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Currently, polylactic acid (PLA) is an attractive alternative to polypropylene (PP) because of its biodegradability. This study introduces a novel modification strategy for PLA by creating a multifunctional ionization layer with ionic salts. This approach achieves humidity sensing, reliable antibacterial properties, and excellent degradability simultaneously. The modified PLA textile sensor exhibits high sensitivity to respiratory humidity (0.92 at 90% RH), with ultrafast response (0.12 s) and recovery times (0.16 s). Additionally, the textile demonstrates excellent antibacterial performance against both E. coli (99.9%) and S. aureus (99.9%) after 1 h of contact. It also shows notable biodegradability with a weight loss rate of 60.38% after 30 days. Also, the ionic salt mechanism is explained through dynamic ionization interactions attributed to the modified ionic salts, which feature both long-chain alkanes and active ions. This work presents a new method to enhance the respiratory detection and antibacterial performance of biodegradable masks.

BackgroundAbusive head trauma (AHT) is a severe form of physical abuse leading to significant morbidity and mortality in children, often presenting with complex brain injuries. Among the varied manifestations, ophthalmologic presentations are critical yet underexplored, which may provide essential clues for early diagnosis and management, improving long-term visual and neurological outcomes.ObjectiveThis study aims to explore the manifestation, management, and outcomes of AHT cases within a single center in China over a five-year period, with a focus on the importance of ophthalmologic evaluation in enhancing the diagnosis, management, and outcome predictions of AHT.MethodsA retrospective case series was conducted at a single institution, involving infants diagnosed with AHT from 2019 to 2023. Data on demographics, medical histories, and clinical management were collected. Ophthalmologic examinations including fundus photography, ocular B-scan ultrasound and fundus fluorescein angiography (FFA), were performed to evaluate retinal vasculature and identify peripheral ischemic retina (PIR). Statistical analyses were performed using SPSS ver. 26.0.ResultsEight AHT patients (16 eyes) were included in the study. Bilateral ocular involvement was observed in all patients, with 81.25% exhibiting retinal hemorrhages (RH). Other manifestations included retinal detachment (31.25%) and optic nerve atrophy (18.75%). Clinical interventions varied, with 68.75% of patients undergoing treatments such as laser photocoagulation and anti-vascular endothelial growth factor (VEGF) injections. Among all eyes, 75% showed resolution of RH. Despite treatment, some patients progressed to severe conditions such as retinal detachment (RD) and iris neovascularization (INV).ConclusionThis study emphasizes the importance of a multidisciplinary approach in the diagnosis and management of AHT, particularly by integrating ophthalmological perspectives into patient care. These findings contribute to the understanding of ophthalmologic presentations in AHT.

PurposeTo observe the vascular development results of tertiary anti-vascular endothelial growth factor (anti-VEGF) therapy following spontaneous second reactivation of retinopathy of prematurity (ROP).MethodsThis retrospective study included 22 infants (42 eyes) with Type 1 or aggressive ROP (A-ROP) who received three anti-VEGF drug treatments for ROP from January 2018 to December 2022. The vascular growth, possible associated risk factors, and the retinal vascularization (DB/DF ratio) were assessed.ResultsThe mean follow-up was 17.6 months. After the 3rd intravitreal injection, seven eyes showed complete vascularization (Group 1), while the remaining 35 eyes demonstrated persistent avascular retina (PAR) (Group 2). In Group 2, 17 eyes maintained a stable state and were classified in the regression subgroup. The other 18 eyes developed a 3rd reactivation (reactivation subgroup) and were treated with laser photocoagulation (LPC).Birth weight (BW) was significantly lower in Group 2 than in Group 1 (p

In response to the issue of harvesting machine failures affecting crop harvesting timing, this study develops an emergency scheduling model and proposes a hybrid optimization algorithm that combines a genetic algorithm and an ant colony algorithm. By enhancing the genetic algorithm’s crossover and mutation methods and incorporating the ant colony algorithm, the proposed algorithm can prevent local optima, thus minimizing disruptions to the overall scheduling plan. Field data from Deyang, Sichuan Province, were utilized, and simulations on various harvesting machines experiencing random faults were conducted. Results indicated that the improved genetic algorithm reduced the optimal comprehensive scheduling cost during random fault occurrences by 47.49%, 19.60%, and 32.45% compared to the basic genetic algorithm and by 34.70%, 14.80%, and 24.40% compared to the ant colony algorithm. The improved algorithm showcases robust global optimization capabilities, high stability, and rapid convergence, offering effective emergency scheduling solutions in case of harvesting machine failures. Furthermore, a visual management system for agricultural machinery scheduling was developed to provide software support for optimizing agricultural machinery scheduling.

