Transcriptional patterns of human retinal pigment epithelial cells under protracted high glucose.

BACKGROUND: The retinal pigment epithelium (RPE) is essential for retinal homeostasis. Comprehensively exploring the transcriptional patterns of diabetic human RPE promotes the understanding of diabetic retinopathy (DR). METHODS AND RESULTS: A total of 4125 differentially expressed genes (DEGs) were screened out from the human primary RPE cells subjected to prolonged high glucose (HG). The subsequent bioinformatics analysis is divided into 3 steps. In Step 1, 21 genes were revealed by intersecting the enriched genes from the KEGG, WIKI, and Reactome databases. In Step 2, WGCNA was applied and intersected with the DEGs. Further intersection based on the enrichments with the GO biological processes, GO cellular components, and GO molecular functions databases screened out 12 candidate genes. In Step 3, 13 genes were found to be simultaneously up-regulated in the DEGs and a GEO dataset involving human diabetic retinal tissues. VEGFA and ERN1 were the 2 starred genes finally screened out by overlapping the 3 Steps. CONCLUSION: In this study, multiple genes were identified as crucial in the pathological process of RPE under protracted HG, providing potential candidates for future researches on DR. The current study highlights the importance of RPE in DR pathogenesis.

An innovative approach for assessing coronary artery lesions: Fusion of wrist pulse and photoplethysmography using a multi-sensor pulse diagnostic device.

Coronary heart disease (CHD) is a leading cause of mortality globally and poses a significant threat to public health. Coronary angiography (CAG) is a gold standard for the clinical diagnosis of CHD, but its invasiveness restricts its widespread application. In this study, we utilized a pulse diagnostic device equipped with pressure and photoelectric sensors to synchronously and non-invasively capture wrist pressure pulse waves and fingertip photoplethysmography (FPPG) of patients undergoing CAG. The extracted features were utilized in constructing random forest-based models to assessing the severity of coronary artery lesions. Notably, Model 3, incorporating both wrist pulse and FPPG features, surpassed Model 1 (solely utilizing wrist pulse features) and Model 2 (solely utilizing FPPG features). Model3 achieved an Accuracy, Precision, Recall, and F1-score of 78.79%, 78.69%, 78.79%, and 78.70%, respectively. Compared to Model1 and Model2, Model 3 exhibited improvements by 4.55%, 5.25%, 4.55%, and 5.12%, and 6.06%, 6.58%, 6.06%, and 6.54% respectively. This fusion of wrist pulse and FPPG features in Model 3 highlights the advantages of multi-source information fusion for model optimization. Additionally, this research provides invaluable insights into the novel development of diagnostic devices imbued with TCM principles and their potential in managing cardiovascular diseases.

The influence of shift work: A bibliometric analysis of research progress and frontiers on health effects.

Shift work has been found to disrupt the circadian system, leading to negative health effects. The objective of this study was to assess the progress and frontiers in research on the health-related influence of shift work. The study analyzed 3,696 data points from Web of Science, using the bibliometric software CiteSpace to visualize and analyze the field. The results showed a steady increase in annual publications, particularly in the last 5 years, with a rapid increase in publications from China. The United States contributed the most to the number of publications and worldwide collaborations. The most prolific institution and author were the Brigham and Women's Hospital and Professor Bjorn Bjorvatn, respectively. The Journal of Chronobiology International ranked at the top and focused primarily on shift worker research. In the first decade of study, the primary focus was on the associations between shift work and cardiovascular disease and metabolic disorders. Over time, research on the health effects of shift work has expanded to include cancer and mental health, with subsequent studies investigating molecular mechanisms. This study provides a comprehensive and intuitive analysis of the negative health impacts of shift work. It highlights existing research hotspots and provides a roadmap for future studies. Further research is needed to explore the adverse health consequences and related mechanisms of shift work exposure, as well as interventions to mitigate its health effects.

High Expression of miR-6785-5p in the Serum Exosomes of Psoriasis Patients Alleviates Psoriasis-Like Skin Damage by Interfering with the MNK2/p-eIF4E Axis in Keratinocytes.

