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Abstract Background Obesity and metabolic syndrome (MetS) have become urgent worldwide health problems, predisposing patients to unfavorable myocardial status and thyroid dysfunction. Low-carbohydrate diet (LCD) and time-restricted eating (TRE) have been confirmed to be effective methods for weight management and improving MetS, but their effects on the myocardium and thyroid are unclear. Methods We conducted a secondary analysis in a randomized clinical diet-induced weight-loss trial. Participants (N = 169) diagnosed with MetS were randomized to the LCD group, the 8 h TRE group, or the combination of the LCD and TRE group for 3 months. Myocardial enzymes and thyroid function were tested before and after the intervention. Pearson’s or Spearman’s correlation was assessed between functions of the myocardium and thyroid and cardiometabolic parameters at baseline. Results A total of 162 participants who began the trial were included in the intention-to-treat (ITT) analysis, and 57 participants who adhered to their assigned protocol were involved in the per-protocol (PP) analysis. Relative to baseline, lactate dehydrogenase, creatine kinase MB, hydroxybutyrate dehydrogenase, and free triiodothyronine (FT3) declined, and free thyroxine (FT4) increased after all 3 interventions (both analyses). Creatine kinase (CK) decreased only in the TRE (− 18 [44] U/L, P

Abstract PHD2 is essential in modulating HIF-1α levels upon oxygen fluctuations. Hypoxia, a hallmark of uterus, and HIF-1α have recently emerged as opposing regulators of mesendoderm specification, suggesting a role for PHD2 therein. We found that PHD2 expression initially covered the epiblast and gradually receded from the primitive streak, which was identical to hypoxia and exclusive to HIF-1α. The investigations performed in mESCs, embryoids, and mouse embryos together demonstrated that PHD2 negatively regulated mesendoderm specification. Single-cell RNA sequencing revealed that PHD2 governed the transition from epiblast to mesendoderm. The downstream effect of PHD2 relied on the HIF-1α regulated Wnt/β-catenin pathway, while it was regulated upstream by miR-429. In summary, our research highlights PHD2’s essential role in mesendoderm specification and its interactions with hypoxia and HIF-1α.

Abstract Effective extraction of building from remote sensing images requires both global and local information. Despite convolutional neural networks (CNNs) excelling at capturing local details, their intrinsic focus on local operations poses challenge in effectively extracting global features, especially in the context of large‐scale buildings. In contrast, transformers excel at capturing global information, but compared to CNNs, they tend to overly rely on large‐scale datasets and pre‐trained parameters. To tackle the challenge, this paper presents the multi‐scale and dual‐branch feature fusion network (MDTrans). Specifically, the CNN and transformer branches are integrated in a dual‐branch parallel manner during both encoding and decoding stages, local information for small‐scale buildings is extracted by utilizing Dense Connection Blocks in the CNN branch, while crucial global information for large‐scale buildings is effectively captured through Swin Transformer Block in the transformer branch. Additionally, Dual Branch Information Fusion Block is designed to fuse local and global features from the two branches. Furthermore, Multi‐Convolutional Block is designed to further enhance the feature extraction capability of buildings with different sizes. Through extensive experiments on the WHU, Massachusetts, and Inria building datasets, MDTrans achieves intersection over union (IoU) scores of 91.36%, 64.69%, and 79.25%, respectively, outperforming other state‐of‐the‐art models.

