Evaluation of parietal pleural adhesion and invasion in subpleural lung cancer: value of B-mode ultrasound and contrast-enhanced ultrasound.

Background: The parietal pleural adhesion/invasion of lung cancer can contribute substantially to poor prognosis and difficulty in surgery. The value of ultrasound in evaluating the parietal pleural adhesion or invasion (pleural adhesion/invasion) of lung cancer remains uncertain. This study investigated the value of B-mode ultrasound and contrast-enhanced ultrasound (CEUS) in diagnosing parietal pleural adhesion/invasion of subpleural lung cancer. Methods: The study animals included 40 male New Zealand white rabbits. A rabbit subpleural lung cancer model was constructed by injecting VX2 tumor tissue under ultrasound guidance. In the 1-3 weeks after subpleural lesion formation, parietal pleural adhesion/invasion of the largest subpleural lesion was evaluated with B-mode ultrasound and CEUS by two sonographers. The parietal pleural adhesion/invasion was also determined using the gold standard method of findings from anatomical and pathological examination. Results: Ultimately, 34 rabbits were subjected to complete ultrasonic evaluation. There were 20 and 14 cases with and without parietal pleural adhesion/invasion, respectively, as confirmed by anatomical and pathological evaluations. The diagnostic sensitivity, specificity, and accuracy of sonographer 1 using B-mode ultrasound were 50.0% [95% confidence interval (CI): 26.0-74.0%], 100%, and 70.6% (95% CI: 54.5-86.7%), respectively; for CEUS, they were 90.0% (95% CI: 75.6-100.0%), 100.0%, and 94.1% (95% CI: 85.8-100.0%), respectively. The diagnostic sensitivity, specificity, and accuracy of sonographer 2 using B-mode ultrasound were 45.0% (95% CI: 21.1-68.9%), 92.9% (95% CI: 77.5-100.0%), and 64.7% (95% CI: 47.8-81.6%), respectively; for CEUS, they were 85.0% (95% CI: 67.9-100.0%), 100.0%, and 91.2% (95% CI: 81.1-100.0%), respectively. The diagnostic accuracy of sonographer 1 was higher with CEUS than with B-mode ultrasound, but not significantly so (94.1% vs. 70.6%; P=0.08). The diagnostic accuracy of sonographer 2 was significantly higher with CEUS than with B-mode ultrasound (91.2% vs. 64.7%; P=0.03). The interrater reliability was higher for CEUS than for B-mode ultrasound (κ=0.941 vs. κ =0.717). Conclusions: Based on an animal model, B-mode ultrasound and CEUS both exhibited good diagnostic efficacy and interrater reliability in evaluating parietal pleural adhesion/invasion of subpleural lung cancer although CEUS outperformed B-mode ultrasound for both measures.

Senaparib as first-line maintenance therapy in advanced ovarian cancer: a randomized phase 3 trial.

Poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors as maintenance therapy after first-line chemotherapy have improved progression-free survival in women with advanced ovarian cancer; however, not all PARP inhibitors can provide benefit for a biomarker-unselected population. Senaparib is a PARP inhibitor that demonstrated antitumor activity in patients with solid tumors, including ovarian cancer, in phase 1 studies. The multicenter, double-blind, phase 3 trial FLAMES randomized (2:1) 404 females with advanced ovarian cancer (International Federation of Gynecology and Obstetrics stage III-IV) and response to first-line platinum-based chemotherapy to senaparib 100 mg (n = 271) or placebo (n = 133) orally once daily for up to 2 years. The primary endpoint was progression-free survival assessed by blinded independent central review. At the prespecified interim analysis, the median progression-free survival was not reached with senaparib and was 13.6 months with placebo (hazard ratio 0.43, 95% confidence interval 0.32-0.58; P < 0.0001). The benefit with senaparib over placebo was consistent in the subgroups defined by BRCA1 and BRCA2 mutation or homologous recombination status. Grade ≥3 treatment-emergent adverse events occurred in 179 (66%) and 27 (20%) patients, respectively. Senaparib significantly improved progression-free survival versus placebo in patients with advanced ovarian cancer after response to first-line platinum-based chemotherapy, irrespective of BRCA1 and BRCA2 mutation status and with consistent benefits observed between homologous recombination subgroups, and was well tolerated. These results support senaparib as a maintenance treatment for patients with advanced ovarian cancer after a response to first-line chemotherapy. ClinicalTrials.gov identifier: NCT04169997 .

