Predicting bond dissociation energies of cyclic hypervalent halogen reagents using DFT calculations and graph attention network model.

Although hypervalent iodine(III) reagents have become staples in organic chemistry, the exploration of their isoelectronic counterparts, namely hypervalent bromine(III) and chlorine(III) reagents, has been relatively limited, partly due to challenges in synthesizing and stabilizing these compounds. In this study, we conduct a thorough examination of both homolytic and heterolytic bond dissociation energies (BDEs) critical for assessing the chemical stability and functional group transfer capability of cyclic hypervalent halogen compounds using density functional theory (DFT) analysis. A moderate linear correlation was observed between the homolytic BDEs across different halogen centers, while a strong linear correlation was noted among the heterolytic BDEs across these centers. Furthermore, we developed a predictive model for both homolytic and heterolytic BDEs of cyclic hypervalent halogen compounds using machine learning algorithms. The results of this study could aid in estimating the chemical stability and functional group transfer capabilities of hypervalent bromine(III) and chlorine(III) reagents, thereby facilitating their development.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment.

Background: Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC). Methods: Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC. Results: ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy. Conclusion: This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

Pulsatilla decoction alleviates DSS-induced UC by activating FXR-ASBT pathways to ameliorate disordered bile acids homeostasis.

Ethnopharmacological relevance: Pulsatilla decoction (PD) is a classical prescription for the treatment of ulcerative colitis. Previous studies have demonstrated that the therapeutic efficacy of PD is closely associated with the activation of Farnesoid X receptor (FXR). The activity of FXR is regulated by apical sodium-dependent bile acid transporter (ASBT), and the FXR-ASBT cascade reaction, centered around bile acid receptor FXR, plays a pivotal role in maintaining bile acid metabolic homeostasis to prevent the occurrence and progression of ulcerative colitis (UC). Aim of the study: To elucidate the underlying mechanism by which PD exerts its proteactive effects against Dextran Sulfate Sodium Salt (DSS)-induced ulcerative colitis, focusing on the modulation of FXR and ASBT. Materials and methods: To establish a model of acute ulcerative colitis, BALB/C mice were administered 3.5% DSS in their drinking water for consecutive 7 days. The disease activity index (DAI) was employed to evaluate the clinical symptoms exhibited by each group of mice. Goblet cell expression in colon tissue was assessed using glycogen schiff periodic acid-Schiff (PAS) and alcian blue staining techniques. Inflammatory cytokine expression in serum and colonic tissues was examined through enzyme-linked immunosorbent assay (ELISA). A PCR Array chip was utilized to screen 88 differential genes associated with the FXR-ASBT pathway in UC treatment with PD. Western blotting (WB) analysis was performed to detect protein expression levels of differentially expressed genes in mouse colon tissue. Results: The PD treatment effectively reduced the Disease Activity Index (DAI) score and mitigated colon histopathological damage, while also restoring weight and colon length. Furthermore, it significantly alleviated the severity of ulcerative colitis (UC), regulated inflammation, modulated goblet cell numbers, and restored bile acid balance. Additionally, a PCR Array analysis identified 21 differentially expressed genes involved in the FXR-ASBT pathway. Western blot results demonstrated significant restoration of FXR, GPBAR1, CYP7A1, and FGF15 protein expression levels following PD treatment; moreover, there was an observed tendency towards increased expression levels of ABCB11 and RXRα. Conclusion: The therapeutic efficacy of PD in UC mice is notable, potentially attributed to its modulation of bile acid homeostasis, enhancement of gut barrier function, and attenuation of intestinal inflammation.

What enlightenment has the development of lung cancer bone metastasis brought in the last 22 years.

BACKGROUND: Lung cancer bone metastasis (LCBM) is a disease with a poor prognosis, high risk and large patient population. Although considerable scientific output has accumulated on LCBM, problems have emerged, such as confusing research structures. AIM: To organize the research frontiers and body of knowledge of the studies on LCBM from the last 22 years according to their basic research and translation, clinical treatment, and clinical diagnosis to provide a reference for the development of new LCBM clinical and basic research. METHODS: We used tools, including R, VOSviewer and CiteSpace software, to measure and visualize the keywords and other metrics of 1903 articles from the Web of Science Core Collection. We also performed enrichment and protein-protein interaction analyses of gene expression datasets from LCBM cases worldwide. RESULTS: Research on LCBM has received extensive attention from scholars worldwide over the last 20 years. Targeted therapies and immunotherapies have evolved into the mainstream basic and clinical research directions. The basic aspects of drug resistance mechanisms and parathyroid hormone-related protein may provide new ideas for mechanistic study and improvements in LCBM prognosis. The produced molecular map showed that ribosomes and focal adhesion are possible pathways that promote LCBM occurrence. CONCLUSION: Novel therapies for LCBM face animal testing and drug resistance issues. Future focus should centre on advancing clinical therapies and researching drug resistance mechanisms and ribosome-related pathways.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Production of constrained L-cyclo-tetrapeptides by epimerization-resistant direct aminolysis.

