Novel anti-inflammatory peptide alleviates liver ischemia-reperfusion injury.

Ischemia-reperfusion injury (IRI) remains inevitable in liver surgeries, macrophages play a critical role in the development of IRI, but little is known about the macrophages regulate pathogenesis of IRI. Based on target-guided screening, we identified a small 3 kDa peptide (SjDX5-271) from various schistosome egg-derived peptides that induced M2 macrophage polarization. SjDX5-271 treatment protected the mice against liver IRI through promoting M2 macrophage polarization, the protective effect was abrogated when the macrophages were depleted. Transcriptomic sequencing showed that the TLR signaling pathway was significantly inhibited in macrophages derived from the SjDX5-271 treatment group. We further identified that SjDX5-271 promotes M2 macrophage polarization by inhibiting the TLR4/MyD88/NF-κB signaling pathway and further alleviates hepatic inflammation in liver IRI. Collectively, SjDX5-271 exhibits promising therapeutic effects in IRI and represents a novel therapeutic approach for IRI, even in immune-related diseases. This study revealed the development of a new biologic from the parasite and enhanced our understanding of host-parasite interplay, providing a blueprint for future therapies for immune-related diseases.

Optical Analysis Method for Turbid Media Based on Wedge-Shaped Cells at Different Angles.

The wedge-shaped sample cell, by offering a comprehensive representation of scattering information in turbid media, significantly enhances the informational content conveyed by spectral images compared to flat sample cells. To further refine the accuracy of turbid medium component detection utilizing wedge-shaped sample cells, this work undertakes modeling and analysis of the influence of different wedge angles on detection precision. In this study, employing a 5° gradient in the incident angle of light, we investigate the impact of incident angles ranging from 10 to 45° on the turbid medium component analysis. Validation experiments are performed by utilizing solutions of Indian ink and fat emulsion at varying ratios. Experimental findings demonstrate that under identical experimental conditions, the wedge-shaped sample cell model at an incident angle of 35° yields optimal analysis results. Utilizing partial least-squares regression (PLSR) for the corresponding optical parameters, the highest value of Rp reached 0.980, with an RMSEP of 0.002. When compared to the model with a 30° incident angle, Rp increased by 0.033, and RMSEP decreased by 0.008. In comparison to the flat sample cell model, Rp increased by 0.041, and RMSEP decreased by 0.004. This study, through continuous variation of wedge angles and PLSR modeling and prediction, further enhances the accuracy of turbid medium component detection, laying an experimental foundation for subsequent analysis of turbid medium components based on wedge-shaped sample cells.

Advances in Non-Invasive Neuromodulation Techniques for Improving Cognitive Function: A Review.

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition.

Effects of paternal ionizing radiation exposure on fertility and offspring's health.

Purpose: The intergenerational effects of ionizing radiation remain controversial. Extensive insights have been revealed for DNA mutations and cancer incidence in progeny, yet many of these results were obtained by immediate post-radiation mating. However, conception at short times after radiation exposure is likely to be avoided. After a long period of fertility recovery, whether unexposed sperm derived from exposed spermatogonia would challenge the health of the offspring is not yet clearly demonstrated. Methods: Ten-week-old C57BL/6J males underwent whole-body acute γ irradiation at 0 and 6.4 Gy. Testes and sperm were collected at different times after radiation to examine reproductive changes. The reproductive, metabolic, and neurodevelopmental parameters were measured in the offspring of controls and the offspring derived from irradiated undifferentiated spermatogonia. Results: Paternal fertility was lost after acute 6.4 Gy γ radiation and recovered at 10-11 weeks post irradiation in mice. The reproductive, metabolic, and neurodevelopmental health of offspring born to irradiated undifferentiated spermatogonia were comparable to those of controls. Conclusion: The male mice could have healthy offspring after recovery from the damage caused by ionizing radiation.

A cuproptosis-related gene expression signature predicting clinical prognosis and immune responses in intrahepatic cholangiocarcinoma detected by single-cell RNA sequence analysis.