Ent-eudesmane sesquiterpenoids with anti-neuroinflammatory activity from the marine-derived fungus Eutypella sp. F0219.

In this study, twenty-three ent-eudesmane sesquiterpenoids (1-23) including fifteen previously undescribed ones, named eutypelides A-O (1-15) were isolated from the marine-derived fungus Eutypella sp. F0219. Their planar structures and relative configurations were established by HR-ESIMS and extensive 1D and 2D NMR investigations. The absolute configurations of the previously undescribed compounds were determined by single-crystal X-ray diffraction analyses, modified Mosher's method, and ECD calculations. Structurally, eutypelide A (1) is a rare 1,10-seco-ent-eudesmane, whereas 2-15 are typically ent-eudesmanes with 6/6/-fused bicyclic carbon nucleus. The anti-neuroinflammatory activity of all isolated compounds (1-23) was accessed based on their ability to NO production in LPS-stimulated BV2 microglia cells. Compound 16 emerged as the most potent inhibitor. Further mechanistic investigation revealed that compound 16 modulated the inflammatory response by decreasing the protein levels of iNOS and increasing ARG 1 levels, thereby altering the iNOS/ARG 1 ratio and inhibiting macrophage polarization. qRT-PCR analysis showed that compound 16 reversed the LPS-induced upregulation of pro-inflammatory cytokines, including iNOS, TNF-α, IL-6, and IL-1ß, at both the transcriptional and translational levels. These effects were linked to the inhibition of the NF-κB pathway, a key regulator of inflammation. Our findings suggest that compound 16 may be a potential structure basis for developing neuroinflammation-related disease therapeutic agents.

Achieving Enhanced Dielectric and Energy Storage Performance in Poly(vinyl chloride-glycidyl methacrylate) through Tuning Interchain Interactions.

Glassy polymer dielectrics exhibit significant advantages in energy storage density and discharge efficiency; however, their potential application in thin-film capacitors is limited by the complexity of the production process, rising costs, and processing challenges arising from the brittleness of the material. In this study, a small amount of the polar monomer glycidyl methacrylate (GMA) was copolymerized with vinyl chloride (VC) using a highly integrated and precisely controlled process. This effectively facilitated the bulk synthesis of P(VC-GMA) copolymers, aimed at enhancing the dielectric properties and energy storage capabilities of the copolymer. Moreover, the incorporation of GMA into PVC induces significant alterations in the structural sequence of the copolymer, resulting in an enhancement of interchain interactions that ultimately contribute to an increase in the modulus and improved breakdown strength. With a GMA content of 2.4 mol %, P(VC-GMA) exhibits a significant enhancement in discharge energy density, surpassing that of a pure PVC copolymer, while maintaining high discharge efficiency and stability. The finding of this study paves the way for future advancements in high-energy-storage polymer dielectrics, thereby expanding the scope of advanced dielectric materials.

Cryoprotective isoliquiritigenin-zein phosphatidylcholine nanoparticles inhibits breast cancer-bone metastasis by targeting JAK-STAT signaling pathways.

