Identification of Potential Ferroptosis Biomarkers and Analysis of Immune Cell Infiltration in Psoriasis Using Machine Learning.

Background: Ferroptosis is a type of cell death characterized by the accumulation of iron-dependent lethal lipid peroxides, which is associated with various pathophysiological processes. Psoriasis is a chronic autoimmune skin disease accompanied by abnormal immune cell infiltration and excessive production of lipid reactive oxygen species (ROS). Currently, its pathogenesis remains elusive, especially the potential role of ferroptosis in its pathophysiological process. Methods: The microarrays GSE13355 (58 psoriatic skin specimens versus 122 healthy skin specimens) and the ferroptosis database were employed to identify the common differentially expressed genes (DEGs) associated with psoriasis and ferroptosis. The functions of common DEGs were investigated through functional enrichment analysis and protein-protein interaction analysis. The potential diagnostic markers for psoriasis among the common DEGs were identified using four machine-learning algorithms. DGIdb was utilized to explore potential therapeutic agents for psoriasis. Additionally, CIBERSORT was employed to investigate immune infiltration in psoriasis. Results: A total of 8 common DEGs associated with psoriasis and ferroptosis were identified, which are involved in intercellular signaling and affect pathways of cell response to stress and stimulation. Four machine-learning algorithms were employed to identify poly (ADP-ribose) polymerase 12 (PARP12), frizzled homolog 7 (FZD7), and arachidonate 15-lipoxygenase (ALOX15B) among the eight common DEGs as potential diagnostic markers for psoriasis. A total of 18 drugs targeting the five common DEGs were identified as potential candidates for treating psoriasis. Additionally, significant changes were observed in the immune microenvironment of patients with psoriasis. Conclusion: This study has contributed to our enhanced comprehension of ferroptosis-related genes as potential biomarkers for psoriasis diagnosis, as well as the alterations in the immune microenvironment associated with psoriasis. Our findings offer valuable insights into the diagnosis and treatment of psoriasis.

Improved cure rate of periprosthetic joint infection through targeted antibiotic therapy based on integrated pathogen diagnosis strategy.

Objectives: This study aimed to determine whether combined of pathogen detection strategies, including specimen acquisition, culture conditions, and molecular diagnostics, can improve treatment outcomes in patients with periprosthetic joint infections (PJI). Methods: This retrospective study included suspected PJI cases from three sequential stages at our institution: Stage A (July 2012 to June 2015), Stage B (July 2015 to June 2018), and Stage C (July 2018 to June 2021). Cases were categorized into PJI and aseptic failure (AF) groups based on European Bone and Joint Infection Society (EBJIS) criteria. Utilization of pathogen diagnostic strategies, pathogen detection rates, targeted antibiotic prescription rates, and treatment outcomes were analyzed and compared across the three stages. Results: A total of 165 PJI cases and 38 AF cases were included in this study. With the progressive implementation of the three optimization approaches across stages A, B and C, pathogen detection rates exhibited a gradual increase (χ2 = 8.282, P=0.016). Similarly, utilization of targeted antibiotic therapy increased stepwise from 57.1% in Stage A, to 82.3% in Stage B, and to 84% in Stage C (χ2 = 9.515, P=0.009). The 2-year infection control rate exceeded 90% in both stages B and C, surpassing stage A (71.4%) (χ2 = 8.317, P=0.011). Combined application of all three optimized protocols yielded the highest sensitivity of 91.21% for pathogen detection, while retaining higher specificity of 92.11%. Conclusion: The utilization of combined pathogen diagnostic strategies in PJI can increase pathogen detection rates, improve targeted antibiotic prescription, reduce the occurrence of antibiotic complications, and achieve better treatment outcomes.

Ionic liquids intercalation in titanium carbide MXenes: A first-principles investigation.

