Iridium(III) complexes conjugated with naproxen exhibit potent anti-tumor activities by inducing mitochondrial damage, modulating inflammation, and enhancing immunity.

A series of Ir(III)-naproxen (NPX) conjugates with the molecular formula [Ir(C^N)2bpy(4-CH2ONPX-4'-CH2ONPX)](PF6) (Ir-NPX-1-3) were designed and synthesized, including C^N = 2-phenylpyridine (ppy, Ir-NPX-1), 2-(2-thienyl)pyridine (thpy, Ir-NPX-2) and 2-(2,4-difluorophenyl)pyridine (dfppy, Ir-NPX-3). Cytotoxicity tests showed that Ir-NPX-1-3 exhibited excellent antitumor activity, especially in A549R cells. The cellular uptake experiment showed that the complexes were mainly localized in mitochondria, and induced apoptosis in A549R cells by damaging the structure and function of mitochondria. The main manifestations are a decrease in the mitochondrial membrane potential (MMP), an increase in reactive oxygen species (ROS) levels, and cell cycle arrest. Furthermore, Ir-NPX-1-3 could inhibit the migration and colony formation of cancer cells, demonstrating potential anti-metastatic ability. Finally, the anti-inflammatory and immunological applications of Ir-NPX-1-3 were verified. The downregulation of cyclooxygenase-2 (COX-2) and programmed death-ligand 1 (PD-L1) expression levels and the release of immunogenic cell death (ICD) related signaling molecules such as damage-associated molecular patterns (DAMPs) (cell surface calreticulin (CRT), high mobility group box 1 (HMGB1), and adenosine triphosphate (ATP)) indicate that these Ir(III) -NPX conjugates are novel ICD inducers with synergistic effects in multiple anti-tumor pathways.

mTOR signaling pathway regulation HIF-1 α effects on LPS induced intestinal mucosal epithelial model damage.

BACKGROUND: Sepsis-induced small-intestinal injury is associated with increased morbidity and mortality. Our previous study and other papers have shown that HIF-1α has a protective effect on intestinal mucosal injury in septic rats. The purpose of this study is to further verify the protective effect of HIF-1α on intestinal mucosa and its molecular mechanism in vitro experiments. METHODS: Caco-2 cells were selected and experiment was divided into 2 parts. Part I: HIF-1α activator and inhibitor were used to treat lipopolysacchrides (LPS)-stimulated Caco-2 cells respectively, to explore the effect of HIF-1α on LPS induced Caco-2 cell epithelial model; Part II: mTOR activator or inhibitor combined with or without HIF-1α activator, inhibitor to treat LPS-stimulated Caco-2 cells respectively, and then the molecular mechanism of HIF-1α reducing LPS induced Caco-2 cell epithelial model damage was detected. RESULTS: The results showed that HIF-1α activator decreased the permeability and up regulated tight junction (TJ) expression, while HIF-1α inhibitor had the opposite effect with the HIF-1α activator. mTOR activation increased, while mTOR inhibition decreased HIF-1α protein and expression of its downstream target molecules, which can be attenuated by HIF-1α activator or inhibitor. CONCLUSION: This study once again confirmed that HIF-1α alleviates LPS-induced mucosal epithelial model damage through P70S6K signalling pathway. It is of great value to explore whether HIF-2α plays crucial roles in the regulation of mucosal epithelial model functions in the future.

Extracorporeal membrane oxygenation technology for adults: an evidence mapping based on systematic reviews.

BACKGROUND: Extracorporeal membrane oxygenation (ECMO) is a cutting-edge life-support measure for patients with severe cardiac and pulmonary illnesses. Although there are several systematic reviews (SRs) about ECMO, it remains to be seen how quality they are and how efficacy and safe the information about ECMO they describe is in these SRs. Therefore, performing an overview of available SRs concerning ECMO is crucial. METHODS: We searched four electronic databases from inception to January 2023 to identify SRs with or without meta-analyses. The Assessment of Multiple Systematic Reviews 2 (AMSTAR-2) tool, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system were used to assess the methodological quality, and evidence quality for SRs, respectively. A bubble plot was used to visually display clinical topics, literature size, number of SRs, evidence quality, and an overall estimate of efficacy. RESULTS: A total of 17 SRs met eligibility criteria, which were combined into 9 different clinical topics. The methodological quality of the included SRs in this mapping was "Critically low" to "Moderate". One of the SRs was high-quality evidence, three on moderate, three on low, and two on very low-quality evidence. The most prevalent study used to evaluate ECMO technology was observational or cohort study with frequently small sample sizes. ECMO has been proven beneficial for severe ARDS and ALI due to the H1N1 influenza infection. For ARDS, ALF or ACLF, and cardiac arrest were concluded to be probably beneficial. For dependent ARDS, ARF, ARF due to the H1N1 influenza pandemic, and cardiac arrest of cardiac origin came to an inconclusive conclusion. There was no evidence for a harmful association between ECMO and the range of clinical topics. CONCLUSIONS: There is limited available evidence for ECMO that large sample, multi-center, and multinational RCTs are needed. Most clinical topics are reported as beneficial or probably beneficial of SRs for ECMO. Evidence mapping is a valuable and reliable methodology to identify and present the existing evidence about therapeutic interventions.

