Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs.

Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Basement-Membrane-Related Gene Signature Predicts Prognosis in WHO Grade II/III Gliomas.

Gliomas that are classified as grade II or grade III lesions by the World Health Organization (WHO) are highly aggressive, and some may develop into glioblastomas within a short period, thus portending the conferral of a poor prognosis for patients. Previous studies have implicated basement membrane (BM)-related genes in glioma development. In this study, we constructed a prognostic model for WHO grade II/III gliomas in accordance with the risk scores of BM-related genes. Differentially expressed genes (DEGs) in the glioma samples relative to normal samples were screened from the GEO database, and five prognostically relevant BM-related genes, including NELL2, UNC5A, TNC, CSPG4, and SMOC1, were selected using Cox regression analyses for the risk score model. The median risk score was calculated, based on which high- and low-risk groups of patients were generated. The clinical information, pathological information, and risk group were combined to establish a prognostic nomogram. Both the nomogram and risk score model performed well in the independent CGGA cohort. Gene set enrichment analysis (GSEA) and immune profile, drug sensitivity, and tumor mutation burden (TMB) analyses were performed in the two risk groups. A significant enrichment of 'Autophagy-other', 'Collecting duct acid secretion', 'Glycosphingolipid biosynthesis-lacto and neolacto series', 'Valine, leucine, and isoleucine degradation', 'Vibrio cholerae infection', and other pathways were observed for patients with high risk. In addition, higher proportions of monocytes and resting CD4 memory T cells were observed in the low- and high-risk groups, respectively. In conclusion, the BM-related gene risk score model can guide the clinical management of WHO grade II and III gliomas.

A subtle connection between crossed cerebellar diaschisis and supratentorial collateral circulation in subacute and chronic ischemic stroke.

OBJECTIVES: It has not been reported whether collateral circulation, a factor closely related to the prognosis of patients with cerebral infarction, is related to the occurrence of crossed cerebellar diaschisis(CCD) or not. Our research attempts to verify the relationship between the collateral circulation grade and the occurrence of CCD, mainly by means of CTA and CTP. MATERIALS AND METHODS: A total of 47 patients were divided into a CCD-positive (Kim et al., 2019) or a CCD-negative group Furlanis et al. (2018) by calculating the asymmetry index (AI) value (<10%) of bilateral cerebellar cerebral blood flow (CBF). A 4-scale grading method was used to evaluate collateral circulation in the supratentorial infarct area, and the four perfusion parameters of the supratentorial and subtentorial brain regions were analyzed and compared between the two groups. The extent of vascular lesions was evaluated by MR sequences including DWI and MRA. RESULTS: Among the four perfusion parameters, except for CBV, were significantly different between the bilateral cerebellum in the CCD-positive group, but only TTP in the supratentorial cerebral infarction area was statistically different in the two groups. Moreover, the collateral circulation sore in the CCD-positive group was significantly lower than that in the CCD-negative group. But no statistical difference was found in the comparison of DWI positive rates between the two groups. CONCLUSION: The collateral score in the supratentorial infarct area is correlated with the occurrence of CCD,which may be used to explain the effect of CCD on the prognosis of patients.

Monolayer Phosphorene-Carbon Nanotube Heterostructures for Photocatalysis: Analysis by Density Functional Theory.

One-dimensional (1D)/2D heterostructures have attracted great attention in electronic and optoelectronic fields because of their unique geometrical structures and rich physics. Here, we systematically explore electronic structure and optical performance of single-wall carbon nanotube (CNT)/phosphorene (BP) hybrids by large-scale density functional theory (DFT) computation. The results show that the interfacial interaction between CNT and BP is a weak van der Waals (vdW) force and correlates with tube diameter of CNTs. The CNT/BP hybrids have strong optical absorption compared with that of individual BP and CNT. A diameter-dependent type I or II heterojunction in CNT/BP hybrids is observed. Moreover, CNTs can not only significantly promote photogenerated carrier transfer, but also effectively improve the photocatalytic activities of BP as a co-catalyst. These findings would enrich our understanding of BP-based 1D/2D heterostructures, providing further insight into the design of highly efficient phosphorene-based or CNT-based nanophotocatalysts.

Simultaneous covalent and noncovalent carbon nanotube/Ag3PO4 hybrids: new insights into the origin of enhanced visible light photocatalytic performance.

Understanding the interfacial interaction is of paramount importance for rationally designing carbon nanomaterial-based hybrids with optimal performance for electronics, optoelectronics, sensing, advanced energy conversion and storage. Here, we firstly reveal that both covalent and noncovalent interactions simultaneously exist in carbon nanotube (CNT)/Ag3PO4 hybrids by studying systematically the electronic and optical properties to elucidate the mechanism of their enhanced photocatalytic performance. Metallic CNT(9,0) may chemically or physically interact with the Ag3PO4(100) surface depending on its relative orientations, whereas semiconducting CNT(10,0) can only noncovalently functionalize Ag3PO4. The C-Ag bond in the covalently bonded hybrid and type-II, staggered, band alignment in noncovalent hybrids lead to a robust separation of photoexcited charge carriers between two constituents, thus enhancing the photocatalytic activity. The small band gap makes the CNT/Ag3PO4 hybrids absorb sunlight from the ultraviolet to infrared region. Moreover, CNTs are not only effective sensitizers, but also highly active co-catalysts in hybrids. The results can be rationalized by the available experiments, thereby partly resolving a debate on the interpretation of the experimental results, and paving the way for developing highly efficient carbon-based nanophotocatalysts.

Highly sensitive surface plasmon resonance sensor utilizing a long period grating with photosensitive cladding.

In this study, we propose and investigate a novel grating-assisted surface plasmon resonance (SPR) platform based on a silver coated long period fiber grating having a photosensitive cladding (C-LPFG). We show that the SPR mode is transited from the higher EH mode with an effective refractive index (ERI) close to that of the surrounding refractive index (SRI) and is highly sensitive to a change in the SRI. Compared with a conventional SPR sensor, a much higher sensitivity is obtained for the novel C-LPFG-based SPR sensor. The sensitivity can be further improved by reducing the fiber diameter. The numerical results show that the highest local sensitivity, which can be as high as ∼4900 nm/RIU, and the corresponding resolution of ∼2.04×10(-6) RIU are achieved for the reduced silver coated C-LPFG sensor. Moreover, the Q-factor and the figure of merit exhibit better characteristics than those of the conventional LPFG-based SPR sensor. Our findings provide insight into the C-LPFG-based SPR platform being a potentially important area to explore for biochemical sensing.