Objective: To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients. Methods: This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets (699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital) between January 2005 and December 2019. The Cancer Hospital dataset was randomly split into a training cohort (559 patients) and a validation cohort (140 patients) to train and tune a deep learning model based on a deep residual network (ResNet). The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model. Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography (HRCT) features for the model. The predictive performance was assessed by area under the curves (AUCs), accuracy, precision, recall, and F1 score. Subgroup analysis was performed to evaluate the potential bias of the study population. Results: A total of 1,009 patients were included in this study; 409 (40.5%) were male and 600 (59.5%) were female. The median age was 57.0 years (inter-quartile range, IQR: 50.0–64.0). The deep learning model achieved AUCs of 0.906 (95% CI: 0.873–0.938) and 0.893 (95% CI: 0.857–0.930) for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule (non-pGGN) testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.622). The precisions of this model for predicting pN0 disease were 0.979 (95% CI: 0.963–0.995) and 0.983 (95% CI: 0.967–0.998) in the testing cohort and the non-pGGN testing cohort, respectively. The deep learning model achieved AUCs of 0.848 (95% CI: 0.798–0.898) and 0.831 (95% CI: 0.776–0.887) for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.657). The recalls of this model for predicting pN2 disease were 0.903 (95% CI: 0.870–0.936) and 0.931 (95% CI: 0.901–0.961) in the testing cohort and the non-pGGN testing cohort, respectively. Conclusions: The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Diabetic wound healing presents a unique and complex challenge due to the impaired cellular and molecular functions associated with diabetes. Chronic wounds in diabetic patients are characterized by prolonged inflammation, reduced angiogenesis, and impaired collagen deposition, which significantly hinder the healing process. This comprehensive review focuses on the innovative applications of designer cytokines in precision therapy for diabetic wound healing, emphasizing the remarkable advancements made in overcoming the limitations of natural cytokines, such as their short half-life, potential cytotoxicity, and lack of specificity. We begin by detailing the intricate biological characteristics of diabetic wounds and the essential role that cytokines play in orchestrating the healing process. The review critically examines the constraints of natural cytokines and traces the evolution of synthetic alternatives, with a particular emphasis on peptide-based and nucleic acid-based artificial cytokines. Advanced strategies for designing these artificial cytokines are discussed, including molecular modifications, functional enhancements, and specificity improvements to better target pathological conditions in diabetic wounds. Furthermore, we explore the utilization of synthetic biology techniques to engineer effective cytokine-based therapies. The promising therapeutic potential of rationally designed cytokines is highlighted, showcasing their ability to modulate the wound microenvironment, enhance tissue regeneration, and reduce chronic inflammation. This review not only provides valuable perspectives on the future research directions but also offers insights into the potential clinical applications of these innovative therapies, aiming to significantly improve the outcomes for patients suffering from diabetic wounds.

Abstract Background Follistatin-like protein 1 (FSTL1) has been identified as a secreted glycoprotein that plays an important role in obesity. However, its role in children with metabolic-associated fatty liver disease (MAFLD) has not been investigated. This study aimed at characterizing the relationship between serum FSTL1 concentration and MAFLD in children with obesity. Methods A total of 121 subjects were recruited from the Second Affiliated Hospital of Xi’an Jiaotong University, including 45 obese children with MAFLD, 31 obese children without MAFLD, and 45 healthy controls. Anthropometric parameters, biochemical data were measured and circulating FSTL1 levels were detected by ELISA. Results The levels of FSTL1 in obese children with MAFLD were higher than that in obese children without MAFLD: 1.31 (0.35–2.29) ng/mL vs. 0.55 (0.36–1.38) ng/mL. Correlation analysis illustrated that FSTL1 was associated with nonesterified free fatty acid and leptin (r = 0.278, P

Abstract Background A combination of prone positioning (PP) and venovenous extracorporeal membrane oxygenation (VV-ECMO) is safe, feasible, and associated with potentially improved survival for severe acute respiratory distress syndrome (ARDS). However, whether ARDS patients, especially non-COVID-19 patients, placed in PP before VV-ECMO should continue PP after a VV-ECMO connection is unknown. This study aimed to test the hypothesis that early use of PP during VV-ECMO could increase the proportion of patients successfully weaned from ECMO support in severe ARDS patients who received PP before ECMO. Methods In this prospective observational study, patients with severe ARDS who were treated with VV-ECMO were divided into two groups: the prone group and the supine group, based on whether early PP was combined with VV-ECMO. The proportion of patients successfully weaned from VV-ECMO and 60-day mortality were analyzed before and after propensity score matching. Results A total of 165 patients were enrolled, 50 in the prone and 115 in the supine group. Thirty-two (64%) and 61 (53%) patients were successfully weaned from ECMO in the prone and the supine groups, respectively. The proportion of patients successfully weaned from VV-ECMO in the prone group tended to be higher, albeit not statistically significant. During PP, there was a significant increase in partial pressure of arterial oxygen (PaO2) without a change in ventilator or ECMO settings. Tidal impedance shifted significantly to the dorsal region, and lung ultrasound scores significantly decreased in the anterior and posterior regions. Forty-five propensity score-matched patients were included in each group. In this matched sample, the prone group had a higher proportion of patients successfully weaned from VV-ECMO (64.4% vs. 42.2%; P = 0.035) and lower 60-day mortality (37.8% vs. 60.0%; P = 0.035). Conclusions Patients with severe ARDS placed in PP before VV-ECMO should continue PP after VV-ECMO support. This approach could increase the probability of successful weaning from VV-ECMO. Trial Registration ClinicalTrials.Gov: NCT04139733. Registered 23 October 2019.

