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Solution processable perovskites have made remarkable strides in direct planar X-ray detectors, promising to reshape X-ray detector industry. Herein, we discuss prevailing challenges, research opportunities, and their synergies with cutting-edge technologies that will benefit both human society and scientific research.

Abstract Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.

Abstract The soil environment plays an important role in urban ecosystems. To study the heavy metal contamination of soil in Beilun District, Ningbo, we collected soil samples from 60 points in urban and peri-urban areas of Beilun District and analyzed the spatiotemporal variation and sources of heavy metal pollution in various land-use types. The results shown that the heavy metal contents in 2015 and 2022 were higher than the background soil values of Ningbo city, and there was an accumulation of heavy metals over these 7 years. The contents of heavy metals in green belts and woodland in 2022 were higher than those in 2015, while there was no significant change in agricultural land. The heavy metal contents in both years were mainly in the order green belts > agricultural land > woodland. The spatiotemporal distribution of heavy metal content showed that heavy metal pollution in Beilun District was concentrated in five industrial areas, and there was a trend toward the disappearance of highly polluted points. But the single-factor pollution index, pollution load index (PLI), and geoaccumulation index (Igeo) indicated that there was no significant heavy metal pollution in Beilun District, and individual elements at specific points showed slight pollution. The source analysis results showed that the main source of Hg is chemical, As is mainly derived from agricultural, Cr, Ni and Cu are mainly derived from natural, the main sources of Zn and Cd are electroplating and machinery activities, and the main source of Pb is traffic. These results specify a reference for future investigation on urban soil heavy metals, and the source apportionment results provide a scientific foundation for subsequent soil heavy metal pollution treatment.

Abstract Reducing interface nonradiative recombination is important for realizing highly efficient perovskite solar cells. In this work, we develop a synergistic bimolecular interlayer (SBI) strategy via 4-methoxyphenylphosphonic acid (MPA) and 2-phenylethylammonium iodide (PEAI) to functionalize the perovskite interface. MPA induces an in-situ chemical reaction at the perovskite surface via forming strong P-O-Pb covalent bonds that diminish the surface defect density and upshift the surface Fermi level. PEAI further creates an additional negative surface dipole so that a more n-type perovskite surface is constructed, which enhances electron extraction at the top interface. With this cooperative surface treatment, we greatly minimize interface nonradiative recombination through both enhanced defect passivation and improved energetics. The resulting p-i-n device achieves a stabilized power conversion efficiency of 25.53% and one of the smallest nonradiative recombination induced V oc loss of only 59 mV reported to date. We also obtain a certified efficiency of 25.05%. This work sheds light on the synergistic interface engineering for further improvement of perovskite solar cells.

Abstract Cryptosporidiosis is a worldwide zoonotic disease. Oxymatrine, an alkaloid extracted and isolated from the plant bitter ginseng, has been reported to have therapeutic effects on cryptosporidiosis. However, the underlying mechanism of its action remains unclear. In this study, we utilized network pharmacology and experimental validation to investigate the mechanism of oxymatrine in the treatment of cryptosporidiosis. First, the potential targets of drugs and diseases were predicted by TCMSP, Gene Cards, and other databases. Following the intersection of drug-disease targets, the DAVID database was used to implement the enrichment analysis of GO functions and KEGG pathways, and then the network diagram of "intersected target-KEGG" relationship was constructed. Autodock 4.2.6 software was used to carry out the molecular docking of core targets to drug components. Based on the establishment of a mouse model of cryptosporidiosis, the validity of the targets in the TNF/NF-κB signaling pathway was confirmed using Western blot analysis and Quantitative Rea-ltime-PCR. A total of 41 intersectional targets of oxymatrine and Cryptosporidium were generated from the results, and five core targets were screened out by network analysis, including RELA, AKT1, ESR1, TNF, and CASP3. The enrichment analysis showed that oxymatrine could regulate multiple gene targets, mediate TNF, Apoptpsis, IL-17, NF-κB and other signaling pathways. Molecular docking experiments revealed that oxymatrine was tightly bound to core targets with stable conformation. Furthermore, we found through animal experiments that oxymatrine could regulate the mRNA and protein expression of IL-6, NF-κB, and TNF-α in the intestinal tissues of post-infected mice through the TNF/NF-κB signaling pathway. Therefore, it can be concluded that oxymatrine can regulate the inflammatory factors TNF-α, NF-κB, and IL-6 through the TNF/NF-κB signaling pathway for the treatment of cryptosporidiosis. This prediction has also been validated by network pharmacology and animal experiments.

This is an article in the field of metallurgical engineering. With the increasing attention of iron and steel enterprises to pollutant emission, non blast furnace ironmaking process has gradually become a hot issue. At present, the industrialized non blast furnace ironmaking technology in China is mainly Corex process of Baogang and HIsmelt process of Shandong Molong. It is also a hot process of non blast furnace ironmaking technology in China. This article expounds the processes of Corex process and HIsmelt process, compares the technical indexes, advantages and disadvantages of the two processes, discusses the research status of the two processes in China, and looks forward to their development direction in combination with the characteristics of each process.

Abstract Stretching elastic materials containing nanoparticle lattices is common in research and industrial settings, yet our knowledge of the deformation process remains limited. Understanding how such lattices reconfigure is critically important, as changes in microstructure lead to significant alterations in their performance. This understanding has been extremely difficult to achieve due to a lack of fundamental rules governing the rearrangements. Our study elucidates the physical processes and underlying mechanisms of three-dimensional lattice transformations in a polymeric photonic crystal from 0% to over 200% strain during uniaxial stretching. Corroborated by comprehensive experimental characterizations, we present analytical models that precisely predict both the three-dimensional lattice structures and the macroscale deformations throughout the stretching process. These models reveal how the nanoparticle lattice and matrix polymer jointly determine the resultant structures, which breaks the original structural symmetry and profoundly changes the dispersion of photonic bandgaps. Stretching induces shifting of the main pseudogap structure out from the 1st Brillouin zone and the merging of different symmetry points. Evolutions of multiple photonic bandgaps reveal potential optical singularities shifting with strain. This work sets a new benchmark for the reconfiguration of soft material structures and may lay the groundwork for the study of stretchable three-dimensional topological photonic crystals.

