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Background Peritoneal metastasis (PM) after radical surgery is an important cause of treatment failure in colorectal cancer (CRC). Intraoperative intraperitoneal perfusion chemotherapy may be an effective method for preventing postoperative PM in patients with CRC. This study aimed to explore the safety and feasibility of intraoperatively preventive intraperitoneal perfusion chemotherapy using lobaplatin for CRC. Methods Between 12 December 2017 and 17 October 2019, 720 eligible CRC patients with T4 or N + clinical TNM stage were recruited from 25 hospitals in China. Eligible patients were randomised in a 1:1 ratio to undergo resection of CRC only (control group) or resection of CRC with intraperitoneal perfusion chemotherapy with lobaplatin intraoperatively (lobaplatin group). The primary endpoint of this trial was the rate of PM after surgery, while secondary endpoints included safety, overall survival (OS) time, recurrence-free survival (RFS) time, peritoneal recurrence-free survival (PRFS) time, and the rate of liver metastasis. Results Of 716 patients included in the full analysis set (FAS), 352 were assigned to the lobaplatin group and 364 to the control group. In the FAS population, adding intraoperatively preventive intraperitoneal perfusion chemotherapy with lobaplatin decreased the primary end point rate of 3-year PM (3.56% vs 8.75%, P  = 0.0053). There was no significant difference in the 3-year OS between the groups (93.2% vs 90.4%, P  = 0.1660). The 3-year RFS rate (88.1% vs 81.6%, 0.0146) and 3-year PRFS rate (96.6% vs 91.5%, P  = 0.0053) were significantly higher in the lobaplatin group than the control group. There were no statistically significant differences between the two groups in the incidence (69.77% vs 64.75%) or severity of adverse events (AEs) in the safety set (SS) population. Conclusions Initiation of intraoperatively preventive intraperitoneal perfusion chemotherapy with lobaplatin reduced the 3-year PM rate in CRC patients while improving both 3-year RFS and PRFS. The treatment was well tolerated, and the safety findings were comparable with those of the control group. Trial registration Chinese Clinical Trial Registry, ChiCTR1800014617.

A non-fullerene molecule named Y6 was incorporated into a binary blend of PBDB-T and IT-M to further enhance photon harvesting in the near-infrared (near-IR) region. Compared with PBDB-T/IT-M binary blend devices, PBDB-T/IT-M/Y6 ternary blend devices exhibited an improved short-circuit current density (JSC) from 15.34 to 19.09 mA cm−2. As a result, the power conversion efficiency (PCE) increased from 10.65% to 12.50%. With an increasing weight ratio of Y6, the external quantum efficiency (EQE) was enhanced at around 825 nm, which is ascribed to the absorption of Y6. At the same time, EQE was also enhanced at around 600–700 nm, which is ascribed to the absorption of IT-M, although the optical absorption intensity of IT-M decreased with increasing weight ratio of Y6. This is because of the efficient energy transfer from IT-M to Y6, which can collect the IT-M exciton lost in the PBDB-T/IT-M binary blend. Interestingly, the EQE spectra of PBDB-T/IT-M/Y6 ternary blend devices were not only increased but also red-shifted in the near-IR region with increasing weight ratio of Y6. This finding suggests that the absorption spectrum of Y6 is dependent on the weight ratio of Y6, which is probably due to different aggregation states depending on the weight ratio. This aggregate property of Y6 was also studied in terms of surface energy.

Renal stones (RS) are common urologic condition with unclear pathogenesis. Role of aging-related differentially expressed genes (ARDEGs) in RS remains poorly understood. This study aims to identify potential aging-related biomarkers for RS, explore the functions of aging-associated genes, and investigate the immunological microenvironment in RS. ARDEGs were collected from the GEO, GeneCards, and Molecular Signatures databases. The roles of ARDEGs were analyzed using Gene Ontology (GO) enrichment analysis. Key genes were identified using machine learning methods. Immune infiltration in RS was assessed using the CIBERSORT and ssGSEA algorithms. A total of 22 ARDEGs were identified through analysis, including 9 up-regulated and 13 down-regulated genes. GO enrichment analysis revealed that these genes were mainly involved in RS-related biological processes such as macrophage proliferation and neuroinflammatory response. GSEA analysis showed that RS-associated genes were predominantly involved in immune regulation-related pathways. Using logistic regression, SVM, and LASSO regression algorithms, a successful early-diagnosis model for RS was developed, yielding 7 key genes: CNR1 , KIT , HTR2A , DES , IL33 , UCP2 , and PPT1 . Immunocyte infiltration analysis of RS samples showed that CD8 + T cells had the strongest positive correlation with M1 macrophages, while resting NK cells had the strongest negative correlation with activated NK cells. The DES gene showed the strongest positive correlation with resting mast cells, and the IL33 gene displayed the highest negative correlation with regulatory T cells. Bioinformatics analysis screened out 7 new potential markers for RS and explored the possible mechanism of RS senescence. These findings provide novel insights into the relationship between RS and senescence, as well as the diagnosis and treatment of RS, and enhance our understanding of the disease’s occurrence and development mechanisms.

