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Multi-temporal synthetic aperture radar interferometry (MT-InSAR) offers unique advantages in monitoring ground deformation and structural stability along the metro lines. However, a vast number of complex deformation points, millions and even more, can be derived from InSAR making it challenging to identify the deformation hotspot in time series automatically. This paper proposes a novel method for quantitatively assessing the MT-InSAR-derived deformation results. We first introduce an iterative seasonal trend decomposition using loess (STL) method to confirm the optimal period for separating seasonal components from the displacement time series. Then, an absolute differences detector with rolling windows is proposed to quantify the subsidence ratio within the time series and allow deformation hotspots to be more visible. To validate the effectiveness of the proposed method, 468 scenes of Sentinel-1A ascending images from Jun. 2015 to Nov. 2023 over the Hong Kong Mass Transit Railway (MTR) are adopted. The results indicate that 99.2% of areas are relatively stable with the displacement velocity ranging from −2 mm/year to 2 mm/year, and 84% of the study area remained a subsidence ratio below 0.3, except for localized hotspots that exhibited either short or long-term subsidence trends. The findings of this study indicate that multiple deformation hotspots were identified at the intersections of several metro lines in the Kowloon Peninsula and along the Island line. In addition to the displacement velocity from the conventional MT-InSAR, the overall and annual subsidence ratios have been demonstrated to be useful indicators for quantitative assessment of the construction-induced deformation.

The efficient and automated identification of landslide hazards is essential for socio-economic development and human safety. Integrating the feature extraction capabilities of deep learning with the millimeter-level precision of Interferometric Synthetic Aperture Radar (InSAR) technology establishes a foundation for this task. However, current methods require unwrapping interferograms, and even converting them into deformation products before identifying landslide hazards. This process is susceptible to unwrapping errors, resulting in inefficient data utilization, and demands considerable time and labor. To overcome these challenges, wrapped interferograms are directly utilized for identifying creeping landslides. In this study, trigonometric functions are applied to improve the representation of interferograms and to further enhance the data through rendering. Secondly, a multi-branch semantic segmentation network (MB-Net) was designed, with parallel branch encoding and progressive feature fusion to optimize the model’s ability to learn interferometric phases. Experimental results indicate a good performance, with the F1-score of 80.91 %, the Intersection over Union (IoU) of 67.94 %, and the Matthews correlation coefficient (MCC) of 80.16 % on the ISSLIDE dataset. To further validate the generalization capability of MB-Net, the public COMET-LiCS Sentinel-1 InSAR portal data was utilized, focusing on the middle reaches of the Jinsha River in China. The results highlight MB-Net’s efficacy in spatial transferability analysis. These findings emphasize the potential of our approach for large-scale landslide hazard identification, providing a crucial foundation for the utilization of interferograms in creeping landslide detection.

Background As one of the most effective biologic treatments for psoriasis, the short-term effectiveness of ustekinumab has yet to be studied extensively.Objective The purpose of this study was to evaluate the short-term effectiveness and potential factors within four weeks after the first-dose ustekinumab treatment based on real-world data.Methods The study enrolled 98 patients with moderate-to-severe psoriasis, given ustekinumab 45 mg at week 0, week 4, and then every 12 weeks. Based on clinical data collected at baseline and week 4, we investigated the short-term effectiveness of ustekinumab after the first dose and potential factors associated with the treatment. For evaluation, we collected demographic information, body data, medical history, laboratory examination results, Psoriasis Area and Severity Index (PASI), body surface area (BSA), and dermatology life quality index (DLQI). Response rates were calculated based on the number of patients that achieved a 75/90/100% reduction in PASI (PASI 75/90/100), and the primary treatment goal was to achieve PASI 75.Results The response rates for PASI 75/90/100 at week 4 were 30.5%, 18.9%, and 16.8%, respectively. For PASI 75, the response rate was higher in patients without metabolic syndrome (MS) (without MS vs. with MS: 36.9% vs. 5.9%, p = 0.013); the serum triglyceride (TG) level was significantly lower in patients achieving PASI 75 (expressed as mean ± standard deviation, achieved vs. unachieved: 1.82 ± 1.79 vs. 3.59 ± 8.89, p = 0.010). For PASI 100, the response rates were higher in female patients (female vs. male: 26.3% vs. 10.5%, p = 0.044) and patients with a family history of psoriasis (with family history vs. without family history: 44.4% vs. 13.9%, p = 0.042). In addition, the possibility of achieving PASI 75/90/100 went up along with the serum high-density lipoprotein cholesterol (HDL-C) level (expressed as adjusted odds ratio < 95% confidence interval>: PASI 75: 28.484 , p = 0.011; PASI 90: 28.226 , p = 0.004; PASI 100: 12.175 , p = 0.009).Conclusion In this study, nearly one-third of patients achieved PASI 75 after only the first-dose ustekinumab treatment. Sex, family history of psoriasis, MS, serum TG level might affect the short-term effectiveness, and serum HDL-C level may be a potential factor. The possibility of achieving treatment goals (PASI 75/90/100) at week 4 increased along with serum HDL-C levels.

With the escalation of global climate change and human activity, geohazards become increasingly frequent which cause severe casualties and property losses to local communities. To alleviate this situation and provide scientific guidance for risk reduction, it is imperative to address some of the basic questions related to geohazards, including: i) how to detect active geohazards (AGs) rapidly and automatically over a wide area; ii) how to determine the region with a high level of hazard activity; iii) what are the primary conditioning factors (CFs) of AGs; and iv) do factors operate independently or are they interconnected. To tackle these issues, we propose a universally applicable framework for wide area automated detection of AGs. The framework is based on multi-source Earth observations which capture surface deformation ranging from millimeters to meters. Our study has focused on the Hexi Corridor (HXC) in Gansu Province, China, covering an area of 210,000 km2 with a length of 1100 km. First, we construct an AGs database for the HXC with high automatic and rapid update capabilities, including a total of 4492 AGs (3652 active landslides and 840 land subsidence areas). Second, using the Geographic Detectors method, we determine the primary CFs including elevation, land surface temperature, and precipitation. We find that faults exert greater control over very slow-moving landslides, but are less effective over slow-moving landslides. Third, we analyzed the interactive effects of dual CFs on geohazard actives. Any interaction effect of dual CFs contributes to the bivariate enhancement of geohazard activity. This study significantly enhances the capabilities of the wide area automated detection of AGs, and provides a crucial dataset for hazard prediction and mitigation along the HXC.

