IEA-Net: Internal and External Dual-Attention Medical Segmentation Network with High-Performance Convolutional Blocks.

Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to adapt to the task of human organ segmentation. Although methods containing attention mechanisms have made good progress in the field of semantic segmentation, most of the current attention mechanisms are limited to a single sample, while the number of samples of human organ images is large, ignoring the correlation between the samples is not conducive to image segmentation. In order to solve these problems, an internal and external dual-attention segmentation network (IEA-Net) is proposed in this paper, and the ICSwR (interleaved convolutional system with residual) module and the IEAM module are designed in this network. The ICSwR contains interleaved convolution and hopping connection, which are used for the initial extraction of the features in the encoder part. The IEAM module (internal and external dual-attention module) consists of the LGGW-SA (local-global Gaussian-weighted self-attention) module and the EA module, which are in a tandem structure. The LGGW-SA module focuses on learning local-global feature correlations within individual samples for efficient feature extraction. Meanwhile, the EA module is designed to capture inter-sample connections, addressing multi-sample complexities. Additionally, skip connections will be incorporated into each IEAM module within both the encoder and decoder to reduce feature loss. We tested our method on the Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods.

Enhanced L-theanine production through semi-rational design of γ-glutamylmethylamide synthetase from Methylovorus mays.

The thermal instability of γ-glutamylmethylamide synthetase (GMAS) from Methylovorus mays has imposed limitations on its industrial applications, affecting both stability and activity at reaction temperatures. In this study, disulfide bridges were introduced through a combination of directed evolution and rational design to enhance GMAS stability. Among the variants that we generated, M12 exhibited a 1.46-fold improvement in relative enzyme activity and a 6.23-fold increase in half-life at 40℃ compared to the wild-type GMAS. Employing variant M12 under optimal conditions, we achieved the production of 645.7 mM (112.49 g/L) L-theanine with a productivity of 29.3 mM/h, from 800 mM substrate in an ATP regeneration system. Our strategy significantly enhances the biosynthesis efficiency of L-theanine by preserving the structural stability of the enzyme during the catalysis process.

Impact of Lymph Node Dissection for Patients With Clinically Node-Negative Intrahepatic Cholangiocarcinoma: A Multicenter Cohort Study.

Background: Lymph node status is a prominent prognostic factor for intrahepatic cholangiocarcinoma (ICC). However, the prognostic value of performing lymph node dissection (LND) in patients with clinical node-negative ICC remains controversial. The aim of this study was to evaluate the clinical value of LND on long-term outcomes in this subgroup of patients. Methods: We retrospectively analyzed patients who underwent radical liver resection for clinically node-negative ICC from three tertiary hepatobiliary centers. The propensity score matching analysis at 1:1 ratio based on clinicopathological data was conducted between patients with and without LND. Recurrence-free survival (RFS) and overall survival (OS) were compared in the matched cohort. Results: Among 303 patients who underwent radical liver resection for ICC, 48 patients with clinically positive nodes were excluded, and a total of 159 clinically node-negative ICC patients were finally eligible for the study, with 102 in the LND group and 57 in the non-LND group. After propensity score matching, two well-balanced groups of 51 patients each were analyzed. No significant difference of median RFS (12.0 vs. 10.0 months, P = 0.37) and median OS (22.0 vs. 26.0 months, P = 0.47) was observed between the LND and non-LND group. Also, LND was not identified as one of the independent risks for survival. Among 51 patients who received LND, 11 patients were with positive lymph nodes (lymph node metastasis (LNM) (+)) and presented significantly worse outcomes than those with LND (-). On the other hand, postoperative adjuvant therapy was the independent risk factor for both RFS (hazard ratio (HR): 0.623, 95% confidence interval (CI): 0.393 - 0.987, P = 0.044) and OS (HR: 0.585, 95% CI: 0.359 - 0.952, P = 0.031). Furthermore, postoperative adjuvant therapy was associated with prolonged survivals of non-LND patients (P = 0.02 for RFS and P = 0.03 for OS). Conclusions: Based on the data, we found that LND did not significantly improve the prognosis of patients with clinically node-negative ICC. Postoperative adjuvant therapy was associated with prolonged survival of ICC patients, especially in non-LND individuals.

Identification and Comprehensive Analysis of OFP Genes for Fruit Shape Influence in Mango.

