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Objective: To explore the genetic basis for a child with 46,XY disorders of sex development (DSD) and explore its genotype-phenotype correlation., Methods: The child was subjected to whole exome sequencing (WES), and exons 1 to 7 of NR5A1 were subjected to multiplex ligation-dependent probe amplification (MLPA) analysis., Results: The patient presented with rudimentary vulva of a female with Tanner stage 1. B-mode ultrasonography has detected ovary and uterus. The child was found to have a chromosome karyotype of 46,XY. WES revealed that the patient has harbored heterozygous deletion of exon 5 of the NR5A1 gene, which was a novel pathogenic variant inherited from the mother. No abnormality was found in the father., Conclusion: The main symptoms of 46,XY DSD children are insufficient external genitalia masculinization, for which variants of the NR5A1 gene are an important cause. WES has improved the detection rate of genetic variants and provided a solid basis for genetic counseling of the affected families.

Purpose: Electronic portal imaging detector (EPID)-based patient positioning verification is an important component of safe radiotherapy treatment delivery. In computer simulation studies, learning-based approaches have proven to be superior to conventional gamma analysis in the detection of positioning errors. To approximate a clinical scenario, the detectability of positioning errors via EPID measurements was assessed using radiomics analysis for patients with thyroid-associated ophthalmopathy., Methods: Treatment plans of 40 patients with thyroid-associated ophthalmopathy were delivered to a solid anthropomorphic head phantom. To simulate positioning errors, combinations of 0-, 2-, and 4-mm translation errors in the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions were introduced to the phantom. The positioning errors-induced dose differences between measured portal dose images were used to predict the magnitude and direction of positioning errors. The detectability of positioning errors was assessed via radiomics analysis of the dose differences. Three classification models-support vector machine (SVM), k-nearest neighbors (KNN), and XGBoost-were used for the detection of positioning errors (positioning errors larger or smaller than 3 mm in an arbitrary direction) and direction classification (positioning errors larger or smaller than 3 mm in a specific direction). The receiver operating characteristic curve and the area under the ROC curve (AUC) were used to evaluate the performance of classification models., Results: For the detection of positioning errors, the AUC values of SVM, KNN, and XGBoost models were all above 0.90. For LR, SI, and AP direction classification, the highest AUC values were 0.76, 0.91, and 0.80, respectively., Conclusions: Combined radiomics and machine learning approaches are capable of detecting the magnitude and direction of positioning errors from EPID measurements. This study is a further step toward machine learning-based positioning error detection during treatment delivery with EPID measurements., (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

The increase of proton beam number might provide higher degrees of freedom in the optimization of intensity-modulated proton therapy planning. In this study, we aimed to quantitatively explore the potential benefits of the increased beam number, including dose volume histogram (DVH), linear energy transfer volume histogram, and DVH bandwidth metrics. Twelve patients with lung cancer are retrospectively selected. Four plans were created based on internal target volume (ITV) robust optimization for each patient using the RayStation treatment planning system. Four plans were generated using different numbers (three, five, seven, and nine) of evenly separated coplanar beams. The three-beam plan was considered as the reference plan. Biologically equivalent doses were calculated using both constant relative biological effectiveness (RBE) and variable RBE models, respectively. To evaluate plan quality, DVH metrics in the target [ITV: D 2% , CI, HI] and organs-at-risk [Lung: V 5Gy[RBE] , V 20Gy[RBE] , V 30Gy[RBE] ; Heart D 2% ; Spinal cord D 2% ] were calculated using both RBE models. To evaluate LET distributions, LET volume histogram metrics [ITV LET mean and LET 2% ; Lung LET mean and LET 2% ; Heart LET 2% ; Spinal cord LET 2% ] were quantified. To evaluate plan robustness, the metrics using DVH bandwidth [ITV: D 2% , D 99% ; Lung: V 5Gy[RBE] , V 20Gy[RBE] , V 30Gy[RBE] ; Heart D 2% ; Spinal cord D 2% ] were also reported. For plan quality, the increase of proton beam number resulted in fewer target hot spots, improved target dose conformity, improved target dose homogeneity, lower median-dose lung volume, and fewer hot spots in spinal cord. As to LET distributions, target mean LET increased significantly as the beam number increased to seven or more. Lung LET hot spots were significantly reduced with the increase of proton beams. With respect to plan robustness, the robustness of target dose coverage, target hot spots, and low-dose lung volume were improved, while the robustness of heart hot spots became worse as the beam number increased to nine. The robustness of cord hot spots became worse using five and seven beams compared to that using three beams. As the proton beam number increased, plan quality and LET distributions were comparable or significantly improved. The robustness of target dose coverage, target dose hot spots, and low-dose lung volume were significantly improved.

