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Abstract Copper-induced cell death is regulated through protein lipoylation, which is critical for gene expression and phenotypic regulation. Neverless, the role of Cuproptosis-related genes in breast cancer (BC) remains unknown. This study aimed to construct a prognostic signature based on the expression of Cuproptosis-related genes in order to guide the diagnosis and treatment for BC. Cuproptosis-related genes prognostic signature has ata of 1250 BC tissues and 583 normal breast tissues were obtained from The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), and GEO GSE65212. The prognostic signature was established and evaluated with nineteen Cuproptosis-related genes. A series of in silico analyses based on SLC31A1, included expression analysis, independent prognostic analysis, correlation analysis, immune-related analysis and survival analysis. Finally, a series of cell experiments (including quantitative real-time polymerase chain reaction and western blot), and mice experiments were applied to evaluate the impact of SLC31A1 on BC. Cuproptosis-related genes prognostic signature has good predictive promising for survival in BC patients. We discovered that SLC31A1SLC31A1 was overexpressed in BC and was its independent prognostic factor. High expression of the SLC31A1 was correlated with poor prognosis and immune infiltrating of BC. SLC31A1 expression is associated with immune, chemotherapeutic and targeted therapy outcomes in BC. The proliferation, migration, and invasiveness of Her2 + enriched BC cells were decreased by SLC31A1 knockdown, also resulting in a decrease in tumor volume in mouse model. SLC31A1 is a candidate biomarker or therapeutic target in precision oncology, with diagnostic and prognostic significance in BC.

Abstract With the increasing urban environmental problems, spontaneous vegetation has been gradually emphasized for high ecological value, whose distribution has been reported as strongly influenced by fine-scale environmental factors. As one of the major zones for urban spontaneous vegetation distribution, urban riparian corridors are well suited for research on the response of spontaneous plants to microhabitats. Taking the Wenyu River-North Canal in Beijing as the study site, we measured six microhabitat factors, including the level of maintenance and visitor activity, canopy density, litter thickness, and distance to water and road, to investigate their effects on spontaneous plant assemblages. The results showed that spontaneous plants respond significantly to fine-scale habitat variation, and were more responsive to human disturbance than other factors. Compared with diversity indicators, the functional trait compositions were more significantly correlated with microhabitat factors. Under lower maintenance of plants, the spontaneous communities had a higher invasion risk for plants. Thick litter can impede the occurrence of invasive species while favoring the growth of native ones. Our findings are important for furthering understanding of the spontaneous plant community establishment, and can serve as a good reference for the maintenance and management of spontaneous plants in urban riparian corridors.

Abstract To examine the influence of Body Mass Index (BMI) on laparoscopic gastrectomy (LG) short-term and long-term outcomes for gastric cancer. A retrospective analysis was conducted on gastric cancer patients undergoing LG at the Third Hospital of Nanchang City from January 2013 to January 2022. Based on WHO BMI standards, patients were categorized into normal weight, overweight, and obese groups. Factors such as operative time, intraoperative blood loss, postoperative complications, and overall survival were assessed. Across different BMI groups, it was found that an increase in BMI was associated with longer operative times (average times: 206.22 min for normal weight, 231.32 min for overweight, and 246.78 min for obese), with no significant differences noted in intraoperative blood loss, postoperative complications, or long-term survival among the groups. The impact of BMI on long-term survival following LG for gastric cancer was found to be insignificant, with no notable differences in survival outcome between different BMI groups. Although higher BMI is associated with increased operative time in LG for gastric cancer, it does not significantly affect intraoperative blood loss, postoperative complications, recovery, or long-term survival. LG is a feasible treatment choice for obese patients with gastric cancer.

Abstract While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan’s deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model—Expectation (EX), Entropy (EN), and Hyper-entropy (HE)—we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan’s forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82–39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79–7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan’s vegetation vertical differentiation.

