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ABSTRACT Objective Gap balancing is a vital process during mobile‐bearing unicompartmental knee arthroplasty (MB‐UKA). However, this process commonly depends on the surgeon's experience and lacks specific unified standards. This study aimed to propose and evaluate a novel “measured resection” method for gap balance in MB‐UKA. Methods This prospective study included 49 consecutive patients (52 knees) who underwent MB‐UKA from February 1, 2023, to September 1, 2023. Gap balance was achieved by the traditional “two‐finger” method (Group 1, 26 knees) or the measured resection method (Group 2, 26 knees). The novel “measured resection” method was performed by measuring the thickness of the resected posterior femoral condyle and resected medial posterior tibial plateau to assess proper meniscal bearing thickness. Data were collected at baseline and the 6‐month follow‐up. Prosthetic angles, range of motion (ROM), visual analog scale (VAS) score, Oxford knee score (OKS), and Global Perceived Scale (GPE) were used to evaluate clinical outcomes. Independent samples t‐test and Mann–Whitney U test were used to compare the differences. Results There were significant improvements in all measured outcomes at the 6‐month follow‐up from baseline in both groups (p 0.05). Conclusions The measured resection method is a reliable method for assisting surgeons in choosing the ideal meniscal bearing thickness in MB‐UKA to achieve proper gap balance and gain better clinical outcomes. Trial Registration ClinicalTrials.gov (NCT03815448)

Abstract Background Breastfeeding is the optimal source of nutrition for infants and young children, essential for their healthy growth and development. However, a gap in cohort studies tracking breastfeeding up to six months postpartum may lead caregivers to miss critical intervention opportunities. Methods This study conducted a three-wave prospective cohort analysis to examine maternal breastfeeding trajectories within the first six months postpartum and to develop risk prediction models for each period using advanced machine learning algorithms. Conducted at a leading Maternal and Children's hospital in China from October 2021 to June 2022, data were gathered via self-administered surveys and electronic health records. Results Of the 3307 women recruited, 3175 completed the surveys, yielding a 96% effective response rate. Breastfeeding(BF) rates were observed at 100%, 96%,93% and 83% at discharge, 42 day, 3 month and 6 month postpartum, respectively. Exclusively breastfeeding(EBF) rates were recorded at 91%, 64%,72% and 58% for the same intervals. Among the five machine learning methods employed, Random Forest (RF) demonstrated superior accuracy in predicting breastfeeding patterns, with classification accuracies of 0.629 and an area under the receiver operating characteristic curve (AUC) of 0.8122 at 42 days, 0.925 and an AUC of 0.9800 at 3 months, and 0.836 and an AUC of 0.9463 at 6 months postpartum, respectively. Key predictive factors for breastfeeding at 42 days postpartum included the newborn’s birth weight and the mother’s pre-delivery and prenatal weights. Predictors for feeding type at 3 months and 6 months postpartum included early feeding types and the scores from the Breastfeeding Self-Efficacy Scale-short Form (BSES-SF) at 6 months. The predictive model based on follow-up data showed strong performance. Conclusion Breastfeeding rates slightly declined from discharge to 6 months postpartum. The breastfeeding context in this region is comparatively optimistic both within China and internationally. Factors such as newborn’s birth weight, gestational age, maternal weight management before and during pregnancy, early support and breastfeeding success, breastfeeding knowledge and self-efficacy are intricately linked to long-term breastfeeding outcomes. This study highlights critical, modifiable risk factors for early breastfeeding stages, providing valuable insights for enhancing breastfeeding intervention programs and informed decision-making.

Abstract Anatomy-based guidelines for shoulder surgery have established the routine preoperative evaluation of the humeral head. Despite recognized sex differences in humeral head size, there has been limited investigation into sex-specific variations in acromiohumeral contact surface (AHCS) arc length. This study aims to assess sex differences in the AHCS arc length within a sample of the Chinese population. We retrospectively analyzed 169 normal shoulder CT images from a single medical center, collected between 2011 and 2021. The AHCS arc length was defined as the distance from the superior glenoid tubercle to the lateral edge of the greater tuberosity, measured using a three-dimensional reconstruction algorithm. Physiological reference values for the AHCS arc length were determined at three abduction angle intervals. Linear regression analysis was used to assess the correlation between the AHCS arc length and abduction angle in both sexes. The reference values for AHCS arc length were significantly lower in women across three abduction angle intervals (male 48.07 ± 3.37 mm vs. female 43.54 ± 2.54 mm, (0–10] °, p 20 °, p = 0.001, respectively). Additionally, the AHCS arc length was linearly and negatively correlated with the abduction angle (male R2 = 0.436, p

Abstract As analogies to magnetic skyrmions, polar skyrmions in ferroelectric superlattices and multilayers have garnered widespread attention for their non-trivial topology and novel properties like negative capacitance and nonlinear optical effect. So far, they have only been theoretically predicted to be able to assemble ordered hexagonal skyrmion lattices (SkLs) in ferroelectric thin films. Here, based on phase-field simulations, we report the critical roles of flexoelectricity playing in the stabilization and transformation of polar SkLs. Different polar SkL patterns can emerge in the ferroelectric thin films, including tetragonal-SkL, and hexagonal-SkLs with diverse orientations, as summarized by phase diagrams. These emergent SkL states are attributed to the material anisotropy modified by the flexoelectric effect. Interestingly, we further found that the hexagonal-SkLs can be rotated by applying strain gradient or in-plane electric field to the films. Moreover, a nonreciprocal bending response of tetragonal-SkL is also induced by the flexoelectric effect. Our results provide useful guidelines for the implementation of polar skyrmion lattices in experiments.

ObjectiveThis study aimed to compare the knowledge, attitudes, and practices of parturients and nurses regarding fall prevention among parturients, with the goal of understanding the cognitive factors contributing to post-delivery fall.MethodsA cross-sectional survey was conducted among obstetric nurses and women in obstetric ward within 2 days after vaginal delivery. The survey instrument included a self-design questionnaire comprising a demographic information sheet and a KAP questionnaire, which demonstrated acceptable reliability and validity.ResultsA total of 705 nurses and 1,220 parturients completed the survey. Over half of both nurses and parturients exhibited reasonably good knowledge, positive attitudes and appropriate practices related to fall prevention. Significant associations were identified between the education level of parturients and their knowledge and attitudes. Nurses scored lower in the attitude dimension compared to parturients (4.32 ± 1.01 vs. 4.84 ± 0.36, P < 0.001), whereas nurses scored higher than parturients in the practice dimension (3.76 ± 0.44 vs. 3.57 ± 0.48, P < 0.001).ConclusionNurses should focus on enhancing the fall practices of parturients by emphasizing key points for fall prevention and providing guidance in situations of improper behaviors (e.g., unaccompanied activities). Further education programs for nurses should prioritize increasing their confidence and proficiency in fall prevention.

BackgroundOccupational fatigue is a widespread condition within the nursing workforce, adversely affecting both nurses’ health and patient safety. The protracted duration of the COVID-19 pandemic, approaching 3 years, has exacerbated the challenges faced by nurses globally. The escalation in patient numbers and the high incidence of infections among healthcare workers have intensified occupational fatigue. This study seeks to explore the enduring impact of the pandemic on occupational fatigue among Chinese nurses through a latent profile analysis, and to identify the associated risk factors.MethodsA comprehensive survey was conducted involving 2,140 nurses from 186 hospitals across China during the initial phase of the post-COVID-19 era. The primary instruments utilized for data collection were the Occupational Fatigue Exhaustion Recovery Scale and the Effort-Reward Imbalance Questionnaire.ResultsThree distinct profiles of occupational fatigue were identified: a low fatigue/high recovery group (18.6%), a moderate fatigue/moderate recovery group (48.8%), and a high fatigue/low recovery group (32.6%). The vast majority of nurses reported experiencing moderate to high levels of occupational fatigue during the early stage of the post-coronavirus era. Significant predictors for membership in these fatigue profiles included marital status, possession of a master’s degree or higher, working over five night shifts per month, experiencing COVID-19 symptoms, and exhibiting higher scores in extrinsic effort and overcommitment.ConclusionChinese nurses exhibit a relatively high level of occupational fatigue in the early post-COVID-19 era, likely influenced by various socio-demographic and work-related factors. It is imperative to develop targeted interventions aimed at alleviating fatigue among specific groups of nurses to effectively address the challenges posed by occupational fatigue in the face of future public health disasters.