Prognosis Prediction of Diffuse Large B-cell Lymphoma in 18F-FDG PET images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

[Research on Position Verification of Multi-Leaf Collimator (MLC) and Dose Verification Based on Electronic Portal Imaging Device].

Objective: A quality control (QC) system based on the electronic portal imaging device (EPID) system was used to realize the Multi-Leaf Collimator (MLC) position verification and dose verification functions on Primus and VenusX accelerators. Methods: The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation. The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation. The dose data obtained by EPID and two-dimensional matrix (MapCheck/PTW) were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis. Results: The position error of VenusX MLC was less than 1 mm. The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID. For the dose reconstructed by EPID, the average γ passing rates of Primus were 98.86% and 91.39% under the criteria of 3%/3 mm, 10% threshold and 2%/2 mm, 10% threshold, respectively. The average γ passing rates of VenusX were 98.49% and 91.11%, respectively. Conclusion: The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

Online quantification of nicotine in e-cigarette aerosols by vacuum ultraviolet photoionization mass spectrometry.

The growing popularity of e-cigarettes and the associated risks of nicotine addiction present a new challenge to global public health security. Measuring the nicotine levels in e-cigarette aerosols is essential to assess the safety of e-cigarettes. In this study, a rapid in situ method was developed for online quantification of nicotine in e-cigarette aerosols by using a homemade vacuum ultraviolet photoionization aerosol mass spectrometer (VUV-AMS). E-cigarette liquids with different nicotine concentrations were prepared to generate aerosols containing different levels of nicotine, which were employed as the calibration sources for nicotine quantification by VUV-AMS. The results showed that the mass concentration of nicotine in e-cigarette aerosols has a good linear relationship with its signal intensity in the mass spectrum, and the limits of detection and quantitation of nicotine by VUV-AMS were found to be 2.0 and 6.2 µg per puff respectively. Then the online method was utilized to measure five commercial e-cigarettes, and their nicotine yields were determined to be between 31 and 188 µg per puff with the nicotine fluxes from 7.7 to 70 µg s-1, agreeing with the results of the gas chromatography with a flame ionization detector (GC-FID). This study demonstrated the feasibility and advantages of VUV-AMS for quick quantification of nicotine in e-cigarette aerosols within seconds.

Identification and validation of prognostic and immunotherapeutic responses in esophageal squamous carcinoma based on hypoxia phenotype-related genes.

The study aimed to investigate the clinical significance of the interaction between hypoxia and the immune system in esophageal squamous cell carcinoma (ESCC) microenvironment. A comprehensive evaluation of 13 hypoxia phenotype-related genes (HPRs) was conducted using data from TCGA-ESCC and two GEO cohorts. Three distinct HPRclusters were identified, and the HPRscore was established as an independent prognostic factor (p = 0.001), with higher scores indicating poorer prognosis. The HPRscore was validated in various immunotherapy cohorts, demonstrating its efficacy in evaluating immunotherapy and chemotherapy outcomes. Additionally, phenome-wide association study (PheWAS) analysis showed that PKP1 had no significant correlation with other traits at the gene level. PKP1 was identified as a potential prognostic marker for ESCC, with upregulated expression observed in ESCC patients. In vitro experiments showed that the knockdown of PKP1 inhibited ESCC cell proliferation and migration. These findings suggest that the novel HPRscore and PKP1 may serve as prognostic tools and therapeutic targets for ESCC patients.

Cyanometallic charge engineering in spin crossover metal-organic frameworks.

In this study, we successfully synthesize cationic/neutral/anionic inverse-Hofmann-type spin crossover (SCO) frameworks with 1,1,2,2-tetrakis(4-(pyridine-4-yl)phenyl)-ethene ligand by means of cyanometallic charge engineering strategy. The cationic and neutral frameworks exhibit single-step thermally induced spin transition behaviors, while the SCO capability of anionic framework can be aroused by partial desolvation. This strategy provides a new idea to construct ionic SCO frameworks and extends the toolkit for SCO materials.

