Deciphering the Factors Controlling Hydrogen and Methyl Spillover on Rh/Cu(111) Single-Atom Alloy.

Spillover of adsorbed species from one active site to another is a key step in heterogeneous catalysis. However, the factors controlling this step, particularly the spillover of polyatomic species, have rarely been studied. Herein, we investigate the spillover dynamics of H* and CH3* species on a single-atom alloy surface (Rh/Cu(111)) upon the dissociative chemisorption of methane (CH4), using molecular dynamics that considers both surface phonons and electron-hole pairs. These dynamical calculations are made possible by a high-dimensional potential energy surface machine learned from density functional theory data. Our results provide compelling evidence that the H* and CH3* can spill over on the metal surface at experimental temperatures and reveal novel dynamical features involving an internal motion during diffusion for CH3*. Increasing surface temperature has minor effect on promoting spillover, as geminate recombinative desorption becomes more prevalent. However, the poisoning of the active site can be mitigated by frequent gaseous molecular collisions that occur under ambient pressure in real-world catalysis, which transfer energy to the trapped adsorbates. Interestingly, the bulky CH3* exhibits a significant spillover advantage over the light H* due to its larger size, which facilitates energy acquisition. These insights help to advance our understanding of spillover in heterogeneous catalysis.

Consistent Thermal Conductivities of Spring-Like Structured Polydimethylsiloxane Composites under Large Deformation.

Flexible and highly thermally conductive materials with consistent thermal conductivity (λ) during large deformation are urgently required to address the heat accumulation in flexible electronics. In this study, spring-like thermal conduction pathways of silver nanowire (S-AgNW) fabricated by 3D printing are compounded with polydimethylsiloxane (PDMS) to prepare S-AgNW/PDMS composites with excellent and consistent λ during deformation. The S-AgNW/PDMS composites exhibit a λ of 7.63 W m-1 K-1 at an AgNW amount of 20 vol%, which is ≈42 times that of PDMS (0.18 W m-1 K-1) and higher than that of AgNW/PDMS composites with the same amount and random dispersion of AgNW (R-AgNW/PDMS) (5.37 W m-1 K-1). Variations in the λ of 20 vol% S-AgNW/PDMS composites are less than 2% under a deformation of 200% elongation, 50% compression, or 180° bending, which benefits from the large deformation characteristics of S-AgNW. The heat-transfer coefficient (0.29 W cm-2 K-1) of 20 vol% S-AgNW/PDMS composites is ≈1.3 times that of the 20 vol% R-AgNW/PDMS composites, which reduces the temperature of a full-stressed central processing unit by 6.8 °C compared to that using the 20 vol% R-AgNW/PDMS composites as a thermally conductive material in the central processing unit.

Centipeda minima and 6-O-angeloylplenolin enhance the efficacy of immune checkpoint inhibitors in non-small cell lung cancer.

BACKGROUND: Chemotherapeutic agents including cisplatin, gemcitabine, and pemetrexed, significantly enhance the efficacy of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) by increasing PD-L1 expression and potentiating T cell cytotoxicity. However, the low response rate and adverse effects limit the application of chemotherapy/ICI combinations in patients. METHODS: We screened for medicinal herbs that could perturb PD-L1 expression and enhance T cell cytotoxicity in the presence of anti-PD-L1 antibody, and investigated the underlying mechanisms. RESULTS: We found that the aqueous extracts of Centipeda minima (CM) significantly enhanced the cancer cell-killing activity and granzyme B expression level of CD8+ T cells, in the presence of anti-PD-L1 antibody. Both CM and its active component 6-O-angeloylplenolin (6-OAP) upregulated PD-L1 expression by suppressing GSK-3ß-ß-TRCP-mediated ubiquitination and degradation. CM and 6-OAP significantly enhanced ICI-induced reduction of tumor burden and prolongation of overall survival of mice bearing NSCLC cells, accompanied by upregulation of PD-L1 and increase of CD8+ T cell infiltration. CM also exhibited anti-NSCLC activity in cells and in a patient-derived xenograft mouse model. CONCLUSIONS: These data demonstrated that the induced expression of PD-L1 and enhancement of CD8+ T cell cytotoxicity underlay the beneficial effects of 6-OAP-rich CM in NSCLCs, providing a clinically available and safe medicinal herb for combined use with ICIs to treat this deadly disease.

