Dynamic prediction of disease processes based on recurrent history and functional principal component analysis of longitudinal biomarkers: Application for ovarian epithelial cancer.

Ovarian epithelial cancer is a gynecological tumor with a high risk of recurrence and death. In the clinical diagnosis of ovarian epithelial cancer, CA125 has become an important indicator of disease burden. To account for patient recurrence and death, a proper method is needed to integrate information from biomarkers and recurrence simultaneously. In the past 10 years, many methods have been proposed for joint modeling of longitudinal biomarkers and survival data, but few of them are applicable to longitudinal data and disease processes, including recurrence and death. In this article, we proposed a new joint frailty model based on functional principal component analysis for dynamic prediction of survival probabilities on the total time scale, which took recurrent history and longitudinal data into account simultaneously. The estimation of the joint frailty model is achieved by maximizing the penalized log-likelihood function. The simulation results demonstrated the advantages of our method in both discrimination and accuracy under different scenarios. To indicate the method's practicality, it is applied to an actual dataset of patients with ovarian epithelial cancer to predict survival dynamically using longitudinal data of biomarker CA125 and recurrent history data.

A Type of Annulus-Based Energy Balanced Data Collection Method in Wireless Rechargeable Sensor Networks.

With the increasing number of ubiquitous terminals and the continuous expansion of network scale, the problem of unbalanced energy consumption in sensor networks has become increasingly prominent in recent years. However, a node scheduling strategy or an energy consumption optimization algorithm may be not enough to meet the requirements of large-scale application. To address this problem a type of Annulus-based Energy Balanced Data Collection (AEBDC) method is proposed in this paper. The circular network is divided into several annular sectors of different sizes. Nodes in the same annulus-sector form a cluster. Based on this model, a multi-hop data forwarding strategy with the help of the candidate cluster headers is proposed to balance energy consumption during transmission and to avoid buffer overflow. Meanwhile, in each annulus, there is a Wireless Charging Vehicle (WCV) that is responsible for periodically recharging the cluster headers as well as the candidate cluster headers. By minimizing the recharging cost, the energy efficiency is enhanced. Simulation results show that AEBDC can not only alleviate the "energy hole problem" in sensor networks, but also effectively prolong the network lifetime.

Development of a Phage-Displayed Nanobody-Based Competitive Immunoassay for the Sensitive Detection of Soybean Agglutinin.

Soybean agglutinin (SBA) is a primary antinutritional factor in soybeans that can inhibit the growth of humans and mammals, disrupt the intestinal environment, and cause pathological changes. Therefore, detecting and monitoring SBA in foods is essential for safeguarding human health. In this paper, M13 phage-displayed nanobodies against SBA were isolated from a naive nanobody library. An M13 phage-displayed nanobody-based competitive enzyme-linked immunosorbent assay (P-cELISA) was then established for SBA analysis using biotinylated anti-M13 phage antibody (biotin-anti-M13) and streptavidin poly-HRP conjugate (SA-poly-HRP). The biotin-anti-M13@SA-poly-HRP probe can easily amplify the detection signal without the chemical modifications of phage-displayed nanobodies. The established P-cELISA presented a linear detection range of 0.56-250.23 ng/mL and a limit of detection (LOD) of 0.20 ng/mL, which was 12.6-fold more sensitive than the traditional phage-ELISA. Moreover, the developed method showed good specificity for SBA and acceptable recoveries (78.21-121.11%) in spiked wheat flour, albumen powder, and whole milk powder. This study proposes that P-cELISA based on biotin-anti-M13@SA-poly-HRP may provide a convenient and effective strategy for the sensitive detection of SBA.

Redistribution of defective mitochondria-mediated dihydroorotate dehydrogenase imparts 5-fluorouracil resistance in colorectal cancer.

Although 5-fluorouracil (5-FU) is the primary chemotherapy treatment for colorectal cancer (CRC), its efficacy is limited by drug resistance. Ferroptosis activation is a promising treatment for 5-FU-resistant cancer cells; however, potential therapeutic targets remain elusive. This study investigated ferroptosis vulnerability and dihydroorotate dehydrogenase (DHODH) activity using stable, 5-FU-resistant CRC cell lines and xenograft models. Ferroptosis was characterized by measuring malondialdehyde levels, assessing lipid metabolism and peroxidation, and using mitochondrial imaging and assays. DHODH function is investigated through gene knockdown experiments, tumor behavior assays, mitochondrial import reactions, intramitochondrial localization, enzymatic activity analyses, and metabolomics assessments. Intracellular lipid accumulation and mitochondrial DHODH deficiency led to lipid peroxidation overload, weakening the defense system of 5-FU-resistant CRC cells against ferroptosis. DHODH, primarily located within the inner mitochondrial membrane, played a crucial role in driving intracellular pyrimidine biosynthesis and was redistributed to the cytosol in 5-FU-resistant CRC cells. Cytosolic DHODH, like its mitochondrial counterpart, exhibited dihydroorotate catalytic activity and participated in pyrimidine biosynthesis. This amplified intracellular pyrimidine pools, thereby impeding the efficacy of 5-FU treatment through molecular competition. These findings contribute to the understanding of 5-FU resistance mechanisms and suggest that ferroptosis and DHODH are promising therapeutic targets for patients with CRC exhibiting resistance to 5-FU.