Herein, we present a density functional theory with dispersion correction (DFT-D) calculations that focus on the intercalation of ionic liquids (ILs) electrolytes into the two-dimensional (2D) Ti3C2Tx MXenes. These ILs include the cation 1-ethyl-3-methylimidazolium (Emim+), accompanied by three distinct anions: bis(trifluoromethylsulfonyl)imide (TFSA-), (fluorosulfonyl)imide (FSA-) and fluorosulfonyl(trifluoromethanesulfonyl)imide (FTFSA-). By altering the surface termination elements, we explore the intricate geometries of IL intercalation in neutral, negative, and positive pore systems. Accurate estimation of charge transfer is achieved through five population analysis models, such as Hirshfeld, Hirshfeld-I, DDEC6 (density derived electrostatic and chemical), Bader, and VDD (voronoi deformation density) charges. In this work, we recommend the DDEC6 and Hirshfeld-I charge models, as they offer moderate values and exhibit reasonable trends. The investigation, aimed at visualizing non-covalent interactions, elucidates the role of cation-MXene and anion-MXene interactions in governing the intercalation phenomenon of ionic liquids within MXenes. The magnitude of this role depends on two factors: the specific arrangement of the cation, and the nature of the anionic species involved in the process.

Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.

Immune-tumor interaction dictates spatially directed evolution of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a poor-prognostic cancer type with extensive intra- and inter-patient heterogeneity in both genomic variations and tumor microenvironment (TME). However, the patterns and drivers of spatial genomic and microenvironmental heterogeneity of ESCC remain largely unknown. Here, we generated a spatial multi-omic atlas by whole-exome, transcriptome, and methylome sequencing of 507 tumor samples from 103 patients. We identified a novel tumor suppressor PREX2, accounting for 22% of ESCCs with frequent somatic mutations or hyper-methylation, which promoted migration and invasion of ESCC cells in vitro. Analysis of the TME and quantification of subclonal expansion indicated that ESCCs undergo spatially directed evolution, where subclones mostly originated from the tumor center but had a biased clonal expansion to the upper direction of the esophagus. Interestingly, we found upper regions of ESCCs often underwent stronger immunoediting with increased selective fitness, suggesting more stringent immune selection. In addition, distinct TMEs were associated with variable genomic and clinical outcomes. Among them, hot TME was associated with high immune evasion and subclonal heterogeneity. We also found that immunoediting, instead of CD8+ T cell abundance, acts as an independent prognostic factor of ESCCs. Importantly, we found significant heterogeneity in previously considered potential therapeutic targets, as well as BRCAness characteristics in a subset of patients, emphasizing the importance of focusing on heterogeneity in ESCC targeted therapy. Collectively, these findings provide novel insights into the mechanisms of the spatial evolution of ESCC and inform precision therapeutic strategies.

Identification of Long Noncoding RNAs Expression Profiles Between Gallstone and Gallbladder Cancer Using Next-Generation Sequencing Analysis.

Background: Gallstone disease (GS) is an important risk factor for Gallbladder cancer (GBC). However, the mechanisms of the progression of GS to GBC remain unclear. Long non-coding RNA (lncRNA), modulates DNA/RNA/proteins at epigenetic, pre-transcriptional, transcriptional and posttranscriptional levels, and plays a potential therapeutic role in various diseases. This study aims to identify lncRNAs that have a potential impact on GS-promoted GBC progression. Methods and Results: Six GBC patients without GS, six normal gallbladder tissues, nine gallstones and nine GBC patients with GS were admitted to our hospital. The next-generation RNA-sequencing was performed to analyze differentially expressed (DE) lncRNA and messenger RNA (mRNA) in four groups. Then overlapping and specific molecular signatures were analyzed. We identified 29 co-DEGs and 500 co-DElncRNAs related to gallstone or GBC. The intersection and concatenation of co-DEGs and co-DElncRNA functionally involved in focal adhesion, Transcriptional misregulation in cancers, Protein digestion and absorption, and ECM-receptor interaction signaling pathways may contribute to the development of gallbladder cancer. Further exploration is necessary for early diagnosis and the potential treatment of GBC. FXYD2, MPZL1 and PAH were observed in both co-DEGs and co-DElncRNA and validated by qRT-PCR. Conclusion: Our data identified a series of DEGs and DElncRNAs, which were involved in the progression of GBC and GS-related metabolism pathways. Compared to GBC, the GS profile was more similar to para-tumor tissues in transcriptome level and lower risk of cancer. Further exploration is necessary from GBC patients with different periods of follow-up gallstone.

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.

