Carbene-Assisted Arene Ring-Opening.

Despite the significant achievements in dearomatization and C-H functionalization of arenes, the arene ring-opening remains a largely unmet challenge and is underdeveloped due to the high bond dissociation energy and strong resonance stabilization energy inherent in aromatic compounds. Herein, we demonstrate a novel carbene assisted strategy for arene ring-opening. The understanding of the mechanism by our DFT calculations will stimulate wide application of bulk arene chemicals for the synthesis of value-added polyconjugated chain molecules. Various aryl azide derivatives now can be directly converted into valuable polyconjugated enynes, avoiding traditional synthesis including multistep unsaturated precursors, poor selectivity control, and subsequent transition-metal catalyzed cross-coupling reactions. The simple conditions required were demonstrated in the late-stage modification of complex molecules and fused ring compounds. This chemistry expands the horizons of carbene chemistry and provides a novel pathway for arene ring-opening.

Plasma-assisted manipulation of vanadia nanoclusters for efficient selective catalytic reduction of NOx.

Supported nanoclusters (SNCs) with distinct geometric and electronic structures have garnered significant attention in the field of heterogeneous catalysis. However, their directed synthesis remains a challenge due to limited efficient approaches. This study presents a plasma-assisted treatment strategy to achieve supported metal oxide nanoclusters from a rapid transformation of monomeric dispersed metal oxides. As a case study, oligomeric vanadia-dominated surface sites were derived from the classic supported V2O5-WO3/TiO2 (VWT) catalyst and showed nearly an order of magnitude increase in turnover frequency (TOF) value via an H2-plasma treatment for selective catalytic reduction of NO with NH3. Such oligomeric surface VOx sites were not only successfully observed and firstly distinguished from WOx and TiO2 by advanced electron microscopy, but also facilitated the generation of surface amide and nitrates intermediates that enable barrier-less steps in the SCR reaction as observed by modulation excitation spectroscopy technologies and predicted DFT calculations.

COVID-19 acts like a stress test, uncovering the vulnerable part of the human body: a retrospective study of 1640 cases in China.

BACKGROUND: Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exhibits multi-organ damage with diverse complications, the correlation between age, gender, medical history and clinical manifestations of novel coronavirus disease 2019 (COVID-19) patients was investigated. METHODS: 1640 patients who were infected with SARS-CoV-2 and hospitalized at the First Affiliated Hospital of Ningbo University from 22 December 2022 to 1 March 2023 were categorized and analysed. Normal distribution test and variance homogeneity test were performed. Based on the test results, one-way analysis of variance, Pearson's chi-squared test and logistic regression analysis were conducted in the study. RESULTS: According to the ANOVA, there was a significant difference in the age distribution (P = .001) between different clinical presentations, while gender did not (P = .06). And regression analysis showed that age, hypertension, atherosclerosis and cancer were significant hazard factors for the development of predominant clinical manifestations in patients hospitalized with novel COVID-19. Additionally, infection with SARS-CoV-2 has the potential to exacerbate the burden on specific diseased or related organs. CONCLUSION: The elderly who are infected with SARS-CoV-2 ought to be treated with emphasis not only on antiviral therapy but also on individualized treatment that takes their medical history and comorbidities into account.

Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing.

Synthetic chemistry plays an indispensable role in drug discovery, contributing to hit compounds identification, lead compounds optimization, candidate drugs preparation, and so on. As Nobel Prize laureate James Black emphasized, "the most fruitful basis for the discovery of a new drug is to start with an old drug"1. Late-stage modification or functionalization of drugs, natural products and bioactive compounds have garnered significant interest due to its ability to introduce diverse elements into bioactive compounds promptly. Such modifications alter the chemical space and physiochemical properties of these compounds, ultimately influencing their potency and druggability. To enrich a toolbox of chemical modification methods for drug discovery, this review focuses on the incorporation of halogen, oxygen, and nitrogen-the ubiquitous elements in pharmacophore components of the marketed drugs-through late-stage modification in recent two decades, and discusses the state and challenges faced in these fields. We also emphasize that increasing cooperation between chemists and pharmacists may be conducive to the rapid discovery of new activities of the functionalized molecules. Ultimately, we hope this review would serve as a valuable resource, facilitating the application of late-stage modification in the construction of novel molecules and inspiring innovative concepts for designing and building new drugs.