Deep learning techniques have been widely adopted in landslide detection by offering powerful feature extraction capabilities and automated processes. However, the pursuit of higher accuracy has led to increasingly complex network structures, which limits the efficiency of models in landslide detection. To tackle this challenge, we have developed a dynamic module, called five-branch feature extraction module (FFEM), based on the theory of structural reparameterization. This module is designed to reconstruct the encoder of the U-shaped network. Our novel network, Re-Net, effectively integrates information from multiple scales during training by utilizing its multibranch structure, which is facilitated by the FFEM. During inference, leveraging the structural reparameterization, the FFEM in Re-Net transforms as a convolutional layer, achieving an impressive 52.9% reduction in parameters, and 34.98% reduction in floating point operations. The efficiency improvement of Re-Net does not come at the expense of sacrificing landslide recognition accuracy. In the public dataset (Bijie dataset), Re-Net achieved improvements of 2.81% in intersection over union (IoU) and 1.93% in F1-Score. In postearthquake landslide detection tasks (Luding Dataset), Re-Net exhibited respective improvements of 2.29% and 1.52%. Moreover, in the task of Landslide Detection, Re-Net demonstrates superior segmentation accuracy compared to other convolutional neural networks, such as Unet++. When compared to other reparameterization modules, FFEM shows significant improvements in IoU and F1-Score in the Bijie dataset, with an average increase of 0.65% and 0.45%, respectively. Similarly, in the Luding dataset, FFEM demonstrates average improvements of 1.56% in IoU and 1.04% in F1-Score.

Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR). We optimize the network paths to enhance the phase unwrapping process of InSAR. Additionally, to address design matrix bias resulting from inaccurate temperature data, we employ the mixed total least squares method to estimate deformation parameters. Subsequently, we utilize independent component analysis to analyze the spatiotemporal deformation characteristics of the bridge. The average standard deviation of the unwrapped phase and the modeling residuals have been reduced by 87% and 5%, respectively. Our findings indicate that thermal expansion deformation is primarily concentrated in the expansion joints, measuring approximately 0.6 mm/°C. In contrast, the cable-stayed bridge deck exhibits the largest deformation magnitude, exceeding 2.0 mm/°C. This research focuses on bridge structures to identify typical deformation locations and evaluate their deformation characteristics. Such analysis is beneficial for conducting safety assessments of bridges.

The mountainous areas of Southwest China are crucial regions for hydropower resource development. The unique geological environment and numerous constructed hydropower stations make landslides the primary geological hazard for the region. Therefore, the deformation monitoring and mechanism analysis of reservoir landslides have attracted extensive attention from the academic community. The Maoergai Reservoir (MEGR) is located in the middle reaches of the Heishui River in Sichuan Province, China. Following impoundment, numerous active slopes have arisen in the reservoir area, with the Xierguazi-Mawo landslide complex (XMLC) potentially being the most threatening. In this paper, we first acquired the surface displacement of the MEGR area, before and after impoundment, using time series Synthetic Aperture Radar Interferometry (InSAR) analysis of the ALOS-1/PALSAR-1, ALOS-2/PALSAR-2 and Sentinel-1 datasets. Then, the three-dimensional (3D) deformation field was inverted by combining multi-track InSAR observations and a topography-constrained model. Results show the presence of 47 active landslides within the 675 km2 reservoir area, a significant increase compared to the seven landslides identified prior to impoundment. Based on the derived 3D velocity vectors, we found that the motion of the XMLC exhibits heterogeneity in terms of spatial and temporal patterns. Further, the displacement time series of all detected active landslides suggests that irrigation and water level fluctuations are two major factors influencing the kinematic behaviors of landslides. Irrigation accelerates landslide movement and thus permanently alters the deformation trend, while water level fluctuations cause seasonal oscillations in the displacement time series with a time lag of approximately 100 days. The above results can contribute to landslide management and prevention in the MEGR area and provide a representation for numerous water reservoirs in southwest China.

The active thickness of the translational landslides plays a pivotal role in evaluating its hazards and simulating its instability. Existing techniques have difficulties in estimating the accurate active thickness due to the limitations of observation conditions and imaging geometry, leading to deviations in failure simulations. To overcome these challenges, this study proposes an enhanced method that utilizes multi-orbit Interferometric Synthetic Aperture Radar (InSAR) observations to estimate the active thickness of the translational landslides and subsequently conduct more accurate instability simulations. The method involves integrating multi-orbit InSAR imaging parameters with the spatial geometry of the landslide to establish a slope coordinate system. This system enables the projection of one-dimensional InSAR Line Of Sight (LOS) displacements onto the three-dimensional displacements of the landslide. Subsequently, the active thickness is estimated by combining InSAR three-dimensional displacements with the mass conservation method. Finally, the estimated thickness is incorporated into the geological model construction to simulate the dynamic movement of the landslide. The method was applied to the Xiongba translational landslide in Gongga County, Tibet Autonomous Region, China. The results show that the deformation is mainly concentrated at its forefront, with maximum three-dimensional deformation rates of 4.7 m/a, 2.3 m/a, and 1.24 m/a. The landslide encompasses an estimated area of around 5.33 square kilometers, and its active thickness varies from 0 to 106.59 m. The maximum displacement distance reaches 1469.76 m, with a peak velocity of 60.37 m/s. The proposed method provides scientific support for assessing, analyzing, and preventing landslide disasters.

Abstract Introduction Previous studies have proposed a possible gut–skin axis, and linked gut microbiota to psoriasis risks. However, there is heterogeneity in existing evidence. Observational research is prone to bias, and it is hard to determine causality. Therefore, this study aims to evaluate possible causal associations between gut microbiota (GM) and psoriasis. Methods With published large-scale GWAS (genome-wide association study) summary datasets, two-sample Mendelian randomization (MR) was performed to sort out possible causal roles of GM in psoriasis and arthropathic psoriasis (PsA). The inverse variance weighted (IVW) method was taken as the primary evaluation of causal association. As complements to the IVW method, we also applied MR-Egger, weighted median. Sensitivity analyses were conducted using Cochrane’s Q test, MR-Egger intercept test, MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) global test, and leave-one-out analysis. Results By primary IVW analysis, we identified nominal protective roles of Bacteroidetes (odds ratio, OR 0.81, P = 0.033) and Prevotella9 (OR 0.87, P = 0.045) in psoriasis risks. Bacteroidia (OR 0.65, P = 0.03), Bacteroidales (OR 0.65, P = 0.03), and Ruminococcaceae UCG002 (OR 0.81, P = 0.038) are nominally associated with lower risks for PsA. On the other hand, Pasteurellales (OR 1.22, P = 0.033), Pasteurellaceae (OR 1.22, P = 0.033), Blautia (OR 1.46, P = 0.014), Methanobrevibacter (OR 1.27, P = 0.026), and Eubacterium fissicatena group (OR 1.21, P = 0.028) are nominal risk factors for PsA. Additionally, E. fissicatena group is a possible risk factor for psoriasis (OR 1.22, P = 0.00018). After false discovery rate (FDR) correction, E. fissicatena group remains a risk factor for psoriasis (P FDR = 0.03798). Conclusion We comprehensively evaluated possible causal associations of GM with psoriasis and arthropathic psoriasis, and identified several nominal associations. E. fissicatena group remains a risk factor for psoriasis after FDR correction. Our results offer promising therapeutic targets for psoriasis clinical management.

Objective:. Up to 85.3% of patients with psoriasis experience sleep disturbance (SD). However, SD has not been characterized in Chinese patients with psoriasis, and the factors that affect SD among adult patients with psoriasis remain unclear. This study was performed to examine the risk of SD in patients with psoriasis compared with a control group of healthy individuals and to identify factors contributing to SD in patients with psoriasis. Methods:. This was a cross-sectional, questionnaire-based, case-control study involving 142 adult participants with psoriasis and 142 healthy controls. The Pittsburgh Sleep Quality Index (PSQI) was administered to assess SD. Mild psoriasis was defined by Psoriasis Area and Severity Index and body surface area cut-offs of

Abstract Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients’ clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan–Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.

