Circ_0044235 regulates the development of osteoarthritis by the modulation of miR-375/PIK3R3 axis.

BACKGROUND: Circular RNAs (circRNAs) play an important role in osteoarthritis (OA). However, the role of circRNA in OA is still unclear. Here, we explored the role and mechanism of circ_0044235 in OA. METHODS: CHON-001 cells were treated with IL-1ß to establish OA model in vitro. The levels of circ_0044235, miR-375 and phosphoinositide 3-kinase (PI3K) regulatory subunit 3 (PIK3R3) were detected by quantitative real-time PCR. Cell count kit-8 assay and flow cytometry assay were used to detect cell viability and apoptosis. The concentrations of inflammation factors were determined by enzyme-linked immunosorbent assay. Western blot was used to detect protein levels. The interaction between miR-375 and circ_0044235 or PIK3R3 was analyzed by dual-luciferase reporter assay and RNA immunoprecipitation assay. RESULTS: Circ_0044235 was significantly decreased in OA cartilage tissue and IL-1ß-treated CHON-001 cells. Overexpression of circ_0044235 promoted IL-1ß-stimulated CHON-001 cell viability and inhibited apoptosis, inflammation, and extracellular matrix (ECM) degradation. In mechanism analysis, circ_0044235 could act as a sponge for miR-375 and positively regulate PIK3R3 expression. In addition, miR-375 ameliorated the effect of circ_0044235 overexpression on IL-1ß-mediated CHON-001 cells injury. In addition, miR-375 inhibition mitigated IL-1ß-induced CHON-001 cell injury, while PIK3R3 silencing restored the effect. CONCLUSION: Circ_0044235 knockdown alleviated IL-1ß-induced chondrocytes injury by regulating miR-375/PIK3R3 axis, confirming that circ_0044235 might be a potential target for OA treatment.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Method The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Results DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. Conclusion DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment.

BACKGROUND: The detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results. PURPOSE: In this work, we fabricated a full-size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose. METHODS: Designed to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10 cm wide and 4 cm thick, embedded with 12 000 lesions (nominal width of 0.6 mm, height of 0.8 mm, and contrast of -350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high-dose reference. Scans were reconstructed with three different settings: filtered-backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs. RESULTS: The printed insert lesions had an average position error of 0.20 mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P < 0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59. CONCLUSIONS: Our 3D-printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

Clozapine-Induced Severe Toxicity: Exploring the Pharmacokinetic Profile of Clozapine and Its Significance in Hemodynamic Instability - A Case Report.

Hemodynamic instability in patients with clozapine intoxication can indirectly reflect the serum concentration of clozapine.We have described a case of a 32-year-old pregnant woman who developed life-threatening clozapine toxicity at 28 weeks of gestation. The levels of clozapine and norclozapine in the serum were high. We initiated hemoperfusion(HP) and other detoxification therapies to remove the drug. The patient had severely dilated peripheral blood vessels, which led to cardiac symptoms such as fatal hypotension and uncontrollable tachycardia, resulting in very high cardiac output and elevated Central venous oxygen saturation (ScvO2). Pharmacological intervention significantly improved the hemodynamics.In light of our observations in the ongoing case, we posit that evaluating hemodynamic parameters before and after blood detoxification could serve as a valuable means to gauge effectiveness and provide guidance for treatment.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective: Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Approach: The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Main Results: DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom. Significance: DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Development of a 3 MV coaxial peaking capacitor for large-scale electromagnetic pulse simulator.