Abstract Background Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions. Methods A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts. Results The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis. Conclusions In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

Abstract Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

Abstract Background Wooden breast (WB) myopathy is a common myopathy found in commercial broiler chickens worldwide. Histological examination has revealed that WB myopathy is accompanied by damage to the pectoralis major (PM) muscle. However, the underlying mechanisms responsible for the formation of WB in broilers have not been fully elucidated. This study aimed to investigate the potential role of hypoxia-mediated programmed cell death (PCD) in the formation of WB myopathy. Results Histological examination and biochemical analysis were performed on the PM muscle of the control (CON) and WB groups. A significantly increased thickness of the breast muscle in the top, middle, and bottom portions (P

Objectives It is now understood that pedicle screw loosening at the implant‐bone interface can lead to poor screw–bone interface purchase and decreased fixation stability. Previous biomechanical tests used cadaveric vertebrae and pull‐out or torque loads to assess the effect of the insertional direction of pedicle screws on screw loosening. However, these tests faced challenges in matching biomechanical differences among specimens and simulating in vivo loads applied on pedicle screws. This study aimed to evaluate the effect of the insertional direction of pedicle screws on screw loosening using tension‐compression‐bending loads and synthetic bone vertebrae. Methods Polyaxial pedicle screws were inserted into nine synthetic bone vertebrae in three directions (three samples per group): cranial, parallel, and caudad (−10°, 0°, +10° of the pedicle screw rod to the upper plane of the vertebra, respectively). Pedicle screws in the vertebrae were loaded using a polyethylene block connected to a material testing machine. Tension‐compression‐bending loads (100N–250N) with 30,000 cycles were applied to the pedicle screws, and displacements were recorded and then cycle‐displacement curve was drawn based on cycle number. Micro‐CT scans were performed on the vertebrae after removing the pedicle screws to obtain images of the screw hole, and the screw hole volume was measured using imaging analysis software. Direct comparison of displacements was conducted via cycle‐displacement curve. Screw hole volume was analyzed using analysis of variance. The correlation between the displacement, screw hole volume and the direction of pedicle screw was assessed by Spearman correlation analysis. Results The smallest displacements were observed in the caudad group, followed by the parallel and cranial groups. The caudad group had the smallest screw hole volume (p

Fault detection and diagnosis (FDD) of heating, ventilation, and air conditioning (HVAC) systems can help to improve the energy saving in building energy systems. However, most data-driven trained FDD models have limited generalizability and can only be applied to specific systems. The diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of FDD. Transfer learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited data. In this study, a novel transfer learning approach for HVAC FDD is proposed. First, the transformer model is modified to incorporate one encoder and two decoders connected, enabling two outputs. This modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer learning. It has effective performance in complex systems and achieves an accuracy of 91.38% for a system with 16 faults and multiple fault severity levels. Second, the adapter-based parameter-efficient transfer learning method, facilitating the transfer of trained models simply by inserting small adapter modules, is investigated as the transfer learning strategy. Results demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable parameters. It works well with limited data amount in target domain. Furthermore, the findings highlight the significance of adapters positioned near the bottom and top layers, emphasizing their critical role in facilitating successful transfer learning.

Objective Bedding deep cutting slopes containing weak interlayers are commonly found in slope engineering, whose stability is influenced by two key factors: The strength of the sliding zone soil and the reserved thickness of the base (the distance from the base of an excavated slope to the weak interlayer). Methods In this research, taking the K42 cutting slope of the Yang-Xuan Expressway as an example, the evolution process of slope deformation was analyzed, especially the characteristics of basal heave deformation. The properties of the deep sliding zone soil in the slope were revealed by ring shear tests, which are suitable for studying the shear strength of soil that has experienced large shear displacements. Moreover, the residual strength parameters of the saturated sliding zone soil were applied to analyze the reserved thickness of the base. Results The results show that sliding zone soils exhibit obvious strain softening characteristics, which become more evident as the normal stress decreases. As the soil shear strength transitions from peak strength to residual strength, both the cohesion force and internal friction angle decrease, with the cohesion force decreasing to a greater extent than the internal friction angle. The residual cohesion force of sliding zone soil varies slightly with the shear rate, while the relationship between the residual internal friction angle and shear rate varies as a logarithmic function. When the shear rate is less than 2 mm/min, the residual shear strength parameter of sliding zone soil is more sensitive to the shear rate and vice versa. Conclusion Furthermore, according to the regression equations of the critical states of slope stability, the reserved thickness of the base under different slope rates was divided into four zones: A (extremely unstable zone), B (unstable zone), C (basically stable zone) and D (stable zone), and based on this, the criterion and early warning model of the reserved thickness of the base for slope excavation were established.

Abstract Purpose This study aims to elucidate the biological functions of ferroptosis-related genes in periodontitis, along with their correlation to tumor microenvironment (TME) features such as immune infiltration. It aims to provide potential diagnostic markers of ferroptosis for clinical management of periodontitis. Methods Utilizing the periodontitis-related microarray dataset GSE16134 from the Gene Expression Omnibus (GEO) and a set of 528 ferroptosis-related genes identified in prior studies, this research unveils differentially expressed ferroptosis-related genes in periodontitis. Subsequently, a protein–protein interaction network was constructed. Subtyping of periodontitis was explored, followed by validation through immune cell infiltration and gene set enrichment analyses. Two algorithms, randomForest and SVM(Support Vector Machine), were employed to reveal potential ferroptosis diagnostic markers for periodontitis. The diagnostic efficacy, immune correlation, and potential transcriptional regulatory networks of these markers were further assessed. Finally, potential targeted drugs for differentially expressed ferroptosis markers in periodontitis were predicted. Results A total of 36 ferroptosis-related genes (30 upregulated, 6 downregulated) were identified from 829 differentially expressed genes between 9 periodontitis samples and the control group. Subsequent machine learning algorithm screening highlighted 4 key genes: SLC1A5(Solute Carrier Family 1 Member 5), SLC2A14(Solute Carrier Family 1 Member 14), LURAP1L(Leucine Rich Adaptor Protein 1 Like), and HERPUD1(Homocysteine Inducible ER Protein With Ubiquitin Like Domain 1). Exploration of these 4 key genes, supported by time-correlated ROC analysis, demonstrated reliability, while immune infiltration results indicated a strong correlation between key genes and immune factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was conducted for the four key genes, revealing enrichment in GO/KEGG pathways that have a significant impact on periodontitis. Finally, the study predicted potential transcriptional regulatory networks and targeted drugs associated with these key genes in periodontitis. Conclusions The ferroptosis-related genes identified in this study, including SLC1A5, SLC2A14, LURAP1L, and HERPUD1, may serve as novel diagnostic and therapeutic targets for periodontitis. They are likely involved in the occurrence and development of periodontitis through mechanisms such as immune infiltration, cellular metabolism, and inflammatory chemotaxis, potentially linking the ferroptosis pathway to the progression of periodontitis. Targeted drugs such as flurofamide, L-733060, memantine, tetrabenazine, and WAY-213613 hold promise for potential therapeutic interventions in periodontitis associated with these ferroptosis-related genes.

