INTRODUCTION:Osimertinib (AZD9291) is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that has shown significant clinical benefits in patients with EGFR-sensitizing mutations or the EGFR T790M mutation. The homologous recombination (HR) pathway is crucial for repairing DNA double-strand breaks (DSBs). Rad51 plays a central role in HR, facilitating the search for homology and promoting DNA strand exchange between homologous DNA molecules. Rad51 is overexpressed in numerous types of cancer cells. B02, a specific small molecule inhibitor of Rad51, inhibits the DNA strand exchange activity of Rad51. Previous studies have indicated that B02 disrupted Rad51 foci formation in response to DNA damage and inhibited DSBs repair in human cells and sensitized them to chemotherapeutic drugs in vitro and in vivo. However, the potential therapeutic effects of combining osimertinib with a Rad51 inhibitor are not well understood. The aim of this study was to elucidate whether the downregulation of Rad51 expression and activity can enhance the osimertinib-induced cytotoxicity in non-small cell lung cancer (NSCLC) cells. METHODS:We used the MTS, trypan blue dye exclusion and colony-formation ability assay to determine whether osimertinib alone or in combination with B02 had cytotoxic effects on NSCLC cell lines. Real-time polymerase chain reaction was conducted to measure the amounts of Rad51 mRNA. The protein levels of phosphorylated AKT and Rad51 were determined by Western blot analysis. RESULTS:We found that osimertinib reduced Rad51 expression by inactivating AKT activity. Rad51 knockdown using small interfering RNA or AKT inactivation through the phosphatidylinositol 3-kinase inhibitor LY294002 or si-AKT RNA transfection enhanced the cytotoxic and growth inhibitory effects of osimertinib. In contrast, AKT-CA (a constitutively active form of AKT) vector-enforced expression could mitigate the cytotoxic and cell growth inhibitory effects of osimertinib. Furthermore, B02 significantly enhanced the cytotoxic and cell growth inhibitory effects of osimertinib in NSCLC cells. Compared to parental cells, the activation of AKT and Rad51 expression in osimertinib-resistant cells could not be significantly inhibited by osimertinib treatment. Moreover, the increased expression of Rad51 is associated with the resistance mechanism in osimertinib-resistant H1975 and A549 cells. CONCLUSION:Collectively, the downregulation of Rad51 expression and activity enhances the cytotoxic effect of osimertinib in human NSCLC cells.

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

Positron emission tomography (PET) is a non-invasive molecular imaging technique. The limited spatial resolution of PET images, due to technological and physical imaging constraints, directly affects the precise localization and interpretation of small lesions and biological processes. The super-resolution (SR) technique aims to enhance image quality by improving spatial resolution, thereby aiding clinicians in achieving more accurate diagnoses. However, most conventional SR methods rely on idealized degradation models and fail to effectively capture both low- and high-frequency information present in medical images. For the challenging SR reconstruction of PET images exhibiting motion-induced artefacts, a degradation model that better aligns with practical scanning scenarios was designed by us. Furthermore, we proposed a PET image SR method based on the deep residual-in-residual network (DRRN), focusing on the recovery of both low- and high-frequency information. By incorporating multi-level residual connections, our approach facilitates direct feature propagation across different network levels. This design effectively mitigates the lack of feature correlation between adjacent convolutional layers in deep networks. Our proposed method surpasses benchmark methods in both full-reference and no-reference metrics and subjective visual effects across small animal PET (SAPET), phantoms, and Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets. The experimental findings confirm the remarkable efficacy of DRRN in enhancing spatial resolution and mitigating blurring in PET images. In comparison to conventional SR techniques, this method demonstrates superior proficiency in restoring low-frequency structural texture information while simultaneously maintaining high-frequency details, thus showcasing exceptional multi-frequency information fusion capabilities.

