Enhancing Precision in Cardiac Segmentation for MR-Guided Radiation Therapy through Deep Learning.

INTRODUCTION: Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than whole-heart metrics. MR-guided radiation therapy (MRgRT) enables substructure visualization during daily localization, allowing potential for enhanced cardiac sparing. We extend a publicly available state-of-the-art deep learning (DL) framework, nnU-Net, to incorporate self-distillation (nnU-Net.wSD) for substructure segmentation for MRgRT. METHODS: Eighteen (Institute A) patients who underwent thoracic or abdominal radiation therapy on a 0.35 T MR-guided linac were retrospectively evaluated. On each image, one of two radiation oncologists delineated reference contours of 12 cardiac substructures (chambers, great vessels, and coronary arteries) used to train (n=10), validate (n=3), and test (n=5) nnU-Net.wSD leveraging a teacher-student network and comparing to standard 3D U-Net. The impact of using simulation data or including 3-4 daily images for augmentation during training was evaluated for nnU-Net.wSD. Geometric metrics (Dice similarity coefficient (DSC), mean distance to agreement (MDA), and 95% Hausdorff distance (HD95)), visual inspection, and clinical dose volume histograms (DVHs) were evaluated. To determine generalizability, Institute A's model was tested on an unlabeled dataset from Institute B (n=22) and evaluated via consensus scoring and volume comparisons. RESULTS: nnU-Net.wSD yielded a DSC (reported mean ± standard deviation) of 0.65±0.25 across the 12 substructures (Chambers: 0.85±0.05, Great Vessels: 0.67±0.19, and Coronary Arteries 0.33±0.16, mean MDA <3 mm, and mean HD95 <9 mm) while outperforming the 3D U-Net (0.583±0.28, p<0.01). Leveraging fractionated data for augmentation improved over a single MR-SIM timepoint (0.579±0.29, p<0.01). Predicted contours yielded DVHs that closely matched the clinical treatment plans where mean and D0.03cc doses deviated by 0.32±0.5 Gy and 1.42±2.6 Gy respectively. No statistically significant differences between Institute A and B volumes (p>0.05) for 11 of 12 substructures with larger volumes requiring minor changes and coronary arteries exhibiting more variability. CONCLUSIONS: This work is a critical step to rapid and reliable cardiac substructure segmentation to improve cardiac sparing in low-field MRgRT.

DKK1 Activates the PI3K/AKT Pathway via CKAP4 to Balance the Inhibitory Effect on Wnt/ß-Catenin Signaling and Regulates Wnt3a-Induced MSC Migration.

Wnt/ß-catenin signaling plays a crucial role in the migration of mesenchymal stem cells (MSCs). However, our study has revealed an intriguing phenomenon where Dickkopf-1 (DKK1), an inhibitor of Wnt/ß-catenin signaling, promotes MSC migration at certain concentrations ranging from 25 to 100 ng/mL while inhibiting Wnt3a-induced MSC migration at a higher concentration (400 ng/mL). Interestingly, DKK1 consistently inhibited Wnt3a-induced phosphorylation of LRP6 at all concentrations. We further identified cytoskeleton-associated protein 4 (CKAP4), another DKK1 receptor, to be localized on the cell membrane of MSCs. Overexpressing the CRD2 deletion mutant of DKK1 (ΔCRD2), which selectively binds to CKAP4, promoted the accumulation of active ß-catenin (ABC), the phosphorylation of AKT (Ser473) and the migration of MSCs, suggesting that DKK1 may activate Wnt/ß-catenin signaling via the CKAP4/PI3K/AKT cascade. We also investigated the effect of the CKAP4 intracellular domain mutant (CKAP4-P/A) that failed to activate the PI3K/AKT pathway and found that CKAP4-P/A suppressed DKK1 (100 ng/mL)-induced AKT activation, ABC accumulation, and MSC migration. Moreover, CKAP4-P/A significantly weakened the inhibitory effects of DKK1 (400 ng/mL) on Wnt3a-induced MSC migration and Wnt/ß-catenin signaling. Based on these findings, we propose that DKK1 may activate the PI3K/AKT pathway via CKAP4 to balance the inhibitory effect on Wnt/ß-catenin signaling and thus regulate Wnt3a-induced migration of MSCs. Our study reveals a previously unrecognized role of DKK1 in regulating MSC migration, highlighting the importance of CKAP4 and PI3K/AKT pathways in this process.

MAGNET: A MODALITY-AGNOSTIC NETWORK FOR 3D MEDICAL IMAGE SEGMENTATION.

