algorithmic and software necessary to improve the efficiency of the management process is proposed. To solve this problem, it is proposed to use the mathematical apparatus of information theory, on the basis of which the information characteristics of the control signal transmission channel from the source to the control object through the external environment are calculated, taking into account the noise of the ecosystem of the control object. The existing systems using monomodal control signals that do not carry a semantic load are analyzed, and the disadvantages and limited possibilities of improving the control systems under consideration are described. Using the example of one of the insect behavior control systems with a trichromic view, a comparison of the results obtained using various technical means used as control signal sources was performed. For improving the efficiency of control systems, which use a multimodal control signal structure, proposed various algorithms for constructing control systems using a multimodal control signal structure are considered, namely: serial and parallel circuits, combined, combining serial and parallel algorithms for using control signal sources. As a method for further improving the efficiency of control systems for biological objects with a high degree of uncertainty, it is proposed to use biological signals containing a large semantic load for the control object, which determines its behavioral response. The directions of further research are formulated in order to maximize the amount of information transmitted to the control object.

The Immunomodulatory effect of exosomes in diabetes: a novel and attractive therapeutic tool in diabetes therapy

Exosomes carry proteins, metabolites, nucleic acids and lipids from their parent cell of origin. They are derived from cells through exocytosis, are ingested by target cells, and can transfer biological signals between local or distant cells. Therefore, exosomes are often modified in reaction to pathological processes, including infection, cancer, cardiovascular diseases and in response to metabolic perturbations such as obesity and diabetes, all of which involve a significant inflammatory aspect. Here, we discuss how immune cell-derived exosomes origin from neutrophils, T lymphocytes, macrophages impact on the immune reprogramming of diabetes and the associated complications. Besides, exosomes derived from stem cells and their immunomodulatory properties and anti-inflammation effect in diabetes are also reviewed. Moreover, As an important addition to previous reviews, we describes promising directions involving engineered exosomes as well as current challenges of clinical applications in diabetic therapy. Further research on exosomes will explore their potential in translational medicine and provide new avenues for the development of effective clinical diagnostics and therapeutic strategies for immunoregulation of diabetes.

Strategy, Design, and Fabrication of Electrochemical Biosensors: A Tutorial.

Advanced healthcare requires novel technologies capable of real-time sensing to monitor acute and long-term health. The challenge relies on converting a real-time quantitative biological and chemical signal into a desired measurable output. Given the success in detecting glucose and the commercialization of glucometers, electrochemical biosensors continue to be a mainstay of academic and industrial research activities. Despite the wealth of literature on electrochemical biosensors, reports are often specific to a particular application (e.g., pathogens, cancer markers, glucose, etc.), and most fail to convey the underlying strategy and design, and if it is transferable to detection of a different analyte. Here we present a tutorial review for those entering this research area that summarizes the basic electrochemical techniques utilized as well as discusses the designs and optimization strategies employed to improve sensitivity and maximize signal output.

Effects of hypertriglyceridemia with or without NEFA elevation on β-cell function and insulin clearance and sensitivity.

Hypertriglyceridemia is a risk factor for developing type 2 diabetes (T2D) and might contribute to its pathogenesis either directly or through elevation of non-esterified fatty acids (NEFAs). This study aimed at comparing the glucometabolic effects of acute hypertriglyceridemia alone or combined with NEFA elevation in non-diabetic subjects. Twenty-two healthy lean volunteers underwent two 5-h intravenous infusions of either saline or Intralipid, without (n=12) or with heparin (I+H; n=10) to activate the release of NEFAs. Oral glucose tolerance tests (OGTTs) were performed during the last 3h of infusion. Insulin sensitivity, insulin secretion rate (ISR), model-derived β-cell function, and insulin clearance were measured after 2h of lipid infusion and during the OGTTs. In fasting conditions, both lipid infusions increased plasma insulin and ISR and reduced insulin clearance, without affecting plasma glucose and insulin sensitivity. These effects on insulin and ISR were more pronounced for I+H than Intralipid alone. During the OGTT, the lipid infusions markedly impaired glucose tolerance, increased plasma insulin and ISR, and decreased insulin sensitivity and clearance, without significant group differences. Intralipid alone inhibited glucose-stimulated insulin secretion (i.e. β-cell glucose sensitivity) and increased β-cell potentiation, whereas I+H had neutral effects on these β-cell functions. In healthy non-obese subjects, mild acute hypertriglyceridemia directly reduces glucose tolerance, insulin sensitivity and clearance, and has selective and opposite effects on β-cell function that are neutralized by NEFAs. These findings provide new insight into plausible biological signals that generate and sustain insulin resistance and chronic hyperinsulinemia in the development of T2D.

