lncRNA BREA2 promotes metastasis by disrupting the WWP2-mediated ubiquitination of Notch1.

Notch has been implicated in human cancers and is a putative therapeutic target. However, the regulation of Notch activation in the nucleus remains largely uncharacterized. Therefore, characterizing the detailed mechanisms governing Notch degradation will identify attractive strategies for treating Notch-activated cancers. Here, we report that the long noncoding RNA (lncRNA) BREA2 drives breast cancer metastasis by stabilizing the Notch1 intracellular domain (NICD1). Moreover, we reveal WW domain containing E3 ubiquitin protein ligase 2 (WWP2) as an E3 ligase for NICD1 at K1821 and a suppressor of breast cancer metastasis. Mechanistically, BREA2 impairs WWP2-NICD1 complex formation and in turn stabilizes NICD1, leading to Notch signaling activation and lung metastasis. BREA2 loss sensitizes breast cancer cells to inhibition of Notch signaling and suppresses the growth of breast cancer patient-derived xenograft tumors, highlighting its therapeutic potential in breast cancer. Taken together, these results reveal the lncRNA BREA2 as a putative regulator of Notch signaling and an oncogenic player driving breast cancer metastasis.

Dual Passivation Strategy for Highly Stable Blue-Luminescent CsPbBr3 Nanoplatelets.

Blue-emitting colloidal CsPbX3 (X = Br, Cl, or I) perovskite nanocrystals have emerged as one of the most fascinating materials for optoelectronic applications. However, their applicability is hindered by poor stability and a low photoluminescence efficiency. Herein, highly stable CsPbBr3 nanoplatelets exhibiting intense blue luminescence are fabricated by employing a strategy in which the morphology is regulated and the surface is subjected to dual passivation through the incorporation of zirconium acetylacetonate [Zr(acac)4]. The passivated CsPbBr3 nanocrystals exhibit adjustable light emission from green to dark blue and a controllable morphology from nanocubes (NCs) to nanoplatelets (NPLs) and nanorods accomplished by varying the content of Zr(acac)4. The optimized NPLs are characterized by a bright blue emission with a central wavelength of 459 nm and a high photoluminescence quantum yield of 90%. The addition of Zr(acac)4 in the synthesis of CsPbBr3 induces oriented growth with a two-dimensional morphology. The Zr(acac)4 can repair the surface defects of the nanocrystal surface, and the surface is also capped with the Zr(OH)4 cluster layer. Therefore, the passivated blue-emitting NPLs exhibit outstanding stability compared to that of pristine NPLs during long-term storage and exposure to light. This work provides a novel strategy for fabricating highly stable PNCs with deep-blue emission and widens their potential applications in blue-emitting optoelectronic devices.

Shell thickness-induced thermal dependence: highly sensitive core-shell CdSe/ZnS/POSS-based temperature probes.

Fluorescence nanothermometry based on quantum dots is a current research hotspot for novel non-contact temperature monitoring, and is of vital significance for the modulation and design of the sensing properties of sensors. Herein, a design strategy to modulate the temperature-sensing characteristics of quantum dots based on the thickness of a shell is proposed. In this study, CdSe/ZnS quantum dot/POSS-based temperature probe films with varying fluorescence characteristics were developed, and the influence of the ZnS shell on temperature sensing was examined by varying the thickness of the ZnS shell. The temperature dependency, linearity, range of applications, and reversibility of quantum dot thin film probes were all considerably regulated by the ZnS shell, according to research on quantum dot/POSS-based films coated with various shell thicknesses. The CdSe/ZnS temperature probe with 4 monolayers (MLs) stood out among the rest due to its strong thermal stability (at least 5 cycles), large usable temperature range (20-80 °C), and excellent temperature sensitivity (R2 > 0.994). The results demonstrated that the temperature sensing performance of quantum dots was the consequence of the combined effect of multiple temperature response properties induced by the thickness of the shell, and the shell control of quantum dots to optimize the temperature sensing performance was an essential approach for the design of temperature probes. This work demonstrates the great potential of the shell in tuning the temperature sensing performance of quantum dots and provides a viable approach for the design of quantum dot temperature probes.