Electromagnetic Field-Assisted Frozen Tissue Planarization Enhances MALDI-MSI in Plant Spatial Omics.

Plant samples with irregular morphology are challenging for longitudinal tissue sectioning. This has restricted the ability to gain insight into some plants using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Herein, we develop a novel technique termed electromagnetic field-assisted frozen tissue planarization (EMFAFTP). This technique involves using a pair of adjustable electromagnets on both sides of a plant tissue. Under an optimized electromagnetic field strength, nondestructive planarization and regularization of the frozen tissue is induced, allowing the longitudinal tissue sectioning that favors subsequent molecular profiling by MALDI-MSI. As a proof of concept, flowers, leaves and roots with irregular morphology from six plant species are chosen to evaluate the performance of EMFAFTP for MALDI-MSI of secondary metabolites, amino acids, lipids, and proteins among others in the plant samples. The significantly enhanced MALDI-MSI capabilities of these endogenous molecules demonstrate the robustness of EMFAFTP and suggest it has the potential to become a standard technique for advancing MALDI-MSI into a new era of plant spatial omics.

Rapid determination of toxic and essential metal binding proteins in biological samples by size exclusion chromatography-inductively coupled plasma tandem mass spectrometry.

Metalloproteins binding with trace elements play a crucial role in biological processes and on the contrary, those binding with exogenous heavy metals have adverse effects. However, the methods for rapid, high sensitivity and simultaneous analysis of these metalloproteins are still lacking. In this study, a fast method for simultaneously determination of both essential and toxic metal-containing proteins was developed by coupling size exclusion chromatography (SEC) with inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). After optimization of the separation and detection conditions, seven metalloproteins with different molecular weight (from 16.0 to 443.0 kDa) were successfully separated within 10 min and the proteins containing iron (Fe), copper (Cu), zinc (Zn), iodine (I) and lead (Pb) elements could be simultaneously detected with the use of oxygen as the collision gas in ICP-MS/MS. Accordingly, the linear relationship between log molecular weight and retention time was established to estimate the molecular weight of unknown proteins. Thus, the trace metal and toxic metal containing proteins could be detected in a single run with high sensitivity (detection limits in the range of 0.0020-2.5 µg/mL) and good repeatability (relative standard deviations lower than 4.5 %). This method was then successfully used to analyze metal (e.g., Pb, Zn, Cu and Fe) binding proteins in the blood of Pb-intoxicated patients, and the results showed a negative correlation between the contents of zinc and lead binding proteins, which was identified to contain hemoglobin subunit. In summary, this work provided a rapid and sensitive tool for screening metal containing proteins in large number of biological samples.

Positive feedback loop of c-myc/XTP6/NDH2/NF-κB to promote malignant progression in glioblastoma.

BACKGROUND: Recent studies have highlighted the significant role of the NF-κB signaling pathway in the initiation and progression of cancer. Furthermore, long noncoding RNAs (lncRNAs) have been identified as pivotal regulators in sustaining the NF-κB signaling pathway's functionality. Despite these findings, the underlying molecular mechanisms through which lncRNAs influence the NF-κB pathway remain largely unexplored. METHODS: Bioinformatic analyses were utilized to investigate the differential expression and prognostic significance of XTP6. The functional roles of XTP6 were further elucidated through both in vitro and in vivo experimental approaches. To estimate the interaction between XTP6 and NDH2, RNA pulldown and RNA Immunoprecipitation (RIP) assays were conducted. The connection between XTP6 and the IκBα promoter was examined using Chromatin Isolation by RNA Purification (ChIRP) assays. Additionally, Chromatin Immunoprecipitation (ChIP) assays were implemented to analyze the binding affinity of c-myc to the XTP6 promoter, providing insights into the regulatory mechanisms at play. RESULTS: XTP6 was remarkedly upregulated in glioblastoma multiforme (GBM) tissues and was connected with adverse prognosis in GBM patients. Our investigations revealed that XTP6 can facilitate the malignant progression of GBM both in vitro and in vivo. Additionally, XTP6 downregulated IκBα expression by recruiting NDH2 to the IκBα promoter, which resulted in elevated levels of H3K27me3, thereby reducing the transcriptional activity of IκBα. Moreover, the progression of GBM was further driven by the c-myc-mediated upregulation of XTP6, establishing a positive feedback loop with IκBα that perpetuated the activation of the NF-κB signaling pathway. Notably, the application of an inhibitor targeting the NF-κB signaling pathway effectively inhibited the continuous activation induced by XTP6, leading to a significant reduction in tumor formation in vivo. CONCLUSION: The results reveal that XTP6 unveils an innovative epigenetic mechanism instrumental in the sustained activation of the NF-κB signaling pathway, suggesting a promising therapeutic target for the treatment of GBM.