The synthesis of constrained 12-membered rings is notably difficult. The main challenges result from constraints during the linear peptide cyclization. Attempts to overcome constraints through excessive activation frequently cause peptidyl epimerization, while insufficient activation of the C-terminus hampers cyclization and promotes intermolecular oligomer formation. We present a ß-thiolactone framework that enables the synthesis of cyclo-tetrapeptides via direct aminolysis. This tactic utilizes a mechanism that restricts C-terminal carbonyl rotation while maintaining high reactivity, thereby enabling efficient head-to-tail amidation, reducing oligomerization, and preventing epimerization. A broad range of challenging cyclo-tetrapeptides ( > 20 examples) are synthesized in buffer and exhibits excellent tolerance toward nearly all proteinogenic amino acids. Previously unattainable macrocycles, such as cyclo-L-(Pro-Tyr-Pro-Val), have been produced and identified as µ-opioid receptor (MOR) agonists, with an EC50 value of 2.5 nM. Non-epimerizable direct aminolysis offers a practical solution for constrained peptide cyclization, and the discovery of MOR agonist activity highlights the importance of overcoming synthetic challenges for therapeutic development.

Deep Spatial-Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection.

Hyperspectral target detection aims to locate targets of interest in the scene, and deep learning-based detection methods have achieved the best results. However, black box network architectures are usually designed to directly learn the mapping between the original image and the discriminative features in a single data-driven manner, a choice that lacks sufficient interpretability. On the contrary, this article proposes a novel deep spatial-spectral joint-sparse prior encoding network (JSPEN), which reasonably embeds the domain knowledge of hyperspectral target detection into the neural network, and has explicit interpretability. In JSPEN, the sparse encoded prior information with spatial-spectral constraints is learned end-to-end from hyperspectral images (HSIs). Specifically, an adaptive joint spatial-spectral sparse model (AS 2 JSM) is developed to mine the spatial-spectral correlation of HSIs and improves the accuracy of data representation. An optimization algorithm is designed for iteratively solving AS 2 JSM, and JSPEN is proposed to simulate the iterative optimization process in the algorithm. Each basic module of JSPEN one-to-one corresponds to the operation in the optimization algorithm so that each intermediate result in the network has a clear explanation, which is convenient for intuitive analysis of the operation of the network. With end-to-end training, JSPEN can automatically capture the general sparse properties of HSIs and faithfully characterize the features of background and target. Experimental results verify the effectiveness and accuracy of the proposed method. Code is available at https://github.com/Jiahuiqu/JSPEN.

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications.

The escalating development and improvement of gas sensing ability in industrial equipment, or "machine olfactory", propels the evolution of gas sensors toward enhanced sensitivity, selectivity, stability, power efficiency, cost-effectiveness, and longevity. Two-dimensional (2D) materials, distinguished by their atomic-thin profile, expansive specific surface area, remarkable mechanical strength, and surface tunability, hold significant potential for addressing the intricate challenges in gas sensing. However, a comprehensive review of 2D materials-based gas sensors for specific industrial applications is absent. This review delves into the recent advances in this field and highlights the potential applications in industrial machine olfaction. The main content encompasses industrial scenario characteristics, fundamental classification, enhancement methods, underlying mechanisms, and diverse gas sensing applications. Additionally, the challenges associated with transitioning 2D material gas sensors from laboratory development to industrialization and commercialization are addressed, and future-looking viewpoints on the evolution of next-generation intelligent gas sensory systems in the industrial sector are prospected.

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer.

Background: Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions. Materials and methods: A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions. Results: The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS. Conclusion: A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.

Copper Difluorocarbene Enables Catalytic Difluoromethylation.

Despite the synthetic versatility of difluorocarbene, its high reactivity severely regulates widespread applications of difluorocarbene in organic synthesis. Here, we report a copper difluorocarbene-involved catalytic coupling, representing a new mode of the difluoromethylation reaction. This method allows difluoromethylation of a wide range of readily available allyl/propargyl electrophiles with NaBH3CN and low-cost difluorocarbene precursor BrCF2CO2K, featuring high cost-efficiency, high stereo- and regioselectivities, and high functional group tolerance, even with complex drug-like molecules. Applying the method led to the efficient synthesis of deuterated difluoromethylated compounds of medicinal interest. The resulting difluoromethylated allyl and allenyl products can serve as versatile synthons for diverse transformations, rendering the approach attractive for synthesizing complex fluorinated structures. Experimental mechanistic studies and computational calculations reveal that the formation of a difluoromethylcopper(I) intermediate through the nucleophilic attack of boron hydride on the copper(I) difluorocarbene is the key step in the reaction.