Biomimetic nanodrug blocks CD73 to inhibit adenosine and boosts antitumor immune response synergically with photothermal stimulation.

Combination of tumor immunotherapy with photothermal therapy (PTT) is a feasible tactic to overcome the drawback of immunotherapy such as poor immune response. Via triggering the immunogenic cells death (ICD), PTT can stimulate the activity of immune cells, but meanwhile, the level of adenosine is elevated via the CD73-induced decomposition of ATP which is overexpressed accompanying with the PTT process, resulting in negative feedback to impair the immune stimulation. Herein, we developed a novel biomimetic photothermal nanodrug to specifically block CD73 for inhibition of adenosine production and more efficient priming of the suppressive immune microenvironments. The nanodrug, named as AptEM@CBA, is constructed by encapsulation of photothermal agent black phosphorus quantum dots (BPQDs) and selective CD73 inhibitor α, ß-Methyleneadenosine 5'-diphosphate (AMPCP) in chitosan nanogels, which are further covered with aptamer AS1411 modified erythrocyte membrane (EM) for biomimetic camouflage. With AS1411 induced active targeting and EM induced long blood circulation time, the enrichment of the nanodrug tumor sites is promoted. The photothermal treatment promotes the maturation of dendritic cells. Meanwhile, the release of AMPCP suppress the adenosine generation via CD73 blockade, alleviating the impairment of adenosine to dendritic cells and suppressing regulatory T cells, synergically stimulate the activity of T cells. The combination of CD73 blockade with PTT, not only suppresses the growth of primary implanted tumors, but also boosts strong antitumor immunity to inhibit the growth of distal tumors, providing good potential for tumor photoimmunotherapy.

Comparison of baseline drifts using three reflector blocks versus using a single reflector block for the calibration of wall-mounted Respiratory Gating for Scanner (RGSC) camera integrated with a CT.

BACKGROUND: The calibration of the Respiratory Gating for SCanner (RGSC) system is critical to achieve better and more stable accuracy. The current procedure for a wall-mounted RGSC system has a relatively large residual error. PURPOSE: To compare the baseline drifts in the image acquisition of DIBH using three reflector blocks versus using a single reflector block in the calibration of a wall-mounted RGSC camera system. MATERIALS AND METHODS: Varian provides a calibration plate with three rows of calibration points: each row is separated by 15 cm longitudinally and by 10 cm laterally. In Varian's single-block calibration method, the reflector block was first placed on the center point of the calibration plate and aligned with the scanner isocenter. The calibration took a picture of the block, then placed the block on the other eight points sequentially. In the proposed three-block method, we placed three reflector blocks on the center row, with the center block aligned with the isocenter, and we took a picture of the center block by manually blocking the other two blocks in calibration. By moving the couch longitudinally in or out 15 cm, the calibration goes through all nine points. Monte Carlo simulation was done using Matlab to analyze the calibration matrix eigenvalue characteristics. RESULTS: For a typical scan length of 40 cm of DIBH, the residual baseline drift in simulated DIBH is 0.02 ± 0.03  versus 0.30 ± 0.12 cm for three-block calibration and single-block calibration, respectively. To achieve 0.5 mm tolerance for the eigenvalue, the laser and reflector box should be within ±3 mm uncertainties based on the eigenvalue simulation. CONCLUSION: Three-block calibration method effectively removes baseline drift caused by couch movement in DIBH/4D CT scan for the wall-mounted camera while the single-block calibration method still has significant residual baseline drift.

Clinical prospects and research strategies of long non-coding RNA encoding micropeptides. / é•¿é“¾éžç¼–ç RNAç¼–ç å°è‚½çš„ç”Ÿç‰©åŒ»å­¦æ„ä¹‰åŠç ”ç©¶ç­–ç•¥.