Studies addressing the anatomical values of the supraspinatus outlet area (SOA) and the available supraspinatus outlet area (ASOA) are insufficient. This study focused on precisely measuring the SOA and ASOA values in a sample from the Chinese population using 3D CT (computed tomography) reconstruction. We analyzed CT imaging of 96 normal patients (59 males and 37 females) who underwent shoulder examinations in a hospital between 2011 and 2021. The SOA, ASOA, acromiohumeral distance (AHD), coracohumeral distance (CHD), coracoacromial arch radius (CAR), and humeral head radius (HHR) were estimated, and statistical correlation analyses were performed. There were significant sex differences observed in SOA (men: 957.62 ± 158.66 mm2; women: 735.87 ± 95.86 mm2) and ASOA (men: 661.35 ± 104.88 mm2; women: 511.49 ± 69.26 mm2), CHD (men: 11.22 ± 2.24 mm; women: 9.23 ± 1.35 mm), CAR (men: 37.18 ± 2.70 mm; women: 33.04 ± 3.15 mm), and HHR (men: 22.65 ± 1.44 mm; women: 20.53 ± 0.95 mm). Additionally, both SOA and ASOA showed positive and linear correlations with AHD, CHD, CAR, and HHR (R: 0.304–0.494, all p < 0.05). This study provides physiologic reference values of SOA and ASOA in the Chinese population, highlighting the sex differences and the correlations with shoulder anatomical parameters.

Abstract Background Gestational diabetes mellitus (GDM) is associated with DNA methylation and lifestyle. The effects of DNA methylation on GDM, and the interaction between DNA methylation and lifestyle factors are not well elucidated. The objective of this study was to explore the association between GDM, DNA methylation and lifestyle factors. Methods A nest case-control design was performed. Sociodemographic data, dietary intake and daily physical activity information of pregnant women were collected. Bisulfate pyrosequencing was used to detect the DNA methylation level of PPARGC1A, HLA-DQA1, and ADCY3 genes. The differences of DNA methylation levels between the GDM group and the control group were compared. The correlation between clinical characteristics, dietary, physical activity and DNA methylation level was analyzed. Results A total of 253 pregnant women were enrolled, of which, 60 participants (GDM: 30; control: 30) were included in the final analysis. There were no significant differences in DNA methylation levels of six methylated sites between the two groups in this study (P > 0.05). Daily intake of potato and poultry were associated with DNA methylation level of the CpG 1 site of the ADCY3 gene in all participants and the control group (P

Abstract The rapid advancement of microservice architecture in the cloud has led to the necessity of effectively detecting, classifying, and diagnosing run failures in microservice applications. Due to the high dynamics of cloud environments and the complex dependencies between microservices, it is challenging to achieve robust real-time system fault identification. This paper proposes an interpretable fault diagnosis framework tailored for microservice architecture, namely Multi-scale Learnable Transformation Graph for Fault Classification and Diagnosis(MTG_CD). Firstly, we employ multi-scale neural transformation and graph structure adjacency matrix learning to enhance data diversity while extracting temporal-structural features from system monitoring metrics Secondly, a graph convolutional network (GCN) is utilized to fuse the extracted temporal-structural features in a multi-feature modeling approach, which helps to improve the accuracy of anomaly detection. To identify the root cause of system faults, we finally conduct a coarse-grained level diagnosis and exploration after obtaining the results of classifying the fault data. We evaluate the performance of MTG_CD on the microservice benchmark SockShop, demonstrating its superiority over several baseline methods in detecting CPU usage overhead, memory leak, and network delay faults. The average macro F1 score improves by 14.05%.

Abstract Transformation optics (TO) provides a powerful tool to manipulate electromagnetic waves, enabling the design of invisibility cloaks, which can render objects invisible. Despite many years of research, however, invisibility cloaks experimentally realized thus far can only operate at a single frequency. The narrow bandwidth significantly restricts the practical applications of invisibility cloaks and other TO devices. Here, a general design strategy is proposed to realize a multiband anisotropic metamaterial characterized by two principal permittivity components, i.e., one infinite and the other spatially gradient. Through a proper transformation and combination of such metamaterials, an omnidirectional invisibility cloak is experimentally implemented, which is impedance‐matched to free space at multiple frequencies. Both far‐field numerical simulations and near‐field experimental mappings confirm that this cloak can successfully suppress scattering from multiple large‐scale objects simultaneously at 5 and 10 GHz. The design strategy and corresponding practical realization bring multiband transformation optical devices one step closer to reality.

Abstract Background The association between TCF7L2 and CAPN10 gene polymorphisms and gestational diabetes mellitus (GDM) has been explored in diverse populations across different geographical regions. Yet, most of these studies have been confined to a limited number of loci, resulting in inconsistent findings. In this study, we conducted a comprehensive review of published literature to identify studies examining the relationship between TCF7L2 and CAPN10 gene polymorphisms and the incidence of GDM in various populations. We specifically focused on five loci that were extensively reported in a large number of publications and performed a meta-analysis. Methods We prioritized the selection of SNPs with well-documented correlations established in existing literature on GDM. We searched eight Chinese and English databases: Cochrane, Elton B. Stephens. Company (EBSCO), Embase, Scopus, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database and retrieved all relevant articles published between the inception of the database and July 2022. The Newcastle Ottawa Scale (NOS) was used to evaluate the selected articles, and the odds ratio (OR) was used as the combined effect size index to determine the association between genotypes, alleles, and GDM using different genetic models. Heterogeneity between the studies was quantified and the I 2 value calculated. Due to large heterogeneities between different ethnic groups, subgroup analysis was used to explore the correlation between genetic polymorphisms and the incidence of GDM in the different populations. The stability of the results was assessed using sensitivity analysis. Begg’s and Egger’s tests were used to assess publication bias. Results A total of 39 articles reporting data on 8,795 cases and 16,290 controls were included in the analysis. The frequency of the rs7901695 genotype was statistically significant between cases and controls in the European population (OR = 0.72, 95% CI: 0.65–0.86) and the American population (OR = 0.61, 95% CI: 0.48–0.77). The frequencies of rs12255372, rs7901695, rs290487, and rs2975760 alleles were also considerably different between the cases and controls in the populations analyzed. Conclusions rs7903146, rs12255372, rs7901695, rs290487, and rs2975760 were associated with the incidence of GDM in different populations.