Landslides have attracted extensive attention and research worldwide because of their tremendous destructiveness, and many catastrophic landslides have occurred along river corridors and reservoir bank slopes. The detection and detailed monitoring of potential landslides are prerequisites for landslide disaster prevention, with recent advance in spaceborne Interferometric Synthetic Aperture Radar (InSAR) having effectively addressed this challenge. In this article, a wide-area landslide detection and monitoring framework, combining multiple InSAR techniques, is proposed to investigate active landslides along the upper reaches of the Yellow River from Longyang Gorge to Liujia Gorge (UYRLL), north-west China. A total of 597 active landslides have been mapped in this region for the first time. Our analyses suggest that a large percentage of the detected landslides are distributed at an elevation of 2000–3000 m with slope angles of 5–30° and their spatial distribution density is correlated with regional tectonic activity. Multitemporal InSAR techniques have also been adopted to analyze the surface motion characteristics of two typical landslides (i.e., the Lijia landslide and the Xijitan landslide), showing that these two landslides are dominated by linear motion. The landslide movements also contain slight nonlinear oscillations, inferred from wavelet analysis to be associated with reservoir water area changes and seasonal rainfall. The proposed framework herein can be extended to the detection and monitoring of active landslides in other areas with similar geomorphic features, and the above findings on landslide characteristics can also contribute to landslide prevention and mitigation in the upper Yellow River area.

ABSTRACTDeep learning techniques have been applied to extract road areas from remote sensing images, leveraging their efficient and intelligent advantages. However, the contradiction between the effective receptive field and coverage range, as well as the conflict between the depth of the network and the density of geographic information, hinders further improvement in extraction accuracy. To address these challenges, we propose a novel semantic segmentation network called D-FusionNet. D-FusionNet integrates the Dilated Convolutional Block (DCB) module, which serves as a technique for expanding the receptive field and mitigating feature loss, resembling a residual mechanism during encoding. We evaluate the extraction capability of D-FusionNet using GF-2 (Gaofen-2) satellite datasets and Massachusetts aerial photography datasets. The experimental results demonstrate that D-FusionNet performs well in road extraction tasks. Compared to FCN, UNet, LinkNet, D-LinkNet, and FusionNet, D-FusionNet achieves an average improvement of 5.35% in F1-score, 7.12% in IoU (Intersection over Union), and 5.61% in MCC (Matthews Correlation Coefficient) on the GF-2 dataset. For the Massachusetts dataset, there is an average improvement of 2.48% in F1-score, 3.25% in IoU, and 2.25% in MCC. This study provides valuable support for road extraction from remote sensing images.

Sichuan-Tibet Railway spans several watersheds such as Jinsha River and Yalong River, and Potential landslides are frequent along the route, which poses serious hazards to the normal construction and operation of the railroad. The traditional time-series InSAR technology is limited by the number of images and other restrictions, and has a long solution time, making it difficult to obtain information on short-term occurrence of deformation and unable to perform wide-area potential landslides monitor quickly. In this paper, taking a geological hazard-prone area in the Jinsha River basin (or a section of the Sichuan-Tibet line) as an example, based on the Sentinel-1 satellite SAR data provided by the Copernicus program of ESA, the model corrects the interferometric superposition deformation results obtained from a small number of SAR images (less than 7), decodes the corrected rate results, and identifies a total of 13 areas where deformation obviously occurs A total of 13 typical areas with significant deformation were identified. The identified typical areas were time-series solved and their deformation was traced. The method provides a new idea for identification and monitor of Potential landslides in a wide area and further promotes the development of disaster prevention and mitigation.

BackgroundAzvudine has been approved in China for the treatment of COVID-19 patients. Previous studies have suggested a correlation between high levels of lactate dehydrogenase (LDH) and the severity of COVID-19. However, the impact of LDH levels in COVID-19 patients receiving Azvudine treatment remains unclear.MethodsIn this retrospective cohort study, we analyzed the data of 351 hospitalized COVID-19 patients who were consecutively treated with Azvudine, with or without high LDH levels. The clinical features, treatment strategies and prognosis data were collected and analyzed.ResultsAmong the 351 hospitalized patients with COVID-19 treated with Azvudine (119 with high-LDH levels), the median age was 69 years (range 58–78), and 213 (60.7%) were male. Common symptoms included cough (86.0%), expectoration (73.5%), fever (69.8%), polypnea (47.6%) and poor appetite (46.4%). Patients with high LDH levels exhibited significantly elevated leucocyte and neutrophil counts, elevated level of myocardial enzymes, as well as higher levels of inflammatory markers such as interleukin-6, interleukin-10, procalcitonin, C reactive protein, ferritin, and prolonged erythrocyte sedimentation rate upon admission. COVID-19 patients with high-LDH levels had higher rates of corticosteroid therapy, non-invasive and invasive mechanical ventilation, worsened and death (2.5% vs. 0%). The Cox proportional hazard model demonstrated that high LDH levels (adjusted hazard ratio = 5.27; 95% confidence interval: 1.19, 14.50) were associated with a more unfavorable composite disease progression outcome among COVID-19 patients treated with Azvudine, after accounting for potential confounding variables.ConclusionHigh-LDH levels predict a worse composite disease progression outcome in COVID-19 patients treated with Azvudine.

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

Background Previous studies have proven that ultraviolet (UV)-based phototherapy, including UVB or psoralen UVA (PUVA), and their combination therapies, is effective for psoriasis treatment. Objective To compare the clinical efficacy and adverse events (AEs) of different UV-based phototherapy in psoriasis. Methods PubMed, Cochrane Library, Scopus and Embase were systematically searched. A random-effect model network meta-analysis with frequentist framework was performed, and results were reported as risk ratios (RRs) with 95% CI. The main variable for assessing effectiveness and safety are PASI 75 response and withdrawal due to AEs. Ranking effects were calculated by surface under the cumulative ranking analysis (SUCRA). Results Thirty-two studies involving a total of 2120 psoriasis patients were included in this network meta-analysis. Overall, no significant difference was reported with respect to withdrawal due to AEs or incidence of erythema. The relatively safest strategy was combined adjuvant therapy with PUVA (cPUVA), especially PUVA combined with calcium/vitamin D derivatives (RR 0.98, 95% CI [0.30–3.17], SUCRA = 80.8%). Both cPUVA (RR 1.39, 95% CI [1.00– 1.94]) and combined adjuvant therapy with UVB (cUVB) (RR 1.27, 95% CI [1.03–1.57]) showed a superior effect than the monotherapy of UVA or UVB, respectively. PUVA combined with vitamin D and its derivatives (PAVD) ranked highest concerning clinical effect and safety (clusterank value = 7393.2). Conclusions The efficacy of all the combination therapy regimens was significantly superior to that of UV monotherapy, without significant differences in tolerability and safety. cUVB and cPUVA, and particularly the combination of UVA with calcium/vitamin D derivatives, was ranked as the overall safest and most effective phototherapy method.