Long non-coding RNAs (lncRNAs) which are usually thought to have no protein coding ability, are widely involved in cell proliferation, signal transduction and other biological activities. However, recent studies have suggested that short open reading frames (sORFs) of some lncRNAs can encode small functional peptides (micropeptides). These micropeptides appear to play important roles in calcium homeostasis, embryonic development and tumorigenesis, suggesting their potential as therapeutic targets and diagnostic biomarkers. Currently, bioinformatic tools as well as experimental methods such as ribosome mapping and in vitro translation are applied to predict the coding potential of lncRNAs. Furthermore, mass spectrometry, specific antibodies and epitope tags are used for validating the expression of micropeptides. Here, we review the physiological and pathological functions of recently identified micropeptides as well as research strategies for predicting the coding potential of lncRNAs to facilitate the further research of lncRNA encoded micropeptides.

GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery.

BACKGROUND: Accurately predicting drug-target binding affinity (DTA) in silico plays an important role in drug discovery. Most of the computational methods developed for predicting DTA use machine learning models, especially deep neural networks, and depend on large-scale labelled data. However, it is difficult to learn enough feature representation from tens of millions of compounds and hundreds of thousands of proteins only based on relatively limited labelled drug-target data. There are a large number of unknown drugs, which never appear in the labelled drug-target data. This is a kind of out-of-distribution problems in bio-medicine. Some recent studies adopted self-supervised pre-training tasks to learn structural information of amino acid sequences for enhancing the feature representation of proteins. However, the task gap between pre-training and DTA prediction brings the catastrophic forgetting problem, which hinders the full application of feature representation in DTA prediction and seriously affects the generalization capability of models for unknown drug discovery. RESULTS: To address these problems, we propose the GeneralizedDTA, which is a new DTA prediction model oriented to unknown drug discovery, by combining pre-training and multi-task learning. We introduce self-supervised protein and drug pre-training tasks to learn richer structural information from amino acid sequences of proteins and molecular graphs of drug compounds, in order to alleviate the problem of high variance caused by encoding based on deep neural networks and accelerate the convergence of prediction model on small-scale labelled data. We also develop a multi-task learning framework with a dual adaptation mechanism to narrow the task gap between pre-training and prediction for preventing overfitting and improving the generalization capability of DTA prediction model on unknown drug discovery. To validate the effectiveness of our model, we construct an unknown drug data set to simulate the scenario of unknown drug discovery. Compared with existing DTA prediction models, the experimental results show that our model has the higher generalization capability in the DTA prediction of unknown drugs. CONCLUSIONS: The advantages of our model are mainly attributed to two kinds of pre-training tasks and the multi-task learning framework, which can learn richer structural information of proteins and drugs from large-scale unlabeled data, and then effectively integrate it into the downstream prediction task for obtaining a high-quality DTA prediction in unknown drug discovery.

MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network.

BACKGROUND: Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computational medication recommendation methods based on EMR, an important challenge is the lack of a large number of longitudinal EMR data with time correlation. Faced with this challenge, this paper proposes a new EMR-based medication recommendation model called MR-KPA, which combines knowledge-enhanced pre-training with the deep adversarial network to improve medication recommendation from both feature representation and the fine-tuning process. Firstly, a knowledge-enhanced pre-training visit model is proposed to realize domain knowledge-based external feature fusion and pre-training-based internal feature mining for improving the feature representation. Secondly, a medication recommendation model based on the deep adversarial network is developed to optimize the fine-tuning process of pre-training visit model and alleviate over-fitting of model caused by the task gap between pre-training and recommendation. RESULT: The experimental results on EMRs from medical and health institutions in Hainan Province, China show that the proposed MR-KPA model can effectively improve the accuracy of medication recommendation on small-scale longitudinal EMR data compared with existing representative methods. CONCLUSION: The advantages of the proposed MR-KPA are mainly attributed to knowledge enhancement based on ontology embedding, the pre-training visit model and adversarial training. Each of these three optimizations is very effective for improving the capability of medication recommendation on small-scale longitudinal EMR data, and the pre-training visit model has the most significant improvement effect. These three optimizations are also complementary, and their integration makes the proposed MR-KPA model achieve the best recommendation effect.

Hypoxia-Inducible Exosomes Facilitate Liver-Tropic Premetastatic Niche in Colorectal Cancer.