Background: This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses. Methods: Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies. Results: Our research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model's broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib. Conclusions: The GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms.

Objective.The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact.Approach.We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms.Results.Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture.Significance.Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for electroencephalogram motor imagery decoding within BCIs.

Exposure to resorcinol bis (diphenyl phosphate) induces colonization of alien microorganisms with potential impacts on the gut microbiota and metabolic disruption in male zebrafish.

Organophosphate esters (OPEs) have been demonstrated to induce various forms of toxicity in aquatic organisms. However, a scarcity of evidence impedes the conclusive determination of whether OPEs manifest sex-dependent toxic effects. Here, we investigated the effects of tris (1-chloro-2-propyl) phosphate (TCPP) and resorcinol bis (diphenyl phosphate) (RDP) on the intestines of both female and male zebrafish. The results indicated that, in comparison to TCPP, RDP induced more pronounced intestinal microstructural damage and oxidative stress, particularly in male zebrafish. 16S rRNA sequencing and metabolomics revealed significant alterations in the species richness and oxidative stress-related metabolites in the intestinal microbiota of zebrafish under exposure to both TCPP and RDP, manifesting gender-specific effects. Based on differential species analysis, we defined invasive species and applied invasion theory to analyze the reasons for changes in the male fish intestinal community. Correlation analysis demonstrated that alien species may have potential effects on metabolism. Overall, this study reveals a pronounced gender-dependent impact on both the intestinal microbiota and metabolic disruptions of zebrafish due to OPEs exposure and offers a novel perspective on the influence of pollutants on intestinal microbial communities and metabolism.

Challenges for sustainable development goal of land degradation neutrality in drylands: Evidence from the Northern Slope of the Tianshan Mountains, China.

The SDG 15.3.1 target of Land Degradation Neutrality (LDN) only has 15 years from conception (in 2015) to realization (in 2030). Therefore, investigating the effectiveness and challenges of LDN has become a priority, especially in drylands, where fragile ecosystems intersect with multiple disturbances. In this study, solutions are proposed and validated based on the challenges of LDN. We chose the Northern Slope of the Tianshan Mountains as a case study and set baselines in 2005 and 2010. The region and degree of land change (including degraded, stable, and improved) were depicted at the pixel scale (100 × 100 m), and LDN realization was assessed at the regional scale (including administrative districts and 5000 × 5000 m grids). The results showed a significant disparity between the two baselines. The number of areas that realized the LDN target was rare, regardless of the scale of the administrative districts or grids. Chord plots, Spearman's correlation, and curve estimation were employed to reveal the relationship between LDN and seven natural or socioeconomic factors. We found that substantial degradation was closely related to the expansion of unused, urban, and mining land and reduction in water, glaciers, and forests. Further evidence suggests that agricultural development both positively and negatively affects LDN, whereas urbanization and mining activities are undesirable for LDN. Notably, the adverse effects of glacier melting require additional attention. Therefore, we consider the easy-to-achieve and hard-to-achieve baselines as the mandatory and desirable targets of LDN, respectively, and focus further efforts in three aspects: preventing agricultural exploitation from occupying ecological resources, defining reasonable zones for urbanization and mining, and reducing greenhouse gas emissions to mitigate warming. Overall, this study is expected to be a beneficial addition to existing LDN theoretical systems and serve as a case validation of the challenges of LDN in drylands.

Efficient Catalyst-Free One-Pot Synthesis of Polysaccharide-Polypeptide Hydrogels in Aqueous Solution.

The preparation of polysaccharide-peptide hydrogels usually involves multiple synthetic steps, thus reducing the effectiveness and practicality of these approaches. Inspired by recent discoveries in aqueous N-carboxyanhydride (NCA) ring-opening polymerization (ROP) and ring-opening polymerization-induced nanogelation, we present an aqueous one-pot strategy to prepare polysaccharide-polypeptide hydrogels. In this study, water-soluble polysaccharide carboxymethyl chitosan is used as the macromolecular initiator to prepare polysaccharide-polypeptide copolymers through the aqueous ROP of NCA. The catalyst-free approach afforded hydrogels with properties that could be controlled by adjusting the type and amount of NCA used, with the elastic modulus ranging from 50 Pa to 18000 Pa. The hydrogen bond-cross-linked hydrogel exhibited self-healing and injectable properties. Morphology characterization revealed that micelles were formed in the early stage of reaction, suggesting that the polymerization follows an aqueous ring-opening polymerization-induced self-assembly (ROPISA) mechanism and that aggregation of micelles during the reaction caused the gelation. Moreover, the hydrogels displayed high swelling ratios (>95% water content), and hemolysis and cytotoxicity experiments demonstrated that the hydrogels had excellent biocompatibility, indicating their potential in medical applications.