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

Developing a Copper(II) Isopropyl 2-Pyridyl Ketone Thiosemicarbazone Compound Based on the IB Subdomain of Human Serum Albumin-Indomethacin Complex: Inhibiting Tumor Growth by Remodeling the Tumor Microenvironment.

To develop a next-generation metal agent and dual-agent multitargeted combination therapy, we developed a copper (Cu) compound based on the properties of the human serum albumin (HSA)-indomethacin (IND) complex to remodel the tumor microenvironment (TME). We optimized a series of Cu(II) isopropyl 2-pyridyl ketone thiosemicarbazone compounds to obtain a Cu(II) compound (C4) with significant cytotoxicity and then constructed an HSA-IND-C4 complex (HSA-IND-C4) delivery system. IND and C4 bind to the hydrophobic cavities of the IB and IIA domains of HSA, respectively. In vivo, the HSA-IND-C4 not only showed enhanced antitumor efficacy relative to C4 and C4 + IND but also improved their targeting ability and decreased their side effects. The antitumor mechanism of C4 + IND involved acting on the different components of the TME. IND inhibited tumor-related inflammation, while C4 not only induced apoptosis and autophagy of cancer cells but also inhibited tumor angiogenesis.

Bhlhe40 deficiency attenuates LPS-induced acute lung injury through preventing macrophage pyroptosis.

BACKGROUND: Acute lung injury (ALI) and its more severe form, acute respiratory distress syndrome (ARDS) as common life-threatening lung diseases with high mortality rates are mostly associated with acute and severe inflammation in lungs. Recently, increasing evidence supports activated inflammation and gasdermin D (GSDMD)-mediated pyroptosis in macrophage are closely associated with ALI. Basic helix-loop-helix family member e40 (Bhlhe40) is a transcription factor that is comprehensively involved in inflammation. However, there is little experimental evidence connecting Bhlhe40 and GSDMD-driven pyroptosis. The study sought to verify the hypothesis that Bhlhe40 is required for GSDMD-mediated pyroptosis in lipopolysaccharide (LPS)-induced inflammatory injury. METHOD: We performed studies using Bhlhe40-knockout (Bhlhe40 -/-) mice, small interfering RNA (siRNA) targeting Bhlhe40 and pyroptosis inhibitor disulfiram to investigate the potential roles of Bhlhe40 on LPS-induced ALI and the underlying mechanisms. RESULTS: Bhlhe40 was highly expressed in total lung tissues and macrophages of LPS-induced mice. Bhlhe40-/- mice showed alleviative lung pathological injury and inflammatory response upon LPS stimulation. Meanwhile, we found that Bhlhe40 deficiency significantly suppressed GSDMD-mediated pyroptosis in macrophage in vivo and in vitro. By further mechanistic analysis, we demonstrated that Bhlhe40 deficiency inhibited GSDMD-mediated pyroptosis and subsequent ALI by repressing canonical (caspase-1-mediated) and non-canonical (caspase-11-mediated) signaling pathways in vivo and in vitro. CONCLUSION: These results indicate Bhlhe40 is required for LPS-induced ALI. Bhlhe40 deficiency can inhibit GSDMD-mediated pyroptosis and therefore alleviate ALI. Targeting Bhlhe40 may be a potential therapeutic strategy for LPS-induced ALI.

Use of 3D printing models for donor tooth extraction in autotransplantation cases.