Abstract The aim of this study was to create and validate a ten-item Domain-specific Grit Scale for College Athletic Students (DGSCAS) to assess the level of grit among college athletic students. College athletic students from a single independent college located in a northern city in China (526 participants at time 1 and 589 participants at time 2) were assessed according to the scale. Various analyses were conducted in this study, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and measurement invariance analysis across different sex and birthplaces. The results of the EFA revealed two factors: consistency of interests and perseverance of effort. The CFA results demonstrated acceptable fit indices (x 2 = 160.048, df = 34, x 2 /df = 4.707, CFI = 0.983, TLI = 0.978, SRMR = 0.021, and RMSEA = 0.079). The scale exhibited satisfactory convergent validity and discriminant validity. The significant correlation of these factors with the Grit scale provided strong evidence of criterion-related validity. Measurement invariance analysis indicated that the scale performed consistently across different sex and birthplaces. Three limitations and corresponding recommendations were discussed, including sample heterogeneity, the lack of a unified test result as a criterion for predictive validity, and the cross-sectional design of the study. In conclusion, the DGSCAS is a practical and validated instrument that can be used to assess the level of grit among college athletic students in an educational context.

Abstract Background The Daur people are one of the 55 minority ethnic groups in China and have lived in Northern China for 300 years. In traditional Daur medicine, medicinal and edible plants (MEPs) are utilised for health benefits and therapeutic purposes; however, related ethnobotanical knowledge is rarely reported, which is disadvantageous for the sustainable development of these MEPs. Methods Semi-structured interviews with 122 informants, six focus group discussions, and a resource survey were conducted in a Daur minority nationality area in Inner Mongolia from 2015 to 2020, and the data statistics were analysed. In this study, we simulated a system dynamics model aimed at understanding the multiple feedback mechanisms involved in the relationships between the cultural influences and socioeconomic factors, sustainable environment, and development of MEPs. Results A total of 52 species of MEPs were identified and relevant ethnobotanical knowledge was assessed using Daur medicinal species data from Inner Mongolia and the Xinjiang region, with the literature and Ewenki ethnic group data used for comparison. The most commonly used medicinal plant species by the Daur were found to be Betula pendula subsp. mandshurica, Artemisia integrifolia, Crataegus pinnatifida, Saposhnikovia divaricata, Artemisia argyi, and Jacobaea cannabifolia. The MEPs most frequently targeted the digestive and rheumatic immunity systems, as well as infectious diseases or parasitic infections and other common diseases and basic health issues. MEP knowledge was primarily limited to older generations; thus, the valuable ethnobotanical knowledge on traditional medicines must be protected from future losses. Conclusions Our findings provide insights for future research aimed at exploiting the rich phytochemical diversity in traditional medicine and promote its use in modern lifestyles. Effective assessment and management of plant resources will lead to their application for the improvement of dietary diversity, nutrition, and health care.

Abstract Background The incidence and mortality of lung cancer is the highest in China and the world. Brain is the most common distant metastasis site of lung cancer. Its transfer mechanism and predictive biomarkers are still unclear. EZH2 participates in the catalysis of transcriptional inhibition complex, mediates chromatin compactness, leads to the silencing of its downstream target genes, participates in the silencing of multiple tumor suppressor genes, and is related to cell proliferation, apoptosis and cycle regulation. In physiology, EZH2 has high activity in stem cells or progenitor cells, inhibits genes related to cell cycle arrest and promotes self-renewal. To detect the expression and mutation of EZH2 gene in patients with brain metastasis of lung cancer, and provide further theoretical basis for exploring the pathogenesis of brain metastasis of lung cancer and finding reliable biomarkers to predict brain metastasis of lung cancer. Methods This study investigated susceptible genes for brain metastasis of lung cancer. The second-generation sequencing technology was applied to screen the differential genes of paired samples (brain metastasis tissues, lung cancer tissues and adjacent tissues) of lung cancer patients with brain metastasi. Results It revealed that there was a significant difference in the G553C genotype of EZH2 between lung cancer brain metastasis tissues and lung cancer tissues (p = 0.045). The risk of lung cancer brain metastasis in G allele carriers was 2.124 times higher than that in C allele carriers. Immunohistochemistry showed that compared with lung cancer patients and lung cancer patients with brain metastasis, the expression level of EZH2 in lung cancer tissues of lung cancer patients was significantly higher than that in adjacent lung tissues (p

Abstract Background Despite abundant evidence on the epidemiological risk factors of metabolic diseases related to hyperuricemia, there is still insufficient evidence regarding the nonlinear relationship between triglyceride-glucose (TyG) index and hyperuricemia. Thus, the purpose of this research is to clarify the nonlinear connection between TyG and hyperuricemia. Methods From 2011 to 2018, a cross-sectional study was carried out using data from the National Health and Nutrition Examination Survey (NHANES). This study had 8572 participants in all. TyG was computed as Ln [triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The outcome variable was hyperuricemia. The association between TyG and hyperuricemia was examined using weighted multiple logistic regression, subgroup analysis, generalized additive models, smooth fitting curves, and two-piecewise linear regression models. Results In the regression model adjusting for all confounding variables, the OR (95% CI) for the association between TyG and hyperuricemia was 2.34 (1.70, 3.21). There is a nonlinear and reverse U-shaped association between TyG and hyperuricemia, with a inflection point of 9.69. The OR (95% CI) before the inflection point was 2.64 (2.12, 3.28), and after the inflection point was 0.32 (0.11, 0.98). The interaction in gender, BMI, hypertension, and diabetes analysis was statistically significant. Conclusion Additional prospective studies are required to corroborate the current findings, which indicate a strong positive connection between TyG and hyperuricemia among adults in the United States.

Abstract Lung diseases globally impose a significant pathological burden and mortality rate, particularly the differential diagnosis between adenocarcinoma, squamous cell carcinoma, and small cell lung carcinoma, which is paramount in determining optimal treatment strategies and improving clinical prognoses. Faced with the challenge of improving diagnostic precision and stability, this study has developed an innovative deep learning-based model. This model employs a Feature Pyramid Network (FPN) and Squeeze-and-Excitation (SE) modules combined with a Residual Network (ResNet18), to enhance the processing capabilities for complex images and conduct multi-scale analysis of each channel's importance in classifying lung cancer. Moreover, the performance of the model is further enhanced by employing knowledge distillation from larger teacher models to more compact student models. Subjected to rigorous five-fold cross-validation, our model outperforms existing models on all performance metrics, exhibiting exceptional diagnostic accuracy. Ablation studies on various model components have verified that each addition effectively improves model performance, achieving an average accuracy of 98.84% and a Matthews Correlation Coefficient (MCC) of 98.83%. Collectively, the results indicate that our model significantly improves the accuracy of disease diagnosis, providing physicians with more precise clinical decision-making support.

