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Porous silicon dioxide (SiO2)/poly(vinylidene fluoride) (PVdF), SiO2/PVdF, and fibrous composite membranes were prepared by electrospinning a blend solution of a SiO2 sol-gel/PVdF. The nanofibers of the SiO2/PVdF (3/7 wt. ratio) blend comprised skin and nanofibrillar structures which were obtained from the SiO2 component. The thickness of the SiO2 skin layer comprising a thin skin layer could be readily tuned depending on the weight proportions of SiO2 and PVdF. The composite membrane exhibited a low thermal shrinkage of ~3% for 2 h at 200 °C. In the prototype cell comprising the composite membrane, the alternating current impedance increased rapidly at ~225 °C, and the open-circuit voltage steeply decreased at ~170 °C, almost becoming 0 V at ~180 °C. After being exposed at temperatures of >270 °C, its three-dimensional network structure was maintained without the closure of the pore structure by a melt-down of the membrane.
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Background: Studies report that diet may have contributed to a 50-60% decrease in human sperm quality over the past few decades. Unhealthy lifestyles affect the structure of spermatozoa, affecting the male reproductive potential. This study aimed to compare the effects of Korean and Western diets on reproductive function in young male Koreans. Methods: Study participants were provided either the Korean Diet (KD group) or the Western Diet (WD group) for 12 weeks. Semen quality parameters such as volume, motility, cell count, and sex hormone levels were evaluated. Sexual function was assessed using the International Index of Erectile Function and the Male Sexual Health Questionnaire. Efficacy and safety evaluations were conducted at baseline, 8 weeks, and 12 weeks. Results: The KD group demonstrated a significantly increased sperm motility after 8 weeks relative to baseline but decreased after 12 weeks. In contrast, sperm motility in the WD group significantly decreased after 8 weeks compared with baseline and remained constant after 12 weeks. Statistically, a near-significant difference was observed between groups (p = 0.057). Similarly, free testosterone levels in the KD group increased after 12 weeks compared with baseline, whereas that in the WD group decreased. The free testosterone levels in the KD group were significantly higher than those in the WD group (p = 0.020). There were no statistically significant differences in other sex hormone and sexual function questionnaires between the groups. None of the participants reported any severe side effects, and no significant alterations in clinical diagnostic test values were detected. Conclusion: The results of the study strongly reveal that KD positively affects sperm motility and male hormone levels in young men, indicating potential benefits for reproductive function.
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Krabbe disease (KD) is an autosomal recessive neurodegenerative disorder caused by deficiency of the galactocerebrosidase (GALC) due to variants in the GALC gene. Here, we provide the first and the largest comprehensive analysis of clinical and genetic characteristics, and genotype-phenotype correlations of KD in Korean in comparison with other ethnic groups. From June 2010 to June 2023, 10 patients were diagnosed with KD through sequencing of GALC. Clinical features, and results of GALC sequencing, biochemical test, neuroimaging, and neurophysiologic test were obtained from medical records. An additional nine previously reported Korean KD patients were included for review. In Korean KD patients, the median age of onset was 2 years (3 months-34 years) and the most common phenotype was adult-onset (33%, 6/18) KD, followed by infantile KD (28%, 5/18). The most frequent variants were c.683_694delinsCTC (23%) and c.1901T>C (23%), while the 30-kb deletion was absent. Having two heterozygous pathogenic missense variants was associated with later-onset phenotype. Clinical features were similar to those of other ethnic groups. In Korean KD patients, the most common phenotype was the adult-onset type and the GALC variant spectrum was different from that of the Caucasian population. This study would further our understanding of KD.