OVATE family proteins (OFPs) are a class of plant-specific proteins with a conserved OVATE domain that play fundamental roles in fruit development and plant growth. Mango (Mangifera indica L.) is an economically important subtropical fruit tree characterized by a diverse array of fruit shapes and sizes. Despite extensive research on OFPs across various species, there remains a scarcity of information regarding OFPs in mango. Here, we have successfully identified 25 OFP genes (MiOFPs) in mango, each of which exhibits the conserved OVATE domains. The MiOFP gene exhibit a range of 2-6 motifs, with all genes containing both motif 1 and motif 2. Phylogenetic analysis on 97 OFPs (including 18 AtOFPs, 24 SlOFPs, 25 MiOFPs, and 30 OsOFPs) indicated that MiOFPs could be divided into three main clades: clade I, II, and III. Comparative morphological analysis identified significant variations in fruit longitudinal diameter, fruit transverse diameter, and fruit shape index between two distinct shaped mango cultivars ('Hongxiangya' and 'Jingpingmang') at DAP5, DAP7, and DAP10 stages. The subsequent examination of paraffin sections revealed distinct patterns of cell elongation. The majority of MiOFP genes exhibited predominantly expressed in developing organs, specifically flowers and immature fruits, while displaying distinct expression patterns. RNA-Seq analysis revealed significant disparities in the expression levels of several OFP genes, including MiOFP5, MiOFP11, MiOFP21, MiOFP22, MiOFP23, and MiOFP25, between the two mango cultivars. These findings suggest that these six genes may play a crucial role for fruit shape in mango, especially the MiOFP22. The findings of this study have established a basis for future investigations into MiOFPs in mango, offering a solid foundation for further research in this field.

Risk Factors and Predictive Nomogram for Survival in Elderly Patients with Brain Glioma.

OBJECTIVE: To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram. METHODS: Data from elderly individuals (age ≥65 years) histologically diagnosed with brain glioma were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio. Additionally, data obtained from Tangdu Hospital constituted an external validation cohort for the study. The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, enabling the construction of a nomogram. Model performance was evaluated using C-index, ROC curves, calibration plot and decision curve analysis (DCA). RESULTS: A cohort of 20 483 elderly glioma patients was selected from the SEER database. Five prognostic factors (age, marital status, histological type, stage, and treatment) were found to significantly impact overall survival (OS) and cancer-specific survival (CSS), with tumor location emerging as a sixth variable independently linked to CSS. Subsequently, nomogram models were developed to predict the probabilities of survival at 6, 12, and 24 months. The assessment findings from the validation queue indicate a that the model exhibited strong performance. CONCLUSION: Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients. They can potentially assist in risk stratification and clinical decision-making.

Light&fast generative adversarial network for high-fidelity CT image synthesis of liver tumor.

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma is a common disease with high mortality. Through deep learning methods to analyze HCC CT, the screening classification and prognosis model of HCC can be established, which further promotes the development of computer-aided diagnosis and treatment in the treatment of HCC. However, there are significant challenges in the actual establishment of HCC auxiliary diagnosis model due to data imbalance, especially for rare subtypes of HCC and underrepresented demographic groups. This study proposes a GAN model aimed at overcoming these obstacles and improving the accuracy of HCC auxiliary diagnosis. METHODS: In order to generate liver and tumor images close to the real distribution. Firstly, we construct a new gradient transfer sampling module to improve the lack of texture details and excessive gradient transfer parameters of the deep model; Secondly, we construct an attention module with spatial and cross-channel feature extraction ability to improve the discriminator's ability to distinguish images; Finally, we design a new loss function for liver tumor imaging features to constrain the model to approach the real tumor features in iterations. RESULTS: In qualitative analysis, the images synthetic by our method closely resemble the real images in liver parenchyma, blood vessels, tumors, and other parts. In quantitative analysis, the optimal results of FID, PSNR, and SSIM are 75.73, 22.77, and 0.74, respectively. Furthermore, our experiments establish classification models for imbalanced data and enhanced data, resulting in an increase in accuracy rate by 21%-34%, an increase in AUC by 0.29 - 0.33, and an increase in specificity to 0.89. CONCLUSION: Our solution provides a variety of training data sources with low cost and high efficiency for the establishment of classification or prognostic models for imbalanced data.