Psoriasis is a chronic inflammatory skin disease characterized by abnormal keratinocyte proliferation and inflammation. MiRNAs and serum exosomes participate in the pathogenesis of many diseases. The objective of this study is to explore the function of miR-6785-5p in psoriatic keratinocytes and its upstream and downstream mechanisms. For our study, we employed qRT-PCR and fluorescence in situ hybridization to evaluate miR-6785-5p in psoriatic keratinocytes and conducted a microRNA microarray for identifying differentially expressed miRNAs in patient serum exosomes. We then cocultured keratinocytes with these exosomes, using immunofluorescence staining and qRT-PCR to assess uptake and miR-6785-5p overexpression. We explored miR-6785-5p's role through transfection with specific mimics and inhibitors and confirmed MNK2 as its target using a luciferase assay. MNK2's function was further examined using siRNA technology. Lastly, we applied an imiquimod-induced psoriasis mouse model, also employing siRNA, to investigate MNK2's role in psoriasis. MiR-6785-5p demonstrates a notable overexpression in the keratinocytes of psoriasis patients as well as in their serum exosomes. These keratinocytes actively uptake the miR-6785-5p-enriched serum exosomes. Functionally, miR-6785-5p appears to alleviate psoriasis-like skin damage, observable both in vitro and in vivo, by downregulating MNK2 expression. Psoriasis keratinocytes uptake serum exosomes highly expressing miR-6785-5p. MiR-6785-5p inhibits the abnormal proliferation and inflammatory state of keratinocytes by reducing MNK2 expression and interfering with the MNK2/p-eIF4E axis.

LPCAT1 Facilitates Keratinocyte Hyperproliferation and Skin Inflammation in Psoriasis by Regulating GLUT3.

Psoriasis is a chronic and relapsing inflammatory skin disorder characterized by keratinocyte hyperproliferation and immune cell infiltration. LPCAT1 has been identified as a cancer promoter in cutaneous squamous cell carcinoma by us, yet its role in psoriasis remains elusive. In this study, we report that LPCAT1 is highly expressed in psoriatic skin lesions. LPCAT1 promotes keratinocyte hyperproliferation and enhances the secretion of IL-1ß, IL-6, CXCL10, CCL20, S100A9, and platelet-activating factor. In psoriasiform keratinocytes, LPCAT1 promotes proliferation and inflammatory mediator production by activating protein kinase B/NF-κB and signal transducer and activator of transcription 3 signaling pathways. Furthermore, LPCAT1 inhibition attenuated epidermal hyperplasia and relieved skin inflammation in imiquimod-treated mice. Importantly, we identify the glucose transporter GLUT3, a recently reported promising target to mitigate T helper 17 cell-mediated inflammatory diseases, as a critical downstream effector of LPCAT1. GLUT3 deficiency impaired the proliferation and inflammation of psoriatic keratinocytes. LPCAT1 regulates GLUT3 in keratinocytes through NF-κB/signal transducer and activator of transcription 3 signaling, enhancing keratinocyte glycolysis and promoting proproliferative and proinflammatory effects. In addition, suppressing GLUT3 in mice alleviated imiquimod-induced dermatitis. Taken together, our study indicates the critical role of the LPCAT1-GLUT3 axis in psoriasis pathogenesis and proposes LPCAT1 or GLUT3 as a potential therapeutic target for psoriasis.

Tear Film Stability Affects Visual Acuity After Implantations of Monofocal and Multifocal Intraocular Lenses: An Evaluation by Objective Scatter Index.