BACKGROUND AND AIMS: Liver metastasis is a frequent occurrence in patients with colorectal cancer (CRC), with 15%-25% of CRC patients having liver metastases at the time of initial diagnosis. Specifically, some regional-stage patients with mild symptoms (stage 1 or 2) will also advance to liver metastases rapidly, even if the CRC lesion in situ is resected in time. Nevertheless, the precise mechanism of liver metastasis is still unclear. APPROACH AND RESULTS: Fresh tumor tissues from patients with CRC, adjacent noncancerous tissues, and colorectal adenoma tissues were subjected to microarray analysis to identify differentially expressed microRNA. Exosomes from human serum and cell culture medium were separated, quantitated, and verified by transmission electronic microscopy and Zetasizer Nano. Luciferase reporter assay, real-time quantitative PCR, western blot, immunoprecipitation, chromatin and re-chromatin immunoprecipitation, migration and invasion assay, PDX mouse model, flow cytometry, immunohistochemistry, and immunofluorescence staining were employed to explore the regulation among CRC liver metastases, immunosuppression, and cell adhesion. In this study, we demonstrated that the hypoxic microenvironment in primary CRC lesions boosted exosome release, selectively initiated favorable premetastatic niche formation in the liver but not in other organs. Mechanistically, Kupffer cells (KCs) can phagocytose exosomes containing highly expressed miR-135a-5p from the blood circulation into the liver. Exosomal miR-135a-5p initiated the large tumor suppressor kinase 2-yes-associated protein-matrix metalloproteinase 7 axis to promote the occurrence of CRC liver metastasis, and cluster of differentiation 30-TNF receptor-associated factor 2-p65-mediated immunosuppression signaling also contributed to this process. CONCLUSIONS: Hypoxia-induced exosomal miR-135a-5p correlates with the development, clinical severity, and prognosis of CRC liver metastases through the premetastatic niche; and our findings revealed that miR-135a-5p might be a promising target in halting CRC liver metastases.

miRNA-130b-5p promotes hepatic stellate cell activation and the development of liver fibrosis by suppressing SIRT4 expression.

Liver fibrosis is a progressive disease accompanied by the deposition of extracellular matrix (ECM). Numerous reports have demonstrated that alterations in the expression of microRNAs (miRNAs) are related to liver disease. However, the effect of individual miRNAs on liver fibrosis has not been studied. Hepatic stellate cells (HSCs), being responsible for producing ECM, exert an important influence on liver fibrosis. Then, microarray analysis of non-activated and activated HSCs induced by transforming growth factor ß1 (TGF-ß1) showed that miR-130b-5p expression was strongly up-regulated during HSC activation. Moreover, the progression of liver fibrosis had a close connection with the expression of miR-130b-5p in different liver fibrosis mouse models. Then, we identified that there were specific binding sites between miR-130b-5p and the 3' UTR of Sirtuin 4 (SIRT4) via a luciferase reporter assay. Knockdown of miR-130b-5p increased SIRT4 expression and ameliorated liver fibrosis in mice transfected with antagomiR-130b-5p oligos. In general, our results suggested that miR-130b-5p promoted HSC activation by targeting SIRT4, which participates in the AMPK/TGF-ß/Smad2/3 signalling pathway. Hence, regulating miR-130b-5p maybe serve as a crucial therapeutic treatment for hepatic fibrosis.

Auto-Trending daily quality assurance program for a pencil beam scanning proton system aligned with TG 224.

The Daily Quality Assurance (DQA) for a proton modality is not standardized. The modern pencil beam scanning proton system is becoming a trend and an increasing number of proton centers with PBS are either under construction or in planning. The American Association of Physicists in Medicine has a Task Group 224 report published in 2019 for proton modality routine QA. Therefore, there is a clinical need to explore a DQA procedure to meet the TG 224 guideline. The MatriXX PT and a customized phantom were used for the dosimetry constancy checking. An OBI box was used for imaging QA. The MyQA(TM) software was used for logging the dosimetry results. An in-house developed application was applied to log and auto analyze the DQA results. Another in-house developed program "DailyQATrend" was used to create DQA databases for further analysis. All the functional and easy determined tasks passed. For dosimetry constancy checking, the outputs for four gantry rooms were within ±3% with room to room baseline differences within ±1%. The energy checking was within ±1%. The spot location checking from the baseline was within 0.63 mm and the spot size checking from the baseline was within -1.41 ± 1.27 mm (left-right) and -0.24 ± 1.27 mm (in-out) by averaging all the energies. We have found that there was also a trend for the beam energies of two treatment rooms slowly going down (0.76% per month and 0.48 per month) after analyzing the whole data trend with linear regression. A DQA program for a PBS proton system has been developed and fully implemented into the clinic. The DQA program meets the TG 224 guideline and has web-based logging and auto treading functions. The clinical data show the DQA program is efficient and has the potential to identify the PBS proton system potential issue.