OBJECTIVE: To clarify whether the 3D printing model has auxiliary functions in toto extraction of donor tooth in autotransplantation cases. METHODS: Two hundred and sixty patients who would have operation of ATT were divided into two groups. In group 1, determination of the tooth extraction in toto was predicted only according to the clinical and imaging examination. In group 2, the prediction was performed according to the clinical and imaging examination as well as the 3D model of donor tooth pre-extraction. A prespctive clinical study was designed on intra-group comparison between the predicted and actual donor teeth situation when extraction in cases of ATT. The consistent rate for the predicted results and the actual results were compared with the two groups. RESULTS: A remarkable difference was observed between the predicted results and the actual results of tooth positions and root numbers in group without model (p < 0,05). The consistency rate of the model group (94.62%) was significantly higher than that of non 3D model group (86.15%) (p = 0.034). CONCLUSION: The 3D printing model for the donor tooth is helpful for dentists to predict the accuracy of toto extraction of donor teeth in autotransplantation cases.

[Construction of prediction model of severe acute pancreatitis based on serum soluble T cell immunogloblulin and mucin domain-containing protein 3].

OBJECTIVE: To investigate the predictive value of the model based on soluble T cell immunogloblulin and mucin domain-containing protein 3 (sTIM3) for the progression of severe acute pancreatitis (SAP) in patients with acute pancreatitis (AP). METHODS: A retrospective cohort study was conducted. The AP patients admitted to Changzhou First People's Hospital and Changzhou Second People's Hospital from June 1, 2020 to June 30, 2022 were enrolled. Mild AP (MAP) and moderately severe AP (MSAP) patients were classified as non-SAP group, and SAP patients were classified as SAP group according to the progression of AP patients during hospitalization. The basic data, blood biological indicators, serum sTIM3 level, bedside index for severity in acute pancreatitis (BISAP), acute physiology and chronic health evaluation II (APACHE II) score, modified computed tomography severity index (MCTSI) score within 48 hours of admission, and prognosis indicators were collected. Multivariate Logistic regression analysis was conducted to analyze the risk factors of the progression of SAP in patients with AP during hospitalization. Based on the results of multivariate analysis and the best parameters selected based on the minimal Akaike information criterion (AIC), the SAP prediction model based on sTIM3 was constructed. The receive operator characteristic curve (ROC curve) was plotted to analyze the predictive efficacy of the model. RESULTS: A total of 99 AP patients were enrolled, 80 patients in the non-SAP group and 19 patients in the SAP group. Compared with the non-SAP group, body mass index (BMI), drinking history ratio, heart rate (HR), respiration rate (RR), white blood cell count (WBC), red blood cell count (RBC), C-reactive protein (CRP), alanine aminotransferase (ALT), serum creatinine (SCr), procalcitonin (PCT), interleukin-6 (IL-6), sTIM3, BISAP score, APACHE II score and MCTSI score were significantly increased, and pulse oxygen saturation (SpO2), direct bilirubin (DBil) and IL-10 were significantly decreased. The length of intensive care unit (ICU) stay and total length of hospital stay of patients in the SAP group were significantly longer than those in the non-SAP group [length of ICU stay (days): 1.0 (0, 1.5) vs. 0 (0, 0), total length of hospital stay (days): 17.11±9.39 vs. 8.40±3.08, both P < 0.01]. Multivariate Logistic regression analysis showed that HR [odds ratio (OR) = 1.059, 95% confidence interval (95%CI) was 1.010-1.110, P = 0.017], DBil (OR = 0.981, 95%CI was 0.950-0.997, P = 0.043), and sTIM3 (OR = 1.002, 95%CI was 1.001-1.003, P = 0.027) were independent risk factors for predicting the progression of SAP in patients with AP, and the SAP prediction model based on sTIM3 was constructed: Logit(P) = -14.602+0.187×BMI+0.057×HR+0.006×CRP-0.020×DBil+0.002×sTIM3. ROC curve analysis showed that among the aforementioned single factor quantitative indicators, IL-6 was the most effective in predicting the progression of AP patients to SAP during hospitalization, but the predictive performance of prediction model based on the sTIM3 was significantly better than IL-6 [area under the ROC curve (AUC) and 95%CI: 0.957 (0.913-1.000) vs. 0.902 (0.845-0.958), P < 0.05]. CONCLUSIONS: The model based on serum sTIM3 demonstrated good predictive value for the progression of SAP in patients with AP.