Timely and accurate monitoring of large-scale crop harvest progress (CHP) is crucial for optimizing agricultural management. Remote sensing shows great potential, but most studies focus on small-scale analysis with limited features and single data sources, lacking large-area mapping. To address this, we developed a regional-scale CHP monitoring framework combining optical and radar data. We analyzed the response of features to corn harvest at Youyi Farm. We developed new indices and algorithms, achieving large-scale CHP mapping. Our findings: First, all 15 features respond to harvest, with shortwave infrared reflectance and the vegetation tillage index (VTI) being the most sensitive. Second, the current binary classification algorithm for mature corn (MC) and crushed straw (CS) is unsuitable for regions with plowed soil (PS). It often misclassifies PS as MC due to similar optical characteristics, causing inaccurate corn harvest monitoring. The new VTI and simple VTI solve this by better distinguishing MC, CS, and PS. Third, corn harvest progress monitoring shows high accuracy, with an overall accuracy of 0.99 for optical data and 0.95 for radar data. Fourth, combining Sentinel-1 and Sentinel-2 data increased the monitoring frequency from 6–8 days to an average of 2–3 days. Fifth, a time-series misclassification correction strategy was developed and applied, correcting an average area of 3.3% of the study area. Sixth, an earlier harvest start time in 2021, aligned with drought occurrences, confirms the feasibility of the CHP mapping framework. This research provides an effective tool for monitoring CHP, which can support agricultural management.

The Songnen Plain is a significant region in China, known for its high grain production and concentrated distribution of soda saline land. It is also considered a priority area for cropland development in the country. However, the Songnen Plain is now facing prominent issues such as soil salinization, soil erosion, and deteriorating cropland quality, which are exacerbated by climate change and intensified human activities. In order to address these challenges, it is crucial to adjust the quantitative structure and layout of different landscapes in a harmonious manner, aiming to achieve synergistic optimization, which is posed as the key scientific approach to guide comprehensive renovation policies, improve saline–alkaline land conditions, and promote sustainable agricultural development. In this study, four scenarios including natural development, priority food production (PFP), ecological security priority (ESP), and economic–ecological-balanced saline soil improvement were set up based on Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Future Land Use Simulation (FLUS) model. The results demonstrated that the SSI scenario, which focused on economic–ecological equilibrium, displayed the most rational quantitative structure and spatial layout of landscape types, with total benefits surpassing those of the other scenarios. Notably, this scenario involved converting unused land into saline cropland and transforming saline cropland into normal cropland, thereby increasing the amount of high-quality cropland and potential cropland while enhancing the habitat quality of the region. Consequently, the conflict between food production and ecological environmental protection was effectively mitigated. Furthermore, the SSI scenario facilitated the establishment of a robust ecological security and protection barrier, offering valuable insights for land use planning and ecological security pattern construction in the Songnen Plain, particularly in salt-affected areas.

Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As a typical region afflicted by soil salinization, the soda saline–alkali soils in the Songnen Plain of China demonstrate a clear cracking phenomena. Nevertheless, the overall spectral response to the cracked soil surface has scarcely been studied. This study intends to study the impact of salt parameters on the soil cracking process and enhance the spectral measurement method used for cracked salt-affected soil. To accomplish this goal, a controlled desiccation cracking experiment was carried out on saline soil samples. A gray-level co-occurrence matrix (GLCM) was calculated for the contrast (CON) texture feature to measure the extent of cracking in the dried soil samples. Additionally, spectroscopy measurements were conducted under different surface conditions. Principal component analysis (PCA) was subsequently performed to downscale the spectral data for band integration. Subsequently, the prediction accuracy of back-propagation artificial neural network (BP-ANN) models developed from the principal components of spectral reflectance was compared for different salt parameters. The results reveal that salt content is the dominant factor determining the cracking process in salt-affected soils, and that cracked soil samples had the highest model prediction accuracy for different salt parameters rather than uncracked blocks and 2 mm comparison soil samples. Furthermore, BP-ANN prediction models combining spectral response and CON were further developed, which can significantly enhance the prediction accuracy of different salt parameters with R2 values of 0.93, 0.91, and 0.74 and a ratio of prediction deviation (RPD) of 3.68, 3.26, and 1.72 for soil salinity, electrical conductivity (EC), and pH, respectively. These findings provide valuable insights into the cracking mechanism in salt-affected soils, thereby advancing the field of hyperspectral remote sensing for monitoring soil salinization. Furthermore, this study also aids in enhancing the design of spectral measurements for saline–alkali soils and is also helpful for local soil remediation with supporting data.

The Dongsheng structural belt in Nanchuan area, southeast Chongqing, is located in the transitional zone of the southeastern margin of Sichuan Basin. It has experienced multi-period tectonic reformation and has typical geological characteristics of complex structure, complex preservation conditions and complex in-situ stress field, which leads to a significant difference in single well production. In order to increase the single well production of normal pressure shale gas wells in Dongsheng complex structural belt and realize profitable development, through target processing of seismic data, analysis of structural characteristics, study of accumulation regularity and analysis of production dynamics, as well as the strengthening of basic research and exploration and development practice, four key technical countermeasures for realizing profitable development of normal pressure shale gas in Dongsheng complex structural belt are defined: (1) improve the accuracy of shallow velocity model and structure interpretation; (2) optimize well pattern deployment, increase well-controlled reserves; (3) integrate geology and engineering, and coordinate drilling, orientation and steering to improve the drilling rate of sweet spots; (4) establish a zonal differentiated fracturing scheme to improve fracture complexity. These four countermea-sures provide technical support for the profitable development of normal pressure shale gas in complex structural zones, and provide reference for the exploration and development of other similar areas along the basin margin.

The precise quantification of soil salinity and the spatial distribution are paramount for proficiently managing and remediating salinized soils. This study aims to explore a pioneering methodology for forecasting soil salinity by combining the spectroscopy of soda saline–alkali soil with crack characteristics, thereby facilitating the ground-based remote-sensing inversion of soil salinity. To attain this objective, a surface cracking experiment was meticulously conducted under controlled indoor conditions for 57 soda saline–alkali soil samples from the Songnen Plain of China. The quantitative parameters for crack characterization, encompassing the length and area of desiccation cracks, together with the contrast texture feature were methodically derived. Furthermore, spectral reflectance of the cracked soil surface was measured. A structural equation model (SEM) was then employed for the estimation of soil salt parameters, including electrical conductivity (EC1:5), Na+, pH, HCO3−, CO32−, and the total salinity. The investigation unveiled notable associations between different salt parameters and crack attributes, alongside spectral reflectance measurements (r = 0.52–0.95), yet both clay content and mineralogy had little effect on the cracking process due to its low activity index. In addition, the presence of desiccation cracks accentuated the overall spectral contrast of salt-affected soil samples. The application of SEMs facilitated the concurrent prediction of multiple soil salt parameters alongside the regression analysis for individual salt parameters. Nonetheless, this study confers the advantage of the swift synchronous observation of multiple salt parameters whilst furnishing lucid interpretation and pragmatic utility. This study helps us to explore the mechanism of soil salinity on the surface cracking of soda saline–alkali soil in the Songnen Plain of China, and it also provides an effective solution for quickly and accurately predicting soil salt content using crack characteristics, which also provides a new perspective for the hyperspectral measurement of saline–alkali soils.

Hyperspectral technology is widely recognized as an effective method for monitoring soil salinity. However, the traditional sieved samples often cannot reflect the true condition of the soil surface. In particular, there is a lack of research on the spectral response of cracked salt-affected soils despite the common occurrence of cohesive saline soil shrinkage and cracking during water evaporation. To address this research, a laboratory was designed to simulate the desiccation cracking progress of 57 soda saline–alkali soil samples with different salinity levels in the Songnen Plain of China. After completion of the drying process, spectroscopic analysis was conducted on the surface of all the cracked soil samples. Moreover, this study aimed to evaluate the predictive ability of multiple linear regression models (MLR) for four main salt parameters. The hyperspectral reflectance data was analyzed using three different band screening methods, namely random forest (RF), principal component analysis (PCA), and Pearson correlation analysis (R). The findings revealed a significant correlation between desiccation cracking and soil salinity, suggesting that salinity is the primary factor influencing surface cracking of saline–alkali soil in the Songnen Plain. The results of the modeling analysis also indicated that, regardless of the spectral dimensionality reduction method employed, salinity exhibited the highest prediction accuracy for soil salinity, followed by electrical conductivity (EC) and sodium (Na+), while the pH model exhibited the weakest predictive performance. In addition, the usage of RF for band selection has the best effect compared with PCA and Pearson methods, which allows salt information of soda saline–alkali soils in Songnen Plain to be predicted precisely.