Purpose: To assess the impact of tear film on postoperative visual acuity after cataract surgery to implant an intraocular lens (IOL). Methods: Tear break-up time (TBUT), Schirmer I test, objective scatter index (OSI), and uncorrected distance visual acuity (UCDVA), intermediate visual acuity (UCIVA), and near visual acuity (UCNVA) were collected 6 months after cataract surgery. Results: Fifteen eyes with monofocal (Mo-) IOLs and 15 eyes with multifocal (Mu-) IOLs from 30 subjects were included. The Mu-IOL group exhibited higher baseline OSI (1.92 ± 0.69, P < 0.001). Negative correlations-both groups: tear film-related OSI (TF-OSI) and TBUT/Schirmer I test; Mo-IOL: TBUT and logMAR UCDVA-and positive correlations-both groups: TF-OSI and baseline OSI, baseline OSI/TF-OSI and logMAR UCDVA; Mu-IOL: baseline OSI/TF-OSI and logMAR UCIVA/UCNVA-were found. Linear regression showed associations between TF-OSI and TBUT (Mo-IOL: R2 = 0.455, P = 0.006; Mu-IOL: R2 = 0.454, P = 0.006)/Schirmer I test (Mo-IOL: R2 = 0.527, P = 0.002; Mu-IOL: R2 = 0.266, P = 0.049). Multiple regression showed associations between baseline OSI (Mo-IOL: R2 = 0.309, P = 0.032; Mu-IOL: R2 = 0.305, P = 0.033)/TF-OSI (Mo-IOL: R2 = 0.332, P = 0.025; Mu-IOL: R2 = 0.523, P = 0.002)/TBUT (Mo-IOL only: R2 = 0.315, P = 0.029) and logMAR UCDVA. Conclusions: TF-OSI reflects the UCDVA performance in eyes with IOLs and facilitates a better understanding of the effects of the tear film. Translational Relevance: TF-OSI offers a developmental and objective approach to assessing the changing visual performance caused by tear film after cataract surgery and IOL implantation in clinical practices.

Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases.

BACKGROUND: Cardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring. OBJECTIVE: Based on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. METHODS: This paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random forest algorithm (RF). RESULTS: RF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states. CONCLUSIONS: The wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves.

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis.

BACKGROUND: The timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' mortality. OBJECTIVE: To evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD. METHODS: A total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were compared. RESULTS: The pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, respectively. CONCLUSIONS: The wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

Mapping of quantitative trait locus reveals PsXI gene encoding xylanase inhibitor as the candidate gene for bruchid (Callosobruchus spp.) resistance in pea (Pisum sativum L.).

Pea (Pisum sativum L.) is an important legume crop for both food and feed. Bruchids (Callosobruchus spp.) are destructive insect pests of pea in the field and during storage. In this study, we identified a major quantitative trait locus (QTL) controlling seed resistance to C. chinensis (L.) and C. maculatus (Fab.) in field pea using F2 populations derived from a cross between PWY19 (resistant) and PHM22 (susceptible). QTL analysis in the two F2 populations grown in different environments consistently identified a single major QTL, qPsBr2.1, controlling the resistance to both bruchid species. qPsBr2.1 was mapped onto linkage group 2 between DNA markers 18339 and PSSR202109 and explained 50.91% to 70.94% of the variation in resistance, depending on the environment and bruchid species. Fine mapping narrowed down qPsBr2.1 to a genomic region of 1.07 Mb on chromosome 2 (chr2LG1). Seven annotated genes were found in this region, including Psat2g026280 (designated as PsXI), which encodes a xylanase inhibitor and was considered as a candidate gene for bruchid resistance. PCR amplification and sequence analysis of PsXI suggested the presence of an insertion of unknown length in an intron of PWY19, which causes variation in the open reading frame (ORF) of PsXI. Moreover, the subcellular localization of PsXI differed between PWY19 and PHM22. These results together suggested that PsXI encoding xylanase inhibitor is responsible for the bruchid resistance of the field pea PWY19.

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks.

The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.

Tongue crack recognition using segmentation based deep learning.

Tongue cracks refer to fissures with different depth and shapes on the tongue's surface, which can characterize the pathological characteristics of spleen and stomach. Tongue cracks are of great significance to the objective study of tongue diagnosis. However, tongue cracks are small and complex, existing methods are difficult to extract them effectively. In order to achieve more accurate extraction and identification of tongue crack, this paper proposes to apply a deep learning network based on image segmentation (Segmentation-Based Deep-Learning, SBDL) to extract and identify tongue crack. In addition, we have studied the quantitative description of tongue crack features. Firstly, the pre-processed tongue crack samples were amplified by using adding salt and pepper noise, changing the contrast and horizontal mirroring; secondly, the annotation tool Crack-Tongue was used to label tongue crack; thirdly, the tongue crack extraction model was trained by using SBDL; fourthly, the cracks on the tongue surface were detected and located by the segmentation network, and then the output and features of the segmentation network were put into the decision network for the classification of crack tongue images; finally, the tongue crack segmentation and identification results were quantitatively evaluated. The experimental results showed that the tongue crack extraction and recognition results based on SBDL were better than Mask Region-based Convolutional Neural Network (Mask R-CNN), DeeplabV3+, U-Net, UNet++ and Semantic Segmentation with Adversarial Learning (SegAN). This method effectively solved the inaccurate tongue crack extraction caused by the tongue crack's color being close to the surrounding tongue coating's color. This method can achieve better tongue crack extraction and recognition results on a small tongue crack data set and provides a new idea for tongue crack recognition, which is of practical value for tongue diagnosis objectification.