BACKGROUND: Cholangiocarcinoma represents a malignant neoplasm originating from the hepatobiliary tree, with a subset of tumors developing inside the liver. Intrahepatic cholangiocarcinomas (ICC) commonly exhibit an asymptomatic presentation, rendering both diagnosis and treatment challenging. Cuproptosis, an emerging regulated cell death pathway induced by copper ions, has garnered attention recently. As cancer cells show altered copper metabolism and comparatively higher copper needs, cuproptosis may play a role in the development of ICC. However, studies investigating this possibility are currently lacking. METHODS: Single-cell and bulk RNA sequence data were analyzed, and correlations were established between the expression of cuproptosis-related molecules and ICC patient survival. Genes with predicting survival were used to create a CUPT score using Cox and LASSO regression and tumor mutation burden (TMB) analysis. The CIBERSORT software was employed to characterize immune cell infiltration within the tumors. Furthermore, immune infiltration prediction, biological function enrichment, and drug sensitivity analyses were conducted to explore the potential implications of the cuproptosis-related signature. The effects of silencing solute carrier family 39 member 4 gene (SLC39A4) expression using siRNA were investigated using assays measuring cell proliferation, colony formation, and cell migration. Key genes of cuproptosis were detected by western blotting. RESULTS: The developed CUPT score divided patients into high and low CUPT score groups. Those with a low score had significantly better prognosis and longer survival. In contrast, high CUPT scores were associated with worse clinical outcomes and significantly higher TMB. Comparisons of the two groups also indicated differences in the immune infiltrate present in the tumors. Finally, we were able to identify 95 drugs potentially affecting the cuproptosis pathway. Some of these might be effective in the treatment of ICC. The in vitro experiments revealed that suppressing the expression of SLC39A4 in ICC cell lines resulted in reduced cell proliferation, colony formation, and cell migration. It also led to an increase in cell death and the upregulation of key genes associated with cuproptosis, namely ferredoxin 1 (FDX1) and dihydrolipoyl transacetylase (DLAT). These findings strongly suggest that this cuproptosis-associated molecule may play a pivotal role in the development and metastasis of ICC. CONCLUSIONS: Changes in the expression of a cuproptosis-related gene signature can predict the clinical prognosis of ICC with considerable accuracy. This supports the notion that cuproptosis influences the diversity and complexity of the immune microenvironment, mutational landscape, and biological behavior of ICC. Understanding this pathway better may hold promise for the development of innovative strategies in the management of this disease.

Sigma-1 receptor regulates the endoplasmic reticulum stress pathway in the protective mechanism of dexmedetomidine against hyperoxia-induced lung injury.

Perioperative hyperoxia therapy is of great significance to save the lives of patients, but little is known about the possible mechanisms that induce hyperoxia-induced acute lung injury (HALI) and the measures for clinical prevention and treatment. In this experiment, the models were established with a feeding chamber with automatic regulation of oxygen concentration. The results showed that with the increase in inhaled oxygen concentration and the prolongation of exposure time, the severity of lung injury also increases significantly, reaching the diagnostic indication of HALI after 48 h of inhaling 95 % oxygen concentration. Subsequently, according to the dynamic changes of apoptosis in lung specimens, and the expression changes in Sig-1R-regulated ER stress pathway proteins (Sig-1R, GRP78, p-PERK, ATF6, IRE1, Caspase-12, ATF4, CHOP, Caspase-3 and p-JNK), it was confirmed that the Sig-1R-regulated ER stress signaling pathway was involved in the occurrence of HALI. To explore the preventive and therapeutic effects of routine clinical medication on HALI during the perioperative period, our research group selected dexmedetomidine (Dex) with lung protection. The experimental results revealed that Dex partially reversed the changes in the expression levels of Sig-1R-regulated ER stress pathway proteins. These results preliminarily confirmed that Dex may inhibit apoptosis induced by high oxygen concentration through the Sig-1R-regulated ER stress signaling pathway, thus playing a protective role in HALI.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices.

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.

Simulation of dynamic monitoring for intracerebral hemorrhage based on magnetic induction phase shift technology.

Intracerebral hemorrhage (ICH) is a common and severe brain disease associated with high mortality and morbidity. Accurate measurement of the ICH area is an essential indicator for doctors to determine whether a surgical operation is necessary. However, although currently used clinical detection methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), provide high-quality images, they may have limitations such as high costs, large equipment size, and radiation exposure to the human body in the case of CT. It makes long-term bedside monitoring infeasible. This paper presents a dynamic monitoring method for ICH areas based on magnetic induction. This study investigates the influence of the bleeding area and the position of ICH on the phase difference at the detection point near the area to be measured. The study applies a neural network algorithm to predict the bleeding area using the phase difference data received by the detection coil as the network input and the bleeding area as the network output. The relative error between the predicted and actual values of the neural network is calculated, and the error of each group of data is less than 4%, which confirms the feasibility of this method for detecting and even trend monitoring of the ICH area.