Focal iron overload is frequently observed in patients with rheumatoid arthritis (RA), yet its functional significance remains elusive. Herein, we report that iron deposition in lesion aggravates arthritis by inducing macrophage ferroptosis. We show that excessive iron in synovial fluid positively correlates with RA disease severity as does lipid hyperoxidation of focal monocyte/macrophages. Further study reveals high susceptibility to iron induced ferroptosis of the anti-inflammatory macrophages M2, while pro-inflammatory M1 are less affected. Distinct glutathione peroxidase 4 (GPX4) degradation depending on p62/SQSTM1 in the two cell types make great contribution mechanically. Of note, ferroptosis inhibitor liproxstatin-1 (LPX-1) can alleviate the progression of K/BxN serum-transfer induced arthritis (STIA) mice accompanied with increasing M2 macrophages proportion. We thus propose that the heterogeneous ferroptosis susceptibility of macrophage subtypes as well as consequent inflammation and immune disorders are potential biomarkers and therapeutic targets in RA.

Objectives The objective was to assess the prevalence of perinatal depressive symptoms and determine the trajectories of marital adjustment and depressive symptoms and their reciprocal relationships among Chinese perinatal women.Design This was a prospective, longitudinal cross-lagged study.Setting The study was conducted at the outpatient department of the largest women’s and children’s hospital in China, which is located in Chengdu, Sichuan Province.Participants Four hundred and sixty-three mothers were conveniently sampled.Main outcome measures The Dyadic Adjustment Scale and the Chinese version of the Edinburgh Postnatal Depression Scale were used to evaluate marital adjustment and depressive symptoms, respectively, at three time points: the first trimester of pregnancy (T1), the third trimester of pregnancy (T2) and 6 weeks after childbirth (T3). Descriptive statistics were used to assess the prevalence of perinatal depressive symptoms, and repeated-measures analysis of variance (ANOVA) was used to determine the trajectories of marital adjustment and depressive symptoms among the participants. A cross-lagged model was used to explore the reciprocal relationship between marital adjustment and depressive symptoms.Results The prevalence of perinatal depressive symptoms among our participants ranged from 21.2% to 24.0%. Repeated-measures ANOVA showed that during the perinatal period there was a significant tendency towards worse marital adjustment (F=33.031, p=0.000) and a slight but not significant reduction in depressive symptoms (F=1.883, p=0.153) among the participants. The cross-lagged model showed that maternal marital adjustment at T1 significantly and negatively predicted depressive symptoms at T2 (β=−0.165, p

Adipose-derived stem cells (ADSCs) are employed as a promising alternative in treating cartilage injury. Regulating the inflammatory “fingerprint” of ADSCs to improve their anti-inflammatory properties could enhance therapy efficiency. Herein, a novel injectable decorin/gellan gum hydrogel combined with ADSCs encapsulation for arthritis cartilage treatment is proposed. Decorin/gellan gum hydrogel was prepared according to the previous manufacturing protocol. The liquid–solid form transition of gellan gum hydrogel is perfectly suitable for intra-articular injection. Decorin-enriched matrix showing an immunomodulatory ability to enhance ADSCs anti-inflammatory phenotype under inflammation microenvironment by regulating autophagy signaling. This decorin/gellan gum/ADSCs hydrogel efficiently reverses interleukin-1β-induced cellular injury in chondrocytes. Through a mono-iodoacetate-induced arthritis mice model, the synergistic therapeutic effect of this ADSCs-loaded hydrogel, including inflammation attenuation and cartilage protection, is demonstrated. These results make the decorin/gellan gum hydrogel laden with ADSCs an ideal candidate for treating inflammatory joint disorders.

In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis.

Pump-rate regulation is frequently used during hydraulic fracturing operations in order to maintain the pressure within a safe range. An emergency pump-rate reduction or pump shutdown is usually applied under the condition of sand screen-out when advancing hydraulic fractures are blocked by injected proppant and develop wellhead overpressure. The drastic regulation of the pump rate induces water-hammer effects—hydraulic shocks—on the wellbore due to the impulsive pressure. This wellbore shock damages the well integrity and then increases the risk of material leakage into water resources or the atmosphere, depending on the magnitude of the impulsive pressure. Therefore, appropriate emergency pump-rate regulation can both secure the fracturing operation and enhance well-completion integrity for environmental requirements—a rare mutual benefit to both sides of the argument. Previous studies have revealed the tube vibration, severe stress concentration, and sand production induced by water-hammer effects in high-pressure wells during oil/gas production. However, the water-hammer effect, the induced impulsive pressures, and the mitigation measures are rarely reported for hydraulic fracturing injections. In this study, we present a data-driven workflow integrating real-time monitoring and regulation strategies, which is applied in four field cases under the emergency operation condition (screen-out or near screen-out). A stepwise pump-rate regulation strategy was deployed in the first three cases. The corresponding maximum impulsive pressure fell in the range of 3.7~7.4 MPa. Furthermore, a sand screen-out case, using a more radical regulation strategy, induced an impulsive pressure 2 or 3 times higher (~14.7 MPa) than the other three cases. Compared with the traditional method of sharp pump-rate regulation in fields, stepwise pump-rate regulation is recommended to constrain the water-hammer effect based on the evolution of impulsive pressures, which can be an essential operational strategy to secure hydraulic fracturing and well integrity, especially for fracturing geologically unstable formations (for instance, formations near faults).

Abstract To explore the association between infertility-related stress, family adaptability and family cohesion in infertile couples and the determinants of infertility-related stress in infertile couples. Fertility Problem Inventory (FPI) and Family Adaptability and Cohesion Evaluation Scales (FACESII-CV) were used to measure the infertility-related stress and family adaptability and cohesion of infertile couples. T-test, ANOVA and multiple comparisons (LSD) were conducted to compare the FPI scores of different demographic characteristics subgroups. Stepwise multivariate linear regression was used to explore the determinants of infertility-related stress. Women had greater global stress than men (P

Introduction Although intrapartum caesarean delivery can resolve dystocia, it would still lead to several adverse outcomes for mothers and children. The obstetric care professionals need effective tools that can help them to identify the possibility and risk factors of intrapartum caesarean delivery, and further implement interventions to avoid unnecessary caesarean birth. This study aims to develop a prediction model for intrapartum caesarean delivery with real-life data based on the artificial neural networks approach.Methods and analysis This study is a prospective nested case–control design. Pregnant women who plan to deliver vaginally will be recruited in a tertiary hospital in Southwest China from March 2022 to March 2024. The clinical data of prelabour, intrapartum period and psychosocial information will be collected. The case group will be the women who finally have a baby with intrapartum caesarean deliveries, and the control group will be those who deliver a baby vaginally. An artificial neural networks approach with the backpropagation algorithm multilayer perceptron topology will be performed to construct the prediction model.Ethics and dissemination Ethical approval for data collection was granted by the Ethics Committee of West China Second University Hospital, Sichuan University, and the ethical number is 2021 (204). Written informed consent will be obtained from all participants and they can withdraw from the study at any time. The results of this study will be published in peer-review journal.