Systemic-inflammatory indices and clinical outcomes in patients with anterior circulation acute ischemic stroke undergoing successful endovascular thrombectomy.

Background: There is a lack of comprehensive profile assessment on complete blood count (CBC)-derived systemic-inflammatory indices, and their correlations with clinical outcome in patients with anterior circulation acute ischemic stroke (AIS) who achieved successful recanalization by endovascular thrombectomy (EVT). Methods: Patients with anterior circulation AIS caused by large vessel occlusion (AIS-LVO) were retrospectively screened from December 2018 to December 2022. Systemic-inflammatory indices including ratios of neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR), and platelet-to-neutrophil (PNR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate inflammation systemic index (AISI) on admission and the first day post-EVT were calculated. Their correlations with symptomatic intracranial hemorrhage (sICH) and unfavorable 90-day functional outcome (modified Rankin Scale score of 3-6) were analyzed. Results: A total of 482 patients [65 (IQR, 56-72) years; 33 % female] were enrolled, of which 231 (47.9 %) had unfavorable 90-day outcome and 50 (10.4 %) developed sICH. Day 1 neutrophil and monocyte counts, NLR, MLR, PLR, SII, SIRI, and AISI were increased, while lymphocyte and PNR were decreased compared to their admission levels. In multivariate analyses, neutrophil count, NLR, SII, and AISI on day 1 were independently associated with 90-day functional outcome. Moreover, day 1 neutrophil count, NLR, MLR, PLR, PNR, SII, and SIRI were independently linked to the occurrence of sICH. No admission variables were identified as independent risk factors for patient outcomes. Conclusion: CBC-derived systemic-inflammatory indices measured on the first day after successful EVT are predictive of 90-day functional outcome and the sICH occurrence in patients with anterior circulation AIS-LVO.

Platelet membrane biomimetic nanoparticle-targeted delivery of TGF-ß1 siRNA attenuates renal inflammation and fibrosis.

The progression of renal fibrosis to end-stage renal disease (ESRD) is significantly influenced by transforming growth factor-beta (TGF-beta) signal pathway. This study aimed to develop nanoparticles (PMVs@PLGA complexes) with platelet membrane camouflage, which can transport interfering RNA to target and regulate the TGF-ß1 pathway in damaged renal tissues. The aim is to reduce the severity of acute kidney injury and to reduce fibrosis in chronic kidney disease. Hence, we formulated PMVs@TGF-ß1-siRNA NP complexes and employed them for both in vitro and in vivo therapy. From the experimental findings we know that the PMVs@siRNA NPs could effectively target the kidneys in unilateral ureteral obstruction (UUO) mice and ischemia/reperfusion injury (I/R) mice. In animal models of treatment, PMVs@siRNA NP complexes effectively decreased the expression of TGF-ß1 and mitigated inflammation and fibrosis in the kidneys by blocking the TGF-ß1/Smad3 pathway. Therefore, these PMVs@siRNA NP complexes can serve as a promising biological delivery system for treating kidney diseases.

Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

PURPOSE: To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. MATERIALS AND METHODS: 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7 days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2 year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. RESULTS: In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4 cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. CONCLUSIONS: SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

In Vivo Classification and Characterization of Carotid Atherosclerotic Lesions with Integrated 18F-FDG PET/MRI.