Integrated biomarker-based ecological risks assessment of tadpole responses to tris(2-chloroethyl) phosphate, tris(1-chloro-2-propyl) phosphate, and their combined environmental exposure.

Tris(2-chloroethyl) phosphate (TCEP) and tris(1-chloro-2-propyl) phosphate (TCPP) are common chlorinated organophosphorus flame retardants (OPFRs) used in industry. They have been frequently detected together in aquatic environments and associated with various hazardous effects. However, the ecological risks of prolonged exposure to these OPFRs at environmentally relevant concentrations in non-model aquatic organisms remain unexplored. This study investigated the effects of long-term exposure (up to 25 days) to TCEP and TCPP on metamorphosis, hepatic antioxidants, and endocrine function in Polypedates megacephalus tadpoles. Exposure concentrations were set at 3, 30, and 90 µg/L for each substance, conducted independently and in equal-concentration combinations, with a control group included for comparison. The integrated biomarker response (IBR) method developed an optimal linear model for predicting the overall ecological risks of TCEP and TCPP to tadpoles in potential distribution areas of Polypedates species. Results showed that: (1) Exposure to environmentally relevant concentrations of TCEP and TCPP elicited variable adverse effects on tadpole metamorphosis time, hepatic antioxidant enzyme activity and related gene expression, and endocrine-related gene expression, with their combined exposure exacerbating these effects. (2) The IBR value of TCEP was consistently greater than that of TCPP at each concentration, with an additive effect observed under their combined exposure. (3) The ecological risk of tadpoles exposed to the combined presence of TCEP and TCPP was highest in China's Taihu Lake and Vietnam's Hanoi than in other distribution locations. In summary, prolonged exposure to environmentally relevant concentrations of TCEP and TCPP presents potential ecological risks to amphibian tadpoles, offering insights for the development of policies and strategies to control TCEP and TCPP pollution in aquatic ecosystems. Furthermore, the methodology employed in establishing the IBR prediction model provides a methodological framework for assessing the overall ecological risks of multiple OPFRs.

Crystal structure of lipase from Pseudomonas aeruginosa reveals an unusual catalytic triad conformation.

The Pseudomonas aeruginosa lipase PaL catalyzes the stereoselective hydrolysis of menthyl propionate to produce L-menthol. The lack of a three-dimensional structure of PaL has so far prevented a detailed understanding of its stereoselective reaction mechanism. Here, the crystal structure of PaL was determined at a resolution of 1.80 Å by single-wavelength anomalous diffraction. In the apo-PaL structure, the catalytic His302 is located in a long loop on the surface that is solvent exposed. His302 is distant from the other two catalytic residues, Asp274 and Ser164. This configuration of catalytic residues is unusual for lipases. Using metadynamics simulations, we observed that the enzyme undergoes a significant conformational change upon ligand binding. We also explored the catalytic and stereoselectivity mechanisms of PaL by all-atom molecular dynamics simulations. These findings could guide the engineering of PaL with an improved diastereoselectivity for L-menthol production.

Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm3) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists' visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses. RESULTS: A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall (p < 0.001), BCLC A (p < 0.001), and BCLC B (p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC An stage; whilst the BCLC B high-TTB patients remained in a BCLC Bn stage. The 2-year ER rate was 30.5% for BCLC An patients vs. 58.1% for BCLC Bn patients (HR = 2.8; p < 0.001). CONCLUSIONS: TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B. CLINICAL RELEVANCE STATEMENT: Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy. KEY POINTS: Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients.