Sub-genomic RNAi-assisted strain evolution of filamentous fungi for enhanced protein production.

Genetic engineering at the genomic scale provides a rapid means to evolve microbes for desirable traits. However, in many filamentous fungi, such trials are daunted by low transformation efficiency. Differentially expressed genes under certain conditions may contain important regulatory factors. Accordingly, although manipulating these subsets of genes only can largely reduce the time and labor, engineering at such a sub-genomic level may also be able to improve the microbial performance. Herein, first using the industrially important cellulase-producing filamentous fungus Trichoderma reesei as a model organism, we constructed suppression subtractive hybridization (SSH) libraries enriched with differentially expressed genes under cellulase induction (MM-Avicel) and cellulase repression conditions (MM-Glucose). The libraries, in combination with RNA interference, enabled sub-genomic engineering of T. reesei for enhanced cellulase production. The ability of T. reesei to produce endoglucanase was improved by 2.8~3.3-fold. In addition, novel regulatory genes (tre49304, tre120391, and tre123541) were identified to affect cellulase expression in T. reesei. Iterative manipulation using the same strategy further increased the yield of endoglucanase activity to 75.6 U/mL, which was seven times as high as that of the wild type (10.8 U/mL). Moreover, using Humicola insolens as an example, such a sub-genomic RNAi-assisted strain evolution proved to be also useful in other industrially important filamentous fungi. H. insolens is a filamentous fungus commonly used to produce catalase, albeit with similarly low transformation efficiency and scarce knowledge underlying the regulation of catalase expression. By combining SSH and RNAi, a strain of H. insolens producing 28,500 ± 288 U/mL of catalase was obtained, which was 1.9 times as high as that of the parent strain.IMPORTANCEGenetic engineering at the genomic scale provides an unparalleled advantage in microbial strain improvement, which has previously been limited only to the organisms with high transformation efficiency such as Saccharomyces cerevisiae and Escherichia coli. Herein, using the filamentous fungus Trichoderma reesei as a model organism, we demonstrated that the advantage of suppression subtractive hybridization (SSH) to enrich differentially expressed genes and the convenience of RNA interference to manipulate a multitude of genes could be combined to overcome the inadequate transformation efficiency. With this sub-genomic evolution strategy, T. reesei could be iteratively engineered for higher cellulase production. Intriguingly, Humicola insolens, a fungus with even little knowledge in gene expression regulation, was also improved for catalase production. The same strategy may also be expanded to engineering other microorganisms for enhanced production of proteins, organic acids, and secondary metabolites.

Small extracellular vesicle CA1 as a promising diagnostic biomarker for nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC), a malignant cancer originating from the epithelial cells of the nasopharynx, presents diagnostic challenges with current methods such as plasma Epstein-Barr virus (EBV) DNA testing showing limited efficacy. This study focused on identifying small extracellular vesicle (sEV) proteins as potential noninvasive biomarkers to enhance NPC diagnostic accuracy. We isolated sEVs from plasma and utilized 4D label-free proteomics to identify differentially expressed proteins (DEPs) among healthy controls (NC = 10), early-stage NPC (E-NPC = 10), and late-stage NPC (L-NPC = 10). Eighteen sEV proteins were identified as potential biomarkers. Subsequently, parallel reaction monitoring (PRM) proteomic analysis preliminarily confirmed sEV carbonic anhydrase 1 (CA1) as a highly promising biomarker for NPC, particularly in early-stage diagnosis (NC = 15; E-NPC = 10; L-NPC = 15). To facilitate this, we developed an automated, high-throughput and highly sensitive CA1 immune-chemiluminescence chip technology characterized by a broad linear detection range and robust controls. Further validation in an independent retrospective cohort (NC = 89; E-NPC = 39; L-NPC = 172) using this technology confirmed sEV CA1 as a reliable diagnostic biomarker for NPC (AUC = 0.9809) and E-NPC (AUC = 0.9893), independent of EBV-DNA testing. Notably, sEV CA1 exhibited superior diagnostic performance compared to EBV-DNA, with a significant incremental net reclassification improvement of 27.61 % for NPC and 72.11 % for E-NPC detection. Thus, this study identifies sEV CA1 as an innovative diagnostic biomarker for NPC and E-NPC independent of EBV-DNA. Additionally, it establishes an immune-chemiluminescence chip technology for the detection of sEV CA1 protein, paving the way for further validation and clinical application.