Breast cancer (BC) is the most common cancer in women and is known for its tendency to spread to the bones, causing significant health issues and mortality. In this study, we aimed to investigate whether cryoprotective isoliquiritigenin-zein phosphatidylcholine nanoparticles (ISL@ZLH NPs) could inhibit BC-induced bone destruction and tumor metastasis in both in vitro and animal models. To evaluate the potential of ISL@ZLH NPs, we conducted various experiments. First, we assessed cell viability, colony formation, transwell migration, and wound healing assays to determine the impact of ISL@ZLH NPs on BC cell behavior. Western blotting, TRAP staining and ALP activity were performed to examine the effects of ISL@ZLH NPs on osteoclast formation induced by MDA-MB-231 cell-conditioned medium and RANKL treated RAW 264.7 cells. Furthermore, we assessed the therapeutic impact of ISL@ZLH NPs on tumor-induced bone destruction using a mouse model of BC bone metastasis. Treatment with ISL@ZLH NPs effectively suppressed BC cell proliferation, colony formation, and motility, reducing their ability to metastasize. ISL@ZLH NPs significantly inhibited osteoclast formation and the expression of factors associated with bone destruction in BC cells. Additionally, ISL@ZLH NPs suppressed JAK-STAT signaling in RAW264.7 cells. In the BCBM mouse model, ISL@ZLH NPs led to a significant reduction in osteolytic bone lesions compared to the control group. Histological analysis and TRAP staining confirmed that ISL@ZLH NPs preserved the integrity of bone structure, preventing invasive metastasis by confining tumor growth to the bone marrow cavity. Furthermore, ISL@ZLH NPs effectively suppressed tumor-induced osteoclastogenesis, a key process in BC-related bone destruction. Our findings demonstrate that ISL@ZLH NPs have the potential to inhibit BC-induced bone destruction and tumor metastasis by targeting JAK-STAT signaling pathways and suppressing tumor-induced osteoclastogenesis. These results underscore the therapeutic promise of ISL@ZLH NPs in managing BC metastasis to the bones.

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study.

PURPOSE: The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism. MATERIALS AND METHODS: This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction. RESULTS: The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization. CONCLUSIONS: The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.

Reconstructing the evolution history of networked complex systems.

The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

EEG analysis in patients with schizophrenia based on microstate semantic modeling method.

Introduction: Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals. Methods: This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences. Results: The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects. Discussion: This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

Establishment of Human Pituitary Neuroendocrine Tumor Derived Organoid and Its Pilot Application for Drug Screening.

CONTEXT: Precision medicine for pituitary neuroendocrine tumors (PitNETs) is limited by the lack of reliable research models. OBJECTIVE: To generate patient-derived organoids (PDOs), which could serve as a platform for personalized drug screening for PitNET patients. DESIGN: From July 2019 to May 2022, a total of 32 human PitNET specimens were collected for the establishment of organoids with an optimized culture protocol. SETTING: This study was conducted at Sun Yat-Sen University Cancer Center. PATIENTS: PitNET patients who were pathologically confirmed were enrolled in this study. INTERVENTIONS: Histological staining and whole-exome sequencing were utilized to confirm the pathologic and genomic features of PDOs. A drug response assay on PDOs was also performed. MAIN OUTCOME MEASURES: PDOs retained key genetic and morphological features of their parental tumors. RESULTS: PDOs were successfully established from various types of PitNET samples with an overall success rate of 87.5%. Clinical nonfunctioning PitNETs-derived organoids (22/23, 95.7%) showed a higher likelihood of successful generation compared to those from functioning PitNETs (6/9, 66.7%). Preservation of cellular structure, subtype-specific neuroendocrine profiles, mutational features, and tumor microenvironment heterogeneity from parental tumors was observed. A distinctive response profile in drug tests was observed among the organoids from patients with different subtypes of PitNETs. With the validation of key characteristics from parental tumors in histological, genomic, and microenvironment heterogeneity consistency assays, we demonstrated the predictive value of the PDOs in testing individual drugs. CONCLUSION: The established PDOs, retaining typical features of parental tumors, indicate a translational significance in innovating personalized treatment for refractory PitNETs.

