Matched Pair Research Articles

Detection and Imaging of Corrosion Defects in Steel Structures Based on Ultrasonic Digital Image Processing

Corrosion is one of the critical factors leading to the failure of steel structures. Ultrasonic C-scans are widely used to identify corrosion damage. Limited by the range of C-scans, multiple C-scans are usually required to cover the whole component. Thus, stitching multiple C-scans into a panoramic image of the area under detection is necessary for interpreting non-destructive testing (NDT) data. In this paper, an image mosaic method for ultrasonic C-scan based on scale invariant feature transform (SIFT) is proposed. Firstly, to improve the success rate of registration, the difference in the probe starting position in two scans is used to filter the matching pairs of feature points obtained by SIFT. Secondly, dynamic programming methods are used to search for the optimal seam path. Finally, the pixels in the overlapping area are fused by fade-in and fade-out fusion along the seam line. The improved method has a higher success rate of registration and lower image distortion than the conventional method in the mosaic of ultrasonic C-scan images. Experimental results show that the proposed method can stitch multiple C-scan images of a testing block containing artificial defects into a panorama image effectively.

Adaptive Uncertainty-Based Learning for Text-Based Person Retrieval

Text-based person retrieval aims at retrieving a specific pedestrian image from a gallery based on textual descriptions. The primary challenge is how to overcome the inherent heterogeneous modality gap in the situation of significant intra-class variation and minimal inter-class variation. Existing approaches commonly employ vision-language pre-training or attention mechanisms to learn appropriate cross-modal alignments from noise inputs. Despite commendable progress, current methods inevitably suffer from two defects: 1) Matching ambiguity, which mainly derives from unreliable matching pairs; 2) One-sided cross-modal alignments, stemming from the absence of exploring one-to-many correspondence, i.e., coarse-grained semantic alignment. These critical issues significantly deteriorate retrieval performance. To this end, we propose a novel framework termed Adaptive Uncertainty-based Learning (AUL) for text-based person retrieval from the uncertainty perspective. Specifically, our AUL framework consists of three key components: 1) Uncertainty-aware Matching Filtration that leverages Subjective Logic to effectively mitigate the disturbance of unreliable matching pairs and select high-confidence cross-modal matches for training; 2) Uncertainty-based Alignment Refinement, which not only simulates coarse-grained alignments by constructing uncertainty representations but also performs progressive learning to incorporate coarse- and fine-grained alignments properly; 3) Cross-modal Masked Modeling that aims at exploring more comprehensive relations between vision and language. Extensive experiments demonstrate that our AUL method consistently achieves state-of-the-art performance on three benchmark datasets in supervised, weakly supervised, and domain generalization settings. Our code is available at https://github.com/CFM-MSG/Code-AUL.

Abstract 5482: Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment

Abstract Glioblastoma remains as one of the deadliest malignancies with most cases likely to recur. Recent work has focused on characterizing not only glioma cells, but the tumor microenvironment via single-cell and spatial transcriptomic technologies. This has improved our understanding of the high heterogeneity of this disease and described potential tumor domains. However, there is still a need to further understand the changes that occur between the primary and the recurrent lesion. We propose that a spatial, multi-omic characterization of matched primary and recurrent glioblastoma can yield insights into recurrence mechanisms occurring throughout the tumor microenvironment. We collected 22 IDH-WT glioblastoma samples from 12 patients (11 primary, 11 recurrent, 10 matched pairs) from the Mayo Clinic Neuroscience Biobank. We performed single-nucleus RNA sequencing (15 samples, 10 patients, 5 matched pairs) and proteomics (16 samples, 6 matched pairs) from fresh-frozen tissue, as well as spatial transcriptomics with 10x Visium (14 samples, 8 patients, 6 matched pairs) and 10x Xenium in-situ molecular imaging (6 samples, 2 matched pairs). Using spatial transcriptomics data, we identified regional subsets present in different proportions and distributions between primary and recurrent glioblastoma. These clusters were characterized with gene set and gene ontology analysis, identifying regions enriched with known glioma subtypes and cellular activity. Spatial-based cluster genes were consistent with significantly differentially expressed proteins. We used single-nucleus RNAseq and in-situ molecular imaging data from matched tissue samples to further interrogate relevant cell populations and location in the tumor micro-environment. We identified regions with enriched extra cellular matrix interaction and remodeling signatures to be highly expressed in recurrent glioblastoma. Primary glioblastoma was enriched with oligodendrocyte-like regions with high cell communication and extracellular vesicle signatures. This study will further our understanding of the changes in the mechanisms at play in the complex ecosystem of glioblastoma as it recurs. Citation Format: Jean R. Clemenceau, Paola Suarez-Meade, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5482.

