LectoScape: A Highly Multiplexed Imaging Platform for Glycome Analysis and Biomedical Diagnosis.

Glycosylation, a fundamental biological process, involves the attachment of glycans to proteins, lipids, and RNA, and it plays a crucial role in various biological pathways. It is of great significance to obtain the precise spatial distribution of glycosylation modifications at the cellular and tissue levels. Here, we introduce LectoScape, an innovative method enabling detailed imaging of tissue glycomes with up to 1 µm resolution through image mass cytometry (IMC). This method utilizes 12 distinct, nonoverlapping lectins selected via microarray technology, enabling the multiplexed detection of a wide array of glycans. Furthermore, we developed an efficient labeling strategy for these lectins. Crucially, our approach facilitates the concurrent imaging of diverse glycan motifs, including N-glycan and O-glycan, surpassing the capabilities of existing technologies. Using LectoScape, we have successfully delineated unique glycan structures in various cell types, enhancing our understanding of the glycan distribution across human tissues. Our method has identified specific glycan markers, such as α2,3-sialylated Galß1, 3GalNAc in O-glycan, and terminal GalNAc, as diagnostic indicators for cervical intraepithelial neoplasia. This highlights the potential of LectoScape in cancer diagnostics through the detection of abnormal glycosylation patterns.

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning.

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR .

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

OBJECTIVE: Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). METHODS: We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. RESULTS: The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. INTERPRETATION: Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Inflammation-related molecular signatures involved in the anticancer activities of brigatinib as well as the prognosis of EML4-ALK lung adenocarcinoma patient.

Although ALK tyrosine kinase inhibitors (ALK-TKIs) have shown remarkable benefits in EML4-ALK positive NSCLC patients compared to conventional chemotherapy, the optimal sequence of ALK-TKIs treatment remains unclear due to the emergence of primary and acquired resistance and the lack of potential prognostic biomarkers. In this study, we systematically explored the validity of sequential ALK inhibitors (alectinib, lorlatinib, crizotinib, ceritinib and brigatinib) for a heavy-treated patient with EML4-ALK fusion via developing an in vitro and in vivo drug testing system based on patient-derived models. Based on the patient-derived models and clinical responses of the patient, we found that crizotinib might inhibit proliferation of EML4-ALK positive tumors resistant to alectinib and lorlatinib. In addition, NSCLC patients harboring the G1269A mutation, which was identified in alectinib, lorlatinib and crizotinib-resistant NSCLC, showed responsiveness to brigatinib and ceritinib. Transcriptomic analysis revealed that brigatinib suppressed the activation of multiple inflammatory signaling pathways, potentially contributing to its anti-tumor activity. Moreover, we constructed a prognostic model based on the expression of IL6, CXCL1, and CXCL5, providing novel perspectives for predicting prognosis in EML4-ALK positive NSCLC patients. In summary, our results delineate clinical responses of sequential ALK-TKIs treatments and provide insights into the mechanisms underlying the superior effects of brigatinib in patients harboring ALKG1269A mutation and resistant towards alectinib, lorlatinib and crizotinib. The molecular signatures model based on the combination of IL6, CXCL1 and CXCL5 has the potential to predict prognosis of EML4-ALK positive NSCLC patients.

Characteristics and prognosis of EGFR mutations in small cell lung cancer patients in the NGS era