RIP3 deficiency alleviates liver fibrosis by inhibiting ROCK1-TLR4-NF-κB pathway in macrophages.

Liver fibrosis is an important pathologic process in injured liver tissues. A protein kinase, receptor-interacting protein (RIP)3, plays a crucial role in mediating different diseases. However, the role of RIP3 in macrophages in liver fibrosis has not yet been studied. In our study, we found that RIP3 expression was up-regulated in liver tissues and macrophages of humans and mice with liver fibrosis. Absence of RIP3 in macrophages could alleviate inflammation and macrophage or neutrophil accumulation in mice after carbon tetrachloride (CCl4) or bile duct ligation (BDL) treatment. Importantly, RIP3 deficiency in macrophages could decrease CCl4-induced and BDL-induced liver fibrosis in mice. Moreover, RIP3 deficiency could inhibit the TLR4-NF-κB pathway through suppressing Rho-associated coiled-coil containing protein kinase (ROCK)1 in macrophages. To explore the connection of ROCK1 and RIP3 in macrophages of mice with liver fibrosis in vivo, ROCK1-overexpressed macrophages were infused to RIP3-deficient mice, which resulted in increased inflammation and liver fibrosis. In conclusion, our findings suggest that RIP3 plays a crucial proinflammatory role in liver fibrosis by regulating the ROCK1-TLR4-NF-κB signaling pathway in macrophages and therefore may be a potential therapeutic target for immune-mediated liver fibrosis.-Wei, S., Zhou, H., Wang, Q., Zhou, S., Li, C., Liu, R., Qiu, J., Shi, C., Lu, L. RIP3 deficiency alleviates liver fibrosis by inhibiting ROCK1-TLR4-NF-κB pathway in macrophages.

The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy.

Deformable image registration (DIR) is the key process for contour propagation and dose accumulation in adaptive radiation therapy (ART). However, currently, ART suffers from a lack of understanding of "robustness" of the process involving the image contour based on DIR and subsequent dose variations caused by algorithm itself and the presetting parameters. The purpose of this research is to evaluate the DIR caused variations for contour propagation and dose accumulation during ART using the RayStation treatment planning system. Ten head and neck cancer patients were selected for retrospective studies. Contours were performed by a single radiation oncologist and new treatment plans were generated on the weekly CT scans for all patients. For each DIR process, four deformation vector fields (DVFs) were generated to propagate contours and accumulate weekly dose by the following algorithms: (a) ANACONDA with simple presetting parameters, (b) ANACONDA with detailed presetting parameters, (c) MORFEUS with simple presetting parameters, and (d) MORFEUS with detailed presetting parameters. The geometric evaluation considered DICE coefficient and Hausdorff distance. The dosimetric evaluation included D95 , Dmax , Dmean , Dmin , and Homogeneity Index. For geometric evaluation, the DICE coefficient variations of the GTV were found to be 0.78 ± 0.11, 0.96 ± 0.02, 0.64 ± 0.15, and 0.91 ± 0.03 for simple ANACONDA, detailed ANACONDA, simple MORFEUS, and detailed MORFEUS, respectively. For dosimetric evaluation, the corresponding Homogeneity Index variations were found to be 0.137 ± 0.115, 0.006 ± 0.032, 0.197 ± 0.096, and 0.006 ± 0.033, respectively. The coherent geometric and dosimetric variations also consisted in large organs and small organs. Overall, the results demonstrated that the contour propagation and dose accumulation in clinical ART were influenced by the DIR algorithm, and to a greater extent by the presetting parameters. A quality assurance procedure should be established for the proper use of a commercial DIR for adaptive radiation therapy.