Abstract Psoriasis is a common, chronic immune-mediated systemic disease that had no effective and durable treatment. Mesenchymal stem cells (MSCs) have immunomodulatory properties. Therefore, we performed a phase 1/2a, single-arm clinical trial to evaluate the safety and efficacy of human umbilical cord-derived MSCs (UMSCs) in the treatment of psoriasis and to preliminarily explore the possible mechanisms. Seventeen patients with psoriasis were enrolled and received UMSC infusions. Adverse events, laboratory parameters, PASI, and PGA were analyzed. We did not observe obvious side effects during the treatment and 6-month follow-up. A total of 47.1% (8/17) of the psoriasis patients had at least 40% improvement in the PASI score, and 17.6% (3/17) had no sign of disease or minimal disease based on the PGA score. And the efficiency was 25% (2/8) for males and 66.7% (6/9) for females. After UMSC transplantation (UMSCT), the frequencies of Tregs and CD4+ memory T cells were significantly increased, and the frequencies of T helper (Th) 17 and CD4+ naive T cells were significantly decreased in peripheral blood (PB) of psoriasis patients. And all responders showed significant increases in Tregs and CD4+ memory T cells, and significant decreases in Th17 cells and serum IL-17 level after UMSCT. And baseline level of Tregs in responders were significantly lower than those in nonresponders. In conclusion, allogeneic UMSCT is safe and partially effective in psoriasis patients, and level of Tregs may be used as a potent biomarker to predict the clinical efficacy of UMSCT. Trial registration Clinical Trials NCT03765957

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on “space-sky-earth” remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors.

Accurately extracting pixel-level buildings from high-resolution remote sensing images is significant for various geographical information applications. Influenced by different natural, cultural, and social development levels, buildings may vary in shape and distribution, making it difficult for the network to maintain a stable segmentation effect of buildings in different areas of the image. In addition, the complex spectra of features in remote sensing images can affect the extracted details of multi-scale buildings in different ways. To this end, this study selects parts of Xi’an City, Shaanxi Province, China, as the study area. A parallel encoded building extraction network (MARS-Net) incorporating multiple attention mechanisms is proposed. MARS-Net builds its parallel encoder through DCNN and transformer to take advantage of their extraction of local and global features. According to the different depth positions of the network, coordinate attention (CA) and convolutional block attention module (CBAM) are introduced to bridge the encoder and decoder to retain richer spatial and semantic information during the encoding process, and adding the dense atrous spatial pyramid pooling (DenseASPP) captures multi-scale contextual information during the upsampling of the layers of the decoder. In addition, a spectral information enhancement module (SIEM) is designed in this study. SIEM further enhances building segmentation by blending and enhancing multi-band building information with relationships between bands. The experimental results show that MARS-Net performs better extraction results and obtains more effective enhancement after adding SIEM. The IoU on the self-built Xi’an and WHU building datasets are 87.53% and 89.62%, respectively, while the respective F1 scores are 93.34% and 94.52%.

Since the last IAEA-FEC in 2021, significant progress on the development of long pulse steady state scenario and its related key physics and technologies have been achieved, including the reproducible 403 s long-pulse steady-state H-mode plasma with pure radio frequency (RF) power heating. A thousand-second time scale (∼1056 s) fully non-inductive plasma with high injected energy up to 1.73 GJ has also been achieved. The EAST operational regime of high β _P has been significantly extended ( H _98y2 > 1.3, β _P ∼ 4.0, β _N ∼ 2.4 and n _e / n _GW ∼ 1.0) using RF and neutral beam injection (NBI). The full edge localized mode suppression using the n = 4 resonant magnetic perturbations has been achieved in ITER-like standard type-I ELMy H-mode plasmas with q _95 ≈ 3.1 on EAST, extrapolating favorably to the ITER baseline scenario. The sustained large ELM control and stable partial detachment have been achieved with Ne seeding. The underlying physics of plasma-beta effect for error field penetration, where toroidal effect dominates, is disclosed by comparing the results in cylindrical theory and MARS-Q simulation in EAST. Breakdown and plasma initiation at low toroidal electric fields (

Summary: We carried out a bidirectional Mendelian randomization (MR) including cases of eczema (N = 218,792), asthma (N = 462,933), and allergic rhinitis (N = 112,583). COVID-19 susceptibility (N = 1,683,768), COVID-19 hospitalization (N = 1,887,658), and COVID-19 severe respiratory symptom (N = 1,388,342) were sampled from GWAS database. The MR analysis was primarily based on inverse variance weighted (IVW), supplemented by several other algorithms. In the bidirectional MR analysis, eczema was negatively associated with COVID-19 susceptibility (odds ratio (OR) IVW = 0.92; p = 0.031) and COVID-19 hospitalization (ORIVW = 0.81, p = 0.010); asthma was negatively associated with COVID-19 susceptibility (ORIVW = 0.65, p = 0.005) and COVID-19 severe respiratory symptom (ORIVW = 0.20, p = 0.001). No significant association was found between allergic rhinitis and COVID-19 susceptibility (ORIVW = 0.80, p = 0.174), COVID-19 hospitalization (ORIVW = 0.71, p = 0.207), or COVID-19 severe respiratory symptom (ORIVW = 0.56; p = 0.167). The reverse MR analysis showed no potential reverse causal association. Our findings provided new evidence that allergic diseases might be associated with different risks of COVID-19 susceptibility, hospitalization, and severe respiratory symptom.

Water detection from SAR imagery has significant values, such as the flood monitoring and environmental protection. Nowadays, significant progress has been achieved in water detection using deep neural network (DNN) methods, but the blackbox behavior incurs many doubts in the performance of deep learning techniques, which undermines its trustworthiness in water detection from SAR imagery. By integrating SAR domain knowledge, DNN and eXplainable Artificial Intelligence (XAI), an explainable DNN framework for surface water detection is proposed for the first time. This framework includes three parts: the water extraction network containing four backbone networks, the Local and Global Mixed Attribution (LGMA) module for performance evaluation of backbone network, and the Semantic Specific-class Activation Mapping (SSAM) module, which performs geo-visualization for the output layers of high-level features. In the experiment, SAR images from different resolutions and frequency-bands are utilized, which are from millimeter-wave and Sentinel-1 systems. The attribution maps and heatmaps of four backbone networks are assessed towards the final water extraction results. The experiment indicates that the proposed framework can glass-box the decision-making process of DNN in water detection and offer corresponding attribution analytics for given input SAR imagery. This work encourages other scholars to conduct extensive research on the explanation of DNN in SAR domain, gradually establish the trustworthiness of DNN, and promote the development of DNN in SAR images analytics.

Interferometric synthetic aperture radar (InSAR) has been widely used to measure ground displacements related to geophysical and anthropic activities over the past three decades. Satellite SAR systems use microwave signals that interact with the ionosphere when they travel through it during the imaging processes. In this context, ionospheric variations can significantly contaminate SAR imagery, which in turn affects spaceborne InSAR measurements. This bias also leads to a decrease in the coherence and accuracy of InSAR measurements, especially for the low-frequency SAR systems. In this paper, we give an overview of the latest methods for mitigating the ionospheric contributions in InSAR, including Faraday rotation method, azimuth shift method, and range split-spectrum method, and only focus on the single pair of InSAR interferograms. The current challenges and future perspectives are outlined at the end of this paper.