To address the problem of inefficient continuous multi-task scheduling operations, a scheduling model was established with the optimization objective of minimum total scheduling time to solve the problem of agricultural machinery continuous performing multiple tasks in agricultural production in hilly areas. An Improved Non-dominated Sorting Genetic Algorithm-III (Improved NSGA-III) was proposed to solve the agricultural machinery scheduling problem. The algorithm employed a two-stage coding mechanism to ensure the uniform distribution of the population and reduce the search solution space. The selection of paternal individuals is based on crowding degree, enabling individuals with better genes to participate in evolution. The domain search strategy of partial solution was introduced to improve the global search ability of the algorithm. The performance of the Improved NSGA-III algorithm and the other three algorithms was compared using farmland data from the experimental area. Results showed that the Improved NSGA-III algorithm outperforms the other three algorithms in terms of minimum total scheduling time, with reductions of 13.89%, 43.81% and 57.19%, respectively, verifying the effectiveness and reliability of the algorithm.

In modern electronic warfare, the jamming environment of radar is more complex than ever. The airborne jammer adapts its jamming method based on diverse raid missions and stages. Recently, the reinforcement learning–based radar anti-jamming method has made some progress in the confrontation scenario of single jamming; however, the gap with respect to actual complex multi-jamming scenarios is large. To address this issue, this paper proposes a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in the complex domain to optimize the anti-jamming strategy of frequency agile radar. First, according to the stage characteristics of the raid mission, noise spot jamming, range deception jamming , and dense false target forwarding jamming models are established. The three jamming sequence strategies were designed to simulate actual jamming scenarios. Second, a reinforcement learning reward function that integrates the signal-to-noise ratio and target trajectory integrity is constructed for the multi-jamming scenario model. Thus, a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in a complex domain is proposed, which is based on the complex domain characteristics of the jamming signal. Finally, radar anti-jamming simulation experiments are performed based on the three jamming sequence strategies. The results show that the proposed method can effectively deal with the main-lobe jamming problem of complex multi-jamming scenarios under time-sequence conditions. Moreover, the average decision-making accuracy was improved, and the average decision-making time was reduced to 405.3 ms compared with the two classical reinforcement learning algorithms.

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) based on deep reinforcement learning (DRL) has made great progress. However, most studies assume relatively simple task scenarios and do not consider the impact of complex task scenarios on UAV flight performance. This paper proposes a DRL-based autonomous navigation algorithm for UAVs, which enables autonomous path planning for UAVs in high-density and highly dynamic environments. This algorithm proposes a state space representation method that contains position information and angle information by analyzing the impact of UAV position changes and angle changes on navigation performance in complex environments. In addition, a dynamic reward function is constructed based on a non-sparse reward function to balance the agent’s conservative behavior and exploratory behavior during the model training process. The results of multiple comparative experiments show that the proposed algorithm not only has the best autonomous navigation performance but also has the optimal flight efficiency in complex environments.

The presence of free gossypol (FG) in cottonseed meal (CSM) greatly limits the use of CSM as a high-quality protein feed. Microbial fermentation is an effective method to simultaneously reduce FG and improve the nutritional value of CSM. In this study, using potato dextrose agar containing acetic gossypol as a selective medium and humus soil from cotton fields as the source, we isolated six strains of fungi capable of tolerating FG. With an inoculation ratio of 8% (8 mL × 106 CFU/mL cells or spores in 100 g fermented CSM), 50% moisture content, and a temperature of 30 °C, CSM was fermented for 5 days. The results showed that strain F had the highest FG removal rate at 56.43%, which was identified as Rhodotorula mucilaginosa (R. mucilaginosa) and named R. mucilaginosa TG529. Further optimization revealed that when the fermentation time was extended to 11 days, TG529 achieved a maximum FG removal rate of 73.29%. Compared to the original sample, treatment with TG529 significantly increased the contents of crude protein, acid-soluble protein, and 18 amino acids, while significantly reducing the contents of crude fiber, neutral detergent fiber (NDF), and acid detergent fiber in fermented cottonseed meal (FCSM). Using atmospheric and room temperature plasma for mutagenesis of TG529, it was found that the mutated TG529 significantly increased the contents of acid-soluble protein and phenylalanine in FCSM, significantly reduced the NDF content, and enhanced the FG removal rate to 76.50%. In summary, this study screened and mutagenized a strain of FG detoxifying fungus, R. mucilagnosa TG529, which can effectively reduce the FG content and improve the nutritional value of CSM by solid-state fermentation.

