BACKGROUNDTo establish the prevalence of hearing deficit in children with Down syndrome (DS) in Hong Kong as measured by brainstem auditory evoked potentials (BAEP). The secondary objective is to examine the agreement between BAEP and clinical questioning in detecting hearing deficit in DS.

METHODSConsecutive DS patients attending the Down's Clinic in a regional pediatric referral center were recruited into this cross-sectional study. BAEP data performed within 12 months were retrieved. The care-taker was interviewed with a structured questionnaire to detect any symptom of hearing impairment. BAEP findings and clinical questionings were compared in an agreement analysis using quadratic weighted kappa statistics.

RESULTSFifty DS patients (35 male, 15 female, mean age 11.70 years ± 5.74 standard deviation) were recruited. Eighteen patients (36.0%) were identified having hearing deficit by BAEP. Among patients with hearing impairment, 13 patients (72.2%) had a conductive deficit, and most have mild to moderate hearing loss. Five patients (27.8%) had sensorineural deficit and most have moderate to severe degree. Eight (44.4%) had bilateral hearing deficit. Care-takers of 13 patients (26.0%) reported symptoms of hearing impairment, with 9 (69.2%) having mild symptoms, 3 (23.1%) had moderate symptoms and 1 (7.7%) had severe symptoms. The weighted kappa was 0.045 (95.0% confidence interval - 0.138-0.229), indicating very poor strength of agreement between BAEP and clinical questioning. For patients with conductive hearing impairment, only 1 patients (7.7%) recalled history of otitis media.

CONCLUSIONSThe estimated point prevalence of hearing impairment in Chinese DS children in Hong Kong is 36%. Our finding of poor strength of agreement between objective testing and symptom questioning reflects significant underestimation of hearing impairment by history taking alone. In view of the high prevalence and low parental awareness, continuous surveillance of hearing is mandatory for DS patients throughout childhood and adolescence.

Adolescent ; Child ; Cross-Sectional Studies ; Down Syndrome ; epidemiology ; physiopathology ; Evoked Potentials, Auditory ; physiology ; Female ; Hearing Loss ; epidemiology ; etiology ; Humans ; Male ; Prevalence

Background/Aims:  Rituximab is known to be associated with high hepatitis B virus (HBV) reactivation rate in patients with resolved HBV infection and hematologic malignancy. However, data regarding HBV reactivation (HBVr) in rheumatic patients receiving rituximab is limited. To assess the HBVr rate in hepatitis B surface antigen (HBsAg)-negative patients receiving rituximab for autoimmune diseases in a large real-world cohort. Methods:  From March 2006 to December 2019, 900 patients with negative HBsAg receiving at least one cycle of rituximab for autoimmune diseases in a tertiary medical center in Taiwan were retrospectively reviewed. Clinical outcome and factors associated with HBVr were analyzed. Results: After a median follow-up period of 3.3 years, 21 patients developed HBVr, among whom 17 patients were positive for hepatitis B core antibody (anti-HBc) and four were negative. Thirteen patients had clinical hepatitis flare, while eight patients had HBsAg seroreversion without hepatitis. Old age, anti-HBc positivity, undetectable serum hepatitis B surface antibody level at rituximab initiation and a higher average rituximab dose were associated with a higher HBVr rate. There was no significant difference in the HBVr risk between rheumatoid arthritis and other autoimmune diseases. Among anti-HBc-negative patients, subjects without HBV vaccination at birth had an increased risk of HBVr (4/368, 1.1%) compared with those who received vaccination (0/126, 0%). Conclusions In HBV endemic areas where occult HBV is prevalent, anti-HBc-negative patients, may still be at risk for HBVr after rituximab exposure. HBVr may still be considered in HBsAgnegative patients developing abnormal liver function after rituximab exposure, even in patients with negative anti-HBc.

