[Microtubule-associated tumor suppressor 1 inhibits hemin-induced apoptosis of vascular endothelial cells via hemeoxygenase 1].

This study aimed to investigate the effects of microtubule associated tumor suppressor 1 (MTUS1) on hemeoxygenase 1 (HMOX1) expression and hemin-induced apoptosis of vascular endothelial cells and its regulatory mechanism. RNA sequencing, RT-qPCR and Western blot were used to assess altered genes of hemin binding proteins, the expression of cAMP response element-binding protein (CREB) and nuclear respiratory factor 2 (NRF2), hemin-induced HMOX1 expression in MTUS1 knockdown human umbilical vein endothelial cells (HUVEC), and the effect of overexpression of CREB and NRF2 on HMOX1 expression in MTUS1 knockdown 293T cells. The effect of MTUS1 or HMOX1 knockdown on hemin-induced apoptosis in HUVEC, and the overexpression of NRF2 on hemin-induced apoptosis in MTUS1 knockdown 293T cells were assayed with CCK8 and Western blot. The results showed that MTUS1 was knocked down significantly in HUVEC by siRNA (P < 0.01), accompanied by decreased HMOX1 expression (P < 0.01). The increased HMOX1 expression induced by hemin was also inhibited by MTUS1 knockdown (P < 0.01). And the apoptosis of HUVEC induced by hemin was amplified by MTUS1 or HMOX1 knockdown (P < 0.01). Moreover the expression of CREB and NRF2 were both inhibited by MTUS1 knockdown in HUVEC (P < 0.01). The decreased HMOX1 regulated by MTUS1 knockdown could be rescued partly by overexpression of NRF2 (P < 0.01), however, not by overexpression of CREB. And the MTUS1 knockdown mediated decreased 293T cells viability induced by hemin could be partly rescued by NRF2 overexpression (P < 0.01). These results suggest that MTUS1 can inhibit hemin-induced apoptosis of HUVEC, and the mechanism maybe related to MTUS1/NRF2/HMOX1 pathway.

Enhancing biomass and lipid accumulation by a novel microalga for unsterilized piggery biogas slurry remediation.

The cost and efficiency of an algal-BS treatment system are determined by the specific microalgal species and BS pretreatment method. This study examines the growth of a novel algae Chlorella sp. YSD-2 and the removal of nutrients from the BS using different pretreatment methods, including dilution ratio and sterilization. The highest biomass production (1.84 g L-1) was achieved in the 1:2 unsterilized biogas slurry, which was 2.03 times higher than that in the sterilized group, as well as higher lipid productivity (17.29 mg L-1 d-1). Nevertheless, the sterilized biogas slurry at a 1:1 dilution ratio exhibited the most notable nutrient-removal efficiency, with COD at 71.97%, TP at 91.32%, and TN at 88.80%. Additionally, the analysis of 16S rRNA sequencing revealed a significant alteration in the indigenous bacterial composition of the biogas slurry by microalgal treatment, with Proteobacteria and Cyanobacteria emerging as the predominant phyla, and unidentified_Cyanobacteria as the primary genus. These findings suggest that Chlorella sp. YSD-2 exhibits favorable tolerance and nutrient-removal capabilities in unsterilized, high-strength biogas slurry, along with high productivity of biomass and lipids. Consequently, these results offer a theoretical foundation for the development of an efficient and economically viable treatment method for algal-BS.

Lysophosphatidic acid receptor 1 (LPA1) antagonists as potential migrastatics for triple negative breast cancer.

Metastasis is responsible for about 90% of cancer deaths. Anti-metastatic drugs, termed as migrastatics, offer a distinctive therapeutic approach to address cancer migration and invasion. However, therapeutic exploitation of metastasis-specific targets remains limited, and the effective prevention and suppression of metastatic cancer continue to be elusive. Lysophosphatidic acid receptor 1 (LPA1) is activated by an endogenous lipid molecule LPA, leading to a diverse array of cellular activities. Previous studies have shown that the LPA/LPA1 axis supports the progression of metastasis for many types of cancer. In this study, we report the synthesis and biological evaluation of fluorine-containing triazole derivatives as potent LPA1 antagonists, offering potential as migrastatic drugs for triple negative breast cancer (TNBC). In particular, compound 12f, the most potent and highly selective in this series with an IC50 value of 16.0 nM in the cAMP assay and 18.4 nM in the calcium mobilization assay, inhibited cell survival, migration, and invasion in the TNBC cell line. Interestingly, the compound did not induce apoptosis in TNBC cells and demonstrated no cytotoxic effects. These results highlight the potential of LPA1 as a migrastatic target. Consequently, the LPA1 antagonists developed in this study hold promise as potential migrastatic candidates for TNBC.

