Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The ...treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.
Lung nodule classification using radiomics based on Computed Tomography (CT) image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.
Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.
The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
Manual contouring is very labor‐intensive, time‐consuming, and subject to intra‐ and inter‐observer variability. An automated deep learning approach to fast and accurate contouring and ...segmentation is desirable during radiotherapy treatment planning.
Purpose
This work investigates an efficient deep‐learning‐based segmentation algorithm in abdomen computed tomography (CT) to facilitate radiation treatment planning.
Methods
In this work, we propose a novel deep‐learning model utilizing U‐shaped multi‐layer perceptron mixer (MLP‐Mixer) and convolutional neural network (CNN) for multi‐organ segmentation in abdomen CT images. The proposed model has a similar structure to V‐net, while a proposed MLP‐Convolutional block replaces each convolutional block. The MLP‐Convolutional block consists of three components: an early convolutional block for local features extraction and feature resampling, a token‐based MLP‐Mixer layer for capturing global features with high efficiency, and a token projector for pixel‐level detail recovery. We evaluate our proposed network using: (1) an institutional dataset with 60 patient cases and (2) a public dataset (BCTV) with 30 patient cases. The network performance was quantitatively evaluated in three domains: (1) volume similarity between the ground truth contours and the network predictions using the Dice score coefficient (DSC), sensitivity, and precision; (2) surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMS); and (3) the computational complexity reported by the number of network parameters, training time, and inference time. The performance of the proposed network is compared with other state‐of‐the‐art networks.
Results
In the institutional dataset, the proposed network achieved the following volume similarity measures when averaged over all organs: DSC = 0.912, sensitivity = 0.917, precision = 0.917, average surface similarities were HD = 11.95 mm, MSD = 1.90 mm, RMS = 3.86 mm. The proposed network achieved DSC = 0.786 and HD = 9.04 mm on the public dataset. The network also shows statistically significant improvement, which is evaluated by a two‐tailed Wilcoxon Mann–Whitney U test, on right lung (MSD where the maximum p‐value is 0.001), spinal cord (sensitivity, precision, HD, RMSD where p‐value ranges from 0.001 to 0.039), and stomach (DSC where the maximum p‐value is 0.01) over all other competing networks. On the public dataset, the network report statistically significant improvement, which is shown by the Wilcoxon Mann–Whitney test, on pancreas (HD where the maximum p‐value is 0.006), left (HD where the maximum p‐value is 0.022) and right adrenal glands (DSC where the maximum p‐value is 0.026). In both datasets, the proposed method can generate contours in less than 5 s. Overall, the proposed MLP‐Vnet demonstrates comparable or better performance than competing methods with much lower memory complexity and higher speed.
Conclusions
The proposed MLP‐Vnet demonstrates superior segmentation performance, in terms of accuracy and efficiency, relative to state‐of‐the‐art methods. This reliable and efficient method demonstrates potential to streamline clinical workflows in abdominal radiotherapy, which may be especially important for online adaptive treatments.
The stereoselectivity and yield in glycosylation reactions are paramount but unpredictable. We have developed a database of acceptor nucleophilic constants (Aka) to quantify the nucleophilicity of ...hydroxyl groups in glycosylation influenced by the steric, electronic and structural effects, providing a connection between experiments and computer algorithms. The subtle reactivity differences among the hydroxyl groups on various carbohydrate molecules can be defined by Aka, which is easily accessible by a simple and convenient automation system to assure high reproducibility and accuracy. A diverse range of glycosylation donors and acceptors with well‐defined reactivity and promoters were organized and processed by the designed software program “GlycoComputer” for prediction of glycosylation reactions without involving sophisticated computational processing. The importance of Aka was further verified by random forest algorithm, and the applicability was tested by the synthesis of a Lewis A skeleton to show that the stereoselectivity and yield can be accurately estimated.
A so‐called “GlycoComputer” program has been developed to foresee and predict the yield and stereoselectivity of glycosylation reactions based on the properties of various donors, acceptors, activation systems and solvents. The program statistically analyzes and compares the relative reactivity value (RRV) of donors and the acceptor nucleophilic constant (Aka) of acceptors.
Over recent years, metabolomics has been featured as the state-of-the-art technology that successfully opens the paths to understanding biological mechanisms and facilitating biomarker discovery. ...However, the inherent dynamic and sensitive nature of the metabolome have been challenging the accuracy of capturing the timepoints of interest while using biofluids such as urine and blood. Hair has thus emerged as a valuable analytical specimen for the long-term and retrospective determinations. Unfortunately, notwithstanding the apparent interest on global hair metabolomics, very few studies have engaged in the optimisation of the extraction strategy. In this study, we systemically investigated the extraction procedures for hair metabolome using a single factor experimental design. Three pH values (acidic, neutral, and basic) in aqueous solution, six extraction solvents (methanol, acetonitrile, acetone, phosphate-buffered saline, deionised water, and dichloromethane), different compositions of selected solvent mixtures and their sequential extraction, and a series of extraction times (15, 45, 60, 120, 240, and 480 min) were evaluated. The ideal condition for hair extraction is ultrasonic-assisted extraction with methanol:phosphate-buffered saline 50:50 (v/v) under +55 °C for 240 min. This strategy may secure the true composition of the metabolome, maximise the signal abundance, and guarantee a high coverage of wide-range metabolites in a straightforward approach. The optimised extraction strategy was then coupled with structure annotation tools for hair metabolome profiling. After a single RPLC-HRMS run, hair metabolite identification was achieved as the annotations of 171 probable structures and 853 tentative structures as well as the assignments of 414 unequivocal molecular formulae. In conclusion, we established an efficient extraction strategy for untargeted hair metabolomics, which the method is deliverable to any analytical laboratories and the sample can be directly profiled by means of a conventional RPLC-HRMS gradient.