Gallium-67 (Ga-67) lung scans assess alveolitis and interstitial lung disease, with Ga-67 accumulation correlating with inflammation intensity as seen in lung biopsy sections. Both semi-quantitative and quantitative methods have been used, with the latter providing an inflammatory activity index linked to lung lavage and histopathological scores. However, differing reference points result in varying outcomes. This study aimed to establish standard Ga-67 index parameters using semi-quantitative analysis and a linear regression model based on planar and SPECT/CT images. Twenty subjects without lung disease received a 5–6 mCi Ga-67 injection via the ankle. Both planar and SPECT/CT images were analyzed, with semi-quantitative values assessed using GraphPad Prism 9.5.1. Compared to planar images, SPECT/CT acquisitions for the lung-to-arm uptake ratio proved the most reliable indicator for assessing lung inflammation. Increased lung Ga-67 uptake was defined as greater than 2 standard deviations from the average Ga-67 uptake for healthy subjects. The normal range was 0.065 ± 0.049 for the right side and 0.076 ± 0.070 for the left side. Lung-to-arm ratios in SPECT/CT images are more accurate than those in planar images. This method should be adopted as a standard clinical practice, providing more reliable diagnostic information and improving patient management. Future studies should compare patients without lung disease to those with lung disease to determine the diagnostic benefit of these norms.

In 5G, network functions can be scaled out/in dynamically to adjust the capacity for network slices. The scale-out/-in procedure, namely autoscaling, enhances performance by scaling out instances and reduces operational costs by scaling in instances. However, the autoscaling problems in 5G networks are different from those in traditional cloud computing. The 5G network functions must be considered the simultaneous deployment of multiple instances; moreover, the deployment of 5G network functions is more frequent than that of traditional cloud computing. Both the number and timing of deployment will substantially affect the cost-effectiveness of the system. In this paper, we first identify the autoscaling issues specifically based on the 3GPP standards. We develop a low-complexity analytical queuing model to formulate the problem and quantify a set of performance metrics with closed-form solutions. The proposed analytical model and closed-form solutions are cross-validated by extensive simulations. The analytical model offers design insights and theoretical guidelines, helping us study the effectiveness of reservations. We proposed a dynamic block-setup reservation algorithm (DBRA) to find the optimal reserved number and threshold value of network slices. Therefore, mobile operators can balance the system's cost-effectiveness without large-scaled testing and real deployment, saving cost on time and money.

In this study, we focus on the issue of calculation reliability and the overhead in vehicular microclouds. We propose a scheduling solution, model the proposed system, and propose an algorithm to obtain the best scheduling configuration. We use simulations to cross-validate our mathematical model, and our evaluation shows that the proposed algorithm performs best.

We aimed to determine whether the hemorrhage risks of cerebral arteriovenous malformation (AVM), evaluated through digital subtraction angiography (DSA) using a neural network, were superior to those assessed through angioarchitecture. We conducted a retrospective review of patients with cerebral AVM who underwent DSA from 2011 to 2017. Angioarchitecture parameters, age, and sex were analyzed using univariate and multivariate logistic regression. Additionally, a neural network was trained using a combination of convolutional neural network (CNN) and recurrent neural network (RNN) architectures. The training dataset consisted of 118 samples, while 29 samples were reserved for testing. After adjusting for age at diagnosis and sex, single venous drainage (odds ratio [OR] = 2.48, p = 0.017), exclusive deep venous drainage (OR = 3.19, p = 0.005), and venous sac (OR = 0.43, p = 0.044) were identified as independent risk factors for hemorrhage. The angioarchitecture-based hemorrhagic prediction model achieved 69% accuracy with an AUC (area under the ROC curve) of 0.757, while the CNN–RNN-based model achieved 76% accuracy with an AUC of 0.748. We present a diagnostic performance for hemorrhagic risk assessment of AVMs that is comparable to the angioarchitectural analysis. By leveraging larger datasets, there is significant potential to enhance prediction accuracy further. The CNN–RNN algorithm not only can potentially streamline workflow within the angio-suite but also serves as a complementary approach to optimize diagnostic accuracy and treatment strategies.

Offloading in edge computing scenarios is considered a prime solution to reduce computational time and also energy resources of the user equipment. This paper focuses on computation offloading from the user equipment to co-located vehicular microclouds. We derive the closed-form system metrics and cross-validate them with simulations. Through a comprehensive observation of vehicular microcloud behavior, the proposed two-state offloading algorithm (TSOA) provides the optimal offloading configuration in both planning and operating states. Finally, our evaluation demonstrates that the proposed TSOA performs optimally among the three offloading schemes.