Feasibility study of individualized optimal positioning selection for left-sided whole breast radiotherapy: DIBH or prone.

The deep inspiration breath hold (DIBH) and prone (P) position are two common heart-sparing techniques for external-beam radiation treatment of left-sided breast cancer patients. Clinicians select the position that is deemed to be better for tissue sparing based on their experience. This approach, however, is not always optimum and consistent. In response to this, we develop a quantitative tool that predicts the optimal positioning for the sake of organs at risk (OAR) sparing. Sixteen left-sided breast cancer patients were considered in the study, each received CT scans in the supine free breathing, supine DIBH, and prone positions. Treatment plans were generated for all positions. A patient was classified as DIBH or P using two different criteria: if that position yielded (1) lower heart dose, or (2) lower weighted OAR dose. Ten anatomical features were extracted from each patient's data, followed by the principal component analysis. Sequential forward feature selection was implemented to identify features that give the best classification performance. Nine statistical models were then applied to predict the optimal positioning and were evaluated using stratified k-fold cross-validation, predictive accuracy and receiver operating characteristic (AUROC). For heart toxicity-based classification, the support vector machine with radial basis function kernel yielded the highest accuracy (0.88) and AUROC (0.80). For OAR overall toxicities-based classification, the quadratic discriminant analysis achieved the highest accuracy (0.90) and AUROC (0.84). For heart toxicity-based classification, Breast volume and the distance between Heart and Breast were the most frequently selected features. For OAR overall toxicities-based classification, Heart volume, Breast volume and the distance between ipsilateral lung and breast were frequently selected. Given the patient data considered in this study, the proposed statistical model is feasible to provide predictions for DIBH and prone position selection as well as indicate important clinical features that affect the position selection.

Controllable fabrication of well-shaped PMBA@CsPbBr3 nanoparticles for highly sensitive detection of HCl and HBr.

We report a facile two-step strategy to construct well-shaped PMBA@CsPbBr3 nanoparticles, with this strategy involving combining in situ adsorption and controlled polymerization. The morphological evolution process and mechanism of formation of the nanoparticles were demonstrated, and the nanoparticles showed high sensitivity to corrosive acid gas. This work has provided an effective approach for fabricating well-structured perovskite-based nanocomposites.

LncFASA promotes cancer ferroptosis via modulating PRDX1 phase separation.

Ferroptosis, a unique type of non-apoptotic cell death resulting from iron-dependent lipid peroxidation, has a potential physiological function in tumor suppression, but its underlying mechanisms have not been fully elucidated. Here, we report that the long non-coding RNA (lncRNA) LncFASA increases the susceptibility of triple-negative breast cancer (TNBC) to ferroptosis. As a tumor suppressor, LncFASA drives the formation of droplets containing peroxiredoxin1 (PRDX1), a member of the peroxidase family, resulting in the accumulation of lipid peroxidation via the SLC7A11-GPX4 axis. Mechanistically, LncFASA directly binds to the Ahpc-TSA domain of PRDX1, inhibiting its peroxidase activity by driving liquid-liquid phase separation, which disrupts intracellular ROS homeostasis. Notably, high LncFASA expression indicates favorable overall survival in individuals with breast cancer, and LncFASA impairs the growth of breast xenograft tumors by modulating ferroptosis. Together, our findings illustrate the crucial role of this lncRNA in ferroptosis-mediated cancer development and provide new insights into therapeutic strategies for breast cancer.

LncRNA LINK-A Remodels Tissue Inflammatory Microenvironments to Promote Obesity.

High-fat diet (HFD)-induced obesity is a crucial risk factor for metabolic syndrome, mainly due to adipose tissue dysfunctions associated with it. However, the underlying mechanism remains unclear. This study has used genetic screening to identify an obesity-associated human lncRNA LINK-A as a critical molecule bridging the metabolic microenvironment and energy expenditure in vivo by establishing the HFD-induced obesity knock-in (KI) mouse model. Mechanistically, HFD LINK-A KI mice induce the infiltration of inflammatory factors, including IL-1ß and CXCL16, through the LINK-A/HB-EGF/HIF1α feedback loop axis in a self-amplified manner, thereby promoting the adipose tissue microenvironment remodeling and adaptive thermogenesis disorder, ultimately leading to obesity and insulin resistance. Notably, LINK-A expression is positively correlated with inflammatory factor expression in individuals who are overweight. Of note, targeting LINK-A via nucleic acid drug antisense oligonucleotides (ASO) attenuate HFD-induced obesity and metabolic syndrome, pointing out LINK-A as a valuable and effective therapeutic target for treating HFD-induced obesity. Briefly, the results reveale the roles of lncRNAs (such as LINK-A) in remodeling tissue inflammatory microenvironments to promote HFD-induced obesity.