Revealing the anti-sintering phenomenon on silica-supported nickel catalysts during CO2 hydrogenation.

The CO2 catalytic hydrogenation represents a promising approach for gas-phase CO2 utilization in a direct manner. Due to its excellent hydrogenation ability, nickel has been widely studied and has shown good activities in CO2 hydrogenation reactions, in addition to its high availability and low price. However, Ni-based catalysts are prone to sintering under elevated temperatures, leading to unstable catalytic performance. In the present study, various characterization techniques were employed to study the structural evolution of Ni/SiO2 during CO2 hydrogenation. An anti-sintering phenomenon is observed for both 9% Ni/SiO2 and 1% Ni/SiO2 during CO2 hydrogenation at 400°C. Results revealed that Ni species were re-dispersed into smaller-sized nanoparticles and formed Ni0 active species. While interestingly, this anti-sintering phenomenon leads to distinct outcomes for two catalysts, with a gradual increase in both reactivity and CH4 selectivity for 9% Ni/SiO2 presumably due to the formation of abundant surface Ni° from redispersion, while an apparent decreasing trend of CH4 selectivity for 1% Ni/SiO2 sample, presumably due to the formation of ultra-small nanoparticles that diffuse and partially filled the mesoporous pores of the silica support over time. Finally, the redispersion phenomenon was found relevant to the H2 gas in the reaction environment and enhanced as the H2 concentration increased. This finding is believed to provide in-depth insights into the structural evolution of Ni-based catalysts and product selectivity control in CO2 hydrogenation reactions.

Revealing the Nature of Active Oxygen Species and Reaction Mechanism of Ethylene Epoxidation by Supported Ag/α-Al2O3 Catalysts.

The oxygen species on Ag catalysts and reaction mechanisms for ethylene epoxidation and ethylene combustion continue to be debated in the literature despite decades of investigation. Fundamental details of ethylene oxidation by supported Ag/α-Al2O3 catalysts were revealed with the application of high-angle annular dark-field-scanning transmission electron microscopy-energy-dispersive X-ray spectroscopy (HAADF-STEM-EDS), in situ techniques (Raman, UV-vis, X-ray diffraction (XRD), HS-LEIS), chemical probes (C2H4-TPSR and C2H4 + O2-TPSR), and steady-state ethylene oxidation and SSITKA (16O2 → 18O2 switch) studies. The Ag nanoparticles are found to carry a considerable amount of oxygen after the reaction. Density functional theory (DFT) calculations indicate the oxidative reconstructed p(4 × 4)-O-Ag(111) surface is stable relative to metallic Ag(111) under the relevant reaction environment. Multiple configurations of reactive oxygen species are present, and their relevant concentrations depend on treatment conditions. Selective ethylene oxidation to EO proceeds with surface Ag4-O2* species (dioxygen species occupying an oxygen site on a p(4 × 4)-O-Ag(111) surface) only present after strong oxidation of Ag. These experimental findings are strongly supported by the associated DFT calculations. Ethylene epoxidation proceeds via a Langmuir-Hinshelwood mechanism, and ethylene combustion proceeds via combined Langmuir-Hinshelwood (predominant) and Mars-van Krevelen (minor) mechanisms.

Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study.