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution (Dmap) of each BM was registered to the planning contrast-enhanced T1 (T1-CE) magnetic resonance imaging (MRI). Axial slices of the Dmap, T1-CE, and planning target volume (PTV) segmentation (PTVseg) intersecting the BM center were extracted within a fixed field of view determined by the 60% isodose volume in Dmap. A spherical projection was implemented to transform planar image content onto a spherical surface using multiple projection centers, and the resultant T1-CE/Dmap/PTVseg projections were stacked as a 3-channel variable. Four Visual Geometry Group (VGG-19) deep encoders were used in an ensemble design, with each submodel using a different spherical projection formula as input for BM outcome prediction. In each submodel, clinical features after positional encoding were fused with VGG-19 deep features to generate logit results. The ensemble's outcome was synthesized from the 4 submodel results via logistic regression. In total, 10 model versions with random validation sample assignments were trained to study model robustness. Performance was compared with (1) a single VGG-19 encoder, (2) an ensemble with a T1-CE MRI as the sole image input after projections, and (3) an ensemble with the same image input design without clinical feature inclusion. RESULTS: The ensemble model achieved an excellent area under the receiver operating characteristic curve (AUCROC: 0.89 ± 0.02) with high sensitivity (0.82 ± 0.05), specificity (0.84 ± 0.11), and accuracy (0.84 ± 0.08) results. This outperformed the MRI-only VGG-19 encoder (sensitivity: 0.35 ± 0.01, AUCROC: 0.64 ± 0.08), the MRI-only deep ensemble (sensitivity: 0.60 ± 0.09, AUCROC: 0.68 ± 0.06), and the 3-channel ensemble without clinical feature fusion (sensitivity: 0.78 ± 0.08, AUCROC: 0.84 ± 0.03). CONCLUSIONS: Facilitated by the spherical image projection method, a deep ensemble model incorporating Dmap and clinical variables demonstrated excellent performance in predicting BM post-SRS local failure. Our novel approach could improve other radiation therapy outcome models and warrants further evaluation.

A Rare Waterborne Outbreak of Bacillus paranthracis in Shandong Province, China, 2020: Epidemiologic Survey, Genomic Insights, and Virulence Characteristics.

ABSTRACTBacillus paranthracis, a Gram-positive conditional pathogen of Bacillus cereus group species, is capable of causing foodborne and waterborne illnesses, leading to intestinal diseases in humans characterized by diarrhea and vomiting. However, documented cases of B. paranthracis infection outbreaks are rare in the world, and the genomic background of outbreak strains is seldom characterized. This study retrospectively analyzed strains obtained from a outbreak in schools, as well as from water systems in peri-urban areas, China, in 2020.In total, 28 B. cereus group isolates were retrieved, comprising 6 from stool samples and 22 from water samples. Epidemiological and phylogenetic investigations indicated that the B. paranthracis isolate from drinking water as the causative agent of the outbreak. Genomic comparison revealed a high degree of consistency among 8 outbreak-related strains in terms of antimicrobial resistance gene profiles, virulence gene profiles, genomic content, and multilocus sequence typing (MLST). The strains related to the outbreak show highly similar genomic ring diagrams and close phylogenetic relationships. Additionally, this study shed light on the pathogenic potential and complexity of B. cereus group through its diversity in virulence genes and mice infection model. The findings highlight the usefulness of B. paranthracis genomes in understanding genetic diversity within specific environments and in tracing the source of pathogens during outbreak situations, thereby enabling targeted infection control interventions.

Effect of Thermo- and pH-Sensitive Block Copolymer Structure and Composition on the Synthesis and Stabilization of Gold Nanoparticles.