A novel disulfidptosis-related prognostic gene signature and experimental validation identify ACTN4 as a novel therapeutic target in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies. OBJECTIVE: To investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD. METHODS: We investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD. RESULTS: Ten differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P< 0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes. CONCLUSIONS: Our study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.

Exosomal MALAT1 promotes the proliferation of esophageal squamous cell carcinoma through glyoxalase 1-dependent methylglyoxal removal.

In previous study we characterized the oncogenic role of long non-coding RNA MALAT1 in esophageal squamous cell carcinoma (ESCC), but the detailed mechanism remains obscure. Here we identified glyoxalase 1 (GLO1) as the most possible executor of MALAT1 by microarray screening. GLO1 is responsible for degradation of cytotoxic methylglyoxal (MGO), which is by-product of tumor glycolysis. Accumulated MGO may lead to glycation of DNA and protein, resulting in elevated advanced glycation end products (AGEs), while glyoxalase 1 detoxify MGO to alleviate its cytotoxic effect to tumor cells. GLO1 interfering led to accumulation of AGEs and following activation of DNA injury biomarkers, which lead to cell cycle arrest and growth inhibition. In silico analysis based on online database revealed abundant enrichment of histone acetylation marker H3K27ac in GLO1 promotor, and acetyltransferase inhibitor C646 declined GLO1 expression. Acetyltransferase KAT2B, which was also identified as a target of MALAT, mediated histone lysine acetylation of GLO1 promotor, which was confirmed by ChIP-qPCR experiment. Shared binding sites of miR-206 were found on MALAT1 and KAT2B mRNA. Dual-luciferase reporter assays confirmed interaction within MALAT1-miR-206-GLO1. Finally, we identified MALAT1 encapsuled by exosome from donor cells, and transferred malignant behaviors to recipient cells. The secreted exosomes may enter circulation, and serum MALAT1 level combined with traditional tumor markers showed potential power for ESCC diagnosis.

A Lightweight Image Super-Resolution Reconstruction Algorithm Based on the Residual Feature Distillation Mechanism.

In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.

Effects of Freeze-Drying Processes on the Acoustic Absorption Performance of Sustainable Cellulose Nanocrystal Aerogels.

Cellulose aerogels have great prospects for noise reduction applications due to their sustainable value and superior 3D interconnected porous structures. The drying principle is a crucial factor in the preparation process for developing high-performance aerogels, particularly with respect to achieving high acoustic absorption properties. In this study, multifunctional cellulose nanocrystal (CNC) aerogels were conveniently prepared using two distinct freeze-drying principles: refrigerator conventional freezing (RCF) and liquid nitrogen unidirectional freezing (LnUF). The results indicate that the rapid RCF process resulted in a denser CNC aerogel structure with disordered larger pores, causing a stronger compressive performance (Young's modulus of 40 kPa). On the contrary, the LnUF process constructed ordered structures of CNC aerogels with a lower bulk density (0.03 g/cm3) and smaller apertures, resulting in better thermal stability, higher diffuse reflection across visible light, and especially increased acoustic absorption performance at low-mid frequencies (600-3000 Hz). Moreover, the dissipation mechanism of sound energy in the fabricated CNC aerogels is predicted by a designed porous media model. This work not only paves the way for optimizing the performance of aerogels through structure control, but also provides a new perspective for developing sustainable and efficient acoustic absorptive materials for a wide range of applications.

A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer.

OBJECTIVE: This study aimed to present a deep-learning network called contrastive learning-based cycle generative adversarial networks (CLCGAN) to mitigate streak artifacts and correct the CT value in four-dimensional cone beam computed tomography (4D-CBCT) for dose calculation in lung cancer patients. METHODS: 4D-CBCT and 4D computed tomography (CT) of 20 patients with locally advanced non-small cell lung cancer were used to paired train the deep-learning model. The lung tumors were located in the right upper lobe, right lower lobe, left upper lobe, and left lower lobe, or in the mediastinum. Additionally, five patients to create 4D synthetic computed tomography (sCT) for test. Using the 4D-CT as the ground truth, the quality of the 4D-sCT images was evaluated by quantitative and qualitative assessment methods. The correction of CT values was evaluated holistically and locally. To further validate the accuracy of the dose calculations, we compared the dose distributions and calculations of 4D-CBCT and 4D-sCT with those of 4D-CT. RESULTS: The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the 4D-sCT increased from 87% and 22.31 dB to 98% and 29.15 dB, respectively. Compared with cycle consistent generative adversarial networks, CLCGAN enhanced SSIM and PSNR by 1.1% (p < 0.01) and 0.42% (p < 0.01). Furthermore, CLCGAN significantly decreased the absolute mean differences of CT value in lungs, bones, and soft tissues. The dose calculation results revealed a significant improvement in 4D-sCT compared to 4D-CBCT. CLCGAN was the most accurate in dose calculations for left lung (V5Gy), right lung (V5Gy), right lung (V20Gy), PTV (D98%), and spinal cord (D2%), with the relative dose difference were reduced by 6.84%, 3.84%, 1.46%, 0.86%, 3.32% compared to 4D-CBCT. CONCLUSIONS: Based on the satisfactory results obtained in terms of image quality, CT value measurement, it can be concluded that CLCGAN-based corrected 4D-CBCT can be utilized for dose calculation in lung cancer.