Exploring the effects of environmentally relevant concentrations of tris(2-chloroethyl) phosphate on tadpole health: A comprehensive analysis of intestinal microbiota and hepatic transcriptome.

Tris(2-chloroethyl) phosphate (TCEP), a chlorinated organophosphate ester, is commonly found in aquatic environments. Due to its various toxic effects, it may pose a risk to the health of aquatic organisms. However, the potential impacts of TCEP exposure on the intestinal microbiota and hepatic function in amphibians have not been reported. This study investigated the impact of long-term exposure to environmentally relevant concentrations of TCEP (0, 3, and 90 µg/L) on the intestinal microbiota and hepatic transcriptome of Polypedates megacephalus tadpoles. The results showed that the body size of the tadpoles decreased significantly with an increase in TCEP concentration. Additionally, TCEP exposure affected the diversity and composition of the intestinal microbiota in tadpoles, leading to significant changes in the relative abundance of certain bacterial groups (the genera Aeromonas decreased and Citrobacter increased) and potentially promoting a more even distribution of microbial species, as indicated by a significant increase in the Simpson index. Moreover, the impact of TCEP on hepatic gene expression profiles in tadpoles was significant, with the majority of differentially expressed genes (DEGs) (709 out of 906 total DEGs in 3 µg/L of TCEP versus control, and 344 out of 387 DEGs in 90 µg/L of TCEP versus control) being significantly down-regulated, which were primarily related to immune response and immune system process. Notably, exposure to TCEP significantly reduced the relative abundance of the genera Aeromonas and Cetobacterium in the tadpole intestine. This reduction was positively correlated with the down-regulated expression of immune-related genes in the liver of corresponding tadpoles. In summary, these findings provide empirical evidence of the potential health risks to tadpoles exposed to TCEP at environmentally relevant concentrations.

Benchmarking quantum versions of the kNN algorithm with a metric based on amplitude-encoded features.

This work introduces a quantum subroutine for computing the distance between two patterns and integrates it into two quantum versions of the kNN classifier algorithm: one proposed by Schuld et al. and the other proposed by Quezada et al. Notably, our proposed subroutine is tailored to be memory-efficient, requiring fewer qubits for data encoding, while maintaining the overall complexity for both QkNN versions. This research focuses on comparing the performance of the two quantum kNN algorithms using the original Hamming distance with qubit-encoded features and our proposed subroutine, which computes the distance using amplitude-encoded features. Results obtained from analyzing thirteen different datasets (Iris, Seeds, Raisin, Mine, Cryotherapy, Data Bank Authentication, Caesarian, Wine, Haberman, Transfusion, Immunotherapy, Balance Scale, and Glass) show that both algorithms benefit from the proposed subroutine, achieving at least a 50% reduction in the number of required qubits, while maintaining a similar overall performance. For Shuld's algorithm, the performance improved in Cryotherapy (68.89% accuracy compared to 64.44%) and Balance Scale (85.33% F1 score compared to 78.89%), was worse in Iris (86.0% accuracy compared to 95.33%) and Raisin (77.67% accuracy compared to 81.56%), and remained similar in the remaining nine datasets. While for Quezada's algorithm, the performance improved in Caesarian (68.89% F1 score compared to 58.22%), Haberman (69.94% F1 score compared to 62.31%) and Immunotherapy (76.88% F1 score compared to 69.67%), was worse in Iris (82.67% accuracy compared to 95.33%), Balance Scale (77.97% F1 score compared to 69.21%) and Glass (40.04% F1 score compared to 28.79%), and remained similar in the remaining seven datasets.

Regulation of macrophage activation by lactylation in lung disease.

Lactylation is a process where lactate, a cellular metabolism byproduct, is added to proteins, altering their functions. In the realm of macrophage activation, lactylation impacts inflammatory response and immune regulation. Understanding the effects of lactylation on macrophage activation is vital in lung diseases, as abnormal activation and function are pivotal in conditions like pneumonia, pulmonary fibrosis, COPD, and lung cancer. This review explores the concept of lactylation, its regulation of macrophage activation, and recent research progress in lung diseases. It offers new insights into lung disease pathogenesis and potential therapeutic targets.