Purpose Targeted therapy has not been effective for small cell lung cancer (SCLC) patients. Although some studies have reported on EGFR mutations in SCLC, a systematic investigation into the clinical, immunohistochemical, and molecular characteristics and prognosis of EGFR-mutated SCLCs is lacking. Methods Fifty-seven SCLC patients underwent next-generation sequencing technology, with 11 in having EGFR mutations (group A) and 46 without (group B). Immunohistochemistry markers were assessed, and the clinical features and first-line treatment outcomes of both groups were analyzed. Results Group A consisted primarily of non-smokers (63.6%), females (54.5%), and peripheral-type tumors (54.5%), while group B mainly comprised heavy smokers (71.7%), males (84.8%), and central-type tumors (67.4%). Both groups showed similar immunohistochemistry results and had RB1 and TP53 mutations. When treated with tyrosine kinase inhibitors (TKIs) plus chemotherapy, group A had a higher treatment response rate with overall response and disease control rates of 80% and 100%, respectively, compared to 57.1% and 100% in group B. Group A also had a significantly longer median progression-free survival (8.20 months, 95% CI 6.91–9.49 months) than group B (2.97 months, 95% CI 2.79–3.15), with a significant difference (P = 0.043). Additionally, the median overall survival was significantly longer in group A (16.70 months, 95% CI 1.20–32.21) than in group B (7.37 months, 95% CI 3.85–10.89) (P = 0.016). Conclusion EGFR-mutated SCLCs occurred more frequently in non-smoking females and were linked to prolonged survival, implying a positive prognostic impact. These SCLCs shared immunohistochemical similarities with conventional SCLCs, and both types had prevalent RB1 and TP53 mutations (AU)

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Advancing Glucose Conjugated Gibberellins Discovery: A Structure-Oriented Screening and Identification Method for Unraveling Gibberellin Metabolites in Plants.

Gibberellins (GAs) play a pivotal role in modulating plant growth and development. Glucose-conjugated gibberellins (Glc-GAs), a prevalent conjugated form of GAs, regulate intracellular GA levels by the coupling and decoupling of glucose groups. However, the diversity of Glc-GAs identified within individual species remains limited, hinting at a multitude of yet undiscovered gibberellin metabolites. This lacuna poses considerable impediments to research efforts dedicated to comprehensively delineating the GA metabolic pathway. In this study, we developed a structure-oriented screening and identification method for Glc-GAs in plant species by employing LC-MS/MS coupled with chemical derivatization. Through the application of chemical derivatization technique, carboxyl groups on Glc-GAs were labeled which effectively enhanced the sensitivity and selectivity of mass spectrometry detection for these compounds. Concurrently, the integration of mass spectrometry fragmentation and chromatographic retention behavior facilitated the efficient screening and identification of potential Glc-GAs. With this strategy, we screened and identified 12 potential Glc-GAs from six plant species. These findings expand the Glc-GA diversity in plants and contribute to understanding GA metabolic pathways.

Exosomes from adipose-derived stem cells restore fibroblast function and accelerate diabetic wound healing.

Background: Diabetes is common yet challenging chronic disease, that affects a wide range of people around the world. Complex cellular environments around diabetic wounds tend to damage the function of effector cells, including vascular endothelial cells (VECs), fibroblasts and epithelial cells. This study aims to analyze the differences between diabetic wounds and normal skin as well as whether adipose-derived stem cell (ADSC) exosome could promote healing of diabetic wound. Methods: Human diabetic wounds and normal skin were collected and stained with HE, Masson, CD31 and 8-hydroxy-2 deoxyguanosine immunohistochemical staining. RNA-seq data were collected for further bioinformatics analysis. ADSC exosomes were isolated and identified by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and western blotting. The effect of ADSC exosomes on diabetic wound healing was assessed on full thickness wounds in mice. To further verify the regulative impact of ADSCs exosomes in high glucose treated fibroblasts, we isolated fibroblasts from normal skin tissue and measured the cell viability, apoptosis rate, proliferation and migration of fibroblasts. In addition, collagen formation and fibrosis-related molecules were also detected. To further disclose the mechanism of ADSC exosomes on the function of high glucose treated fibroblasts, we detected the expression of apoptosis related molecules including BCL2, Bax, and cleaved caspase-3. Results: Histological observation indicated that perilesional skin tissues from diabetic patients showed structural disorder, less collagen disposition and increased injury compared with normal skin. Bioinformatics analysis showed that the levels of inflammatory and collagen synthesis related molecules, as well as oxidative stress and apoptosis related molecules, were significantly changed. Furthermore, we found that ADSC exosomes could not only speed up diabetic wound healing, but could also improve healing quality. ADSC exosomes restored high glucose induced damage to cell viability, migration and proliferation activity, as well as fibrosis-related molecules such as SMA, collagen 1 and collagen 3. In addition, we verified that ADSC exosomes downregulated high glucose induced increased apoptosis rate in fibroblast and the protein expression of Bax as well as cleaved caspases 3. Conclusions: This study indicated that ADSC exosomes alleviated high glucose induced damage to fibroblasts and accelerate diabetic wound healing by inhibiting Bax/caspase 3.