Abstract Objectives To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. Methods Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015–June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. Results For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. Conclusion Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. Critical relevance statement Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. Key Points Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies. Graphical Abstract

Chinese wingnut (Pterocarya stenoptera) is a medicinally and economically important tree species within the family Juglandaceae. However, the lack of high-quality reference genome has hindered its in-depth research. In this study, we successfully assembled its chromosome-level genome and performed multi-omics analyses to address its evolutionary history and synthesis of medicinal components. A thorough examination of genomes has uncovered a significant expansion in the Lateral Organ Boundaries Domain gene family among the winged group in Juglandaceae. This notable increase may be attributed to their frequent exposure to flood-prone environments. After further differentiation between Chinese wingnut and Cyclocarya paliurus, significant positive selection occurred on the genes of NADH dehydrogenase related to mitochondrial aerobic respiration in Chinese wingnut, enhancing its ability to cope with waterlogging stress. Comparative genomic analysis revealed Chinese wingnut evolved more unique genes related to arginine synthesis, potentially endowing it with a higher capacity to purify nutrient-rich water bodies. Expansion of terpene synthase families enables the production of increased quantities of terpenoid volatiles, potentially serving as an evolved defense mechanism against herbivorous insects. Through combined transcriptomic and metabolomic analysis, we identified the candidate genes involved in the synthesis of terpenoid volatiles. Our study offers essential genetic resources for Chinese wingnut, unveiling its evolutionary history and identifying key genes linked to the production of terpenoid volatiles.

When a curling rock slides on an ice sheet with an initial rotation, a lateral movement occurs, which is known as the curling phenomenon. The force of friction between the curling rock and the ice sheet changes continually with changes in the environment; thus, the sport of curling requires great skill and experience. The throwing of the curling rock is a great challenge in robot design and control, and existing curling robots usually adopt a combination scheme of a wheel chassis and gripper that differs significantly from human throwing movements. A hexapod curling robot that imitates human kicking, sliding, pushing, and curling rock rotating was designed and manufactured by our group, and completed a perfect show during the Beijing 2022 Winter Olympics Games. Smooth switching between the walking and throwing tasks is realized by the robot’s morphology transformation based on leg configuration switching. The robot’s controlling parameters, which include the kicking velocity vk, pushing velocity vp, orientation angle θc, and rotation velocity ω, are determined by aiming and sliding models according to the estimated equivalent friction coefficient μequ and ratio e of the front and back frictions. The stable errors between the target and actual stopping points converge to 0.2 and 1.105 m in the simulations and experiments, respectively, and the error shown in the experiments is close to that of a well-trained wheelchair curling athlete. This robot holds promise for helping ice-makers rectify ice sheet friction or assisting in athlete training.

ABSTRACTLow thermal conductivity and excellent high-temperature stability make lanthanum zirconate (La2Zr2O7, LZO) a very promising material for high-temperature insulation applications. In this study, LZO porous ceramics with ultra-high porosity and an open-pore structure were prepared by the replica method using a polyurethane template. The flowability and coating quality of the ceramic slurry were optimized. When the dispersant content of the slurry was 0.6 wt% and the alcohol to water ratio was 1:1, the slurry had low surface tension and low viscosity, leading to good coating quality of the ceramic slurry on the sponge template. The pore size between hollow skeletons was 100–500 μm with the hollow structure of the skeletons forming secondary pores. LZO porous ceramics showed high porosity of 87.6%-96.0%, low thermal conductivity of 0.048–0.126 W/(m·K), and compressive strength of 0.13–0.74 MPa by adjusting the ceramic content. A high temperature insulation performance test showed that the temperature difference between the cold side and the hot side of the sample was over 1000°C, indicating the excellent thermal insulation properties of LZO porous ceramics. LZO porous ceramics had good high-temperature stability without sintering shrinkage at heat treatment temperatures below 1500°C.

Abstract Exercise is usually regarded to have short-term beneficial effects on immune health. Here we show that early-life regular exercise exerts long-term beneficial effects on inflammatory immunity. Swimming training for 3 months in male mice starting from 1-month-old curbs cytokine response and mitigates sepsis when exposed to lipopolysaccharide challenge, even after an 11-month interval of detraining. Metabolomics analysis of serum and liver identifies pipecolic acid, a non-encoded amino acid, as a pivotal metabolite responding to early-life regular exercise. Importantly, pipecolic acid reduces inflammatory cytokines in bone marrow-derived macrophages and alleviates sepsis via inhibiting mTOR complex 1 signaling. Moreover, early-life exercise increases histone 3 lysine 4 trimethylation at the promoter of Crym in the liver, an enzyme responsible for catalyzing pipecolic acid production. Liver-specific knockdown of Crym in adult mice abolishes this early exercise-induced protective effects. Our findings demonstrate that early-life regular exercise enhances anti-inflammatory immunity during middle-aged phase in male mice via epigenetic immunometabolic modulation, in which hepatic pipecolic acid production has a pivotal function.

Abstract Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.

Safe, high-efficient, green and low-carbon mining is an eternal theme of coal mines. Near zero rock burst, near zero ecological damage and low-carbon, zero-carbon and carbon-negative green mining will become new requirements to ensure China's energy security supply and green low-carbon development. Backfill mining is the inevitable way to achieve these requirements. However, the existing theories, technologies, and methods of backfill mining are difficult to overcome the technical bottlenecks of high yield, high efficiency, and low-carbon mining, and it is imperative to reform the filling materials and filling modes. In view of the strategic goal of low-carbon coal mining of “kilometer deep mine resource development and ten-million-ton productivity mine filling (two thousands) ” and “near zero ecological damage and near zero rock burst (two near zeros)”. The definition and concept of carbon-negative & high-efficient backfill mining in coal mines has been systematically expounded, and the theoretical development for carbon-negative & high-efficient backfill mining in coal mines has been proposed, including the topological configuration and strength theory of CGIF (CO2 Gangue Innovative Framework) for high porosity filling materials structure, the carbon sequestration theory of CGIF mixture filling body, the reaction kinetics theory of fast adhesive gel bonding material, and the prevention and control of rock burst by filling mining in mining area. The key technical systems have been proposed, such as the preparation technology of gangue fast and efficient cementation high porosity filling material, the green and efficient preparation technology of fast and efficient cementation gel binding material, the negative carbon efficient filling mining technology of CGIF backfill, the negative carbon efficient filling mining technology, the technology of multi-face mining, and the full cycle three-dimensional efficient filling mining and rock burst prevention technology. On this basis, the “three stage” development plan of “basic research, technical research, and engineering demonstration” for carbon-negative & high-efficient backfill mining in coal mines has been clarified, and a theoretical and technical system for carbon-negative & high-efficient backfill mining in coal mines has been constructed. The CO2 storage capacity with carbon-negative & high-efficient backfill mining in coal mines has been evaluated. It is expected to achieve a new pattern of carbon neutrality in the entire process of coal development and utilization through carbon-negative mining and low-carbon utilization.

