A syndromic neurodevelopmental disorder caused by rare variants in PPFIA3.

PPFIA3 encodes the protein-tyrosine phosphatase, receptor-type, F-polypeptide-interacting-protein-alpha-3 (PPFIA3), which is a member of the LAR-protein-tyrosine phosphatase-interacting-protein (liprin) family involved in synapse formation and function, synaptic vesicle transport, and presynaptic active zone assembly. The protein structure and function are evolutionarily well conserved, but human diseases related to PPFIA3 dysfunction are not yet reported in OMIM. Here, we report 20 individuals with rare PPFIA3 variants (19 heterozygous and 1 compound heterozygous) presenting with developmental delay, intellectual disability, hypotonia, dysmorphisms, microcephaly or macrocephaly, autistic features, and epilepsy with reduced penetrance. Seventeen unique PPFIA3 variants were detected in 18 families. To determine the pathogenicity of PPFIA3 variants in vivo, we generated transgenic fruit flies producing either human wild-type (WT) PPFIA3 or five missense variants using GAL4-UAS targeted gene expression systems. In the fly overexpression assays, we found that the PPFIA3 variants in the region encoding the N-terminal coiled-coil domain exhibited stronger phenotypes compared to those affecting the C-terminal region. In the loss-of-function fly assay, we show that the homozygous loss of fly Liprin-α leads to embryonic lethality. This lethality is partially rescued by the expression of human PPFIA3 WT, suggesting human PPFIA3 function is partially conserved in the fly. However, two of the tested variants failed to rescue the lethality at the larval stage and one variant failed to rescue lethality at the adult stage. Altogether, the human and fruit fly data reveal that the rare PPFIA3 variants are dominant-negative loss-of-function alleles that perturb multiple developmental processes and synapse formation.

A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images.

Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard.

Professional Oversight of Emergency-Use Interventions and Monitoring Systems: Ethical Guidance From the Singapore Experience of COVID-19.

High degrees of uncertainty and a lack of effective therapeutic treatments have characterized the COVID-19 pandemic and the provision of drug products outside research settings has been controversial. International guidelines for providing patients with experimental interventions to treat infectious diseases outside of clinical trials exist but it is unclear if or how they should apply in settings where clinical trials and research are strongly regulated. We propose the Professional Oversight of Emergency-Use Interventions and Monitoring System (POEIMS) as an alternative pathway based on guidance developed for the ethical provision of experimental interventions to treat COVID-19 in Singapore. We support our proposal with justifications that establish moral duties for physicians to record outcomes data and for institutions to establish monitoring systems for reporting information on safety and effectiveness to the relevant authorities. Institutions also have a duty to support generation of evidence for what constitutes good clinical practice and so should ensure the unproven intervention is made the subject of research studies that can contribute to generalizable knowledge as soon as practical and that physicians remain committed to supporting learning health systems. We outline key differences between POEIMS and other pathways for the provision of experimental interventions in public health emergencies.

Retinal Degeneration Associated With the G1606A Mitochondrial Mutation.

The guanine-to-adenine substitution at nucleotide 1606 (G1606A) mutation in the mitochondrial DNA transfer RNA-valine gene has been reported to cause sensorineural deafness, ataxia, myoclonus, seizures, and mental retardation. This study hereby presents a single case report of a new retinal phenotype associated with this mutation: a middle-aged woman with retinal pigment epithelium stippling, atrophy, and peripapillary (retinal pigment epithelium) dropout on fundus examination. The patient was administered an empiric trial of a mitochondrial cocktail with close monitoring of her systemic symptoms. This study identified a novel G1606A mutation to cause early-onset macular pathology resembling that previously described in the A3243G mutation. [Ophthalmic Surg Lasers Imaging Retina. 2022;53:116-119.].

Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation.

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository.

CryoNuSeg: A dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images.