Abstract To evaluate the diagnostic accuracy of a new modified MR dual precision positioning of thin-slice oblique sagittal fat suppression proton density-weighted imaging (DPP-TSO-Sag-FS-PDWI) sequence in detecting ACL injuries and its grades compared to standard sequences using arthroscopy as the standard reference. 42 patients enrolled in this retrospective study received the 1.5-T MRI with standard sequences and the new modified DPP-TSO-Sag-FS-PDWI sequence, and their arthroscopy results was recorded. The Mc Nemer-Bowker and weighted Kappa was performed to compare the consistency of MRI diagnosis with arthroscopic results. Finally, the diagnostic accuracy was calculated based on the true positive, true negative, false negative and false positive values. The diagnostic consistency of the DPP-TSO-Sag-FS-PDWI were higher than standard sequences for both reader 1 (K = 0.876 vs. 0.620) and reader 2 (K = 0.833 vs. 0.683) with good diagnostic repeatability (K = 0.794 vs. 0.598). Furthermore, the DPP-TSO-Sag-FS-PDWI can classify and diagnose three grades of ACL injury [the sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value were more than 84%], especially for grade II injury as the PPV was superior for reader 1 (92.3% vs. 53.9%) and reader 2 (84.6% vs. 69.2%). The new modified DPP-TSO-Sag-FS-PDWI sequence can display the ACL injury on one or continuous levels by maximizing the acquisition of complete ligament shape and true anatomical images, and excluding the influence of anatomical differences between individuals. It can improve the diagnostic accuracy with good repeatability and classify three grades of the ACL injury.

Abstract Forest soils are important components of forest ecosystems, and soil quality assessment as a decision-making tool to understand forest soil quality and maintain soil productivity is essential. Various methods of soil quality assessment have been developed, which have occasionally generated inconsistent assessment results between soil types. We assessed the soil quality of five communities (herb, shrub, Quercus acutissima, Pinus thunbergii, and Q. acutissima–P. thunbergii mixed plantation) using two common methods of dry and barren mountains in the Yimeng Mountain area, China. Sixteen soil physical, chemical and biological properties were analysed. The soil quality index was determined using the established minimum data set based on the selection results of principal component analysis and Pearson analysis. Silt, soil total phosphorus (P), soil total nitrogen (N), L-leucine aminopeptidase, acid phosphatase and vector length were identified as the most representative indicators for the minimum data set. Linear regression analysis showed that the minimum data set can adequately represent the total data set to quantify the impact of different communities on soil quality (P

Abstract This study presents a parametric analysis of the steady-state temperature elevation in rat skin models due to millimeter wave exposure at frequencies from 6–100 GHz. The statistical data of the thickness of skin layers, namely epidermis, dermis, dermal white adipose tissue, and panniculus carnosus, were measured for the first time using the excised tissues of real male Sprague–Dawley rats. Based on the precise structure obtained from the histological analysis of rat skin, we solve the bioheat transfer equation to investigate the effects of changes in parameters, such as body parts and thermal constants, on the absorbed power density and temperature elevation of biological tissues. Owing to the notably thin dermal white adipose tissue layer, the surface temperature elevation in the rat head and dorsal skin at 6–100 GHz is 52.6–32.3% and 83.3–58.8% of the average values of different human skin models, respectively. Our results also reveal that the surface temperature elevation of rat skin may correlate with the tissue thickness and deep blood perfusion rates.

Abstract Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.

Abstract We will use micro-computed tomography to scan 31 sets of the adult lower cervical vertebrae (155 vertebrae) to observe the morphological characteristics and direction of trabeculae in the lower cervical vertebrae by outlining and reconstructing the regions of interest and to calculate the variation laws of the microstructure in the regions of interest to reveal their structural characteristics and weak areas. As a result, the images showed that the trabeculae in the lower cervical pedicle near the medial and lateral cortices were relatively dense, and their bone plates were lamellar. There were cavities between the superior and inferior articular processes where the ossification centers had not been absorbed after ossified. The lamellar trabeculae in the vertebral plates near the cortical bones were only 1–2 layers, extended and transformed into rod-shaped trabeculae in a radial shape toward the medullary space. The lamellar trabeculae of the vertebral plate extend over the spinous process near the cortical bone. The statistical results of the trabeculae's morphological parameters showed significant differences in bone volume fraction values among the four parts (P

Abstract In recent years, joint triple extraction methods have received extensive attention because they have significantly promoted the progress of information extraction and many related downstream tasks in the field of natural language processing. However, due to the inherent complexity of language such as relation overlap, joint extraction model still faces great challenges. Most of the existing models to solve the overlapping problem adopt the strategy of constructing complex semantic shared encoding features with all types of relations, which makes the model suffer from redundancy and poor inference interpretability in the prediction process. Therefore, we propose a new model for entity role attribute recognition based on triple holistic fusion features, which can extract triples (including overlapping triples) under a limited number of relationships, and its prediction process is simple and easy explain. We adopt the strategy of low-level feature separation and high-level concept fusion. First, we use the low-level token features to perform entity and relationship prediction in parallel, then use the residual connection with attention calculation to perform feature fusion on the candidate triples in the entity-relation matrix, and finally determine the existence of triple by identifying the entity role attributes. Experimental results show that the proposed model is very effective and achieves state-of-the-art performance on the public datasets.