The hardness of recognising poorly matchable graphs and the hunting of the d-snark

Abstract. An r-graph is an r-regular graph G on an even number of vertices where every odd set X ⊆V(G) is connected by at least r edges to its complement V(G) \ X. Every r-graph has a perfect matching and in a poorly matchable r-graph every pair of perfect matchings intersect, which implies that poorly matchable r-graphs are not r-edge-colourable. We prove, for each fixed r ≥ 3, that poorly matchable r-graph recognition is coNP-complete, an indication that, for each odd d ≥ 3, it may be a hard problem to recognise d-regular (d−1)-edge-connected non-d-edge-colourable graphs, referred to as d-snarks in this paper. We show how to construct, for every fixed odd d ≥ 5, an infinite family of d-snarks. These families provide a natural extension to the well-known Loupekine snarks. We also discuss how the hunting of the smallest d-snarks may help in strengthening and better understanding the major Overfull Conjecture on edge-colouring simple graphs.

Diverse Pairs of Matchings

AbstractWe initiate the study of the Diverse Pair of (Maximum/ Perfect) Matchings problems which given a graph G and an integer k, ask whether G has two (maximum/perfect) matchings whose symmetric difference is at least k. Diverse Pair of Matchings (asking for two not necessarily maximum or perfect matchings) is $$\textsf{NP}$$ NP -complete on general graphs if k is part of the input, and we consider two restricted variants. First, we show that on bipartite graphs, the problem is polynomial-time solvable, and second we show that Diverse Pair of Maximum Matchings is $$\textsf{FPT}$$ FPT parameterized by k. We round off the work by showing that Diverse Pair of Matchings has a kernel on $${\mathcal {O}}(k^2)$$ O ( k 2 ) vertices.

Binocular Visual Measurement Method Based on Feature Matching.

To address the issues of low measurement accuracy and unstable results when using binocular cameras to detect objects with sparse surface textures, weak surface textures, occluded surfaces, low-contrast surfaces, and surfaces with intense lighting variations, a three-dimensional measurement method based on an improved feature matching algorithm is proposed. Initially, features are extracted from the left and right images obtained by the binocular camera. The extracted feature points serve as seed points, and a one-dimensional search space is established accurately based on the disparity continuity and epipolar constraints. The optimal search range and seed point quantity are obtained using the particle swarm optimization algorithm. The zero-mean normalized cross-correlation coefficient is employed as a similarity measure function for region growing. Subsequently, the left and right images are matched based on the grayscale information of the feature regions, and seed point matching is performed within each matching region. Finally, the obtained matching pairs are used to calculate the three-dimensional information of the target object using the triangulation formula. The proposed algorithm significantly enhances matching accuracy while reducing algorithm complexity. Experimental results on the Middlebury dataset show an average relative error of 0.75% and an average measurement time of 0.82 s. The error matching rate of the proposed image matching algorithm is 2.02%, and the PSNR is 34 dB. The algorithm improves the measurement accuracy for objects with sparse or weak textures, demonstrating robustness against brightness variations and noise interference.

Transarterial chemoembolization with/without immune checkpoint inhibitors plus tyrosine kinase inhibitors for unresectable hepatocellular carcinoma: a single center, propensity score matching real-world study

ObjectivesTo explore the efficacy and safety of Transarterial chemoembolization (TACE) in combination with immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) in patients with unresectable hepatocellular carcinoma (uHCC).Methods456 patients with HCC receiving either TACE in combination with ICIs and TKIs (combination group, n = 139) or TACE monotherapy (monotherapy group, n = 317) were included from Apr 2016 to Dec 2021 in this retrospective study. We employed propensity score matching (PSM), performed 1:2 optimal pair matching, to balance potential bias.ResultsThe mean follow-up time is 24.7 months (95% CI 22.6–26.8) for matched patients as of March 2022. After matching, the combination group achieved longer OS and PFS (median OS:21.9 vs. 16.3 months, P = 0.022; median PFS: 8.3 vs. 5.1 months, P < 0.0001) than TACE monotherapy group. The combination group had better objective response rate (ORR) and disease control rate (DCR) (ORR: 52.5% vs. 32.8%, P < 0.001; DCR: 82.7% vs. 59.6%, P < 0.001). Subgroup analysis showed that patients who received “TKIs + ICIs” after the first TACE procedure (after TACE group) achieved longer OS than those before the first TACE procedure (before TACE group) (26.8 vs. 19.2 months, P = 0.011). Adverse events were consistent with previous studies of TACE-related trials.ConclusionsTACE plus TKIs and ICIs appeared to deliver longer PFS and OS in HCC patients than TACE monotherapy. “TKIs + ICIs” co-treatment within 3 months after the first TACE procedure might be a better medication strategy.