KMT2C Induced by FABP5P3 Aggravates Keratinocyte Hyperproliferation and Psoriasiform Skin Inflammation by Upregulating the Transcription of PIK3R3.

The extensive involvement of lysine methyltransferase 2C (KMT2C) in the inflammatory response is well-documented. However, little is known about the role of KMT2C in psoriasis. We identified that KMT2C was significantly upregulated in the epidermis of psoriatic skin lesions and the psoriasiform cell model. KMT2C knockdown diminished keratinocyte proliferation and the secretion of IL-6, IL-8, CCL20, and S100A9 in vitro and in vivo. In psoriasiform keratinocytes, KMT2C promoted the transcription of PIK3R3 by regulating the enrichment of histone H3 lysine 4 trimethylation at the PIK3R3 promoter and histone 3 lysine 4 monomethylation at the enhancer. The PIK3R3/protein kinase B/NF-κB pathway is a vital step in KMT2C-mediated alleviation of cytokine-primed inflammation. The long noncoding RNA FABP5P3 sustained KMT2C mRNA stability by recruiting human antigen R. Furthermore, inhibition of KMT2C attenuated epidermal hyperplasia and skin inflammation in mice with psoriasis. Taken together, our findings indicated a link between KMT2C and psoriasis and opened the possibility of using KMT2C as a potential therapeutic target for psoriasis treatment.

Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network.

Tongue texture analysis is of importance to inspection diagnosis in traditional Chinese medicine (TCM), which has great application and irreplaceable value. The tough and tender classification for tongue image relies mainly on image texture of tongue body. However, texture discontinuity adversely affects the classification of the tough and tender tongue classification. In order to promote the accuracy and robustness of tongue texture analysis, a novel tongue image texture classification method based on image inpainting and convolutional neural network is proposed. Firstly, Gaussian mixture model is applied to separate the tongue coating and body. In order to exclude the interference of tongue coating on tough and tender tongue classification, a tongue body image inpainting model is built based on generative image inpainting with contextual attention to realize the inpainting of the tongue body image to ensure the continuity of texture and color change of tongue body image. Finally, the classification model of the tough and tender tongue inpainting image based on ResNet101 residual network is used to train and test. The experimental results show that the proposed method achieves better classification results compared with the existing methods of texture classification of tongue image and provides a new idea for tough and tender tongue classification.

Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network.

Purpose: Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method: This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result: The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion: LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals.

Downregulation of miRNA miR-1305 and upregulation of miRNA miR-6785-5p may be associated with psoriasis.

Background: The role of serum extracellular vesicles (EVs) is less known in psoriasis. Objectives: To explore the transcriptomic profile of serum EVs and the potential biomarkers in psoriasis. Methods: EVs were isolated by differential ultracentrifugation and identified by transmission electron microscope. The diameters of EVs were detected using nanoparticle tracking analysis. Serum EVs-keratinocyte interaction was observed through confocal fluorescence microscopy. miRNA microarray and mRNA microarray were performed in serum EVs (n = 4) and skin lesions (n = 3), respectively. Quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) and fluorescence in situ hybridization were used to detect the expression of miRNAs in serum EVs and skin lesions (n = 15). Bioinformatics analysis was performed to predict the potential target genes and functions of miR-1305 and miR-6785-5p. Western blot, CCK-8 and enzyme-linked immunosorbent assay (ELISA) were used to detect the EVs' biomarkers, keratinocytes proliferation and cytokines secretion. Results: A total of 16 miRNAs and 1,725 mRNAs were significantly dysregulated in serum EVs and skin lesions, respectively. miR-1305 was down-regulated and miR-6785-5p was upregulated in both serum EVs and skin lesions. Serum EVs could be taken up by keratinocytes. miR-1305 was downregulated and miR-6785-5p were upregulated in keratinocytes after co-cultured with psoriasis serum EVs compared with controls. Psoriasis serum EVs promoted keratinocyte proliferation and the secretion of CCL20 and IL-8. Serum EVs miR-1305 and miR-6785-5p were associated with disease severity. Conclusion: Serum EVs might be involved in the activation of keratinocytes through loaded miRNAs in psoriasis. Serum EVs miR-1305 and miR-6785-5p may be associated with psoriasis.