Abstract Background In recent years, spiritual well-being has gradually gained the attention of health care providers in China, especially those in oncology departments, who have recognized the importance of improving spiritual well-being in cancer patients. Since most of the current research on spiritual well-being has been carried out in areas with religious beliefs, this study was conducted in the context of no development of formal religion. The purpose of this study was to explore the relationship between death anxiety and spiritual well-being and the related factors of spiritual well-being among gynecological cancer patients. Methods This cross-section study was conducted among 586 gynecological cancer patients. The European Organization for Research and Treatment for Cancer Quality of Life Questionnaire-spiritual well-being32 (EORTC QLQ-SWB32) and Templer's Death Anxiety Scale (T-DAS) were used to measure spiritual well-being and death anxiety. The Multiple Linear Regression Model was used to determine the relationship between spiritual well-being and death anxiety. Results For all participants, the highest QLQ-SWB32 centesimal score was 75.13 on the Relationship with Other scale, and the lowest was 60.33 on the Relationship with Someone or Something Greater Scale. The mean Death Anxiety score was 5.31 (SD 3.18). We found that Relationship with Someone or Something Greater was the only scale not associated with death anxiety. Overall, patients with lower death anxiety have a higher level of spiritual well-being. Besides, a high Relationship with Other score was associated with living with a partner (B = 2.471, P

After being processed into a certain shape by a stirrup bending machine, steel bars are used as the skeleton of reinforced concrete in construction. The accuracy of their bending directly affects the bearing capacity of reinforced concrete. In order to improve the accuracy of the springback angle of steel bars after bending, this paper focuses on optimizing the structural parameters of the steel bar bending machine and controlling the springback angle of steel bars. The influence of the support shaft diameter, bending crankshaft diameter, and bending crankshaft radius on the springback angle of steel bars is analyzed using ANSYS explicit dynamic simulation and experimental analysis. By combining orthogonal experimental methods, the bending structural parameters were optimized, including a support shaft diameter of 20 mm, a crankshaft bending diameter of 40 mm, and a crankshaft bending radius of 80 mm. This can effectively reduce the springback angle of the steel bars and improve the accuracy of the bending angle after bending. In addition, in order to improve the control accuracy of the steel bar springback angle, a cubic spline interpolation method was proposed, and the collected steel bar springback angle data were fitted using Matlab. At the same time, comparisons were made between the least squares method, polynomial interpolation method, and cubic spline interpolation method. The results show that the compensation formula obtained by using the cubic spline interpolation method can effectively improve the control accuracy of the steel bar bending springback angle.

Soil surface roughness is an important factor affecting hydrology and soil erosion processes, and its development is influenced by precipitation, topography, and tillage practices. In this study, the typical mollisol area in northeast China was taken as the research object. Then, the variation in soil surface roughness with time was analyzed under different terrains, as well as different tillage methods, and the effect of the precipitation condition on roughness was also discussed in detail. Through the design of field experiments, the height information of the soil surface was measured using a probe-type roughness plate. Two parameters, the root-mean-square height (RMSH) and the correlation length (CL), were selected to quantitatively characterize the soil surface roughness. In addition, the dynamic change patterns of surface roughness resulting from five tillage methods, including rotary tillage, combined tillage, no tillage, conventional tillage, and reduced tillage, under both sloping and flat land, were compared and analyzed throughout the soybean growing season, under the influence of rainfall. The results show that with the increase in rainfall, the RMSH of the soil surface, under different tillage methods, showed a trend of first decreasing, and then increasing. The results also showed that the RMSHs under rotary tillage, combined tillage, conventional tillage, and reduced tillage in flat land were greater than those in sloping land, and that the CLs of the soil surface under different tillage methods in flat land were smaller than those in sloping land. In addition, the degree of variation in the soil surface roughness was greater in flat land than that in sloping land under all tillage practices, indicating that this study is of great practical importance in the rational selection of tillage methods, and in the scientific quantification of soil erosion, which also show obvious significance for soil and water conservation.

Soil salinity is one of the parameters used for determining the extent of soil salinization. During water evaporation, the surface of salt-affected soils in the Songnen Plain, China, exhibits obvious shrinkage and cracking phenomena due to the high salt content. The aim of this current study is to quantify the influence of the salt content on the surface shrinkage–cracking process and to achieve quantitative extraction of soil salinity parameters based on different crack parameter types. In order to achieve the above objectives, a controlled shrinkage–cracking experiment was conducted. Subsequently, three kinds of crack characteristics such as crack length, box-counting dimension, and 12 gray-level co-occurrence matrix (GLCM) texture features were quantitatively extracted from the standard binary crack patterns. In order to predict the soil physical–chemical properties of salt-affected soils in the Songnen Plain, three models such as multiple linear regression (MLR), multiple stepwise regression (MSR), and artificial neural network (ANN) were developed and compared based on the crack length, box-counting dimension, and the first two principal components of GLCM texture features. The results show that the extent of desiccation cracks was determined by soil salinity since the water film caused by exchangeable cations and the thickness of DDL determined by soil salinity can promote desiccation cracking. Although the three methods have high prediction accuracy for Na+, electrical conductivity (EC), and total soil salinity, the ANN-based method showed the best prediction with R2 values for Na+, EC, and soil salinity as high as 0.91, 0.91, and 0.89, and ratio of performance to deviation (RPD) values for Na+, EC, and soil salinity corresponding to 2.96, 3.47, and 2.95.

Mesenchymal stem cells (MSCs) have been described to induce angiogenesis in various tissues and have been used for the development of novel cell‐based therapies. Increasing evidence suggests that MSCs execute their paracrine function via the secretion of exosomes, especially under hypoxic conditions. However, the mechanisms by which MSC‐derived exosomes secreted under hypoxia enhance angiogenesis still remain unclear. To study exosome physiology under hypoxic or normoxic conditions, we isolated exosomes from bone marrow mesenchymal stem cells (BMSCs). Furthermore, we detected the uptake of exosomes by human umbilical vein endothelial cells (HUVECs) by immunofluorescence staining. In addition, we determined the effects of exosomes on cell viability, migration and tube formation in HUVECs by Cell Counting Kit‐8, migration and tube formation assays, respectively. We examined the expression of key proteins related to exosome‐induced angiogenesis by BMSCs cultured under hypoxic conditions by western blot. Exosomes released by BMSCs cultured under hypoxic conditions enhanced cell proliferation, migration and angiogenesis of HUVECs. Hypoxia induced the expression of high mobility group box 1 protein (HMGB1) in BMSC‐derived exosomes, and silencing of HMGB1 abolished the angiogenic effect in HUVECs. Furthermore, exosomal HMGB1 activated the JNK signaling pathway and induced hypoxia‐inducible factor‐1α/vascular endothelial growth factor expression, consequently enhancing angiogenesis in HUVECs. Our data reveal that exosomal HMGB1 promotes angiogenesis via JNK/hypoxia‐inducible factor‐1α signaling. Therefore, BMSC exosomes derived under hypoxia may have potential for development of novel treatment strategies for angiogenesis‐related diseases.

Joint contracture (also known as arthrofibrosis) is a fibrotic joint disorder characterized by excessive collagen production to form fibrotic scar tissue and adhesions within joint capsules. This can severely affect day‐to‐day activities and quality of life because of a restricted range of motion in affected joints. The precise pathogenic mechanism underlying joint contractures is not fully understood. Lumican belongs to the class II small leucine‐rich repeat proteoglycan superfamily, which makes up collagen fibrils in the extracellular matrix. Lumican is ubiquitously expressed in the skin, liver, heart, uterus and articular cartilage and has reported roles in cell migration, proliferation, angiogenesis and Toll‐like receptor 4 signaling. Previous research has suggested that lumican is involved in the pathogenesis of several fibrotic diseases. Because joint contracture resembles a fibrotic disease, we aimed to investigate the role of lumican in the development of joint contracture in vitro. Here, we showed that protein levels were up‐regulated in the fibrotic joint capsule versus control. We observed that lumican significantly enhanced the proliferation, migration and fibroblast–myofibroblast transition of synovial fibroblasts. Moreover, lumican led to increased transcription of alpha‐smooth muscle actin, matrix metallopeptidase 9, Collagen I, plasminogen activator inhibitor 1 and transforming growth factor‐β in vitro. Lumican treatment promoted collagen lattice contraction in a dose‐dependent manner as early as 24 h after treatment. Thus, our studies reveal that lumican could promote fibroblast–myofibroblast transition and joint contracture.