Cancelable templates for secure face verification based on deep learning and random projections

Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.

The Impact of Intracranial Pressure Telesensors: An Observational Propensity-Matched Control Analysis of Service Demand and Costs.

Implantable telemetric intracranial pressure (ICP) sensors (telesensors) enable routine, noninvasive ICP feedback, aiding clinical decision-making and attribution of pressure-related symptoms in patients with cerebrospinal fluid shunt systems. Here, we aim to explore the impact of these devices on service demand and costs in patients with adult hydrocephalus. We performed an observational propensity-matched control study, comparing patients who had an MScio/Sensor Reservoir (Christoph Miethke, GmbH & Co) against those with a nontelemetric reservoir inserted between March 2016 and March 2018. Patients were matched on demographics, diagnosis, shunt-type, and revision status. Service usage was recorded with frequencies of neurosurgical admissions, outpatient clinics, scans, and further surgical procedures in the 2 years before and after shunt insertion. In total, 136 patients, 73 telesensors, and 63 controls were included in this study (48 matched pairs). Telesensor use led to a significant decrease in neurosurgical inpatient admissions, radiographic encounters, and procedures including ICP monitoring. After multivariate adjustment, the mean cumulative saving after 2 years was £5236 ($6338) in telesensor patients (£5498 on matched pair analysis). On break-even analysis, cost-savings were likely to be achieved within 8 months of clinical use, postimplantation. Telesensor patients also experienced a significant reduction in imaging-associated radiation (4 mSv) over 2 years. The findings of this exploratory study reveal that telesensor implantation is associated with reduced service demand and provides net financial savings from an institutional perspective. Moreover, telesensor patients required fewer appointments, invasive procedures, and had less radiation exposure, indicating an improvement in both their experience and safety.

Extinct and extant termites reveal the fidelity of behavior fossilization in amber

Fossils encompassing multiple individuals provide rare direct evidence of behavioral interactions among extinct organisms. However, the fossilization process can alter the spatial relationship between individuals and hinder behavioral reconstruction. Here, we report a Baltic amber inclusion preserving a female-male pair of the extinct termite species Electrotermes affinis. The head-to-abdomen contact in the fossilized pair resembles the tandem courtship behavior of extant termites, although their parallel body alignment differs from the linear alignment typical of tandem runs. To solve this inconsistency, we simulated the first stage of amber formation, the immobilization of captured organisms, by exposing living termite tandems to sticky surfaces. We found that the posture of the fossilized pair matches trapped tandems and differs from untrapped tandems. Thus, the fossilized pair likely is a tandem running pair, representing the direct evidence of the mating behavior of extinct termites. Furthermore, by comparing the postures of partners on a sticky surface and in the amber inclusion, we estimated that the male likely performed the leader role in the fossilized tandem. Our results demonstrate that past behavioral interactions can be reconstructed despite the spatial distortion of body poses during fossilization. Our taphonomic approach demonstrates how certain behaviors can be inferred from fossil occurrences.

Extrinsic calibration for large FOV based on inverse depth parameterized bundle adjustment

To overcome the reliance on high-precision calibration plates in camera calibration, this paper proposes an extrinsic calibration method suitable for three-dimensional digital image correlation (3D-DIC) in large field of view (FOV). The method combines image feature algorithms with DIC techniques to extract matching point pairs (MPPs) from the left and right images of binocular cameras. These MPPs are then homogenized within the FOV. Next, initial values of the extrinsic parameters are solved based on epipolar constraint theory. Finally, the calibration parameters are nonlinearly optimized using the bundle adjustment method. To achieve stable and reliable numerical optimization in large FOV applications, the calibration control points (CCPs)’ spatial coordinates are represented using inverse depth parameterization. In scenarios where there might be a lack of sufficient CCPs, speckle patterns are artificially introduced to supplement the scene features. However, there is a lack of reliable experimental basis on how to add CCPs within the FOV. Therefore, through simulations, the factors affecting calibration accuracy are analyzed to guide the actual calibration process. The proposed method’s reliability and accuracy in large FOV 3D measurement are verified through experiments.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi etal. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modelingframework.

