Dual mutations in the whitefly nicotinic acetylcholine receptor ß1 subunit confer target-site resistance to multiple neonicotinoid insecticides.

Neonicotinoid insecticides, which target insect nicotinic acetylcholine receptors (nAChRs), have been widely and intensively used to control the whitefly, Bemisia tabaci, a highly damaging, globally distributed, crop pest. This has inevitably led to the emergence of populations with resistance to neonicotinoids. However, to date, there have been no reports of target-site resistance involving mutation of B. tabaci nAChR genes. Here we characterize the nAChR subunit gene family of B. tabaci and identify dual mutations (A58T&R79E) in one of these genes (BTß1) that confer resistance to multiple neonicotinoids. Transgenic D. melanogaster, where the native nAChR Dß1 was replaced with BTß1A58T&R79E, were significantly more resistant to neonicotinoids than flies where Dß1 were replaced with the wildtype BTß1 sequence, demonstrating the causal role of the mutations in resistance. The two mutations identified in this study replace two amino acids that are highly conserved in >200 insect species. Three-dimensional modelling suggests a molecular mechanism for this resistance, whereby A58T forms a hydrogen bond with the R79E side chain, which positions its negatively-charged carboxylate group to electrostatically repulse a neonicotinoid at the orthosteric site. Together these findings describe the first case of target-site resistance to neonicotinoids in B. tabaci and provide insight into the molecular determinants of neonicotinoid binding and selectivity.

DDInter 2.0: an enhanced drug interaction resource with expanded data coverage, new interaction types, and improved user interface.

Drug interactions pose significant challenges in clinical practice, potentially leading to adverse drug reactions, reduced efficacy, and even life-threatening consequences. As polypharmacy becomes increasingly common, the risk of harmful drug interactions rises, underscoring the need for comprehensive and user-friendly drug interaction resources to ensure patient safety. To address these concerns and support healthcare professionals in optimizing drug therapy, we present DDInter 2.0, a significantly expanded and enhanced update to our drug interaction database. This new version incorporates additional interaction types, including drug-food interactions (DFIs), drug-disease interactions (DDSIs), and therapeutic duplications, providing a more complete resource for clinical decision-making. The updated database covers 2310 drugs, with 302 516 drug-drug interaction (DDI) records accompanied by 8398 distinct, high-quality mechanism descriptions and management recommendations. DDInter 2.0 also includes 857 DFIs, 8359 DDSIs and 6033 therapeutic duplication records, each supplemented with detailed information and guidance. Furthermore, the enhanced user interface and advanced filtering options in this second release facilitate easy access to and analysis of the comprehensive drug interaction data. By providing healthcare professionals and researchers with a more complete and user-friendly resource, DDInter 2.0 aims to support clinical decision-making and ultimately improve patient outcomes. DDInter 2.0 is freely accessible at https://ddinter2.scbdd.com.

Wnt5a-mediated autophagy contributes to the epithelial-mesenchymal transition of human bronchial epithelial cells during asthma.

BACKGROUND: The epithelial-mesenchymal transition (EMT) of human bronchial epithelial cells (HBECs) is essential for airway remodeling during asthma. Wnt5a has been implicated in various lung diseases, while its role in the EMT of HBECs during asthma is yet to be determined. This study sought to define whether Wnt5a initiated EMT, leading to airway remodeling through the induction of autophagy in HBECs. METHODS: Microarray analysis was used to investigate the expression change of WNT5A in asthma patients. In parallel, EMT models were induced using 16HBE cells by exposing them to house dust mites (HDM) or interleukin-4 (IL-4), and then the expression of Wnt5a was observed. Using in vitro gain- and loss-of-function approaches via Wnt5a mimic peptide FOXY5 and Wnt5a inhibitor BOX5, the alterations in the expression of the epithelial marker E-cadherin and the mesenchymal marker protein were observed. Mechanistically, the Ca2+/CaMKII signaling pathway and autophagy were evaluated. An autophagy inhibitor 3-MA was used to examine Wnt5a in the regulation of autophagy during EMT. Furthermore, we used a CaMKII inhibitor KN-93 to determine whether Wnt5a induced autophagy overactivation and EMT via the Ca2+/CaMKII signaling pathway. RESULTS: Asthma patients exhibited a significant increase in the gene expression of WNT5A compared to the healthy control. Upon HDM and IL-4 treatments, we observed that Wnt5a gene and protein expression levels were significantly increased in 16HBE cells. Interestingly, Wnt5a mimic peptide FOXY5 significantly inhibited E-cadherin and upregulated α-SMA, Collagen I, and autophagy marker proteins (Beclin1 and LC3-II). Rhodamine-phalloidin staining showed that FOXY5 resulted in a rearrangement of the cytoskeleton and an increase in the quantity of stress fibers in 16HBE cells. Importantly, blocking Wnt5a with BOX5 significantly inhibited autophagy and EMT induced by IL-4 in 16HBE cells. Mechanistically, autophagy inhibitor 3-MA and CaMKII inhibitor KN-93 reduced the EMT of 16HBE cells caused by FOXY5, as well as the increase in stress fibers, cell adhesion, and autophagy. CONCLUSION: This study illustrates a new link in the Wnt5a-Ca2+/CaMKII-autophagy axis to triggering airway remodeling. Our findings may provide novel strategies for the treatment of EMT-related diseases.

IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication.

Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.

Controlling nonlinear collapse of ellipticity and orientation of a co-variant vector optical field.

A vector optical field with inhomogeneous spatial polarization distribution offers what we believe to be a new paradigm to form controllable filaments. However, it is challenging to steer multiple performances (e.g. number, orientation, and interval) of filaments in transparent nonlinear media at one time. Herein, we theoretically self-design and generate a kind of believed to be novel ellipticity and orientation co-variant vector optical field to interact with Kerr medium to solve this issue. The collapsing behaviors of such a new hybrid vector optical field reveal that, by judiciously adjusting the inherent topological charge and initial phase of incident optical field, we are able to give access to stable collapsing filamentation with tunable numbers, orientations and interval. Additionally, the collapsing patterns presented are immune nearly to the extra random noise. The relevant mechanism behind the collapse of the vector optical field is elucidated as well. The findings in this work may have huge potential in optical signal processing, laser machining, and other related applications.

Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer.

BACKGROUND: Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. METHODS: A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. RESULTS: Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. CONCLUSIONS: Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Electrostatic-Field-Induced Collapse of Nanobubbles in Nanochannels.

The adsorbed nanobubbles inside the nanochannels can cause fluid transport blockages, which will obviously degrade the nanodevice performance and reduce the lifetime. However, due to small-scale effects, the removal of nanobubbles is a huge challenge at the nanoscale. Herein, molecular dynamics simulations are carried out to study the effect of the electrostatic field on underwater nitrogen nanobubbles confined in nanochannels. It is found that the nanobubbles will collapse under an appropriate electrostatic field, thereby unblocking the transport of water in the nanochannels. The formation of ordered water structures induced by electrostatic fields plays an important role in the removal of nanobubbles from the nanochannels. Our findings provide a convenient, controllable, and remote way to address the blockage problem of nanobubbles in nanochannels, which may have potential applications in improving the performance of fuel cells.

Optical trapping-induced crystallization promoted by gold and silicon nanoparticles.

This study investigates the promotion of sodium chlorate (NaClO3) crystallization through optical trapping, enhanced by the addition of gold nanoparticles (AuNPs) and silicon nanoparticles (SiNPs). Using a focused laser beam at the air-solution interface of a saturated NaClO3 solution with AuNPs or SiNPs, the aggregates of these particles were formed at the laser focus, the nucleation and growth of metastable NaClO3 (m-NaClO3) crystals were induced. Continued laser irradiation caused these m-NaClO3 crystals to undergo repeated cycles of growth and dissolution, eventually transitioning to a stable crystal form. Our comparative analysis showed that AuNPs, due to their significant heating due to higher photon absorption efficiency, caused more pronounced size fluctuations in m-NaClO3 crystals compared to the stable behavior observed with SiNPs. Interestingly, the maximum diameter of the m-NaClO3 crystals that appeared during the size fluctuation step was consistent, regardless of nanoparticle type, concentration, or size. The crystallization process was also promoted by using polystyrene nanoparticles, which have minimal heating and electric field enhancement, suggesting that the reduction in activation energy for nucleation at the particle surface is a key factor. These findings provide critical insights into the mechanisms of laser-induced crystallization, emphasizing the roles of plasmonic heating, particle surfaces, and optical forces.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities.

