A 540° posterior-anterior-posterior approach for 360° fused rigid severe cervical kyphosis: patient series

BACKGROUND Treatment of severe rigid 360° fused cervical kyphosis (CK) is challenging and often requires a combined approach for ankylosis release, establishment of sagittal balance, and fixation with fusion. OBSERVATIONS Four patients with iatrogenic 360° fused severe rigid CK (Cobb angle ≥40°) were enrolled for this retrospective analysis. All patients in the case series were female, with an average age of 27 years. All patients previously underwent posterior laminectomy/laminoplasty and cervical tumor resection when they were children (13–17 years). They underwent correction surgery with a 540° posterior-anterior-posterior approach. Preoperative and final follow-up radiography and computed tomography (CT) were used to evaluate kyphosis correction, internal fixation implants, and bone fusion. The preoperative and final follow-up average C2–7 Cobb angles were −32.4° ± 12.0° and 5.3° ± 7.1°, respectively. Preoperative and final follow-up CK angles averaged −47.2° ± 7.4° and −0.9° ± 16.1°, respectively. The mean correction angle was 46.3° ± 9.6°. At final follow-up, CT showed stable fixation and solid bone fusion. LESSONS The rare iatrogenic severe kyphosis with 360° ankylosis requires a combined approach. The 540° posterior-anterior-posterior approach can completely release the bony fusion, and the CK can be corrected using an anterior plate. This technique can achieve good results and is an effective strategy.

Case Report: Favorable Response and Manageable Toxicity to the Combination of Camrelizumab, Oxaliplatin, and Oral S-1 in a Patient With Advanced Epstein–Barr Virus-Associated Gastric Cancer

Some pertinent studies have demonstrated that Epstein–Barr virus-associated gastric cancer (EBVaGC) patients showed a favorable clinical outcome to immunotherapy and Epstein–Barr virus (EBV)-positive status might be a potential biomarker for immunotherapy in gastric cancer (GC). However, knowledge of given exposure to EBVaGC to the first-line immunotherapy is largely inadequate. Moreover, whether camrelizumab can be as effective as other PD-1 inhibitors in the treatment of advanced EBVaGC has not been reported. We report a case of advanced EBVaGC patient with a positive expression of PD-L1, enriched PD-L1+CD68+macrophages, and high TMB who had a long-term partial response and manageable toxicity to the combined approach of camrelizumab (a novel PD-1 inhibitor) and oxaliplatin plus oral S-1 (SOX). As the first-line treatment of advanced EBVaGC patients, camrelizumab combined with SOX regimen may provide a novel combined approach with favorable response and manageable safety. Combination of multiple biomarkers could have a higher effective predictive capacity to immunotherapy. Integrated treatment (chemo-immunotherapy and radiotherapy) might be the optimal strategy for patients with oligometastasis. It deserves prospective research to further validate the efficacy.

Methodology for the Assessment of Imprecise Multi-State System Availability

Most existing studies of a system’s availability in the presence of epistemic uncertainties assume that the system is binary. In this paper, a new methodology for the estimation of the availability of multi-state systems is developed, taking into consideration epistemic uncertainties. This paper formulates a combined approach, based on continuous Markov chains and interval contraction methods, to address the problem of computing the availability of multi-state systems with imprecise failure and repair rates. The interval constraint propagation method, which we refer to as the forward–backward propagation (FBP) contraction method, allows us to contract the probability intervals, keeping all the values that may be consistent with the set of constraints. This methodology is guaranteed, and several numerical examples of systems with complex architectures are studied.

Segmentation of Pectoral Muscle in Mammograms Using Granular Computing

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and layering was employed to locate the pectoral muscle in mammograms. In most cases, the pectoral muscle is found to be triangular in shape and hence, the ant colony optimization algorithm is employed to accurately estimate the pectoral muscle boundary. The proposed method works with the left mediolateral oblique (MLO) view of mammograms to avoid artifacts. For the right MLO view, the method automatically mirrors the image to the left MLO view. The performance of this method was evaluated using the standard mini MIAS dataset (mammographic image analysis society). The algorithm was tested on 322 images and the overall accuracy of the system was about 97.47 %. The method is robust with respect to the view, shape, size and reduces the processing time. The approach correctly identifies images when the pectoral muscle is completely absent.

Challenges, Roles, and Axiology of Indonesian Digital Literacy During the Pandemic

Digital literacy is one of the new media, which has a vital role in Indonesian people's lives, especially during the pandemic. In this article, the author will discuss the challenges faced by the community in implementing digital literacy, the role and value of axiology contained in digital literacy during a pandemic. Indonesian society is expected to have good digital literacy skills to balance these urgent needs, especially during a pandemic. This article aims to increase public awareness of the importance of understanding digital literacy. Besides, make efforts to utilize digital literacy to the fullest and realize the challenges faced. This research method uses a combined approach. The results show that digital literacy is critical in various aspects and has axiological value. However, Indonesia still has several challenges implementing digital literacy during the pandemic. Therefore, the government and society are expected to work together to improve the ability and understanding of digital literacy. However, this article still needs to be reviewed further due to the limited data and time available.

LPIN: A Lightweight Progressive Inpainting Network for Improving the Robustness of Remote Sensing Images Scene Classification

At present, the classification accuracy of high-resolution Remote Sensing Image Scene Classification (RSISC) has reached a quite high level on standard datasets. However, when coming to practical application, the intrinsic noise of satellite sensors and the disturbance of atmospheric environment often degrade real Remote Sensing (RS) images. It introduces defects to them, which affects the performance and reduces the robustness of RSISC methods. Moreover, due to the restriction of memory and power consumption, the methods also need a small number of parameters and fast computing speed to be implemented on small portable systems such as unmanned aerial vehicles. In this paper, a Lightweight Progressive Inpainting Network (LPIN) and a novel combined approach of LPIN and the existing RSISC methods are proposed to improve the robustness of RSISC tasks and satisfy the requirement of methods on portable systems. The defects in real RS images are inpainted by LPIN to provide a purified input for classification. With the combined approach, the classification accuracy on RS images with defects can be improved to the original level of those without defects. The LPIN is designed on the consideration of lightweight model. Measures are adopted to ensure a high gradient transmission efficiency while reducing the number of network parameters. Multiple loss functions are used to get reasonable and realistic inpainting results. Extensive tests of image inpainting of LPIN and classification tests with the combined approach on NWPU-RESISC45, UC Merced Land-Use and AID datasets are carried out which indicate that the LPIN achieves a state-of-the-art inpainting quality with less parameters and a faster inpainting speed. Furthermore, the combined approach keeps the comparable classification accuracy level on RS images with defects as that without defects, which will improve the robustness of high-resolution RSISC tasks.