Abstract In recent years, terahertz imaging technology has been widely used in security, medicine, and other fields. However, the image resolution is low due to the limits of imaging equipment and diffraction. Traditional super‐resolution methods based on machine learning use artificial paired image datasets, and their image degradation process is quite different from the actual terahertz imaging mechanism. In the paper, an unpaired hybrid attention network is proposed, using the real unpaired high‐resolution and low‐resolution terahertz images as the original input, two sub‐networks are trained: degenerate reconstruction network and super‐resolution network. The degenerate reconstruction network is used to learn the degradation process of the real terahertz imaging system, and reconstruct the generated degradation images into the fixed mapping down‐sampled image of high‐resolution terahertz image. The super‐resolution network uses the generated paired image with fixed correspondence to train the super‐resolution network of paired terahertz images, to improve the imaging resolution of the network in the super‐resolution work of real low‐resolution terahertz images. In addition, the no‐reference image quality evaluation system is introduced to objectively evaluate the network performance. The experimental results show that the UHA‐CycleGAN network outperforms the traditional paired super‐resolution network on both open‐source and self‐built datasets.

Abstract Background Cancer‐associated fibroblasts (CAFs) are potential targets for cancer therapy. Due to the heterogeneity of CAFs, the influence of CAF subpopulations on the progression of lung cancer is still unclear, which impedes the translational advances in targeting CAFs. Methods We performed single‐cell RNA sequencing (scRNA‐seq) on tumour, paired tumour‐adjacent, and normal samples from 16 non‐small cell lung cancer (NSCLC) patients. CAF subpopulations were analyzed after integration with published NSCLC scRNA‐seq data. SpaTial enhanced resolution omics‐sequencing (Stereo‐seq) was applied in tumour and tumour‐adjacent samples from seven NSCLC patients to map the architecture of major cell populations in tumour microenvironment (TME). Immunohistochemistry (IHC) and multiplexed IHC (mIHC) were used to validate marker gene expression and the association of CAFs with immune infiltration in TME. Results A subcluster of myofibroblastic CAFs, POSTN+ CAFs, were significantly enriched in advanced tumours and presented gene expression signatures related to extracellular matrix remodeling, tumour invasion pathways and immune suppression. Stereo‐seq and mIHC demonstrated that POSTN+ CAFs were in close localization with SPP1+ macrophages and were associated with the exhausted phenotype and lower infiltration of T cells. POSTN expression or the abundance of POSTN+ CAFs were associated with poor prognosis of NSCLC. Conclusions Our study identified a myofibroblastic CAF subpopulation, POSTN+ CAFs, which might associate with SPP1+ macrophages to promote the formation of desmoplastic architecture and participate in immune suppression. Furthermore, we showed that POSTN+ CAFs associated with cancer progression and poor clinical outcomes and may provide new insights on the treatment of NSCLC.

Abstract Background Nonkeratinizing nasopharyngeal carcinoma (NK-NPC) has a strong association with Epstein-Barr virus (EBV) infection. The role of NK cells and the tumor cell evolutionary trajectory in NK-NPC remain unclear. In this study, we aim to investigate the function of NK cell and the evolutionary trajectory of tumor cells in NK-NPC by single-cell transcriptomic analysis, proteomics and immunohistochemistry. Methods NK-NPC (n = 3) and normal nasopharyngeal mucosa cases (n = 3) were collected for proteomic analysis. Single-cell transcriptomic data of NK-NPC (n = 10) and nasopharyngeal lymphatic hyperplasia (NLH, n = 3) were obtained from Gene Expression Omnibus (GSE162025 and GSE150825). Quality control, dimension reduction and clustering were based on Seurat software (v4.0.2) process and batch effects were removed by harmony (v0.1.1) software. Normal cells of nasopharyngeal mucosa and tumor cells of NK-NPC were identified using copykat software (v1.0.8). Cell-cell interactions were explored using CellChat software (v1.4.0). Tumor cell evolutionary trajectory analysis was performed using SCORPIUS software (v1.0.8). Protein and gene function enrichment analyses were performed using clusterProfiler software (v4.2.2). Results A total of 161 differentially expressed proteins were obtained between NK-NPC (n = 3) and normal nasopharyngeal mucosa (n = 3) by proteomics (log2 fold change > 0.5 and P value

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.