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Galactosilceramidase , Estudos de Associação Genética , Leucodistrofia de Células Globoides , Fenótipo , Humanos , Leucodistrofia de Células Globoides/genética , Leucodistrofia de Células Globoides/patologia , Leucodistrofia de Células Globoides/diagnóstico , Leucodistrofia de Células Globoides/fisiopatologia , Galactosilceramidase/genética , Masculino , Feminino , República da Coreia/epidemiologia , Pré-Escolar , Adulto , Lactente , Criança , Adolescente , Adulto Jovem , Mutação/genética , Genótipo , Predisposição Genética para Doença , Idade de InícioRESUMO
There is an increasing need for an objective grading system to evaluate the severity of dry eye disease (DED). In this study, a fully automated deep learning-based system for the assessment of DED severity was developed. Corneal fluorescein staining (CFS) images of DED patients from one hospital for system development (n = 1400) and from another hospital for external validation (n = 94) were collected. Three experts graded the CFS images using NEI scale, and the median value was used as ground truth. The system was developed in three steps: (1) corneal segmentation, (2) CFS candidate region classification, and (3) estimation of NEI grades by CFS density map generation. Also, two images taken on different days in 50 eyes (100 images) were compared to evaluate the probability of improvement or deterioration. The Dice coefficient of the segmentation model was 0.962. The correlation between the system and the ground truth data was 0.868 (p<0.001) and 0.863 (p<0.001) for the internal and external validation datasets, respectively. The agreement rate for improvement or deterioration was 88% (44/50). The fully automated deep learning-based grading system for DED severity can evaluate the CFS score with high accuracy and thus may have potential for clinical application.
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Aprendizado Profundo , Síndromes do Olho Seco , Humanos , Córnea , Síndromes do Olho Seco/diagnóstico , Gravidade do PacienteRESUMO
The utility of the next-generation sequencing (NGS) panel could be increased in hereditary peripheral neuropathies, given that the duplication of PMP22 is a major abnormality. In the present study, the analytical performance of an algorithm for detecting PMP22 copy number variation (CNV) from the NGS panel data was evaluated. The NGS panel covers 141 genes, including PMP22 and five genes within 1.5-megabase duplicated region at 17p11.2. CNV calling was performed using a laboratory-developed algorithm. Among the 92 cases subjected to targeted NGS panel from March 2018 to January 2021, 26 were suggestive of PMP22 CNV. Multiplex ligation-dependent probe amplification analysis was performed in 58 cases, and the results were 100% concordant with the NGS data (23 duplications, 2 deletions, and 33 negatives). Analytical performance of the pipeline was further validated by another blind data set, including 14 positive and 20 negative samples. Reliable detection of PMP22 CNV was possible by analyzing not only PMP22 but also the adjacent genes within the 1.5-megabase region of 17p11.2. On the basis of the high accuracy of CNV calling for PMP22, the testing strategy for diagnosis of peripheral polyneuropathies could be simplified by reducing the need for multiplex ligation-dependent probe amplification.
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Doenças do Sistema Nervoso Periférico , Humanos , Doenças do Sistema Nervoso Periférico/genética , Variações do Número de Cópias de DNA/genética , Reprodutibilidade dos Testes , Testes Genéticos/métodos , Proteínas da Mielina/genéticaRESUMO
BACKGROUND: Marfan syndrome (MFS), caused by pathogenic variants of FBN1 (fibrillin-1), is a systemic connective tissue disorder with variable phenotypes and treatment responsiveness depending on the variant. However, a significant number of individuals with MFS remain genetically unexplained. In this study, we report novel pathogenic intronic variants in FBN1 in two unrelated families with MFS. METHODS: We evaluated subjects with suspected MFS from two unrelated families using Sanger sequencing or multiplex ligation-dependent probe amplification of FBN1 and/or panel-based next-generation sequencing. As no pathogenic variants were identified, whole-genome sequencing was performed. Identified variants were analyzed by reverse transcription-PCR and targeted sequencing of FBN1 mRNA harvested from peripheral blood or skin fibroblasts obtained from affected probands. RESULTS: We found causative deep intronic variants, c.6163+1484A>T and c.5788+36C>A, in FBN1. The splicing analysis revealed an insertion of in-frame or out-of-frame intronic sequences of the FBN1 transcript predicted to alter function of calcium-binding epidermal growth factor protein domain. Family members carrying c.6163+1484A>T had high systemic scores including prominent skeletal features and aortic dissection with lesser aortic dilatation. Family members carrying c.5788+36C>A had more severe aortic root dilatation without aortic dissection. Both families had ectopia lentis. CONCLUSION: Variable penetrance of the phenotype and negative genetic testing in MFS families should raise the possibility of deep intronic FBN1 variants and the need for additional molecular studies. This study expands the mutation spectrum of FBN1 and points out the importance of intronic sequence analysis and the need for integrative functional studies in MFS diagnosis.