The surface of cohesive soda saline-alkali soil in the Songnen Plain of China exhibits obvious desiccation cracking phenomenon during water evaporation. Quantitative research on the process and influencing factors of desiccation cracking are significant for determining the mechanical properties of salt-affected soil, improving the salinization status and the local ecological environment. This study aims to conduct laboratory-controlled cracking experiments on soda saline-alkali soils, and to regularly measure the soil moisture and the cracking status of 40 soil samples. After processing the crack images uniformly, crack characteristics including crack length, crack area, contrast texture feature, and box-counting fractal dimension are extracted. The results indicate that the decrease in soil moisture can be divided into three stages such as fast evaporation stage, slow evaporation stage and stable evaporation stage, and that the evaporation rate shows a linear negative correlation with soil salinity. The measurements also indicate that crack length rapidly increases and reaches stability with a decrease in water content compared with the crack area. The high correlation between different crack characteristics and the soil total salinity (correlation coefficient from 0.44 to 0.95) shows that salt content is the dominant factor affecting the desiccation cracking of soda saline-alkali soil. A multiple linear regression prediction model was finally established based on crack characteristics for different salinity parameters with high prediction accuracy for Na+, EC and total salinity (R2 > 0.91 and RPD > 2.5) and also certain prediction accuracy for Cl− and CO32− (R2 > 0.66 and RPD > 1.5).

Pointer meters have been widely used in industrial field due to their strong stability; it is an important issue to be able to accurately read the meter. At present, patrol robots with computer vision function are often used to detect and read meters in some situations that are not suitable for manual reading of the meter. However, existing object detection algorithms are often misread and miss detection due to factors such as lighting, shooting angles, and complex background environments. To address these problems, this paper designs a YOLOv4-Tiny-based pointer meter detection model named pointer meter detection-YOLO (PMD-YOLO) for the goal of practical applications. Firstly, to reduce weight of the model and ensure the accuracy of object detection, a feature extraction network named GhostNet with a channel attention mechanism is implemented in YOLOv4-Tiny. Then, to enhance feature extraction ability of small- and medium-sized targets, an improved receptive field block (RFB) module is added after the backbone network, and a convolutional block attention module (CBAM) is introduced into the feature pyramid network (FPN). Finally, the FPN is optimized to improve the feature utilization, which further improves the detection accuracy. In order to verify the effectiveness and superiority of the PMD-YOLO proposed in this paper, the PMD-YOLO is used for experimental research on the constructed dataset of the pointer meter, and the target detection algorithms such as Faster region convolutional neural network (RCNN), YOLOv4, YOLOv4-Tiny, and YOLOv5-s are compared under the same conditions. The experimental results show that the mean average precision of the PMD-YOLO is 97.82%, which is significantly higher than the above algorithms. The weight of the PMD-YOLO is 9.38 M, which is significantly lower than the above algorithms. Therefore, the PMD-YOLO not only has high detection accuracy, but can also reduce the weight of the model and can meet the requirements of practical applications.

ObjectivesAlthough studies shows that symptom clusters and illness perceptions are negatively associated with quality of life (QoL), it is unclear how these variables of cervical cancer patients who receive concurrent chemoradiotherapy (CCRT) relate to each other. This study aimed to identify the symptom clusters in cervical cancer patients who receive CCRT and evaluate the mediating effect of illness perceptions on the relationship between symptom clusters and QoL.MethodsA cross-sectional survey was conducted on 286 cervical cancer patients receiving CCRT from October 2019 to October 2020. M.D. Anderson Symptom Inventory, Brief Illness Perception Questionnaire, and Functional Assessment Cancer Therapy-Cervix were applied to investigate the symptom clusters, illness perceptions and QoL of the participants, respectively. Exploratory factor analysis was conducted to identify symptom clusters. The relationships among symptom clusters, illness perceptions, and QoL were analyzed with the structural equation modeling.ResultsA total of four symptom clusters were identified, including psychological status symptom cluster, therapy side-effect symptom cluster, sickness symptom cluster, and gastrointestinal symptom cluster (χ2 = 1,552.282, Df = 78, P < 0.001). Symptom clusters, illness perceptions, and QoL were significantly correlated. Symptom clusters had significant direct (β = −0.38, P < 0.001) and indirect effects (β = −0.21, P < 0.001) on QoL.ConclusionIllness perceptions played a significant mediating role between symptom clusters and QoL in cervical cancer patients receiving CCRT. Strategies like prompting effective symptom management for the purposes of alleviating illness perceptions may contribute to improving their QoL.

The complex application environments of frame structures and the similar vibration signals between different locations make it difficult to accurately diagnose damage using traditional methods. Based on modifying the parameters and configuration of the convolution neural network with training interference (TICNN), this paper proposes a new model for damage diagnosis of frame structures by implanting a squeeze-and-excitation neural network (SENet) and Res2Net modules. Taking the frame structure model from the University of British Columbia as the research object, the proposed damage diagnosis model was used to diagnose its damage type. The proposed new model was compared with other models in terms of accuracy and anti-noise ability. The experimental results show that the accuracy of the proposed model was 99.44% when the training epoch was 30 and 99.78% when training epoch was 100. It is superior to other similar models in terms of convergence speed and accuracy. At the same time, the proposed model also has an excellent advantage in anti-noise ability. Therefore, the proposed damage diagnosis model has the advantages of fast convergence and higher damage diagnosis accuracy under a strong noise environment. It can realize the accurate damage diagnosis of structural frames.

The whistle of the rail train is usually directly controlled by the driver. However, in long-distance transportation, there is a risk of traffic accidents due to driver fatigue or distraction. In addition, the noise pollution of the train whistle has also been criticized. In order to solve the above two problems, an intelligent whistling system for railway trains based on deep learning is proposed. The system judges whether to whistle and intelligently adjusts the volume of the whistle according to the road conditions of the train. The system consists of a road condition sensing module and a whistling decision module. The former includes the target detection model based on YOLOv4 and the semantic segmentation model based on U-Net, which can extract the key information of the road conditions ahead; the latter is to carry out logical analysis of the data after the intelligent recognition and processing and make the whistling decision. Based on the train-running data set, the intelligent whistle system model is tested. The results of this research show that the whistling accuracy of the model on the test set is 99.22%, the average volume error is 1.91 dB/time, and the Frames Per Second (FPS) is 18.7 f/s. Therefore, the intelligent whistle system model proposed in this paper has high reliability and is suitable for further development and application in actual scenes.

Excessive drinking can significantly damage people’s health and well-being. Although some lactic acid bacterial strains have been previously shown to alleviate the symptoms of alcohol injury, the mechanism underlying these effects remains unclear. The aim of this study was to establish an alcohol injury model and examine the protective effect and mechanism of B. animalis A12 and L. salivarius M18-6. The results showed that A12 freeze-dried powder could maintain the survival rate of mice with alcohol injury at 100%. Compared with Alco group, L. salivarius M18-6 dead cell improved the survival rate of mice, attenuated liver steatosis, and significantly down-regulated serum Alanine transaminase (ALT) level; at the same time, it activated keap1-Nrf2 signaling pathway and up-regulated Superoxide dismutase (SOD), it protects mouse liver cells from oxidative stress induced by alcohol injury. In addition, B. animalis A12 can reduce the stress response to short-term alcohol intake and improve the ability of anti-oxidative stress by upregulating the level of isobutyric acid, reducing the level of keap1 protein in the liver of mice and upregulating the expression of thioredoxin genes (Txnrd1, Txnrd3, Txn1). Taken together, the results showed that B. animalis A12 and L. salivarius M18-6 alleviate alcohol injury in mice through keap1-Nrf2 signaling pathway and thioredoxin system.