Enhancing multi-gas detectability using photoacoustic spectroscopy capable of simultaneous detection, highly selectivity and less cross-interference is essential for dissolved gas sensing application. A T-type photoacoustic cell was designed and verified to be an appropriate sensor, due to the resonant frequencies of which are determined jointly by absorption and resonant cylinders. The three designated resonance modes were investigated from both simulation and experiments to present the comparable amplitude responses by introducing excitation beam position optimization. The capability of multi-gas detection was demonstrated by measuring CO, CH4 and C2H2 simultaneously using QCL, ICL and DFB lasers as excitation sources respectively. The influence of potential cross-sensitivity towards humidity have been examined in terms of multi-gas detection. The experimentally determined minimum detection limits of CO, CH4 and C2H2 were 89ppb, 80ppb and 664ppb respectively, corresponding to the normalized noise equivalent absorption coefficients of 5.75 × 10−7 cm−1 W Hz−1/2, 1.97 × 10−8 cm−1 W Hz−1/2 and 4.23 × 10−8 cm−1 W Hz−1/2.

The loss of life and property in economically developed areas due to geological hazards caused by intense ground subsidence is incalculable. As one of the fastest growing areas in the Guangdong-Hong Kong-Macao Greater Bay Area, the study of ground subsidence in Nansha will help to provide a scientific basis for urban planning and improve the capacity of monitoring and prevention of ground subsidence. The combination of coastal soft soil foundation and urbanization conditions creates a certain risk of land subsidence. We chose Nansha District, the geographical center of the Greater Bay Area, as the study area to analyze its surface subsidence characteristics in recent years. The 20-view Sentinel-1A data and SBAS-InSAR technique were used to monitor the ground subsidence in Nansha from 2017 to 2023. The main rate of ground subsidence in Nansha ranges from −19.4 to 7.7 mm/yr and is distributed in the urban area, along the rivers, in the construction area, and in the reclamation area. As of 4 May 2023, the average ground settlement in Nansha is 10.05 mm and the maximum settlement can be up to 142.45 mm. The 6-year total settlement at all four settlement intensities is greater than 60 mm, with the highest value exceeding 110 mm. The cumulative settlement increases with time, but inverse settlement and no settlement also occur at points where settlement is severe. For settlement caused by soft soil consolidation, it is recommended that drainage pipes be installed to accelerate drainage as a means of stabilizing settlement. For settlement caused by groundwater extraction and additional loads on the road surface, it is recommended to rationally extract groundwater and reinforce the foundation of the road surface with severe settlement.

A discernible gap has materialized between the expectations for object detection tasks in optical remote sensing images and the increasingly sophisticated design methods. The flexibility of deep learning object detection algorithms allows the selection and combination of multiple basic structures and model sizes, but this selection process relies heavily on human experience and lacks reliability when faced with special scenarios or extreme data distribution. To address these inherent challenges, this study proposes an approach that leverages deep reinforcement learning within the framework of vision tasks. This study introduces a Task-Risk Consistent Intelligent Detection Framework (TRC-ODF) for object detection in optical remote sensing images. The proposed framework designs a model optimization strategy based on deep reinforcement learning that systematically integrates the available information from images and vision processes. The core of the reinforcement learning agent is the proposed task-risk consistency reward mechanism, which is the driving force behind the optimal prediction allocation in the decision-making process. To verify the effectiveness of the proposed framework, multiple sets of empirical evaluations are conducted on representative optical remote sensing image datasets: RSOD, NWPU VHR-10, and DIOR. When applying the proposed framework to representative advanced detection models, the mean average precision (mAP@0.5 and mAP@0.5:0.95) is improved by 0.8–5.4 and 0.4–2.7, respectively. The obtained results showcase the considerable promise and potential of the TRC-ODF framework to address the challenges associated with object detection in optical remote sensing images.