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Doenças da Aorta , Dissecção Aórtica , Síndrome de Marfan , Humanos , Fibrilina-1/genética , Mutação/genética , Síndrome de Marfan/genética , Síndrome de Marfan/complicações , Síndrome de Marfan/diagnóstico , Testes Genéticos , Adipocinas/genéticaRESUMO
Circulating tumor DNA (ctDNA) has emerged as a promising tool for various clinical applications, including early diagnosis, therapeutic target identification, treatment response monitoring, prognosis evaluation, and minimal residual disease detection. Consequently, ctDNA assays have been incorporated into clinical practice. In this review, we offer an in-depth exploration of the clinical implementation of ctDNA assays. Notably, we examined existing evidence related to pre-analytical procedures, analytical components in current technologies, and result interpretation and reporting processes. The primary objective of this guidelines is to provide recommendations for the clinical utilization of ctDNA assays.
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DNA Tumoral Circulante , Humanos , DNA Tumoral Circulante/genética , Biomarcadores Tumorais/genética , Prognóstico , Neoplasia Residual/genética , Mutação , Sequenciamento de Nucleotídeos em Larga EscalaRESUMO
Missense mutations in the alpha-B crystallin gene (CRYAB) have been reported in desmin-related myopathies with or without cardiomyopathy and have also been reported in families with only a cataract phenotype. Dilated cardiomyopathy (DCM) is a disorder with a highly heterogeneous genetic etiology involving more than 60 causative genes, hindering genetic diagnosis. In this study, we performed whole genome sequencing on 159 unrelated patients with DCM and identified an unusual stop-loss pathogenic variant in NM_001289808.2:c.527A>G of CRYAB in one patient. The mutant alpha-B crystallin protein is predicted to have an extended strand with addition of 19 amino acid residues, p.(Ter176TrpextTer19), which may contribute to aggregation and increased hydrophobicity of alpha-B crystallin. The proband, diagnosed with DCM at age 32, had a history of bilateral congenital cataracts but had no evidence of myopathy or associated symptoms. He also has a 10-year-old child diagnosed with bilateral congenital cataracts with the same CRYAB variant. This study expands the mutational spectrum of CRYAB and deepens our understanding of the complex phenotypes of alpha-B crystallinopathies.
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Cardiomiopatias , Cardiomiopatia Dilatada , Catarata , Doenças Musculares , Masculino , Criança , Humanos , Adulto , Cardiomiopatia Dilatada/genética , Mutação , Catarata/genética , Fenótipo , Linhagem , Cadeia B de alfa-Cristalina/genéticaRESUMO
Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9-91.8%) for internal validation. The AUROC of the MP-CNN model were 91.3% (95% CI 90.7-91.9%) and 79.5% (95% CI 78.2-80.8%) for the extra-internal and external validation, respectively. The MP-CNN based heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.
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Aprendizado Profundo , Estenose Espinal , Humanos , Estenose Espinal/diagnóstico por imagem , Redes Neurais de Computação , Imageamento por Ressonância Magnética/métodos , AlgoritmosRESUMO
Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.
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Aprendizado Profundo , Fraturas do Quadril , Adulto , Humanos , Feminino , Idoso , Estudos Retrospectivos , Fraturas do Quadril/diagnóstico por imagem , Área Sob a Curva , Tomografia Computadorizada por Raios XRESUMO
Rationale: Differential diagnosis of pleural effusion is challenging in clinical practice. Objectives: We aimed to develop a machine learning model to classify the five common causes of pleural effusions. Methods: This retrospective study collected 49 features from clinical information, blood, and pleural fluid of adult patients who underwent diagnostic thoracentesis between October 2013 and December 2018. Pleural effusions were classified into the following five categories: transudative, malignant, parapneumonic, tuberculous, and other. The performance of five different classifiers, including multinomial logistic regression, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGB), was evaluated in terms of accuracy and area under the receiver operating characteristic curve through fivefold cross-validation. Hybrid feature selection was applied to determine the most relevant features for classifying pleural effusion. Results: We analyzed 2,253 patients (training set, n = 1,459; validation set, n = 365; extra-validation set, n = 429) and found that the LGB model achieved the best performance in both validation and extra-validation sets. After feature selection, the accuracy of the LGB model with the selected 18 features was equivalent to that with all 49 features (mean ± standard deviation): 0.818 ± 0.012 and 0.777 ± 0.007 in the validation and extra-validation sets, respectively. The model's mean area under the receiver operating characteristic curve was as high as 0.930 ± 0.042 and 0.916 ± 0.044 in the validation and extra-validation sets, respectively. In our model, pleural lactate dehydrogenase, protein, and adenosine deaminase levels were the most important factors for classifying pleural effusions. Conclusions: Our LGB model showed satisfactory performance for differential diagnosis of the common causes of pleural effusions. This model could provide clinicians with valuable information regarding the major differential diagnoses of pleural diseases.