An increasing interest in the use of low frequency Synthetic Aperture Radar (SAR) systems, e.g., L- and P-bands, makes the research of the ionospheric effects on SAR interferograms become urgent and significant. As the most pronounced signature in interferograms, the ionosphere-induced azimuth streak was thoroughly investigated in this study through processing of the 19 L-band Advanced Land-Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR)images over the Chongqing City, China. The investigations show that the visible ionosphere-induced stripe-shape azimuth shifts with the invariable direction of 26°E, 113°N are observed in some interferometric pairs. Relating these anomalous azimuth shifts to the International GNSS Service (IGS) final ionospheric products shows that the detected ionosphere-contaminated SAR images display the relatively large ionospheric variation with time during SAR satellite travelled through the study area, indicating a somewhat correlation between them. After detecting the ionosphere-contaminated interferograms, we estimated the Ionospheric Phase Streak (IPS) based on an approximate linear relationship between IPS and azimuth shift, and then removed them from the original interferograms. The corrected results show that ionospheric phase patterns are largely removed from the ionosphere-contaminated interferograms. The investigation indicates that the direction of the IPS keeps approximately constant in space and time, which provides the potential chance to develop methods to correct the ionospheric effect. Furthermore, this study once more proves that the ionospheric effect on SAR interferogram can be detected, estimated and corrected from azimuth shifts.

BackgroundPsoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. ObjectiveThis study aimed to develop an image–artificial intelligence (AI)–based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. MethodsA deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. ResultsThe proposed image-AI–based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. ConclusionsAn image-AI–based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists’ accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Psoriasis is a chronic inflammatory skin disease featuring rapid proliferation of epidermal cells. Although elevated glycolysis flux has been reported in psoriasis, the molecular mechanisms underlying its pathogenesis remain unclear. We investigated the role of the integral membrane protein CD147 in psoriasis pathogenesis, observing its high expression in psoriatic skin lesions of humans and imiquimod (IMQ)-induced mouse models. In mouse models, genomic deletion of epidermal CD147 markedly attenuated IMQ-induced psoriatic inflammation. We found that CD147 interacted with glucose transporter 1 (Glut1). Depletion of CD147 in the epidermis blocked glucose uptake and glycolysis in vitro and in vivo. In CD147-knockout mice and keratinocytes, oxidative phosphorylation was increased in the epidermis, indicating CD147's pivotal role in glycolysis reprogramming during pathogenesis of psoriasis. Using non-targeted and targeted metabolic techniques, we found that epidermal deletion of CD147 significantly increased the production of carnitine and α-ketoglutaric acid (α-KG). Depletion of CD147 also increased transcriptional expression and activity of γ-butyrobetaine hydroxylase (γ-BBD/BBOX1), a crucial molecule for carnitine metabolism, by inhibiting histone trimethylations of H3K9. Our findings demonstrate that CD147 is critical in metabolic reprogramming through the α-KG–H3K9me3–BBOX1 axis in the pathogenesis of psoriasis, indicating that epidermal CD147 is a promising target for psoriasis treatment.

The Qinghai–Tibet Plateau is the highest and largest permafrost area in the middle and low latitudes of China. In this region, permafrost thaw settlement is the main form of expressway subgrade disaster. Therefore, the quantitative analysis and regionalization study of permafrost thaw settlement deformation are of great significance for expressway construction and maintenance in the Qinghai–Tibet region. This paper establishes a thaw settlement prediction model using the thaw settlement coefficient and thaw depth. The thaw depth was predicted by the mean annual ground temperatures and active-layer thicknesses using the Radial Basis Function (RBF) neural network model, and the thaw settlement coefficient was determined according to the type of ice content. Further, the distribution characteristics of thaw settlement risk of the permafrost subgrade in the study region were mapped and analyzed. The results showed that the thaw settlement risk was able to be divided into four risk levels, namely significant risk, high risk, medium risk and low risk levels, with the areas of these four risk levels covering 3868.67 km2, 1594.21 km2, 2456.10 km2 and 558.78 km2, respectively, of the total study region. The significant risk level had the highest proportion among all the risk levels and was mainly distributed across the Chumar River Basin, Beiluhe River Basin and Gaerqu River Basin regions. Moreover, ice content was found to be the main factor affecting thaw settlement, with thaw settlement found to increase as the ice content increased.

It is of great significance to explore the spatial and temporal evolution of soil and rockfill dam deformation, ensuring the safety of people’s lives and healthy economic development. The spatial and temporal evolution patterns of deformation of 17 large soil and rockfill dams in Henan Province were analyzed by using the PS-InSAR technique and 55 Sentinel-1A images from March 2017 to September 2021. Based on factors such as reservoir water level and rainfall, a series of analyses were conducted on the Xiaolangdi soil and rockfill dam, which has the highest dam height and the most prominent deformation problem. The monitoring results show that all the soil and rockfill dams in Henan Province have different degrees of deformation, and there is a close relationship between dam height and deformation. In addition, the deformation rate of the Xiaolangdi soil and rockfill dam in the past five years presents a “Stepped Shape” deformation trend from the top to the bottom of the dam. The deformation of the upper, middle, and lower parts of the dam body reaches 80 mm, 40 mm, and 20 mm, respectively, among which the middle part of the dam crest has the largest deformation. Furthermore, the time series prediction model for sparrow search algorithm Long Short-Term Memory considering the moving average filter (MAF-SSA-LSTM) is proposed to predict and accurately analyze the future deformation of Xiaolangdi soil and rockfill dam with RMSE of 1.526 mm, MAE of 1.447 mm, and MAPE of 2.22%, which proved that the model has high prediction accuracy. It can truly reflect the overall deformation trend of the dam body. The results provide a theoretical basis and decision basis for the census of reservoir safety conditions and deformation history retrieval in Henan Province.

Psoriasis can be provoked or exacerbated by environmental exposures such as certain microbiomes. The distinction between plaque psoriasis (PP) and guttate psoriasis (GP) in the skin or pharyngeal microbiota is not yet clear. High-throughput sequencing using Illumina MiSeq was used in this study to characterize skin and pharyngeal microbial composition in patients with PP [large PP (LPP, n = 62), small PP (SPP, n = 41)] and GP (n = 14). The alpha- and beta-diversity of skin microbiota LPP was similar to that of the SPP group, but different from the GP group. There were no differences in pharyngeal microbiota among the groups. According to linear discriminant analysis effect size (LEfSe) analysis, Staphylococcus, Stenotrophomonas, Enhydrobacter, Brevundimonas, and Allorhizobium–Neorhizobium–Pararhizobium–Rhizobium were the dominant genera of skin microbiota in PP. Diversity of skin microbiota correlated with Psoriasis Area and Severity Index (PASI). Moderate-to-severe psoriasis and mild psoriasis have different microbiota compositions. The skin microbiota may be related to the pharyngeal microbiota. Furthermore, two microbiota-based models could distinguish psoriasis subtypes with area under the receiver-operating characteristic curve (AUC-ROC) of 0.935 and 0.836, respectively. In conclusion, the skin microbiota in patients with LPP is similar to that in patients with SPP, but displays variations compared to that of GP, no differences are noted between subtypes in pharyngeal microbiota. Skin microbiota diversity correlated with PASI.