AHNAK2 Regulates NF-κB/MMP-9 Signaling to Promote Pancreatic Cancer Progression.

Early metastasis of pancreatic cancer (PaC) is a major cause of its high mortality rate. Previous studies have shown that AHNAK2 is involved in the progression of some tumors and is predicted to be an independent prognostic factor for PaC; however, the specific mechanisms through which AHNAK2 regulates PaC remain unclear. In this study, we examined the role of AHNAK2 in PaC and its potential molecular mechanisms. AHNAK2 mRNA and protein expression in PaC tissues and cells were measured using qRT-PCR and western blot analysis. After AHNAK2 knockdown using small interfering RNA, PaC cells were subjected to CCK-8 scratch, and Transwell assays to assess cell proliferation, migration, and invasion, respectively. Furthermore, the validation of the mechanistic pathway was achieved by western blot analysis. AHNAK2 mRNA and protein levels were up-regulated in PaC and silencing AHNAK2 significantly inhibited the proliferation, migration, and invasion of PaC cells. Mechanistically, AHNAK2 knockdown decreased the expression of phosphorylated p65, phosphorylated IκBα, and matrix metalloproteinase-9 (MMP-9), suggesting that activation of the NF-κB/MMP-9 signaling pathway was inhibited. Importantly, activation of NF-κB reversed the effects of AHNAK2 knockdown. Our findings indicate that AHNAK2 promotes PaC progression through the NF-kB/MMP-9 pathway and provides a theoretical basis for targeting AHNAK2 for the treatment of PaC.

Assessing the association between a sedentary lifestyle and prevalence of primary osteoporosis: a community-based cross-sectional study among Chinese population.

OBJECTIVES: To reveal the association between a sedentary lifestyle and the prevalence of primary osteoporosis (POP). DESIGN: A community-based cross-sectional study was conducted. SETTING: This study was conducted in communities in Hefei city, Anhui province, China. PARTICIPANTS: A total of 1346 residents aged 40 and above underwent POP screening via calcaneus ultrasound bone mineral density (BMD) testing and completed a questionnaire survey. OUTCOME MEASURES: The average daily sitting time was included in the study variable and used to assess sedentary behaviour. The 15 control variables included general information, dietary information and life behaviour information. Logistic regression was used to analyse the association between the POP prevalence and study or control variables in different models. RESULTS: 1346 participants were finally included in the study. According to the 15 control variables, the crude model and 4 models were established. The analysis revealed that the average daily sitting time showed a significant correlation with the prevalence of POP in the crude model (OR=2.02, 95% CI=1.74 to 2.36, p<0.001), Model 1 (OR=2.65, 95% CI=2.21 to 3.17, p<0.001), Model 2 (OR=2.63, 95% CI=2.19 to 3.15, p<0.001), Model 3 (OR=2.62, 95% CI=2.18 to 3.15, p<0.001) and Model 4 (OR=2.58, 95% CI=2.14 to 3.11, p<0.001). Besides, gender, age and body mass index showed a significant correlation with the POP prevalence in all models. CONCLUSIONS: This study suggests a potential association between a sedentary lifestyle and the prevalence of POP within the Chinese population. Modifying sedentary behaviours could contribute to a reduction in POP risk. However, longitudinal cohort studies are necessary to confirm this hypothesis in the future.

Observation of quantum nonlocality in Greenberger-Horne-Zeilinger entanglement on a silicon chip.

Nonlocality is the defining feature of quantum entanglement. Entangled states with multiple particles are of crucial importance in fundamental tests of quantum physics as well as in many quantum information tasks. One of the archetypal multipartite quantum states, Greenberger-Horne-Zeilinger (GHZ) state, allows one to observe the striking conflict of quantum physics to local realism in the so-called all-versus-nothing way. This is profoundly different from Bell's theorem for two particles, which relies on statistical predictions. Here, we demonstrate an integrated photonic chip capable of generating and manipulating the four-photon GHZ state. We perform a complete characterization of the four-photon GHZ state using quantum state tomography and obtain a state fidelity of 0.729±0.006. We further use the all-versus-nothing test and the Mermin inequalities to witness the quantum nonlocality of GHZ entanglement. Our work paves the way to perform fundamental tests of quantum physics with complex integrated quantum devices.

Self-Assembled Nanocomposite DOX/TPOR4@CB[7]4 for Enhanced Synergistic Photodynamic Therapy and Chemotherapy in Neuroblastoma.