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Derrame Pleural , Adulto , Humanos , Diagnóstico Diferencial , Estudos Retrospectivos , Derrame Pleural/diagnóstico , Derrame Pleural/etiologia , Exsudatos e Transudatos , Aprendizado de Máquina , Adenosina Desaminase/metabolismoRESUMO
BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF). METHODS: Optical coherence tomography (OCT) images were obtained at baseline and after the loading phase. The first recurrence was defined as the initial appearance of a new retinal hemorrhage or intra/subretinal fluid accumulation after the initial resolution of exudative changes after three loading injections. Standard U-Net architecture was used to identify the three retinal fluid compartments, which include pigment epithelial detachment, subretinal fluid, and intraretinal fluid. To predict the first recurrence of nAMD, classification learning was conducted to determine whether the first recurrence occurred within three months after the loading phase. The recurrence classification architecture was built using ResNet50. The model with retinal regions of interest of the entire region and fluid region on OCT at baseline and after the loading phase is presented. RESULTS: A total of 1,444 eyes of 1,302 patients were included. The mean duration until the first recurrence after the loading phase was 8.20 ± 15.56 months. The recurrence classification system revealed that the model with the fluid region of OCT after the loading phase provided the highest classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.725 ± 0.012. Heatmap analysis revealed that three pathological fluids, subsided choroidal neovascularization lesions, and hyperreflective foci were important areas for the first recurrence. CONCLUSIONS: The deep learning algorithm allowed for the prediction of the first recurrence for three months after the loading phase with adequate feasibility. An automated prediction system may assist in establishing patient-specific treatment plans and the provision of individualized medical care for patients with nAMD.
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Aprendizado Profundo , Degeneração Macular , Degeneração Macular Exsudativa , Humanos , Inibidores da Angiogênese/uso terapêutico , Fator A de Crescimento do Endotélio Vascular , Retina/patologia , Líquido Sub-Retiniano , Tomografia de Coerência Óptica , Injeções Intravítreas , Degeneração Macular/tratamento farmacológico , Degeneração Macular Exsudativa/diagnóstico , Degeneração Macular Exsudativa/tratamento farmacológico , Ranibizumab/uso terapêuticoRESUMO
Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories-signal property, inter-/intra-position correlation, peak value/interval variability, and demographics-were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.
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Oximetria , Oxigênio , Humanos , Feminino , Oximetria/métodos , Complicações Pós-Operatórias , Mecânica Respiratória , EspirometriaRESUMO
Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.
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Epilepsia do Lobo Temporal , Epilepsia , Humanos , Epilepsia do Lobo Temporal/tratamento farmacológico , Estudos Retrospectivos , Eletroencefalografia , Aprendizado de MáquinaRESUMO
Introduction: RNA sequence analysis can be effectively used to identify aberrant splicing, and tumor suppressor genes are adequate targets considering their loss-of-function mechanisms. Sanger sequencing is the simplest method for RNA sequence analysis; however, because of its insufficient sensitivity in cases with nonsense-mediated mRNA decay (NMD), the use of cultured specimens with NMD inhibition has been recommended, hindering its wide adoption. Method: The results of Sanger sequencing of peripheral blood RNA without NMD inhibition performed on potential splicing variants of tumor suppressor genes were retrospectively reviewed. For negative cases, in which no change was identified in the transcript, the possibility of false negativity caused by NMD was assessed through a review of the up-to-date literature. Results: Eleven potential splice variants of various tumor suppressor genes were reviewed. Six variants were classified as pathogenic or likely pathogenic based on the nullifying effect identified by Sanger RNA sequencing. Four variants remained as variants of uncertain significance because of identified in-frame changes or normal expression of both alleles. The result of one variant was suspected to be a false negative caused by NMD after reviewing a recent study that reported the same variant as causing a nullifying effect on the affected transcript. Conclusion: Although RNA changes found in the majority of cases were expected to undergo NMD by canonical rules, most cases (10/11) were interpretable by Sanger RNA sequencing without NMD inhibition due to incomplete NMD efficiency or allele-specific expression despite highly efficient NMD.