The Weihe Fault is an important basement fault that is buried deep and controls the formation, evolution, and seismicity of the Weihe Basin. It has been quiescent for more than 300 years with only a few moderate and small earthquakes distributed unevenly. Therefore, it is necessary to investigate the current tectonic deformation pattern in order to assess regional seismic risk. In this context, the tectonic deformation velocities of the Weihe Fault were analyzed using an interferometric synthetic aperture radar (InSAR), a global navigation satellite system (GNSS) and leveling observations. The line of slight (LOS) deformation rates spanning from 2015 to 2019 were estimated from stacking-InSAR technology. Subsequently, the three-dimensional deformation rates in the north–south, east–west, and vertical directions were separated through the integration of GNSS-derived horizontal deformation and InSAR-derived LOS deformation. After that, the long-wavelength tectonic deformation was decomposed from the separated vertical deformation based on the spherical wavelet multiscale approach. Finally, the slip rate and locking depth were inverted for the assessment of the seismic hazard and tectonic activity of the Weihe Fault. The results show that the separated vertical deformation is consistent with the leveling observations, where the standard deviation between them is 1.69 mm/yr and the mean value is 0.6 mm/yr, demonstrating the reliability of the proposed method. The decomposed long-wavelength tectonic deformation exhibits uplift in the north and subsidence in the south, as well as the obvious vertical velocity gradient. The inversion result shows that the slip rate of the Weihe Fault gradually decreases from the west to the east, and the dip gradually increases from the west to the east, indicating a segmented activity and the geometric characteristics of the fault. The locking depth of the Weihe Fault gradually increases from the west (~5 km) to the east (~14 km), implying a higher stress accumulation and seismic risk on the eastern section of the fault. Taking into account the higher locking depth and frequent historical earthquakes on the eastern section of the Weihe Fault, further attention should be paid to the earthquake risk of the eastern section of the Weihe Fault.

Underground coal mining can cause the deformation, failure, and collapse of the overlying rock mass of a coal seam. If the mining design, monitoring, early warning, or emergency disposal are improper, in that case, it can often lead to mining disasters such as roof falls, water inrush, surface collapse, and ground fissures, seriously threatening the safety of mine engineering and the geological environment protection in mining areas. To ensure the intrinsic security of the entire coal mining process, aspace–time continuous sensing system of overburden deformation and failure was developed, which breaks through the limitations of traditional monitoring methods that characterize the evolution process of overlying rock deformation and ground subsidence. This paper summarizes the classification of typical overburden deformation and failure modes. It researches the space–time continuous sensing of rock–soil mass above the coal seam based on Distributed Fiber Optic Sensing (DFOS). A multi-range strain optical fiber sensing neural series from micron to meter was developed to achieve synchronous sensing of overburden separation, internal micro–cracks, and large rock mass deformation. The sensing cable–rock mass coupling test verified the reliability of the optical fiber monitoring data. The sensing neural network of overburden deformation was constructed using integrated optical fiber layout technology on the ground and underground. Different sensing nerves’ performance and application effects in overburden deformation and failure monitoring were compared and analyzed with field monitoring examples. A physical model was used to carry out the experimental study on the overburden subsidence prediction during coal mining. The results showed that the optical fiber monitoring data were reliable and could be used to predict overburden subsidence. The reliability of the calculation model for overlying rock subsidence based on space–time continuous optical fiber sensing data was verified in the application of mining subsidence evaluation. A systematic review of the shortcomings of current overburden deformation observation technology during coal mining was conducted, and a space–time continuous sensing system for overburden deformation and failure was proposed. This system integrated sensing, transmission, processing, early warning, decision-making, and emergency response. Based on the fusion of multi-parameter sensing, multi-method transmission, multi-algorithm processing, and multi-threshold early warning, the system realized the real-time acquisition of space–time continuous information for the overburden above coal seams. This system utilizes long-term historical monitoring data from the research area for data mining and modeling, realizing the prediction and evaluation of the evolution process of overburden deformation as well as the potential for mining subsidence. This work provides a theoretical reference for the prevention and control of mining disasters and the environmental carrying capacity evaluation of coal development.

ObjectiveThis study aimed to develop a risk of psoriatic arthritis (PsA) predictive model for plaque psoriasis patients based on the available features.MethodsPatients with plaque psoriasis or PsA were recruited. The characteristics, skin lesions, and nail clinical manifestations of the patients have been collected. The least absolute shrinkage was used to optimize feature selection, and logistic regression analysis was applied to further select features and build a PsA risk predictive model. Calibration, discrimination, and clinical utility of the prediction model were evaluated by using the calibration plot, C-index, the area under the curve (AUC), and decision curve analysis. Internal validation was performed using bootstrapping validation. The model was subjected to external validation with two separate cohorts.ResultsAge at onset, duration, nail involvement, erythematous lunula, onychorrhexis, oil drop, and subungual hyperkeratosis were presented as predictors to perform the prediction nomogram. The predictive model showed good calibration and discrimination (C-index: 0.759; 95% CI: 0.707–0.811). The AUC of this prediction model was 0.7578092. Excellent performances of the C-index were reached in the internal validation and external cohort validation (0.741, 0.844, and 0.845). The decision curve indicated good effect of the PsA nomogram in guiding clinical practice.ConclusionThis novel PsA nomogram could assess the risk of PsA in plaque psoriasis patients with good efficiency.

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides.

Background: Psoriasis is a skin condition associated with increased risks of developing metabolic diseases, such as diabetes and hyperlipidaemia. Retinoid drugs, including acitretin, are commonly used to treat psoriasis due to its low cost and tolerable side effects.Objective: This study aimed to explore the influence of acitretin on patients' metabolism levels, especially lipid and glucose.Methods: In this retrospective study, a total of 685 psoriatic patients and 395 age/sex matched controls were enrolled. The demographic and biochemical indexes of each participant were recorded. Acitretin (30 mg/d) combined with the topical ointment calcipotriol was used to treat the psoriatic patients, and the glucose and lipid profiles of patients before and after acitretin treatment were analyzed.Results: The blood glucose levels of 685 psoriasis patients were significantly higher than that of the control group (P < 0.001), while the blood lipid levels showed no difference between psoriatic patients and the matched controls. Triglyceride and low-density lipoprotein levels were significantly increased in 247 patients (P < 0.05) after 8 weeks of treatment with acitretin. Interestingly, there was a remarkable downward trend in body mass index (BMI) and blood glucose levels (P < 0.05) after acitretin treatment. Additionally, expression of both GLUT1 and GLUT4 in HaCaT and HepG2 cells were significantly increased when treated with acitretin. Compared to acitretin-free cells, the uptake of 2-NBDG was significantly higher in HaCaT and HepG2 cells after incubation with 5000 ng/mL acitretin for 36 h.Conclusion: Acitretin plays a significant role of reducing the blood glucose level in psoriasis patients. The mechanism of lowering blood glucose may be through increasing glucose intake by cells, thereby reducing glucose levels in the peripheral blood.