Abstract Conventional food production is restricted by energy conversion efficiency of natural photosynthesis and demand for natural resources. Solar-driven artificial food synthesis from CO2 provides an intriguing approach to overcome the limitations of natural photosynthesis while promoting carbon-neutral economy, however, it remains very challenging. Here, we report the design of a hybrid electrocatalytic−biocatalytic flow system, coupling photovoltaics-powered electrocatalysis (CO2 to formate) with five-enzyme cascade platform (formate to sugar) engineered via genetic mutation and bioinformatics, which achieves conversion of CO2 to C6 sugar (L-sorbose) with a solar-to-food energy conversion efficiency of 3.5%, outperforming natural photosynthesis by over three-fold. This flow system can in principle be programmed by coupling with diverse enzymes toward production of multifarious food from CO2. This work opens a promising avenue for artificial food synthesis from CO2 under confined environments.

Abstract. Background:. The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate. Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome, who subjected to preimplantation genetic testing for aneuploidy (PGT-A) treatment. However, it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement. Methods:. From February 2020 to May 2021, we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos, the clinical information of couples, and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment. Initially, we developed a novel deep learning model called the Attentive Multi-Focus Selection Network (AMSNet) to analyze time-lapse videos in real time and predict blastocyst formation. Building upon AMSNet, we integrated additional clinically predictive variables and created a second deep learning model, the Attentive Multi-Focus Video and Clinical Information Fusion Network (AMCFNet), to assess the euploidy status of embryos. The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements. The receiver operating characteristic curve (ROC) was used to evaluate the superiority of the model. Results:. A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task, and 1422 qualified blastocysts received PGT-A (n = 589) or PGT for chromosomal structural rearrangement (PGT-SR, n = 833) were enrolled for the euploidy assessment task in this study. The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy. The real-time accuracy for AMSNet to predict blastocyst formation reached above 70% on the day 2 of embryo culture, and then increased to 80% on the day 4 of embryo culture. Combing with 4 clinical features of couples, the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement. Conclusion:. Integrating seven focal raw time-lapse images of embryos and parental clinical information, AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement.

Abstract Background Spinal muscular atrophy (SMA) is a rare autosomal recessive hereditary neuromuscular disease caused by survival motor neuron 1 (SMN1) gene deletion or mutation. Homozygous deletions of exon 7 in SMN1 result in 95% of SMA cases, while the remaining 5% are caused by other pathogenic variants of SMN1. Methods We analyzed two SMA-suspected cases that were collected, with no SMN1 gene deletion and point mutation in whole-exome sequencing. Exon 1 deletion of the SMN gene was detected using Multiplex ligation-dependent probe amplification (MLPA) P021. We used long-range polymerase chain reaction (PCR) to isolate the SMN1 template, optimized-MLPA P021 for copy number variation (CNV) analysis within SMN1 only, and validated the findings via third-generation sequencing. Results Two unrelated families shared a genotype with one copy of exon 7 and a novel variant, g.70919941_70927324del, in isolated exon 1 of the SMN1 gene. Case F1-II.1 demonstrated no exon 1 but retained other exons, whereas F2-II.1 had an exon 1 deletion in a single SMN1 gene. The read coverage in the third-generation sequencing results of both F1-II.1 and F2-II.1 revealed a deletion of approximately 7.3 kb in the 5’ region of SMN1. The first nucleotide in the sequence data aligned to the 7385 bp of NG_008691.1. Conclusion Remarkably, two proband families demonstrated identical SMN1 exon 1 breakpoint sites, hinting at a potential novel mutation hotspot in Chinese SMA, expanding the variation spectrum of the SMN1 gene and corroborating the specificity of isolated exon 1 deletion in SMA pathogenesis. The optimized-MLPA P021 determined a novel variant (g.70919941_70927324del) in isolated exon 1 of the SMN1 gene based on long-range PCR, enabling efficient and affordable detection of SMN gene variations in patients with SMA, providing new insight into SMA diagnosis to SMN1 deficiency and an optimized workflow for single exon CNV testing of the SMN gene.

Abstract Background In observational studies, there exists an association between obesity and epigenetic age as well as telomere length. However, varying and partially conflicting outcomes have notably arisen from distinct studies on this topic. In the present study, two-way Mendelian randomization was used to identify potential causal associations between obesity and epigenetic age and telomeres. Methods A genome-wide association study was conducted using data from individuals of European ancestry to investigate bidirectional Mendelian randomization (MR) regarding the causal relationships between obesity, as indicated by three obesity indicators (body mass index or BMI, waist circumference adjusted for BMI or WCadjBMI, and waist-to-hip ratio adjusted for BMI or WHRadjBMI), and four epigenetic age measures (HannumAge, HorvathAge, GrimAge, PhenoAge), as well as telomere length. To assess these causal associations, various statistical methods were employed, including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode. To address the issue of multiple testing, we applied the Bonferroni correction. These methods were used to determine whether there is a causal link between obesity and epigenetic age, as well as telomere length, and to explore potential bidirectional relationships. Forest plots and scatter plots were generated to show causal associations between exposures and outcomes. For a comprehensive visualization of the results, leave-one-out sensitivity analysis plots, individual SNP-based forest plots for MR analysis, and funnel plots were included in the presentation of the results. Results A strong causal association was identified between obesity and accelerated HannumAge, GrimAge, PhenoAge and telomere length shrinkage. The causal relationship between WCadjBMI and PhenoAge acceleration (OR: 2.099, 95%CI: 1.248—3.531, p = 0.005) was the strongest among them. However, only the p-values for the causal associations of obesity with GrimAge, PhenoAge, and telomere length met the criteria after correction using the Bonferroni multiple test. In the reverse MR analysis, there were statistically significant causal associations between HorvathAge, PhenoAge and GrimAge and BMI, but these associations exhibited lower effect sizes, as indicated by their Odds Ratios (ORs). Notably, sensitivity analysis revealed the robustness of the study results. Conclusions The present findings reveal a causal relationship between obesity and the acceleration of epigenetic aging as well as the reduction of telomere length, offering valuable insights for further scientific investigations aimed at developing strategies to mitigate the aging process in humans.