Nuclei instance segmentation plays an important role in the analysis of hematoxylin and eosin (H&E)-stained images. While supervised deep learning (DL)-based approaches represent the state-of-the-art in automatic nuclei instance segmentation, annotated datasets are required to train these models. There are two main types of tissue processing protocols resulting in formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS), respectively. Although FFPE-derived H&E stained tissue sections are the most widely used samples, H&E staining of frozen sections derived from FS samples is a relevant method in intra-operative surgical sessions as it can be performed more rapidly. Due to differences in the preparation of these two types of samples, the derived images and in particular the nuclei appearance may be different in the acquired whole slide images. Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality. In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and inter-observer variabilities. Moreover, we investigate the effects of tissue fixation/embedding protocol (i.e., FS or FFPE) on the automatic nuclei instance segmentation performance and provide a baseline segmentation benchmark for the dataset that can be used in future research. A step-by-step guide to generate the dataset as well as the full dataset and other detailed information are made available to fellow researchers at https://github.com/masih4/CryoNuSeg.

Multimodal imaging of an RPGR carrier female.

Background: Retinitis pigmentosa GTPase regulator (RPGR) gene mutations are a common cause of X-linked retinitis pigmentosa and X-linked cone-rod dystrophy. There have been no previous reports of association with crystalline retinopathy or pseudo-crystalline retinopathy.Materials and Methods: We describe the history, clinical findings, retinal imaging, and electrodiagnostic studies of a patient with a tapetal-like reflex (TLR) and pseudo-crystalline retinopathy secondary to RPGR mutation.Case Description: Asymptomatic TLR secondary to RPGR mutation was diagnosed in a 14-year-old African American female with a family history of retinal dystrophy and no other past ophthalmic or medical history. Pseudo-crystalline retinopathy was observed on the Optos scanning laser ophthalmoscopy (SLO) imaging system but not on color fundus photography (CFP). Evidence of a TLR secondary to RPGR mutation was confirmed by CFP, autofluorescence, and genetic testing.Conclusion: We present a case of pseudo-crystalline retinopathy seen on Optos imaging in a patient with a TLR secondary to RPGR mutation.

A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation.

Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.

"Who is watching the watchdog?": ethical perspectives of sharing health-related data for precision medicine in Singapore.

BACKGROUND: We aimed to examine the ethical concerns Singaporeans have about sharing health-data for precision medicine (PM) and identify suggestions for governance strategies. Just as Asian genomes are under-represented in PM, the views of Asian populations about the risks and benefits of data sharing are under-represented in prior attitudinal research. METHODS: We conducted seven focus groups with 62 participants in Singapore from May to July 2019. They were conducted in three languages (English, Mandarin and Malay) and analysed with qualitative content and thematic analysis. RESULTS: Four key themes emerged: nuanced understandings of data security and data sensitivity; trade-offs between data protection and research benefits; trust (and distrust) in the public and private sectors; and governance and control options. Participants were aware of the inherent risks associated with data sharing for research. Participants expressed conditional support for data sharing, including genomic sequence data and information contained within electronic medical records. This support included sharing data with researchers from universities and healthcare institutions, both in Singapore and overseas. Support was conditional on the perceived social value of the research and appropriate de-identification and data security processes. Participants suggested that a data sharing oversight body would help strengthen public trust and comfort in data research for PM in Singapore. CONCLUSION: Maintenance of public trust in data security systems and governance regimes can enhance participation in PM and data sharing for research. Contrary to themes in much prior research, participants demonstrated a sophisticated understanding of the inherent risks of data sharing, analysed trade-offs between risks and potential benefits of PM, and often adopted an international perspective.

Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification.