Comparison of mutation landscapes of pretreatment versus recurrent squamous cell carcinoma of the oral cavity: The possible mechanism of resistance to standard treatment.

A high recurrent rate of oral squamous cell carcinoma (OSCC) is a major concern in head and neck cancer treatment. The study of the genetic mutation landscape in recurrent OSCC may provide information on certain mutations associated with the pathobiology and treatment response of the OSCC. We investigated the mutation landscape of matched pretreatment and recurrent tumors to understand the influence of genetic mutations on the pathobiology and clinical outcomes in OSCC. We sequenced 33 formalin-fixed paraffin-embedded (FFPE) recurrent tumors, primary tumors, and primary tumors before recurrence that matched the recurrent tumors collected from Rajavithi Hospital during 2019-2021. We identified recurrent mutations from these samples by the Oncomine Ion Torrent-based next-generation sequencing on the 517 cancer-associated gene panel. From the results, we found that the most commonly mutated gene in the cohort is a histone methyltransferase KMT2D (54.55%), implicating that aberrance in epigenetic regulation may play a role in oral cancer tumorigenesis. Functional protein association network analysis of the gene frequently mutated in the recurrent tumors showed enrichment of genes that regulate the cancer cell cycle, that is, MRE11A, CDKN2A, and CYLD. This finding was confirmed in the primary-recurring matched pair. We found that recurrent tumors possess a small but recurring group of genes, with presumably the subclonal mutations driving the recurrence of the tumor, suggesting that the recurrent disease originated from a small fraction of the cancer cell that survives standard treatment. These genes were absent in the primary tumor with a good response to the standard treatment. On the other hand, we found an enrichment of DNA repair genes, namely ATR, BRCA1, BRCA2, RAD50, and MUTYH, in nonrecurrent tumors suggesting that the mutations in the DNA repair pathway may at least partially explain the different response to the standard treatment. Our pilot study identified pathways of carcinogenesis in oral cancer and specific gene sets that indicate treatment responses and prognoses in this group of patients.

FDG imaging with long-axial field-of-view PET/CT in patients with high blood glucose—a matched pair analysis

PurposeHigh blood glucose (hBG) in patients undergoing [18F]FDG PET/CT scans often results in rescheduling the examination, which may lead to clinical delay for the patient and decrease productivity for the department. The aim of this study was to evaluate whether long-axial field-of-view (LAFOV) PET/CT can minimize the effect of altered bio-distribution in hBG patients and is able to provide diagnostic image quality in hBG situations.Materials and methodsOncologic patients with elevated blood glucose (≥ 8.0 mmol/l) and normal blood glucose (< 8.0 mmol/l, nBG) levels were matched for tumor entity, gender, age, and BMI. hBG patients were further subdivided into two groups (BG 8–11 mmol/l and BG > 11 mmol/l). Tracer uptake in the liver, muscle, and tumor was evaluated. Furthermore, image quality was compared between long acquisitions (ultra-high sensitivity mode, 360 s) on a LAFOV PET/CT and routine acquisitions equivalent to a short-axial field-of-view scanner (simulated (sSAFOV), obtained with high sensitivity mode, 120 s). Tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) were used as the main image quality criteria.ResultsThirty-one hBG patients met the inclusion criteria and were matched with 31 nBG patients. Overall, liver uptake was significantly higher in hBG patients (SUVmean, 3.07 ± 0.41 vs. 2.37 ± 0.33; p = 0.03), and brain uptake was significantly lower (SUVmax, 7.58 ± 0.74 vs. 13.38 ± 3.94; p < 0.001), whereas muscle (shoulder/gluteal) uptake showed no statistically significant difference. Tumor uptake was lower in hBG patients, resulting in a significantly lower TBR in the hBG cohort (3.48 ± 0.74 vs. 5.29 ± 1.48, p < 0.001). CNR was higher in nBG compared to hBG patients (12.17 ± 4.86 vs. 23.31 ± 12.22, p < 0.001). However, subgroup analysis of nBG 8–11 mmol/l on sSAFOV PET/CT compared to hBG (> 11 mmol/l) patients examined with LAFOV PET/CT showed no statistical significant difference in CNR (19.84 ± 8.40 vs. 17.79 ± 9.3, p = 0.08).ConclusionWhile elevated blood glucose (> 11 mmol) negatively affected TBR and CNR in our cohort, the images from a LAFOV PET-scanner had comparable CNR to PET-images acquired from nBG patients using sSAFOV PET/CT. Therefore, we argue that oncologic patients with increased blood sugar levels might be imaged safely with LAFOV PET/CT when rescheduling is not feasible.