Enhancing light extraction efficiency of the inclined-sidewall-shaped DUV micro-LED array by hybridizing a nanopatterned sapphire substrate and an air-cavity reflector.

In this work, we hybridize an air cavity reflector and a nanopatterned sapphire substrate (NPSS) for making an inclined-sidewall-shaped deep ultraviolet micro light-emitting diode (DUV micro-LED) array to enhance the light extraction efficiency (LEE). A cost-effective hybrid photolithography process involving positive and negative photoresist (PR) is explored to fabricate air-cavity reflectors. The experimental results demonstrate a 9.88% increase in the optical power for the DUV micro-LED array with a bottom air-cavity reflector when compared with the conventional DUV micro-LED array with only a sidewall metal reflector. The bottom air-cavity reflector significantly contributes to the reduction of the light absorption and provides more escape paths for light, which in turn increases the LEE. Our investigations also report that such a designed air-cavity reflector exhibits a more pronounced impact on small-size micro-LED arrays, because more photons can propagate into escape cones by experiencing fewer scattering events from the air-cavity structure. Furthermore, the NPSS can enlarge the escape cone and serve as scattering centers to eliminate the waveguiding effect, which further enables the improved LEE for the DUV micro-LED array with an air-cavity reflector.

A Reconfigurable All-Optical-Controlled Synaptic Device for Neuromorphic Computing Applications.

Retina-inspired visual sensors play a crucial role in the realization of neuromorphic visual systems. Nevertheless, significant obstacles persist in the pursuit of achieving bidirectional synaptic behavior and attaining high performance in the context of photostimulation. In this study, we propose a reconfigurable all-optical controlled synaptic device based on the IGZO/SnO/SnS heterostructure, which integrates sensing, storage and processing functions. Relying on the simple heterojunction stack structure and the role of energy band engineering, synaptic excitatory and inhibitory behaviors can be observed under the light stimulation of ultraviolet (266 nm) and visible light (405, 520 and 658 nm) without additional voltage modulation. In particular, junction field-effect transistors based on the IGZO/SnO/SnS heterostructure were fabricated to elucidate the underlying bidirectional photoresponse mechanism. In addition to optical signal processing, an artificial neural network simulator based on the optoelectrical synapse was trained and recognized handwritten numerals with a recognition rate of 91%. Furthermore, we prepared an 8 × 8 optoelectrical synaptic array and successfully demonstrated the process of perception and memory for image recognition in the human brain, as well as simulated the situation of damage to the retina by ultraviolet light. This work provides an effective strategy for the development of high-performance all-optical controlled optoelectronic synapses and a practical approach to the design of multifunctional artificial neural vision systems.

Serum metabolomics analysis of patients with chronic obstructive pulmonary disease and 'frequent exacerbator' phenotype.

Chronic obstructive pulmonary disease (COPD) exacerbations accelerate loss of lung function and increased mortality. The complex nature of COPD presents challenges in accurately predicting and understanding frequent exacerbations. The present study aimed to assess the metabolic characteristics of the frequent exacerbation of COPD (COPD­FE) phenotype, identify potential metabolic biomarkers associated with COPD­FE risk and evaluate the underlying pathogenic mechanisms. An internal cohort of 30 stable patients with COPD was recruited. A widely targeted metabolomics approach was used to detect and compare serum metabolite expression profiles between patients with COPD­FE and patients with non­frequent exacerbation of COPD (COPD­NE). Bioinformatics analysis was used for pathway enrichment analysis of the identified metabolites. Spearman's correlation analysis assessed the associations between metabolites and clinical indicators, while receiver operating characteristic (ROC) analysis evaluated the ability of metabolites to distinguish between two groups. An external cohort of 20 patients with COPD validated findings from the internal cohort. Out of the 484 detected metabolites, 25 exhibited significant differences between COPD­FE and COPD­NE. Metabolomic analysis revealed differences in lipid, energy, amino acid and immunity pathways. Spearman's correlation analysis demonstrated associations between metabolites and clinical indicators of acute exacerbation risk. ROC analysis demonstrated that the area under the curve (AUC) values for D­fructose 1,6­bisphosphate (AUC=0.871), arginine (AUC=0.836), L­2­hydroxyglutarate (L­2HG; AUC=0.849), diacylglycerol (DG) (16:0/20:5) (AUC=0.827), DG (16:0/20:4) (AUC=0.818) and carnitine­C18:2 (AUC=0.804) were >0.8, highlighting their discriminative capacity between the two groups. External validation results demonstrated that DG (16:0/20:5), DG (16:0/20:4), carnitine­C18:2 and L­2HG were significantly different between patients with COPD­FE and those with COPD­NE. In conclusion, the present study offers insights into early identification, mechanistic understanding and personalized management of the COPD­FE phenotype.