This research employs NYCU-developed open source 5G core network $\boldsymbol{free5GC}$ and Intel open source edge computing platform OpenNESS to build a 5G private network. An online multi-person chorus application is then deployed on the edge platform to (1) achieve High Reliability and Low Latency Communication (URLLC) requirements, and (2) improve the backhaul bandwidth occupancy rate from the edge to the core network. In addition, this research implements the traffic influence function proposed in the 3GPP 5G standards, which can dynamically change traffic rules of the 5G core during execution, directing specific traffic to the edge applications in order to improve the performance of private networks. Finally, to verify the effectiveness of this schema, this research uses the example application deployed to compare the performance of the system equipped with edge computing with that without edge platform. Our analysis is done with both a physical RAN and the UERANSIM simulator.

Network function virtualization (NFV) is a novel concept that enables an architectural transition from dedicated hardware to orchestrated resource and function management. As an integral part of the core network, NFV offers a fine-grained network capability to cellular operators by scaling out or scaling in network resources in an on-demand manner to meet the performance requirements. However, designing an autoscaling algorithm with low operation cost and low latency in non-standalone networks, where legacy network equipment coexists with a virtual evolved packet core (EPC), is a challenging task. In this paper, we propose a dynamic NFV instance autoscaling algorithm that considers the tradeoff between performance and operation cost. Furthermore, we develop an analytical framework to assess the performance of the scheme by modeling the hybrid network as a queueing system that includes both legacy network equipment and NFV instances. The virtualized network function (VNF) instances are powered on or off according to the number of job requests. Numerical results based on extensive simulations validate the correctness of the model and the effectiveness of the algorithm.

Background:This study developed and validated a deep learning (DL) model based on whole slide imaging (WSI) for predicting the treatment response to chemotherapy and radiotherapy (CRT) among patients with non-small cell lung cancer (NSCLC).Methods:We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile). A voting method was employed, by which the label of tiles with the greatest quantity from 1 patient would be used as the label of the patient.Results:The tissue classification model had a great performance (accuracy in the training set/internal validation set =0.966/0.956). Based on 181,875 tumor-tiles selected by the tissue classification model, the model for predicting the treatment response demonstrated strong predictive ability (accuracy of patient-level prediction in the internal validation set/external validation set 1/external validation set 2 =0.786/0.742/0.737).Conclusions:A DL model was constructed based on WSI to predict the treatment response of patients with NSCLC. This model can help doctors to formulate personalized CRT plans and improve treatment outcomes.

Next-generation intelligent transportation systems aim to achieve many cooperative perception and cooperative driving functions that require considerable computational resources. Offloading such tasks via mobile edge computing is considered part of the solution; this approach is currently being studied within the scope of 5G networks and beyond. In the automotive context, such edge systems could be roadside units (RSUs), which can easily be overloaded at peak times. Vehicular microcloud approaches have been proposed to overcome such problems by sharing the computational resources of nearby cars. In this study, we propose an offloading system architecture to enable such offloading in a vehicular microcloud interconnected by a 5G core network. We model the system as a queueing model to derive closed-form solutions for selected performance metrics. Based on our insights, we propose the triple-check offloading algorithm (TCOA) to obtain both the best offloading ratio of the vehicular microcloud to the entire offloading system and the optimal maximum number of the remaining vehicle instances in the vehicular microcloud. Our simulation results show that the proposed TCOA achieves better system performance than four other offloading schemes in terms of cost, response time, service rate, and cost response-time production service rate division (CRPSD).