Efficient 2D DOA Estimation via Decoupled Projected Atomic Norm Minimization

This paper presents an efficient two-dimensional (2D) direction of arrival (DOA) estimation method, termed as decoupled projected atomic norm minimization (D-PANM), to solve the angle-ambiguity problem. It first introduces a novel atomic metric via projecting the original atom set onto a smoothing space, based on which we formulate an equivalent semi-definite programming (SDP) problem. Then, two relatively low-complexity decoupled Toeplitz matrices can be obtained to estimate the DOAs. We further exploit the structural information hidden in the newly constructed data to avoid pair matching for the azimuth and elevation angles when the number of sensors is odd, and then propose a fast and feasible decoupled alternating projections (D-AP) algorithm, reducing computational complexity to a great extent. Numerical simulations are performed to demonstrate that the proposed algorithm is no longer restricted by angle ambiguity scenarios, but instead provides a more stable estimation performance, even when multiple signals share the same angles in both azimuth and elevation dimensions. Additionally, it greatly improves the resolution, with control of the computation load compared with the existing atomic norm minimization (ANM) algorithm.

A domain-label-guided translation model for molecular optimization

Molecular optimization, which aims to improve molecular properties by modifying complex molecular structures, is a crucial and challenging task in drug discovery. In recent years, translation models provide a promising way to transform low-property molecules to high-property molecules, which enables molecular optimization to achieve remarkable progress. However, most existing models require matched molecular pairs, which are prone to be limited by the datasets. Although some models do not require matched molecular pairs, their performance is usually sacrificed due to the lack of useful supervising information. To address this issue, a domain-label-guided translation model is proposed in this paper, namely DLTM. In the model, the domain label information of molecules is exploited as a control condition to obtain different embedding representations, enabling the model to generate diverse molecules. Besides, the model adopts a classifier network to identify the property categories of transformed molecules, guiding the model to generate molecules with desired properties. The performance of DLTM is verified on two optimization tasks, namely the quantitative estimation of drug-likeness and penalized logP. Experimental results show that the proposed DLTM is superior to the compared baseline models.

A robust method for multi-view 3D data stitching based on pasted marked points

High-precision three-dimensional (3D) measurement techniques commonly perform 3D point cloud data stitching using pasted marked points. However, pasted marked points exhibit a high degree of randomness and are susceptible to mismatching due to their structural similarity. Therefore, this paper presents a robust method for multi-view 3D data stitching based on pasted marked points (RMSM). First, potential matched marked point pairs (MMPP) are identified according to spatial feature invariance constraints, and a matching strength calculation model is established to find the fiducial point pairs. Then, a quick public MMPP searching algorithm using the congruent triangle search method is proposed. Thus, the problem of mismatching caused by structurally similar marked points can be eliminated, and robust stitching of 3D data from different views can be achieved. In simulation and measurement experiments, the RMSM demonstrates superior accuracy compared to alternative methods and holds promise for application in various multi-view 3D data stitching methodologies.

PPARG activation promotes the proliferation of colorectal cancer cell lines and enhances the antiproliferative effect of 5-fluorouracil