Limb-girdle muscular dystrophies (LGMDs) are a group of neuromuscular diseases that are characterized by progressive muscle weakness. LGMD type 2A (LGMD2A), caused by variants in the calpain-3 (CAPN3) gene, is the most prevalent type. The present study aimed to analyze pathogenic CAPN3 gene variants in two pedigrees affected by LGMD2A. Each family contains three patients who are siblings and sought genetic counseling. Genomic DNA was extracted from the peripheral blood samples collected from the probands and family members and whole-exome sequencing (WES) was used to detect the pathogenic genes in the probands. Suspected variants were subsequently validated by Sanger sequencing. In family 1, WES revealed that the proband carried the compound heterogeneous variants c.1194-9A>G and c.1437C>T (p.Ser479=) in CAPN3 (NM_000070.2). In family 2, WES identified that the proband carried the compound heterogeneous variants c.632+4A>G and c.1468C>T (p.Arg490Trp) in CAPN3 (NM_000070.2). In conclusion, the present study indicated that the compound heterogeneous variants of the CAPN3 gene were most likely responsible for LGMD2A in the two Chinese families., (Copyright: © Zheng et al.)

Objective: To analyze the molecular etiology of a Chinese child affected with dihydropyrimidinase deficiency., Methods: Genomic DNA was extracted from peripheral blood samples of the family members. Pathogenic variant was determined by whole exome sequencing and verified by Sanger sequencing., Results: The child was found to harbor homozygous c.905G>A (p.Arg302Gln) variants in exon 5 of the DPYS gene, for which her parents were both heterozygous carriers., Conclusion: The homozygous c.905G>A (p.Arg302Gln) variants of the DPYS gene probably underlies the dihydropyrimidinase deficiency in the child. Above result has enabled genetic counseling and prenatal diagnosis for this family.

Objective: To evaluate rectal toxicity of radiotherapy for prostate cancer using a novel predictive model based on multi-modality and multi-classifier fusion., Methods: We retrospectively collected the clinical data from 44 prostate cancer patients receiving external beam radiation (EBRT), including the treatment data, clinical parameters, planning CT data and the treatment plans. The clinical parameter features and dosimetric features were extracted as two different modality features, and a subset of features was selected to train the 5 base classifiers (SVM, Decision Tree, K-nearest-neighbor, Random forests and XGBoost). To establish the multi-modality and multi-classifier fusion model, a multi-criteria decision-making based weight assignment algorithm was used to assign weights for each base classifier under the same modality. A repeat 5-fold cross-validation and the 4 indexes including the area under ROC curve (AUC), accuracy, sensitivity and specificity were used to evaluate the proposed model. In addition, the proposed model was compared quantitatively with different feature selection methods, different weight allocation algorithms, the model based on single mode single classifier, and two integrated models using other fusion methods., Results: Repeated (5 times) 5-fold cross validation of the proposed model showed an accuracy of 0.78 for distinguishing toxicity from non-toxicity with an AUC of 0.83, a specificity of 0.79 and a sensitivity of 0.76., Conclusions: Compared with the models based on a single mode or a single classifier and other fusion models, the proposed model can more accurately predict rectal toxicity of radiotherapy for prostate cancer.

With the current accumulation of metagenome data, it is possible to build an integrated platform for processing of rigorously selected metagenomic samples (also referred as "metagenomic communities" here) of interests. Any metagenomic samples could then be searched against this database to find the most similar sample(s). However, on one hand, current databases with a large number of metagenomic samples mostly serve as data repositories but not well annotated database, and only offer few functions for analysis. On the other hand, the few available methods to measure the similarity of metagenomic data could only compare a few pre-defined set of metagenome. It has long been intriguing scientists to effectively calculate similarities between microbial communities in a large repository, to examine how similar these samples are and to find the correlation of the meta-information of these samples. In this work we propose a novel system, Meta-Mesh, which includes a metagenomic database and its companion analysis platform that could systematically and efficiently analyze, compare and search similar metagenomic samples. In the database part, we have collected more than 7 000 high quality and well annotated metagenomic samples from the public domain and in-house facilities. The analysis platform supplies a list of online tools which could accept metagenomic samples, build taxonomical annotations, compare sample in multiple angle, and then search for similar samples against its database by a fast indexing strategy and scoring function. We also used case studies of "database search for identification" and "samples clustering based on similarity matrix" using human-associated habitat samples to demonstrate the performance of Meta-Mesh in metagenomic analysis. Therefore, Meta-Mesh would serve as a database and data analysis system to quickly parse and identify similar

Objective: To simulate the multi-leaf collimator of Varian linear accelerator using Monte Carlo method., Methods: The multi-leaf collimator model was established using the DYNVMLC module of BEAMnrc and validated by comparison of Monte Carlo simulation and actual measurement results., Results: The simulation results were well consistent with the actual measurement results with a bias of less than 3%., Conclusion: The multi-leaf collimator of Varian linear accelerator can be successfully modeled using Monte Carlo method for analysis of the impact of the geometric properties of the multi-leaf collimator on the dose distribution.