Abstract A number of strict lockdown measures were implemented in the areas most affected by COVID-19 in China, including Ji’nan city, from 24 January to 7 February 2020. Due to these forced restrictions, the pollution levels in cities across the country drastically decreased within just a few days. Since traffic pollution and industrial emissions are important factors affecting regional air quality, congestion has a significant impact on the environment. Therefore, using the aid of air quality data for six pollutants (PM10, PM2.5, SO2, NO2, CO and O3) from 11 monitoring stations (located in urban, suburban and urban-industrial regions) across Ji’nan, we employed the air quality index (AQI) to investigate the spatial pattern of air quality in the pre-COVID-19 (pre-COVID) and COVID-19-related lockdown (COVID lockdown) periods. The results showed that air quality significantly improved during the COVID lockdown period. Among the selected pollutants, compared to the corresponding pre-COVID levels, the greatest reduction was observed for the concentration of NO2 (54.02%), while the smallest reduction was observed for the concentration of SO2 (27.92%). The PM2.5 (38.73%), PM10 (44.92%) and CO (30.60%) levels also decreased during the COVID lockdown period; only the O3 concentration increased (37.42%) during this period. Overall, air quality improved by approximate improvements of 37.33% during the COVID lockdown period. Approximately 35.48%, 37.01% and 43.43% in the AQI were observed in urban, suburban and urban-industrial regions, respectively. Therefore, the AQI exhibited remarkable regional differences in Ji'nan. This study demonstrates the contributions of the transportation sector and local emissions to improving air quality in typical urban areas, and these research results can provide guidance for the further monitoring of air pollution in northern Chinese cities.

Abstract Previous studies showed that mutation of folC caused decreased expression of the dihydropteroate synthase encoding gene folP2 in Mycobacterium tuberculosis (M. tuberculosis). We speculated that mutation of folC in M. tuberculosis might affect the susceptibility to sulfamethoxazole (SMX). To prove this, 53 clinical isolates with folC mutations were selected and two folC mutants (I43A, I43T) were constructed based on M. tuberculosis H37Ra. The results showed that 42 of the 53 clinical isolates (79.2%) and the two lab-constructed folC mutants were more sensitive to SMX. To probe the mechanism by which folC mutations make M. tuberculosis more sensitive to SMX, folP2 was deleted in H37Ra, and expression levels of folP2 were compared between H37Ra and the two folC mutants. Although deletion of folP2 resulted in increased susceptibility to SMX, no difference in folP2 expression was observed. Furthermore, production levels of para-aminobenzoic acid (pABA) were compared between the folC mutants and the wild-type strain, and results showed that folC mutation resulted in decreased production of pABA. Taken together, we show that folC mutation leads to decreased production of pABA in M. tuberculosis and thus affects its susceptibility to SMX, which broadens our understanding of mechanisms of susceptibilities to antifolates in this bacterium.

Abstract Image fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.

Abstract Cardiovascular disease is one of the main causes of death in diabetes mellitus (DM) patients. The aim of the current study was to explore the value of peak strain dispersion (PSD) for discovering early-stage left ventricular (LV) dysfunction in type 2 diabetes mellitus (T2DM) patients. One hundred and one T2DM patients and sixty healthy subjects were selected for this study. T2DM patients were further divided into controlled blood glucose (HbA1c

Abstract To study the fractal dimensions of the soil particle size distributions (PSDs) within different plantations (of Pinus densiflora, Quercus acutissima, Robinia pseudoacacia, and Larix kaempferi) and evaluate PSDs as an indicator of the likelihood of Robinia pseudoacacia invasion, the soil porosity of 0–20 cm soil layers was measured at different plantations in the Yaoxiang Forest Farm, Shandong Province, China. The results showed that the fractal dimension (D m) values varied from 2.59 to 2.70 among the different plantations and were significantly negatively correlated to sand content and positively correlated to silt content and clay content. Significant negative correlations were observed between D m and both soil organic matter (SOM) (P