BACKGROUND: Pneumocystis jirovecii pneumonia (PCP) could be fatal to patients without human immunodeficiency virus (HIV) infection. Current diagnostic methods are either invasive or inaccurate. We aimed to establish an accurate and non-invasive radiomics-based way to identify the risk of PCP infection in non-HIV patients with computed tomography (CT) manifestation of pneumonia. METHODS: This is a retrospective study including non-HIV patients hospitalized for suspected PCP from January 2010 to December 2022 in one hospital. The patients were randomized in a 7:3 ratio into training and validation cohorts. Computed tomography (CT)-based radiomics features were extracted automatically and used to construct a radiomics model. A diagnostic model with traditional clinical and CT features was also built. The area under the curve (AUC) were calculated and used to evaluate the diagnostic performance of the models. The combination of the radiomics features and serum ß-D-glucan levels was also evaluated for PCP diagnosis. RESULTS: A total of 140 patients (PCP: N = 61, non-PCP: N = 79) were randomized into training (N = 97) and validation (N = 43) cohorts. The radiomics model consisting of nine radiomic features performed significantly better (AUC = 0.954; 95% CI: 0.898-1.000) than the traditional model consisting of serum ß-D-glucan levels (AUC = 0.752; 95% CI: 0.597-0.908) in identifying PCP (P = 0.002). The combination of radiomics features and serum ß-D-glucan levels showed an accuracy of 95.8% for identifying PCP infection (positive predictive value: 95.7%, negative predictive value: 95.8%). CONCLUSIONS: Radiomics showed good diagnostic performance in differentiating PCP from other types of pneumonia in non-HIV patients. A combined diagnostic method including radiomics and serum ß-D-glucan has the potential to provide an accurate and non-invasive way to identify the risk of PCP infection in non-HIV patients with CT manifestation of pneumonia. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05701631).

Global-scale magnetosphere convection driven by dayside magnetic reconnection.

Plasma convection on a global scale is a fundamental feature of planetary magnetosphere. The Dungey cycle explains that steady-state convection within the closed part of the magnetosphere relies on magnetic reconnection in the nightside magnetospheric tail. Nevertheless, time-dependent models of the Dungey cycle suggest an alternative scenario where magnetospheric convection can be solely driven by dayside magnetic reconnection. In this study, we provide direct evidence supporting the scenario of dayside-driven magnetosphere convection. The driving process is closely connected to the evolution of Region 1 and Region 2 field-aligned currents. Our global simulations demonstrate that intensified magnetospheric convection and field-aligned currents progress from the dayside to the nightside within 10-20 minutes, following a southward turning of the interplanetary magnetic field. Observational data within this short timescale also reveal enhancements in both magnetosphere convection and the ionosphere's two-cell convection. These findings provide insights into the mechanisms driving planetary magnetosphere convection, with implications for the upcoming Solar-Wind-Magnetosphere-Ionosphere Link Explorer (SMILE) mission.

Human Serum Albumin-Platinum(II) Agent Nanoparticles Inhibit Tumor Growth Through Multimodal Action Against the Tumor Microenvironment.

To overcome the limitations of traditional platinum (Pt)-based drugs and further improve the targeting ability and therapeutic efficacy in vivo, we proposed to design a human serum albumin (HSA)-Pt agent complex nanoparticle (NP) for cancer treatment by multimodal action against the tumor microenvironment. We not only synthesized a series of Pt(II) di-2-pyridone thiosemicarbazone compounds and obtained a Pt(II) agent [Pt(Dp44mT)Cl] with significant anticancer activity but also successfully constructed a novel HSA-Pt(Dp44mT) complex nanoparticle delivery system. The structure of the HSA-Pt(Dp44mT) complex revealed that Pt(Dp44mT)Cl binds to the IIA subdomain of HSA and coordinates with His-242. The HSA-His242-Pt-Dp44mT NPs had an obvious effect on the inhibition of tumor growth, which was superior to that of Dp44mT and Pt(Dp44mT)Cl, and they had almost no toxicity. In addition, the HSA-His242-Pt-Dp44mT NPs were found to kill cancer cells by inducing apoptosis, autophagy, and inhibiting angiogenesis.