Coaxial peaking capacitor is a key component in high-altitude electromagnetic pulse (EMP) simulators with fast front pulse output. It poses significant technical and engineering challenges in limiting radiation field amplitude and test space. This paper presents the design and testing of a 180 pF, 3 MV coaxial peaking capacitor with improved insulation performance. In the insulation design, the length of the dielectric film is extended to reduce the background electric field on the flashover path. The electric field threshold obtained from image diagnosis is used as a reference. During capacitor testing, the insulation characteristics are diagnosed using both direct and indirect methods. The voltage measured by a D-dot probe, the output waveform of the Marx generator in the primary source, and the radiation field waveform are analyzed to understand the flashover characteristics of the capacitor and to improve the reliability of the test results. The experimental results demonstrate that the peaking capacitor can operate stably at 3.0 MV. If flashover occurring on the dropping edge of the pulse is permitted, the operating voltage can be greater than 3.7 MV without significantly affecting the radiation field waveform. The analysis on the surface flashover morphology of the peaking capacitor reveals that the flashover mainly occurs at the dropping edge of the capacitor's waveform, indicating that the damage to the film is not serious. This research significantly increases the working voltage of coaxial peaking capacitors and contributes to the development of high-altitude EMP simulation technology.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

PixelPrint: A collection of three-dimensional printed CT phantoms of different respiratory diseases.

Imaging is often a first-line method for diagnostics and treatment. Radiological workflows increasingly mine medical images for quantifiable features. Variability in device/vendor, acquisition protocol, data processing, etc., can dramatically affect quantitative measures, including radiomics. We recently developed a method (PixelPrint) for 3D-printing lifelike computed tomography (CT) lung phantoms, paving the way for future diagnostic imaging standardization. PixelPrint generates phantoms with accurate attenuation profiles and textures by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. The present study introduces a library of 3D printed lung phantoms covering a wide range of lung diseases, including usual interstitial pneumonia with advanced fibrosis, chronic hypersensitivity pneumonitis, secondary tuberculosis, cystic fibrosis, Kaposi sarcoma, and pulmonary edema. CT images of the patient-based phantom are qualitatively comparable to original CT images, both in texture, resolution and contrast levels allowing for clear visualization of even subtle imaging abnormalities. The variety of cases chosen for printing include both benign and malignant pathology causing a variety of alveolar and advanced interstitial abnormalities, both clearly visualized on the phantoms. A comparison of regions of interest revealed differences in attenuation below 6 HU. Identical features on the patient and the phantom have a high degree of geometrical correlation, with differences smaller than the intrinsic spatial resolution of the scans. Using PixelPrint, it is possible to generate CT phantoms that accurately represent different pulmonary diseases and their characteristic imaging features.

Revealing pelvic structures in the presence of metal hip prothesis via non-circular CBCT orbits.

As the expansion of Cone Beam CT (CBCT) to new interventional procedures continues, the burdensome challenge of metal artifacts remains. Photon starvation and beam hardening from metallic implants and surgical tools in the field of view can result in the anatomy of interest being partially or fully obscured by imaging artifacts. Leveraging the flexibility of modern robotic CBCT imaging systems, implementing non-circular orbits designed for reducing metal artifacts by ensuring data-completeness during acquisition has become a reality. Here, we investigate using non-circular orbits to reduce metal artifacts arising from metallic hip prostheses when imaging pelvic anatomy. As a first proof-of-concept, we implement a sinusoidal and a double-circle-arc orbit on a CBCT test bench, imaging a physical pelvis phantom, with two metal hip prostheses, housing a 3D-printed iodine-filled radial line-pair target. A standard circular orbit implemented with the CBCT test bench acted as comparator. Imaging data collection and processing, geometric calibration and image reconstruction was completed using in-house developed software programs. Imaging with the standard circular orbit, image artifacts were observed in the pelvic bones and only 33 out of the possible 45 line-pairs of the radial line-pair target were partially resolvable in the reconstructed images. Comparatively, imaging with both the sinusoid and double-circle-arc orbits reduced artifacts in the surrounding anatomy and enabled all 45 line-pairs to be visibly resolved in the reconstructed images. These results indicate the potential of non-circular orbits to assist in revealing previously obstructed structures in the pelvic region in the presence of metal hip prosthesis.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that have realistic image texture and densities, which are critical in evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized stone-based filament to increase Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that have realistic image texture and densities, which are critical in evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized stone-based filament to increase Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study.