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Since the majority of patients with pancreatic cancer (PC) develop insulin resistance and/or diabetes mellitus (DM) prior to PC diagnosis, PC-induced diabetes mellitus (PC-DM) has been a focus for a potential platform for PC detection. In previous studies, the PC-derived exosomes were shown to contain the mediators of PC-DM. In the present study, the response of normal pancreatic islet cells to the PC-derived exosomes was investigated to determine the potential biomarkers for PC-DM, and consequently, for PC. Specifically, changes in microRNA (miRNA) expression were evaluated. The miRNA specimens were prepared from the untreated islet cells as well as the islet cells treated with the PC-derived exosomes (from 50 patients) and the healthy-derived exosomes (from 50 individuals). The specimens were subjected to next-generation sequencing and bioinformatic analysis to determine the differentially expressed miRNAs (DEmiRNAs) only in the specimens treated with the PC-derived exosomes. Consequently, 24 candidate miRNA markers, including IRS1-modulating miRNAs such as hsa-miR-144-5p, hsa-miR-3148, and hsa-miR-3133, were proposed. The proposed miRNAs showed relevance to DM and/or insulin resistance in a literature review and pathway analysis, indicating a potential association with PC-DM. Due to the novel approach used in this study, additional evidence from future studies could corroborate the value of the miRNA markers discovered.
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Diabetes Mellitus , Exossomos , Resistência à Insulina , Ilhotas Pancreáticas , MicroRNAs , Neoplasias Pancreáticas , Humanos , Exossomos/genética , Exossomos/metabolismo , MicroRNAs/genética , MicroRNAs/metabolismo , Neoplasias Pancreáticas/metabolismo , Diabetes Mellitus/metabolismo , Ilhotas Pancreáticas/metabolismo , Neoplasias PancreáticasRESUMO
BACKGROUND: Neurodevelopmental disorders (NDDs) have diverse phenotypes. Their genetic diagnoses are often challenged by difficulties of targeting causative genes due to heterogeneous genetic etiologies. The objective of this study was to perform genetic diagnosis of children with NDDs using whole genome sequencing. METHODS: This study included 78 pediatric patients with NDDs and their 152 family members for whole genome sequencing (WGS). All cases except one were families with at least two members. Seventy-five patients had previously undergone other genetic tests besides WGS. Detected variants were classified according to the guidelines of the American College of Medical Genetics and Genomics. RESULTS: Among 78 probands, 26 patients were genetically diagnosed with NDDs through WGS, showing a diagnostic rate of 33.3%. Of them, 22 cases had de novo variants (DNVs) identified through trio analysis. Of these DNVs, half were novel variants. Three structural variants, including a multiexon deletion, a contiguous gene deletion involving 13 Mb, and a retrotransposon insertion, were revealed by WGS. All cases except one had defects in different genes, consistent with the phenotypically diverse nature of NDDs. In addition, three patients were inconclusive, two of them had one likely pathogenic variant in a gene associated with autosomal recessive disease and the other one had no clinical phenotypes associated with the detected DNV. CONCLUSIONS: Our experience demonstrates the advantage of WGS in the diagnosis of NDDs, including detection of copy number variations and also the advantage of trio sequencing for interpretation of DNVs.
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Variações do Número de Cópias de DNA , Transtornos do Neurodesenvolvimento , Humanos , Criança , Transtornos do Neurodesenvolvimento/diagnóstico , Transtornos do Neurodesenvolvimento/genética , Sequenciamento Completo do Genoma , Testes Genéticos , FenótipoRESUMO
Male climacteric syndrome (MCS) is a medical condition that can affect middle-aged men whose testosterone levels begin to decline considerably. These symptoms may include fatigue, decreased libido, mood swings, and disturbed sleep. MCS can be managed with lifestyle modifications and testosterone replacement. However, testosterone therapy may cause number of side effects, including an increased risk of cardiovascular issues. This study aims to evaluate the efficacy and safety of unripe black raspberry extract (BRE) against MCS and voiding dysfunction in men with andropause symptoms. A total of 30 subjects were enrolled and randomly assigned to the BRE group (n = 15) or the placebo group (n = 15). Participants were supplemented with 4800 mg BRE or placebo twice daily for 12 weeks. The impact of BRE was assessed using the Aging Male's Symptoms (AMS scale), International Prostate Symptom Score (IPSS) and the IPSS quality of life index (IPSS-QoL). Additionally, male sex hormones, lipid profiles, and anthropometric indices were assessed 6 and 12 weeks after treatment. The AMS scores did not differ significantly between the two groups. In the BRE group, the total IPSS and IPSS-QoL scores decreased significantly after 12 weeks compared to baseline (p < 0.05), but there was no significant difference compared to the placebo group. However, a significant difference was observed in the IPSS voiding symptoms sub-score compared to the placebo group. Furthermore, LDL-C and TC levels were also significantly lower in the BRE group than in the placebo group (p < 0.05). Collectively, the study provides strong evidence supporting the safety of BRE as a functional food and its supplementation potentially enhances lipid metabolism and alleviates MCS and dysuria symptoms, limiting the development of BPH.