Display omitted
•Hair records long-term and retrospective metabolomic data.•Ideal condition for extracting hair metabolome is 240-min UAE with MeOH:PBS 50:50.•This strategy maximises signal abundance and covers wide-range metabolites.•Coupling with structural annotation tools, 1438 compounds are profiled in hair.
Low-dimensional materials could display anomalous thermal conduction that the thermal conductivity (κ) diverges with increasing lengths, in ways inconceivable in any bulk materials. However, previous ...theoretical or experimental investigations were plagued with many finite-size effects, rendering the results either indirect or inconclusive. Indeed, investigations on the anomalous thermal conduction must demand the sample length to be sufficiently long so that the phenomena could emerge from unwanted finite-size effects. Here we report experimental observations that the κ's of single-wall carbon nanotubes continuously increase with their lengths over 1 mm, reaching at least 8640 W/mK at room temperature. Remarkably, the anomalous thermal conduction persists even with the presence of defects, isotopic disorders, impurities, and surface absorbates. Thus, we demonstrate that the anomalous thermal conduction in real materials can persist over much longer distances than previously thought. The finding would open new regimes for wave engineering of heat as well as manipulating phonons at macroscopic scales.
Statins have been shown to be a beneficial treatment as chemotherapy and target therapy for lung cancer. This study aimed to investigate the effectiveness of statins in combination with epidermal ...growth factor receptor‐tyrosine kinase inhibitor therapy for the resistance and mortality of lung cancer patients. A population‐based cohort study was conducted using the Taiwan Cancer Registry database. From January 1, 2007, to December 31, 2012, in total 792 non‐statins and 41 statins users who had undergone EGFR‐TKIs treatment were included in this study. All patients were monitored until the event of death or when changed to another therapy. Kaplan‐Meier estimators and Cox proportional hazards regression models were used to calculate overall survival. We found that the mortality was significantly lower in patients in the statins group compared with patients in the non‐statins group (4‐y cumulative mortality, 77.3%; 95% confidence interval (CI), 36.6%‐81.4% vs. 85.5%; 95% CI, 78.5%‐98%; P = .004). Statin use was associated with a reduced risk of death in patients the group who had tumor sizes <3 cm (hazard ratio HR, 0.51, 95% CI, 0.29‐0.89) and for patients in the group who had CCI scores <3 (HR, 0.6; 95% CI, 0.41‐0.88; P = .009). In our study, statins were found to be associated with prolonged survival time in patients with lung cancer who were treated with EGFR‐TKIs and played a synergistic anticancer role.
In this article, we describe the use of statins on the beneficial mortality of lung cancer patients with EGFR‐TKIs therapy. We found that statins were associated with prolonged survival time in patients with lung cancer who were taking EGFR‐TKIs. These could be playing a synergic anticancer role during the TKI treatment period, as well as improving quality of life worldwide and medical practice overall.
This work reveals the intrinsic carrier transport behavior of 2D organolead halide perovskites based on phase‐pure homologous (n = 1, 2, and 3) Ruddelsden–Popper perovskite (RPP) (BA)2(MA)n−1PbnI3n+1 ...single crystals. The 2D perovskite field effect transistors with high‐quality exfoliated 2D perovskite bulk crystals are fabricated, and characteristic output and transfer curves are measured from individual single‐crystal flakes with various n values under different temperatures. Unipolar n‐type transport dominated the electrical properties of all these 2D RPP single crystals. The transport behavior of the 2D organolead halide hybrid perovskites exhibits a strong dependence on the n value and the mobility substantially increases as the ratio of the number of inorganic perovskite slabs per organic spacer increases. By extracting the effect of contact resistances, the corrected mobility values for n = 1, 2, and 3 are 2 × 10−3, 8.3 × 10−2, and 1.25 cm2 V−1 s−1 at 77 K, respectively. Furthermore, by combining temperature‐dependent electrical transport and optical measurements, it is found that the origin of the carrier mobility dependence on the phase transition for 2D organolead halide perovskites is very different from that of their 3D counterparts. Our findings offer insight into fundamental carrier transport behavior of 2D organic–inorganic hybrid perovskites based on phase‐pure homologous single crystals.
2D organolead halide perovskite field effect transistors, which are fabricated based on phase‐pure homologous (n = 1, 2, and 3) Ruddelsden–Popper perovskite (BA)2(MA)n−1PbnI3n+1 single crystals are demonstrated. A strong dependence of carrier transport behavior of the 2D organolead halide hybrid perovskites on the n value is revealed.