Background: Patients with psoriasis are prone to suffer from anxiety and depression during their lifetime. This study aimed to investigate the association of psoriasis with anxiety and depression in Chinese patients.Methods: A case-control study in Chinese patients with psoriasis vulgaris and healthy controls was conducted. Clinical information based on patient-reported, clinical information, and reliable structured questionnaires were collected. Multivariable logistic regression was used to investigate the associations, in terms of adjusted odds ratios (AORs).Results: We continuously selected 1,571 patients who were firstly diagnosed with psoriasis vulgaris, and 1,571 healthy controls, matched by age and sex. The risk of depression in the psoriasis vulgaris group was higher than that in the healthy controls (AOR = 1.30, P = 0.047), while no differences were found in the risk of anxiety between the two groups (AOR = 1.18, P = 0.381). Subgroup analysis by disease onsets showed that late-onset psoriasis (LOP) was significantly associated with a higher risk of anxiety (AOR = 1.47, P = 0.033) and depression symptoms (AOR = 1.85, P = 0.012) but not with early-onset psoriasis (EOP). Subgroup analysis by disease severity indicated that no difference was observed in the associations of mild psoriasis vulgaris, moderate-to-severe psoriasis vulgaris with anxiety and depression.Conclusion: Patients with psoriasis vulgaris were more likely to develop depression compared with the general population. LOP patients were positively associated with anxiety and depression. We believe the screening of emotional disorders should be included in the daily management of psoriasis patients.

BackgroundChronic urticaria (CU) is a chronic inflammatory skin disease associated with Th2 immune response. The two most common subtypes of CU, i.e., chronic spontaneous urticaria and symptomatic dermographism (CSD), often coexist. However, the pathogenesis of CSD is still unclear. Gut microbiota plays an important role in immune-related inflammatory diseases. The purpose of this study was to explore the correlation between gut microbiota and CSD.MethodsA case-control study was conducted on CSD patients as well as gender- and age-matched normal controls (NCs). The 16S ribosomal DNA sequencing of fecal samples was used to detect the gut microbiota of all subjects. QPCR was used to further verify the species with differences between the two groups.ResultsThe alpha diversity of gut microbiota decreased in CSD patients, accompanied by significant changes of the structure of gut microbiota. Subdoligranulum and Ruminococcus bromii decreased significantly in CSD patients and had a potential diagnostic value for CSD according to receiver operating characteristic curve (ROC) analysis. Enterobacteriaceae and Klebsiella were found to be positively correlated with the duration of CSD, while Clostridium disporicum was positively correlated with the dermatology life quality index (DLQI).ConclusionsThe gut microbiota of CSD patients is imbalanced. Subdoligranulum and Ruminococcus bromii are the gut microbiota biomarkers in CSD.

Landslides pose a greater potential risk to the Sichuan-Tibet Transportation Project, and extensive landslide inventory and mapping are essential to prevent and control geological hazards along the Sichuan-Tibet Transportation Corridor (STTC). Recently proposed landslide detection methods mainly focused on new landslides with high vegetation. In addition, there are still challenges in automatic detection of old landslides using optical images. In this paper, two methods, namely mask region-based convolutional neural networks (Mask R-CNN) and transfer learning Mask R-CNN (TL-Mask R-CNN), are presented for detecting and segmenting new and old landslides, respectively. An optical remote sensing dataset for landslide recognition along the Sichuan-Tibet Transportation Corridor (LRSTTC) is constructed as an evaluation benchmark. Our experimental results show that the recall rate and F1-score of the proposed method for new landslide detection can reach 78.47% and 79.80%, respectively. Transfer learning is adopted to detect old landslides, and our experimental results show that evaluation indices can be further improved by about 10%. Furthermore, TL-Mask R-CNN has been applied to identify ice avalanches based on the characteristics of landslides. It appears that our proposed methods can detect and segment landslides effectively along the STTC with the constructed LRSTTC dataset, which is essential for studying and preventing landslide hazards in mountainous areas.

BackgroundSkin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited. ObjectiveThis study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases. MethodsEach image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences. ResultsXiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%). ConclusionsXiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.

Purpose: Melanocytic nevi are common cutaneous lesions. This study aimed to demonstrate the concordance and discordance between clinical and histopathological diagnoses of melanocytic nevi and the importance of histological evaluation in differentiating malignant lesions from diseases with similar clinical manifestations.Patients and Methods: We studied 4,561 consecutive patients with a clinical diagnosis of melanocytic nevi from 2014 to 2019. We compared the clinical diagnosis with the histopathological diagnosis to establish a histopathological concordance rate and then investigated the effects of clinical characteristics and the reasons for removal on misclassification.Results: Among 4,561 patients who were clinically diagnosed with melanocytic nevi, the overall histopathological concordance rate was 82.11% (3,745 of 4,561 patients), while the histopathological discordance rate was 17.89% (816 of 4,561 patients). The histopathological concordance included 90.25% common acquired melanocytic nevi (3,380 of 3,745 patients) and 9.75% other benign melanocytic neoplasms (365 of 3,745 patients). The most common diagnostic change was to seborrheic keratosis (n = 470, 10.30%), followed by basal cell carcinoma (n = 64, 1.40%), vascular tumor (n = 53, 1.16%), fibroma (n = 43, 0.94%), epidermoid cyst (n = 34, 0.75%), wart (n = 30, 0.66%), melanoma (n = 24, 0.53%), Bowen's disease (n = 16, 0.35%), squamous cell carcinoma (n = 4, 0.09%), keratoacanthoma (n = 2, 0.04%), and other neoplasms (n = 76, 1.67%). Male sex, old age, location of the lesion, and the reasons for removal have a potential effect on misclassification. The percentages of misclassified lesions on the trunk and limbs and the perineum and buttocks were higher than those in lesions without a change in diagnosis. Importantly, locations of lesions on the head and neck were significantly related to a change in diagnosis to non-melanoma skin cancer, while locations on the hands and feet were significantly related to a change in diagnosis to melanoma. In addition to a typical clinical features, removal due to lesion changes or repeated stimulation was significantly associated with a change in diagnosis to melanoma.Conclusions: Our study emphasizes the clinical differential diagnosis of melanocytic nevi, especially the possibility of malignant tumors. The occurrence of clinical features associated with clinicopathological discordance should raise the clinical suspect and be carefully differentiated from malignant tumors.