Abstract We performed this meta-analysis to examine the diagnostic accuracy of MRI for the diagnosis of anterior cruciate ligament (ACL) injury in comparison to arthroscopy. We also compared the diagnostic accuracy of MRI with magnetic field intensities (MFI) greater than or equal to 1.5T with those below 1.5T, in addition to different MRI sequences. Studies relevant to the diagnosis of ACL injury by MRI and arthroscopy were analyzed. Computer and manual retrieval were carried out on studies published between January 1, 2006 and May 31, 2016. Twenty-one papers were included. Neither threshold nor non-threshold effects were present (p = 0.40, p = 0.06). The pooled sensitivity (SE), specificity (SP), positive likelihood ratio (LR+), negative likelihood ratio (LR−) and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were 87% (84–90%), 90% (88–92%), 6.78 (4.87–9.44), 0.16 (0.13–0.20) and 44.70 (32.34–61.79), respectively. The area under the curve (AUC) was 0.93. The risk of publication bias was negligible (p = 0.75). In conclusion, examination by MRI is able to provide appreciable diagnostic performance. However, the principle, which states that the higher the MFI, the better the diagnostic accuracy, could not be verified. Additionally, conventional sequences (CSs) associated with proton density-weighted imaging (PDWI) are only slightly better than CSs alone, but not statistically different.

Abstract Macrophages and resident microglia play an import role in the secondary neuroinflammation response following spinal cord injury. Reprogramming of macrophage/microglia polarization is an import strategy for spinal cord injury restoration. Low-level laser therapy (LLLT) is a noninvasive treatment that has been widely used in neurotrauma and neurodegenerative diseases. However, the influence of low-level laser on polarization of macrophage/microglia following spinal cord injury remains unknown. The present study applied low-level laser therapy on a crush spinal cord injury rat model. Using immunofluorescence, flow cytometry, RT-qPCR, and western blot assays, we found that low-level laser therapy altered the polarization state to a M2 tendency. A greater number of neurons survived in the pare injury site, which was accompanied by higher BBB scores in the LLLT group. Furthermore, low-level laser therapy elevated expression of interleukin 4 (IL-4) and interleukin 13 (IL-13). Results from this study show that low-level laser therapy has the potential for reducing inflammation, regulating macrophage/microglia polarization, and promoting neuronal survival. These beneficial effects demonstrate that low-level laser therapy may be an effective candidate for clinical treatment of spinal cord injury.

Abstract Urolithiasis is a leading urological disorder where accurate preoperative identification of stone types is critical for effective treatment. Deep learning has shown promise in classifying urolithiasis from CT images, yet faces challenges with model size and computational efficiency in real clinical settings. To address these challenges, we developed a non-invasive prediction approach for determining urinary stone types based on CT images. Through the refinement and improvement of the self-distillation architecture, coupled with the incorporation of feature fusion and the Coordinate Attention Module (CAM), we facilitated a more effective and thorough knowledge transfer. This method circumvents the extra computational expenses and performance reduction linked with model compression and removes the reliance on external teacher models, markedly enhancing the efficacy of lightweight models. achieved a classification accuracy of 74.96% on a proprietary dataset, outperforming current techniques. Furthermore, our method demonstrated superior performance and generalizability on two public datasets. This not only validates the effectiveness of our approach in classifying urinary stones but also showcases its potential in other medical image processing tasks. These results further reinforce the feasibility of our model for actual clinical deployment, potentially assisting healthcare professionals in devising more precise treatment plans and reducing patient discomfort.

Abstract Early postoperative cerebral infarction (ePCI) is a serious complication of spontaneous intracerebral hemorrhage (SICH). Yet, no study has specifically focused on ePCI among SICH patients. Our study aims to investigate the characteristics, predictors, and outcomes of ePCI observed on computed tomography (CT) within 72 h after surgery in patients with supratentorial SICH. Data from a single-center SICH study conducted from May 2015 to September 2022 were retrospectively analyzed. We described the characteristics of ePCI. Predictors were identified through logistic regression analysis, and the impact of ePCI on six-month mortality was examined using a Cox regression model. Subgroup analyses and the “E-value” approach assessed the robustness of the association between ePCI and mortality. A retrospective analysis of 637 out of 3938 SICH patients found that 71 cases (11.1%) developed ePCI. The majority of ePCI cases occurred on the bleeding side (40/71, 56.3%) and affected the middle cerebral artery (MCA) territory (45/71, 63.4%). Multivariable analysis showed that the Glasgow Coma Scale (GCS) score (odds ratio (OR), 0.62; 95% CI, 0.48–0.8; p