BACKGROUND: The aim of this study was to exploit integrated PET/MRI to simultaneously evaluate the morphological, component, and metabolic features of advanced atherosclerotic plaques and explore their incremental value. METHODS: In this observational prospective cohort study, patients with advanced plaque in the carotid artery underwent 18F-FDG PET/MRI. Plaque morphological features were measured, and plaque component features were determined via MRI according to AHA lesion-types. Maximum standardized uptake values (SUVmax) and tissue to background ratio (TBR) on PET were calculated. Area under the receiver-operating characteristic curve (AUC) and net reclassification improvement (NRI) were used to compare the incremental contribution of FDG uptake when added to AHA lesion-types for symptomatic plaque classification. RESULTS: A total of 280 patients with advanced plaque in the carotid artery were recruited. A total of 402 plaques were confirmed, and 87 of 402 (21.6%) were symptomatic plaques. 18F-FDG PET/MRI was performed a mean of 38 days (range 1-90) after the symptom. Increased stenosis degree (61.5% vs. 50.0%, p < 0.001) and TBR (2.96 vs. 2.32, p < 0.001) were observed in symptomatic plaques compared with asymptomatic plaques. The performance of the combined model (AHA lesion type VI + stenosis degree + TBR) for predicting symptomatic plaques was the best among all models (AUC = 0.789). The improvement of the combined model (AHA lesion type VII + stenosis degree + TBR) over AHA lesion type VII model for predicting symptomatic plaques was the highest (AUC = 0.757/0.454, combined model/AHA lesion type VII model), and the NRI was 50.7%. CONCLUSIONS: Integrated PET/MRI could simultaneously evaluate the morphological component and inflammation features of advanced atherosclerotic plaques and provide supplementary optimization information over AHA lesion-types for identifying vulnerable plaques in atherosclerosis subjects to achieve further stratification of stroke risk.

Novel Metabolites from the Marine-Derived Fungus Peniophora sp. SCSIO41203 Show Promising In Vitro Antitumor Activity as Methuosis Inducers in PC-3 Cells.

Two new cytochalasin derivatives, peniotrinins A (1) and B (2), three new citrinin derivatives, peniotrinins C-E (4, 5, 7), and one new tetramic acid derivative, peniotrinin F (12), along with nine structurally related known compounds, were isolated from the solid culture of Peniophora sp. SCSIO41203. Their structures, including the absolute configurations of their stereogenic carbons, were fully elucidated based on spectroscopic analysis, quantum chemical calculations, and the calculated ECD. Interestingly, 1 is the first example of a rare 6/5/5/5/6/13 hexacyclic cytochalasin. We screened the above compounds for their anti-prostate cancer activity and found that compound 3 had a significant anti-prostate cancer cell proliferation effect, while compounds 1 and 2 showed weak activity at 10 µM. We then confirmed that compound 3 exerts its anti-prostate cancer effect by inducing methuosis through transmission electron microscopy and cellular immunostaining, which suggested that compound 3 might be first reported as a potential anti-prostate methuosis inducer.

Gut virome alterations in patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is one of the primary causes of mortality and morbidity worldwide. The gut microbiome, particularly the bacteriome, has been demonstrated to contribute to the progression of COPD. However, the influence of gut virome on the pathogenesis of COPD is rarely studied. Recent advances in viral metagenomics have enabled the rapid discovery of its remarkable role in COPD. In this study, deep metagenomics sequencing of fecal virus-like particles and bacterial 16S rRNA sequencing was performed on 92 subjects from China to characterize alterations of the gut virome in COPD. Lower richness and diversity of the gut virome were observed in the COPD subjects compared with the healthy individuals. Sixty-four viral species, including Clostridium phage, Myoviridae sp., and Synechococcus phage, showed positive relationships with pulmonary ventilation functions and had markedly declined population in COPD subjects. Multiple viral functions, mainly involved in bacterial susceptibility and the interaction between bacteriophages and bacterial hosts, were significantly declined in COPD. In addition, COPD was characterized by weakened viral-bacterial interactions compared with those in the healthy cohort. The gut virome showed diagnostic performance with an area under the curve (AUC) of 88.7%, which indicates the potential diagnostic value of the gut virome for COPD. These results suggest that gut virome may play an important role in the development of COPD. The information can provide a reference for the future investigation of diagnosis, treatment, and in-depth mechanism research of COPD. IMPORTANCE: Previous studies showed that the bacteriome plays an important role in the progression of chronic obstructive pulmonary disease (COPD). However, little is known about the involvement of the gut virome in COPD. Our study explored the disease-specific virome signatures of patients with COPD. We found the diversity and compositions altered of the gut virome in COPD subjects compared with healthy individuals, especially those viral species positively correlated with pulmonary ventilation functions. Additionally, the declined bacterial susceptibility, the interaction between bacteriophages and bacterial hosts, and the weakened viral-bacterial interactions in COPD were observed. The findings also suggested the potential diagnostic value of the gut virome for COPD. The results highlight the significance of gut virome in COPD. The novel strategies for gut virome rectifications may help to restore the balance of gut microecology and represent promising therapeutics for COPD.

