BACKGROUND Acephalic spermatozoa syndrome(ASS)is an extremely rare form of severe teratozoospermia,where in most of the sperm either appear to lack heads or have disconnected or poorly connected heads and tails.CASE S...BACKGROUND Acephalic spermatozoa syndrome(ASS)is an extremely rare form of severe teratozoospermia,where in most of the sperm either appear to lack heads or have disconnected or poorly connected heads and tails.CASE SUMMARY We reported the case of a male patient with secondary infertility whose sperm showed typical ASS upon morphological analysis.Whole-exome sequencing was performed on the patient’s peripheral blood,which revealed two heterozygous variants of the PMFBP1 gene:PMFBP1c.414+1G>T(p.?)and PMFBP1c.393del(p.C132Afs*3).CONCLUSION It is speculated that the compound homozygous mutation of PMFBP1 may be the cause of ASS.We conducted a literature review in order to provide the basis for genetic counseling and clinical diagnosis of patients with ASS.展开更多
Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,000.First-tier clinical diagnostic tests often return many false positives[fve false positi...Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,000.First-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of MMA.Methods We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction.We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature construction.Feature selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model development.Results Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity.The best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor.The model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%sensitivity.Conclusions The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric patients.The improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.展开更多
基金Supported by Shenzhen Key Medical Discipline Construction Fund,Grant/Award,No.SZXK031.
文摘BACKGROUND Acephalic spermatozoa syndrome(ASS)is an extremely rare form of severe teratozoospermia,where in most of the sperm either appear to lack heads or have disconnected or poorly connected heads and tails.CASE SUMMARY We reported the case of a male patient with secondary infertility whose sperm showed typical ASS upon morphological analysis.Whole-exome sequencing was performed on the patient’s peripheral blood,which revealed two heterozygous variants of the PMFBP1 gene:PMFBP1c.414+1G>T(p.?)and PMFBP1c.393del(p.C132Afs*3).CONCLUSION It is speculated that the compound homozygous mutation of PMFBP1 may be the cause of ASS.We conducted a literature review in order to provide the basis for genetic counseling and clinical diagnosis of patients with ASS.
基金supported by the National Key R&D Program of China grand No.2022YFC2703103the Clinical Research Plan of SHDC(SHDC2020CR6028,SHDC2020CR1047B)+1 种基金the Science and Technology Commission of Shanghai Municipality grant 22Y11906900the Second Century Fund(C2F),Chulalongkorn University,Bangkok,Thailand.
文摘Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,000.First-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of MMA.Methods We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction.We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature construction.Feature selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model development.Results Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity.The best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor.The model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%sensitivity.Conclusions The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric patients.The improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.