Abstract Acute kidney injury (AKI) is one of the most important lethal factors for patients admitted to intensive care units (ICUs), and timely high-risk prognostic assessment and intervention are essential to improving patient prognosis. In this study, a stacking model using the MIMIC-III dataset with a two-tier feature selection approach was developed to predict the risk of in-hospital mortality in ICU patients admitted for AKI. External validation was performed using separate MIMIC-IV and eICU-CRD. The area under the curve (AUC) was calculated using the stacking model, and features were selected using the Boruta and XGBoost feature selection methods. This study compares the performance of a stacking model using two-tier feature selection with a model using single-tier feature selection (XGBoost: 85; Boruta: 83; two-tier: 0.91). The predictive effectiveness of the stacking model was further validated by using different datasets (Validation 1: 0.83; Validation 2: 0.85) and comparing it with a simpler model and traditional clinical scores (SOFA: 0.65; APACH IV: 0.61). In addition, this study combined interpretable techniques and causal inference to analyze the causal relationship between features and predicted outcomes.

Abstract To evaluate the effect of diabetic retinopathy (DR) status or severity on all-cause and cause-specific mortality among diabetic older adults in the United States using the most recent National Health and Nutrition Examination Survey (NHANES) follow-up mortality data. The severity of DR was graded according to the Early Treatment Diabetic Retinopathy Study (ETDRS) grading scale. Multiple covariate-adjusted Cox proportional hazards regression models, Fine and Gray competing risk regression models, and propensity score matching (PSM) methods were used to assess the risk of all-cause and cause-specific mortality in individuals with diabetes. All analyses adopted the weighted data and complex stratified design approach proposed by the NHANES guidelines. Time to death was calculated based on the time between baseline and date of death or December 31, 2019, whichever came first. Ultimately 1077 participants, representing 3,025,316 US non-hospitalized individuals with diabetes, were included in the final analysis. After a median follow-up of 12.24 years (IQR, 11.16–13.49), 379 participants were considered deceased from all-causes, with 43.90% suffering from DR, including mild DR (41.50%), moderate to severe DR (46.77%), and proliferative DR (PDR) (67.21%). DR was associated with increased all-cause, cardiovascular disease (CVD) and diabetes mellitus (DM)-specific mortality, which remained consistent after propensity score matching (PSM). Results of DR grading assessment suggested that the presence of mild, moderate to severe NPDR was significantly associated with increased risk of all-cause and CVD-specific mortality, while the presence and severity of any DR was associated with increased DM-specific mortality, with a positive trend. The presence of DR in elderly individuals with diabetes is significantly associated with the elevated all-cause and CVD mortality. The grading or severity of DR may reflect the severity of cardiovascular disease status and overall mortality risk in patients with diabetes.

Abstract Ambient air temperature is a key factor affecting human health. Female reproductive disorders are representative health risk events under low temperature. However, the mechanism involving in cold-induced female reproductive disorders remains largely unknown. Female mice were intermittently exposed to cold conditions (4 °C) to address the health risk of low temperature on female reproductive system. Primary granulosa cells (GCs) were prepared and cultured under low temperature (35 °C) or exposed to β3-adrenoreceptor agonist, isoproterenol, to mimic the condition of cold exposure. Western-blot, RT-PCR, co-IP, ELISA, pharmacological inhibition or siRNA-mediated knockdown of target gene were performed to investigate the possible role of hormones, gap conjunction proteins, and ER stress sensor protein in regulating female reproductive disorders under cold exposure. Cold exposure induced estrous cycle disorder and follicular dysplasia in female mice, accompanying with abnormal upregulation of progesterone and its synthetic rate-limiting enzyme, StAR, in the ovarian granulosa cells. Under the same conditions, an increase in connexin 43 (CX43) expressions in the GCs was also observed, which contributed to elevated progesterone levels in the ovary. Moreover, ER stress sensor protein, PERK, was activated in the ovarian GCs after cold exposure, leading to the upregulation of downstream NRF2-dependent CX43 transcription and aberrant increase in progesterone synthesis. Most importantly, blocking PERK expression in vivo significantly inhibited NRF2/CX43/StAR/progesterone pathway activation in the ovary and efficiently rescued the prolongation of estrous cycle and the increase in follicular atresia of the female mice induced by cold stress. We have elucidated the mechanism of ovarian PERK/NRF2/CX43/StAR/progesterone pathway activation in mediating female reproductive disorder under cold exposure. Targeting PERK might be helpful for maintaining female reproductive health under cold conditions.