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

Updated pharmaceutical progress on plant antibiotic rhein and its analogs: Bioactivities, structure-activity relationships and future perspectives.

Rhein, as a plant antibiotic, demonstrates a broad spectrum of pharmacological effects. Nevertheless, its limited water solubility, low bioavailability, and potential hepatotoxicity and nephrotoxicity making it difficult to directly become a medicine, thereby imposing significant constraints on its clinical application. In recent decades, extensive researches have been proceeded on the multifaceted structural modifications of rhein, resulting in notable improvements on pharmacological activities and druggabilities. This review offers a comprehensive overview and advanced update on the biological potential and structural-activity relationships (SARs) of various rhein derivatives, delineating the sites of structural modification and corresponding activity trends of rhein derivatives for future.

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment.

Nuclear magnetic resonance (NMR) chemical shift calculations are powerful tools for structure elucidation and have been extensively employed in both natural product and synthetic chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks with a high requirement on quality. Herein, we have constructed a 54-layer-deep graph convolutional network for 13C NMR chemical shift calculations, which achieved high accuracy with low time-cost and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semiempirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K ring junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculations to date. This model has been developed into user-friendly software, providing a useful tool for routine rapid structure validation and assignation as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculations.

DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

Co-culturing Propionibacterium freudenreichii and Bifidobacterium animalis subsp. lactis improves short-chain fatty acids and vitamin B12 contents in soy whey.

The lack of vitamin B12 in unprocessed plant-based foods can lead to health problems in strict vegetarians and vegans. The main aim of this study was to investigate the potential synergy of co-culturing Bifidobacterium animalis subsp. lactis and Propionibacterium freudenreichii in improving production of vitamin B12 and short-chain fatty acids in soy whey. Different strategies including mono-, sequential and simultaneous cultures were adopted. Growth, short-chain fatty acids and vitamin B12 were assessed throughout the fermentation while free amino acids, volatiles, and isoflavones were determined on the final day. P. freudenreichii monoculture grew well in soy whey, whereas B. lactis monoculture entered the death phase by day 4. Principal component analysis demonstrates that metabolic changes in both sequential cultures did not show drastic differences to those of P. freudenreichii monoculture. However, simultaneous culturing significantly improved vitamin B12, acetic acid and propionic acid contents (1.3 times, 5 times, 2.5 times, compared to the next highest treatment [sequential cultures]) in fermented soy whey relative to other culturing modes. Hence, co-culturing of P. freudenreichii and B. lactis would provide an alternative method to improve vitamin B12, acetic acid and propionic acid contents in fermented foods.

Invited review: Camel milk and gut health-Understanding digestibility and the effect on gut microbiota.

Camel milk (CM), known for its immune-regulatory, anti-inflammatory, antiapoptotic, and antidiabetic properties, is a natural healthy food. It is easily digestible due to the high levels of ß-casein and diverse secreted antibodies, exhibiting superior antibacterial and antiviral activities compared with bovine milk. ß-casein is less allergic and more digestible because it is more susceptible to digestive hydrolysis in the gut; therefore, higher levels of ß-casein make CM advantageous for human health. Furthermore, antibodies help the digestive system by destroying the antigens, which are then overwhelmed and digested by macrophages. The connection between the gut microbiota and human health has gained substantial research attention, as it offers potential benefits and supports disease treatment. The gut microbiota has a vital role in regulating the host's health because it helps in several biological functions, such as protection against pathogens, immune function regulation, energy harvesting from digested foods, and reinforcement of digestive tract biochemical barriers. These functions could be affected by the changes in the gut microbiota profile, and gut microbiota differences are associated with several diseases, such as inflammatory bowel disease, colon cancer, irritable bowel disorder, mental illness, allergy, and obesity. This review focuses on the digestibility of CM components, particularly protein and fat, and their influence on gut microbiota modulation. Notably, the hypoallergenic properties and small fat globules of CM contribute to its enhanced digestibility. Considering the rapid digestion of its proteins under conditions simulating infant gastrointestinal digestion, CM exhibits promise as a potential alternative for infant formula preparation due to the high ß-/αs-casein ratio and protective proteins, in addition to the absence of ß-lactoglobulin.

