<i>Scedosporium apiospermum </i>infection of the lumbar vertebrae: A case report

Shi, Xue-Wen; Li, Sheng-Tang; Lou, Jin-Peng; Xu, Bo; Wang, Jian; Wang, Xin; Liu, Hua; Li, Song-Kai; Peng, Zhen; Zhang, Tao

doi:10.12998/wjcc.v10.i10.3251

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…The intricate biomechanical stability system responsible for sustaining the natural curvature of the human spine comprises 2 fundamental components. [4][5][6] The endogenous stabilization system involves the vertebral body, appendages, lumbar spinal canal, and connecting ligaments to maintain static balance. The outside of the spinal canal is a bone structure with obvious grayscale and is evenly distributed; however, the inside of it is a soft tissue structure, composed of the spinal fluid and capsule.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis

Zhou,

Wang,

Zhang

et al. 2024

Medicine

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Background: Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals. Methods: This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy. Results: Analysis reveals a progressive increase in the size of vertebrae and intervertebral discs from the cervical to lumbar regions. Vertebrae, bearing weight and safeguarding the spinal cord and nerves, are interconnected by intervertebral discs, resilient structures that counteract spinal pressure. Experimental findings demonstrate a lack of pronounced anteroposterior bending during flexion and extension, maintaining displacement and rotation angles consistently approximating zero. This consistency maintains uniform anterior and posterior vertebrae heights, coupled with parallel intervertebral disc heights, aligning with theoretical expectations. Conclusions: Accuracy assessment employs 2 methods: IoU and Dice, and the average accuracy of IoU is 88% and that of Dice is 96.4%. The proposed deep learning-based system showcases promising results in spinal canal segmentation, laying a foundation for precise stenosis diagnosis in computed tomography images. This contributes significantly to advancements in spinal pathology understanding and treatment.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis

Zhou,

Wang,

Zhang

et al. 2024

Medicine

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“…Scedosporium species are human pathogenic moulds. They are the agents of eumycetoma [1][2][3][4][5], a chronic deep fungal infection of the skin and subcutaneous tissues, and are responsible for a broad spectrum of localised and life-threatening disseminated infections in immunocompetent and immunocompromised individuals [6][7][8][9][10] affecting numerous organs of the body (Figure 1A), including the bones and joints [11][12][13], the central nervous system [14][15][16][17], the eyes [18][19][20][21][22][23][24], the lungs [14,[25][26][27][28][29][30][31][32][33], the sinuses [34][35][36], and other body sites [37][38][39] of cystic fibrosis patients [8,28,[40][41][42], hematopoietic stem-cell and solid organ transplant recipients…”

Section: Introductionmentioning

confidence: 99%

A Lateral-Flow Device for the Rapid Detection of Scedosporium Species

Davies,

Thornton

2024

Preprint

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Scedosporium species are human pathogenic fungi, responsible for chronic, localised, and life-threatening disseminated infections in both immunocompetent and immunocompromised individuals. Diagnosis of Scedosporium infections currently relies on non-specific CT, lengthy and insensitive culture from invasive biopsy, and time-consuming histopathology of tissue samples. At present, there are no rapid antigen tests that detect Scedosporium-specific biomarkers. Here we report the development of a rapid (30 min) and sensitive (pmol/L sensitivity) lateral-flow device (LFD) test, incorporating a Scedosporium-specific IgG1 monoclonal antibody (mAb), HG12, which binds to extracellular polysaccharide (EPS) antigens of between ~15 kDa and 250 kDa secreted during hyphal growth of the pathogens. The test is compatible with human serum, and allows the detection of the Scedosporium species most-frequently reported as agents of human disease (Scedosporium apiospermum, Scedosporium aurantiacum, and Scedosporium boydii), with limits of detection (LOD) of the EPS biomarkers in human serum of ~0.81 ng/mL (S. apiospermum), ~0.94 ng/mL (S. aurantiacum), and ~1.95 ng/mL (S. boydii). The Scedosporium-specific LFD (ScedLFD) test therefore provides a potential novel opportunity for the detection of infections caused by different Scedosporium species.

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“…Scedosporium species are human pathogenic moulds. They are the agents of eumycetoma [1][2][3][4][5], a chronic deep fungal infection of the skin and subcutaneous tissues, and are responsible for a broad spectrum of localised and life-threatening disseminated infections in immunocompetent and immunocompromised individuals [6][7][8][9][10] affecting numerous organs of the body (Figure 1A), including the bones and joints [11][12][13], the central nervous system [14][15][16][17], the eyes [18][19][20][21][22][23][24], the lungs [14,[25][26][27][28][29][30][31][32][33], the sinuses [34][35][36], and other body sites [37][38][39] of cystic fibrosis patients [8,28,[40][41][42], haematopoietic stem cell and solid organ transplant recipients…”

Section: Introductionmentioning

confidence: 99%

A Lateral-Flow Device for the Rapid Detection of Scedosporium Species

Davies,

Thornton

2024

Diagnostics

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Scedosporium species are human pathogenic fungi, responsible for chronic, localised, and life-threatening disseminated infections in both immunocompetent and immunocompromised individuals. The diagnosis of Scedosporium infections currently relies on non-specific CT, lengthy and insensitive culture from invasive biopsy, and the time-consuming histopathology of tissue samples. At present, there are no rapid antigen tests that detect Scedosporium-specific biomarkers. Here, we report the development of a rapid (30 min) and sensitive (pmol/L sensitivity) lateral-flow device (LFD) test, incorporating a Scedosporium-specific IgG1 monoclonal antibody (mAb), HG12, which binds to extracellular polysaccharide (EPS) antigens between ~15 kDa and 250 kDa secreted during the hyphal growth of the pathogens. The test is compatible with human serum and allows for the detection of the Scedosporium species most frequently reported as agents of human disease (Scedosporium apiospermum, Scedosporium aurantiacum, and Scedosporium boydii), with limits of detection (LODs) of the EPS biomarkers in human serum of ~0.81 ng/mL (S. apiospermum), ~0.94 ng/mL (S. aurantiacum), and ~1.95 ng/mL (S. boydii). The Scedosporium-specific LFD (ScedLFD) test therefore provides a potential novel opportunity for the detection of infections caused by different Scedosporium species.

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Scedosporium apiospermum infection of the lumbar vertebrae: A case report

Cited by 7 publications

References 22 publications

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis

A Lateral-Flow Device for the Rapid Detection of Scedosporium Species

A Lateral-Flow Device for the Rapid Detection of Scedosporium Species

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