P117 Surgical management of Ureteropelvic Junction Obstruction (UPJO) in Neonates amid COVID-19 pandemic; Review of Literature and a Cohort Study in Iran

Introduction The current study presents a systematic review of literature on surgical management of ureteropelvic junction obstruction (UPJO) alongside an ongoing cohort study of neonates presenting with high grades of hydronephrosis due to UPJO requiring urgent treatment in a developing country amid the COVID-19 pandemic. The aim of this study is to investigate the efficacy and cost effectiveness of laparoscopic-assisted pyeloplasty. Methods Patients’ demographics, clinical presentations and treatment outcomes are recorded. The cohort is classified into 3 groups based on type of surgical treatment offered including open, laparoscopic and laparoscopic-assisted pyeloplasty. All patients admitted with grade 3-4 hydronephrosis due to UPJO are included. Patients with UPJO as part of a complex multisystemic syndrome are excluded. Literature review was conducted from 2000/1/1 to 2020/1/1 to include all original research papers on surgical management of UPJO. The age group was limited to neonates (under 1 year-old). Results 32 articles were included in the review. 20 papers (62.5%) recommended open pyeloplasty, 10 papers (31.3%) recommended laparoscopic pyeloplasty and 2 papers (6.25%) recommended laparoscopic-assisted pyeloplasty. The cohort study is currently recruiting patients treated by the 3 surgical approaches. Conclusion The majority of studies are focusing on open pyeloplasty. However, as more surgeons are going through the learning curve of laparoscopic pyeloplasty, the trend is towards more laparoscopic management in the future. It is important to discover the advantages of laparoscopic-assisted approach as a new technique to improve the outcome and shorten the hospital stay amid the COVID-19 pandemic

Stimuli-responsive piezoelectricity in electrospun polycaprolactone (PCL)/Polyvinylidene fluoride (PVDF) fibrous scaffolds for bone regeneration

Polymeric scaffolds are a determinant part of modern tissue engineering owing to their great diversity, adaptability, and processability. Interestingly, the physical properties of these scaffolds, e.g., porosity, mechanical properties, and biocompatibility, can be tuned to make them smart and stimuli-responsive. In this regard, piezoelectric materials can be applied to stimulate bone regeneration by converting mechanical impulses to electrical signals. In the present research, fibers made of various blend ratios of polyvinylidene fluoride (PVDF)/polycaprolactone (PCL) were fabricated, investigated and optimized to promote bone regeneration. Uniform fibers containing β-phase PVDF were obtained due to the simultaneous stretching and high voltage applied during electrospinning. Furthermore, components interaction, crystallinity, and piezoelectric behavior were estimated through fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and piezometery, respectively. The samples showed improved wettability and controlled biodegradability, and the piezoelectric charge output reached up to 7.5 pC/N in the sample containing 70 wt% PVDF. At the same time, these scaffolds could provide mechanical properties close to the native bone tissue relying on the PVDF component. In vitro assessments demonstrated that the composite scaffolds were biocompatible and could support cell attachment and proliferation. Moreover, their piezoelectric behavior promoted stem cell differentiation into osteoblasts. Considering the obtained results, the potential of piezoelectric PVDF/PCL blend fibers for bone scaffolds is indisputable

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community