Unveiling the Dynamic Electrocatalytic Activity of Online Synthesized Bimetallic Nanocatalysts via Electrochemiluminescence Microscopy.

Effective bimetallic nanoelectrocatalysis demands precise control of composition, structure, and understanding catalytic mechanisms. To address these challenges, we employ a two-in-one approach, integrating online synthesis with real-time imaging of bimetallic Au@Metal core-shell nanoparticles (Au@M NPs) via electrochemiluminescence microscopy (ECLM). Within 120 s, online electrodeposition and in situ catalytic activity screening alternate. ECLM captures transient faradaic processes during potential switches, visualizes electrochemical processes in real-time, and tracks catalytic activity dynamics at the single-particle level. Analysis using ECL photon flux density eliminates size effects and yields quantitative electrocatalytic activity results. Notably, a nonlinear activity trend corresponding to the shell metal to Au surface atomic ratio is discerned, quantifying the optimal surface component ratio of Au@M NPs. This approach offers a comprehensive understanding of catalytic behavior during the deposition process with high spatiotemporal resolution, which is crucial for tailoring efficient bimetallic nanocatalysts for diverse applications.

Development and validation of a risk prediction model for valve regurgitation in Behçet's disease.

BACKGROUND: In Behçet's disease (BD), mild-to-severe valvular regurgitation (VR) poses a serious complication that contributes significantly to heart failure and eventually death. The accurate prediction of VR is crucial in the early stages of BD subjects for improved prognosis. Accordingly, this study aimed to develop a nomogram that can detect VR early in the course of BD. METHODS: One hundred seventy-two patients diagnosed with Behçet's disease (BD) were conducted to assess cardiac valve regurgitation as the primary outcome. The severity of regurgitation was classified as mild, moderate, or severe. The parameters related to the diagnostic criteria were used to develop model 1. The combination of stepAIC, best subset, and random forest approaches was employed to identify the independent predictors of VR and thus establish model 2 and create a nomogram for predicting the probability of VR in BD. Receiver operating characteristics (ROC) and decision curve analysis (DCA) were used to evaluate the model performance. RESULTS: Thirty-four patients experienced mild-to-severe VR events. Model 2 was established using five variables, including arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. In comparison with model 1 (0.635, 95% CI: 0.512-0.757), the ROC of model 2 (0.879, 95% CI: 0.793-0.966) was improved significantly. DCA suggested that model 2 was more feasible and clinically applicable than model 1. CONCLUSION: A predictive model and a nomogram for predicting the VR of patients with Behçet's disease were developed. The good performance of this model can help us identify potential high-risk groups for heart failure. Key Points • In this study, the predictors of VR in BD were evaluated, and a risk prediction model was developed for the early prediction of the occurrence of VR in patients with BD. • The VR prediction model included the following indexes: arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. • The risk model that we developed was better and more optimized than the models built with diagnostic criteria parameters, and visualizing and personalizing the model, a nomogram, provided clinicians with an easy and intuitive tool for practical prediction.

Chronic Opioid Treatment Arrests Neurodevelopment and Alters Synaptic Activity in Human Midbrain Organoids.

Understanding the impact of long-term opioid exposure on the embryonic brain is critical due to the surging number of pregnant mothers with opioid dependency. However, this has been limited by human brain inaccessibility and cross-species differences in animal models. Here, a human midbrain model is established that uses hiPSC-derived midbrain organoids to assess cell-type-specific responses to acute and chronic fentanyl treatment and fentanyl withdrawal. Single-cell mRNA sequencing of 25,510 cells from organoids in different treatment groups reveals that chronic fentanyl treatment arrests neuronal subtype specification during early midbrain development and alters synaptic activity and neuron projection. In contrast, acute fentanyl treatment increases dopamine release but does not significantly alter gene expression related to cell lineage development. These results provide the first examination of the effects of opioid exposure on human midbrain development at the single-cell level.

Regulation of Dihydroartemisinin on the pathological progression of laryngeal carcinoma through the periostin/YAP/IL-6 pathway.