Modified transport medium for improving influenza virus detection.

Background: Accurate detection of influenza virus in clinical samples requires correct execution of all aspects of the detection test. If the viral load in a sample is below the detection limit, a false negative result may be obtained. To overcome this issue, we developed a modified transport medium (MTM) for clinical sample transportation to increase viral detection sensitivity. Method: We first validated the MTM using laboratory-stocked influenza A viruses (IAVs: H1N1, H3N2, H7N3, H9N2) and influenza B viruses (IBVs: Yamagata, Victoria). We also tested clinical samples. A total of 110 patients were enrolled and a pair of samples were collected to determine the sensitivity of real-time polymerase chain reaction (RT-PCR) following MTM treatment. Result: After 24 h culturing in MTM, the viral loads were increased, represented by a 10-fold increase in detection sensitivity for H1N1, H9N2, and IBVs, a 100-fold increase for H3N2, and a 1,000-fold increase for H7N3. We further tested the effects of MTM on 19 IAV and 11 IBV stored clinical samples. The RT-PCR results showed that the positive detection rate of IAV samples increased from 63.16% (12/19) without MTM culturing to 78.95% (15/19) after 48 h culturing, and finally 89.47% (17/19) after 72 h culturing. MTM treatment of IBV clinical samples also increased the positive detection rate from 36.36% (4/11, 0 h) to 63.64% (7/11, 48 h) to 72.73% (8/11, 72 h). For clinical samples detected by RT-PCR, MTM outperformed other transport mediums in terms of viral detection rate (11.81% increase, P=0.007). Conclusion: Our results demonstrated that the use of MTM for clinical applications can increase detection sensitivity, thus facilitating the accurate diagnosis of influenza infection.

Cost-effectiveness of dostarlimab plus chemotherapy for primary advanced or recurrent endometrial cancer.

Objective: In the double-blind, phase III, placebo-controlled RUBY randomized clinical trial, dostarlimab plus carboplatin-paclitaxel significantly increased survival among patients with primary advanced or recurrent endometrial cancer (EC). We conducted a cost-effectiveness analysis of dostarlimab in combination with chemotherapy in these patients stratified by mismatch repair-deficient (dMMR) and mismatch repair-proficient (pMMR) subgroups from the perspective of a United States payer. Materials and methods: A Markov model with three states was employed to simulate patients who were administered either dostarlimab in combination with chemotherapy or chemotherapy based on the RUBY trial. Quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratio (ICER) were calculated with a willingness-to-pay (WTP) threshold of $150,000 per QALY. Both univariate and probabilistic sensitivity analyses were carried out to explore the robustness of the model. Results: In dMMR EC, the combination of dostarlimab and chemotherapy achieved an additional 5.48 QALYs at an incremental cost of $330,747 compared to chemotherapy alone, resulting in an ICER of $60,349.30 per QALY. In pMMR EC, there were 1.51 additional QALYs gained at an extra cost of $265,148, yielding an ICER of $175,788.47 per QALY. With a 15.2% discount on dostarlimab, the ICER decreased to $150,000 per QALY in the pMMR EC. The univariate sensitivity analysis revealed that the cost of dostarlimab, utility of progression-free survival (PFS), and progressive disease (PD) had the most significant impacts on the outcomes. Probabilistic sensitivity analysis revealed that dostarlimab had a 100% likelihood of being considered cost-effective for patients at a WTP threshold of $150,000 per QALY for dMMR EC, whereas this likelihood was only 0.5% for pMMR EC. Conclusion: Dostarlimab in combination with chemotherapy was cost-effective for primary advanced or recurrent dMMR EC from the perspective of a United States payer at a WTP threshold of $150,000 per QALY, but not for pMMR EC. Lowering the prices of dostarlimab could potentially enhance the cost-effectiveness of treatment for pMMR EC.

