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Axion-like particles (ALPs) are a class of hypothetical pseudo-scalar particles and can be created in hot astrophysical plasma through the interaction between photons. I calculate the production of ALPs in type Ia supernovae. It is found that many ALPs lighter than a few MeV can be produced in type Ia supernovae. Once produced, heavy ALPs decay into photons during propagation in the interstellar space. I calculate the flux of the decay photons and find that it may be detected by future MeV γ-ray telescopes if a type Ia supernova explodes near the Solar System.

Ossification of the posterior longitudinal ligament (OPLL) can cause severe and irreversible paralysis in not only the cervical spine but also the thoracolumbar spine. To date, however, the prevalence and distribution of OPLL in the whole spine has not been precisely evaluated in patients with cervical OPLL. Therefore, we conducted a multi-center study to comprehensively evaluate the prevalence and distribution of OPLL using multi-detector computed tomography (CT) images in the whole spine and to analyze what factors predict the presence of ossified lesions in the thoracolumbar spine in patients who were diagnosed with cervical OPLL by plain X-ray. Three hundred and twenty-two patients with a diagnosis of cervical OPLL underwent CT imaging of the whole spine. The sum of the levels in which OPLL was present in the whole spine was defined as the OP-index and used to evaluate the extent of ossification. The distribution of OPLL in the whole spine was compared between male and female subjects. In addition, a multiple regression model was used to ascertain related factors that affected the OP-index. Among patients with cervical OPLL, women tended to have more ossified lesions in the thoracolumbar spine than did men. A multiple regression model revealed that the OP-index was significantly correlated with the cervical OP-index, sex (female), and body mass index. Furthermore, the prevalence of thoracolumbar OPLL in patients with a cervical OP-index ≥ 10 was 7.8 times greater than that in patients with a cervical OP-index ≤ 5. The results of this study reveal that the extent of OPLL in the whole spine is significantly associated with the extent of cervical OPLL, female sex, and obesity.

We attempted to establish a magnetic resonance imaging (MRI)-based radiomic model for stratifying prognostic subgroups of newly diagnosed glioblastoma (GBM) patients and predicting O (6)-methylguanine-DNA methyltransferase promotor methylation ( pMGMT -met) status of the tumor. Preoperative MRI scans from 201 newly diagnosed GBM patients were included in this study. A total of 489 texture features including the first-order feature, second-order features from 162 datasets, and location data from 182 datasets were collected. Supervised principal component analysis was used for prognostication and predictive modeling for pMGMT -met status was performed based on least absolute shrinkage and selection operator regression. 22 radiomic features that were correlated with prognosis were used to successfully stratify patients into high-risk and low-risk groups ( p  = 0.004, Log-rank test). The radiomic high- and low-risk stratification and pMGMT status were independent prognostic factors. As a matter of fact, predictive accuracy of the pMGMT methylation status was 67% when modeled by two significant radiomic features. A significant survival difference was observed among the combined high-risk group, combined intermediate-risk group (this group consists of radiomic low risk and pMGMT -unmet or radiomic high risk and pMGMT -met), and combined low-risk group ( p  = 0.0003, Log-rank test). Radiomics can be used to build a prognostic score for stratifying high- and low-risk GBM, which was an independent prognostic factor from pMGMT methylation status. On the other hand, predictive accuracy of the pMGMT methylation status by radiomic analysis was insufficient for practical use.

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient age. Using all features, we succeeded in classifying patients with an accuracy of 63.1%, which was significantly higher than the accuracy obtained from using either the radiomic features or patient age alone. In particular, prediction of the pTERT mutation was significantly improved by the CNN texture features. In conclusion, the pretrained CNN texture features capture the information of IDH and TERT genotypes in grade II/III gliomas better than the conventional radiomic features.

Molecular biological characterization of tumors has become a pivotal procedure for glioma patient care. The aim of this study is to build conventional MRI-based radiomics model to predict genetic alterations within grade II/III gliomas attempting to implement lesion location information in the model to improve diagnostic accuracy. One-hundred and ninety-nine grade II/III gliomas patients were enrolled. Three molecular subtypes were identified: IDH1/2 -mutant, IDH1/2 -mutant with TERT promoter mutation, and IDH- wild type. A total of 109 radiomics features from 169 MRI datasets and location information from 199 datasets were extracted. Prediction modeling for genetic alteration was trained via LASSO regression for 111 datasets and validated by the remaining 58 datasets. IDH mutation was detected with an accuracy of 0.82 for the training set and 0.83 for the validation set without lesion location information. Diagnostic accuracy improved to 0.85 for the training set and 0.87 for the validation set when lesion location information was implemented. Diagnostic accuracy for predicting 3 molecular subtypes of grade II/III gliomas was 0.74 for the training set and 0.56 for the validation set with lesion location information implemented. Conventional MRI-based radiomics is one of the most promising strategies that may lead to a non-invasive diagnostic technique for molecular characterization of grade II/III gliomas.

