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Background: Adolescent use of e-cigarettes has been recognized as a significant public health concern as rates of adolescent vaping increase. As evidence of respiratory damage and other health concerns continue to emerge, educational resources for parents are critical in combating the vaping epidemic. A vaping educational tool can serve as a resource that parents of adolescents can reference to learn about e-cigarette use and how to address this topic with their families. Objective: The purpose of this study was to examine parents' perspectives on the design and use of an educational infographic (Parent E-Cigarette and Vaping Educational Resource; P-EVER) to inform parents of adolescents and their families about e-cigarette use, risks, and quitting resources. Methods: Parents who had an adolescent aged 12 to 18 years were recruited through community pharmacies in Wisconsin through recruitment fliers, emails, and word of mouth between February and June 2023. Participants reviewed the vaping educational handout in a semi-structured interview that was recorded and transcribed verbatim. Two members of the research team independently coded each transcript using NVivo software to conduct inductive thematic analysis. Bi-weekly meetings were held to refine codes, develop the master codebook, and identify prevalent themes (intercoder reliability - 0.83). Results: Thirty-five parents were interviewed between February and June 2023. Four themes were identified: vaping awareness, P-EVER content and implementation, pharmacist's role, and barriers to pharmacist intervention on vaping. Participants believed the vaping educational handout was an effective resource for disseminating information to parents of adolescents. Responses highlighted the importance of providing educational resources to initiate conversations surrounding health risks to adolescents. Conclusion: Pharmacists played a crucial role in successfully disseminating the P-EVER education tool to parents of adolescents. Future work is required to determine effective interventions for pharmacists and adolescents to discuss the topic of vaping awareness and safety.

Preclinical models serve as indispensable tools in translational medicine. Specifically, patient-derived models such as patient-derived xenografts (PDX), induced pluripotent stem cells (iPSC), organoids, and recently developed technique of conditional reprogramming (CR) have been employed to reflect the host characteristics of diseases. CR technology involves co-culturing epithelial cells with irradiated Swiss-3T3-J2 mouse fibroblasts (feeder cells) in the presence of a Rho kinase (ROCK) inhibitor, Y-27632. CR technique facilitates the rapid conversion of both normal and malignant cells into a “reprogrammed stem-like” state, marked by robust in vitro proliferation. This is achieved without reliance on exogenous gene expression or viral transfection, while maintaining the genetic profile of the parental cells. So far, CR technology has been used to study biology of diseases, targeted therapies (precision medicine), regenerative medicine, and noninvasive diagnosis and surveillance. Respiratory diseases, ranking as the third leading cause of global mortality, pose a significant burden to healthcare systems worldwide. Given the substantial mortality and morbidity rates of respiratory diseases, efficient and rapid preclinical models are imperative to accurately recapitulate the diverse spectrum of respiratory conditions. In this article, we discuss the applications and future potential of CR technology in modeling various respiratory tract diseases, including lung cancer, respiratory viral infections (such as influenza and Covid-19 and etc.), asthma, cystic fibrosis, respiratory papillomatosis, and upper aerodigestive track tumors. Furthermore, we discuss the potential utility of CR in personalized medicine, regenerative medicine, and clinical translation.

Nonaqueous flow batteries hold promise given their high cell voltage and energy density, but their performance is often plagued by the crossover of redox compounds. In this study, we used permselective lithium superionic conducting (LiSICON) ceramic membranes to enable reliable long-term use of organic redox molecules in nonaqueous flow cells. With different solvents on each side, enhanced cell voltages were obtained for a flow battery using viologen-based negolyte and TEMPO-based posolyte molecules. The thermoplastic assembly of the LiSICON membrane realized leakless cell sealing, thus overcoming the mechanical brittleness challenge. As a result, stable cycling was achieved in the flow cells, which showed good capacity retention over an extended test time.

The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.

Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommendation system that leverages multi-model and multimodal human-computer interaction (HCI) techniques to provide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper and ingredient image recognition through GPT-Vision. It employs an advanced recipe filtering system that utilizes user-provided ingredients to fetch recipes, which are then evaluated through multi-model AI through integrations of OpenAI, Google Gemini, Claude, and/or Anthropic APIs to deliver highly personalized recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary restrictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future recommendations, demonstrating the system’s adaptability. Chef Dalle showcases potential applications ranging from home kitchens to grocery stores and restaurant menu customization, addressing accessibility and promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field.

BACKGROUND: Seizures are an under-reported feature of the SATB2-associated syndrome phenotype. We describe the electroencephalographic findings and seizure semiology and treatment in a population of individuals with SATB2-associated syndrome. METHODS: We performed a retrospective review of 101 individuals with SATB2-associated syndrome who were reported to have had a previous electroencephalographic study to identify those who had at least one reported abnormal result. For completeness, a supplemental survey was distributed to the caregivers and input from the treating neurologist was obtained whenever possible. RESULTS: Forty-one subjects were identified as having at least one prior abnormal electroencephalography. Thirty-eight individuals (93%) had epileptiform discharges, 28 (74%) with central localization. Sleep stages were included as part of the electroencephalographies performed in 31 individuals (76%), and epileptiform activity was recorded during sleep in all instances (100%). Definite clinical seizures were diagnosed in 17 individuals (42%) with a mean age of onset of 3.2 years (four months to six years), and focal seizures were the most common type of seizure observed (42%). Six subjects with definite clinical seizures needed polytherapy (35%). Delayed myelination and/or abnormal white matter hyperintensities were seen on neuroimaging in 19 individuals (61%). CONCLUSIONS: Epileptiform abnormalities are commonly seen in individuals with SATB2-associated syndrome. A baseline electroencephalography that preferably includes sleep stages is recommended during the initial evaluation of all individuals with SATB2-associated syndrome, regardless of clinical suspicion of epilepsy.