AAV-mediated gene therapy restores natural fertility and improves physical function in the Lhcgr-deficient mouse model of Leydig cell failure.

Leydig cell failure (LCF) caused by gene mutations leads to testosterone deficiency, infertility and reduced physical function. Adeno-associated virus serotype 8 (AAV8)-mediated gene therapy shows potential in treating LCF in the Lhcgr-deficient (Lhcgr-/-) mouse model. However, the gene-treated mice still cannot naturally sire offspring, indicating the modestly restored testosterone and spermatogenesis in AAV8-treated mice remain insufficient to support natural fertility. Recognizing this, we propose that enhancing gene delivery could yield superior results. Here, we screened a panel of AAV serotypes through in vivo transduction of mouse testes and identified AAVDJ as an impressively potent vector for testicular cells. Intratesticular injection of AAVDJ achieved markedly efficient transduction of Leydig cell progenitors, marking a considerable advance over conventional AAV8 vectors. AAVDJ-Lhcgr gene therapy was well tolerated and resulted in significant recovery of testosterone production, substantial improvement in sexual development, and remarkable restoration of spermatogenesis in Lhcgr-/- mice. Notably, this therapy restored fertility in Lhcgr-/- mice through natural mating, enabling the birth of second-generation. Additionally, this treatment led to remarkable improvements in adipose, muscle, and bone function in Lhcgr-/- mice. Collectively, our findings underscore AAVDJ-mediated gene therapy as a promising strategy for LCF and suggest its broader potential in addressing various reproductive disorders.

[Content of secondary metabolites and antioxidant activities of Lonicera japonica flowers at different developmental stages from Shandong province].

This study established an ultrasound-assisted extraction-high performance liquid chromatography method for simulta-neously determinining the content of 11 bioactive compounds including iridoids, phenolic acids, and flavonoids in Lonicera japonica flowers. The flowers at six stages from the rice bud stage(ML) to the golden flower stage(JH) of L. japonica varieties 'Sijuhua' and 'Beihua No.1' in two planting bases in Shandong province were collected. The established method was employed to determine the content of 11 target compounds, on the basis of which the dynamics of active components in L. japonica sampels during different development stages was investigated. The correlation analysis was carried out to reveal the correlations of the content of iridoids, phenolic acids, and flavonoids. Furthermore, the antioxidant activities of samples at different developmental stages were determined, and the relationship between antioxidant activity and chemical components was analyzed by the correlation analysis. The results showed that the total content of the 11 components in 'Sijihua' changed in a "W" pattern from the ML to JH, being the highest at the ML and the second at the slight white stage(EB). The total content of 11 compounds in 'Beihua No.1' was the highest at the ML and decreased gra-dually from the ML to JH. The samples of 'Sijihua' had higher content of iridoids and lower content of phenolic acids than those of 'Beihua No.1'. The content of flavonoids and phenolic acids showed a positive correlation(R~2=0.90, P<0.05) in 'Sijihua' but no obvious correlation in 'Beihua No.1'. The antioxidant activity and phenolic acid content showed positive correlations, with the determination coefficients(R~2) of 0.84(P<0.05) in 'Beihua No.1' and 0.73(P<0.05) in 'Sijihua'. The antioxidant activity of both varieties was the strongest at the ML and the second at the EB. This study revealed that the content dynamics of iridoids, phenolic acids, and flavonoids in 'Sijihua' and 'Beihua No.1' cultivated in Shandong province during different developmental stages. The results indicated that the antioxidant activity of L. japonica flowers was significantly correlated with the content of phenolic acids at different deve-lopmental stages, which provided a basis for determining the optimum harvest time of L. japonica flowers.