To evaluate the radiological differences between diffuse idiopathic skeletal hyperostosis (DISH) and ankylosing spondylitis (AS) using whole spine computed tomography (CT), including the spine and sacroiliac joint (SIJ). The ossification and bridging of spinal ligament and fusion of the facet joint and SIJ were evaluated in 111 patients who were diagnosed with DISH and 27 patients with AS on the whole spine CT. The number of anterior bridging and shape of bridging (candle-wax-type/ smooth-type) were also evaluated. We further evaluated patients with DISH and AS by matching their age and sex. Complete SIJ fusion was more common in AS, whereas anterior and posterior bony bridging around SIJ was more common in DISH. However, 63% of patients with DISH had a partial or complete fusion. In spinal anterior bony bridging, the majority of patients with AS had the smooth-type, whereas those with DISH had the candle-wax-type. However, some of the patients with DISH (11%) had smooth-type. Intervertebral facet joint fusion is more common in AS. The number of anterior spinal bony bridging was greater in AS than in DISH, especially in the lumbar spine. These results are useful in differentiating DISH from AS and should therefore be considered when making a diagnosis.

Cervical ossification of the posterior longitudinal ligament of the spine (c-OPLL) is an intractable disease that impairs activities of daily living and quality of life. There are few reports on patients’ return to work (RTW) after surgery. The aim of this study was to investigate the RTW status and its associated factors after c-OPLL surgery through a nationwide multicenter questionnaire survey and to provide reference evidence for determining the future treatment of this disease. We were able to study 286 patients (205 males, 81 females) from 13 institutions. We analyzed RTW in 198 patients, excluding 88 patients who were unemployed before surgery. RTW was confirmed in 151 patients (124 males, 27 females), and RTW rate was 76.3%. Significant differences were observed between RTW + and—groups in age, body mass index, preoperative workload, and preoperative JOA score. Notably, this study was the first to demonstrate that postoperative residual neuropathic pain is a significant factor associated with RTW after c-OPLL surgery. The results of this study will provide useful information for determining future treatment plans for patients with an eye toward returning to work after surgery, and may also have an impact on surgical indications for this disease.

The prognostic impact of TERT mutations has been controversial in IDH -wild tumors, particularly in glioblastomas (GBM). The controversy may be attributable to presence of potential confounding factors such as MGM T methylation status or patients’ treatment. This study aimed to evaluate the impact of TERT status on patient outcome in association with various factors in a large series of adult diffuse gliomas. We analyzed a total of 951 adult diffuse gliomas from two cohorts (Cohort 1, n  = 758; Cohort 2, n  = 193) for IDH1/2 , 1p/19q, and TERT promoter status. The combined IDH/TERT classification divided Cohort 1 into four molecular groups with distinct outcomes. The overall survival (OS) was the shortest in IDH wild-type/ TERT mutated groups, which mostly consisted of GBMs ( P  < 0.0001). To investigate the association between TERT mutations and MGMT methylation on survival of patients with GBM, samples from a combined cohort of 453 IDH -wild-type GBM cases treated with radiation and temozolomide were analyzed. A multivariate Cox regression model revealed that the interaction between TERT and MGMT was significant for OS ( P  = 0.0064). Compared with TERT mutant- MGMT unmethylated GBMs, the hazard ratio (HR) for OS incorporating the interaction was the lowest in the TERT mutant- MGMT methylated GBM (HR, 0.266), followed by the TERT wild-type- MGMT methylated (HR, 0.317) and the TERT wild-type- MGMT unmethylated GBMs (HR, 0.542). Thus, patients with TERT mutant- MGMT unmethylated GBM have the poorest prognosis. Our findings suggest that a combination of IDH , TERT , and MGMT refines the classification of grade II-IV diffuse gliomas.

Shiro Ikegawa and colleagues report the results of a genome-wide association study for ossification of the posterior longitudinal ligament of the spine in a Japanese cohort. They identify six new loci, three of which showed decreased expression in a mouse model of endochondral ossification. Ossification of the posterior longitudinal ligament of the spine (OPLL) is a common spinal disorder among the elderly that causes myelopathy and radiculopathy. To identify genetic factors for OPLL, we performed a genome-wide association study (GWAS) in ∼8,000 individuals followed by a replication study using an additional ∼7,000 individuals. We identified six susceptibility loci for OPLL: 20p12.3 (rs2423294: P = 1.10 × 10 −13 ), 8q23.1 (rs374810: P = 1.88 × 10 −13 ), 12p11.22 (rs1979679: P = 4.34 × 10 −12 ), 12p12.2 (rs11045000: P = 2.95 × 10 −11 ), 8q23.3 (rs13279799: P = 1.28 × 10 −10 ) and 6p21.1 (rs927485: P = 9.40 × 10 −9 ). Analyses of gene expression in and around the loci suggested that several genes are involved in OPLL etiology through membranous and/or endochondral ossification processes. Our results bring new insight to the etiology of OPLL.

PurposePostoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL).MethodsThis prospective multicenter study was conducted by the 28 institutions, and 478 patients were included in the analysis. Deep learning was used to create two predictive models of the overall postoperative complications and neurological complications, one of the major complications. These models were constructed by learning the patient's preoperative background, clinical symptoms, surgical procedures, and imaging findings. These logistic regression models were also created, and these accuracies were compared with those of the DLM.ResultsOverall complications were observed in 127 cases (26.6%). The accuracy of the DLM was 74.6 ± 3.7% for predicting the overall occurrence of complications, which was comparable to that of the logistic regression (74.1%). Neurological complications were observed in 48 cases (10.0%), and the accuracy of the DLM was 91.7 ± 3.5%, which was higher than that of the logistic regression (90.1%).ConclusionA new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.