A target-based discovery from a parasitic helminth as a novel therapeutic approach for autoimmune diseases.

BACKGROUND: Regulatory T cells (Tregs) can alleviate the development of autoimmune and inflammatory diseases, thereby proposing their role as a new therapeutic strategy. Parasitic helminths have co-evolved with hosts to generate immunological privilege and immune tolerance through inducing Tregs. Thus, constructing a "Tregs-induction"-based discovery pipeline from parasitic helminth is a promising strategy to control autoimmune and inflammatory diseases. METHODS: The gel filtration chromatography and reverse-phase high-performance liquid chromatography (RP-HPLC) were used to isolate immunomodulatory components from the egg extracts of Schistosoma japonicum. The extracted peptides were evaluated for their effects on Tregs suppressive functions using flow cytometry, ELISA and T cell suppression assay. Finally, we carried out colitis and psoriasis models to evaluate the function of Tregs induced by helminth-derived peptide in vivo. FINDINGS: Here, based on target-driven discovery strategy, we successfully identified a small 3 kDa peptide (SjDX5-53) from egg extracts of schistosome, which promoted both human and murine Tregs production. SjDX5-53 presented immunosuppressive function by arresting dendritic cells (DCs) at an immature state and augmenting the proportion and suppressive capacity of Tregs. In mouse models, SjDX5-53 protected mice against autoimmune-related colitis and psoriasis through inducing Tregs and inhibiting inflammatory T-helper (Th) 1 and Th17 responses. INTERPRETATION: SjDX5-53 exhibited the promising therapeutic effects in alleviating the phenotype of immune-related colitis and psoriasis. This study displayed a screening and validation pipeline of the inducer of Tregs from helminth eggs, highlighting the discovery of new biologics inspired by co-evolution of hosts and their parasites. FUNDING: This study was supported by the Natural Science Foundation of China (82272368) and Natural Science Foundation of Jiangsu Province (BK20211586).

A method for obtaining object defocus information in the RC observation mode.

The micro-operation robot is widely used in micro-manipulations of biological cells in biological and medical experiments. It plans and controls micro-effector movement based on image feedback information to achieve micro-operations. However, the displacement information of the micro-effector on the x-y plane can be obtained from the image, but not the position information of the micro-effector in the z-axis direction. This makes the micro-effector movement in the z-axis direction discontinuous, which is time-consuming and reduces operational efficiency. In this study, starting from the optical imaging principle of Robert Hoffman modulation contrast method (RC), we propose a defocus detection method for the RC observation mode of an optical microscope. Our method can determine the direction of defocus, which is not available in previous defocusing detection methods. Utilizing this method, we achieve rapid focus for the micro-effector while it is moving along the z-axis direction.

Non-invasive continuous blood pressure prediction based on ECG and PPG fusion map.

To achieve real-time blood pressure monitoring, a novel non-invasive method is proposed in this article. Electrocardiographic (ECG) and pulse wave signals (PPG) are fused from a multi-omics signal-level perspective. A physiological signal fusion matrix and fusion map, which can estimate the blood pressure of blood loss(BPBL), are constructed. The results demonstrate the efficacy of the fusion map model, with correlation values of 0.988 and 0.991 between the estimated BPBL and the true systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The root mean square errors for SBP and DBP were 3.21 mmHg and 3.00 mmHg, respectively. The model validation showed that the fusion map method is capable of simultaneous highlighting of the respective characteristics of ECG and PPG and their correlation, improving the utilization of the information and the accuracy of BPBL. This article validates that physiological signal fusion map can effectively improve the accuracy of BPBL estimation and provides a new perspective for the field of physiological information fusion.

The identification of blood pressure variation with hypovolemia based on the volume compensation method.