Background: Psoriasis vulgaris is an immune-mediated inflammatory skin disease. Although the pathogenesis of psoriasis is unclear, genetic susceptibility, such as HLA-C*06:02, is believed to be a major risk factor. However, there is a paucity of knowledge regarding the relationship between genetics and the response to systemic treatment of psoriasis. We hypothesized that genetic variations in human leukocyte antigen (HLA) genes may act as predictors of acitretin treatment in psoriasis. The aim of our study was to explore the presence of HLA gene variants in patients with moderate-to-severe psoriasis receiving acitretin treatment. Methods: A total of 100 Han Chinese patients with psoriasis completed the study. 24 patients including 16 responders and 8 non-responders underwent deep sequencing by MHC targeted region capture and 76 samples were genotyped by Sanger sequencing (SBT) based HLA typing for validation. Results: Regressions with adjustment for age, sex, body mass index (BMI), and baseline psoriasis area and severity index (PASI) revealed that two HLA alleles (HLA-DQA1*:02:01, DQB*:02:02) were associated with the response to acitretin. The DQA1*0201-positive patients showed a better response to acitretin compared to the DQA1*0201-negative patients (relative risk (RR) = 10.34, 95% confidence interval (CI): 2.62–40.77, p = 0.001), and the DQB1*0202-positive patients manifested a better response to acitretin when compared to the DQB1*0202-negative patients (RR = 21.01, 95% CI: 2.53–174.27, p = 0.005). Conclusions: Our observations support the potential role of HLA-DQA1*:02:01 and DQB*:02:02 as pharmacogenetic markers of the acitretin response in patients with psoriasis.

Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya’an-Kangding), Yaxi (Ya’an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future.

The Weihe Basin, located in central China, is a Cenozoic rift basin that is characterized by a complicated geological background, with intense tectonic movement and relatively significant seismic activity. The faulting behaviors, slip rates, geometrical structures, and possible surface traces of the active faults in the Weihe Basin are still not well known. The goal of this work is to provide a more complete picture of recent vertical ground deformation of the basin and to identify active faults. We derived two basin-wide average InSAR deformation maps during 2015–2019 using C-band Sentinel-1A/B and L-band ALOS PALSAR2 ScanSAR imagery. The basin-wide vertical and east–west deformation components decomposed from ascending and descending InSAR measurements show that vertical movement dominates the deformation of the Weihe Basin. Subsidence and uplift maxima both occurred over the Xi’an region at rates of about −146 and 20 mm/y, respectively. A subsidence of ~38 mm/y was found at Sanyuan, Fuping, Weinan, Lantian, Qianxian, and Xingping while an uplift of ~11 mm/y was found at Fufeng, Huxian, Jingyang, Fuping, Pucheng, and Huaxian. The significant vertical deformation in these regions is spatially linked to previously identified or unmapped faults. A slip rate of ~13.7 mm/y on faults F20, F6, F10, and F7 explained the observed uplift of up to 5 mm/y in the Fufeng and Wugong areas. Furthermore, surface fault traces were clearly identified for faults F7-1, F8-1, F20, F25, and F26 based on discontinuities in the InSAR-derived vertical deformation measurements. Our results provide an accurate and economical way to delineate the surface deformation and fault movement and the associated geohazards over the Weihe Basin, and offer independent unprecedented data for further geological and geophysical interpretation.

Objective: To explore the possible mechanism of improving the imiquimod (IMQ)-induced psoriasis-like inflammation by using polyethylene glycol (PEG) ointment.Methods: We evaluated the appearance of psoriasis lesions by Psoriasis Area and Severity Index (PASI), observed the epidermal proliferation by histopathological staining and immunohistochemical staining, and explored the key molecules and signaling pathways of improving psoriasis-like inflammation treated with PEG ointment by RNA sequencing. Finally, we verified the expression of inflammatory cells and inflammatory factors by flow cytometry, immunohistochemical staining, and Q-PCR.Results: PEG ointment could improve the appearance of psoriasis lesions and the epidermis thickness of psoriasis mouse, inhibit the proliferation of keratinocytes, and down-regulate the relative mRNA levels of IL-23, IL-22, IL-6, IL-17C, IL-17F, S100A7, S100A8, S100A9, CXCL1, CXCL2, and IL-1β in the skin lesions of psoriasis mouse by down-regulating the numbers of myeloid-derived suppressor cells (MDSCs) and T helper 17 (Th17) cells.Conclusion: PEG ointment could improve the IMQ-induced psoriasis-like inflammation by down-regulating the functions of Th17 cells and MDSCs.

Increased numbers of myeloid-derived suppressor cells (MDSCs) are involved in the development of psoriasis. Acitretin is used to treat psoriasis by regulating the proliferation and differentiation of keratinocytes, but little is known about the effect of acitretin on immune cells. Here, we reported that psoriasis patients had an expansion of MDSCs and monocytic-MDSCs (M-MDSCs) in peripheral blood and skin lesions. The number of MDSCs and M-MDSCs in peripheral blood correlated positively with disease severity. Acitretin could reduce the number of MDSCs and M-MDSCs in the peripheral blood of psoriasis patients as well as the spleen and skin lesions of IMQ-induced psoriasis-like model mice. Moreover, acitretin promoted the differentiation of MDSCs into macrophages, especially CD206+ M2 macrophages, and CD11c+MHC-II+ dendritic cells. Mechanically, acitretin dramatically increased the glutathione synthase (GSS) expression and glutathione (GSH) accumulation in MDSCs. Interruption of GSH synthesis abrogated the acitretin effect on MDSCs differentiation. Acitretin regulated GSS expression via activation of extracellular signal-regulated kinase 1/2. Thus, our data demonstrated a novel mechanism underlying the effects of acitretin on psoriasis by promoting MDSCs differentiation.

Psoriasis affects the health of myriad populations around the world. The pathogenesis is multifactorial, and the exact driving factor remains unclear. This condition arises from the interaction between hyperproliferative keratinocytes and infiltrating immune cells, with poor prognosis and high recurrence. Better clinical treatments remain to be explored. There is much evidence that alterations in the skin and intestinal microbiome play an important role in the pathogenesis of psoriasis, and restoration of the microbiome is a promising preventive and therapeutic strategy for psoriasis. Herein, we have reviewed recent studies on the psoriasis-related microbiome in an attempt to confidently identify the “core” microbiome of psoriasis patients, understand the role of microbiome in the pathogenesis of psoriasis, and explore new therapeutic strategies for psoriasis through microbial intervention.

Abstract Background Y‐chromosomal short tandem repeats (Y‐STRs) have been certified to be the serviceable markers for some paternity cases in the last few years. Methods We presented the gene diversity, haplotypic diversity, and forensic statistical parameters of 340 unrelated Uighur males from Kashi region based on the 27 Y‐STRs. Genomic DNA was extracted from bloodstain samples using the Chelex‐100 method and amplified by Yfiler® Plus PCR Amplification kit. Results Gene diversity values on the 27 Y‐STRs ranged from 0.4749 (at DYS437 locus) to 0.9416 (at DYS385a,b loci). According to forensic parameters of the 27 Y‐STR loci, 295 disparate haplotypes were acquired, 258 of which were unique. The haplotypic diversities and discrimination capacities at Yfiler plus 27 loci, Yfiler 17 loci, extended 11 loci, and minimal 9 loci were 0.9990 and 0.8676; 0.9961 and 0.6912; 0.9952 and 0.5941; and 0.9919 and 0.5676, respectively. Multidimensional scaling plot and neighbor‐joining tree between the studied Uighur group and 17 reference populations were conducted, and the obtained results indicated the Kashi Uighur group had the closer genetic relationships with Uighur groups living in different regions. Conclusion To sum up, the present study may provide valuable population data and background information of Kashi Uighur group.