Abstract Background This experimental study used peanut shell biochar and fermented cow manure as the main raw materials forming a substrate for tomato plants. Results Substrates were created from peanut shell biochar, fermented cow manure, slag, and vermiculite mixed in volume ratios of 6:0:1:2, 5:1:1:2, 4:2:1:2, and 3:3:1:2, respectively. Comparisons were made to a control substrate composed of peat, slag, and vermiculite in a volume ratio of 6:1:2, respectively. As the proportion of biochar in the substrate increased, the bulk density showed a downward trend while the total porosity, aeration porosity, and water holding capacity showed upward trends. As the proportion of cow manure increased, the total N, available K, Ca, and Mg in the substrate increased. Tomatoes demonstrated similar or better growth than the control at experimental substrate composition ratios of 6:0:1:2 and 5:1:1:2. This was reflected in seedling strength index, seedling growth, chlorophyll content, root growth, plant carbohydrates, purine metabolism, caffeine metabolism, galactose metabolism, and starch and sucrose metabolism. The results of this study indicate the experimental substrate composition ratios of 6:0:1:2 and 5:1:1:2 were the most beneficial in terms of supporting the growth of tomato plants. Conclusions The study confirms biochar in composite substrate promotes plant growth by improving the root environment and plant metabolism. This investigation provides new information to moderate the use of peat and support efforts to achieve carbon neutrality through the creative utilization of agricultural waste. Graphical abstract

Abstract The energy efficiency identification of machining process plays an indispensable part in achieving energy-efficient manufacturing and improving energy utilization as well as productivity and surface quality. However, there is a great difficulty to track energy efficiency in real-time based on one kind of traditional power signal. Because energy consumption is affected by many factors such as machine tool current performance, tool wear conditions and cutting parameters selection. This paper puts forward an energy efficiency recognition method as well as surface roughness prediction model based on the cutting force signals. The CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is employed to decompose the cutting force signal into multiple IMF (intrinsic mode function) components; and characterization of energy efficiency of machining process is recognized through proportion of components based on PCA—Fast ICA algorithm. Then, a surface roughness prediction model is proposed using support vector regression (SVR) based on specific cutting energy consumption (SCEC). The orthogonal test is designed considering spindle speed, feed rate, depth of cutting and width of cutting in 3 levels to obtain the influence degree of cutting parameters on cutting force, specific energy consumption, and the surface roughness. The energy efficiency of 27 group experiments is classified into high, medium and low levels according to energy efficiency value. Finally, using the data of orthogonal test, energy efficiency state was identified. The result show that time–frequency of cutting force signals for high, medium and low energy efficiency could be extracted, and the average absolute error of surface roughness predict is 0.058. That illustrated that the proposed method could meet the industry requirement for energy efficiency monitoring and surface roughness prediction to achieve sustainable manufacturing.