Magnetically driven formation of 3D freestanding soft bioscaffolds.

3D soft bioscaffolds have great promise in tissue engineering, biohybrid robotics, and organ-on-a-chip engineering applications. Though emerging three-dimensional (3D) printing techniques offer versatility for assembling soft biomaterials, challenges persist in overcoming the deformation or collapse of delicate 3D structures during fabrication, especially for overhanging or thin features. This study introduces a magnet-assisted fabrication strategy that uses a magnetic field to trigger shape morphing and provide remote temporary support, enabling the straightforward creation of soft bioscaffolds with overhangs and thin-walled structures in 3D. We demonstrate the versatility and effectiveness of our strategy through the fabrication of bioscaffolds that replicate the complex 3D topology of branching vascular systems. Furthermore, we engineered hydrogel-based bioscaffolds to support biohybrid soft actuators capable of walking motion triggered by cardiomyocytes. This approach opens new possibilities for shaping hydrogel materials into complex 3D morphologies, which will further empower a broad range of biomedical applications.

Investigation of the Alternations in Lipid Oxidation and Lipase Activity in Air-Dried Hairtail (Trichiurus lepturus) during Chilled Storage.

The effects of water content and water activity on the lipid stability of air-dried hairtail (Trichiurus haumela) were investigated during chilled storage. Air-dried hairtail samples with high and low water contents were comparatively analyzed over 8 days of storage at 4 °C. The results indicated that the decreases in water activity and increases in the NaCl content significantly inhibited lipid oxidation in the air-dried hairtail samples. The peroxidation value (PV), conjugated diene value (CD), thiobarbituric acid reactive substance (TBARS) value, and p-anisidine value (p-AnV) of the air-dried hairtail significantly increased with the extension of storage time. The low water content significantly inhibited the activity of neutral and alkaline lipase, in addition to lipoxygenase, and retarded the rapid increases in the non-esterified fatty acid (NEFA) content in the hairtail samples. The correlation analysis results showed that the TBARS, p-AnV, and lipase activity were positively correlated in the air-dried hairtail samples, and the lower water content significantly inhibited the progress of lipid oxidation. This study offers a theoretical framework for the industrial processing and storage of air-dried hairtail products.

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion.

Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan-Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.

A Comprehensive Analysis of Auxin Response Factor Gene Family in Melastoma dodecandrum Genome.

Auxin Response Factors (ARFs) mediate auxin signaling and govern diverse biological processes. However, a comprehensive analysis of the ARF gene family and identification of their key regulatory functions have not been conducted in Melastoma dodecandrum, leading to a weak understanding of further use and development for this functional shrub. In this study, we successfully identified a total of 27 members of the ARF gene family in M. dodecandrum and classified them into Class I-III. Class II-III showed more significant gene duplication than Class I, especially for MedARF16s. According to the prediction of cis-regulatory elements, the AP2/ERF, BHLH, and bZIP transcription factor families may serve as regulatory factors controlling the transcriptional pre-initiation expression of MedARF. Analysis of miRNA editing sites reveals that miR160 may play a regulatory role in the post-transcriptional expression of MeARF. Expression profiles revealed that more than half of the MedARFs exhibited high expression levels in the stem compared to other organs. While there are some specific genes expressed only in flowers, it is noteworthy that MedARF16s, MedARF7A, and MedARF9B, which are highly expressed in stems, also demonstrate high expressions in other organs of M. dodecandrum. Further hormone treatment experiments revealed that these MedARFs were sensitive to auxin changes, with MedARF6C and MedARF7A showing significant and rapid changes in expression upon increasing exogenous auxin. In brief, our findings suggest a crucial role in regulating plant growth and development in M. dodecandrum by responding to changes in auxin. These results can provide a theoretical basis for future molecular breeding in Myrtaceae.