ObjectiveThis study aims to develop and evaluate a predictive nomogram for early assessment risk factors of gestational diabetes mellitus (GDM) during early pregnancy term, so as to help early clinical management and intervention.MethodsA total of 824 pregnant women at Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province from 1 February 2020 to 30 April 2020 were enrolled in a retrospective observational study and comprised the training dataset. Routine clinical and laboratory information was collected; we applied least absolute shrinkage and selection operator (LASSO) logistic regression and multivariate ROC risk analysis to determine significant predictors and establish the nomogram, and the early pregnancy files (gestational weeks 12–16, n = 392) at the same hospital were collected as a validation dataset. We evaluated the nomogram via the receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis (DCA).ResultsWe conducted LASSO analysis and multivariate regression to establish a GDM nomogram during the early pregnancy term; the five selected risk predictors are as follows: age, blood urea nitrogen (BUN), fibrinogen-to-albumin ratio (FAR), blood urea nitrogen-to-creatinine ratio (BUN/Cr), and blood urea nitrogen-to-albumin ratio (BUN/ALB). The calibration curve and DCA present optimal predictive power. DCA demonstrates that the nomogram could be applied clinically.ConclusionAn effective nomogram that predicts GDM should be established in order to help clinical management and intervention at the early gestational stage.

Acute pandysautonomia-associated distal esophageal spasm is a rare disease with an unclear etiology. Here, we describe a 12-year-old boy with an acute pandysautonomia-associated distal esophageal spasm who was treated using a peroral endoscopic myotomy (POEM). The patient's clinical features included recurrent dysphagia, nausea, vomiting, growth retardation, and signs of autonomic nerve dysfunction (e.g., a decreased production of tears and sweat, and an increased production of saliva). Signs of the distal esophageal spasm were visible in upper gastrointestinal radiography, endoscopy, and high-resolution esophageal manometry. After the POEM, the patient exhibited improvements in nausea and vomiting, and his dysphagia symptoms were relieved by the 6-month follow-up visit. However, the patient's neurological problems persisted. The satisfactory short-term clinical responses in our patient suggest that POEM is feasible, safe, and effective for the treatment of acute pandysautonomia-associated distal esophageal spasms in children.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging hemorrhagic fever caused by tick-borne SFTS virus. Here, Wang et al. characterize transcriptomic and epigenetic changes in infected patients and correlate them with clinical parameters to improve the understanding of disease progression.

Abstract Cerebral ischemia, a common cerebrovascular disease, is characterized by functional deficits and apoptotic cell death. Autophagy, a type of programmed cell death, plays critical roles in controlling neuronal damage and metabolic homeostasis, and has been inextricably linked to cerebral ischemia. We previously identified a short peptide aptamer from collapsin response mediator protein 2 (CRMP2), designated the Ca2+ channel-binding domain 3 (CBD3) peptide, that conferred protection against excitotoxicity and traumatic brain injury. ST2-104, a nona-arginine (R9)-fused CBD3 peptide, exerted beneficial effects on neuropathic pain and was neuroprotective in a model of Alzheimer’s disease; however, the effect of ST2-104 on cerebral ischemia and its mechanism of action have not been studied. In this study, we modeled cerebral ischemia–reperfusion injury in rats with the middle cerebral artery occlusion (MCAO) as well as challenged SH-SY5Y neuroblastoma cells with glutamate to induce toxicity to interrogate the effects of ST2-104 on autophagy following ischemic/excitotoxic insults. ST2-104 reduced the infarct volume and improved the neurological score of rats subjected to MCAO. ST2-104 protected SH-SY5Y cells from death following glutamate exposure via blunting apoptosis and autophagy as well as limiting excessive calcium entry. 3-Methyladenine (3-MA), an inhibitor of autophagy, promoted the effects of ST2-104 in inhibiting apoptosis triggered by glutamate while rapamycin, an activator of autophagy, failed to do so. ST2-104 peptide reversed glutamate-induced apoptosis via inhibiting Ca2+/CaM-dependent protein kinase kinase β (CaMKKβ)-mediated autophagy, which was partly enhanced by STO-609 (an inhibitor of CaMKKβ). ST2-104 attenuated neuronal apoptosis by inhibiting autophagy through CaMKKβ/AMPK/mTOR pathway. Our results suggest that the neuroprotective effect of ST2-104 are due to actions on the crosstalk between apoptosis and autophagy via the CaMKKβ/AMPK/mTOR signaling pathway. The findings present novel insights into the potential neuroprotection of ST2-104 in cerebral ischemia.