Objective: The purpose of this study is to identify the blood pressure variation, which is important in continuous blood pressure monitoring, especially in the case of low blood volume, which is critical for survival. Methods: A pilot study was conducted to identify blood pressure variation with hypovolemia using five Landrace pigs. New multi-dimensional morphological features of Photoplethysmography (PPG) were proposed based on experimental study of hemorrhagic shock in pigs, which were strongly correlated with blood pressure changes. Five machine learning methods were compared to develop the blood pressure variation identification model. Results: Compared with the traditional blood pressure variation identification model with single characteristic based on single period area of PPG, the identification accuracy of mean blood pressure variation based on the proposed multi-feature random forest model in this paper was up to 90%, which was 17% higher than that of the traditional blood pressure variation identification model. Conclusion: By the proposed multi-dimensional features and the identification method, it is more accurate to detect the rapid variation in blood pressure and to adopt corresponding measures. Significance: Rapid and accurate identification of blood pressure variation under low blood volume ultimately has the potential to effectively avoid complications caused by abnormal blood pressure in patients with clinical bleeding trauma.

Adaptive window space direction laser speckle contrast imaging to improve vascular visualization.

Vascular visualization is crucial in monitoring, diagnosing, and treating vascular diseases. Laser speckle contrast imaging (LSCI) is widely used for imaging blood flow in shallow or exposed vessels. However, traditional contrast computation using a fixed-sized sliding window introduces noise. In this paper, we propose dividing the laser speckle contrast image into regions and using the variance criterion to extract pixels more suitable for the corresponding regions for calculation, and changing the shape and size of the analysis window at the vascular boundary regions. Our results show that this method has a higher noise reduction and better image quality in deeper vessel imaging, revealing more microvascular structure information.

Constructing big data prevention and control model for public health emergencies in China: A grounded theory study.

Big data technology plays an important role in the prevention and control of public health emergencies such as the COVID-19 pandemic. Current studies on model construction, such as SIR infectious disease model, 4R crisis management model, etc., have put forward decision-making suggestions from different perspectives, which also provide a reference basis for the research in this paper. This paper conducts an exploratory study on the construction of a big data prevention and control model for public health emergencies by using the grounded theory, a qualitative research method, with literature, policies, and regulations as research samples, and makes a grounded analysis through three-level coding and saturation test. Main results are as follows: (1) The three elements of data layer, subject layer, and application layer play a prominent role in the digital prevention and control practice of epidemic in China and constitute the basic framework of the "DSA" model. (2) The "DSA" model integrates cross-industry, cross-region, and cross-domain epidemic data into one system framework, effectively solving the disadvantages of fragmentation caused by "information island". (3) The "DSA" model analyzes the differences in information needs of different subjects during an outbreak and summarizes several collaborative approaches to promote resource sharing and cooperative governance. (4) The "DSA" model analyzes the specific application scenarios of big data technology in different stages of epidemic development, effectively responding to the disconnection between current technological development and realistic needs.

Noninvasive and simultaneous quantitative analysis of multiple human blood components based on the grey analysis system.

Noninvasive detection of human blood components is the dream of human beings and the goal of clinical detection. From the perspective of mathematical analysis, based on the grey analysis system, the principle of spectral chemical quantitative analysis and the solution method of multivariate linear equation, this paper pioneers the spectrum elimination method, and obtains a complete, high-precision, synchronous and noninvasive detection system for a variety of human blood components. The spectral elimination method applies the principle of elimination method in mathematics to the noninvasive quantitative analysis of human blood components by spectral method, reduces the influence of non-target components on the detection of target components, and improves the accuracy of noninvasive quantitative analysis of human blood components. To demonstrate the effectiveness of the method, taking the analysis of the contents of seven blood components (hemoglobin, red blood cell count, neutrophils, lymphocytes, monocytes, eosinophils and basophils) in blood as an example, fourteen models were established by two different methods. From the comparison of modeling results, it can be concluded that when the seven models established by spectral elimination method predict the corresponding seven components of all samples, the predicted correlation coefficients are more than 0.9500. The experimental results show that the spectral elimination method and non-invasive detection system proposed can predict the content of human blood components with high accuracy. This paper studies a high-precision, simultaneous and noninvasive quantitative analysis system of multiple human blood components for the first time, which not only makes great progress in the non-invasive chemical quantitative analysis of human blood components by spectroscopy, but also has great application value for clinical medical treatment and disease diagnosis.

Evaluation and Obstacle Analysis of Emergency Response Capability in China.