Is Type 2 Diabetes Mellitus Associated with Spinal Degenerative Disorders?: Evidence from Observational and 2-Sample Mendelian Randomization Analyses.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and spinal degenerative disorders (SDD) are common diseases that frequently coexist. However, both traditional observational studies and recent Mendelian randomization (MR) studies have demonstrated conflicting evidence on the association between T2DM and SDD. This comparative study explored and compared the association between T2DM and SDD using observational and MR analyses. METHODS: For observational analyses, cross-sectional studies (44,972 participants with T2DM and 403,095 participants without T2DM), case-control studies (38,234 participants with SDD and 409,833 participants without SDD), and prospective studies (35,550 participants with T2DM and 392,046 participants without T2DM with follow-up information until 2022) were performed to test the relationship between T2DM and SDD using individual-level data from the U.K. Biobank from 2006 to 2022. For MR analyses, the associations between single-nucleotide polymorphisms with SDD susceptibility obtained using participant data from the U.K. Biobank, which had 407,938 participants from 2006 to 2022, and the FinnGen Consortium, which had 227,388 participants from 2017 to 2022, and genetic predisposition to T2DM obtained using summary statistics from a pooled genome-wide association study involving 1,407,282 individuals were examined. The onset and severity of T2DM are not available in the databases being used. RESULTS: Participants with T2DM were more likely to have SDD than their counterparts. Logistic regression analysis identified T2DM as an independent risk factor for SDD, which was confirmed by the Cox proportional hazard model results. However, using single-nucleotide polymorphisms as instruments, the MR analyses demonstrated no causal relationship between T2DM and SDD. The lack of such an association was robust in the sensitivity analysis, and no pleiotropy was seen. CONCLUSIONS: Our results suggest that the association between T2DM and SDD may be method-dependent. Researchers and clinicians should be cautious in interpreting the association, especially the causal association, between T2DM and SDD. Our findings provide fresh insights into the association between T2DM and SDD by various analysis methods and guide future research and clinical efforts in the effective prevention and management of T2DM and SDD. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Validation Study of the Newly Proposed Refined Diagnostic Criteria for Malignant Phyllodes Tumor With 136 Borderline and Malignant Phyllodes Tumor Cases.

The World Health Organization (WHO) diagnostic criteria for malignant phyllodes tumor (MPT) may miss a significant number of MPTs with metastatic potential. New refined diagnostic criteria (Refined Criteria) for MPT were recently proposed. The aim of this study is to validate the Refined Criteria. This validation study included 136 borderline (borderline phyllodes tumor [BoPT]) and MPT cases that were not included in the initial study. We evaluated tumor classifications based on both the Refined Criteria and the WHO criteria. The Refined Criteria defines MPT when these criteria are met (1) stromal overgrowth with ≥ 1 feature(s) of marked stromal cellularity, marked stromal cytologic atypia, or ≥10 mitoses per 10 high-power fields (10 mitoses/10 HPFs) or (2) marked stromal cellularity with ≥1 feature(s) of marked stromal cytologic atypia, ≥10 mitoses/10 HPFs or permeative border. The WHO criteria require all 5 morphologic features (stromal overgrowth, permeative border, marked stromal cellularity, marked stromal cytologic atypia, and ≥10 mitoses/10 HPFs) for an MPT diagnosis. Using the Refined Criteria, none of the 61 BoPTs developed metastasis and 40.0% of the 75 MPTs developed metastases; local recurrence was seen in 11.5% BoPTs and 25.3% MPTs. Using the WHO criteria, 9.6% of the 94 BoPTs developed metastases and 50.0% of the 42 MPTs developed metastases; 14.9% of the BoPTs had local recurrence and 28.6% of the MPTs had local recurrence. Nine (30.0%) of the 30 tumors that developed distant metastases were diagnosed as BoPTs by the WHO criteria. When we combined the 75 MPTs from this validation cohort with the 65 MPT cases from the published data using the Refined Criteria, 50 (35.7%) of the 140 MPTs developed metastases, whereas 8 cases with metastases were <5 cm. In the univariate analysis with log-rank test, stromal overgrowth, marked stromal cellularity, marked stromal cytologic atypia, ≥10 mitoses/10 HPFs, presence of heterologous components other than liposarcomatous component, and presence of stromal necrosis were significantly associated with the risk of metastasis (all with P < 0.05). In multivariate analysis with Cox proportional hazard regression, stromal overgrowth and marked stromal cellularity were significantly associated with metastasis (both with P < 0.001). The Refined Criteria are superior to the WHO criteria in predicting the clinical outcomes of BoPTs and MPTs. Using the Refined Criteria, 35.7% of 140 patients with MPT developed metastases, whereas none (0%) of the patients with BoPT developed metastases. Patients with MPT have a high metastatic rate; these patients may benefit from systemic chemotherapy or targeted therapies. In contrast, patients with BoPT may be managed with complete local excision alone without chemotherapy.