Homopolymers of poly[N-(2-(diethylamino)ethyl) acrylamide] exhibit the ability to adsorb onto the surface of preformed or growing gold nanoparticles. The resulting hybrid materials possess a pH and thermo-sensitive nature. Consequently, their optical properties can be modulated by manipulating either the temperature or the pH. Moreover, introducing monomers based on poly(N-isopropyl acrylamide) into block or random statistical polymers enables further modulation of the thermosensitive properties. These copolymers, employed for the in-situ synthesis and/or stabilization of gold nanoparticles, lead to hybrid materials whose properties and/or particle size depend on the polymer composition and microstructure: statistical polymers emerge as superior stabilizing agents compared to their block counterparts at a constant composition.

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.

Purpose: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. Approach: A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N=69; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model. Results: FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (p=0.0158 and p=0.0180, respectively; log-rank test). Conclusions: Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.

TMEM158 functions as an oncogene and promotes lung adenocarcinoma progression through the PI3K/AKT pathway via interaction with TWIST1.

Lung adenocarcinoma (LUAD) is a common and deadly form of lung cancer, with high rates of metastasis and unsatisfactory clinical outcomes. Herein, we examined the influence of TMEM158 on the LUAD progression. A combination of bioinformatic analyses was used to assess the TMEM158 expression pattern, prognostic implications, and potential function in LUAD. The levels of TMEM158 and TWIST1 were evaluated in clinical samples from LUAD patients using Western blot analysis and qRT-PCR. To discover the function and underlying molecular pathways of TMEM158 in LUAD, we employed a combination of experimental approaches in vitro, such as flow cytometry analysis and colony formation, Co-IP, CCK-8, Transwell, and wound-healing assays. Elevated expression of TMEM158 in LUAD is associated with increased cancer aggressiveness and a poor prognosis. In vitro experiments demonstrated that high levels of TMEM158 promote cell proliferation, progression through the cell cycle, migration, and invasion while suppressing apoptosis. Knockdown of TMEM158 produced opposite effects. The underlying mechanism involves TMEM158 and TWIST1 directly interacting, stimulating the PI3K/AKT signaling pathway in LUAD cells. This investigation emphasizes the molecular functions of TMEM158 in LUAD progression and proposes targeting it as a promising treatment approach for managing LUAD.

Potential actions of capsaicin for preventing vascular calcification of vascular smooth muscle cells in vitro and in vivo.

Vascular calcification (VC) is an accurate risk factor and predictor of adverse cardiovascular events; however, there is currently no effective therapy to specifically prevent VC progression. Capsaicin (Cap) is a bioactive alkaloid isolated from Capsicum annuum L., a traditional medicinal and edible plant that is beneficial for preventing cardiovascular diseases. However, the effect of Cap on VC remains unclear. This study aimed to explore the effects and related mechanisms of Cap on aortic calcification in a mouse and on Pi-induced calcification in vascular smooth muscle cells (VSMCs). First, we established a calcification mouse model with vitamin D3 and evaluated the effects of Cap on calcification mice using von Kossa staining, calcium content, and alkaline phosphatase activity tests. The results showed that Cap significantly improved calcification in mice. VSMCs were then cultured in 2.6 mM Na2HPO4 and 50 µg/mL ascorbic acid for 7 days to obtain a calcification model, and we investigated the effects and mechanisms of Cap on VSMCs calcification by assessing the changes of calcium deposition, calcium content, and subsequent VC biomarkers. These results showed that Cap alleviated VSMCs calcification by upregulating the expressions of TRPV1. Moreover, Cap reduced the expression of Wnt3a and ß-catenin, whereas DKK1 antagonised the inhibitory effect of Cap on VSMC calcification. This study is the first to offer direct evidence that Cap inhibits the Wnt/ß-catenin signaling pathway by upregulating the expression of the TRPV1 receptor, resulting in the decreased expression of Runx2 and BMP-2, thereby reducing VSMC calcification. Our study may provide novel strategies for preventing the progression of VC. This could serve as a theoretical basis for clinically treating VC with spicy foods.

Are radiomic spleen features useful for assessing the response to infliximab in patients with Crohn's disease? A multicentre study.