Emergency response capability evaluation is an essential means to strengthen emergency response capacity-building and improve the level of government administration. Based on the whole life cycle of emergency management, the emergency capability evaluation index system is constructed from four aspects: prevention and emergency preparedness, monitoring and early warning, emergency response and rescue, and recovery and reconstruction. Firstly, the entropy method is applied to measure the emergency response capability level of 31 Chinese provinces from 2011 to 2020. Second, the Theil index and ESDA (Exploratory Spatial Data Analysis) are applied in exploring the regional differences and spatial-temporal distribution characteristics of China's emergency response capacity. Finally, the obstacle degree model is used to explore the obstacle factors and obstacle degrees that affect the emergency response capability. The results show that: (1) The average value of China's emergency response capacity is 0.277, with a steady growth trend and a gradient distribution of "high in the east, low in the west, and average in center and northeast" in the four major regions. (2) From the perspective of spatial distribution characteristics, the unbalanced regional development leads to the obvious aggregation effect of "high-efficiency aggregation and low-efficiency aggregation", and the interaction of the "centripetal effect" and "centrifugal effect" finally forms the spatial clustering result of emergency response capability level in China. (3) Examining the source of regional differences, inter-regional differences are the decisive factor affecting the overall differences in emergency response capability, and the inter-regional differences show a reciprocating fluctuation of narrowing-widening-narrowing from 2011 to 2020. (4) Main obstacles restricting the improvement of China's emergency response capabilities are "the business volume of postal and telecommunication services per capita", "the daily disposal capacity of city sewage" and "the general public budget revenue by region". The extent of the obstacles' impacts in 2020 are 12.19%, 7.48%, and 7.08%, respectively. Based on the evaluation results, the following countermeasures are proposed: to realize the balance of each stage of emergency management during the holistic process; to strengthen emergency coordination and balanced regional development; and to implement precise measures to make up for the shortcomings of emergency response capabilities.

Classification of Alzheimer's Disease Based on Deep Learning of Brain Structural and Metabolic Data.

To improve the diagnosis and classification of Alzheimer's disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.

Method for improving the accuracy of fluorescence molecular tomography based on multi-wavelength concurrent reconstruction.

A Concurrent-wavelength Reconstruction Algorithm (CRA) based on multi-wavelength information fusion is proposed in this paper that aims to further improve the accuracy of Fluorescence Molecular Tomography (FMT) reconstruction. Combining multi-spectral data with FMT technology, the information of 650 and 750 nm wavelengths near-infrared was used to increase the feature information of the dominant 850 nm wavelength near-infrared effectively. Principal component analysis, which can remove redundant information and achieve data dimensionality reduction, was then utilized to extract the feature information. Finally, tomographic reconstruction of the anomalies was performed based on the stacked auto-encoder neural network model. The comparison results of numerical experiments showed that the reconstruction effect of CRA was superior to the performance of the single wavelength model. The correlation coefficient between CRA reconstructed anomalies' fluorescence yield values and the real fluorescence yield values remained at 0.95 or more under the noise of different levels of signal-to-noise ratios. Therefore, the CRA proposed in this paper could effectively improve on the ill-posedness of the inverse problem, which could further enhance the accuracy of FMT reconstruction.

Happy or sad? Recognizing emotions with wavelet coefficient energy mean of EEG signals.

BACKGROUND: Emotional intelligence plays a vital role in human-computer interaction, and EEG signals are an objective response to human emotions. OBJECTIVE: We propose a method to extract the energy means of detail coefficients as feature values for emotion recognition helps to improve EEG signal-based emotion recognition accuracy. METHOD: We used movie clips as the eliciting material to stimulate the real emotions of the subjects, preprocessed the collected EEG signals, extracted the feature values, and classified the emotions based on them using Support Vector Machine (SVM) and Stacked Auto-Encoder (SAE). The method was verified based on the SJTU emotion EEG database (SEED) and the self-acquisition experiment. RESULTS: The results show that the accuracy is better using SVM. The results based on the SEED database are 89.06% and 79.90% for positive-negative and positive-neutral-negative, respectively. The results based on the self-acquisition data are 98.05% and 89.83% for the same, with an average recognition rate of 86.57% for the four categories of fear, sad (negative), peace (neutral) and happy (positive). CONCLUSION: The results demonstrate the validity of the feature values and provide a theoretical basis for implementing human-computer interaction.