A novel Gaussian sum quaternion constrained cubature Kalman filter algorithm for GNSS/SINS integrated attitude determination and positioning system.

The quaternion cubature Kalman filter (QCKF) algorithm has emerged as a prominent nonlinear filter algorithm and has found extensive applications in the field of GNSS/SINS integrated attitude determination and positioning system (GNSS/SINS-IADPS) data processing for unmanned aerial vehicles (UAV). However, on one hand, the QCKF algorithm is predicated on the assumption that the random model of filter algorithm, which follows a white Gaussian noise distribution. The noise in actual GNSS/SINS-IADPS is not the white Gaussian noise but rather a ubiquitous non-Gaussian noise. On the other hand, the use of quaternions as state variables is bound by normalization constraints. When applied directly in nonlinear non-Gaussian system without considering normalization constraints, the QCKF algorithm may result in a mismatch phenomenon in the filtering random model, potentially resulting in a decline in estimation accuracy. To address this issue, we propose a novel Gaussian sum quaternion constrained cubature Kalman filter (GSQCCKF) algorithm. This algorithm refines the random model of the QCKF by approximating non-Gaussian noise with a Gaussian mixture model. Meanwhile, to account for quaternion normalization in attitude determination, a two-step projection method is employed to constrain the quaternion, which consequently enhances the filtering estimation accuracy. Simulation and experimental analyses demonstrate that the proposed GSQCCKF algorithm significantly improves accuracy and adaptability in GNSS/SINS-IADPS data processing under non-Gaussian noise conditions for Unmanned Aerial Vehicles (UAVs).

Psathrosterols C-E, highly conjugated ergosterol derivatives from Psathyrella rogueiana with anti-inflammatory and immunosuppressive activity.

Three new ergosterols featuring with a highly conjugated ring system, psathrosterols C-E (1-3), have been isolated from the fungus Psathyrella rogueiana. The structures with the absolute configurations were elucidated by means of spectroscopic methods and single crystal X-ray diffraction. Compounds 1-3 exhibit inhibitory activity against NO production with IC50 values ranging from 8.4 to 21.8 µM. Compound 1 inhibits the LPS-induced proliferation of B lymphocyte cells with an IC50 value of 12.3 µM.

Deep learning-based automated scan plane positioning for brain magnetic resonance imaging.

Background: Manual planning of scans in clinical magnetic resonance imaging (MRI) exhibits poor accuracy, lacks consistency, and is time-consuming. Meanwhile, classical automated scan plane positioning methods that rely on certain assumptions are not accurate or stable enough, and are computationally inefficient for practical application scenarios. This study aims to develop and evaluate an effective, reliable, and accurate deep learning-based framework that incorporates prior physical knowledge for automatic head scan plane positioning in MRI. Methods: A deep learning-based end-to-end automated scan plane positioning framework has been developed for MRI head scans. Our model takes a three-dimensional (3D) pre-scan image input, utilizing a cascaded 3D convolutional neural network to detect anatomical landmarks from coarse to fine. And then, with the determined landmarks, accurate scan plane localization can be achieved. A multi-scale spatial information fusion module was employed to aggregate high- and low-resolution features, combined with physically meaningful point regression loss (PRL) function and direction regression loss (DRL) function. Meanwhile, we simulate complex clinical scenarios to design data augmentation strategies. Results: Our proposed approach shows good performance on a clinically wide range of 229 MRI head scans, with a point-to-point absolute error (PAE) of 0.872 mm, a point-to-point relative error (PRE) of 0.10%, and an average angular error (AAE) of 0.502°, 0.381°, and 0.675° for the sagittal, transverse, and coronal planes, respectively. Conclusions: The proposed deep learning-based automated scan plane positioning shows high efficiency, accuracy and robustness when evaluated on varied clinical head MRI scans with differences in positioning, contrast, noise levels and pathologies.