Water hammer in pipelines based on different friction models.

Water hammer in pipelines is a difficult problem in fluid transmission field. Especially, there exists some friction items of pipeline transient model such that the simulation model is not consistent to the experimental results. By using the friction model proposed by Kagawa and the model of impulse response function, the pressure transients are calculated with and without cavitation. The corresponding simulation results involving pressure, velocity, steady and dynamic frictions, cavitation volume are analyzed to reveal the effect of friction item on pressure transients. Moreover, the features of steady and dynamic frictions are captured in pipelines with upstream and downstream valves. The comparative simulation results of two friction models have verified that the friction model using an impulse response function has higher consistency between simulation and experimental results of pipeline transients.

Characteristics and prognosis of EGFR mutations in small cell lung cancer patients in the NGS era.

PURPOSE: Targeted therapy has not been effective for small cell lung cancer (SCLC) patients. Although some studies have reported on EGFR mutations in SCLC, a systematic investigation into the clinical, immunohistochemical, and molecular characteristics and prognosis of EGFR-mutated SCLCs is lacking. METHODS: Fifty-seven SCLC patients underwent next-generation sequencing technology, with 11 in having EGFR mutations (group A) and 46 without (group B). Immunohistochemistry markers were assessed, and the clinical features and first-line treatment outcomes of both groups were analyzed. RESULTS: Group A consisted primarily of non-smokers (63.6%), females (54.5%), and peripheral-type tumors (54.5%), while group B mainly comprised heavy smokers (71.7%), males (84.8%), and central-type tumors (67.4%). Both groups showed similar immunohistochemistry results and had RB1 and TP53 mutations. When treated with tyrosine kinase inhibitors (TKIs) plus chemotherapy, group A had a higher treatment response rate with overall response and disease control rates of 80% and 100%, respectively, compared to 57.1% and 100% in group B. Group A also had a significantly longer median progression-free survival (8.20 months, 95% CI 6.91-9.49 months) than group B (2.97 months, 95% CI 2.79-3.15), with a significant difference (P = 0.043). Additionally, the median overall survival was significantly longer in group A (16.70 months, 95% CI 1.20-32.21) than in group B (7.37 months, 95% CI 3.85-10.89) (P = 0.016). CONCLUSION: EGFR-mutated SCLCs occurred more frequently in non-smoking females and were linked to prolonged survival, implying a positive prognostic impact. These SCLCs shared immunohistochemical similarities with conventional SCLCs, and both types had prevalent RB1 and TP53 mutations.

Pharmacokinetic and Bioequivalence Study of Lisinopril/Hydrochlorothiazide Tablet Under Fasting and Postprandial Conditions in Healthy Chinese Subjects.

The objective of this research was to evaluate and compare the pharmacokinetic profiles and safety of lisinopril/hydrochlorothiazide (10 mg/12.5 mg) tablets in the test and reference formulations administered to participants in both fasting and postprandial states and to evaluate the bioequivalence of the 2 products in healthy Chinese volunteers. This study employed a single-center, randomized, open-label, single-dose dosing trial involving a cumulative 96 healthy adult participants (60 in the fasting group and 36 in the postprandial group). Each group comprised 2 sequence sets, and a 2-week washout period was implemented. There were no statistically significant differences in time to maximum concentration and terminal elimination half-life between the test and control groups under fasting and postprandial conditions (P > .05), and the 90% CIs for area under the plasma concentration-time curve and maximum plasma concentration were within the bioequivalence range of 80%-125%. Pharmacokinetic results indicate a large food effect for lisinopril, meaning that there is a loss of approximately 20%-25% of systemic exposure from fasting to postprandial administration for both preparations. The study demonstrated that a single oral dose of generic lisinopril/hydrochlorothiazide is bioequivalent to the reference product and well tolerated, with no significant adverse events observed, and that both products are similarly safe in a cohort of healthy Chinese male and female participants, following administration under fasting and postprandial conditions.