OBJECTIVES: To develop and validate a radiomics nomogram for assessing the response of patients with Crohn's disease (CD) to infliximab. METHODS: Radiomics features of the spleen were extracted from computed tomography enterography (CTE) images of each patient's arterial phase. The feature selection process was performed using the least absolute shrinkage and selection operator (LASSO) algorithm, and a radiomics score (Rad-score) was calculated based on the radiomics signature formula. Subsequently, the radiomic model and the clinical risk factor model were separately established based on the Rad-score and clinically significant features, respectively. The performance of both models was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, and clinical impact curves (CICs). RESULTS: Among the 175 CD patients, 105 exhibited a clinical response, and 60 exhibited clinical remission after receiving infliximab (IFX) treatment. Our radiomic model, comprising 20 relevant features, demonstrated excellent predictive performance. The radiomic nomogram for predicting clinical response showed good calibration and discrimination in the training cohort (AUC=0.909, 95% CI=0.840-0.978), the validation cohort (AUC=0.954, 95% CI=0.889-1) and the external cohort (AUC=0.902, 95% CI=0.83-0.974). Accordingly, the nomogram was also suitable for predicting clinical remission. Decision curve analysis and clinical impact curves highlighted the clinical utility of our nomogram. CONCLUSIONS: Our radiomics nomogram is a non-invasive predictive tool constructed from radiomic features of the spleen. It also demonstrated good predictive accuracy in evaluating CD patients' response to infliximab treatment. Multicentre validation provided high-level evidence for its clinical application.

Donor-recipient intermicrobial interactions impact transfer of subspecies and fecal microbiota transplantation outcome.

Studies on fecal microbiota transplantation (FMT) have reported inconsistent connections between clinical outcomes and donor strain engraftment. Analyses of subspecies-level crosstalk and its influences on lineage transfer in metagenomic FMT datasets have proved challenging, as single-nucleotide polymorphisms (SNPs) are generally not linked and are often absent. Here, we utilized species genome bin (SGB), which employs co-abundance binning, to investigate subspecies-level microbiome dynamics in patients with autism spectrum disorder (ASD) who had gastrointestinal comorbidities and underwent encapsulated FMT (Chinese Clinical Trial: 2100043906). We found that interactions between donor and recipient microbes, which were overwhelmingly phylogenetically divergent, were important for subspecies transfer and positive clinical outcomes. Additionally, a donor-recipient SGB match was indicative of a high likelihood of strain transfer. Importantly, these ecodynamics were shared across FMT datasets encompassing multiple diseases. Collectively, these findings provide detailed insight into specific microbial interactions and dynamics that determine FMT success.

Independent Monte Carlo dose calculation identifies single isocenter multi-target radiosurgery targets most likely to fail pre-treatment measurement.

PURPOSE: For individual targets of single isocenter multi-target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre-treatment Quality Assurance (QA) measurement. METHODS: Treatment plans from 31 SIMT SRS patients were recalculated in a series of scenarios designed to investigate sources of discrepancy between TPS and independent MC. Targets with > 5% discrepancy in DMean[Gy] after progressing through all scenarios were measured with SRS MapCHECK. A matched pair analysis was performed comparing SRS MapCHECK results for these targets with matched targets having similar characteristics (volume & distance from isocenter) but no such MC dose discrepancy. RESULTS: Of 217 targets analyzed, individual target mean dose (DMean[Gy]) fell outside a 5% threshold for 28 and 24 targets before and after removing tissue heterogeneity effects, respectively, while only 5 exceeded the threshold after removing effect of patient geometry (via calculation on StereoPHAN geometry). Significant factors affecting agreement between the TPS and MC included target distance from isocenter (0.83% decrease in DMean[Gy] per 2 cm), volume (0.15% increase per cc), and degree of plan modulation (0.37% increase per 0.01 increase in modulation complexity score). SRS MapCHECK measurement had better agreement with MC than with TPS (2%/1 mm / 10% threshold gamma pass rate (GPR) = 99.4 ± 1.9% vs. 93.1 ± 13.9%, respectively). In the matched pair analysis, targets exceeding 5% for MC versus TPS also had larger discrepancies between TPS and measurement with no GPR (2%/1 mm / 10% threshold) exceeding 90% (71.5% ± 16.1%); whereas GPR was high for matched targets with no such MC versus TPS difference (96.5% ± 3.3%, p = 0.01). CONCLUSIONS: Independent MC complements pre-treatment QA measurement for SIMT SRS by identifying problematic individual targets prior to pre-treatment measurement, thus enabling plan modifications earlier in the planning process and guiding selection of targets for pre-treatment QA measurement.