Abstract Background The latent and incubation periods characterize the transmission of infectious viruses and are the basis for the development of outbreak prevention and control strategies. However, systematic studies on the latent period and associated factors with the incubation period for SAS-CoV-2 variants are still lacking. We inferred the two durations of Delta, BA.1, and BA.2 cases and analyzed the associated factors. Methods The Delta, BA.1, and BA.2 (and its lineages BA.2.2 and BA.2.76) cases with clear transmission chains and infectors from 10 local SAS-CoV-2 epidemics in China were enrolled. The latent and incubation periods were fitted by the Gamma distribution, and associated factors were analyzed using the accelerated failure time model. Results The mean latent period for 672 Delta, 208 BA.1, and 677 BA.2 cases was 4.40 (95%CI: 4.24 ~ 4.63), 2.50 (95%CI: 2.27 ~ 2.76), and 2.58 (95%CI: 2.48 ~ 2.69) days, respectively, with 85.65% (95%CI: 83.40 ~ 87.77%), 97.80% (95%CI: 96.35 ~ 98.89%), and 98.87% (95%CI: 98.40 ~ 99.27%) of them starting to shed viruses within 7 days after exposure. In 405 Delta, 75 BA.1, and 345 BA.2 symptomatic cases, the mean latent period was 0.76, 1.07, and 0.79 days shorter than the mean incubation period [5.04 (95%CI: 4.83 ~ 5.33), 3.42 (95%CI: 3.00 ~ 3.89), and 3.39 (95%CI: 3.24 ~ 3.55) days], respectively. No significant difference was observed in the two durations between BA.1 and BA.2 cases. After controlling for the sex, clinical severity, vaccination history, number of infectors, the length of exposure window and shedding window, the latent period [Delta: exp(β) = 0.81, 95%CI: 0.66 ~ 0.98, p = 0.034; Omicron: exp(β) = 0.82, 95%CI: 0.71 ~ 0.94, p = 0.004] and incubation period [Delta: exp(β) = 0.69, 95%CI: 0.55 ~ 0.86, p

Abstract As testicular mesenchymal stromal cells, stem Leydig cells (SLCs) show great promise in the treatment of male hypogonadism. The therapeutic functions of mesenchymal stromal cells are largely determined by their reciprocal regulation by immune responses. However, the immunoregulatory properties of SLCs remain unclear. Here, we observe that SLCs transplantation restore male fertility and testosterone production in an ischemia‒reperfusion injury mouse model. SLCs prevent inflammatory cascades through mitochondrial transfer to macrophages. Reactive oxygen species (ROS) released from activated macrophages inducing mitochondrial transfer from SLCs to macrophages in a transient receptor potential cation channel subfamily member 7 (TRPM7)-mediated manner. Notably, knockdown of TRPM7 in transplanted SLCs compromised therapeutic outcomes in both testicular ischemia‒reperfusion and testicular aging mouse models. These findings reveal a new mechanism of SLCs transplantation that may contribute to preserve testis function in male patients with hypogonadism related to immune disorders.

Abstract Purpose The clinical outcomes of patients who received a cervical collar after anterior cervical decompression and fusion were evaluated by comparison with those of patients who did not receive a cervical collar. Methods All of the comparative studies published in the PubMed, Cochrane Library, Medline, Web of Science, and EMBASE databases as of 1 October 2023 were included. All outcomes were analysed using Review Manager 5.4. Results Four studies with a total of 406 patients were included, and three of the studies were randomized controlled trials. Meta-analysis of the short-form 36 results revealed that wearing a cervical collar after anterior cervical decompression and fusion was more beneficial (P 0.05) between the cervical collar group and the no cervical collar group. Conclusions This systematic review and meta-analysis revealed no significant differences in the 6-week postoperative cervical range of motion, fusion rate, or neck disability index between the cervical collar group and the no cervical collar group. However, compared to patients who did not wear a cervical collar, patients who did wear a cervical collar had better scores on the short form 36. Interestingly, at the final follow-up visit, the neck disability index scores were better in the no cervical collar group than in the cervical collar group. PROSPERO registration number: CRD42023466583.

Abstract Bone is a mechanosensitive tissue and undergoes constant remodeling to adapt to the mechanical loading environment. However, it is unclear whether the signals of bone cells in response to mechanical stress are processed and interpreted in the brain. In this study, we found that the hypothalamus of the brain regulates bone remodeling and structure by perceiving bone prostaglandin E2 (PGE2) concentration in response to mechanical loading. Bone PGE2 levels are in proportion to their weight bearing. When weight bearing changes in the tail-suspension mice, the PGE2 concentrations in bones change in line with their weight bearing changes. Deletion of cyclooxygenase-2 (COX2) in the osteoblast lineage cells or knockout of receptor 4 (EP4) in sensory nerve blunts bone formation in response to mechanical loading. Moreover, knockout of TrkA in sensory nerve also significantly reduces mechanical load-induced bone formation. Moreover, mechanical loading induces cAMP-response element binding protein (CREB) phosphorylation in the hypothalamic arcuate nucleus (ARC) to inhibit sympathetic tyrosine hydroxylase (TH) expression in the paraventricular nucleus (PVN) for osteogenesis. Finally, we show that elevated PGE2 is associated with ankle osteoarthritis (AOA) and pain. Together, our data demonstrate that in response to mechanical loading, skeletal interoception occurs in the form of hypothalamic processing of PGE2-driven peripheral signaling to maintain physiologic bone homeostasis, while chronically elevated PGE2 can be sensed as pain during AOA and implication of potential treatment.

Abstract Objective To assess the efficacy of ibutilide administration during radiofrequency catheter ablation of persistent atrial fibrillation (AF), to explore the success rate of conversion and related influential factors, and to analyze the effects of ibutilide on postoperative recurrence. Methods A total of 192 patients with persistent AF who underwent catheter ablation from January 1, 2019, to December 31, 2021. These patients failed in conversion of AF to normal sinus rhythm by intraoperative catheter ablation. Patients were categorized into effective group (115 cases) and ineffective group (77 cases) based on whether sinus rhythm was restored after application of ibutilide. Results The overall success rate of conversion using ibutilide administration was 59.9%. The success rate was associated with weight ((68.12 ± 11.72 vs. 72.83 ± 12.08) kg, P = 0.008), the duration of AF ((34.67 ± 55.68 vs. 66.52 ± 95.21) months, p = 0.008), diameter of left atrium (LAD) ((44.39 ± 5.80 vs. 47.36 ± 6.10) mm,P = 0.002), and N-terminal pro-brain natriuretic peptide (NT-proBNP) level ((854.85 ± 770.84 vs. 662.88 ± 659.18) pg/ml,P = 0.030). The results showed the duration of AF was associated with early recurrence, while early recurrence was not a risk factor for late recurrence. And duration of AF was associated with postoperative maintenance time of normal sinus rhythm, whereas successful conversion into normal sinus rhythm using ibutilide administration had no influence on postoperative maintenance time of normal sinus rhythm. Conclusion Ibutilide showed to be effective in catheter ablation of AF, the success rate of conversion was correlated with the duration of AF, LA diameter, and NT-proBNP level. Besides, the duration of AF was found as a risk factor for early postoperative recurrence, while ibutilide administration for successful conversion had no influence on predicting postoperative recurrence and had no influence on postoperative maintenance time of sinus rhythm.