Real-time respiratory motion prediction using photonic reservoir computing.

Respiration induced motion is a well-recognized challenge in many clinical practices including upper body imaging, lung tumor motion tracking and radiation therapy. In this work, we present a recurrent neural network algorithm that was implemented in a photonic delay-line reservoir computer (RC) for real-time respiratory motion prediction. The respiratory motion signals are quasi-periodic waveforms subject to a variety of non-linear distortions. In this work, we demonstrated for the first time that RC can be effective in predicting short to medium range of respiratory motions within practical timescales. A double-sliding window technology is explored to enable the real-time establishment of an individually trained model for each patient and the real-time processing of live-streamed respiratory motion data. A breathing dataset from a total of 76 patients with breathing speeds ranging from 3 to 20 breaths per minute (BPM) is studied. Motion prediction of look-ahead times of 66.6, 166.6, and 333 ms are investigated. With a 333 ms look-ahead time, the real-time RC model achieves an average normalized mean square error (NMSE) of 0.025, an average mean absolute error (MAE) of 0.34 mm, an average root mean square error (RMSE) of 0.45 mm, an average therapeutic beam efficiency (TBE) of 94.14% for an absolute error (AE) < 1 mm, and 99.89% for AE < 3 mm. This study demonstrates that real-time RC is an efficient computing framework for high precision respiratory motion prediction.

Evaluation of repeatability and reproducibility of radiomic features produced by the fan-beam kV-CT on a novel ring gantry-based PET/CT linear accelerator.

BACKGROUND: The RefleXion X1 is a novel radiotherapy delivery system on a ring gantry equipped with fan-beam kV-CT and PET imaging subsystems. The day-to-day scanning variability of radiomics features must be evaluated before any attempt to utilize radiomics features. PURPOSE: This study aims to characterize the repeatability and reproducibility of radiomic features produced by the RefleXion X1 kV-CT. MATERIALS AND METHODS: The Credence Cartridge Radiomics (CCR) phantom includes six cartridges of varied materials. It was scanned 10 times on the RefleXion X1 kVCT imaging subsystem over a 3-month period using the two most frequently used scanning protocols (BMS and BMF). Fifty-five radiomic features were extracted for each ROI on each CT scan and analyzed using LifeX software. The coefficient of variation (COV) was computed to evaluate the repeatability. Intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) were used to evaluate the repeatability and reproducibility of the scanned images using 0.9 as the threshold. This process is repeated on a GE PET-CT scanner using several built-in protocols as a comparison. RESULTS: On average, 87% of the features on both scan protocols on the RefleXion X1 kVCT imaging subsystem can be considered repeatable as they met COV < 10% criteria. On GE PET-CT, this number is similar at 86%. When we tighten the criteria to COV <5%, the RefleXion X1 kVCT imaging subsystem showed much better repeatability with 81% of features on average whereas GE PET-CT showed only 73.5% on average. About 91% and 89% of the features with ICC > 0.9 respectively for BMS and BMF protocols on RefleXion X1. On the other hand, the percentage of features with ICC > 0.9 on GE PET-CT ranges from 67% to 82%. The RefleXion X1 kVCT imaging subsystem showed excellent intra-scanner reproducibility between the scanning protocols much better than the GE PET CT scanner. For the inter-scanner reproducibility, the percentage of features with CCC > 0.9 ranged from 49% to 80%. between X1 and GE PET-CT scanning protocols. CONCLUSIONS: Clinically useful CT radiomic features produced by the RefleXion X1 kVCT imaging subsystem are reproducible and stable over time, demonstrating its utility as a quantitative imaging platform.