Oral administration of grape-derived nanovesicles for protection against LPS/D-GalN-induced acute liver failure.

Although the exploration of the molecular mechanisms of Acute liver failure (ALF) is supported by a growing number of studies, the lack of effective therapeutic agents and measures indicates an urgent clinical need for the development of new drugs and treatment strategies. Herein, we focused on the treatment of ALF with grape-derived nanovesicles (GDNVs), and assessed its protective effects and molecular mechanisms against liver injury. In the mice model of ALF, prophylactic administration for three consecutive days and treatment with GDNVs after successful induction of ALF showed a significant reduction of ALT and AST activity in mouse serum, as well as a blockade of the release of inflammatory cytokines IL6, IL-1ß and TNF-α. Treatment with GDNVs significantly prevented the massive apoptosis of hepatocytes caused by LPS/D-GalN and down-regulated the activation and expression of the mitochondrial apoptosis-related proteins p53, Caspase 9, Caspase 8, Caspase 3 and Bax. GDNVs downregulated the release of chemokines during the inflammatory eruption in mice and inhibited the infiltration of peripheral monocytes into the liver by inhibiting CCR2/CCR5. Moreover, the pro-inflammatory phenotype of macrophages in the liver was reversed by GDNVs. GDNVs further reduced the activation of NLRP3-related pathways, and treatment with GDNVs activated the expression of autophagy-related proteins Foxo3a, Sirt1 and LC3-II in the damaged mouse liver, inducing autophagy to occur. GDNVs could exert hepatoprotective and inflammatory suppressive functions by increasing nuclear translocation of Nrf2 and upregulating HO-1 expression against exogenous toxin-induced oxidative stress in the liver. In conclusion, these results demonstrate that GDNVs alleviate LPS/D-GalN-induced ALF and have the potential to be used as a novel hepatoprotective agent for clinical treatment.

Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.

BACKGROUND: Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated. PURPOSE: The purpose of this work was to show a proof-of-concept of a new radiomics paradigm for sparse, time-series imaging data, where features are extracted from a spatial-temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC). METHODS: To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time t = 0 $t = 0$ and t > 0 $t > 0$ . These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker-Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial-temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker-Planck estimation and simulated ground-truth. To demonstrate feasibility and clinical impact, we applied our approach to 18F-FDG-PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre-treatment and 2-weeks intra-treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k-means clustering and compared by Kaplan-Meier analyses with log-rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach. RESULTS: Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan-Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray-Level-Size-Zone-Matrix gray-level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722). CONCLUSIONS: We developed, verified, and demonstrated the prognostic value of a novel, physics-based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations.

tRF3-IleAAT reduced extracellular matrix synthesis in diabetic kidney disease mice by targeting ZNF281 and inhibiting ferroptosis.

It is well established that the synthesis of extracellular matrix (ECM) in mesangial cells is a major determinant of diabetic kidney disease (DKD). Elucidating the major players in ECM synthesis may be helpful to provide promising candidates for protecting against DKD progression. tRF3-IleAAT is a tRNA-derived fragment (tRF) produced by nucleases at tRNA-specific sites, which is differentially expressed in the sera of patients with diabetes mellitus and DKD. In this study we investigated the potential roles of tRFs in DKD. Db/db mice at 12 weeks were adapted as a DKD model. The mice displayed marked renal dysfunction accompanied by significantly reduced expression of tRF3-IleAAT and increased ferroptosis and ECM synthesis in the kidney tissues. The reduced expression of tRF3-IleAAT was also observed in high glucose-treated mouse glomerular mesangial cells. We administered ferrostatin-1 (1 mg/kg, once every two days, i.p.) to the mice from the age of 12 weeks for 8 weeks, and found that inhibition of the onset of ferroptosis significantly improved renal function, attenuated renal fibrosis and reduced collagen deposition. Overexpression of tRF3-IleAAT by a single injection of AAV carrying tRF3-IleAAT via caudal vein significantly inhibited ferroptosis and ECM synthesis in DKD model mice. Furthermore, we found that the expression of zinc finger protein 281 (ZNF281), a downstream target gene of tRF3-IleAAT, was significantly elevated in DKD models but negatively regulated by tRF3-IleAAT. In high glucose-treated mesangial cells, knockdown of ZNF281 exerted an inhibitory effect on ferroptosis and ECM synthesis. We demonstrated the targeted binding of tRF3-IleAAT to the 3'UTR of ZNF281. In conclusion, tRF3-IleAAT inhibits ferroptosis by targeting ZNF281, resulting in the mitigation of ECM synthesis in DKD models, suggesting that tRF3-IleAAT may be an attractive therapeutic target for DKD.