Sodium-Based Cylindrical Plasmonic Waveguides in the Near-Infrared.

Subwavelength optical field confinement and low-loss propagation are of great significance for compact photonic integration. However, the field confinement capability of plasmonic devices is always accompanied by the inherent Ohmic loss. Although recent studies have shown that sodium (Na) exhibits lower loss than noble metals in the near-infrared band, the field confinement ability has not been adequately assessed. Meanwhile, the high chemical reactivity of Na should be regulated for practical application. Two dielectric-coated Na nanowires, consisting of cylindrical Na nanowires with one or two dielectric layers as claddings, are proposed and investigated in this paper. Based on finite element calculations, we thoroughly study the modal fields and low-loss propagation properties of dielectric-coated Na nanowires. The results demonstrate that Na exhibits lower loss and stronger field confinement than the typical plasmonic material silver. These findings indicate the performance of plasmonic devices can be considerably improved by employing the metal Na compared with devices using noble metals, which may promote the applications in subwavelength photonic devices.

ILF2 Contributes to Hyperproliferation of Keratinocytes and Skin Inflammation in a KLHDC7B-DT-Dependent Manner in Psoriasis.

Background: The extensive involvement of interleukin enhancer binding factor 2 (ILF2) in RNA stability and the inflammatory response is well documented. Aberrant long noncoding RNA (lncRNA) expression contributes to the pathogenesis of psoriasis. However, little is known about the role of ILF2 in psoriasis. Objective: To investigate the role of ILF2 and KLHDC7B-DT in psoriasis. Methods: LncRNA expression in psoriatic tissues was measured by lncRNA microarray and qRT-PCR. Normal human epidermal keratinocytes (NHEKs), HaCaT cells, and Ker-CT cells stimulated with M5 (IL-17A, IL-22, IL-1α, oncostatin M, and TNF-α) were used to establish a psoriasis model in vitro. Fluorescence in situ hybridization was used to detect the distribution of KLHDC7B-DT and ILF2 in keratinocytes. The proliferative effects of KLHDC7B-DT and ILF2 on keratinocytes were demonstrated by EdU assay and flow cytometry. ELISA was used to detect the secretion levels of cytokines. RNA pull-down and RNA immunoprecipitation (RIP) were used to detect the direct binding of KLHDC7B-DT with ILF2. Western blotting was used to detect the proteins related to STAT3/JNK signalling pathways. Results: ILF2 and KLHDC7B-DT were significantly overexpressed in psoriatic tissues and M5-induced keratinocytes. KLHDC7B-DT promoted the proliferation of keratinocytes and induced the secretion of IL-6 and IL-8. KLHDC7B-DT could directly bind to ILF2 and activate the STAT3 and JNK signalling pathways. KLHDC7B-DT expression was regulated by ILF2. M5-induced proliferation and inflammatory cytokine secretion in keratinocytes was inhibited after ILF2 knockdown. Furthermore, we found that ILF2 promoted keratinocyte proliferation and the inflammatory response in a KLHDC7B-DT-dependent manner. Conclusions: ILF2 and KLHDC7B-DT are involved in the hyperproliferation of keratinocytes and skin inflammation in psoriasis. In addition, ILF2 functions in a KLHDC7B-DT-dependent manner.

Par3 regulates the asymmetric division of basal stem cells in psoriasis via the Par3/mInsc/LGN signaling axis.