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations.

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 µg/m3 to 44.8416 µg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Evaluation of DNA-protein complex structures using the deep learning method.

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

Protective effects of MiR-146b in cerebral infarction via targeting SIRT1/FOXO1 signaling pathway.

To observe the therapeutic effect of micro ribonucleic acid (miR)-146b on brain tissue injury in rats with cerebral infarction (CI) by regulating the Sirtuin 1 (SIRT1)/forkhead box protein O1 (FOXO1) signaling pathway, a rat model of CI was established. Lentiviral cells were used to transfect and silence or overexpress miR-146b. The rats were divided into the miR-146b inhibitor group (Inhibitors), miR-146b mimic group (Mimics) and normal group (Control). Then quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the transfection rate of miR-146b in rat brain tissues in each group. The improved method was adopted to score the nerves of rats, and the infarction volume of rats in each group was determined. Subsequently, the levels of superoxide dismutase (SOD) and reactive oxygen species (ROS) in the brain tissues in each group were measured via enzyme-linked immunosorbent assay (ELISA), the apoptosis of nerve cells in the brain tissues was detected by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), and glial fibrillary acidic protein (GFAP), S100ß gene and SIRT1/FOXO1 pathway-related genes and proteins in the brain tissues were determined through qRT-PCR and Western blotting. MiR-146b exhibited a high expression in Mimics and an extremely low expression in Inhibitors. Rats in Mimics were normal in movement, and their neurological scores were close to those in Control. Rats in Inhibitors could walk normally, and their neurological scores were notably higher than those in other groups (P<0.05). In addition, Inhibitors had a remarkably larger CI volume (P<0.05), a remarkably increased ROS level and a significantly reduced SOD level compared with those in other groups. Moreover, TUNEL staining results manifested that apoptotic cells, especially glial cells, were notably increased in Inhibitors compared with those in Mimics. Besides, the messenger RNA (mRNA) expression levels of S100ß and GFAP in Inhibitors were higher than those in other groups (P<0.05). SIRT1 and FOXO1 genes were increased in Mimics, which were close to those in Control. According to Western blotting results, the protein expression levels of SIRT1 and FOXO1 in Mimics were notably higher than those in Inhibitors. MiR-146b can play a protective role in CI rats by activating the SIRT1/FOXO1 signaling pathway, lowering the oxidative stress level and reducing brain tissue apoptosis.

MiR-182 promotes apoptosis of neural cells in cerebral infarction rats by PI3K/AKT signaling pathway.

To explore the effects of micro ribonucleic acid (miR)-182 on the proliferation and apoptosis of neural cells in cerebral infarction rats and its underlying mechanism. The rat model of cerebral infarction was established, and neural cells were extracted accordingly. The cell proliferation ability was detected via cell counting kit-8 (CCK8) assay. In addition, the apoptosis rate was determined through flow cytometry and the activity of active caspase-3. Furthermore, the interaction between miR-182 and PI3K was explored via dual luciferase reporter assay, and the protein expression levels were observed via Western blotting. The neural cells in mouse brain tissues significantly decreased in the model group compared with that in the control group via HE stain. Additionally, the expression level of miR-182 was significantly increased in the model group compared with that in the control group. Furthermore, overexpression of miR-182 could inhibit the proliferation of neural cells through inducing cell apoptosis. Besides, the results of the luciferase reporter assay showed that the relative luciferase activity in neural cells could be inhibited by the transfection with miR-182 (P<0.05). Overexpression of miR-182 significantly reduced the protein expression levels of phosphatidylinositol 3-hydroxy kinase (PI3K) and p-AKT. MiR-182 induces apoptosis of neural cells through inhibiting the PI3K/AKT signaling pathway, which plays an important regulatory role in the apoptosis of neural cells in cerebral infarction rats.