Objective: Laryngeal cancer (LC) is one of the most common squamous cell carcinomas of the head and neck in clinical practice, and its incidence has been increasing in recent years, but the prognosis of the patients is not favorable. Hence, it is critical to re-understand and deeply study the causes and mechanisms of LC and explore new effective treatment methods and strategies. In this study, we analyzed the effect of Dihydroartemisinin (DHA) on the pathological progression of LC through the periostin (POSTN)/Yes-associated protein (YAP)/interleukin (IL)-6 pathway, which can provide new clinical references and guidelines. Methods: POSTN, YAP, and IL-6 levels in 18 pairs of fresh LC tissues and adjacent counterparts in our hospital were detected. Additionally, LC TU686 cell line was purchased for DHA treatment of various concentrations to detect changes in cell biological behavior. Finally, we built a tumor-bearing mouse model with C57BL/6 mice and intragastrically administrated DHA to the animals to observe the growth of living tumors and to measure POSTN, YAP, and IL-6 expression in tumor tissues. Results: As indicated by PCR, Western blotting, and immunohistochemistry, POSTN, YAP, and IL-6 presented higher expression in LC tissues than in adjacent counterparts. In cell experiments, the cloning rate of LC cells decreased and the apoptosis rate increased after DHA intervention, with 160 µmol/L DHA contributing to the most significant effect on LC activity inhibition. Furthermore, DHA-intervened cells exhibited markedly reduced POSTN, YAP, and IL-6 levels. Finally, the tumorigenesis experiment in nude mice showed inhibited tumor growth after DHA administration. And consistently, the expressions of POSTN, YAP, and IL-6 in living tumors decreased. Conclusions: DHA can inhibit POSTN/YAP/IL-6 transduction, accelerate LC cell apoptosis, and alleviate the malignant progression of LC.

Amendment of straw with decomposing inoculants benefits the ecosystem carbon budget and carbon footprint in a subtropical wheat cropping field.

The incorporation of straw with decomposing inoculants into soils has been widely recommended to sustain agricultural productivity. However, comprehensive analyses assessing the effects of straw combined with decomposing inoculants on greenhouse gas (GHG) emissions, net primary production (NPP), the net ecosystem carbon budget (NECB), and the carbon footprint (CF) in farmland ecosystems are scant. Here, we carried out a 2-year field study in a wheat cropping system with six treatments: rice straw (S), a straw-decomposing Bacillus subtilis inoculant (K), a straw-decomposing Aspergillus oryzae inoculant (Q), a combination of straw and Bacillus subtilis inoculant (SK), a combination of straw and Aspergillus oryzae inoculant (SQ), and a control with no rice straw or decomposing inoculant (Control). We found that all the treatments resulted in a positive NECB ranging between 838 and 5065 kg C ha-1. Relative to the Control, the S treatment increased CO2 emissions by 16%, while considerably enhancing the NECB by 349%. This difference might be attributed to the straw C input and an increase in plant productivity (NPP, 30%). More importantly, in comparison to that in S, the NECB in SK and SQ significantly increased by 27-35% due to the positive response of NPP to the decomposing inoculants. Although the combination of straw and decomposing inoculants yielded a 3% increase in indirect GHG emissions, it also exhibited the lowest CF (0.18 kg CO2-eq kg-1 of grain). This result was attributed to the synergistic effects of straw and decomposing inoculants, which reduced direct N2O emissions and increased wheat productivity. Overall, the findings of the present study suggested that the combined amendment of straw and decomposing inoculants is an environmentally sustainable management practice in wheat cropping systems that can generate win-win scenarios through improvements in soil C stock, crop productivity, and GHG mitigation.

N-CF3 Imidoyl Chlorides: Scalable N-CF3 Nitrilium Precursors for the Construction of N-CF3 Compounds.