BackgroundPeroxisome proliferator-activated receptor gamma (PPARG) is a member of the nuclear receptor family. It is involved in the regulation of adipogenesis, lipid metabolism, insulin sensitivity, vascular homeostasis and inflammation. In addition, PPARG agonists, known as thiazolidinediones, are well established in the treatment of type 2 diabetes mellitus. PPARGs role in cancer is a matter of debate, as pro- and anti-tumour properties have been described in various tumour entities. Currently, the specific role of PPARG in patients with colorectal cancer (CRC) is not fully understood.Material and methodsThe prognostic impact of PPARG expression was investigated by immunohistochemistry in a case-control study using a matched pair selection of CRC tumours (n = 246) with either distant metastases to the liver (n = 82), lung (n = 82) or without distant metastases (n = 82). Its effect on proliferation as well as the sensitivity to the chemotherapeutic drug 5-fluorouracil (5-FU) was examined after activation, inhibition, and transient gene knockdown of PPARG in the CRC cell lines SW403 and HT29.ResultsHigh PPARG expression was significantly associated with pulmonary metastasis (p = 0.019). Patients without distant metastases had a significantly longer overall survival with low PPARG expression in their tumours compared to patients with high PPARG expression (p = 0.045). In the pulmonary metastasis cohort instead, a trend towards longer survival was observed for patients with high PPARG expression in their tumour (p = 0.059). Activation of PPARG by pioglitazone and rosiglitazone resulted in a significant dose-dependent increase in proliferation of CRC cell lines. Inhibition of PPARG by its specific inhibitor GW9662 and siRNA-mediated knockdown of PPARG significantly decreased proliferation. Activating PPARG significantly increased the CRC cell lines sensitivity to 5-FU while its inhibition decreased it.ConclusionThe prognostic effect of PPARG expression depends on the metastasis localization in advanced CRC patients. Activation of PPARG increased malignancy associated traits such as proliferation in CRC cell lines but also increases sensitivity towards the chemotherapeutic agent 5-FU. Based on this finding, a combination therapy of PPARG agonists and 5-FU-based chemotherapy constitutes a promising strategy which should be further investigated.

Primary Sjögren's syndrome in Italy: Real-world evidence of a rare disease through administrative healthcare data

ObjectivesPrimary Sjögren's syndrome (pSS) is a systemic autoimmune disease with significant impact on morbidity, mortality, and quality of life. This study aimed to evaluate epidemiology, healthcare needs and related costs of pSS patients from the Italian National Health Service perspective. MethodsFrom the Fondazione Ricerca e Salute's database (∼5 million inhabitants/year), pSS prevalence in 2018 was calculated. Demographics, mean healthcare consumptions and direct costs at one year following index date (first in-hospital diagnosis/disease waiver claim) were analysed through an individual direct matched pair case-control analysis (age, sex, residency). ResultsIn Italy, 3.8/10,000 inhabitants were identified as affected by pSS (1,746 case: 1,746 controls) in 2018. In the year following index date, 53.7% of cases and 42.7% of controls received ≥1 drug (p<0.001); mean per capita cost was €501 and €161, respectively (p<0.01). At least one hospitalization occurred to 7.8% of cases and 3.9% of controls (p<0.001) with mean per capita costs of €416 and €129, respectively (p = 0.46). At least one outpatient specialist service was performed in 49.8% of cases and 30.6% of controls (p<0.001); mean per capita costs were €200 and €75, respectively (p<0.01). Overall, mean annual costs were €1,171 per case and €372 per control (p < 0.01). ConclusionAccording to results of this population-based study, the prevalence of pSS in Italy appears to be consistent with the definition of rare disease. Patients with pSS have higher pharmacological, in-hospital and outpatient specialist care needs, leading to three-times higher overall cost for the INHS, compared to the general population.

A numerical study attitude of compatibility in scintillator detectors through matching pair scintillator-photodiode materials

In a group of scintillator detectors where photodiodes have replaced photomultiplier tubes, one way to increase the efficiency of the device is proper matching between the radiation spectrum of the scintillator and the spectral sensitivity of the photodiode. This study seeks to find the most compatible pair of scintillators with photodiodes using the simulation of photodiodes in COMSOL Multiphysics. In this research, three scintillators NaI(Tl), CsI(Tl), and Bi4Ge3O12 (BGO), along with three photodiodes silicon (Si) and gallium arsenide (GaAs) and indium phosphide (InP), have been studied to find the best compatible pair. Among the selected scintillators and photodiodes, the pair of Si with BGO produced the highest output current. Generating more current in a detector for constant input radiation means more sensitivity of that detector to the incident beam. In another part of this research, using the simulated photodiode in COMSOL Multiphysics, the current generated in the photodiode has been obtained for a fixed value of the ratio of the length of the photodiode to the junction depth of doping for various lengths. Then, using the Origin Pro software, the best curve corresponding to the data obtained from the simulation was determined. The results showed that these points coincide with an exponential curve with an excellent approximation. Using these types of detectors in the International Linear Collider (ILC) and in the Positron Emission Tomography (PET) field shows the unique applications of detectors.