Evaluation of haematological parameters in haemolytic anaemia caused by tick-borne pathogens in grazing cattle.

BACKGROUND: No tick-borne pathogens (TBPs) causing haemolytic anaemia in cattle have been reported, except Theileria orientalis and complete blood count (CBC) profile is the only haematological parameter to determine the severity of regenerative haemolytic anaemia. OBJECTIVES: To identify the causative agents of TBP-induced haemolytic anaemia and determine haematological parameters that indicate haemolytic anaemia in grazing cattle. METHODS: Eighty-two Korean indigenous cattle (Hanwoo) were divided into two groups: grazing (n = 67) and indoor (n = 15) groups. CBC and serum biochemistry were performed. PCR was conducted using whole blood-extracted DNA to investigate the prevalence of TBPs. RESULTS: TBP-induced haemolytic anaemia was observed in the grazing group. In grazing cattle, co-infection (43.3%, 29/67) was most frequently detected, followed by T. orientalis (37.6%, 25/67) and Anaplasma phagocytophilum infections (1.5%, 1/67). In indoor cattle, only co-infection (20%, 3/15) was identified. Grazing cattle exhibited regenerative haemolytic anaemia with marked monocytosis, mild neutropenia, and thrombocytopenia. According to grazing frequency, the 1st-time grazing group had more severe anaemia than the 2nd-time grazing group. Elevations in indirect bilirubin and L-lactate due to haemolytic anaemia were identified, and correlations with the respective markers were determined in co-infected grazing cattle. CONCLUSIONS: Quantitative evaluation of haematocrit, mean corpuscular volume, and reticulocytes (markers of regenerative haemolytic anaemia in cattle) was performed for the first time. Our results show that, in addition to T. orientalis, A. phagocytophilum is strongly associated with anaemia. The correlation between haemolytic anaemia severity and haematological parameters (indirect bilirubin, reticulocytes, and L-lactate) was confirmed.

Treatment and Rehabilitation of a Patient with Neuromyelitis Optica Spectrum Disorder-Induced Complete Spinal Cord Injury Following COVID-19 Vaccination: A Case Report.

Neuromyelitis optica spectrum disease (NMOSD) is a rare autoimmune disorder of the central nervous system characterized by optic neuritis, myelitis, or brain lesions. Its symptoms overlap with those of multiple sclerosis (MS), making a diagnosis of NMOSD challenging. Here, we report a rare case of NMOSD-induced complete spinal cord injury following COVID-19 vaccination. A 52-year-old female patient developed NMOSD-induced complete spinal cord injury after receiving their third dose of the Pfizer-BioNTech COVID-19 vaccine (BNT162b2). Despite the initial diagnosis of complete spinal cord injury, the patient underwent intensive treatment, including rituximab therapy and rehabilitation. As a result, she made a full recovery and transitioned from the ASIA Impairment Scale(AIS)-A to AIS-E. The remarkable neurological recovery from complete spinal cord injury to functional independence highlights the efficacy of a comprehensive treatment approach. In addition, this case emphasizes the need to recognize NMOSD as a potential adverse outcome of COVID-19 vaccination and emphasizes the importance of early diagnosis, timely intervention, and thorough rehabilitation for optimizing patient results. Further case reports and studies are needed to investigate the association between COVID-19 vaccination and the occurrence of NMOSD.

Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

OBJECTIVE: To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors. METHODS: This study presents a deep-learning-based bone suppression method where a residual U-Net model is trained for chest X-rays using data set generated from the single-energy material decomposition (SEMD) technique on CT. Synthetic projection images and soft-tissue selective images were obtained from the CT data set via the SEMD, which were then used as the input and label data of the U-Net network. The trained network was tested on synthetic chest X-rays and two real chest radiographs. RESULTS: Bone-suppressed images of the real chest radiographs obtained by the proposed method were similar to the results from the American Association of Physicists in Medicine lung CT data; pulmonary nodules in the soft-tissue selective images appeared more clearly than in the synthetic projection images. The peak signal-to-noise ratio and structural similarity values measured between the output and the corresponding label images were approximately 17.85 and 0.90, respectively. CONCLUSION: The proposed method effectively yielded bone-suppressed chest X-ray images, indicating its clinical usefulness, and it can improve the detection of lung abnormalities in chest X-rays. ADVANCES IN KNOWLEDGE: The idea of using SEMD to obtain large amounts of paired images for deep-learning-based bone suppression algorithms is novel.

Direct Identification of Antisite Cation Intermixing and Correlation with Electronic Conduction in CuBi2O4 for Photocathodes.

Cu-based p-type semiconducting oxides have been sought for water-reduction photocathodes to enhance the energy-conversion efficiency in photoelectrochemical cells. CuBi2O4 has recently attracted notable attention as a new family of p-type oxides, based on its adequate band gap. Although the identification of a major defect structure should be the first step toward understanding the electronic conduction behavior, no direct experimental analysis has been carried out yet. Using atomic-scale scanning transmission electron microscopy together with chemical probing, we identify a substantial amount of BiCu-CuBi antisite intermixing as a major point-defect type. Our density functional theory calculations also show that antisite BiCu can seriously hinder the hole-polaron hopping between Cu, in agreement with lower conductivity and a larger thermal activation barrier under a higher degree of intermixing. These findings highlight the value of the direct identification of point defects for a better understanding of electronic properties in complex oxides.

A Metal Artifact Reduction Method Using a Fully Convolutional Network in the Sinogram and Image Domains for Dental Computed Tomography.

The reconstruction quality of dental computed tomography (DCT) is vulnerable to metal implants because the presence of dense metallic objects causes beam hardening and streak artifacts in the reconstructed images. These metal artifacts degrade the images and decrease the clinical usefulness of DCT. Although interpolation-based metal artifact reduction (MAR) methods have been introduced, they may not be efficient in DCT because teeth as well as metallic objects have high X-ray attenuation. In this study, we investigated an effective MAR method based on a fully convolutional network (FCN) in both sinogram and image domains. The method consisted of three main steps: (1) segmentation of the metal trace, (2) FCN-based restoration in the sinogram domain, and (3) FCN-based restoration in image domain followed by metal insertion. We performed a computational simulation and an experiment to investigate the image quality and evaluated the effectiveness of the proposed method. The results of the proposed method were compared with those obtained by the normalized MAR method and the deep learning-based MAR algorithm in the sinogram domain with respect to the root-mean-square error and the structural similarity. Our results indicate that the proposed MAR method significantly reduced the presence of metal artifacts in DCT images and demonstrated better image performance than those of the other algorithms in reducing the streak artifacts without introducing any contrast anomaly.