Lanthanum oxide powder was added to improve the microstructure and properties of a Ti-6Al-4V alloy part during cold metal transfer (CMT) based on wire arc additive manufacturing (WAAM). The macrostructure, microstructure and properties of the part were studied by using an optical microscope (OM), scanning electron microscope (SEM), electron backscatter(ed) diffraction (EBSD), microhardness evaluation and a tensile test. The results showed that the β grain size and martensite (α) size were reduced by adding lanthanum oxide powder. The texture intensity of the part also decreased due to the change in microstructure. Accordingly, the microhardness and tensile properties of the part obtained by adding lanthanum oxide were improved. In addition, based on the interdependence theory model, adding La2O3 particles in the molten pool as heterogeneous nucleation points could reduce the distance of XSD, so fine equiaxed β grains can be formed in the deposition layer.

ObjectiveTo investigate the effects of helichrysum arenarium flavonoid extract on high glucose damaged cardiomyocytes and the alleviation of myocardial inflammation in diabetic rats.MethodsThe study was divided into two parts, the first part was a cellular experiment in which a high-glucose cardiomyocyte injury model (H9C2) was established using a high-glucose culture medium, divided into low (group N1, 6.25 μg/mL), medium (group N2, 12.5 μg/mL), high dose group (group N3, 25 μg/mL) of helichrysum arenarium intervention and a model control group. The levels of enzyme activities [creatine kinase (CK) and lactate dehydrogenase (LDH)] in each group of H9c2 cells were measured by Enzyme-linked immunosorbent assay (ELISA), the expression levels of apoptotic proteins (Bax and Bcl-2) by western blot (WB), and the expression levels of inflammatory factors [tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6)] by RT-qPCR. The second part is animal experiments, after establishing the diabetic rat model, we used helichrysum arenarium flavonoid extract to intervene SD rats, divided into helichrysum arenarium intervention low (group S1, 250 mg/kg), medium (group S2, 500 mg/kg), high dose group (group S3, 1 g/kg), SD rat model group. Hematoxylin-eosin (HE) staining was used to observe myocardial tissue lesions, and Real Time Quantitative PCR (RT-qPCR) method was used to detect inflammatory (TNF-α, IL-1β, and IL-6) infiltration in myocardial tissue.ResultsCellular experiments: The activity levels of enzymes such as CK and LDH and the levels of inflammatory factors such as TNF-α, IL-1β, and IL-6 in damaged cardiac myocytes were significantly decreased after helichrysum arenarium intervention; the expression levels of Bax protein were significantly down-regulated and the expression levels of Bcl-2 protein expression were significantly up-regulated. Animal experiment: HE staining showed that the model group had widened intercellular spaces, interstitial edema and obvious inflammatory cell infiltration in cardiac muscle tissue. After the intervention of helichrysum arenarium, the collagen fibers of rat myocardial cells were significantly reduced and cell degeneration was alleviated. Animal experiment: HE staining showed that the model group had widened intercellular spaces, interstitial edema and obvious inflammatory cell infiltration in cardiac muscle tissue. After the intervention of helichrysum arenarium, the collagen fibers of rat myocardial cells were significantly reduced and cell degeneration was alleviated; the levels of TNF-α, IL-1β, IL-6 and other inflammatory factors in myocardial tissues were significantly decreased.ConclusionThe helichrysum arenarium flavonoid extract can reduce the degree of damage of H9C2 cells induced by high glucose and decrease the cellular inflammatory response, and its mechanism of action may be achieved by regulating the apoptotic factors Bax and Bcl-2. In addition, the extract of helichrysum arenarium can reduce the histopathological damage of myocardium in diabetic rats, decrease the inflammatory response in the tissue, and achieve the effect of myocardial protection.

Objective The clinical importance of aberrantly expressed microRNAs (miRNAs) in diagnosing inflammatory bowel disease (IBD) has not been well established, so was investigated in this systematic review and meta-analysis. Methods Articles in online databases from inception to March 17, 2021 were retrieved. Random effects meta-analysis was used to obtain sensitivity, specificity, positive (PLRs) and negative likelihood ratios (NLRs), diagnostic odds ratios (DORs), and areas under the curve (AUC) with 95% confidence intervals (CI) for IBD diagnosis. Results Of 117 studies reporting altered miRNA expression in IBD included in the systematic review, 15 involving 937 patients with IBD and 707 controls, 22 miRNAs, and two miRNA panels were eligible for meta-analysis. Pooled analyses showed a moderate diagnostic accuracy for miRNAs in the IBD diagnosis, with a sensitivity of 0.80 (95% CI: 0.79–0.82), specificity of 0.84 (95% CI: 0.82–0.86), DOR of 21.19 (95% CI: 13.90–32.31), and AUC of 0.89. Subgroup analyses revealed a better performance in patients with ulcerative colitis (AUC, 0.93) than Crohn’s disease (AUC, 0.84). Consistent upregulation of miR-21, miR-16, and miR-192 in blood with a high-moderate diagnostic accuracy was found in at least two studies. Conclusions These findings suggest miRNAs are credible diagnostic biomarkers in IBD.