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes

BackgroundAs most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance.ResultsWe compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0–97%) and false positive rates (0–30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered.ConclusionsTogether, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals.

The Role of p38 Mitogen-Activated Protein Kinase-Mediated F-Actin in the Acupuncture-Induced Mitigation of Inflammatory Pain in Arthritic Rats.

The analgesic efficacy of acupuncture has been widely recognized. However, the mechanism by which manual acupuncture-generated mechanical stimuli translate into biological signals remains unclear. This study employed a CFA-induced inflammatory pain rat model. Acupuncture intervention was then performed following standardized procedures. Enzyme-linked immunosorbent assay (ELISA) assessed inflammatory cytokines levels, while immunofluorescence and qRT-PCR screened the level of p38 and F-actin expression in the ST36 acupoint area of rats. Results indicated increased inflammatory factors, including IL-1β and TNFα, with reduced paw withdrawal mechanical threshold (PWMT) and paw withdrawal thermal latency (PWTL) in CFA rats compared to unmodeled rats. After acupuncture intervention, the heightened expression level of F-actin and p38 mRNA and the phosphorylation of p38 in the acupoint area was observed alongside decreased inflammatory factors in diseased ankle joints. The application of lifting and thrusting manipulations further enhanced the effect of acupuncture, in which the molecular expression level of muscle and connective tissue increased most significantly, indicating that these two tissues play a major role in the transformation of acupuncture stimulation. Moreover, antagonizing p38 expression hindered acupuncture efficacy, supporting the hypothesis that p38 MAPK-mediated F-actin transduces mechanical signals generated by acupuncture and related manipulation into biological signals.

NANOBIOMATERIALS/BIOINKS BASED SCAFFOLDS IN 3D BIOPRINTING FOR TISSUE ENGINEERING AND ARTIFICIAL HUMAN ORGANS

Bioinks are combinations of live cells and biomaterials that are sometimes blended with tissue factor or other biomolecules for the purpose of extrusion if the desired effect is desired. The term “bioprinting” refers to a relatively new method that involves the application or depositing of biomaterial solutions or bioinks in order to produce three-dimensional (3D) constructions that have topologies and mechanical/biological qualities that are similar to those of genuine human tissue or organs. Printed structures are widely used in tissue engineering to heal or repair damaged tissues or organs, as well as in vitro tissue modeling to test and validate newly created medications and vaccinations before being administered to patients. Supporting and directing cell development toward its native surroundings. When it comes to the formation of biological structures, one of the most important challenges is to ensure that specific physicochemical and biological signals are present in a harmonious manner in order to regulate the activity of cells. Additionally, in order to stimulate stem cell differentiation toward certain tissues, exact arrays of stimuli must be established. The introduction of bioactive material with a nanoscale can control the destiny of cells, contributing to their differentiation and enabling the biofabrication of useful structures. Using nano-composite bio-ink, it is possible to create scaffolds that are instructive for cells or cells can be high quality images onto the media. In addition, the addition of nano into 3D printed configurations may make it possible for these structures to be manipulated by a range of external physical stimuli, thereby providing an additional instrument for usage in healthcare applications. With that being said, there is an interest in the development of biological systems that have functionalities such as motion, shape alteration or sensing. In this study, we emphasize how the use of nano-biomaterials in bioprinting techniques results in strategies for tissue regeneration that show promise.

Assessing the usability of aromatic mouthwashes in alleviating physiological stress responses.

Mouthwashes play a pivotal role in oral care, and their efficacy has been explored extensively across various dimensions. As a contribution to the development of novel oral care products, this study aims to investigate the psychophysiological effects of aromatic mouthwashes during the resilience period from a short-term cognitive stressor utilizing biological signals and subjective evaluations. A within-participant experimental design with 22 healthy females was conducted with four mouthwashes; peppermint (Mint), peppermint + bergamot (MB), peppermint + sweet orange (MO), and peppermint + lavender (ML), and water as the control (Ctl), after a 20-min calculation task. Subjective evaluations and physiological responses including skin conductance level and electrocardiogram were recorded throughout the experiment. Citrus mouthwashes (MO and MB) showed a greater decrease in heart rate and a significant increase in the high-frequency component of heart rate variability. The participants indicated a significant effect in terms of "flavor preference" and "refreshing sensation" for mouthwash use compared to the Ctl. The results suggest that rinsing with citrus-flavored mouthwashes has a positive impact in alleviating the physiological stress response (in terms of cardiac activity). These findings may have implications for the development of innovative, novel oral care products that promote stress reduction and improve oral health.

IMAR in Frequency Domain for Separating Speech Signals by STFT

Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

IoT driven joint compressed sensing and shallow learning approach for ECG signal-reconstruction

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations.