Evolution mechanism of scratch removal based on the implementation of magnetorheological finishing.

Scratches on optical components induce laser damage and limit the increase in laser power. Magnetorheological finishing (MRF) is a highly deterministic optical manufacturing technology that can improve the surface roughness of optical components. Although MRF has exhibited significant potential for reducing subsurface damage and removing scratches, the principle and mechanism behind the scratch removal are not sufficiently understood. In this study, the theory of fluid mechanics is used to analyze the pressure, velocity, and particle trajectory distribution near a scratch. A physical model was developed for the differential removal of scratches at the bottom and surface of the optical components. The morphological evolution of the scratch was predicted during removal, and detailed experiments were performed to verify the effectiveness of the proposed model. The results indicate that scratches expand laterally rather than being completely removed. Furthermore, scratch removal efficiency is greater when the removal direction is perpendicular to the scratch rather than being parallel. This study offers an intrinsic perspective for a comprehensive understanding of the MRF technique used for scratch removal, which can be beneficial for removing scratches from aspherical optical systems.

Long-term and combined heavy-metal contamination forms a unique microbiome and resistome: A case study in a Yellow River tributary sediments.

Microorganisms have developed mechanisms to adapt to environmental stress, but how microbial communities adapt to long-term and combined heavy-metal contamination under natural environmental conditions remains unclear. Specifically, this study analyzed the characteristics of heavy metal composition, microbial community, and heavy metal resistance genes (MRGs) in sediments along Mang River, a tributary of the Yellow River, which has been heavily polluted by industrial production for more than 40 years. The results showed that the concentrations of Cr, Zn, Pb, Cu and As in most sediments were higher than the ambient background values. Bringing the heavy metals speciation and concentration into the risk evaluation method, two-thirds of the sediment samples were at or above the moderate risk level, and the ecological risk of combined heavy metals in the sediments decreased along the river stream. The high ecological risk of heavy metals affected the microbial community structure, metabolic pathways and MRG distribution. The formation of a HM-resistant microbiome possibly occurred through the spread of insertion sequences (ISs) carrying multiple MRGs, the types of ISs carrying MRGs outnumber those of plasmids, and the quantity of MRGs on ISs is also higher than that on plasmids. These findings could improve our understanding of the adaptation mechanism of microbial communities to long-term combined heavy metal contamination.

Immediate effect of kinesiology taping on muscle strength, static balance and proprioception after eccentric muscle fatigue on ankle: a randomized cross-over trial.

BACKGROUND: Kinesiology Taping(KT) is commonly used as a physical therapy to prevent exercise-induced fatigue. This study aims to evaluate the immediate effects of KT on muscle strength, static balance, and proprioception after eccentric muscle fatigue on ankle. METHODS: Twenty healthy male university students were recruited. The experimental protocol was structured into four sessions, each separated by a one-week washout period to prevent carryover effects. Participants were randomly allocated to one of four intervention conditions in each session, ensuring no participant received the same intervention twice. These conditions were: no taping(NT),sham taping(ST),athletic taping(AT),and kinesiology taping(KT).Taping was applied immediately following an eccentric muscle fatigue protocol targeting the ankle, and assessments were conducted in the order of proprioception, muscle strength and static balance. Isometric muscle strength and proprioception were evaluated using the Biodex isokinetic system. Static balance was measured using the TecnoBody balance platform. RESULTS: KT had a significantly higher plantarflexion/dorsiflexion peak torque, dorsiflexion average peak torque, and plantarflexion/dorsiflexion average power at 60°/s compared with NT and ST in terms of isometric muscle strength (p < 0.05).Furthermore, the plantarflexion peak torque of KT was significantly greater than AT at 60°/s[p = 0.005,95% confidence interval(CI) = 3.39 to 18.20] and 180°/s[p = 0.006,95%CI(2.62,21.98)]. In terms of proprioception, KT showed a lower absolute error in 25° plantarflexion and 10° dorsiflexion compared to NT, ST and AT. For static balance with eyes-open and eyes-closed conditions, AT and KT had a lower total sway area than NT and ST (p < 0.05). Additionally, a significant difference in total sway length with eyes-open condition was observed between AT and KT[p < 0.001,95%CI(-431.81,-168.25)];total sway area and the center of pressure(COP) velocity in the mediolateral(ML) and anteroposterior(AP) directions with eyes-closed condition were significantly lower in AT compared to KT. CONCLUSION: This study suggests that KT is more effective than other taping conditions in improving muscle strength and proprioception after eccentric muscle fatigue on ankle. However, AT is more helpful in increasing static postural control ability after ankle muscle fatigue than KT. TRIAL REGISTRATION: This study was registered with www.chictr.org.cn (registration number: ChiCTR2300068278) on 13/2/2023.

Clinical value and mechanistic analysis of HIIT on modulating risk and symptoms of depression: A systematic review / Valor clínico y análisis mecanicista del HIIT sobre la modulación del riesgo y los síntomas de la depresión: una revisión sistemática

Background: The exact causal mechanisms of depression remain unclear due to the complexity of the triggers, which has led to limitations in treating depression using modern drugs. High-intensity interval training (HIIT) is as effective as medication in treating depression without toxic side effects. Typically, HIIT requires less time commitment (i.e., shorter exercise duration) and exhibits pronounced benefits on depressive symptoms than other forms of physical exercise. This review summarizes the risk reduction and clinical effects of HIIT for depression and discusses the underlying mechanisms, providing a theoretical basis for utilizing HIIT in treating depression. Methods: A database search was conducted in PubMed, Embase, Web of Science, and Scopus from inception up to October 2022. The methodological quality of the included literature was evaluated by the physiotherapy evidence database (PEDro) scale criteria. The review focused on evaluating the changes in depression risk or symptoms of HIIT interventions in healthy individuals, patients with depression, and patients with other disorders co-morbid with depression. Consequently, the mechanisms associated with depression related HIIT were summarized. Results: A total of 586 participants (52 % female; mean age: 43.58±8.93 years) from 22 studies were included. Implementing HIIT using different exercise types alleviates depressive symptoms in individuals with depression and in individuals with depression who have exhibited comorbidities and reduced depression scale scores in subjects immediately after acute exercise. In addition, the long-interval HIIT and short-interval HIIT in the treatment of patients with cardiovascular or psychiatric disorders may reduce depressive symptoms via complex exercise-related changes on several levels, including by effecting the following measures: releasing monoamines, reducing neuronal death, inducing neurogenesis, modulating the functional...(AU)

Nighttime color constancy using robust gray pixels.

Color constancy is a basic step for achieving stable color perception in both biological visual systems and the image signal processing (ISP) pipeline of cameras. So far, there have been numerous computational models of color constancy that focus on scenes under normal light conditions but are less concerned with nighttime scenes. Compared with daytime scenes, nighttime scenes usually suffer from relatively higher-level noise and insufficient lighting, which usually degrade the performance of color constancy methods designed for scenes under normal light. In addition, there is a lack of nighttime color constancy datasets, limiting the development of relevant methods. In this paper, based on the gray-pixel-based color constancy methods, we propose a robust gray pixel (RGP) detection method by carefully designing the computation of illuminant-invariant measures (IIMs) from a given color-biased nighttime image. In addition, to evaluate the proposed method, a new dataset that contains 513 nighttime images and corresponding ground-truth illuminants was collected. We believe this dataset is a useful supplement to the field of color constancy. Finally, experimental results show that the proposed method achieves superior performance to statistics-based methods. In addition, the proposed method was also compared with recent deep-learning methods for nighttime color constancy, and the results show the method's advantages in cross-validation among different datasets.

Coxsackievirus B3 HFMD animal models in Syrian hamster and rhesus monkey.

Coxsackievirus B3 (CVB3) is the pathogen causing hand, foot and mouth disease (HFMD), which manifests across a spectrum of clinical severity from mild to severe. However, CVB3-infected mouse models mainly demonstrate viral myocarditis and pancreatitis, failing to replicate human HFMD symptoms. Although several enteroviruses have been evaluated in Syrian hamsters and rhesus monkeys, there is no comprehensive data on CVB3. In this study, we have first tested the susceptibility of Syrian hamsters to CVB3 infection via different routes. The results showed that Syrian hamsters were successfully infected with CVB3 by intraperitoneal injection or nasal drip, leading to nasopharyngeal colonization, acute severe pathological injury, and typical HFMD symptoms. Notably, the nasal drip group exhibited a longer viral excretion cycle and more severe pathological damage. In the subsequent study, rhesus monkeys infected with CVB3 through nasal drips also presented signs of HFMD symptoms, viral excretion, serum antibody conversion, viral nucleic acids and antigens, and the specific organ damages, particularly in the heart. Surprisingly, there were no significant differences in myocardial enzyme levels, and the clinical symptoms resembled those often associated with common, mild infections. In summary, the study successfully developed severe Syrian hamsters and mild rhesus monkey models for CVB3-induced HFMD. These models could serve as a basis for understanding the disease pathogenesis, conducting pre-trial prevention and evaluation, and implementing post-exposure intervention.

Ultrasensitive fiber sensor with enhanced Vernier effect for simultaneous measurements of transverse load and temperature.

Based on enhanced Vernier effect, a compact fiber sensor with ultrahigh sensitivity is proposed for simultaneous transverse load (TL) and temperature measurements. A single mode fiber (SMF) is spliced with a segment of hollow-core fiber (HCF) coated with polydimethylsiloxane (PDMS), some PDMS is injected into the HCF, forming a Vernier sensor with an air cavity adjacent to a PDMS cavity. It is shown that TL and temperature changes give rise to opposite and remarkable different variations in lengths of the two cavities, thereby enhancing Vernier effect and in favor of simultaneous measurements of TL and temperature. Moreover, the limited sensitivity magnification due to the length mismatch between the two cavities is compensated for by reconstructing the Vernier envelope with a broadened free spectrum range (FSR) from output signal. As a result, the highest TL sensitivity reported so far of -2637.47 nm/N and a good condition number of 69.056 for the sensitivity coefficient matrix have been achieved.

Multi-Omics Approach Reveals OsPIL1 as a Regulator Promotes Rice Growth, Grain Development, and Blast Resistance.

Rice (Oryza sativa) is a crucial crop, achieving high yield concurrent pathogen resistance remains a challenge. Transcription factors play roles in growth and abiotic tolerance. However, rice phytochrome-interacting factor-like 1 (OsPIL1) in pathogen resistance and agronomic traits remains unexplored. We generated OsPIL1 overexpressing (OsPIL1 OE) rice lines and evaluated their impact on growth, grain development, and resistance to Magnaporthe oryzae. Multiomics analysis (RNA-seq, metabolomics, and CUT&Tag) and RT-qPCR validated OsPIL1 target genes and key metabolites. In the results, OsPIL1 OE rice lines exhibited robust growth, longer grains, and enhanced resistance to M. oryzae without compromising growth. Integrative multiomics analysis revealed a coordinated regulatory network centered on OsPIL1, explaining these desirable traits. OsPIL1 likely acts as a positive regulator, targeting transcriptional elements or specific genes with direct functions in several biological programs. In particular, a range of key signaling genes (phosphatases, kinases, plant hormone genes, transcription factors), and metabolites (linolenic acid, vitamin E, trigonelline, d-glucose, serotonin, choline, genistein, riboflavin) contributed to enhanced rice growth, grain size, pathogen resistance, or a combination of these traits. These findings highlight OsPIL1's regulatory role in promoting important traits and provide insights into potential strategies for rice breeding.

Clinical value and mechanistic analysis of HIIT on modulating risk and symptoms of depression: A systematic review.

Background: The exact causal mechanisms of depression remain unclear due to the complexity of the triggers, which has led to limitations in treating depression using modern drugs. High-intensity interval training (HIIT) is as effective as medication in treating depression without toxic side effects. Typically, HIIT requires less time commitment (i.e., shorter exercise duration) and exhibits pronounced benefits on depressive symptoms than other forms of physical exercise. This review summarizes the risk reduction and clinical effects of HIIT for depression and discusses the underlying mechanisms, providing a theoretical basis for utilizing HIIT in treating depression. Methods: A database search was conducted in PubMed, Embase, Web of Science, and Scopus from inception up to October 2022. The methodological quality of the included literature was evaluated by the physiotherapy evidence database (PEDro) scale criteria. The review focused on evaluating the changes in depression risk or symptoms of HIIT interventions in healthy individuals, patients with depression, and patients with other disorders co-morbid with depression. Consequently, the mechanisms associated with depression related HIIT were summarized. Results: A total of 586 participants (52 % female; mean age: 43.58±8.93 years) from 22 studies were included. Implementing HIIT using different exercise types alleviates depressive symptoms in individuals with depression and in individuals with depression who have exhibited comorbidities and reduced depression scale scores in subjects immediately after acute exercise. In addition, the long-interval HIIT and short-interval HIIT in the treatment of patients with cardiovascular or psychiatric disorders may reduce depressive symptoms via complex exercise-related changes on several levels, including by effecting the following measures: releasing monoamines, reducing neuronal death, inducing neurogenesis, modulating the functional homeostasis of the HPA axis, and enhancing the level of inflammation in the body. Conclusion: HIIT is a relatively safe and effective antidepressant, which may involve multiple neurobiological mechanisms (release of monoamines, reducing neuronal death, inducing neurogenesis, modulating the functional homeostasis of the HPA axis, and enhancing the level of inflammation in the body), thereby reducing the risk or symptoms of depression in participants.

Pan-Cancer Analysis of P3H1 and Experimental Validation in Renal Clear Cell Carcinoma.

Prolyl 3-hydroxylase 1 (P3H1) has been implicated in cancer development, but no pan-cancer analysis has been conducted on P3H1. In this study, for the first time, aspects associated with P3H1, such as the mRNA expression, any mutation, promoter methylation, and prognostic significance, the relationship between P3H1 and clinicopathological parameters, drug sensitivity, and immune cell infiltration were investigated by searching several databases including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, and The Tumor Immune Evaluation Resource (TIMER2.0) using bioinformatics tools. The findings indicate significant differential expression of P3H1 in most tumors when compared to normal tissues, with a strong association with clinical prognosis. A pan-cancer Cox regression analysis revealed that high P3H1 expression is significantly associated with low overall survival in patients with brain lower grade glioma, kidney clear cell carcinoma, adrenocortical cancer, liver hepatocellular carcinoma, mesothelioma, sarcoma, uveal melanoma, bladder urothelial carcinoma, kidney papillary cell carcinoma, kidney chromophobe, thymoma, and thyroid carcinoma. A negative correlation was observed between P3H1 DNA methylation and its expression. P3H1 is significantly associated with infiltrating cells, immune-related genes, tumor mutation burden, microsatellite instability, and mismatch repair. Finally, A significant correlation was found between P3H1 expression and sensitivity to nine drugs. Thus, enhanced P3H1 expression is associated with poor prognosis in a variety of tumors, which may be due to its role in tumor immune regulation and tumor microenvironment. This pan-cancer analysis provides insight into the function of P3H1 in tumorigenesis of different cancers and provides a theoretical basis for further in-depth studies to follow.

Cortical thickness and white matter microstructure predict freezing of gait development in Parkinson's disease.

The clinical applications of the association of cortical thickness and white matter fiber with freezing of gait (FoG) are limited in patients with Parkinson's disease (PD). In this retrospective study, using white matter fiber from diffusion-weighted imaging and cortical thickness from structural-weighted imaging of magnetic resonance imaging, we investigated whether a machine learning-based model can help assess the risk of FoG at the individual level in patients with PD. Data from the Parkinson's Disease Progression Marker Initiative database were used as the discovery cohort, whereas those from the Fujian Medical University Union Hospital Parkinson's Disease database were used as the external validation cohort. Clinical variables, white matter fiber, and cortical thickness were selected by random forest regression. The selected features were used to train the support vector machine(SVM) learning models. The median area under the receiver operating characteristic curve (AUC) was calculated. Model performance was validated using the external validation cohort. In the discovery cohort, 25 patients with PD were defined as FoG converters (15 men, mean age 62.1 years), whereas 60 were defined as FoG nonconverters (38 men, mean age 58.5 years). In the external validation cohort, 18 patients with PD were defined as FoG converters (8 men, mean age 66.9 years), whereas 37 were defined as FoG nonconverters (21 men, mean age 65.1 years). In the discovery cohort, the model trained with clinical variables, cortical thickness, and white matter fiber exhibited better performance (AUC, 0.67-0.88). More importantly, SVM-radial kernel models trained using random over-sampling examples, incorporating white matter fiber, cortical thickness, and clinical variables exhibited better performance (AUC, 0.88). This model trained using the above mentioned features was successfully validated in an external validation cohort (AUC, 0.91). Furthermore, the following minimal feature sets that were used: fractional anisotropy value and mean diffusivity value for right thalamic radiation, age at baseline, and cortical thickness for left precentral gyrus and right dorsal posterior cingulate gyrus. Therefore, machine learning-based models using white matter fiber and cortical thickness can help predict the risk of FoG conversion at the individual level in patients with PD, with improved performance when combined with clinical variables.