Selenium-based metabolic oligosaccharide engineering strategy for quantitative glycan detection.

Metabolic oligosaccharide engineering (MOE) is a classical chemical approach to perturb, profile and perceive glycans in physiological systems, but probes upon bioorthogonal reaction require accessibility and the background signal readout makes it challenging to achieve glycan quantification. Here we develop SeMOE, a selenium-based metabolic oligosaccharide engineering strategy that concisely combines elemental analysis and MOE,enabling the mass spectrometric imaging of glycome. We also demonstrate that the new-to-nature SeMOE probes allow for detection, quantitative measurement and visualization of glycans in diverse biological contexts. We also show that chemical reporters on conventional MOE can be integrated into a bifunctional SeMOE probe to provide multimodality signal readouts. SeMOE thus provides a convenient and simplified method to explore the glyco-world.

RNet: a network strategy to predict RNA binding preferences.

Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community.

Different delocalized ranges in mixed valence cyanido-metal-bridged Fe-Ru-Fe complexes controlled by terminal ligand substitution modification.

In order to investigate the properties of metal to metal charge transfer (MMCT) influenced by the relative energy level between the bridging unit and the terminal unit, two groups of heterotrimetallic cyanido-metal-bridged complexes, trans-[Cp(dppe)Fe-CN-Ru(MeOpy)4-NC-Fe(dppe)Cp][X]n (1[X]n; n = 2, 3, or 4; X = PF6 or BF4) (Cp = cyclopentadiene, dppe = 1,2-bis(diphenylphosphino)ethane, MeOpy = 4-methoxypyridine) and [Cp*(dppe)Fe-CN-Ru(MeOpy)4-NC-Fe(dppe)Cp*] [X]n (2[X]n; Cp* = 1,2,3,4,5-pentamethylcyclopentadiene; n = 2, 3, or 4; X = PF6 or BF4) were synthesized and fully characterized. The crystallography data suggest different oxidation sites in the ground state for one-electron oxidation products 13+ and 23+, and the electrochemical and Mössbauer spectra suggest that in the one-electron oxidation compounds 13+, the charge is delocalized all along the trimetal backbone Fe-Ru-Fe, while in 23+, the charge is rather delocalized between the two metal parts Fe-Ru. Further oxidation of N3+ gives N4+ (N = 1 or 2), during which a spin transfer towards the terminal units is observed in both series.

Use of a radiomics-clinical model based on magnetic diffusion-weighted imaging for preoperative prediction of lymph node metastasis in rectal cancer patients.

Rectal cancer is the eighth most prevalent malignancy worldwide with a 3.2% mortality rate and 3.9% incidence rate. Radiologists still have difficulty in correctly diagnosing lymph node metastases that have been suspected preoperatively. To assess the effectiveness of a model combining clinical and radiomics features for the preoperative prediction of lymph node metastasis in rectal cancer. We retrospectively analyzed data from 104 patients with rectal cancer. All patients were selected as samples for the training (n = 72) and validation cohorts (n = 32). Lymph nodes (LNs) in diffusion-weighted images were analyzed to obtain 842 radiomic characteristics, which were then used to draw the region of interest. Logistic regression, least absolute shrinkage and selection operator, and between-group and within-group correlation analyses were combined to establish the radiomic score (rad-score). Receiver operating characteristic curves were used to estimate the prediction accuracy of the model. A calibration curve was constructed to test the predictive ability of the model. A decision curve analysis was performed to analyze the model's value in clinical application. The area under the curve for the radiomics-clinical, clinical, and radiomics models was 0.856, 0.810, and 0.781, respectively, in the training cohort and 0.880, 0.849, and 0.827, respectively, in the validation cohort. The calibration curve and DCA showed that the radiomics-clinical prediction model had good prediction accuracy, which was higher than that of the other models. The radiomics-clinical model showed a favorable predictive performance for the preoperative prediction of LN metastasis in patients with rectal cancer.