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. PURPOSE: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. METHODS: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). RESULTS: The developed SPU-Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U-Net. Quantitatively, the SPU-Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the LSU-Net (scaling factor = 2). The SPU-Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. CONCLUSION: The SPU-Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image-related deep-learning applications for uncertainty evaluation.

P450-Modified Ribosomally Synthesized Peptides with Aromatic Cross-Links.

Cyclization of linear peptides is an effective strategy to convert flexible molecules into rigid compounds, which is of great significance for enhancing the peptide stability and bioactivity. Despite significant advances in the past few decades, Nature and chemists' ability to macrocyclize linear peptides is still quite limited. P450 enzymes have been reported to catalyze macrocyclization of peptides through cross-linkers between aromatic amino acids with only three examples. Herein, we developed an efficient workflow for the identification of P450-modified RiPPs in bacterial genomes, resulting in the discovery of a large number of P450-modified RiPP gene clusters. Combined with subsequent expression and structural characterization of the products, we have identified 11 novel P450-modified RiPPs with different cross-linking patterns from four distinct classes. Our results greatly expand the structural diversity of P450-modified RiPPs and provide new insights and enzymatic tools for the production of cyclic peptides.

Rapamycin Exacerbates Staphylococcus aureus Pneumonia by Inhibiting mTOR-RPS6 in Macrophages.

Purpose: This study aimed to explore the effect of Rapamycin (Rapa) in Staphylococcus aureus (S. aureus) pneumonia and clarify its possible mechanism. Methods: We investigated the effects of Rapa on S. aureus pneumonia in mouse models and in macrophages cultured in vitro. Two possible mechanisms were investigated: the mTOR-RPS6 pathway phosphorylation and phagocytosis. Furthermore, for the mechanism verification in vivo, mice with specific Mtor knockout in myeloid cells were constructed for pneumonia models. Results: Rapa exacerbated S. aureus pneumonia in mouse models, promoting chemokines secretion and inflammatory cells infiltration in lung. In vitro, Rapa upregulated the secretion of chemokines and cytokines in macrophages induced by S. aureus. Mechanistically, the mTOR-ribosomal protein S6 (RPS6) pathway in macrophages was phosphorylated in response to S. aureus infection, and the inhibition of RPS6 phosphorylation upregulated the inflammation level. However, Rapa did not increase the phagocytic activity. Accordingly, mice with specific Mtor knockout in myeloid cells experienced more severe S. aureus pneumonia. Conclusion: Rapa exacerbates S. aureus pneumonia by increasing the inflammatory levels of macrophages. Inhibition of mTOR-RPS6 pathway upregulates the expression of cytokines and chemokines in macrophages, thus increases inflammatory cells infiltration and exacerbates tissue damage.

Study on the treatment of corn alcohol wastewater by the internal circulation anaerobic reactor.

To comprehensively assess the efficacy of employing the internal circulation (IC) anaerobic reactor for corn alcohol wastewater treatment and investigate its feasibility, this study focused on anaerobic digestion parameters, energy balance, and the composition of the prokaryotic microbial community. During the operation of the reactor, the hydraulic retention time was progressively reduced from 4.8 to 1.6 days while achieving an average organic loading rate of 12.46 kg chemical oxygen demand (COD)/(m3·d). Moreover, the removal rate of COD exceeded 98%, and the energy balance (ΔE) reached 10.29 kJ/g fed COD. The initial manifestation of organic acidosis in the reactor was a decline in gas production, which is primarily caused by propionic acid accumulation. The subsequent analysis revealed a high diversity of prokaryotes in granular sludge, with the predominant archaea primarily involved in methane production through the acetic acid pathway. The IC anaerobic reactor shows exceptional performance in treating corn alcohol wastewater by optimizing its operating conditions. Energy balance analysis confirmed the feasibility of the process. The findings of this study may offer valuable insights for optimizing control strategies and engineering applications.