A wide range of N-CF3 imidoyl chlorides were synthesized for the first time via the N-trifluoromethylation of nitriles in DCM by using AlCl3-activated PhICF3Cl as the CF3 source. The reactions of them with N-/O-/S-nucleophiles, as well as with 1,3-dipoles, were carried out to efficiently deliver N-CF3 amidines/imidates/thioimidates and N-CF3 azoles, demonstrating that they are a class of scalable NCF3-containing synthons in the synthesis of N-CF3 compounds.

Air oxidation of carbon supports boosts the low-humidity fuel cell performance.

We introduce a straightforward, yet effective strategy to combat the performance decline of proton-exchange membrane fuel cells in low-humidity environments. Our method centers on air-oxidizing carbon supports, significantly improving proton and oxygen transport within the cathode catalyst layer.

Epidemiology and risk factors of bloodstream infections among adolescents and young adults with acute lymphoblastic leukaemia: An 11-year retrospective cohort study.

Adolescent and young adults (AYAs) belong to a unique category of patients diagnosed with acute lymphoblastic leukaemia (ALL). Bloodstream infection (BSI) is a leading cause of treatment-related mortality in ALL patients. However, the epidemiology and risk factors for mortality from BSIs in AYA patients remain unclear. In this study, we analysed these aspects in AYAs patients and compared similarities and differences with children (<15 years old) and older adults (>39 years old). We analysed the pathogenic epidemiology, antibiotic resistance and BSI risk factors of 73 children, 180 AYAs, and 110 older adults with ALL in three comprehensive hospitals from January 2010 to August 2021. The data on BSIs in AYAs were compared to that of the other two groups. In this study, the epidemiology of BSIs in AYAs was similar to that of older adult patients. Concerning clinical characteristics, most AYAs and older adults with BSIs were in a relapsed or uncontrolled state (34.5% vs. 35.4%, p = 0.861). In terms of pathogen distribution, Gram-negative bacteria (GNB) were the most common causative pathogens in AYAs and older adult groups. Extended-spectrum beta-lactamase (ESBL)-producing bacteria were more commonly found in AYAs than in children (32.8% vs. 16.4%, p = 0.09). Regarding risk factors, the length of hospitalization (>14 days) and renal inadequacy (creatinine ≥ 177 µmol/L) were influencing factors for 30-day mortality in AYAs patients with BSIs. In our study, AYA patients with BSIs showed clinical characteristics and pathogen distributions similar to those of older adult patients but quite different from those of children.

Synthesis and characterization of iron clusters with an icosahedral [Fe@Fe12]16+ Core.

Iron-metal clusters are crucial in a variety of critical biological and material systems, including metalloenzymes, catalysts, and magnetic storage devices. However, a synthetic high-nuclear iron cluster has been absent due to the extreme difficulty in stabilizing species with direct iron-iron bonding. In this work, we have synthesized, crystallized, and characterized a (Tp*)4W4S12(Fe@Fe12) cluster (Tp* = tris(3,5-dimethyl-1-pyrazolyl)borate(1-)), which features a rare trideca-nuclear, icosahedral [Fe@Fe12] cluster core with direct multicenter iron-iron bonding between the interstitial iron (Fei) and peripheral irons (Fep), as well as Fep···Fep ferromagnetic coupling. Quantum chemistry studies reveal that the stability of the cluster arises from the 18-electron shell-closing of the [Fe@Fe12]16+ core, assisted by its bonding interactions with the peripheral tridentate [(Tp*)WS3]4- ligands which possess both S→Fe donation and spin-polarized Fe-W σ bonds. The ground-state electron spin is theoretically predicted to be S = 32/2 for the cluster. The existence of low oxidation-state (OS ∼ +1.23) iron in this compound may find interesting applications in magnetic storage, spintronics, redox chemistry, and cluster catalysis.

Effects of Tetrabasic Zinc Chloride on the Diarrhea Rate, Intestinal Morphology, Immune Indices and Microflora of Weaned Piglets.

