Treatment Patterns and Prognosis of Palliative Chemotherapy Combined With Targeting Agents in Patients With Unresectable Metastatic Colorectal Cancer: CHOICE, A Multicenter Longitudinal Observational Study.

BACKGROUND/AIM: This study investigated the treatment patterns and prognosis of patients with metastatic or unresectable colorectal cancer (mCRC) treated with chemotherapy with targeting agents. PATIENTS AND METHODS: This longitudinal multicenter study included 963 patients with mCRC who were treated in Korea between 2016 and 2020. Treatment patterns and efficacy were compared according to the mutation status and clinical factors. RESULTS: As first-line therapy, most of the patients (83.5%) received FOLFOX plus bevacizumab (35.4%), followed by FOLFIRI plus bevacizumab (18.8%), FOLFIRI plus cetuximab (17.0%), and FOLFOX plus cetuximab (12.3%). Bevacizumab was the most frequent agent (78.8%) combined with chemotherapy in RAS-mutated CRC, while cetuximab (57.2%) in RAS wild-type CRC. Cetuximab was frequently combined with a doublet regimen in patients with left-sided CRC than in those with right-sided CRC (34.4% vs. 16%). As second-line therapy, most patients (63.4%) also received doublet regimens with bevacizumab, and FOLFIRI plus aflibercept was administered in 15.1%. The objective response rate with FOLFIRI plus cetuximab was significantly higher in patients with left-sided CRC than in those with right-sided CRC (59.2% vs. 30.8%, p=0.008) and marginally higher in patients with RAS wild-type CRC than in those with RAS-mutated CRC (55.6% vs. 0.0%, p=0.092). Progression-free survival (PFS) with FOLFOX plus bevacizumab was significantly shorter than that with FOLFIRI plus bevacizumab (p=0.030) in RAS-mutated CRC, whereas there were no significant differences between regimens in RAS wild-type CRC. CONCLUSION: In patients with unresectable metastatic colorectal cancer, doublet chemotherapy with targeting agents is the most common therapy and efficacy depends on the mutation status as well as clinical factors.

Comparative RNA-Seq Analysis Revealed Tissue-Specific Splicing Variations during the Generation of the PDX Model.

Tissue-specific gene expression generates fundamental differences in the function of each tissue and affects the characteristics of the tumors that are created as a result. However, it is unclear how much the tissue specificity is conserved during grafting of the primary tumor into an immune-compromised mouse model. Here, we performed a comparative RNA-seq analysis of four different primary-patient derived xenograft (PDX) tumors. The analysis revealed a conserved RNA biotype distribution of primary-PDX pairs, as revealed by previous works. Interestingly, we detected significant changes in the splicing pattern of PDX, which was mainly comprised of skipped exons. This was confirmed by splicing variant-specific RT-PCR analysis. On the other hand, the correlation analysis for the tissue-specific genes indicated overall strong positive correlations between the primary and PDX tumor pairs, with the exception of gastric cancer cases, which showed an inverse correlation. These data propose a tissue-specific change in splicing events during PDX formation as a variable factor that affects primary-PDX integrity.

Reformulation of frequency-difference matched-field processor for high-frequency known-source localization.

Frequency-difference matched-field processing is a high-frequency source localization technique formulated by matching the frequency-difference autoproduct of the measured field and replicas at the difference-frequency. Although it successfully localizes sound sources by sparse vertical array in shallow or deep ocean with an environmental mismatch, there is still some ambiguity in replica modeling and signal processing. Here, the existing conventional processor is modified to match the bandwidth-averaged autoproduct of the measured field with replicas of the bandwidth-averaged autoproduct, or approximately its self-term for the expected source locations. The proposed processor is consistent with the perspective of matched-field processing and can naturally relieve some drawbacks of the existing one, such as low peak or low dynamic range on the ambiguity surface. Numerical tests are carried out in several shallow ocean environments and the source localization using experimental data are performed to confirm the properties of the proposed processor. It is found that the high-frequency diffracted field always leaves traces on its bandwidth-averaged autoproduct field. These high-frequency marks cause a bias in source localization in the presence of a sound speed mismatch even in low difference-frequencies.

Active SMA Fibers' Effect on the Pullout Behavior of a Steel Bar Embedded in Concrete.