Abstract The current gait planning for legged robots is mostly based on human presets, which cannot match the flexible characteristics of natural mammals. This paper proposes a gait optimization framework for hexapod robots called Smart Gait. Smart Gait contains three modules: swing leg trajectory optimization, gait period & duty optimization, and gait sequence optimization. The full dynamics of a single leg, and the centroid dynamics of the overall robot are considered in the respective modules. The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion, mostly, it enables the hexapod robot to determine its gait pattern transitions based on its current state, instead of repeating the formalistic clock-set step cycles. Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time. The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures, and it can run efficiently on board in real-time after deployment. Various experiments are carried out on the hexapod robot LittleStrong. The results show that the energy consumption is reduced by 15.9% when in locomotion. Adaptive gait patterns can be generated spontaneously both in regular and challenge environments, and when facing external interferences.

Abstract Unicellular eukaryotes represent tremendous evolutionary diversity. However, the molecular mechanisms underlying this diversity remain largely unexplored, partly due to a limitation of genetic tools to only a few model species. Paramecium caudatum is a well-known unicellular eukaryote with an unexpectedly large germline genome, of which only two percent is retained in the somatic genome following sexual processes, revealing extensive DNA elimination. However, further progress in understanding the molecular mechanisms governing this process is hampered by a lack of suitable genetic tools. Here, we report the successful application of gene knockdown and protein localization methods to interrogate the function of both housekeeping and developmentally regulated genes in P. caudatum. Using these methods, we achieved the expected phenotypes upon RNAi by feeding, and determined the localization of these proteins by microinjection of fusion constructs containing fluorescent protein or antibody tags. Lastly, we used these methods to reveal that P. caudatum PiggyMac, a domesticated piggyBac transposase, is essential for sexual development, and is likely to be an active transposase directly involved in DNA cleavage. The application of these methods lays the groundwork for future studies of gene function in P. caudatum and can be used to answer important biological questions in the future.

Abstract Background Diagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease. Methods We enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts. Results The seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%. Conclusions This seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.

Abstract Rockbursts are a complex phenomenon characterized by a violent ejection of rock fragments from the free face of deep underground mines. Due to the sudden, disruptive, and complex nature of these events, accurate predictions of rockbursts are difficult. Therefore, geotechnical intervention is imperative to prevent the occurrence of a rockburst. The scenarios of microwave destressing is introduced here as an application method to potentially prevent their occurrence. To understand the influences of microwave destressing, such as microwave exposure duration, spatial layout, and geometric parameters of the microwave preconditioned zone (MPZ), on the occurrence of a rockburst, numerical simulation was conducted based on an impact-induced rockburst model. The feasibility of microwave destressing technology was validated numerically. This paper also provides guidance for the utilization of microwave destressing in practice. Discontinuous MPZ is suitable for a known blasting source with a fixed position, and continuous MPZ can be applied for a working condition with multiple blasting sources with variable positions. A closer distance of MPZ from the free face results in a better microwave destressing effect. However, the closer distance of MPZ from the free face also introduces more negative influences on the static stability of surrounding rock.

BackgroundMetabolic dysregulation represents a defining characteristic of Type 2 diabetes (T2DM). Nevertheless, there remains an absence of substantial evidence establishing a direct causal link between circulating blood metabolites and the promotion or prevention of T2DM. In addressing this gap, we employed Mendelian randomization (MR) analysis to investigate the potential causal association between 1,091 blood metabolites, 309 metabolite ratios, and the occurrence of T2DM.MethodsData encompassing single-nucleotide polymorphisms (SNPs) for 1,091 blood metabolites and 309 metabolite ratios were extracted from a Canadian Genome-wide association study (GWAS) involving 8,299 participants. To evaluate the causal link between these metabolites and Type 2 diabetes (T2DM), multiple methods including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode were employed. p-values underwent correction utilizing False Discovery Rates (FDR). Sensitivity analyses incorporated Cochran’s Q test, MR-Egger intercept test, MR-PRESSO, Steiger test, leave-one-out analysis, and single SNP analysis. The causal effects were visualized via Circos plot, forest plot, and scatter plot. Furthermore, for noteworthy, an independent T2DM GWAS dataset (GCST006867) was utilized for replication analysis. Metabolic pathway analysis of closely correlated metabolites was conducted using MetaboAnalyst 5.0.ResultsThe IVW analysis method utilized in this study revealed 88 blood metabolites and 37 metabolite ratios demonstrating a significant causal relationship with T2DM (p < 0.05). Notably, strong causal associations with T2DM were observed for specific metabolites: 1-linoleoyl-GPE (18:2) (IVW: OR:0.930, 95% CI: 0.899–0.962, p = 2.16 × 10−5), 1,2-dilinoleoyl-GPE (18:2/18:2) (IVW: OR:0.942, 95% CI: 0.917–0.968, p = 1.64 × 10−5), Mannose (IVW: OR:1.133, 95% CI: 1.072–1.197, p = 1.02 × 10−5), X-21829 (IVW: OR:1.036, 95% CI: 1.036–1.122, p = 9.44 × 10−5), and Phosphate to mannose ratio (IVW: OR:0.870, 95% CI: 0.818–0.926, p = 1.29 × 10−5, FDR = 0.008). Additionally, metabolic pathway analysis highlighted six significant pathways associated with T2DM development: Valine, leucine and isoleucine biosynthesis, Phenylalanine metabolism, Glycerophospholipid metabolism, Alpha-Linolenic acid metabolism, Sphingolipid metabolism, and Alanine, aspartate, and glutamate metabolism.ConclusionThis study identifies both protective and risk-associated metabolites that play a causal role in the development of T2DM. By integrating genomics and metabolomics, it presents novel insights into the pathogenesis of T2DM. These findings hold potential implications for early screening, preventive measures, and treatment strategies for T2DM.

The corrosion performance of martensitic steel remains a matter of dispute, with no consensus on whether it exhibits heightened or diminished corrosion resistance. In this study, the corrosion behavior and mechanism of martensitic steel were systematically investigated by simulating a cargo oil tank environment. The results indicate that under strong acidic conditions, the corrosion rate of martensite (1.1404 mm/y) is significantly higher than that of ferrite-pearlite (0.7430 mm/y) due to its increased dislocation density and internal stress. Additionally, the presence of high-energy defects provides abundant active sites for the redeposition of Cu-bearing particles, while their uneven distribution further exacerbates corrosion. Therefore, we propose a competitive mechanism that governs the corrosion behavior of martensite, complementing the understanding of its behavior and mechanism.