OBJECTIVE: This research aimed to investigate the mechanism in psoriasis with the involvement of Par3-containing exosomes from macrophages by regulating the asymmetric division of basal stem cells. METHODS: BrdU labeling and double immunofluorescence assays were conducted to detect the proportion of asymmetric division in psoriasis mice. Western blot assay was conducted to examine the expression of Par3/mInsc/LGN signaling pathway-related proteins in psoriasis mice. Next, the asymmetric division of keratinocytes in normal mice treated with macrophages and their secreted exosomes were determined, together with the related protein detection. After establishing a macrophage-specific Par3 knockout mouse model, the asymmetric division of isolated keratinocytes and the related proteins were measured. An epidermal-specific mInsc, LGN, or NuMA knockout mouse model was induced, followed by the determination of the asymmetric division of isolated keratinocytes. RESULTS: The asymmetric division of basal stem cells was increased, and the expression of Par3/mInsc/LGN signaling pathway-related proteins was elevated in psoriasis. Par3-containing macrophage-derived exosomes enhanced asymmetric division of basal stem cells and expression of Par3/mInsc/LGN signaling pathway-related proteins in mice. However, mice with Par3 loss presented opposite trends. There was a decreased asymmetric division of basal stem cells in epidermal-specific mInsc, LGN, and NUMA knockout mice. CONCLUSION: Our study suggests that macrophage-derived exosomes-shuttled Par3 are absorbed by the basal stem cells and regulate the asymmetric division of cells to produce a large number of transit-amplifying cells, thus causing psoriasis-related symptoms in conjunction with various other factors.

CircEIF5 contributes to hyperproliferation and inflammation of keratinocytes in psoriasis via p-NFκB and p-STAT3 signalling pathway.

Psoriasis is a chronic, immune-mediated skin disease accompanied by hyperproliferation and inflammation of keratinocytes. Circular RNAs (circRNAs) as new players regulating the development of psoriasis have been reported in recent years. However, its mechanism has not yet been fully revealed. In this study, we identified that hsa_circ_0033469 (circEIF5) was highly expressed in psoriasis tissues compared with the normal skin. We investigated the functional roles of circEIF5 in proliferation and inflammatory of HaCat cells under M5-stimulated inflammatory condition. By using a approach of knockdown and overexpression of circEIF5, we showed that circEIF5 could promote proliferation by facilitating the G1/S transition and increase secretion of chemokines in HaCat cells. These moderating effects of circEIF5 were associated with the activating of the NF-κB and STAT3 signalling pathways. Moreover, NF-κB and STAT3 inhibition abrogated circEIF5-induced promotion of cell proliferation and chemokine secretion. These results indicated that through NF-κB and STAT3 signalling pathways, circEIF5 regulated the proliferation and chemokine secretion of HaCat cells and contributed to the pathogenesis of psoriasis, which might become a potent target for psoriasis treatment.

Tai Chi Program to Improve Glucose Control and Quality of Life for the Elderly With Type 2 Diabetes: A Meta-analysis.

OBJECTIVE: To systematically evaluate the effects of Tai chi for improving elderly patients with type 2 diabetes. METHODS: According to PRISMA checklist, we conducted this standard meta-analysis. The multiple databases like Pubmed, Embase, and Cochrane databases were used to search for the relevant studies, and full-text articles involved in the evaluation of Tai chi in improving elderly patients with type 2 diabetes. Review manager 5.2 was adopted to estimate the effects of the results among selected articles. Forest plots, sensitivity analysis and funnel plot for the articles included were also conducted. RESULTS: Finally, 7 relevant studies were eventually satisfied the included criteria. We found that Tai chi group had lower glucose than control group (mean difference (MD)=-12.47, 95%CI [-21.20, -3.73], P=.005; I2 = 32%), Tai chi group had higher activities-specific balance confidence (ABC) scale than control group (MD =9.26 with 95%CI [6.68, 11.83], P < .001) and Tai chi group had higher single limb standing test score than control group (MD = 8.38, 95%CI [4.02, 12.74], P = .001). The study was robust and limited publication bias was observed in this study. CONCLUSION: Since we found Tai chi had better performance than usual care in improving old diabetes patients' glucose and life quality, the study supports that Tai chi can help old diabetes patients from several aspects including disease indicators, independence and life quality.