This study was aimed to investigate the effects of different dietary zinc sources on the diarrhea rate, intestinal morphology, immune indexes and intestinal microbial composition of weaned piglets. A total of 240 weaned piglets (Duroc × Landrace × Yorkshire), at the age of 21 days, were randomly assigned to five dietary treatments for a four-week feeding trial to determine the effects of different amounts of tetrabasic zinc chloride (TBZC) supplementation on intestinal morphology, intestinal immune indices and intestinal microflora in weaned piglets, compared with the pharmacological dose of ZnO. The dietary treatments included a negative control (CON), (T1) ZnO (ZnO, 1500 mg/kg), (T2) tetrabasic zinc chloride (TBZC, 800 mg/kg), (T3) tetrabasic zinc chloride (TBZC, 1000 mg/kg), and (T4) tetrabasic zinc chloride (TBZC, 1200 mg/kg). Each treatment comprised six replicate pens, with eight pigs (four barrows and four gilts) per pen. Dietary TBZC of 1200 mg/kg improved the duodenum villus height, jejunum villus height and crypt depth of ileum, and increased the ratio of villus height to crypt depth of ileum (p < 0.05). The dietary supplementation of TBZC at a dosage of 1200 mg/kg has the potential to increase the levels of immunoglobulin G (IgG) and immunoglobulin A (IgA) in the duodenal mucosa. Furthermore, it shows a significant increase in the levels of immunoglobulin A (IgA) in the ileum. Compared with CON, TBZC significantly (p < 0.05) decreased pH values of stomach contents. It also increased the number of Firmicutes in intestinal contents. Compared with CON, the abundance of Firmicutes in jejunum contents of other treatments was significantly improved (p < 0.05), while the abundance of Proteobacteria in ileum contents of high-zinc treatments (T2 and T5) was decreased (p < 0.05). In conclusion, dietary TBZC of 1200 mg/kg improved the digestibility of crude protein in weaned piglets, altered the intestinal morphology of piglets, changed the intestinal microflora of piglets, reduced the diarrhea rate, and significantly improved the development of the small intestine of weaned piglets, and its regulation mechanism on intestinal tract needs further study. In summary, TBZC is likely to be an effective substitute source for the pharmacological dose of ZnO to control diarrhea in weaned piglets.

Identification of macrophage-related genes correlated with prognosis and immunotherapy efficacy in non-small cell lung cancer.

Background: Malignant tumours, particularly non-small cell lung cancer (NSCLC), pose a significant threat to human health due to their prevalence and lethality. Treatment methods for NSCLC vary greatly among individuals, making it crucial to identify predictive markers. Moreover, during tumour initiation and progression, tumour cells can release signaling molecules to induce polarization of macrophages towards a more tumour friendly M2 phenotype, which can promote tumour growth, metastasis, and drug resistance. Methods: We employed a comprehensive approach, combining bulk RNA-seq and single-cell sequencing analysis. Results: In our study, we used bulk RNA-seq and single-cell sequencing methods to analyze differential cells in NSCLC and adjacent tissues, searching for relevant marker genes that can predict prognosis and drug efficacy. We scrutinized biological phenomena such as macrophage-related gene methylation, copy number variation, and alternative splicing. Additionally, we utilized a co-culture technique of immune and tumour cells to explore the role of these genes in macrophage polarization. Our findings revealed distinct differences in macrophages between cancerous and adjacent tissues. We identified ANP32A, CCL20, ERAP2, MYD88, TMEM126B, TUBB6, and ZNF655 as macrophage-related genes that correlate with NSCLC patient prognosis and immunotherapy efficacy. Notably, ERAP2, TUBB6, CCL20, and TMEM126B can induce macrophage M0 to M2 polarization, promoting tumour proliferation. Conclusion: These findings significantly contribute to our understanding of the NSCLC tumour immune microenvironment. They pave the way for further research into the potential of these genes as targets for regulating tumour occurrence and development.