We have experienced a case of 13 ring chromosome in a 40-month-old girl who demonstrated psychomotor retardation with delayed speech, growth retardation, hearing loss(left), microcephaly, trigonocephaly with flat occiput, hypertelorism, epicanthal folds, microophthalmia, broad prominamt nasal bridge, high arched palate, micrognathia, large auricles and other anomalies. Cytogenetic studies of peripheral blood lymphocytes with differential staining of chromosomes revealed 46, XX, r13. Her parents' karyotypes were normal. We reported the case with the review of the associated literatures.
Child, Preschool ; Craniosynostoses ; Cytogenetics ; Female ; Hearing ; Humans ; Hypertelorism ; Karyotype ; Lymphocytes ; Microcephaly ; Palate ; Ring Chromosomes*

PURPOSE: The purpose of this study was to describe the clinical characteristics of neonatal urinary tract infection (UTI) caused by Klebsiella pneumoniae and non- Klebsiella pneumoniae UTI. METHODS: We compared clinical characteristics of 84 neonatal patients with UTI caused by Klebsiella pneumoniae who were hospitalized at the Department of Pediatricsat Han Dong University, Sunlin Hospital during the period between May, 1994 and August, 1998. The cases were divided into two groups depending upon causative organisms' Klebsiella pneumoniae UTI vs non-Klebsiella pneumoniae UTI, and the clinical characteristics of these groups were compared. RESULTS: Escherichia coli was the most common bacterial pathogen causing neonatal UTI, followed by Klebsiella pneumoniae. There was no significant difference in the sex distribution of Klebsiella pneumoniae UTI, but non-Klebsiella pneumoniae UTI showed male predominence. There were no significant differences in the incidences of hematologic, urologic, radiologic findings and perinatal complications in between these 2 groups. CONCLUSION: Klebsiella pneumoniae is the second most common pathogen causing neonatal UTI. There were no specific differences in the laboratory, symptomatologic, and radiologic findings in these two groups.
Escherichia coli ; Humans ; Incidence ; Klebsiella pneumoniae* ; Klebsiella* ; Male ; Pneumonia ; Sex Distribution ; Urinary Tract Infections* ; Urinary Tract*

Background and Objectives: Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify voice recordings into normal, vox senilis, and laryngocele. Performance was assessed using precision, recall, F1-score, and accuracy. Results: The CNN model demonstrated high classification performance, with precision, recall, and F1-scores of 1.00 for normal and 0.99 for vox senilis and laryngocele. Accuracy reached 1.00 after 50 epochs and remained stable through 100 epochs. Time-frequency analysis supported the model’s ability to differentiate between classes. Conclusion This study highlights the potential of deep learning for voice disorder detection, achieving high accuracy and precision. Future research should address dataset diversity and realworld integration for broader clinical adoption.

Background and Objectives: Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify voice recordings into normal, vox senilis, and laryngocele. Performance was assessed using precision, recall, F1-score, and accuracy. Results: The CNN model demonstrated high classification performance, with precision, recall, and F1-scores of 1.00 for normal and 0.99 for vox senilis and laryngocele. Accuracy reached 1.00 after 50 epochs and remained stable through 100 epochs. Time-frequency analysis supported the model’s ability to differentiate between classes. Conclusion This study highlights the potential of deep learning for voice disorder detection, achieving high accuracy and precision. Future research should address dataset diversity and realworld integration for broader clinical adoption.

Background and Objectives: Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify voice recordings into normal, vox senilis, and laryngocele. Performance was assessed using precision, recall, F1-score, and accuracy. Results: The CNN model demonstrated high classification performance, with precision, recall, and F1-scores of 1.00 for normal and 0.99 for vox senilis and laryngocele. Accuracy reached 1.00 after 50 epochs and remained stable through 100 epochs. Time-frequency analysis supported the model’s ability to differentiate between classes. Conclusion This study highlights the potential of deep learning for voice disorder detection, achieving high accuracy and precision. Future research should address dataset diversity and realworld integration for broader clinical adoption.

No abstract available.
Carcinoma, Adenoid Cystic* ; Humans ; Knee* ; Male

No abstract available.
Hyperplasia* ; Melanoma*

No abstract available.
Child* ; Humans

Carnitine (beta-hydroxy-gamma-trimethylaminobutyric acid) is involved in the transport of long-chain fatty acids into the mitochondrial matrix and the removal of potentially toxic acylcarnitine esters. Transient carnitine transport defect is a rare condition in newborns reported in 1/90,000 live births. In this paper, we describe a case of transient carnitine transport defect found in a premature baby who had prolonged cholestatic jaundice and poor weight gain, and who responded dramatically to oral carnitine supplementation.
Carnitine ; Esters ; Fatty Acids ; Humans ; Infant, Newborn ; Jaundice, Obstructive ; Live Birth ; Weight Gain

Carnitine (beta-hydroxy-gamma-trimethylaminobutyric acid) is involved in the transport of long-chain fatty acids into the mitochondrial matrix and the removal of potentially toxic acylcarnitine esters. Transient carnitine transport defect is a rare condition in newborns reported in 1/90,000 live births. In this paper, we describe a case of transient carnitine transport defect found in a premature baby who had prolonged cholestatic jaundice and poor weight gain, and who responded dramatically to oral carnitine supplementation.
Carnitine ; Esters ; Fatty Acids ; Humans ; Infant, Newborn ; Jaundice, Obstructive ; Live Birth ; Weight Gain