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Climatério , Hiperplasia Prostática , Rubus , Pessoa de Meia-Idade , Humanos , Masculino , Hiperplasia Prostática/tratamento farmacológico , Qualidade de Vida , Testosterona/uso terapêutico , Método Duplo-Cego , Resultado do TratamentoRESUMO
Whole-genome sequencing is the most comprehensive form of next-generation sequencing method. We aimed to assess the additional diagnostic yield of whole-genome sequencing in patients with clinically diagnosed Charcot-Marie-Tooth disease when compared with whole-exome sequencing, which has not been reported in the literature. Whole-genome sequencing was performed on 72 families whose genetic cause of clinically diagnosed Charcot-Marie-Tooth disease was not revealed after the whole-exome sequencing and 17p12 duplication screening. Among the included families, 14 (19.4%) acquired genetic diagnoses that were compatible with their phenotypes. The most common factor that led to the additional diagnosis in the whole-genome sequencing was genotype-driven analysis (four families, 4/14), in which a wider range of genes, not limited to peripheral neuropathy-related genes, were analysed. Another four families acquired diagnosis due to the inherent advantage of whole-genome sequencing such as better coverage than the whole-exome sequencing (two families, 2/14), structural variants (one family, 1/14) and non-coding variants (one family, 1/14). In conclusion, an evident gain in diagnostic yield was obtained from whole-genome sequencing of the whole-exome sequencing-negative cases. A wide range of genes, not limited to inherited peripheral neuropathy-related genes, should be targeted during whole-genome sequencing.
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BACKGROUND: Despite the wide use of next generation sequencing, there are still many difficulties in detecting structural variants. A split read is one of the clues of structural variants and is represented as a soft-clipped read in the raw sequencing data. Considering that most of the breakpoints of structural variants reside in non-coding regions, split read information has not been routinely used in exome sequencing or targeted panel sequencing. Recently, SCRAMble, a software capable of detecting mobile element insertion (MEI) and deletion based on soft-clipped read clusters (SCRCs), was shown to provide an additional diagnostic yield of 0.03 - 0.25%. SCRAMble is the only software that can be used for exome sequencing or targeted panel sequencing to detect structural variants based on SCRC information. The aim of present study was to establish a working procedure of utilizing SCRC information using SCRAMble in clinical exome sequencing and to assess its diagnostic yield. METHODS: Raw sequencing data of clinical exome sequencing were retrospectively analyzed using SCRAMble to search MEIs and deletions. SCRAMble software was installed according to the manufacturer's instructions and default parameters except for one, mei-score, which was adjusted for sensitivity, were used. RefSeq gene annotation was performed for both MEI and deletion calls using ANNOVAR. Blacklist-based filtering was used to reduce candidate MEI/deletion calls. Clinical relevance was manually evaluated for the remaining variant calls. RESULTS: One diagnostic MEI, which is a founder variant in East Asia, was detected in two cases (2/266, 0.75%). In addition, two diagnostic deletions, which had been previously detected in depth-of-coverage (DOC)-based copy number variant (CNV) callers, were detected (2/266, 0.75%). Base-level breakpoints that could not be derived by the DOC-based callers were identified for these two deletions using SCRAMble. Most SCRCs were repetitive among cases and blacklist-based filtering reduced candidate MEI/deletion calls by 49.5 - 94.5%, leaving a considerable number of variant calls to be manually validated. CONCLUSIONS: SCRC screening in exome or targeted panel sequencing may provide additional diagnostic yield either by pathogenic MEI detection or reassurance of deletions identified by DOC-based CNV callers. Development of an efficient filtering algorithm is warranted.