Severe asthma is a complex and heterogeneous clinical condition presented as chronic inflammation of the airways. Conventional treatments are mainly focused on symptom control; however, there has ...been a shift towards personalized medicine. Identification of different phenotypes driven by complex pathobiological mechanisms (endotypes), especially those driven by type-2 (T2) inflammation, has led to improved treatment outcomes. Combining biomarkers with T2-targeting monoclonal antibodies is crucial for developing personalized treatment strategies. Several biological agents, including anti-immunoglobulin E, anti-interleukin-5, and anti-thymic stromal lymphopoietin/interleukin-4, have been approved for the treatment of severe asthma. These biological therapies have demonstrated efficacy in reducing asthma exacerbations, lowering eosinophil count, improving lung function, diminishing oral corticosteroid use, and improving the quality of life in selected patients. Severe asthma management is undergoing a profound transformation with the introduction of ongoing and future biological therapies. The availability of novel treatment options has facilitated the adoption of phenotype/endotype-specific approaches and disappearance of generic interventions. The transition towards precision medicine plays a crucial role in meticulously addressing the individual traits of asthma pathobiology. An era of tailored strategies has emerged, allowing for the successful targeting of immune-inflammatory responses that underlie uncontrolled T2-high asthma. These personalized approaches hold great promise for improving the overall efficacy and outcomes in the management of severe asthma. This article comprehensively reviews currently available biological agents and biomarkers for treating severe asthma. With the expanding repertoire of therapeutic options, it is becoming increasingly crucial to comprehend the influencing factors, understand the pathogenesis, and track treatment progress in severe asthma.
Acute kidney injury (AKI) is a common complication of acute myocardial infarction (AMI), and is associated with adverse outcomes. The study aimed to identify a miRNA signature for the early diagnosis ...of post-AMI AKI.
A total of 108 patients admitted to a coronary care unit (CCU) were divided into four subgroups: AMI
AKI
, AMI
AKI
, AMI
AKI
, and AMI
AKI
. Thirty-six miRNA candidates were selected based on an extensive literature review. Real-time quantitative RT-PCR analysis was used to determine the expression levels of these miRNAs in the serum collected on the day of CCU admittance. TargetScan 7.1 and miRDB databases were used for target prediction and Metacore 6.13 was used for pathway analysis.
Through a stepwise selection based on abundance, hemolytic effect and differential expression between four groups, 9 miRNAs were found to have significantly differential expression levels as potential biomarkers for post-AMI AKI specifically. Noticeably, the expression levels of miR-24, miR-23a and miR-145 were significantly down-regulated in AMI
AKI
patients compared to those in AMI
AKI
patients. Combination of the three miRNAs as a panel showed the best performance in the early detection of AKI following AMI (AUC = 0.853, sensitivity 95.65%), compared to the analysis of serum neutrophil gelatinase-associated lipocalin (AUC = 0.735, sensitivity 63.16%). Furthermore, bioinformatic analysis indicated that these three miRNAs regulate the transforming growth factor beta signaling pathway and involve in apoptosis and fibrosis in AKI.
For the first time, this study identify a unique circulating miRNA signature (miR-24-3p, miR-23a-3p, miR-145-5p) that can potentially early detect AKI following AMI and may be involved in renal injury and fibrosis in post-AMI AKI pathogenesis.
Organic photodetectors (OPDs) with spectral response extending from ultraviolet to near‐infrared domains are of great interest for many applications. Morphological impact on the performance of ...photomultiplication (PM)‐type OPDs, however, is still rarely investigated so far. Herein, a non‐fullerene acceptor, Y6‐Se‐HD, is synthesized, in which heavier selenium atoms substitute the sulfur atoms in the core of Y6, and used for fabricating PM‐OPDs. The resulting devices exhibit remarkable PM effects in a very broadband spectral range covering from 320 to 1090 nm. A maximum EQE value of ≈6500% at 860 nm is achieved at a low bias of −1.0 V. As compared with the device prepared with Y6 molecules, the Y6‐Se‐HD OPDs exhibited much enhanced performance. From the morphological analysis, we infer that Y6‐Se‐HD almost covers the entire active layer and avoids the direct contact of the hole‐trapping donor polymers with the Ag electrode, thereby resulting in stronger charge trapping and preventing possible charge recombination and/or quenching. Furthermore, finer phase separation between the donor and acceptor molecules also facilitates hole trapping and strengthens the PM effects. This research highlights the importance of morphological effects on the PM‐OPDs and demonstrates one approach for controlling the device morphology.
A selenium‐containing non‐fullerene acceptor is synthesized and used for constructing photomultiplication organic photodetectors. Remarkable photomultiplication effects with a broadband spectral range is observed and the effect of atomic substitution on the morphology is studied. The optimized molecular arrangement ensures strong charge trapping and finer phase separation between the donor and acceptor molecules facilitates hole trapping, thereby strengthening the photomultiplication effects.
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