A numerical framework for interstitial fluid pressure imaging in poroelastic MRE

A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed

Single-cell transcriptome and antigen-immunoglobin analysis reveals the diversity of B cells in non-small cell lung cancer

Background Malignant transformation and progression of cancer are driven by the co-evolution of cancer cells and their dysregulated tumor microenvironment (TME). Recent studies on immunotherapy demonstrate the efficacy in reverting the anti-tumoral function of T cells, highlighting the therapeutic potential in targeting certain cell types in TME. However, the functions of other immune cell types remain largely unexplored. Results We conduct a single-cell RNA-seq analysis of cells isolated from tumor tissue samples of non-small cell lung cancer (NSCLC) patients, and identify subtypes of tumor-infiltrated B cells and their diverse functions in the progression of NSCLC. Flow cytometry and immunohistochemistry experiments on two independent cohorts confirm the co-existence of the two major subtypes of B cells, namely the naïve-like and plasma-like B cells. The naïve-like B cells are decreased in advanced NSCLC, and their lower level is associated with poor prognosis. Co-culture of isolated naïve-like B cells from NSCLC patients with two lung cancer cell lines demonstrate that the naïve-like B cells suppress the growth of lung cancer cells by secreting four factors negatively regulating the cell growth. We also demonstrate that the plasma-like B cells inhibit cancer cell growth in the early stage of NSCLC, but promote cell growth in the advanced stage of NSCLC. The roles of the plasma-like B cell produced immunoglobulins, and their interacting proteins in the progression of NSCLC are further validated by proteomics data. Conclusion Our analysis reveals versatile functions of tumor-infiltrating B cells and their potential clinical implications in NSCLC

Effectiveness of neoadjuvant immunochemotherapy compared to neoadjuvant chemotherapy in non-small cell lung cancer patients: Real-world data of a retrospective, dual-center study

BackgroundStudying the application of neoadjuvant immunochemotherapy (NICT) in the real world and evaluating its effectiveness and safety in comparison with neoadjuvant chemotherapy (NCT) are critically important.MethodsThis study included the II-IIIB stage non-small cell lung cancer (NSCLC) patients receiving NCT with or without PD-1 inhibitors and undergoing surgery after neoadjuvant treatments between January 2019 to August 2022. The clinical characteristics and treatment outcomes were retrospectively reviewed and analyzed.ResultsA total of 66 patients receiving NICT and 101 patients receiving NCT were included in this study. As compared to NCT, NICT showed similar safety while not increasing the surgical difficulty. The ORR in the NICT and NCT groups was 74.2% and 53.5%, respectively, P = 0.009. A total of 44 patients (66.7%) in the NICT group and 21 patients (20.8%) in the NCT group showed major pathology response (MPR) (P <0.001). The pathology complete response (pCR) rate was also significantly higher in NICT group than that in NCT group (45.5% vs. 10.9%, P <0.001). After Propensity Score Matching (PSM), 42 pairs of patients were included in the analysis. The results showed no significant difference in the ORR between the two groups (52.3% vs. 43.2%, P = 0.118), and the proportions of MPR (76.2%) and pCR (50.0%) in NICT group were significantly higher than those of MPR (11.9%) and pCR (4.7%) in the NCT group (P <0.001). The patients with driver mutations might also benefit from NICT.ConclusionsAs compared to NCT, the NICT could significantly increase the proportions of patients with pCR and MPR without increasing the operation-related bleeding and operation time

Length scales and pinning of interfaces

The pinning of interfaces and free discontinuities by defects and heterogeneities plays an important role in a variety of phenomena, including grain growth, martensitic phase transitions, ferroelectricity, dislocations and fracture. We explore the role of length scale on the pinning of interfaces and show that the width of the interface relative to the length scale of the heterogeneity can have a profound effect on the pinning behaviour, and ultimately on hysteresis. When the heterogeneity is large, the pinning is strong and can lead to stick–slip behaviour as predicted by various models in the literature. However, when the heterogeneity is small, we find that the interface may not be pinned in a significant manner. This shows that a potential route to making materials with low hysteresis is to introduce heterogeneities at a length scale that is small compared with the width of the phase boundary. Finally, the intermediate setting where the length scale of the heterogeneity is comparable to that of the interface width is characterized by complex interactions, thereby giving rise to a non-monotone relationship between the relative heterogeneity size and the critical depinning stress

Learning modality-invariant features for heterogeneous face recognition

This paper addresses the problem of heterogeneous face recognition where the gallery and probe face samples are captured from two different modalities. Due to large discrepancies yet weak relationships across heterogeneous face image sets, most existing face recognition algorithms usually suffer from this application scenario. To address this problem, we propose in this paper to learn modality-invariant features (MIF) for heterogeneous face recognition. In our proposed method, a pair of heterogeneous face datasets are used as generic training datasets, and the relationship between both gallery and probe samples and generic training datasets are computed as modality-invariant features for matching heterogeneous face images. The rationale of our method is motivated by the fact the local geometrical information of each pair of heterogeneous face samples are usually similar in the corresponding generic training sets. Experimental results are presented to show the efficacy of the proposed method

Co-Learned Multi-View Spectral Clustering for Face Recognition Based on Image Sets

Different from the existing approaches that usually utilize single view information of image sets to recognize persons, multi-view information of image sets is exploited in this paper, where a novel method called Co-Learned Multi-View Spectral Clustering (CMSC) is proposed to recognize faces based on image sets. In order to make sure that a data point under different views is assigned to the same cluster, we propose an objective function that optimizes the approximations of the cluster indicator vectors for each view and meanwhile maximizes the correlations among different views. Instead of using an iterative method, we relax the constraints such that the objective function can be solved immediately. Experiments are conducted to demonstrate the efficiency and accuracy of the proposed CMSC method.ASTAR (Agency for Sci., Tech. and Research, S’pore)Accepted versio