Bifidobacterium animalis A12 was used for the development of fermented sausage. The growth activity, tolerance, and enzyme activity of B. animalis A12 and its contribution to the texture and flavour of fermented sausages were evaluated. Additionally, the sensory texture, flavour components, and amino acid nutrients during the fermentation process were assessed. B. animalis had high tolerance to NaCl and nitrite, and B. animalis A12 had protease and lipase activities. The pH value of sausage fermented with B. animalis A12 was lower than that of sausage fermented without any fermentation strain. Hexanal, heptanal, decanal, cis-2-decanal, and 4-methoxy-benzaldehyde are the unique aldehydes flavour components of fermented sausages in the A12 group. The highest content of volatile flavour substances and amino acids, and the color and texture characteristics of fermented sausage in the experimental group at 18 h were better than those at other times. These results suggest that B. animalis A12 has the potential to be used as a starter culture for im-proving flavour and texture in fermented sausage.

Accurate damage location diagnosis of frame structures is of great significance to the judgment of damage degree and subsequent maintenance of frame structures. However, the similarity characteristics of vibration data at different damage locations and noise interference bring great challenges. In order to overcome the above problems and realize accurate damage location diagnosis of the frame structure, the existing convolutional neural network with training interference (TICNN) is improved in this paper, and a high-precision neural network model named convolutional neural network based on Inception (BICNN) for fault diagnosis with strong anti-noise ability is proposed by adding the Inception module to TICNN. In order to effectively avoid the overall misjudgment problem caused by using single sensor data for damage location diagnosis, an integrated damage location diagnosis method is proposed. Taking the four-story steel frame model of the University of British Columbia as the research object, the method proposed in this paper is tested and compared with other methods. The experimental results show that the diagnosis accuracy of the proposed method is 97.38%, which is higher than other methods; at the same time, it has greater advantages in noise resistance. Therefore, the method proposed in this paper not only has high accuracy, but also has strong anti-noise ability, which can solve the problem of accurate damage location diagnosis of complex frame structures under a strong noise environment.

Abstract Background Joint contracture is a fibrous disease characterized as joint capsule fibrosis that results in joint dysfunction and disability. The purpose of this study was to analyze the biological activities of chaperonin containing T-complex polypeptide (CCT) subunits and to determine the role of CCT chaperone in joint contracture in a rat model. Methods In this study, the rat model of joint contracture was established by immobilizing the rat knee for 8 weeks. Then, fibroblasts were isolated from the posterior joint capsule and were cultured for functional analysis such as qRT-PCR, Western blot, transwell assay, and collagen assay. The effect of CCT subunit was determined by employing a lentivirus containing target gene and transfecting it into fibroblasts. Results Results of qRT-PCR and Western blot showed that among all CCT subunits, CCT6b significantly decreased in the fibroblasts from contractive joints compared to cells from normal joints (p < 0.05). Overexpression of CCT6b by transfection of lentivirus containing CCT6b gene to active fibroblasts significantly inhibited fibrous marker (α-SMA, COL-1) expressions, fibroblast migration, and collagen synthesis (all p < 0.05). Moreover, fibrosis-related chaperone CCT7 expression was decreased with CCT6b overexpression (p < 0.05). Conclusion The biological activities of CCT subunits in fibroblasts from the joint contracture rat model were analyzed in this study. CCT6b significantly decreased in the active fibroblasts, and overexpression of CCT6b significantly inhibited fibroblast functions. These findings indicate that CCT6b appears to be a potential molecular biomarker and therapeutic target for the novel therapies of joint contracture.

Steam flooding is crucial for the development of heavy oil reservoirs, and the development of the steam cavity significantly determines the efficiency of steam flooding. Previous studies have elucidated the concept of steam overburden and pseudomobility ratio; however, the thermal energy loss in deep heavy oil reservoirs during steam injection needs further investigation. Therefore, in this study, the vapour–liquid interface theory and mathematical integration were used to establish a steam cavity expansion model. The wellbore heat loss rate coefficient, steam overlay, and pseudomobility ratio were used to accurately describe the development of the steam cavity in deep heavy oil reservoirs. The proposed model was experimentally validated, and it was observed that the model could accurately reflect the actual mine conditions. In addition, the pressure gradient distribution of the steam belt and the heat dissipation areas of the top and bottom layers of the steam cavity were evaluated. The results showed that the influence of the wellbore heat loss rate coefficient on the pressure gradient of the oil layer was primarily in the range of 5–20 m away from the steam injection well. Furthermore, it was observed that the pseudomobility ratio is inversely proportional to the development of the steam cavity. As the wellbore heat loss rate coefficient increased, the wellbore heat loss increased. The larger the area ratio, the more pronounced the steam overlay phenomenon, and the large area ratio does not meet the development requirements of the steam chamber. The research closely combines theory with production, and the results of this study can help actual mines by providing theoretical support for the development of deep heavy oil reservoirs.

Background: Angiogenesis is an essential step in tissue engineering. MSC exosomes play an important role in angiogenesis. Functional biomolecules in exosomes vested by the culture microenvironment can be transferred to recipient cells and affects their effect. 3D culture can improve the proliferation and activity of MSCs. However, whether exosomes derived from 3D culture of MSCs have an enhanced effect on angiogenesis is unclear. Methods: Herein, we compared the bioactivity of exosomes produced by conventional 2D culture (2D-exos) and 3D culture (3D-exos) of bone marrow stem cells (BMSCs) in angiogenesis. Results: A series of in vitro and in vivo experiments indicated that 3D-exos exhibited stronger effects on HUVEC cell proliferation, migration, tube formation, and in vivo angiogenesis compared with 2D-exos. Moreover, the superiority of 3D-exos might be attributed to the activation of HMGB1/AKT signaling. Conclusions: These results indicate that exosomes from 3D culture of MSCs may serve as a potential therapeutic approach for pro-angiogenesis.

Inflammatory damage contributes to β cell failure in type 1 and 2 diabetes (T1D and T2D, respectively). Mitochondria are damaged by inflammatory signaling in β cells, resulting in impaired bioenergetics and initiation of proapoptotic machinery. Hence, the identification of protective responses to inflammation could lead to new therapeutic targets. Here, we report that mitophagy serves as a protective response to inflammatory stress in both human and rodent β cells. Utilizing in vivo mitophagy reporters, we observed that diabetogenic proinflammatory cytokines induced mitophagy in response to nitrosative/oxidative mitochondrial damage. Mitophagy-deficient β cells were sensitized to inflammatory stress, leading to the accumulation of fragmented dysfunctional mitochondria, increased β cell death, and hyperglycemia. Overexpression of CLEC16A, a T1D gene and mitophagy regulator whose expression in islets is protective against T1D, ameliorated cytokine-induced human β cell apoptosis. Thus, mitophagy promotes β cell survival and prevents diabetes by countering inflammatory injury. Targeting this pathway has the potential to prevent β cell failure in diabetes and may be beneficial in other inflammatory conditions.