In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03-0.29, using a 1-5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint's production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms. Classification: CT lung phantoms, reader study.

Iterative Joint Estimation Procedure of Channel and PDP for OFDM Systems.

The power-delay profile (PDP) estimation of wireless channels is an important step to generate a channel correlation matrix for channel linear minimum mean square error (LMMSE) estimation. Estimated channel frequency response can be used to obtain time dispersion characteristics that can be exploited by adaptive orthogonal frequency division multiplexing (OFDM) systems. In this paper, a joint estimator for PDP and LMMSE channel estimation is proposed. For LMMSE channel estimation, we apply a candidate set of frequency-domain channel correlation functions (CCF) and select the one that best matches the current channel to construct the channel correlation matrix. The initial candidate set is generated based on the traditional CCF calculation method for different scenarios. Then, the result of channel estimation is used as an input for the PDP estimation whereas the estimated PDP is further used to update the candidate channel correlation matrix. The enhancement of LMMSE channel estimation and PDP estimation can be achieved by the iterative joint estimation procedure. Analysis and simulation results show that in different communication scenarios, the PDP estimation error of the proposed method can approach the Cramér-Rao lower bound (CRLB) after a finite number of iterations. Moreover, the mean square error of channel estimation is close to the performance of accurate PDP-assisted LMMSE.

Supercritical fluid extract of Angelica sinensis promotes the anti-colorectal cancer effect of oxaliplatin.

Oxaliplatin-based chemotherapy regimens are recommended for patients with advanced colorectal cancer (CRC). However, oxaliplatin (OXA) can cause toxic side effects at the recommended dosage. Therefore, it is necessary to find new drug candidates that can synergize with OXA and thereby lower the OXA dose while still maintaining its efficacy. Angelica sinensis is a common drug in traditional Chinese medicine and has demonstrated a significant anti-CRC effect in modern pharmacological studies. The active ingredients in Angelica sinensis can be effectively extracted by a supercritical fluid extract. In this study, the supercritical fluid extract of Angelica sinensis (A-SFE) was obtained by a stable extraction process and was chemically characterized by GC/MS. The anti-cancer effect of A-SFE when applied individually was explored in vitro through MTT, scratch, and Transwell assay. The effect of A-SFE on CRC cells under the influence of tumor-associated macrophages (TAMs) was explored by a co-culture model. The results showed that A-SFE could inhibit the viability, metastasis, and invasion of HCT116 cells, especially under the influence of TAMs. When 20-100 µg/ml of A-SFE and 8-64 µg/ml of OXA were used in combination in HCT116 cells, synergistic or additive effects were shown in different concentration combinations. The CT26 syngeneic mouse model was used to explore the anti-CRC effect of OXA combined with A-SFE in vivo. The tumor volume, expression levels of Ki67, MMP9, and CD206 in the OXA + A-SFE group were less than those in the OXA group. In conclusion, A-SFE has the potential to become an adjuvant drug for OXA in the treatment of CRC, which provides new strategies for anti-colorectal cancer research.

miR-187/PDLIM1 Gets Involved in Gastric Cancer Progression and Cisplatin Sensitivity of Cisplatin by Mediating the Hippo-YAP Signaling Pathway.