Properties of low-fat Cheddar cheese prepared from bovine-camel milk blends: Chemical composition, microstructure, rheology, and volatile compounds.

Making cheese from camel milk (CM) presents various challenges due to its different physicochemical properties compared with bovine milk (BM). In this study, we investigated the chemical composition, proteolysis, meltability, oiling off, texture profile, color, microstructure, and rheological properties of low-fat Cheddar cheese (LFCC) prepared from BM-CM blends. LFCC was produced from BM or BM supplemented with 15% CM (CM15) and 30% CM (CM30), and analyzed after 14, 60, 120, and 180 d of ripening at 8°C. Except for salt content, no significant differences were observed among LFCC from BM, CM15, and CM30. The addition of CM increased the meltability and oiling off in the resulting cheese throughout storage. With respect to color properties, after melting, LFCC CM30 showed lower L* values than LFCC made from BM and CM15, and a* and b* values were higher than those of BM and CM15 samples. LFCC from CM30 also exhibited lower hardness compared with the other cheeses. Moreover, LFCC made from BM showed a rough granular surface, but cheese samples made from BM-CM blends exhibited a smooth surface. The rheological parameters, including storage modulus, loss modulus, and loss tangent, varied among cheese treatments. The determined acetoin and short-chain volatile acids (C2-C6) in LFCC were affected by the use of CM, because CM15 showed significantly higher amounts than BM and CM30, respectively. The detailed interactions between BM and CM in the cheese matrix should be further investigated.

Harnessing AI for precision tonsillitis diagnosis: a revolutionary approach in endoscopic analysis.

BACKGROUND: Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists; however, it can increase the infection risk for healthcare professionals amidst the coronavirus pandemic. In recent years, with the advancement of artificial intelligence (AI), its application in medical imaging has also thrived. This research is to identify the optimal convolutional neural network (CNN) algorithm for accurate diagnosis of tonsillitis and early precision treatment. METHODS: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our CNN, with the algorithm including UNet, PSPNet, and FPN. A total of 485 pharyngoscopic images from 485 participants were included, comprising healthy individuals (133 cases), patients with the common cold (295 cases), and patients with tonsillitis (57 cases). Both color and texture features from 485 images are extracted for analysis. RESULTS: UNet outperformed PSPNet and FPN in accurately segmenting oropharyngeal anatomy automatically, with average Dice coefficient of 97.74% and a pixel accuracy of 98.12%, making it suitable for enhancing the diagnosis of tonsillitis. The normal tonsils generally have more uniform and smooth textures and have pinkish color, similar to the surrounding mucosal tissues, while tonsillitis, particularly the antibiotic-required type, shows white or yellowish pus-filled spots or patches, and shows more granular or lumpy texture in contrast, indicating inflammation and changes in tissue structure. After training with 485 cases, our algorithm with UNet achieved accuracy rates of 93.75%, 97.1%, and 91.67% in differentiating the three tonsil groups, demonstrating excellent results. CONCLUSION: Our research highlights the potential of using UNet for fully automated semantic segmentation of oropharyngeal structures, which aids in subsequent feature extraction, machine learning, and enables accurate AI diagnosis of tonsillitis. This innovation shows promise for enhancing both the accuracy and speed of tonsillitis assessments.

Cystoderma yongpingense sp. nov. (Squamanitaceae, Agaricales) a new species from southwestern China.

The present study introduces a novel fungus, Cystoderma yongpingense, which was identified in the southwestern region of China. The new species is characterized by a pileus that ranges in color from light orange-red to orange-red; the pileus has a wrinkled surface and is accompanied by a persistent annulus that is membranous and floccose-scaly. Above the annulus, the color transitions from white to yellowish brown. This proposal is substantiated through analyses encompassing both morphological characteristics and phylogenetic relationships. The phylogenetic position of the newly discovered species has been further corroborated through comprehensive maximum likelihood and Bayesian sequence analyses of the ITS + nrLSU DNA regions. Additionally, the technical description of C. yongpingense is enhanced by detailed illustrations and comparative studies with species that are closely related.

Comprehensive profiling of the chemical constituents in Dayuanyin decoction using UPLC-QTOF-MS combined with molecular networking.