Abstract The prevention and treatment of gastrointestinal mucosal injury caused by a plateau hypoxic environment is a clinical conundrum due to the unclear mechanism of this syndrome; however, oxidative stress and microbiota dysbiosis may be involved. The Robinia pseudoacacia L. flower, homologous to a functional food, exhibits various pharmacological effects, such as antioxidant, antibacterial, and hemostatic activities. An increasing number of studies have revealed that plant exosome-like nanoparticles (PELNs) can improve the intestinal microbiota and exert antioxidant effects. In this study, the oral administration of Robinia pseudoacacia L. flower exosome-like nanoparticles (RFELNs) significantly ameliorated hypoxia-induced gastric and small intestinal mucosal injury in mice by downregulating hypoxia-inducible factor-1α (HIF-1α) and HIF-2α expression and inhibiting hypoxia-mediated ferroptosis. In addition, oral RFELNs partially improved hypoxia-induced microbial and metabolic disorders of the stomach and small intestine. Notably, RFELNs displayed specific targeting to the gastrointestinal tract. In vitro experiments using gastric and small intestinal epithelial cell lines showed that cell death caused by elevated HIF-1α and HIF-2α under 1% O2 mainly occurred via ferroptosis. RFELNs obviously inhibited HIF-1α and HIF-2α expression and downregulated the expression of NOX4 and ALOX5, which drive reactive oxygen species production and lipid peroxidation, respectively, suppressing ferroptosis under hypoxia. In conclusion, our findings underscore the potential of oral RFELNs as novel, naturally derived agents targeting the gastrointestinal tract, providing a promising therapeutic approach for hypoxia-induced gastric and small intestinal mucosal ferroptosis. Graphical Abstract

Abstract A comparative study of two force perception skill learning approaches for robot‐assisted spinal surgery, the impedance model method and the imitation learning (IL) method, is presented. The impedance model method develops separate models for the surgeon and patient, incorporating spring‐damper and bone‐grinding models. Expert surgeons' feature parameters are collected and mapped using support vector regression and image navigation techniques. The imitation learning approach utilises long short‐term memory networks (LSTM) and addresses accurate data labelling challenges with custom models. Experimental results demonstrate skill recognition rates of 63.61%–74.62% for the impedance model approach, relying on manual feature extraction. Conversely, the imitation learning approach achieves a force perception recognition rate of 91.06%, outperforming the impedance model on curved bone surfaces. The findings demonstrate the potential of imitation learning to enhance skill acquisition in robot‐assisted spinal surgery by eliminating the laborious process of manual feature extraction.

Abstract Recent research in multi-principal element alloys (MPEAs) has increasingly focused on the role of short-range order (SRO) on material performance. However, the mechanisms of SRO formation and its precise control remain elusive, limiting the progress of SRO engineering. Here, leveraging advanced additive manufacturing techniques that produce samples with a wide range of cooling rates (up to 107 K s−1) and an enhanced semi-quantitative electron microscopy method, we characterize SRO in three CoCrNi-based face-centered-cubic (FCC) MPEAs. Surprisingly, irrespective of the processing and thermal treatment history, all samples exhibit similar levels of SRO. Atomistic simulations reveal that during solidification, prevalent local chemical order arises in the liquid-solid interface (solidification front) even under the extreme cooling rate of 1011 K s−1. This phenomenon stems from the swift atomic diffusion in the supercooled liquid, which matches or even surpasses the rate of solidification. Therefore, SRO is an inherent characteristic of most FCC MPEAs, insensitive to variations in cooling rates and even annealing treatments typically available in experiments.

Abstract “Cuohu Bazi” (CHBZ) is an ancient sorghum variety collected from the fields of China, known for its agronomic traits like dwarf stature, early maturation. In this study, we present the first telomere-to-telomere (T2T) and gap-free genome assembly of CHBZ using PacBio HiFi reads, Oxford Nanopore Technologies, and Hi-C data. The assembled genome comprises 724.85 Mb, effectively resolving all 3,913 gaps that were present in the previous sorghum BTx623 reference genome. Notably, the T2T assembly captures 10 centromeres and all 20 telomeres, providing strong support for their integrity. This assembly is of high quality in terms of contiguity (contig N50: 71.1 Mb), completeness (BUSCO score: 99.01%, k-mer completeness: 98.88%), and correctness (QV: 61.60). Repetitive sequences accounted for 70.41% of the genome and a total of 32,855 protein-coding genes have been annotated. Furthermore, 161 CHBZ-specific presence/absence variants genes have been identified when comparing to BTx623 genome. This study provides valuable insights for future research on sorghum genetics, genomics, and evolutionary history.