Gastric cancer (GC) is one of the most prevalent malignancies in the digestive system across the world. The function and mechanism of PDLIM1, a cancer-suppressing gene, in gastric cancer progression remain unclear. This study is aimed at investigating the expression features and function of PDLIM1 in GC. RT-qPCR and western blot were used to compare the profiles of PDLIM1 and miR-187 between GC and normal tissues. The cell models of PDLIM1 overexpression and low expression were established in gastric cancer cell lines MKN45 and AGS. CCK8 and BrdU assays measured cell proliferation. Flow cytometry monitored cell apoptosis. Transwell analyzed cell invasion and migration. The influence of miR-187 overexpression on gastric cancer development was assessed. We predicted the targeted correlation between miR-187 and PDLIM1 through bioinformatics, which was corroborated via dual luciferase activity assay and RIP. Meanwhile, the cell model of PDLIM1 overexpression was built in AGS cells transfected with miR-187 mimics. A rescue experiment was conducted to assess the impact of PDLIM1 overexpression on the procancer function of miR-187. As a result, in contrast with normal paracancer tissues, PDLIM1 was substantially downregulated in GC tissues. Moreover, PDLIM1 overexpression considerably dampened proliferation, invasion, and migration in GC cells, boosted the cell apoptosis, and bolstered their sensitivity to cisplatin. PDLIM1 knockdown or miR-187 overexpression dramatically fostered GC cell proliferation, invasion, and migration and repressed cell apoptosis. Mechanism studies demonstrated that PDLIM1 vigorously restrained the profiles of the Hippo-YAP signaling pathway and the downstream target genes. miR-187 targeted PDLIM1, while miR-187 overexpression cramped PDLIM1 expression. The rescue experiment suggested that PDLIM1 overexpression weakened the procancer function of miR-187 in GC cells. In conclusion, our study demonstrated that PDLIM1 presented a low expression in GC tissues, while miR-187/PDLIM1 participated in GC development and cisplatin sensitivity by mediating the Hippo-YAP signaling pathway.

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for CT imaging.

Phantoms are essential tools for assessing and verifying performance in computed tomography (CT). Realistic patient-based lung phantoms that accurately represent textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D-printing solution to create patient-specific lung phantoms with accurate contrast and textures. PixelPrint converts patient images directly into printer instructions, where density is modeled as the ratio of filament to voxel volume to emulate local attenuation values. For evaluation of PixelPrint, phantoms based on four COVID-19 pneumonia patients were manufactured and scanned with the original (clinical) CT scanners and protocols. Density and geometrical accuracies between phantom and patient images were evaluated for various anatomical features in the lung, and a radiomic feature comparison was performed for mild, moderate, and severe COVID-19 pneumonia patient-based phantoms. Qualitatively, CT images of the patient-based phantoms closely resemble the original CT images, both in texture and contrast levels, with clearly visible vascular and parenchymal structures. Regions-of-interest (ROIs) comparing attenuation demonstrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist revealed a high degree of geometrical correlation between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the images. Radiomic feature analysis revealed high correspondence, with correlations of 0.95-0.99 between patient and phantom images. Our study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast that will enable protocol optimization, CT research and development advancements, and generation of ground-truth datasets for radiomic evaluations.

[Preparation of microemulsion gel loading enriched ingredients of Epimedii Folium and its pharmacodynamics evaluation].

On the basis of previous studies, this study prepared and evaluated microemulsion gel loading enriched ingredients of Epimedii Folium and investigated its protective effect against peripheral nervous system damage caused by chemotherapeutics. The preparation method and the type and dosage of the matrix were investigated from rheology, preparation difficulty, and drug loading. Then the optimal prescription was determined and the microemulsion gel loading enriched ingredients of Epimedii Folium was prepared. The in vitro release and transdermal behaviors of the gel were investigated in the Franz diffusion cell with epimedin A1,A,B,C, and icariin as evaluation indicators. The oxaliplatin-induced peripheral neuropathy(OIPN) model was established in Wistar rats. The protective effect of the microemulsion gel loading enriched ingredients of Epimedii Folium against peripheral nervous system damage caused by chemotherapeutics was evaluated by behavioral measurement after drug administration and histopathological examination of dorsal root ganglia and sciatic nerve. The preparation process of the microemulsion gel loading enriched ingredients of Epimedii Folium was stable, and the release of the five components was consistent with the Hixson-Crowell cube root law. Behavioral indicators intuitively showed that the drug could effectively relieve mechanical allodynia caused by oxaliplatin. The histopathological examination showed that the drug can improve neuron damage in the dorsal root ganglia, axon degeneration, and demyelination caused by oxaliplatin. Therefore, the preparation process of the microemulsion gel loading enriched ingredients of Epimedii Folium is feasible, which can achieve stable drug release. It has a certain therapeutic effect on chemotherapy-induced peripheral neuropathy(CIPN).