BACKGROUND AND OBJECTIVE: Skin cancer is among the most common cancer types in the white population and consequently computer aided methods for skin lesion classification based on dermoscopic images are of great interest. A promising approach for this uses transfer learning to adapt pre-trained convolutional neural networks (CNNs) for skin lesion diagnosis. Since pre-training commonly occurs with natural images of a fixed image resolution and these training images are usually significantly smaller than dermoscopic images, downsampling or cropping of skin lesion images is required. This however may result in a loss of useful medical information, while the ideal resizing or cropping factor of dermoscopic images for the fine-tuning process remains unknown. METHODS: We investigate the effect of image size for skin lesion classification based on pre-trained CNNs and transfer learning. Dermoscopic images from the International Skin Imaging Collaboration (ISIC) skin lesion classification challenge datasets are either resized to or cropped at six different sizes ranging from 224 × 224 to 450 × 450. The resulting classification performance of three well established CNNs, namely EfficientNetB0, EfficientNetB1 and SeReNeXt-50 is explored. We also propose and evaluate a multi-scale multi-CNN (MSM-CNN) fusion approach based on a three-level ensemble strategy that utilises the three network architectures trained on cropped dermoscopic images of various scales. RESULTS: Our results show that image cropping is a better strategy compared to image resizing delivering superior classification performance at all explored image scales. Moreover, fusing the results of all three fine-tuned networks using cropped images at all six scales in the proposed MSM-CNN approach boosts the classification performance compared to a single network or a single image scale. On the ISIC 2018 skin lesion classification challenge test set, our MSM-CNN algorithm yields a balanced multi-class accuracy of 86.2% making it the currently second ranked algorithm on the live leaderboard. CONCLUSIONS: We confirm that the image size has an effect on skin lesion classification performance when employing transfer learning of CNNs. We also show that image cropping results in better performance compared to image resizing. Finally, a straightforward ensembling approach that fuses the results from images cropped at six scales and three fine-tuned CNNs is shown to lead to the best classification performance.

A Multi-Organ Nucleus Segmentation Challenge.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Constitutive activation of the PI3K-AKT pathway and cardiovascular abnormalities in an individual with Kosaki overgrowth syndrome.

Kosaki overgrowth syndrome is a recently described syndrome characterized by distinctive facial features, brain white matter lesions, and developmental delay. Germline activating heterozygous PDGFRB mutations have been reported in this condition. Systemic connective tissue-type findings have been described in some individuals. We describe a 19-year-old Caucasian female with a history of hydrocephalus, Dandy-Walker malformation, cervical spine arachnoid cyst, progressive scoliosis, and overgrowth. Her physical exam included distinctive craniofacial dysmorphism, as well as soft and hyperextensible skin. Cardiovascular imaging during adolescence revealed saccular aneurysms in both coronary artery systems and subtle tortuosity of the cervical vertebral arteries. Exome sequencing trio analysis identified a de novo previously reported pathogenic variant in PDGFRB, c.1696T>C (p.[Trp566Arg]). Further functional studies included platelet-derived growth factor cellular metabolic pathway activity that confirmed the variant causes a constitutive activation of the PI3K-AKT pathway. This is the first report to characterize the activating nature of this PDGFRB variant. We also highlight the connective tissue findings seen in Kosaki overgrowth syndrome and recommend baseline echocardiographic evaluation in all individuals with this condition with particular emphasis on coronary arteries.

Fusing fine-tuned deep features for skin lesion classification.

Malignant melanoma is one of the most aggressive forms of skin cancer. Early detection is important as it significantly improves survival rates. Consequently, accurate discrimination of malignant skin lesions from benign lesions such as seborrheic keratoses or benign nevi is crucial, while accurate computerised classification of skin lesion images is of great interest to support diagnosis. In this paper, we propose a fully automatic computerised method to classify skin lesions from dermoscopic images. Our approach is based on a novel ensemble scheme for convolutional neural networks (CNNs) that combines intra-architecture and inter-architecture network fusion. The proposed method consists of multiple sets of CNNs of different architecture that represent different feature abstraction levels. Each set of CNNs consists of a number of pre-trained networks that have identical architecture but are fine-tuned on dermoscopic skin lesion images with different settings. The deep features of each network were used to train different support vector machine classifiers. Finally, the average prediction probability classification vectors from different sets are fused to provide the final prediction. Evaluated on the 600 test images of the ISIC 2017 skin lesion classification challenge, the proposed algorithm yields an area under receiver operating characteristic curve of 87.3% for melanoma classification and an area under receiver operating characteristic curve of 95.5% for seborrheic keratosis classification, outperforming the top-ranked methods of the challenge while being simpler compared to them. The obtained results convincingly demonstrate our proposed approach to represent a reliable and robust method for feature extraction, model fusion and classification of dermoscopic skin lesion images.

McArdle Disease Presenting With Muscle Pain in a Teenage Girl: The Role of Whole-Exome Sequencing in Neurogenetic Disorders.