Retinal Layer Segmentation in OCT Images With Boundary Regression and Feature Polarization.

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github.com/tyb311/TCCT.

Glycation of fibronectin impairs angiopoietin-1/Tie-2 signaling through uncoupling Tie-2-α5ß1 integrin crosstalk.

The dysfunction of angiopoietin-1 (Ang-1)/Tie-2 signaling pathways has been implicated in diabetic complications. However, the underlying molecular mechanisms remain unclear. Fibronectin (FN) is thought to have an important role in regulating Ang-1/Tie-2 signaling activation. But no previous study has investigated the effects of FN glycation on Ang-1/Tie-2 signaling. In the present study, FN was glycated by methylglyoxal (MGO) to investigate whether the glycation of FN contributes to diabetes-induced Ang-1/Tie-2 signaling impairment and to understand the molecular mechanisms involved. The results demonstrated that MGO-glycated FN significantly impaired Ang-1-evoked phosphorylation of Tie-2 and Akt, Ang-1-induced endothelial cell migration and tube formation and Ang-1-mediated cell survival. The glycation of FN also inhibited the binding of α5ß1 integrin to Tie-2. Moreover, FN was remarkably modified by AGEs in aortae derived from db/db mice, indicating the glycation of FN in vivo. Ang-1-induced aortic ring vessel outgrowth and Ang-1-mediated cell survival were also both significantly inhibited in aortae from db/db mice compared to that from the wild type littermates. Moreover, FN, rather than glycated FN partly restored aortic ring angiogenesis in db/db mice, indicating that the angiogenesis defect in the db/db mice are due to FN glycation. Collectively, the results in the present study suggest that the glycation of FN impairs Ang-1/Tie-2 signaling pathway by uncoupling Tie-2-α5ß1 integrin crosstalk. This may provide a mechanism for Ang-1/Tie-2 signaling dysfunction and angiogenesis failure in diabetic ischaemic diseases.

Differences in subthalamic oscillatory activity in the two hemispheres associated with severity of Parkinson's disease.

Background: It is well known that motor features of Parkinson's disease (PD) commonly begin on one side of the body and extend to the other side with disease progression. The onset side generally remains more severely affected over the course of the disease. However, the pathophysiology underlying the asymmetry of motor manifestations remains unclear. The purpose of the present study is to examine whether alterations in neuronal activity in the subthalamic nucleus (STN) associate with PD severity. Methods: Microelectrode recording was performed in the STN during targeting for 30 patients in the treatment of deep brain stimulation. The mean spontaneous firing rate (MSFR), power density spectral analysis, and correlations were calculated. Characteristics of subthalamic oscillatory activity were compared between two hemispheres. UPDRS III scores during "Off" and "On" states were obtained for the body side of initial symptoms (BSIS) and the body side of extended symptoms (BSES). Results: There were significant differences of MSFR (41.3 ± 11.0 Hz vs 35.2 ± 10.0 Hz) and percentage of ß frequency oscillatory neurons (51.3% vs 34.9%) between BSIS and BSES. The percentage of ß frequency oscillatory neurons correlated with the bradykinesia/rigidity scores for both sides (p < 0.05). In contrast, the percentage of tremor frequency oscillatory neurons was significantly higher in the BSES than that in the BSIS. In particular, these neurons only correlated with the tremor scores of the BSES (p < 0.05). Conclusion: The results suggest that increased neuronal firing rate and ß frequency oscillatory neurons in the STN are associated with contralateral side motor severity and its progression. Tremor frequency oscillatory neurons are less observed in the STN of the BSIS suggesting that ß oscillatory activity dominates and tremor frequency oscillatory activity reciprocally declines.