PixelPrint: Three-dimensional printing of patient-specific soft tissue and bone phantoms for CT.

Patient-based CT phantoms, with realistic image texture and densities, are essential tools for assessing and verifying CT performance in clinical practice. This study extends our previously presented 3D printing solution (PixelPrint) to patient-based phantoms with soft tissue and bone structures. To expand the Hounsfield Unit (HUs) range, we utilize a stone-based filament. Applying PixelPrint, we converted patient DICOM images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. Two different phantoms were designed to demonstrate the high reproducibility of our approach with micro-CT acquisitions, and to determine the mapping between filament line widths and HU values on a clinical CT system. Moreover, a third phantom based on a clinical cervical spine scan was manufactured and scanned with a clinical spectral CT scanner. CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels. Measured differences between patient and phantom are around 10 HU for bone marrow voxels and around 150 HU for cortical bone. In addition, stone-based filament can accurately represent boney tissue structures across the different x-ray energies, as measured by spectral CT. This study demonstrates the feasibility of our 3D-printed patient-based phantoms to be extended to soft-tissue and bone structure while maintaining accurate organ geometry, image texture, and attenuation profiles for spectral CT.

Three-dimensional printing of patient-specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures.

PURPOSE: Phantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures. METHODS: PixelPrint, a software tool, was developed to convert patient digital imaging and communications in medicine (DICOM) images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. A calibration phantom was designed to determine the mapping between filament line width and Hounsfield units (HU) within the range of human lungs. For evaluation of PixelPrint, a phantom based on a single human lung slice was manufactured and scanned with the same CT scanner and protocol used for the patient scan. Density and geometrical accuracy between phantom and patient CT data were evaluated for various anatomical features in the lung. RESULTS: For the calibration phantom, measured mean HU show a very high level of linear correlation with respect to the utilized filament line widths, (r > 0.999). Qualitatively, the CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels (from -800 to 0 HU), with clearly visible vascular and parenchymal structures. Regions of interest comparing attenuation illustrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist reveal a high degree of geometrical correlation of details between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the scans. CONCLUSION: The present study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate organ geometry, image texture, and attenuation profiles. PixelPrint will enable applications in the research and development of CT technology, including further development in radiomics.

Diagnostic significance of CyclinD1 and D2-40 expression for follicular neoplasm of the thyroid.

OBJECTIVES: To evaluate the expression and differential diagnostic significance of CyclinD1 and D2-40 in follicular neoplasm (FN) and other thyroid adenomatoid lesions. METHODS: A total of 144 cases of thyroid adenomatoid lesions were enrolled. Immunohistochemistry for CyclinD1 and D2-40 was performed. RESULTS: We found two patterns of CyclinD1 expression: nuclear (N) and cytoplasmic (C). The expression of N-CyclinD1 / C-CyclinD1 in FN (77.4%, 48/62; 50.0%, 31/62) was much higher than that in multinodular goiters with dominant nodules (MNG-DN) (16.4%, 10/61; 4.9%, 3/61) (p < 0.05). In contrast, the expression of D2-40 in MNG-DN (82.0%,50/61) was much higher than that in FN (4.8%, 3/62) (p < 0.05). In addition, unique staining patterns were observed: CyclinD1 showed no immunostaining only in all 8 cases of oncocytic cell tumors (OCT); D2-40 staining showed the characteristic wide distribution of lymphatic vessels in all 8 cases of poorly differentiated thyroid carcinoma (PDTC). Finally, the expression of CyclinD1 and D2-40 did not differ among follicular thyroid adenoma and follicular thyroid carcinoma / noninvasive follicular thyroid neoplasm with papillary-like nuclear features (p > 0.05). CONCLUSIONS: CyclinD1 and D2-40 are helpful diagnostic markers of FN, which can assist to discern FN from MNG-DN / OCT / PDTC.