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The clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) is a genome editing tool widely used in biological research and clinical therapeutics. Natural human genetic variations, through altering the sequence context of CRISPR-Cas9 target regions, can significantly affect its DNA repair outcomes and ultimately lead to different editing efficiencies. However, these effects have not been systematically studied, even as CRISPR-Cas9 is broadly applied to primary cells and patient samples that harbor such genetic diversity. Here, we present comprehensive investigations of natural genetic variations on CRISPR-Cas9 outcomes across the human genome. The utility of our analysis is illustrated in two case studies, on both preclinical discoveries of CD33 knockout in chimeric antigen receptor-T cell therapy and clinical applications of transthyretin (TTR) inactivation for treating TTR amyloidosis. We further expand our analysis to genome-scale, population-stratified common variants that may lead to gene editing disparity. Our analyses demonstrate pitfalls of failing to account for the widespread genetic variations in Cas9 target selection and how they can be effectively examined and avoided using our method. To facilitate broad access to our analysis, a web platform CROTONdb is developed, which provides predictions for all possible CRISPR-Cas9 target sites in the coding and noncoding regulatory regions, spanning over 5.38 million guide RNA targets and 90.82 million estimated variant effects. We anticipate CROTONdb having broad clinical utilities in gene and cellular therapies.

Objective: Child neglect is a public health concern with negative consequences that impact children, families, and society. While neglect is involved with many pediatric hospitalizations, few studies explore characteristics associated with neglect types, social needs, and post-discharge care., Methods: Data on neglect type, sociodemographics, social needs, inpatient consultations, and post-discharge care were collected from the electronic medical record for children aged 0-5 years who were hospitalized with concern for neglect during 2016-2020. Frequencies and percentages were calculated to determine sample characteristics. The Chi-square Test for Independence was used to evaluate associations between neglect type and other variables., Results: The most common neglect types were inadequate nutrition (40%), inability to provide basic care (37%), intrauterine substance exposure (25%), combined types (23%), and inadequate medical care (10%). Common characteristics among neglect types included age less than 1 year, male sex, Hispanic ethnicity, public insurance, past involvement with Child Protective Services, and inpatient consultation services (social work, physical therapy, and occupational therapy), and post-discharge recommendations (primary care, physical therapy, and regional center). Neglect type groups varied by child medical history, social needs, and discharge recommendations. Statistically significant associations supported differences per neglect type., Conclusions: Our findings highlight five specific types of neglect seen in an impoverished and ethnically diverse geographic region. Post-discharge care needs should focus on removing social barriers and optimizing resources, in particular mental health, to mitigate the risk of continued neglect. Future studies should focus on prevention strategies, tailored interventions, and improved resource allocations per neglect type and discharge location., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Fecal filtrate transfer (FFT) is emerging as a safer alternative to traditional fecal microbiota transplantation (FMT) - particularly in the context of necrotizing enterocolitis (NEC), a severe gastrointestinal condition affecting preterm infants. Using a preterm piglet model, FFT has demonstrated superiority over FMT in safety and NEC prevention. Since FFT is virtually devoid of bacteria, prokaryotic viruses (bacteriophages) are assumed to mediate the beneficial effects. However, this assumption remains unproven. To address this gap, we separated virus-like particles (30 kDa to 0.45 µm) of donor feces from the residual postbiotic fluid. We then compared clinical and gut microbiota responses to these fractions with the parent FFT solution after transferring them to NEC-susceptible preterm piglets. Virome transfer was equally effective as FFT in reducing the severity of NEC-like pathology. The bacterial compositional data corroborated clinical findings as virome transfer reduced the relative abundance of several NEC-associated pathogens e.g. Klebsiella pneumoniae and Clostridium perfringens . Virome transfer diversified gut viral communities with concomitant constraining effects on the bacterial composition. Unexpectedly, virome transfer, but not residual postbiotic fluid, led to earlier diarrhea. While diarrhea may be a minor concern in human infants, future work should identify ways of eliminating this side effect without losing treatment efficacy.

Studies into the viral fraction of complex microbial communities, like in the mammalian gut, have recently garnered much interest. Yet there is still no standardized protocol for extracting viruses from such samples, and the protocols that exist employ procedures that skew the viral community of the sample one way or another. The first step of the extraction pipeline often consists of the basic filtering of macromolecules and bacteria, yet even this affects the viruses in a strain-specific manner. In this study, we investigate a protocol for viral extraction based on ultrafiltration and how the choice of ultrafilter might influence the extracted viral community. Clinical samples (feces, vaginal swabs, and tracheal suction samples) were spiked with a mock community of known phages (T4, c2, Φ6, Φ29, Φx174, and Φ2972), filtered, and quantified using spot and plaque assays to estimate the loss in recovery. The enveloped Φ6 phage is especially severely affected by the choice of filter, but also tailed phages such as T4 and c2 have a reduced infectivity after ultrafiltration. We conclude that the pore size of ultrafilters may affect the recovery of phages in a strain- and sample-dependent manner, suggesting the need for greater thought when selecting filters for virus extraction.