Genome-Wide Identification and Expression Analysis of the Class III Peroxidase Gene Family under Abiotic Stresses in Litchi (Litchi chinensis Sonn.).

Class III peroxidases (CIII PRXs) are plant-specific enzymes with high activity that play key roles in the catalysis of oxidation-reduction reactions. In plants, CIII PRXs can reduce hydrogen peroxide to catalyze oxidation-reduction reactions, thereby affecting plant growth, development, and stress responses. To date, no systematic analysis of the CIII PRX gene family in litchi (Litchi chinensis Sonn.) has been documented, although the genome has been reported. In this study, a total of 77 CIII PRX (designated LcPRX) gene family members were predicted in the litchi genome to provide a reference for candidate genes in the responses to abiotic stresses during litchi growth and development. All of these LcPRX genes had different numbers of highly conserved PRX domains and were unevenly distributed across fifteen chromosomes. They were further clustered into eight clades using a phylogenetic tree, and almost every clade had its own unique gene structure and motif distribution. Collinearity analysis confirmed that there were eleven pairs of duplicate genes among the LcPRX members, and segmental duplication (SD) was the main driving force behind the LcPRX gene expansion. Tissue-specific expression profiles indicated that the expression levels of all the LcPRX family members in different tissues of the litchi tree were significantly divergent. After different abiotic stress treatments, quantitative real-time PCR (qRT-PCR) analysis revealed that the LcPRX genes responded to various stresses and displayed differential expression patterns. Physicochemical properties, transmembrane domains, subcellular localization, secondary structures, and cis-acting elements were also analyzed. These findings provide insights into the characteristics of the LcPRX gene family and give valuable information for further elucidating its molecular function and then enhancing abiotic stress tolerance in litchi through molecular breeding.

A new artificial intelligence system for both stomach and small-bowel capsule endoscopy.

BACKGROUND AND AIMS: Despite the benefits of artificial intelligence in small-bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking. METHODS: In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning-based SmartScan (SS), which has been described previously. A total of 1069 magnetically controlled GI CE examinations (comprising 2,672,542 gastric images) were used in the training phase for recognizing gastric pathologies, producing a new artificial intelligence algorithm named SS Plus. A total of 342 fully automated, magnetically controlled CE examinations were included in the validation phase. The performance of both senior and junior endoscopists with both the SS Plus-assisted reading (SSP-AR) and conventional reading (CR) modes was assessed. RESULTS: SS Plus was designed to recognize 5 types of gastric lesions and 17 types of SB lesions. SS Plus reduced the number of CE images required for review to 873.90 (median, 1000; interquartile range [IQR], 814.50-1000) versus 44,322.73 (median, 42,393; IQR, 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endoscopists took 9.54 minutes (median, 8.51; IQR, 6.05-13.13) to complete the CE video reading. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, whereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, junior endoscopists remarkably improved their CE image reading ability with SSP-AR. CONCLUSIONS: Our study shows that the newly upgraded deep learning-based algorithm SS Plus can detect GI lesions and help improve the diagnostic performance of junior endoscopists in interpreting CE videos.

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression.

In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

Healthcare workers perceptions of patient safety culture in selected Ghanaian regional hospitals: a qualitative study.