In this paper, we proposed MAGNET, a novel modality-agnostic network for 3D medical image segmentation. Different from existing learning methods, MAGNET is specifically designed to handle real medical situations where multiple modalities/sequences are available during model training, but fewer ones are available or used at time of clinical practice. Our results on multiple datasets show that MAGNET trained on multi-modality data has the unique ability to perform predictions using any subset of training imaging modalities. It outperforms individually trained uni-modality models while can further boost performance when more modalities are available at testing.

A Bioinformatics-Based Analysis of an Anoikis-Related Gene Signature Predicts the Prognosis of Patients with Low-Grade Gliomas.

Low-grade glioma (LGG) is a highly aggressive disease in the skull. On the other hand, anoikis, a specific form of cell death induced by the loss of cell contact with the extracellular matrix, plays a key role in cancer metastasis. In this study, anoikis-related genes (ANRGs) were used to identify LGG subtypes and to construct a prognostic model for LGG patients. In addition, we explored the immune microenvironment and enrichment pathways between different subtypes. We constructed an anoikis-related gene signature using the TCGA (The Cancer Genome Atlas) cohort and investigated the differences between different risk groups in clinical features, mutational landscape, immune cell infiltration (ICI), etc. Kaplan-Meier analysis showed that the characteristics of ANRGs in the high-risk group were associated with poor prognosis in LGG patients. The risk score was identified as an independent prognostic factor. The high-risk group had higher ICI, tumor mutation load (TMB), immune checkpoint gene expression, and therapeutic response to immune checkpoint blockers (ICB). Functional analysis showed that these high-risk and low-risk groups had different immune statuses and drug sensitivity. Risk scores were used together with LGG clinicopathological features to construct a nomogram, and Decision Curve Analysis (DCA) showed that the model could enable patients to benefit from clinical treatment strategies.

An Energy Harvester with Temperature Threshold Triggered Cycling Generation for Thermal Event Autonomous Monitoring.

This paper proposes a temperature threshold triggered energy harvester for potential application of heat-event monitoring. The proposed structure comprises an electricity generation cantilever and a bimetallic cantilever that magnetically attract together. When the structure is heated to a pre-set temperature threshold, the heat absorption induced bimetallic effect of the bimetallic cantilever will cause sufficient bending of the generation cantilever to get rid of the magnetic attraction. The action triggers the freed generation cantilever into resonance to piezoelectrically generate electricity, and the heated bimetallic cantilever dissipates heat to the environment. With the heat dissipated, the bimetallic cantilever will be restored to attract with the generation cantilever again and the structure returns to the original state. Under continual heating, the temperature threshold triggered cycle is repeated to intermittently generate electric power. In this paper, the temperature threshold of the harvester is modeled, and the harvester prototype is fabricated and tested. The test results indicate that, with the temperature threshold of 71 °C, the harvesting prototype is tested to generate 1.14 V peak-to-peak voltage and 1.077 µW instantaneous power within one cycle. The thermal harvesting scheme shows application potential in heat event-driven autonomous monitoring.

Transplantation of neural scaffolds consisting of dermal fibroblast-reprogrammed neurons and 3D silk fibrous materials promotes the repair of spinal cord injury.

Transplantation of tissue-engineered neural scaffolds bears great potential for reconstructing neural circuits after spinal cord injury (SCI). In this study, a 3D porous silk fibrous scaffold (3D-SF) with biomimetic interconnected micro- to nanofibrous structure and good biocompatibility is fabricated. Then, a small-molecule combination CFLSSVY (CHIR99021, Forskolin, LDN193189, SB431542, SP600125, VPA, and Y27632) that efficiently reprograms rat dermal fibroblasts into neurons is screened, and these chemically induced neurons (CiNs) are shown to readily communicate on the 3D-SF and form neural scaffolds. After transplantation of these silk-based neural scaffolds into the stumps of transected spinal cords in rats, the damaged tissue is repaired significantly, as indicated by the reduced cavity areas, decreased GFAP expression, and improved axonal regeneration and myelination in the injury site. Moreover, the hindlimb movement and motor-nerve conductivity are greatly improved as indicated by the elevated BBB score, the alternate movement of two hindlimbs during the 45° inclined grid test, and the shortened latency and enhanced amplitude in cMEP detection. Together, these results demonstrate that transplantation of neural scaffolds consisting of 3D-SF and dermal fibroblast-reprogrammed neurons leads to significant nerve regeneration and functional recovery, providing a promising therapeutic strategy for SCI.