Tillage practices are critical for sustaining soil quality necessary for successful crop growth and productivity, but there are only few studies for strip tillage (ST) in the Mollisols region of Northeast China at present. A long-term (≥10-year) study was carried out to investigate the influence of within the tilled row (IR) and between rows (BR) in ST (10-year), conventional tillage (CT, 14-year) and no tillage (NT, 14-year) treatments on soil physicochemical properties. Soil samples were taken in May of 2019 at 0–5, 5–10, 10–20 and 20–30 cm depths and used to analyze bulk density (BD), soil aggregate distribution and stability, and soil organic carbon (SOC). Meanwhile, our study also explored the differences in seed emergence, soil moisture, and temperature during the seed emergence period, and yield of maize (Zea mays L.) among the different treatments. Similar soil properties were observed between ST-BR and NT, which showed they had a significantly greater BD, >0.25 mm water stable aggregate content (WR0.25) (especially in the amount of >2 mm and 1–2 mm size proportion), aggregate stability, and SOC than ST-IR and CT-IR at a depth of 0–20 cm. By improving soil conditions of seedbed, ST-IR and CT-IR increased soil temperature above NT by 1.64 °C and 1.80 °C, respectively, and ST-IR had a slight greater soil moisture than CT-IR in the top 10 cm layer during the seed emergence period. Late maize seed emergence was observed NT in than ST-IR and CT-IR and the average annual yields in ST were slightly greater than NT and CT, but the differences were not significant. Our results also showed that CT-BR had a poor soil structure and lower SOC than other treatments at 0–30 cm depth. We conclude from these long-term experimental results that ST could improve soil water-heat conditions to promote seed germination, maintain soil structure, and increase the maize yield and it should be applied in the Mollisols region of Northeast China.

Cracking on the surface of soda saline-alkali soil is very common. In most previous studies, spectral prediction models of soil salinity were less accurate since spectral measurements were usually performed on 2 mm soil samples which cannot represent true soil surface condition very well. The objective of our research is to provide a procedure to improve soil property estimation of soda saline-alkali soil based on spectral measurement considering the texture feature of the soil surface with cracks. To achieve this objective, a cracking test was performed with 57 soil samples from Songnen Plain of China, the contrast (CON) texture feature of crack images of soil samples was then extracted from grey level co-occurrence matrix (GLCM). The original reflectance was then measured and the mixed reflectance considering the CON texture feature was also calculated from both the block soil samples (soil blocks separated by crack regions) and the comparison soil samples (soil powders with 2 mm particle size). The results of analysis between spectra and the main soil properties indicate that surface cracks can reduce the overall reflectivity of the soda saline-alkali soil and thus increasing the spectral difference among the block soil samples with different salinity levels. The results also show that both univariate and multivariate linear regression models considering the CON texture feature can greatly improve the prediction accuracy of main soil properties of soda saline-alkali soils, such as Na+, EC and salinity, which also can reduce the intensity of field spectral measurements under natural condition.

The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na+, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2⁻11% decrease in the parameters mean absolute error (MAE).

AimThe aim of this study is to assess the depression of pregnant women in the aftermath of an earthquake, and to identify the social support that they obtained, their coping styles and socio-demographic factors associated with depression.MethodsA total of 128 pregnant women from three hospitals in the epicenter area were recruited immediately after the Ya'an earthquake. Their depression was investigated using the Edinburgh Postnatal Depression Scale (EPDS) with a cutoff score of 14; the social support that they obtained was measured using the Social Support Questionnaire; and their coping styles were assessed using the Coping Styles Questionnaire.ResultsImmediately after the earthquake, the incidence rate of depression in pregnant women was 35.2%, higher than that of the general pregnant population (7%-14%). The EPDS scores were significantly correlated with gestation age at the time of the earthquake, objective support, subjective support, use of support, negative coping style, and positive coping style. The regression analysis indicated that risk factors of prenatal depression include the number of children, relatives wounded, subjective support, and coping styles. A further analysis of the interaction between social support and two types of coping styles with depression showed that there was interaction effect between subjective social support and positive coping styles in relation to EPDS scores. There was an inverse relationship between low EPDS scores and positive coping styles and high social support, and vice versa.ConclusionThe timing of the occurrence of the earthquake may not necessarily affect the progress of the illness and recovery from depression, and psychological intervention could be conducted in the immediate aftermath after the earthquake. The impact of coping styles on prenatal depression appeared to be linked with social support. Helping pregnant women to adopt positive coping styles with good social support after a recent major earthquake, which is a stressor, may reduce their chances of developing prenatal depression.

Offering high school students' exposure to university level laboratory research encourages interest in science, develops critical thinking, and prepares the new generation for college and careers. In this study, we report on the development of an immersive 2-week summer camp with the aim of training high school students in cancer research. Carefully selected laboratory methods were chosen to yield distinct visible results with the sole goal of making complex concepts more accessible and engaging to high school students. The 20 participants were randomly selected from a bigger pool of candidates without any prior registration requirements, such as grades or prior lab experience. Survey questionnaires given to students on the first day of the program outlined the background of each student and helped the instructors to better understand their goals and expectations. The postcamp survey, which measured satisfaction and potential areas for improvement, was completed by students after their departure from the campus and served as an unbiased evaluation of our program. Considering the feedback from students, our program was very successful. Whereas information for such programs is not broadly available, research laboratories and future summer camps that aim to train high school or undergraduate students with minimal laboratory experience may utilize this pedagogy as a template for their programs.

Fe-Ni alloys were fabricated on steel substrates by means of pulse electrodeposition in sulfate solutions. The layers were electrodeposited using different peak current densities, duty cycles and frequencies. Fe contents, microhardnesses and crystalline phases were examined systematically. The Fe content in the deposit decreased and the microhardness increased with increasing duty cycle and peak current density. The pulse frequency had little effect on Fe content but led to a slight decrease in microhardness. X-ray diffraction patterns show that the crystalline phases vary with changes in peak current density and duty cycle but are barely influenced by frequency. When the peak current density or duty cycle is relatively low, crystalline Fe-Ni alloy and pure Fe phases coexist; the pure Fe phases disappear as the peak current density or duty cycle increases. At still larger peak current densities or duty cycles, crystalline Fe-Ni alloy and pure Ni phases coexist.

Osteoarthritis (OA) is a degenerative joint disease that affects both cartilage and bone. A better understanding of the early molecular changes in subchondral bone may help elucidate the pathogenesis of OA. We used microarray technology to investigate the time course of molecular changes in the subchondral bone in the early stages of experimental osteoarthritis in a rat model. We identified 2,234 differentially expressed (DE) genes at 1 week, 1,944 at 2 weeks and 1,517 at 4 weeks post-surgery. Further analyses of the dysregulated genes indicated that the events underlying subchondral bone remodeling occurred sequentially and in a time-dependent manner at the gene expression level. Some of the identified dysregulated genes that were identified have suspected roles in bone development or remodeling; these genes include Alp, Igf1, Tgf β1, Postn, Mmp3, Tnfsf11, Acp5, Bmp5, Aspn and Ihh. The differences in the expression of these genes were confirmed by real-time PCR, and the results indicated that our microarray data accurately reflected gene expression patterns characteristic of early OA. To validate the results of our microarray analysis at the protein level, immunohistochemistry staining was used to investigate the expression of Mmp3 and Aspn protein in tissue sections. These analyses indicate that Mmp3 protein expression completely matched the results of both the microarray and real-time PCR analyses; however, Aspn protein expression was not observed to differ at any time. In summary, our study demonstrated a simple method of separation of subchondral bone sample from the knee joint of rat, which can effectively avoid bone RNA degradation. These findings also revealed the gene expression profiles of subchondral bone in the rat OA model at multiple time points post-surgery and identified important DE genes with known or suspected roles in bone development or remodeling. These genes may be novel diagnostic markers or therapeutic targets for OA.