We present the case of a young woman with worsening attacks of muscle pain and rhabdomyolysis beginning at age 14. Initial metabolic testing and electromyography revealed findings of a nonspecific myopathy. Diagnostic options were discussed among the members of a neurogenetics clinic team. Whole-exome sequencing was selected as a first tier test. This testing revealed a known disease causing mutation in the PYGM gene consistent with McArdle disease. We discuss the decision to use whole-exome sequencing in diagnostics and the rationale for making this our choice as a first-level test modality.

A parent-of-origin analysis of paternal genetic variants and increased risk of conotruncal heart defects.

The association between conotruncal heart defects (CTHDs) and maternal genetic and environmental exposures is well studied. However, little is known about paternal genetic or environmental exposures and risk of CTHDs. We assessed the effect of paternal genetic variants in the folate, homocysteine, and transsulfuration pathways on risk of CTHDs in offspring. We utilized National Birth Defects Prevention Study data to conduct a family-based case only study using 616 live-born infants with CTHDs, born October 1997-August 2008. Maternal, paternal and infant DNA was genotyped using an Illumina® Golden Gate custom single nucleotide polymorphism (SNP) panel. Relative risks (RR) and 95% confidence intervals (CI) from log-linear models determined parent of origin effects for 921 SNPs in 60 candidate genes involved in the folate, homocysteine, and transsulfuration pathways on risk of CTHDs. The risk of CTHD among children who inherited a paternally derived copy of the A allele on GLRX (rs17085159) or the T allele of GLRX (rs12109442) was 0.23 (95%CI: 0.12, 0.42; p = 1.09 × 10-6 ) and 0.27 (95%CI: 0.14, 0.50; p = 2.06 × 10-5 ) times the risk among children who inherited a maternal copy of the same allele. The paternally inherited copy of the GSR (rs7818511) A allele had a 0.31 (95%CI: 0.18, 0.53; p = 9.94 × 10-6 ] risk of CTHD compared to children with the maternal copy of the same allele. The risk of CTHD is less influenced by variants in paternal genes involved in the folate, homocysteine, or transsulfuration pathways than variants in maternal genes in those pathways.

Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System.

This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

A novel approach in pediatric telegenetic services: geneticist, pediatrician and genetic counselor team.

PurposeOur aim was to improve access to genetic services in an underserved region by developing a collaborative telegenetic service delivery model with a pediatrician, medical geneticist, and genetics counselor (GC).MethodsProtocols for the evaluation of common genetic indications were developed. Patients referred with indications suggestive of a syndromic etiology were scheduled to see the geneticist directly via telegenetics. Other patients were scheduled to see the pediatrician and GC in person before follow-up with the geneticist if indicated. Patients seen by the geneticist and/or pediatrician/GC were enumerated and the next available appointment was tracked. Patient satisfaction surveys were conducted.ResultsOf the 265 patients evaluated during the study period, 116 (44%) were evaluated by a pediatrician and GC in person first, after which 82 (71% of those evaluated) required further follow-up with the geneticist. The next available appointment with a pediatrician and GC never exceeded 6 weeks, while new appointments with a geneticist ranged from 3 to 9 months. All patients reported high satisfaction with this genetic service model.ConclusionThe pediatrician/GC clinic provides a model of collaborative care that is a medical home neighbor and exemplifies the integration of genetics into primary care. The telegenetics clinic offers a viable solution to providing competent and convenient access to a geneticist for patients in chronically underserved regions.

Alu-mediated deletion of PIGL in a Patient with CHIME syndrome.

CHIME syndrome is a rare autosomal recessive neuroectodermal disorder associated with biallelic mutations in PIGL. To date, six molecularly confirmed cases of CHIME syndrome have been reported. Here, we report the seventh patient with biallelic PIGL mutations associated with CHIME syndrome and describe the first characterization of an intragenic deletion in PIGL. Our characterization of the deletion breakpoint junction demonstrated that the breakpoints occurred within Alu repeats and the deletion was most likely mediated by a microhomology event. Analysis of PIGL genomic sequences for repetitive elements demonstrated that Alu repeats represent ∼34% of its intronic sequence, suggesting that the genomic architecture may predispose the gene to disease-causing copynumber changes. Taken together, these findings indicate that patients with a clinical diagnosis of CHIME syndrome and a single identifiable mutation in PIGL warrant further investigation for copynumber changes involving PIGL.