Improved Calculations of Heavy Metal Toxicity Coefficients for Evaluating Potential Ecological Risk in Sediments Based on Seven Major Chinese Water Systems.

Several methods have been used to assess heavy metal contamination in sediments. However, an assessment that considers both composite heavy metal speciation and concentration is necessary to accurately study ecological risks. This study improved the potential ecological risk index method and calculated the toxicity coefficients of seven heavy metals: Arsenic (As), Cadmium (Cd), Chromium (Cr), Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn). The newly calculated toxicity coefficients were validated by using previously published heavy metal distribution data of the Henan section of the Yellow River. The calculation procedure is based on the principle that the abundance of heavy metals in the environment and their bioavailable forms affect the toxicity of heavy metals. The toxicity coefficients for the seven heavy metals were calculated as follows: As = 10, Cd = 20, Cr = 5, Cu = 2, Ni = 5, Pb = 5, Zn = 1. Ecological risk assessment of the Henan section of the Yellow River using the improved toxicity coefficients revealed that the ecological risk of Cd and total heavy metals is higher than previous calculations, reaching the strength and moderate risk levels, respectively. The improved potential ecological risk index method is more sensitive to heavy metal pollution and thus provides a better indication of ecological risk. This is a necessary improvement to provide more accurate pollution assessments.

A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study.

BACKGROUND: The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors' writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning-based institution name normalization model can increase the accuracy of institution name normalization at the semantic level. OBJECTIVE: This study aimed to train a deep learning-based model for institution name normalization based on the feature fusion of affiliation data from multisource literature, which would realize the normalization of institution name variants with the help of authority files and achieve a high specification accuracy after several rounds of training and optimization. METHODS: In this study, an institution name normalization-oriented model was trained based on bidirectional encoder representations from transformers (BERT) and other deep learning models, including the institution classification model, institutional hierarchical relation extraction model, and institution matching and merging model. The model was then trained to automatically learn institutional features by pretraining and fine-tuning, and institution names were extracted from the affiliation data of 3 databases to complete the normalization process: Dimensions, Web of Science, and Scopus. RESULTS: It was found that the trained model could achieve at least 3 functions. First, the model could identify the institution name that is consistent with the authority files and associate the name with the files through the unique institution ID. Second, it could identify the nonstandard institution name variants, such as singular forms, plural changes, and abbreviations, and update the authority files. Third, it could identify the unregistered institutions and add them to the authority files, so that when the institution appeared again, the model could identify and regard it as a registered institution. Moreover, the test results showed that the accuracy of the normalization model reached 93.79%, indicating the promising performance of the model for the normalization of institution names. CONCLUSIONS: The deep learning-based institution name normalization model trained in this study exhibited high accuracy. Therefore, it could be widely applied in the evaluation of the competitiveness of research institutions, analysis of research fields of institutions, and construction of interinstitutional cooperation networks, among others, showing high application value.

Extracellular vesicle-packaged ILK from mesothelial cells promotes fibroblast activation in peritoneal fibrosis.