Purpose/objectives: Malignant pleural mesothelioma (MPM) is a rare but aggressive cancer arising from the cells of the thoracic pleura with a poor prognosis. We aimed to develop a model, via interpretable machine learning (ML) methods, predicting overall survival for MPM following radiotherapy based on dosimetric metrics as well as patient characteristics., Materials/methods: Sixty MPM (37 right, 23 left) patients treated on a Tomotherapy unit between 2013 and 2018 were retrospectively analyzed. All patients received 45 Gy (25 fractions). The multivariable Cox regression (Cox PH) model and Survival Support Vector Machine (sSVM) were applied to build predictive models of overall survival (OS) based on clinical, dosimetric, and combined variables., Results: Significant differences in dosimetric endpoints for critical structures, i.e., the lung, heart, liver, kidney, and stomach, were observed according to target laterality. The OS was found to be insignificantly different ( p = 0.18) between MPM patients who tested left- and right-sided, with 1-year OS of 77.3% and 75.0%, respectively. With Cox PH regression, considering dosimetric variables for right-sided patients alone, an increase in PTV_Min, Total_Lung_PTV_Mean, Contra_Lung_Volume, Contra_Lung_V20, Esophagus_Mean, and Heart_Volume had a greater hazard to all-cause death, while an increase in Total_Lung_PTV_V20, Contra_Lung_V5, and Esophagus_Max had a lower hazard to all-cause death. Considering clinical variables alone, males and increases in N stage had greater hazard to all-cause death; considering both clinical and dosimetric variables, increases in N stage, PTV_Mean, PTV_Min, and esophagus_Mean had greater hazard to all-cause death, while increases in T stage and Heart_V30 had lower hazard to all-cause-death. In terms of C-index, the Cox PH model and sSVM performed similarly and fairly well when considering clinical and dosimetric variables independently or jointly., Conclusions: Clinical and dosimetric variables may predict the overall survival of mesothelioma patients, which could guide personalized treatment planning towards a better treatment response. The identified predictors and their impact on survival offered additional value for translational application in clinical practice.

Purpose: This retrospective analysis aims to examine the effectiveness of the current chest imaging guidelines regarding COVID-19 positive pediatric patients on our study group of patients aged 0 to 18., Materials and Methods: We examined clinical and imaging data of 178 pediatric COVID-19 positive patients confirmed by PCR admitted to the Children's Hospital of Los Angeles between March 6, 2020 and June 23, 2020., Results: Of 178 patients, only 46 (27%) patients underwent any form of chest imaging. Thirteen (28%) of 46 imaged patients had positive chest X-rays (CXR) or computed tomography (CT) chest findings, with 8 (62%) of the 13 patients suggesting pneumonia or multifocal pneumonia, 3 (23%) patients having acute respiratory distress syndrome, and 2 (15%) patients demonstrating left sided pleural effusions thought to be the result of ruptured appendicitis unrelated to their COVID-19 diagnosis. All but one patient had significant prior medical histories with an associated comorbid medical condition. Of the 46 imaged patients, 17 (37%) patients had a negative chest X-ray, and 15 (33%) patients had suggestive findings of viral etiology. 132 patients were not imaged., Conclusion: Our study population corroborated current chest imaging guidelines in pediatric patients. Chest imaging modalities such as CXR and CT should be reserved for patients who are severely symptomatic and/or possess prior comorbidities such as immunosuppression, diabetes, asthma, obesity, or where other differential etiologies must be entertained., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Motivation: CRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 editing outcomes depend on local DNA sequences at the target site and are thus predictable. However, existing prediction methods are dependent on both feature and model engineering, which restricts their performance to existing knowledge about CRISPR/Cas9 editing., Results: Herein, deep multi-task convolutional neural networks (CNNs) and neural architecture search (NAS) were used to automate both feature and model engineering and create an end-to-end deep-learning framework, CROTON (CRISPR Outcomes Through cONvolutional neural networks). The CROTON model architecture was tuned automatically with NAS on a synthetic large-scale construct-based dataset and then tested on an independent primary T cell genomic editing dataset. CROTON outperformed existing expert-designed models and non-NAS CNNs in predicting 1 base pair insertion and deletion probability as well as deletion and frameshift frequency. Interpretation of CROTON revealed local sequence determinants for diverse editing outcomes. Finally, CROTON was utilized to assess how single nucleotide variants (SNVs) affect the genome editing outcomes of four clinically relevant target genes: the viral receptors ACE2 and CCR5 and the immune checkpoint inhibitors CTLA4 and PDCD1. Large SNV-induced differences in CROTON predictions in these target genes suggest that SNVs should be taken into consideration when designing widely applicable gRNAs., Availability and Implementation: https://github.com/vli31/CROTON., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)