Novel mono-, bi-, tri- and tetra-nuclear copper complexes that inhibit tumor growth through apoptosis and anti-angiogenesis.

To develop the next-generation metal agents for efficiently inhibiting tumor growth, a series of novel mononuclear, binuclear and trinuclear copper (Cu) thiophene-2-formaldehyde thiosemicarbazone complexes and a tetranuclear Cu 1,2,4-triazole-derived complex have been synthesized and their structure-activity relationships have been studied. The trinucleated Cu complex showed the strongest inhibitory activity against T24 cells among all the Cu complexes. Its antitumor effect in vivo was superior to that of cisplatin, with reduced side effects. Further studies on the antitumor mechanism have showed that Cu complexes not only induced apoptosis of cancer cells but also inhibited tumor angiogenesis by inhibiting the migration and invasion of vascular endothelial cells, blocking the cell cycle in the G1 phase, and inducing autophagy.

FTO demethylates regulates cell-cycle progression by controlling CCND1 expression in luteinizing goat granulosa cells.

In mammals, N6-methyladenosine (m6A) stands out as one of the most abundant internal mRNA modifications and plays a crucial role in follicular development. Nonetheless, the precise mechanism by which the demethylase FTO regulates the progression of the goat luteinizing granulosa cells (LGCs) cycle remains to be elucidated. In our study, we primarily assessed the protein and mRNA expression levels of genes using Western blotting and quantitative real-time polymerase chain reaction (qRT-PCR), cell proliferation via EdU, cell viability with CCK-8, and apoptosis and cell cycle progression through flow cytometry. Here, the results demonstrated that knockdown of FTO significantly enhanced apoptosis, impeded cell proliferation, and increased autophagy levels in goat LGCs. Furthermore, the silencing of FTO substantially reduced cyclin D1 (CCND1) expression through the recognition and degradation of YTHDF2, consequently prolonging the cell cycle progression. This study sheds light on the mechanism by which FTO demethylation governs cell cycle progression by controlling the expression of CCND1 in goat LGCs, underscoring the dynamic role of m6A modification in the regulation of cell cycle progression.

Electrochemical Synthesis of Copper Mesh-Supported Thermo-Catalysts.

Metallic substrates, widely studied in the context of monolithic catalysts, offer inherent advantages in heterogeneous catalysis due to their exceptional thermal conductivity and mechanical properties. However, synthesizing stable monolithic catalysts with metallic substrates in a well-controlled manner remains a significant challenge. Here, this work introduces a simple, cost-efficient method to fabricate robust Cu mesh-supported thermo-catalysts using a modified cycling chronopotentiometry approach, where the Cu mesh serves as a donor of Cu ions. In this method, the Cu mesh surface generates two distinct layers of CuO and Cu2 O. In this context, CuO acts as the active phase, accounting for the high CO oxidation activity of Cu mesh catalysts with T90 ≈ 120 °C. Additionally, these catalysts exhibit considerable potential in electrocatalysis, showcasing significant research and application value.

Developing a Hetero-Trinuclear Erbium(III)-Copper(II) Complex Based on Apoferritin: Targeted Photoacoustic Imaging and Multimodality Therapy of Tumor.

For the integration of targeted diagnosis and treatment of tumor, we innovatively designed and synthesized a single-molecule hetero-multinuclear Er(III)-Cu(II) complex (ErCu2) and then constructed an ErCu2@apoferritin (AFt) nanoparticle (NP) delivery system. ErCu2 and ErCu2@AFt NPs not only provided an evident photoacoustic imaging (PAI) signal of the tumor but also effectively inhibited tumor growth by integrating photothermal therapy, chemotherapy, and immunotherapy. ErCu2@AFt NPs improved the targeting ability and decreased the systemic toxicity of ErCu2 in vivo. Furthermore, we confirmed that ErCu2 and ErCu2@AFt NPs inhibited tumor growth by inducing apoptosis and autophagy of tumor cells and activating the immune system. The study not only provides a novel strategy to develop therapeutic metal agents but also reveals their potential for targeted accurate diagnosis and multimodality therapy of cancer.