BACKGROUND: Patient safety culture is an integral part of healthcare delivery both in Ghana and globally. Therefore, understanding how frontline health workers perceive patient safety culture and the factors that influence it is very important. This qualitative study examined the health workers' perceptions of patient safety culture in selected regional hospitals in Ghana. OBJECTIVE: This study aimed to provide a voice concerning how frontline health workers perceive patient safety culture and explain the major barriers in ensuring it. METHOD: In-depth semi-structured interviews were conducted with 42 health professionals in two regional government hospitals in Ghana from March to June 2022. Participants were purposively selected and included medical doctors, nurses, pharmacists, administrators, and clinical service staff members. The inclusion criteria were one or more years of clinical experience. Interviews were recorded and transcribed. Thematic analysis was used to identify themes. RESULT: The health professionals interviewed were 38% male and 62% female, of whom 54% were nurses, 4% were midwives, 28% were medical doctors; lab technicians, pharmacists, and human resources workers represented 2% each; and 4% were critical health nurses. Among them, 64% held a diploma and 36% held a degree or above. This study identified four main areas: general knowledge of patient safety culture, guidelines and procedures, attitudes of frontline health workers, and upgrading patient safety culture. CONCLUSIONS: This qualitative study presents a few areas for improvement in patient safety culture. Despite their positive attitudes and knowledge of patient safety, healthcare workers expressed concerns about the implementation of patient safety policies outlined by hospitals. Healthcare professionals perceived that curriculum training on patient safety during school education and the availability of dedicated officers for patient safety at their facilities may help improve patient safety.

Regiodivergent and Enantioselective Synthesis of Cyclic Sulfones via Ligand-Controlled Nickel-Catalyzed Hydroalkylation.

Cyclic sulfones have demonstrated important applications in drug discovery. However, the catalytic and enantioselective synthesis of chiral cyclic sulfones remains challenging. Herein, we develop nickel-catalyzed regiodivergent and enantioselective hydroalkylation of sulfolenes to streamline the synthesis of chiral alkyl cyclic sulfones. The method has broad scope and high functional group tolerance. The regioselectivity can be controlled by ligands only. A neutral PYROX ligand favors C3-alkylation whereas an anionic BOX ligand favors C2-alkylation. This control is kinetic in origin as the C2-bound Ni intermediates are always thermodynamically more stable. Reactivity study of a wide range of relevant Ni intermediates reveal a NiI/NiIII catalytic cycle with a NiII-H species as the resting state. The regio- and enantio-determining step is the insertion of this NiII-H species into 2-sulfolene. This work provides an efficient catalytic method for the synthesis of an important class of organic compounds and enhances the mechanistic understanding of Ni-catalyzed stereoselective hydroalkylation.

Identification, diversity, and evolution analysis of Commd gene family in Haliotis discus hannai and immune response to biotic and abiotic stresses.

The Commd (Copper Metabolism gene MURR1 Domain) family genes play crucial roles in various biological processes, including copper and sodium transport regulation, NF-κB activity, and cell cycle progression. Their function in Haliotis discus hannai, however, remains unclear. This study focused on identifying and analyzing the Commd genes in H. discus hannai, including their gene structure, phylogenetic relationships, expression profiles, sequence diversity, and alternative splicing. The results revealed significant homology between H. discus hannai's Commd genes and those of other mollusks. Both transcriptome quantitative analysis and qRT-PCR demonstrated the responsiveness of these genes to heat stress and Vibrio parahaemolyticus infection. Notably, alternative splicing analysis revealed that COMMD2, COMMD4, COMMD5, and COMMD7 produce multiple alternative splice variants. Furthermore, sequence diversity analysis uncovered numerous missense mutations, specifically 9 in COMMD5 and 14 in COMMD10. These findings contribute to expanding knowledge on the function and evolution of the Commd gene family and underscore the potential role of COMMD in the innate immune response of H. discus hannai. This research, therefore, offers a novel perspective on the molecular mechanisms underpinning the involvement of Commd genes in innate immunity, paving the way for further explorations in this field.

[Intervention effect of Erchen Decoction on methionine and choline deficient diet-induced non-alcoholic steatohepatitis in mice].