A 45-year-old woman presented with right hip pain for a month. Imaging results revealed that the left peritoneal mass was accompanied by metastases of the right sciatic branch, lung, and retroperitoneal lymph nodes. A biopsy of the left peritoneal mass was performed. The pathological morphology demonstrated clear cell adenocarcinoma. Immunohistochemical staining revealed a positive expression of keratin7 and PAX8 and a negative expression of keratin20, GCDFP-15, ER, PR, WT1, CDX2, villin, TTF-1, napsin-A, vimentin, calretinin, and GATA3. Finally, the diagnosis of primary retroperitoneal müllerian adenocarcinoma (PRMA) was confirmed. PRMA is a very rare type of primary retroperitoneal tumor. PRMA should be considered for the retroperitoneal mass.

Abstract Harvesting indoor light to power electronic devices for the Internet of Things has become an application scenario for emerging photovoltaics, especially utilizing organic photovoltaics (OPVs). Combined liquid‐ and solid‐state processing, such as printing and lamination used in industry for developing indoor OPVs, also provides a new opportunity to investigate the device structure, which is otherwise hardly possible based on the conventional approach due to solvent orthogonality. This study investigates the impact of fullerene‐based acceptor interlayer on the performance of conjugated polymer–fullerene‐based laminated OPVs for indoor applications. We observe open‐circuit voltage (VOC) loss across the interface despite this arrangement being presumed to be ideal for optimal device performance. Incorporating insulating organic components such as polyethyleneimine (PEI) or polystyrene (PS) into fullerene interlayers decreases the work function of the cathode, leading to better energy level alignment with the active layer (AL) and reducing the VOC loss across the interface. Neutron reflectivity studies further uncover two different mechanisms behind the VOC increase upon the incorporation of these insulating organic components. The self‐organized PEI layer could hinder the transfer of holes from the AL to the acceptor interlayer, while the gradient distribution of the PS‐incorporated fullerene interlayer eliminates the thermalization losses. This work highlights the importance of structural dynamics near the extraction interfaces in OPVs and provides experimental demonstrations of interface investigation between solution‐processed cathodic fullerene layer and bulk heterojunction AL.

Background: Hyperlipidemia (HLP) presents a significant challenge to global public health. Mounting evidence suggests that statins, the recommended first-line lipid-lowering agents, have significant adverse effects. Consequently, the quest for natural and efficacious alternative therapies is steadily emerging as a research priority for HLP prevention and treatment. Consumption of tea, which is rich in diverse biologically active compounds with the capacity to regulate lipid metabolism and combat obesity, has emerged as a promising alternative therapy. Sea buckthorn leaves are rich in a multitude of biologically active substances, have a hypolipidemic effect, and can be used as a raw material for tea because of their unique flavor. There is a suggestion that combining Aspergillus cristatus with tea could modify or boost the lipid-lowering active compounds present in tea, thereby increasing its efficacy in regulating lipid metabolism. Results: Sea Buckthorn Leaf Fu Tea (SBLFT) was obtained by fermentation when sea buckthorn leaves contained 42 % moisture, inoculated with Aspergillus cristatus 0.2 mL/g, and incubated for 8 d at constant temperature. Animal experiments demonstrated that SBLFT significantly inhibited body weight gain in HLP rats and reduced lipid content and serum oxidative stress. In addition, liver tissue sections and functional indices showed that SBLFT can improve liver morphology and function abnormalities. Reverse transcription-polymerase chain reaction results indicated that the expression of Liver kinase B1 (LKB1), adenosine 5‘-monophosphate (AMP)-activated protein kinase (AMPK), acetyl CoA carboxylase 1 (ACC1), and sterol-regulatory element binding protein-1 (SREBP1c) gene related to lipid metabolism was altered. Conclusion: SBLFT improved HLP, specifically via promoting the expression of LKB1 in the liver of HLP rats, activating AMPK, and inhibiting ACC1 and SREBP1c expression, resulting in the inhibition of fatty acid and triglyceride synthesis-related enzymes at the transcriptional level.

BackgroundDespite the efficacy of efgartigimod demonstrated in ADAPT phase 3 trial, data specifically derived from Chinese participants are not available. Therefore, we aimed to evaluate the efficacy and safety of efgartigimod in Chinese patients with generalized myasthenia gravis (gMG).MethodsThis is a prospective cohort study conducted in 8 hospitals across China. gMG patients received weekly intravenous infusions of efgartigimod (10 mg/kg) under a named patient program (NPP). The present study is an 8-week study, consisting of 4 consecutive doses of efgartigimod administered over 3 weeks (one cycle), followed by a 5-week follow-up period to assess the tolerability of efgartigimod’s therapeutic effects. The primary outcome was the mean change in MG activities of daily living (MG-ADL) total score from baseline to 4 weeks. MG-ADL responder was defined as a ≥ 2-point improvement that persisted for 4 weeks, starting by week 4. Safety evaluations encompassed the monitoring of adverse events (AE) and serious AE (SAE) throughout the study.ResultsBetween 5 July 2022 and 25 August 2023, a total of 14 gMG patients were included. The mean age was 57.7 years, with a mean MG-ADL score of 10.86 ± 3.32. At week 4, MG-ADL scores showed a mean reduction of 6 points, reaching a maximum decline of 13 points. Among the patients, 85.7% (12/14) achieved MG-ADL responder status after one cycle of treatment. The most significant reduction in quantitative MG (QMG) scores also occurred at week 4, with a mean decrease of 7 points. Notably, the improvements in MG-ADL and QMG scores persisted until week 8. During treatment and follow-up period, only two mild neck rashes occurred and resolved promptly. No infections or SAE were reported.DiscussionA single cycle of efgartigimod treatment demonstrates effectiveness and the tolerability through week 8, with no new safety signals observed in Chinese gMG patients.

Abstract The authors investigate new Automated Guided Vehicle (AGV) Routing Problem with Simultaneous Pickup and Delivery with Mixed Time Windows (VRPSPDMTW) in smart workshops, a variation of the classic Vehicle Routing Problem (VRP). A mixed time window vehicle routing model was developed for simultaneous deliveries. This model reduces the cost of AGVs used and distribution cost, along with time window penalties. To address this complex challenge, a Hybrid Adaptive Genetic Algorithm using Variable Neighbourhood Search (AGA‐VNS) is proposed. This algorithm enhances the genetic algorithm's local search capabilities while preserving solution diversity, thereby improving both efficiency and quality of solutions. Comprehensive computational experiments are conducted, which include both VRPSPDTW test benchmark and real‐world smart factory instance studies. The outcomes reveal that the AGA‐VNS algorithm outperforms both professional solver software and advanced heuristic methods significantly. Moreover, the newly developed mixed time window model is more aligned with the requirements of real‐world production processes compared to the traditional time window model. Thus, this research not only presents novel insights into the domain of vehicle routing problems but also demonstrates its significant applicability and potential in the background of intelligent workshops.