Carbon-Carbon Bond Cleavage for Late-Stage Functionalization.

Late-stage functionalization (LSF) introduces functional group or structural modification at the final stage of the synthesis of natural products, drugs, and complex compounds. It is anticipated that late-stage functionalization would improve drug discovery's effectiveness and efficiency and hasten the creation of various chemical libraries. Consequently, late-stage functionalization of natural products is a productive technique to produce natural product derivatives, which significantly impacts chemical biology and drug development. Carbon-carbon bonds make up the fundamental framework of organic molecules. Compared with the carbon-carbon bond construction, the carbon-carbon bond activation can directly enable molecular editing (deletion, insertion, or modification of atoms or groups of atoms) and provide a more efficient and accurate synthetic strategy. However, the efficient and selective activation of unstrained carbon-carbon bonds is still one of the most challenging projects in organic synthesis. This review encompasses the strategies employed in recent years for carbon-carbon bond cleavage by explicitly focusing on their applicability in late-stage functionalization. This review expands the current discourse on carbon-carbon bond cleavage in late-stage functionalization reactions by providing a comprehensive overview of the selective cleavage of various types of carbon-carbon bonds. This includes C-C(sp), C-C(sp2), and C-C(sp3) single bonds; carbon-carbon double bonds; and carbon-carbon triple bonds, with a focus on catalysis by transition metals or organocatalysts. Additionally, specific topics, such as ring-opening processes involving carbon-carbon bond cleavage in three-, four-, five-, and six-membered rings, are discussed, and exemplar applications of these techniques are showcased in the context of complex bioactive molecules or drug discovery. This review aims to shed light on recent advancements in the field and propose potential avenues for future research in the realm of late-stage carbon-carbon bond functionalization.

Brain tumor feature extraction and edge enhancement algorithm based on U-Net network.

Background: Statistics show that each year more than 100,000 patients pass away from brain tumors. Due to the diverse morphology, hazy boundaries, or unbalanced categories of medical data lesions, segmentation prediction of brain tumors has significant challenges. Purpose: In this thesis, we highlight EAV-UNet, a system designed to accurately detect lesion regions. Optimizing feature extraction, utilizing automatic segmentation techniques to detect anomalous regions, and strengthening the structure. We prioritize the segmentation problem of lesion regions, especially in cases where the margins of the tumor are more hazy. Methods: The VGG-19 network structure is incorporated into the coding stage of the U-Net, resulting in a deeper network structure, and an attention mechanism module is introduced to augment the feature information. Additionally, an edge detection module is added to the encoder to extract edge information in the image, which is then passed to the decoder to aid in reconstructing the original image. Our method uses the VGG-19 in place of the U-Net encoder. To strengthen feature details, we integrate a CBAM (Channel and Spatial Attention Mechanism) module into the decoder to enhance it. To extract vital edge details from the data, we incorporate an edge recognition section into the encoder. Results: All evaluation metrics show major improvements with our recommended EAV-UNet technique, which is based on a thorough analysis of experimental data. Specifically, for low contrast and blurry lesion edge images, the EAV-Unet method consistently produces forecasts that are very similar to the initial images. This technique reduced the Hausdorff distance to 1.82, achieved an F1 score of 96.1%, and attained a precision of 93.2% on Dataset 1. It obtained an F1 score of 76.8%, a Precision of 85.3%, and a Hausdorff distance reduction to 1.31 on Dataset 2. Dataset 3 displayed a Hausdorff distance cut in 2.30, an F1 score of 86.9%, and Precision of 95.3%. Conclusions: We conducted extensive segmentation experiments using various datasets related to brain tumors. We refined the network architecture by employing smaller convolutional kernels in our strategy. To further improve segmentation accuracy, we integrated attention modules and an edge enhancement module to reinforce edge information and boost attention scores.