This study investigated the effect of Erchen Decoction(ECD) on the prevention of non-alcoholic steatohepatitis(NASH) in mice and explored its possible mechanism, so as to provide scientific data for the clinical application of ECD in the prevention of NASH. C57BL/6 male mice were randomly divided into normal group(methionine and choline supplement, MCS), model group(methionine and choline deficient, MCD), low-dose ECD group(ECD＿L, 6 g·kg~(-1)), medium-dose ECD group(ECD＿M, 12 g·kg~(-1)), and high-dose ECD group(ECD＿H, 24 g·kg~(-1)), with eight mice in each group. The MCS group was fed with an MCS diet, and the other groups were fed with an MCD diet. The mice in each group were given corresponding diets, but the drug intervention group was given low-, medium-, and high-dose ECD(10 mL·kg~(-1)·d~(-1)) by intragastric administration for six weeks on the basis of MCD diet feeding, and the mice could eat and drink freely during the whole experiment. At the end of the experiment, mice were fasted overnight(12 h) and were anesthetized with 20% urethane. Thereafter, the blood and liver tissue were collected. The serum was used to detect the levels of alanine aminotransferase(ALT), aspartate aminotransaminase(AST), interleukin-1ß(IL-1ß), interleukin-6(IL-6), interleukin-10(IL-10), and tumor necrosis factor-α(TNF-α). Liver tissue was processed by hematoxylin-eosin(HE) staining and used for hepatic histological analysis and detection of the expression levels of genes and proteins related to nuclear factor erythroid 2-related factor 2/glutathione peroxidase 4(Nrf2/GPX4) pathway by real-time quantitative reverse transcriptase-polymerase chain reaction(RT-qPCR) and Western blot analysis, respectively. The results showed that compared with the MCS group, the MCD group showed higher serum ALT and AST levels; the HE staining exhibited fat vacuoles and obvious inflammatory cell infiltration in liver tissue; serum IL-1ß, IL-6, and TNF-α levels were significantly increased, and the serum IL-10 level was significantly decreased. The mRNA expressions of fatty acid synthase(FASN), monocyte chemoattractant protein-1(MCP-1), and IL-1ß in liver tissue were significantly up-regulated, while those of GPX4, Nrf2, and NAD(P)H:quinine oxidoreductase(NQO1) were significantly down-regulated. Compared with the MCD group, the serum ALT and AST levels of ECD＿M and ECD＿H groups were significantly decreased, and the AST level in the ECD＿L group was significantly decreased. The number of fat vacuoles and the degree of inflammatory cell infiltration in liver tissue were improved; serum IL-1ß, IL-6, and TNF-α levels were significantly decreased, but the serum IL-10 level was significantly increased only in the ECD＿H group. The mRNA expressions of FASN, MCP-1, and IL-1ß in liver tissue were significantly down-regulated, and those of GPX4 and NQO1 were significantly up-regulated. The mRNA expressions of Nrf2 in ECD＿M and ECD＿H groups were significantly up-regulated. Western blot results showed that compared with the MCD group, the protein expression levels of Nrf2 and GPX4 in each group were significantly increased after ECD administration, and the protein expression level of FASN was significantly decreased; the protein expression of NQO1 was increased in ECD＿M and ECD＿H groups. In summary, ECD can reduce hepatic lipid accumulation, oxidative stress, liver inflammation, and liver injury in NASH mice, which may be related to the activation of the Nrf2/GPX4 pathway.

Quantifying the impacts of Canadian wildfires on regional air pollution networks.

Wildfire smoke greatly impacts regional atmospheric systems, causing changes in the behavior of pollution. However, the impacts of wildfire smoke on pollution behavior are not easily quantifiable due to the complex nature of atmospheric systems. Air pollution correlation networks have been used to quantify air pollution behavior during ambient conditions. However, it is unknown how extreme pollution events impact these networks. Therefore, we propose a multidimensional air pollution correlation network framework to quantify the impacts of wildfires on air pollution behavior. The impacts are quantified by comparing two time periods, one during the 2023 Canadian wildfires and one during normal conditions with two complex network types for each period. In this study, the value network represents PM2.5 concentrations and the rate network represents the rate of change of PM2.5 concentrations. Wildfires' impacts on air pollution behavior are captured by structural changes in the networks. The wildfires caused a discontinuous phase transition during percolation in both network types which represents non-random organization of the most significant spatiotemporal correlations. Additionally, wildfires caused changes to the connectivity of stations leading to more interconnected networks with different influential stations. During the wildfire period, highly polluted areas are more likely to form connections in the network, quantified by an 86 % and 19 % increase in the connectivity of the value and rate networks respectively compared to the normal period. In this study, we create novel understandings of the impacts of wildfires on air pollution correlation networks, show how our method can create important insights into air pollution patterns, and discuss potential applications of our methodologies. This study aims to enhance capabilities for wildfire smoke exposure mitigation and response strategies.