Hyperphenylalaninemia is one of the commonest inborn errors of metabolism affecting approximately 1 in 15,000 livebirths. Among Chinese, BH4 deficiency leading to hyperphenylalaninemia is much commoner than in Caucasians. Exact diagnosis is important for the treatment and genetic counseling. In 2000, newborn screening for phenylketonuria is mandatory by law in China throughout the whole country. However, it is not yet included in the newborn screening program of the Hong Kong Special Administrative Region, China. Published data on hyperphenylalaninemia among HongKong Chinese are largely lacking. We report a 1-year-old Hong Kong Chinese girl with severe 6-pyruvoyl-tetrahydropterin synthase (PTPS) deficiency. The patient presented with infantile hypotonia and was misdiagnosed as cerebral palsy. She had very mild hyperphenylalaninemia (95 μmol/L), significantly high phenylalnine-to-tyrosine ratio (3.1), and elevated prolactin of 1109 mIU/L. Genetic analysis confirmed a homozygous known disease-causing mutation PTS NM_000317.1:c.259C>T; NP_000308.1: p.P87S in the proband. In our local experience, while the estimated prevalence of hyperphenylalaninemia due to PTPS deficiency was reported to be 1 in 29,542 live births, not a single case of phenylalanine hydroxylase deficiency has been reported. Furthermore, there is a general lack of awareness of inherited metabolic diseases in the community as well as among the medical professionals. Very often, a low index of clinical suspicion will lead to delay in diagnosis, multiple unnecessary and costly investigations, prolonged morbidity and anxiety to the family affected. We strongly recommend that expanded newborn screening for hyperphenylalaninemia should be implemented for every baby born in the Hong Kong Special Administrative Region, China.
Asian Continental Ancestry Group ; China ; Female ; Hong Kong ; Humans ; Infant ; Mass Screening ; Phenylketonurias ; diagnosis

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

OBJECTIVETo further investigate the {ptin vitro} effects of an osteoprotective herbal formula "ELP" (Herba Epimedii, Fructus Ligustri Lucidi and Fructus Psoraleae) using seropharmacological approach.

METHODSRats were fed with ELP or its individual component herbs for 2 days. The serum containing the postabsorbed ingredients of the herbal items were collected for cell culture using UMR106 cell, RAW264.7 cell and mesenchymal stem cell (MSC) isolated from the bone marrow of the rats. The effects of the herbal-containing serum on cell toxicity were detected by 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assay; bromodeoxyuridine assay was conducted to measure the cell proliferation of UMR106 cell and MSC; cell activity was measured using colorimetric method, and mRNA expression of runt-related transcription factor 2 (Runx2), alkaline phosphatase (ALP) and osteopontin (OPN) of UMR106 and MSC as well as matrix metalloproteinase 9 (MMP-9), tartrate-resistant acid phosphatase (TRAP) and cathepsin K of RAW264.7 were analyzed using real-time reverse-transcription polymerase chain reaction.

RESULTSELP and its component serum exhibited no cytotoxic effects on the cells. The ELP-containing serum increased the proliferation of UMR106 cell and MSC by 25.7% and 14.4 %, respectively and the alkaline phosphatase activity of MSC was increased by 42.6%. On the contrary, it inhibited the RAW264.7 cell differentiation by 29.2 %. ELP serum upregulated the Runx2 expression of UMR and MSC by 1.18 fold and 1.27 fold, respectively. It also upregulated ALP and OPN expression in MSC by 1.69- and 2.12-fold, respectively. On the other hand, ELP serum down-regulated MMP-9 and cathepsin K expression of RAW264.7 cell by 0.46- and 0.36-fold, respectively.

CONCLUSIONSThe serum of the animals fed with ELP contains active ingredients which are effective in promoting osteogenesis and inhibiting osteoclastogenesis.

Absorption, Physiological ; drug effects ; Animals ; Bone and Bones ; drug effects ; pathology ; Cell Differentiation ; drug effects ; Cell Proliferation ; drug effects ; Cell Survival ; drug effects ; Drugs, Chinese Herbal ; pharmacology ; Male ; Mice ; Osteoclasts ; drug effects ; metabolism ; pathology ; Osteogenesis ; drug effects ; Protective Agents ; pharmacology ; RAW 264.7 Cells ; RNA, Messenger ; genetics ; metabolism ; Rats ; Rats, Sprague-Dawley ; Real-Time Polymerase Chain Reaction ; Serum ; metabolism