BACKGROUND:Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative joint disease in which quantitative analysis based on magnetic resonance image (MRI) or cone-beam computed tomography (CBCT) remains limited. Moreover, the long-term effects of soft food on the adaptive condylar remodeling process in TMJ-OA remain unclear. This study aimed to assess the effects of food hardness on adaptive condylar remodeling in a healthy TMJ, TMJ-OA, and controlled TMJ-OA.METHODS:Complete Freund's adjuvant (CFA) was used for TMJ-OA induction and Link-N (LN) for TMJ repair. Eighteen mature rats were randomly divided into six groups: (1) control/normal diet (Ctrl-N); (2) control/soft diet (Ctrl-S); (3) TMJ-OA/normal diet (CFA-N); (4) TMJ-OA/soft diet (CFA-S); (5) Link-N-controlled TMJ-OA/normal diet (LN-N); and (6) Link-N-controlled TMJ-OA/soft diet (LN-S). Micro-CT was performed 14, 21, and 28 days after CFA injection to analyze the bone volume, bone volume fraction (BVF), bone mineral density (BMD), and trabecular bone number and thickness (Tb.N, Tb.Th). MRI and histological imaging were performed to support the analysis.RESULTS:Under CFA treatment, the BVF and BMD decreased significantly (p < 0.01) and later recovered to normal. However, more significant improvements occurred in normal-diet groups than soft-diet groups. Additionally, bone volume changes were more predictable in the normal-diet groups than in the soft-diet groups. The normal-diet groups presented a significant decrease and increase in the Tb.N and Tb.Th, respectively (p < 0.05), while the Tb.N and Tb.Th in the soft-diet groups remained largely unchanged. Furthermore, a significantly higher frequency of irregularities on the condylar articular surface was found in the soft-diet groups.CONCLUSIONS:Compared with a soft diet, a normal diet may be beneficial for preserving condyle articular surface and directing bone remodeling in TMJ-OA rats.

The demands of today’s 5G mobile network, especially low latency and high bandwidth, are a big challenge for the 5G Core (5GC) provider. The most critical user data packet handler in the 5GC Network Function (NF) is the User Plane Function (UPF), which is responsible for moving data from the user equipment to the destination data network, and vice versa. Existing work mainly focuses on implementing UPF using the key technologies of high-speed data processing. In this paper, with a mobile core provider called free5GC for a stand-alone (SA) 5G network, we share our experience with the implementation of UPF by using a programmable hardware appliance, which can offer more Tbps compared to the implementation of software UPF that can offer only a few hundred Gbps. For that, we demonstrate how to build up a more flexible architecture of UPF by using the Software-Defined Networking (SDN) concept due to the opacity of protocol specification. We split the UPF control signal implementation into a software application, and user data packet processing into a programmable hardware appliance. We also show how to integrate a number of current UPF data plane free5GC implementations such as Data Plane Development Kit (DPDK), Linux kernel module, and SmartNIC. Furthermore, we analyze and make use of microservices to support the specific features of the UPF data plane that cannot be implemented in a programmable hardware appliance. We tested our free5GC mobile network and the new UPF design architecture that can run on a real programmable hardware appliance from Accton CSP-7551. The evaluation results show that our programmable user plane can reach the line rate.

Positron emission tomography (PET) is a popular research topic. People are becoming more interested in PET images as they become more widely available. However, the partial volume effect (PVE) in PET images remains one of the most influential factors causing the resolution of PET images to degrade. It is possible to reduce this PVE and achieve better image quality by measuring and modeling the point spread function (PSF) and then accounting for it inside the reconstruction algorithm. In this work, we examined the response characteristics of the MetisTM PET/CT system by acquiring 22Na point source at different locations in the field of view (FOV) of the scanner and reconstructing with small pixel size for images to obtain their radial, tangential, and axial full-width half maximum (FWHM). An image-based model of the PSF model was then obtained by fitting asymmetric two-dimensional Gaussians on the 22Na images. This PSF model determined by FWHM in three directions was integrated into a three-dimensional ordered subsets expectation maximization (3D-OSEM) algorithm based on a list-mode format to form a new PSF-OSEM algorithm. We used both algorithms to reconstruct point source, Derenzo phantom, and mouse PET images and performed qualitative and quantitative analyses. In the point source study, the PSF-OSEM algorithm reduced the FWHM of the point source PET image in three directions to about 0.67 mm, and in the phantom study, the PET image reconstructed by the PSF-OSEM algorithm had better visual effects. At the same time, the quantitative analysis results of the Derenzo phantom were better than the original 3D-OSEM algorithm. In the mouse experiment, the results of qualitative and quantitative analyses showed that the imaging quality of PSF-OSEM algorithm was better than that of 3D-OSEM algorithm. Our results show that adding the PSF model to the 3D-OSEM algorithm in the MetisTM PET/CT system helps to improve the resolution of the image and satisfy the qualitative and quantitative analysis criteria.