Association between COVID-19 severity and relatively high serum adiponectin levels at the time of admission.

At the beginning of 2020, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to epidemics worldwide. Obesity and visceral fat accumulation have been reported to be independent risk factors for severe COVID-19. Several reports have focused on the levels of adipocytokines/adipokines, including adiponectin (APN), which is exclusively secreted from adipocytes, although the importance of these factors in acute disease conditions remains unclear. Therefore, we investigated the relationship between serum adiponectin levels and COVID-19 severity. Patients with COVID-19 who were admitted to Sumitomo Hospital (Osaka, Japan) from May through October 2021 were included. A total of 107 patients were enrolled in this study. We obtained the anthropometric and clinical laboratory data of the patients at the time of admission and examined the associations between various parameters and COVID-19 severity. The mean period from onset to admission was 6.5 ± 2.8 days. We divided the patients into "non-severe" (mild, moderate-I and moderate-II) (n = 80) and "severe" (n = 27) groups. The "severe" patients were significantly older than "non-severe" patients. Additionally, no significant differences were observed in BMI, sex, or the period from onset to admission. The serum adiponectin levels of "severe" patients at the time of admission were significantly greater than those of "non-severe" patients even after adjusting for age, sex, and BMI. These results suggest that the serum APN levels at the time of admission can predict COVID-19 severity. However, further investigations on the changes in APN levels in acute diseases are needed.

Realization of reliable cerebral-blood-flow maps from low-dose CT perfusion images by statistical noise reduction using nonlinear diffusion filtering.

X-ray computed tomographic perfusion (CTP) imaging, a rapid method for measuring cerebral blood flow (CBF), is an effective modality for assessment of the severity and extent of brain tissue ischemia. Low-dose scanning has been required for CTP imaging for reducing the radiation exposure to patients, because the same plane is scanned repeatedly. Low-dose CTP imaging, however, results in substantial statistical noise in the images, which may negatively impact the accuracy of CBF values. Because CBF values are calculated from the set of CTP images, it is important to reduce the statistical noise in raw CTP images to make the values reliable. Noise reduction must be performed without blurring of vessel structures, because such blurring will overestimate CBF values. For this purpose, two-dimensional nonlinear diffusion filtering (NLDF) was introduced. It was applied to CTP images of a CTP phantom for evaluating the accuracy of CBF values in low-dose CTP and to clinical low-dose CTP images for determining its effectiveness in actual CTP examinations. NLDF successfully reduced the statistical noise in the CTP images while preserving the sharp edges. This feature generated CBF values close to the reference value, producing reliable CBF maps from low-dose CT perfusion images. The CBF maps obtained with NLDF were comparable to or better than those obtained by other, commercial CTP software programs. The use of NLDF was thus effective for manipulation of low-dose CT perfusion images.

New technique for visualizing cerebral vessels in MR angiographic images using three-dimensional discrete wavelet transform.

Visualization techniques for magnetic resonance (MR) angiographic images are generally based on the projection ray concept; for example, maximum intensity projection (MIP) and volume rendering (VR). A new technique based on a different concept from projection rays is explored in this study: three-dimensional (3-D) discrete wavelet transforms are used for visualizing cerebral vessels in the MR angiographic image. This technique successfully visualizes cerebral vessels and represents the spatial relationship between the cerebral vessels, as in the case of VR. The proposed technique is, thus, indicated to be promising for visualizing cerebral vessels in 3-D MR angiographic images.

MgATP-induced conformational changes in a single myosin molecule observed by atomic force microscopy: periodicity of substructures in myosin rods.

This paper discusses the conformational changes in a single myosin molecule directly observed using atomic force microscopy (AFM). The myosin molecules were pretreated in rigor solutions without MgATP or in relaxed solutions with various concentrations of MgATP. The images of these molecules were obtained using a tapping mode AFM. The results indicate that the orientation of the myosin's heads and tail strongly depend on the MgATP concentration. Without using MgATP, almost all of the myosin molecules are in the extended form; however, when MgATP is used, the molecules bend according to the level of MgATP concentration. The mean-square end-to-end distance of the myosin molecules is significantly shorter with p[MgATP] = 4 than with p[MgATP] = 6. The rod region did not show the same level of intensity along their length in the extended form. The rods exhibited clusters of discontinuity, which were identified as substructures. The size of these substructures change at intervals that are multiples of 14.3-14.5 nm, which reflects the periodicity of the alpha-helical coiled coils. The substructure clusters also correspond to the myosin crossbridge spacing in muscles (14.3 or 43 nm). These results suggest that the myosin's head bends in conjunction with the bending or tilting in the helical substructures. Conformational changes of the myosin molecule induced by MgATP seem to mimic the molecular motions in a muscle's force generation process.

The application of a new predictive technique for lossless compression of medical images.

A new predictive technique for lossless compression is applied to CT and MR images in order to achieve high compression in the lossless mode. To accomplish this, a prediction value was obtained by assuming the horizontal and vertical components of each pixel. Wide and narrow predictive coding modes were adaptively selected to achieve an accurate prediction. The performance of the predictive technique was compared with the performance of four representative compression techniques in the lossless mode, using different types of CT and MR image sets with different bit rates. Overall, the proposed predictive technique yielded the best compression, particularly with the lower bit-rate images tested. While the compression results are largely dependent on the images used, the results confirm the strong potential of the proposed predictive technique as a strategy for obtaining high lossless-compression in medical images.

Emission CT Image Reconstruction Using the Augmented Lagrangian Method with a Modified Form of Nonnegativity Constraints.

The augmented Lagrangian method was applied to emission CT image reconstruction to attain the satisfactory elimination of negative pixels using a modified form of the original nonnegativity constraints. A penalty function was included in the augmented Lagrangian method to suppress image noise due to statistical noise in the projection data. A slight change in the original constraint form achieved almost complete nonnegativity without loss in the closeness of reconstruction to the original image.