Progressive peritoneal fibrosis and the loss of peritoneal function often emerged in patients undergoing long-term peritoneal dialysis (PD), resulting in PD therapy failure. Varieties of cell-cell communications among peritoneal cells play a significant role in peritoneal fibrogenesis. Extracellular vesicles (EVs) have been confirmed to involve in intercellular communication by transmitting proteins, nucleic acids or lipids. However, their roles and functional mechanisms in peritoneal fibrosis remain to be determined. Using integrative analysis of EV proteomics and single-cell RNA sequencing, we characterized the EVs isolated from PD patient's effluent and revealed that mesothelial cells are the main source of EVs in PD effluent. We demonstrated that transforming growth factor-ß1 (TGF-ß1) can substitute for PD fluid to stimulate mesothelial cells releasing EVs, which in turn promoted fibroblast activation and peritoneal fibrogenesis. Blockade of EVs secretion by GW4869 or Rab27a knockdown markedly suppressed PD-induced fibroblast activation and peritoneal fibrosis. Mechanistically, injured mesothelial cells produced EVs containing high level of integrin-linked kinase (ILK), which was delivered to fibroblast and activated them via p38 MAPK signalling pathway. Clinically, the expression of ILK was up-regulated in fibrotic peritoneum of patients undergoing long-term PD. The percentage of ILK positive EVs in PD effluent correlated with peritoneal dysfunction and the degree of peritoneal damage. Our study highlights that peritoneal EVs mediate communications between mesothelial cells and fibroblasts to initiate peritoneal fibrogenesis. Targeting EVs or ILK could provide a novel therapeutic strategy to combat peritoneal fibrosis.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer.

BACKGROUND: Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. METHODS: Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. RESULTS: From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. CONCLUSIONS: This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Effects of whole-body vibration training with different frequencies on the balance ability of the older adults: a network meta-analysis.

Purpose: To compare the effects of whole-body vibration training (WBVT) with different frequencies on the balance ability of older adults. Methods: Randomized controlled trials (RCTs) on the WBVT interventions on balance ability in older adults were searched through PubMed, Web of Science, The Cochrane Library, ProQuest, Embase, Opengrey, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database (CSTJ) databases from the establishment of the database to August 2022, and all literature that met the PICOS (Participants, Intervention, Comparison, Outcomes, Study design) criteria were enrolled. Two reviewers screened and assessed the methodological quality of the included literature according to the physiotherapy evidence database (PEDro) scale criteria. Statistical analysis was performed using Stata 14.0 software after data extraction. Results: Twenty-five RCTs with a total of 1267 subjects were finally included. The results of the pairwise comparison of the Network Meta-analysis showed that the Timed Up and Go Test (TUGT) values of Low-frequency whole-body vibration training (LF-WBVT) was lower than the placebo and traditional rehabilitation groups, and the difference was statistically significant [WMD = -1.37, 95% CI (-2.53, -0.20)] [WMD = -1.84, 95% CI(-3.17,-0.51)]. The Five-repetition Sit-to-Stand Test (5STS) values of LF-WBVT, Medium-frequency whole-body vibration training (MF-WBVT), and High-frequency whole-body vibration training (HF-WBVT) were lower than the placebo and traditional rehabilitation groups, but none of them were statistically significant. In addition, the TUGT and 5STS values of HF-WBVT had a tendency to be lower than those of LF-WBVT and MF-WBVT, but neither of them was statistically different. The cumulative probability ranking results of both TUGT and 5STS showed that HF-WBVT was the best protocol. Conclusion: Current evidence shows that HF-WBVT may be the best protocol for improving balance in older adults. Due to the study's limitations, the conclusion obtained in this study still needs to be further confirmed by more high-quality studies. Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42021250405].

A lightweight network guided with differential matched filtering for retinal vessel segmentation.

The geometric morphology of retinal vessels reflects the state of cardiovascular health, and fundus images are important reference materials for ophthalmologists. Great progress has been made in automated vessel segmentation, but few studies have focused on thin vessel breakage and false-positives in areas with lesions or low contrast. In this work, we propose a new network, differential matched filtering guided attention UNet (DMF-AU), to address these issues, incorporating a differential matched filtering layer, feature anisotropic attention, and a multiscale consistency constrained backbone to perform thin vessel segmentation. The differential matched filtering is used for the early identification of locally linear vessels, and the resulting rough vessel map guides the backbone to learn vascular details. Feature anisotropic attention reinforces the vessel features of spatial linearity at each stage of the model. Multiscale constraints reduce the loss of vessel information while pooling within large receptive fields. In tests on multiple classical datasets, the proposed model performed well compared with other algorithms on several specially designed criteria for vessel segmentation. DMF-AU is a high-performance, lightweight vessel segmentation model. The source code is at https://github.com/tyb311/DMF-AU.