In modern industrial systems, fault diagnosis is crucial for ensuring safety, stability, and efficiency. Traditional physics - based methods struggle with increasing process complexity, prompting growing attention to flexible and adaptable data - driven approaches. The Kernel Entropy Component Analysis (KECA) algorithm is a standout feature extraction and dimensionality reduction technique for nonlinear data. Yet, conventional KECA algorithms have fault - diagnosis limitations like underutilized fault data and noise sensitivity. This paper presents the Fault Data Enhanced Optimized Kernel Entropy Component Analysis (FDE-OKECA) algorithm. It constructs two sub - models via the OKECA framework, extracting kernel entropy components from normal and fault data. Bayesian inference fuses statistical parameters from these sub - models for fault detection and diagnosis. The normal data sub - model extracts components through data standardization, kernel matrix construction, eigenvalue decomposition, and rotational optimization. The fault data sub - model uses the OKECA-LDA algorithm to boost fault feature separability. TE process simulations show FDE-OKECA improves fault detection and type identification versus traditional OKECA, with fewer false alarms.

Surgical repair of bone defects remains challenging, and the search for alternative procedures is ongoing. Devices made of Mg for bone repair have received much attention owing to their good biocompatibility and mechanical properties. We developed a new type of scaffold made of a Mg-Zn-Ca alloy with a shape that mimics cortical bone and can be filled with morselized bone. We evaluated its durability and efficacy in a rabbit ulna-defect model. Three types of scaffold-surface coating were evaluated: group A, no coating; group B, a 10-μm microarc oxidation coating; group C, a hydrothermal duplex composite coating; and group D, an empty-defect control. X-ray and micro-computed tomography(micro-CT) images were acquired over 12 weeks to assess ulnar repair. A mechanical stress test indicated that bone repair within each group improved significantly over time (P < 0.01). The degradation behavior of the different scaffolds was assessed by micro-CT and quantified according to the amount of hydrogen gas generated; these measurements indicated that the group C scaffold better resisted corrosion than did the other scaffold types (P < 0.05). Calcein fluorescence and histology revealed that greater mineral densities and better bone responses were achieved for groups B and C than for group A, with group C providing the best response. In conclusion, our Mg-Zn-Ca-alloy scaffold effectively aided bone repair. The group C scaffold exhibited the best corrosion resistance and osteogenesis properties, making it a candidate scaffold for repair of bone defects.

Early identification of unresectable hepatocellular carcinoma (HCC) patients who may benefit from immune checkpoint inhibitors (ICIs) is crucial for optimizing outcomes. Here, we developed a multimodal fusion (MMF) system integrating CT-derived deep learning features and clinical data to predict overall survival (OS) and progression-free survival (PFS). Using retrospective multicenter data ( n  = 859), the MMF combining an ensemble deep learning (Ensemble-DL) model with clinical variables achieved strong external validation performance (C-index: OS = 0.74, PFS = 0.69), outperforming radiomics (29.8% OS improvement), mRECIST (27.6% OS improvement), clinical benchmarks (C-index: OS = 0.67, p  = 0.0011; PFS = 0.65, p  = 0.033), and Ensemble-DL (C-index: OS = 0.69, p  = 0.0028; PFS = 0.66, p  = 0.044). The MMF system effectively stratified patients across clinical subgroups and demonstrated interpretability through activation maps and radiomic correlations. Differential gene expression analysis revealed enrichment of the PI3K/Akt pathway in patients identified by the MMF system. The MMF system provides an interpretable, clinically applicable approach to guide personalized ICI treatment in unresectable HCC.

Background The clinical decision support system can effectively break the limitations of doctors’ knowledge and reduce the possibility of misdiagnosis to enhance health care. The traditional genetic data storage and analysis methods based on stand-alone environment are hard to meet the computational requirements with the rapid genetic data growth for the limited scalability. Methods In this paper, we propose a distributed gene clinical decision support system, which is named GCDSS. And a prototype is implemented based on cloud computing technology. At the same time, we present CloudBWA which is a novel distributed read mapping algorithm leveraging batch processing strategy to map reads on Apache Spark. Results Experiments show that the distributed gene clinical decision support system GCDSS and the distributed read mapping algorithm CloudBWA have outstanding performance and excellent scalability. Compared with state-of-the-art distributed algorithms, CloudBWA achieves up to 2.63 times speedup over SparkBWA. Compared with stand-alone algorithms, CloudBWA with 16 cores achieves up to 11.59 times speedup over BWA-MEM with 1 core. Conclusions GCDSS is a distributed gene clinical decision support system based on cloud computing techniques. In particular, we incorporated a distributed genetic data analysis pipeline framework in the proposed GCDSS system. To boost the data processing of GCDSS, we propose CloudBWA, which is a novel distributed read mapping algorithm to leverage batch processing technique in mapping stage using Apache Spark platform.

Sequence alignment algorithms are a basic and critical component of many bioinformatics fields. With rapid development of sequencing technology, the fast growing reference database volumes and longer length of query sequence become new challenges for sequence alignment. However, the algorithm is prohibitively high in terms of time and space complexity. In this paper, we present DSA, a scalable distributed sequence alignment system that employs Spark to process sequences data in a horizontally scalable distributed environment, and leverages data parallel strategy based on Single Instruction Multiple Data (SIMD) instruction to parallelize the algorithm in each core of worker node. The experimental results demonstrate that 1) DSA has outstanding performance and achieves up to 201x speedup over SparkSW. 2) DSA has excellent scalability and achieves near linear speedup when increasing the number of nodes in cluster.