Paenibacillus assamensis in Joint Fluid of Man with Suspected Tularemia, China.

Paenibacillus assamensis is a bacterium usually found in warm springs. We detected P. assamensis in a man with suspected tularemia. The strain isolated from the man's knee joint fluid was identified as P. assamensis after analysis of a homologous sequence of the 16S rRNA gene.

Wearable energy harvesters generating electricity from low-frequency human limb movement.

A wearable energy harvester technology is developed for generating electricity from the movement of human joints. A micro-electroplated ferromagnetic nickel cantilever is integrated with a piezoelectric element and bonded on a flexible substrate. Based on the magnetic interaction between the magnetized cantilever and a magnet on the substrate, a novel vertical-vibration frequency-up-conversion (FUC) structure is formed to generate stable amounts of electric energy per cycle from the horizontal substrate stretching/rebounding. The two ends of the flexible substrate are attached on both sides of a limb joint to transform joint rotation into substrate stretching. During limb movement, the flexible substrate is horizontally stretched and rebounded, causing the cantilever to vertically release from and return to the magnet, thereby exciting the piezoelectric cantilever into resonant generation. Since the horizontal low-frequency limb movement is perpendicular to the vertical high-frequency resonance, the stretch has little influence on the resonance of the cantilever. Thus the generated energy is always stable within a wide frequency range of limb movements. The performance of the novel harvester is experimentally verified using a stretching/rebounding movement cycle, where the cycle corresponds to the frequency range of 0.5-5.0 Hz. Within one stretching/rebounding movement cycle, the generated electric energy is stable in the approximate range of 0.56-0.69 µJ for the whole frequency range. Two flexible harvesters are worn on the human elbow and knee for a body kinetic energy harvesting test. Considerable power can always be generated under typical low-frequency limb movements, such as squatting, walking, jogging, and fast running, where the peak-to-peak generated voltages are always approximately 4.0 V. Additionally, energy harvesting under two-directional area stretching is also realized by adjusting the FUC structure layout. The flexible-substrate harvester is promising for various wearable applications.

The novel H1N1 Influenza A global airline transmission and early warning without travel containments.

A novel influenza A (H1N1) has been spreading worldwide. Early studies implied that international air travels might be key cause of a severe potential pandemic without appropriate containments. In this study, early outbreaks in Mexico and some cities of United States were used to estimate the preliminary epidemic parameters by applying adjusted SEIR epidemiological model, indicating transmissibility infectivity of the virus. According to the findings, a new spatial allocation model totally based on the real-time airline data was established to assess the potential spreading of H1N1 from Mexico to the world. Our estimates find the basic reproductive number R0 of H1N1 is around 3.4, and the effective reproductive number fall sharply by effective containment strategies. The finding also implies Spain, Canada, France, Panama, Peru are the most possible country to be involved in severe endemic H1N1 spreading.

Risk analysis for the highly pathogenic avian influenza in Mainland China using meta-modeling.

A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in Mainland China for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.

[Spectral data analysis of salinity soils with ground objects in the delta oasis of Weigan and Kuqa Rivers].

The characteristic of landmark spectrum is not only the physical base of remote sensing technical application but also the base of the quantificational analysis of remote sensing, and the study of landmark spectrum is the main content in the basic research of remote sensing. In the present paper, the authors adopted CI700 portable spectrum apparatus made in American CID Company, and investigated or examined some spots in the delta oasis of Weigan and Kuqa rivers located in the north of Tarim Basin considered as the typical area, based on a great deal of spectral data for different kinds of geo-targets, and the spectral features and changing law of saline-alkaline ground, silver sand ground, dune, cotton ground etc. Alhagi sparsifolia., Phragmites australis, Tamarix, Halostachys caspica etc. were analyzed. According to the actual conditions, we analyzed the data noise characteristic of the spectrum and got rid of the noise. Meanwhile, derivative spectrum technology was used to remove the environmental background influence. Finally, in order to take full advantage of multi-spectrum data, ground information is absolutely necessary, and it is important to build a representative spectral library. The ENVI software was used to build the spectral library of surface features by field survey of the delta oasis of Weigan and Kuqa Rivers, Xinjiang Uygur Autonomous Region. This library can be used for features investigation, vegetation surveys, vegetation classification and environmental monitoring in the delta oasis of Weigan and Kuqa Rivers by remote sensing. The result of this research will be significant to the research on the saline-alkali soil in the arid area.