This study investigated the bond behavior and radial crack between concrete and reinforcing bars using cold-drawn shape memory alloy (SMA) crimped fibers controlled by the temperature and volume fraction of the fibers. In this novel approach, the concrete specimens containing cold-drawn SMA crimped fibers with 1.0% and 1.5% volume fractions of cold-drawn SMA fibers were prepared. After that, the specimens were heated to 150 °C to generate recovery stress and activate prestressing within the concrete. The bond strength of specimens was estimated by pullout test using the universal testing machine (UTM). Furthermore, the cracking patterns were investigated using radial strain measured by a circumferential extensometer. The results showed that adding up to 1.5% of SMA fibers improved the bond strength by 47.9% and reduced radial strain by more than 54%. Thus, heating specimens containing SMA fibers showed improved bond behavior compared with non-heated specimens with the same volume fraction.

Predicting Long-Term Deformation of Soundproofing Resilient Materials Subjected to Compressive Loading: Machine Learning Approach.

Soundproofing materials are widely used within structural components of multi-dwelling residential buildings to alleviate neighborhood noise problems. One of the critical mechanical properties for the soundproofing materials to ensure its appropriate structural and soundproofing performance is the long-term compressive deformation under the service loading conditions. The test method in the current test specifications only evaluates resilient materials for a limited period (90-day). It then extrapolates the test results using a polynomial function to predict the long-term compressive deformation. However, the extrapolation is universally applied to materials without considering the level of loads; thus, the calculated deformation may not accurately represent the actual compressive deformation of the materials. In this regard, long-term compressive deformation tests were performed on the selected soundproofing resilient materials (i.e., polystyrene, polyethylene, and ethylene-vinyl acetate). Four levels of loads were chosen to apply compressive loads up to 350 to 500 days continuously, and the deformations of the test specimens were periodically monitored. Then, three machine learning algorithms were used to predict long-term compressive deformations. The predictions based on machine learning and ISO 20392 method are compared with experimental test results, and the accuracy of machine learning algorithms and ISO 20392 method are discussed.

Prognostic Value of CD200R1 mRNA Expression in Head and Neck Squamous Cell Carcinoma.

Immune system dysfunction is associated with head and neck squamous cell carcinoma (HNSCC) development and progression and immune checkpoint inhibitors have demonstrated substantial survival benefits in platinum-refractory HNSCC; therefore, we examined the prognostic value of immune-related gene (IRG) expression in HNSCC. We analyzed the expression of 82 IRGs in 71 patients with HNSCC enrolled in a feasibility study for a prospective HNSCC biomarker-driven umbrella trial (Korean Cancer Study Group TRIUMPH study, NCT03292250). CD200R1 was identified as an independent prognostic factor and validated in GEO and TCGA database. CD2000R1 mRNA expression was found to be an independent favorable prognostic factor in patients with HNSCC. Moreover, CD200R1 was found to affect genes and pathways associated with the immune response, while seven differentially expressed genes (CD8A, DOK2, CX3CR1, TYROBP, CXCL9, CD300LF, IFNG) were associated with CD200R1 expression. Samples with higher CD200R1 expression displayed higher tumor-infiltrating immune cell counts both in silico and in histological analysis. These findings will help in the development of more accurate prognostic tools and suggest CD200R1 modulation as a HNSCC immunotherapy.

Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning.

Four data-driven methods-random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)-are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data-the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.

Probabilistic Analysis for Strain-Hardening Behavior of High-Performance Fiber-Reinforced Concrete.

The practical application of fiber-reinforced concrete (FRC) in structural components has gained growing interest due to structural advantages such as improved tensile strength, distributed load transfer, crack width control, as well as superior durability. To this end, reliable structural assessment techniques and analytical models have been developed, placing emphasis on tension-softening behavior owing to the bond and pull-out mechanisms of fibers at a local crack. However, these models could not be directly applicable to evaluate the multiple cracking mechanisms of high-performance fiber-reinforced concrete (HPFRC), which exhibits strain-hardening behavior. To overcome this challenge, this paper presents a probabilistic analytical technique. This approach has employed the simplified diverse embedment model (SDEM). Then, an HPFRC member was modeled with multiple segments considering the most probable number of cracks. It was assumed that material properties had a normal probability distribution and were randomly assigned to each segment. To have reliable results, 10,000 analyses were performed for each analysis case and validated using experimental test data. Based on the analysis results, the actual strain-hardening tensile behavior of an HPFRC member could be reasonably predicted with the number of segments chosen on the basis of the fiber length.