123 research outputs found

    Being Motivated to Protect : The Influence of Sexual Communal Motivations on Sexual Risk Taking

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    College-aged students are a high-risk population for unplanned pregnancy with 40% of women between the ages of 18-20 experiencing an unplanned pregnancy. This can cause physical, mental, and emotional stress resulting in withdrawal from college for the student. Communal motivation (being oriented towards other’s needs) positively predicts condom use. WISE interventions, a simple yet impactful type of interventions targeted towards addressing a problem, have been shown to be successful. Participants completed a sexual risk behavior measure, sexual risk-taking measure and communal motivations (CM) measure following a sexual health video, and reflection activity were participants either applied the sexual health information to their relationship (experimental) or reflected on the sexual health material presented (control). CM was positively correlated with number of sexual partners in the past 3 months, r(262) = .162,

    Distinguishing cancerous from non-cancerous cells through analysis of electrical noise

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    Since 1984, electric cell-substrate impedance sensing (ECIS) has been used to monitor cell behavior in tissue culture and has proven sensitive to cell morphological changes and cell motility. We have taken ECIS measurements on several cultures of non-cancerous (HOSE) and cancerous (SKOV) human ovarian surface epithelial cells. By analyzing the noise in real and imaginary electrical impedance, we demonstrate that it is possible to distinguish the two cell types purely from signatures of their electrical noise. Our measures include power-spectral exponents, Hurst and detrended fluctuation analysis, and estimates of correlation time; principal-component analysis combines all the measures. The noise from both cancerous and non-cancerous cultures shows correlations on many time scales, but these correlations are stronger for the non-cancerous cells.Comment: 8 pages, 4 figures; submitted to PR

    Psychosocial predictors of primiparous breastfeeding initiation and duration

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    Background: Many US women fall short of meeting the recommendations on breastfeeding.Whereas prenatal demographic factors have been well researched in relation to breastfeeding,psychosocial maternal characteristics are less understood but could be important predictors ofbreastfeeding initiation and duration.Objective: This study examined primiparous maternal psychosocial characteristics andtemperamentally based negative infant affect as predictors of breastfeeding initiation andduration while accounting for depression and sociodemographic covariates.Methods: Prenatally, 237 primiparous women were administered the Adult AttachmentInterview and completed a measure of beliefs related to infant crying. At 6 months postpartum,negative infant affect was assessed via mother report. Breastfeeding was assessed at 6 monthsand 1 year postpartum via mother report.Results: Results indicated that younger, low income, less educated, single, ethnic minoritymothers and mothers with elevated depressive symptoms were less likely to initiate breastfeedingand breastfed for a shorter period than other women. Women who initiated breastfeeding tendedto have higher adult attachment coherence scores (more secure attachment) than those who didnot initiate breastfeeding (median score of 6.00 vs 4.00). An interaction was observed betweennegative infant affect and beliefs about crying related to spoiling, such that earlier cessation ofbreastfeeding was observed among mothers who reported high levels of negative infant affectand strongly endorsed the belief that responding to cries spoils infants (hazard ratio = 1.71, P Conclusion: Although these psychosocial variables predicted relatively little variation inbreastfeeding over and above covariates, the results suggest some novel approaches to promotebreastfeeding

    Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics

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    Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MICL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD4 takes into account the available metadata and summaries all relevant statistics generated in steps 1–3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making

    An automated, low volume, and high-throughput analytical platform for aggregate quantitation from cell culture media

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    High throughput screening methods have driven a paradigm shift in biopharmaceutical development by reducing the costs of good manufactured (COGM) and accelerate the launch to market of novel drug products. Scale-down cell culture systems such as shaken 24- and 96-deep-well plates (DWPs) are used for initial screening of hundreds of recombinant mammalian clonal cell lines to quickly and efficiently select the best producing strains expressing product quality attributes that fit to industry platform. A common modification monitored from early-stage product development is protein aggregation due to its impact on safety and efficacy. This study aims to integrate high-throughput analysis of aggregation-prone therapeutic proteins with 96-deep well plate screening to rank clones based on the aggregation levels of the expressed proteins. Here we present an automated, small-scale analytical platform workflow combining the purification and subsequent aggregation analysis of protein biopharmaceuticals expressed in 96-DWP cell cultures. Product purification was achieved by small-scale solid-phase extraction using dual flow chromatography (DFC) automated on a robotic liquid handler for the parallel processing of up to 96 samples at a time. At-line coupling of size-exclusion chromatography (SEC) using a 2.1 mm ID column enabled the detection of aggregates with sub-2 µg sensitivity and a 3.5 min run time. The entire workflow was designed as an application to aggregation-prone mAbs and “mAb-like” next generation biopharmaceuticals, such as bispecific antibodies (BsAbs). Application of the high-throughput analytical workflow to a shake plate overgrow (SPOG) screen, enabled the screening of 384 different clonal cell lines in 32 h, requiring < 2 μg of protein per sample. Aggregation levels expressed by the clones varied between 9 and 76%. This high-throughput analytical workflow allowed for the early elimination of clonal cell lines with high aggregation, demonstrating the advantage of integrating analytical testing for critical quality attributes (CQAs) earlier in product development to drive better decision making

    Polypeptide-grafted macroporous polyHIPE by surface-initiated N-Carboxyanhydride (NCA) polymerization as a platform for bioconjugation

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    A new class of functional macroporous monoliths from polymerized high internal phase emulsion (polyHIPE) with tunable surface functional groups was developed by direct polypeptide surface grafting. In the first step, amino-functional polyHIPEs were obtained by the addition of 4-vinylbenzyl or 4-vinylbenzylphthalimide to the styrenic emulsion and thermal radical polymerization. The obtained monoliths present the expected open-cell morphology and a high surface area. The incorporated amino group was successfully utilized to initiate the ring-opening polymer- ization of benzyl-L-glutamate N-carboxyanhydride (BLG NCA) and benzyloxycarbonyl-L-lysine (Lys(Z)) NCA, which resulted in a dense homogeneous coating of polypeptides throughout the internal polyHIPE surfaces as confirmed by SEM and FTIR analysis. The amount of polypeptide grafted to the polyHIPE surfaces could be modulated by varying the initial ratio of amino acid NCA to amino-functional polyHIPE. Subsequent removal of the polypeptide protecting groups yielded highly functional polyHIPE-g-poly(glutamic acid) and polyHIPE-g- poly(lysine). Both types of polypeptide-grafted monoliths responded to pH by changes in their hydrohilicity. The possibility to use the high density of function (−COOH or −NH2) for secondary reaction was demonstrated by the successful bioconjugation of enhanced green fluorescent protein (eGFP) and fluorescein isocyanate (FITC) on the polymer 3D-scaffold surface. The amount of eGFP and FITC conjugated to the polypeptide-grafted polyHIPE was significantly higher than to the amino- functional polyHIPE, signifying the advantage of polypeptide grafting to achieve highly functional polyHIPEs
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