General Analysis of Heat Shock Factors in the Cymbidium ensifolium Genome Provided Insights into Their Evolution and Special Roles with Response to Temperature.

Heat shock factors (HSFs) are the key regulators of heat stress responses and play pivotal roles in tissue development and the temperature-induced regulation of secondary metabolites. In order to elucidate the roles of HSFs in Cymbidium ensifolium, we conducted a genome-wide identification of CeHSF genes and predicted their functions based on their structural features and splicing patterns. Our results revealed 22 HSF family members, with each gene containing more than one intron. According to phylogenetic analysis, 59.1% of HSFs were grouped into the A subfamily, while subfamily HSFC contained only two HSFs. And the HSF gene families were differentiated evolutionarily between plant species. Two tandem repeats were found on Chr02, and two segmental duplication pairs were observed on Chr12, Chr17, and Chr19; this provided evidence for whole-genome duplication (WGD) events in C. ensifolium. The core region of the promoter in most CeHSF genes contained cis-acting elements such as AP2/ERF and bHLH, which were associated with plant growth, development, and stress responses. Except for CeHSF11, 14, and 19, each of the remaining CeHSFs contained at least one miRNA binding site. This included binding sites for miR156, miR393, and miR319, which were responsive to temperature and other stresses. The HSF gene family exhibited significant tissue specificity in both vegetative and floral organs of C. ensifolium. CeHSF13 and CeHSF15 showed relatively significant expression in flowers compared to other genes. During flower development, CeHSF15 exhibited markedly elevated expression in the early stages of flower opening, implicating critical regulatory functions in organ development and floral scent-related regulations. During the poikilothermic treatment, CeHSF14 was upregulated over 200-fold after 6 h of heat treatment. CeHSF13 and CeHSF14 showed the highest expression at 6 h of low temperature, while the expression of CeHSF15 and CeHSF21 continuously decreased at a low temperature. The expression patterns of CeHSFs further confirmed their role in responding to temperature stress. Our study may help reveal the important roles of HSFs in plant development and metabolic regulation and show insight for the further molecular design breeding of C. ensifolium.

[Multisystem inflammatory syndrome in children in the context of coronavirus disease 2019 pandemic]. / æ–°åž‹å† çŠ¶ç— æ¯’æ„ŸæŸ“æµè¡ŒèƒŒæ™¯ä¸‹çš„å„¿ç«¥å¤šç³»ç»Ÿç‚Žç—‡ç»¼åˆå¾.

Multisystem inflammatory syndrome in children (MIS-C) is a complex syndrome characterized by multi-organ involvement that has emerged in the context of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak. The clinical presentation of MIS-C is similar to Kawasaki disease but predominantly presents with fever and gastrointestinal symptoms, and severe cases can involve toxic shock and cardiac dysfunction. Epidemiological findings indicate that the majority of MIS-C patients test positive for SARS-CoV-2 antibodies. The pathogenesis and pathophysiology of MIS-C remain unclear, though immune dysregulation following SARS-CoV-2 infection is considered a major contributing factor. Current treatment approaches for MIS-C primarily involve intravenous immunoglobulin therapy and symptomatic supportive care. This review article provides a comprehensive overview of the definition, epidemiology, pathogenesis, clinical presentation, diagnosis, treatment, and prognosis of MIS-C.

Efficient Defect Detection of Rotating Goods under the Background of Intelligent Retail.

Dynamic visual vending machines are rapidly growing in popularity, offering convenience and speed to customers. However, there is a prevalent issue with consumers damaging goods and then returning them to the machine, severely affecting business interests. This paper addresses the issue from the standpoint of defect detection. Although existing industrial defect detection algorithms, such as PatchCore, perform well, they face challenges, including handling goods in various orientations, detection speeds that do not meet real-time monitoring requirements, and complex backgrounds that hinder detection accuracy. These challenges hinder their application in dynamic vending environments. It is crucial to note that efficient visual features play a vital role in memory banks, yet current memory repositories for industrial inspection algorithms do not adequately address the problem of location-specific feature redundancy. To tackle these issues, this paper introduces a novel defect detection algorithm for goods using adaptive subsampling and partitioned memory banks. Firstly, Grad-CAM is utilized to extract deep features, which, in combination with shallow features, mitigate the impact of complex backgrounds on detection accuracy. Next, graph convolutional networks extract rotationally invariant features. The adaptive subsampling partitioned memory bank is then employed to store features of non-defective goods, which reduces memory consumption and enhances training speed. Experimental results on the MVTec AD dataset demonstrate that the proposed algorithm achieves a marked improvement in detection speed while maintaining accuracy that is comparable to state-of-the-art models.