DOX/TPOR4@CB[7]4 was synthesized via self-assembly, and its physicochemical properties and ability to generate reactive oxygen species (ROS) were evaluated. The impact of photodynamic therapy on SH-SY5Y cells was assessed using the MTT assay, while flow cytometry analysis was employed to detect cell apoptosis. Confocal laser scanning microscopy was utilized to observe the intracellular distribution of DOX/TPOR4@CB[7]4 in SH-SY5Y cells. Additionally, fluorescence imaging of DOX/TPOR4@CB[7]4 in nude mice bearing SH-SY5Y tumors and examination of the combined effects of photodynamic and chemical therapies were conducted. The incorporation of CB[7] significantly enhanced the optical properties of DOX/TPOR4@CB[7]4, resulting in increased ROS production and pronounced toxicity towards SH-SY5Y cells. Moreover, both the apoptotic and mortality rates exhibited significant elevation. In vivo experiments demonstrated that tumor growth inhibition was most prominent in the DOX/TPOR4@CB[7]4 group. π-π interactions facilitated the binding between DOX and photosensitizer TPOR, with TPOR's naphthalene hydrophilic groups encapsulated within CB[7]'s cavity through host-guest interactions with CB[7]. Therefore, CB[7] can serve as a nanocarrier to enhance the combined application of chemical therapy and photodynamic therapy, thereby significantly improving treatment efficacy against neuroblastoma tumors.

Radical-Mediated α-tert-Alkylation of Aldehydes by Consecutive 1,4- and 1,3-(Benzo)thiazolyl Migrations.

The direct alkylation of the α-position of aldehydes is an effective method for accessing a wide range of structurally diverse aldehydes, yet tert-alkylation has proven to be a challenging task. In this study, we present a novel radical-mediated tert-alkylation approach targeting the α-position of aldehydes, enabling the synthesis of complex aliphatic aldehydes. The transformation is initiated by the interaction between an in situ generated enamine intermediate and α-bromo sulfone, forming an electron donor-acceptor (EDA) complex, followed by consecutive 1,4- and 1,3-functional group migrations. This protocol operates under metal-free and mild photochemical conditions, delivering a broad scope of products and providing new mechanistic insights into radical rearrangement reactions.

A Principle Design of Registration-Fusion Consistency: Toward Interpretable Deep Unregistered Hyperspectral Image Fusion.

For hyperspectral image (HSI) and multispectral image (MSI) fusion, it is often overlooked that multisource images acquired under different imaging conditions are difficult to be perfectly registered. Although some works attempt to fuse unregistered images, two thorny challenges remain. One is that registration and fusion are usually modeled as two independent tasks, and there is no yet a unified physical model to tightly couple them. Another is that deep learning (DL)-based methods may lack sufficient interpretability and generalization. In response to the above challenges, we propose an unregistered HSI fusion framework energized by a unified model of registration and fusion. First, a novel registration-fusion consistency physical perception model (RFCM) is designed, which uniformly models the image registration and fusion problem to greatly reduce the sensitivity of fusion performance to registration accuracy. Then, an HSI fusion framework (MoE-PNP) is proposed to learn the knowledge reasoning process for solving RFCM. Each basic module of MoE-PNP one-to-one corresponds to the operation in the optimization algorithm of RFCM, which can ensure clear interpretability of the network. Moreover, MoE-PNP captures the general fusion principle for different unregistered images and therefore has good generalization. Extensive experiments demonstrate that MoE-PNP achieves state-of-the-art performance for unregistered HSI and MSI fusion. The code is available at https://github.com/Jiahuiqu/MoE-PNP.

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.

Ten-year observation of corneal densitometry and associated factors following small incision lenticule extraction.

AIM: To investigate the long-term changes of corneal densitometry (CD) and its contributing elements after small incision lenticule extraction (SMILE). METHODS: Totally 31 eyes of 31 patients with mean spherical equivalent of -6.46±1.50 D and mean age 28.23±7.38y were enrolled. Full-scale examinations were conducted on all patients preoperatively and during follow-up. Visual acuity, manifest refraction, axial length, corneal thickness, corneal higher-order aberrations, and CD were evaluated. RESULTS: All surgeries were completed successfully without complications or adverse events. Ten-year safety index was 1.17±0.20 and efficacy 1.04±0.28. CD value of 0-6 mm zones in central layer was statistically significantly lower 10y postoperatively, compared with preoperative values (0-2 mmΔ=-1.62, 2-6 mmΔ=-1.24, P<0.01). There were no correlations between CD values and factors evaluated. CONCLUSION: SMILE is a safe and efficient procedure for myopia on a long-term basis. CD values get lower 10y postoperatively, whose mechanism is to be further discussed.