Though divalent-europium-based complexes are promising materials for next-generation light-emitting devices, their poor air stability limits their applicability. Here, the authors report the design of air stable divalent-europium-based complexes for efficient organic light-emitting diodes.

When the Dempster–Shafer evidence theory is applied to the field of information fusion, how to reasonably transform the basic probability assignment (BPA) into probability to improve decision-making efficiency has been a key challenge. To address this challenge, this paper proposes an efficient probability transformation method based on neural network to achieve the transformation from the BPA to the probabilistic decision. First, a neural network is constructed based on the BPA of propositions in the mass function. Next, the average information content and the interval information content are used to quantify the information contained in each proposition subset and combined to construct the weighting function with parameter r. Then, the BPA of the input layer and the bias units are allocated to the proposition subset in each hidden layer according to the weight factors until the probability of each single-element proposition with the variable is output. Finally, the parameter r and the optimal transform results are obtained under the premise of maximizing the probabilistic information content. The proposed method satisfies the consistency of the upper and lower boundaries of each proposition. Extensive examples and a practical application show that, compared with the other methods, the proposed method not only has higher applicability, but also has lower uncertainty regarding the transformation result information.

Despite the well-known benefits of breastfeeding and the World Health Organization’s breastfeeding recommendations for COVID-19 infected mothers, whether these mothers should be encouraged to breastfeed is under debate due to concern about the risk of virus transmission and lack of evidence of breastmilk’s protective effects against the virus. Here, we provide a molecular basis for the breastfeeding recommendation through mass spectrometry (MS)-based proteomics and glycosylation analysis of immune-related proteins in both colostrum and mature breastmilk collected from COVID-19 patients and healthy donors. The total protein amounts in the COVID-19 colostrum group were significantly higher than in the control group. While casein proteins in COVID-19 colostrum exhibited significantly lower abundances, immune-related proteins, especially whey proteins with antiviral properties against SARS-CoV-2, were upregulated. These proteins were detected with unique site-specific glycan structures and improved glycosylation diversity that are beneficial for recognizing epitopes and blocking viral entry. Such adaptive differences in milk from COVID-19 mothers tended to fade in mature milk from the same mothers one month postpartum. These results suggest that feeding infants colostrum from COVID-19 mothers confers both nutritional and immune benefits, and provide molecular-level insights that aid breastmilk feeding decisions in cases of active infection.

Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance.

In the title compound, C17H19N3O·CH3OH, the hydrazone molecule exists in a trans geometry with respect to the methylidene unit and the dihedral angle between the two substituted benzene rings is 42.6 (2)°. In the crystal, the components are linked through N—H...O and O—H...O hydrogen bonds, forming [100] chains of alternating hydrazone and methanol molecules.

In the title compound, C14H9ClN4O5, the molecule exists in a trans geometry with respect to the methylidene unit. The dihedral angle between the two substituted benzene rings is 62.7 (2)°. In the crystal, inversion dimers linked by pairs of N—H...O hydrogen bonds generate R22(8) loops.

In the title compound, C16H17N3O2·0.5H2O, the two hydrazide molecules are approximately planar: the dihedral angles between the two substituted benzene rings are 7.7 (2) and 4.2 (2)°. Both hydrazone molecules exist in a trans geometry with respect to their methylidene units. In the crystal, the water molecule lies between the two organic molecules and makes bifurcated O—H...(N,O) hydrogen bonds to both of them. The hydrazide molecules form N—H...O and O—H...O hydrogen bonds, resulting in a three-dimensional network.

There are two independent PdII complex molecules in the asymmetric unit of the title compound, [PdCl2{Fe(C5H5)(C24H19NP)}]·0.5CH2Cl2. One ferrocenyl ring of one complex molecule is disordered over two sites with half-occupancy for each component. Both PdII cations adopt a distorted square-planar coordination geometry with a bidentate [2-(diphenylphosphino)phenyliminomethyl]ferrocene ligand and two chloride anions.