CONTEXT: Dayuanyin decoction is a traditional Chinese medicine formulation that is commonly used in modern clinical practice to treat viral infections such as viral pneumonia, viral fever, influenza, and hepatitis. Although the usage rate of Dayuanyin decoction is gradually increasing in clinical practice, its pharmacological constituents are still unclear. OBJECTIVE: This study comprehensively characterized the chemical constituents in Dayuanyin decoction using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and molecular networking. MATERIALS AND METHODS: The overall strategy involved retrieving structural information, such as fragment ions and precursor ion masses, from self-built databases to identify the target constituents of the Dayuanyin decoction extract. The identification of non-targeted constituents was achieved by analyzing different categories, fragment pathways, mass spectrometry data, and the relationships between clusters of structures in molecular networking. Unannotated constituents were inferred from secondary mass spectrometry similarity and molecular weight differences and annotated constituents in the same constituent cluster. A few predicted constituents were selected and validated by comparing them to reference standards under identical mass spectrometry conditions. RESULTS: This study preliminarily identified 216 constituents, including flavonoids, amino acids, alkaloids, triterpenes, steroidal saponins, phenylpropanoids, and other constituents. CONCLUSIONS: This integrated strategy using UPLC-QTOF-MS and molecular networking lays the foundation for clinical research on pharmacologically active substances in Dayuanyin decoction and could be popularized for the comprehensive profiling of chemical constituents of other traditional Chinese medicines.

Integrated "Two-in-One" Strategy for High-Rate Electrocatalytic CO2 Reduction to Formate.

The electrochemical CO2 reduction reaction (ECR) is a promising pathway to producing valuable chemicals and fuels. Despite extensive studies reported, improving CO2 adsorption for local CO2 enrichment or water dissociation to generate sufficient H* is still not enough to achieve industrial-relevant current densities. Herein, we report a "two-in-one" catalyst, defective Bi nanosheets modified by CrOx (Bi-CrOx), to simultaneously promote CO2 adsorption and water dissociation, thereby enhancing the activity and selectivity of ECR to formate. The Bi-CrOx exhibits an excellent Faradic efficiency (≈ 100 %) in a wide potential range from ‒0.4 to ‒0.9 V. In addition, it achieves a remarkable formate partial current density of 687 mA cm‒2 at a moderate potential of ‒0.9 V without iR compensation, the highest value at ‒0.9 V reported so far. Control experiments and theoretical simulations revealed that the defective Bi facilitates CO2 adsorption/activation while the CrOx accounts for enhancing the protonation process via accelerating H2O dissociation. This work presents a pathway to boosting formate production through tuning CO2 and H2O species at the same time.

Surgical treatment of multiple magnet ingestion by children: A single-center experience from China.

BACKGROUND: The incidence of magnet ingestion in children has escalated concurrent with the rise in popularity of magnetic playthings, bearing the capacity to induce substantial morbidity. AIM: The objective of this study was to encapsulate our accumulated expertise in handling pediatric cases featuring multiple magnetic foreign bodies within the gastrointestinal tract sometimes necessitating surgical intervention, as well as to formulate a clinical management algorithm. METHODS: This was a retrospective review of patients with multiple magnetic foreign bodies in the digestive tract, admitted to Shenzhen Children's Hospital, between January 2018 and December 2022. RESULTS: A total of 100 cases were included in this study, including 66 males and 34 females. The main clinical manifestation ns were abdominal pain and vomiting. All patients had abdominal x-ray, all of which indicated foreign bodies in the digestive tract. 33 patients had to undergo a surgical intervention. Among these cases, the gastrointestinal complications occurred in 31 patients, including gastric rupture (n = 9), intestinal obstruction (n = 11) and intestinal perforation (n = 30). Postoperative intestinal obstruction occurred in 6 children. There was no statistical significant difference in age and gender between the Surgical group and Non-surgical group, but the Surgical group had a higher number of magnets ([7.5(2-44) vs 4(2-20)], p = 0.009), a longer interval between time of misingestion to clinical visit ([48(7.2-480) vs 5(2-336)]hours, p < 0.001), and a longer length of hospital stay ([10(6-19) vs 2(1-8)]days, p < 0.001). CONCLUSIONS: Multiple magnet ingestion in children can lead to serious complications and carry severe risks. Timely diagnosis and effective treatment are crucial for managing such patients.