Abstract The association between sarcopenia and OA still presents many uncertainties. We aimed to assess whether sarcopenia is associated with occurrence of OA in US adults. We conducted a cross-sectional study consisting of 11,456 participants from National Health and Nutrition Examination Survey 1999–2006. Sarcopenia was defined by a low muscle mass. The skeletal muscle index (SMI) was calculated as the appendicular skeletal muscle mass divided by body mass indexes (BMI) or body weight. OA status was assessed by using self-reported questionnaire. We evaluated the association between sarcopenia and OA using multivariate regression models. In addition, subgroup and interaction analysis were performed. Sarcopenia was associated with OA when it was defined by the BMI-adjusted SMI (OR = 1.23 [95% CI, 1.01, 1.51]; P = 0.038) and defined by the weight-adjusted SMI (OR = 1.30 [95% CI, 1.10, 1.55]; P = 0.003). Subgroup and interaction analysis found that the strongest positive association mainly exists in smoker (OR = 1.54 [95% CI, 1.21, 1.95], Pint = 0.006), and this association is not significant in other groups. In conclusion, we found that sarcopenia was associated with occurrence of OA. Subgroup analysis revealed that the association between sarcopenia and OA was more pronounced in smoker. Further well-designed prospective cohort studies are needed to assess our results.

Abstract Improving the quality of medical images is crucial for accurate clinical diagnosis; however, medical images are often disrupted by various types of noise, posing challenges to the reliability and diagnostic accuracy of the images. This study aims to enhance the Black Widow optimization algorithm and apply it to the task of denoising medical images to improve both the quality of medical images and the accuracy of diagnostic results. By introducing Tent mapping, we refined the Black Widow optimization algorithm to better adapt to the complex features of medical images. The algorithm's denoising capabilities for various types of noise were enhanced through the combination of multiple filters, all without the need for training each time to achieve preset goals. Simulation results, based on processing a dataset containing 1588 images with Gaussian, salt-and-pepper, Poisson, and speckle noise, demonstrated a reduction in Mean Squared Error (MSE) by 0.439, an increase in Peak Signal-to-Noise Ratio (PSNR) by 4.315, an improvement in Structural Similarity Index (SSIM) by 0.132, an enhancement in Edge-to-Noise Ratio (ENL) by 0.402, and an increase in Edge Preservation Index (EPI) by 0.614. Simulation experiments verified that the proposed algorithm has a certain advantage in terms of computational efficiency. The improvement, incorporating Tent mapping and a combination of multiple filters, successfully elevated the performance of the Black Widow algorithm in medical image denoising, providing an effective solution for enhancing medical image quality and diagnostic accuracy.

Abstract To analyze the deformation behavior of short glass fiber-reinforced poly-ether-ether-ketone (SGFR-PEEK) under various conditions through numerical simulations, it is crucial to construct a constitutive model that can describe its stress–strain behavior over a wide range of strain rates and temperatures. In this study, quasi-static compression tests were conducted on SGFR-PEEK composites with varying mass fractions, and dynamic tests were performed using a Split Hopkinson Pressure Bar to acquire the material's compressive stress–strain response under quasi-static and dynamic conditions. The results indicate that, under compression, the yield stress of SGFR-PEEK composites increases with an augmentation in glass fiber content, rises with increasing strain rate, and decreases with elevated temperature. Based on experimental findings, a modified Johnson–Cook constitutive model was established to characterize the mechanical performance of SGFR-PEEK. In comparison to the traditional Johnson–Cook intrinsic structure model, the modified model takes into account the glass fiber mass fraction as comprehensively as possible and better predicts the material's flow behavior at high strain rates. Finally, this modified constitutive model was implemented in the ABAQUS software using the user-defined subroutine VUMAT to simulate the compression behavior of SGFR-PEEK composites under different loading conditions, and the model was validated. This research provides valuable insights for the practical application of SGFR-PEEK composites in engineering.