Background:. Cytomegalovirus (CMV) infection causes significant morbidity and mortality in lung transplant recipients. Current guidelines use pretransplant donor and recipient CMV serostatus to predict the risk of subsequent CMV replication and length of antiviral prophylaxis. Immunological monitoring may better inform the risk of CMV infection in patients, thereby allowing for improved tailoring of antiviral prophylaxis. In this study, we compared 2 commercially available assays, the QuantiFERON-CMV (QFN-CMV) and T-Track-CMV (enzyme-linked immunosorbent spot assay), to predict the risk of CMV disease in lung transplant recipients. Methods:. We performed CMV immunity assays on 32 lung transplant recipients at risk of CMV disease as defined by serostatus (CMV-seropositive recipients, n = 26; or CMV-seronegative lung transplant recipient receiving a CMV-seropositive donor organ, n = 6). QFN-CMV and T-Track were performed on peripheral blood mononuclear cells, and episodes of CMV replication in both serum and bronchoalveolar lavage were found to be correlated to the CMV immune assays. The predictive ability of the assays was determined using Kaplan–Meier curves. Results:. There was a degree of concordance between tests, with 44% of recipients positive for both tests and 28% negative for both tests; however, test results were discordant in 28% of cases. A negative result in either the QFN-CMV (P < 0.01) or T-Track (P < 0.05) assays was obtained in a significantly higher number of recipients who experienced CMV replication in the blood. Using these assays together gave higher predictability of CMV replication, with only 1 recipient experiencing CMV replication in the blood who obtained a positive test result for both assays. Neither assay was able to predict recipients who experienced CMV replication in the lung allograft. Conclusions:. Our study demonstrates that CMV immunity assays can predict viremia; however, the lack of association with allograft infection suggests that CMV-specific T-cell immunity in the circulation is not associated with the control of CMV replication within the transplanted lung allograft.

Abstract Vaccines represent the single most cost-efficient and equitable way to combat and eradicate infectious diseases. While traditional licensed vaccines consist of either inactivated/attenuated versions of the entire pathogen or subunits of it, most novel experimental vaccines against emerging infectious diseases employ nucleic acids to produce the antigen of interest directly in vivo. These include DNA plasmid vaccines, mRNA vaccines, and recombinant viral vectors. The advantages of using nucleic acid vaccines include their ability to induce durable immune responses, high vaccine stability, and ease of large-scale manufacturing. In this review, we present an overview of pre-clinical and clinical data on recombinant viral vector vaccines and discuss the advantages and limitations of the different viral vector platforms.

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter’s capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI’s computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development.

Integrase defective lentiviral vectors (IDLVs) are a promising vaccine delivery platform given their ability to induce high magnitude and durable antigen-specific immune responses. IDLVs based on the simian immunodeficiency virus (SIV) are significantly more efficient at transducing human and simian dendritic cells (DCs) compared to HIV-based vectors, resulting in a higher expansion of antigen-specific CD8+ T cells. Additionally, IDLV persistence and continuous antigen expression in muscle cells at the injection site contributes to the durability of the vaccine-induced immune responses. Here, to further optimize transgene expression levels in both DCs and muscle cells, we generated ten novel lentiviral vectors (LVs) expressing green fluorescent protein (GFP) under different hybrid promoters. Our data show that three of the tested hybrid promoters resulted in the highest transgene expression levels in mouse DCs, monkey DCs and monkey muscle cells. We then used the three LVs with the highest in vitro transgene expression levels to immunize BALB/c mice and observed high magnitude T cell responses at 3 months post-prime. Our study demonstrates that the choice of the vector promoter influences antigen expression levels in target cells and the ensuing magnitude of T cell responses in vivo.

Preclinical in vitro models play an important role in studying cancer cell biology and facilitating translational research, especially in the identification of drug targets and drug discovery studies. This is particularly relevant in breast cancer, where the global burden of disease is quite high based on prevalence and a relatively high rate of lethality. Predictive tools to select patients who will be responsive to invasive or morbid therapies (radiotherapy, chemotherapy, immunotherapy, and/or surgery) are relatively lacking. To be clinically relevant, a model must accurately replicate the biology and cellular heterogeneity of the primary tumor. Addressing these requirements and overcoming the limitations of most existing cancer cell lines, which are typically derived from a single clone, we have recently developed conditional reprogramming (CR) technology. The CR technology refers to a co-culture system of primary human normal or tumor cells with irradiated murine fibroblasts in the presence of a Rho-associated kinase inhibitor to allow the primary cells to acquire stem cell properties and the ability to proliferate indefinitely in vitro without any exogenous gene or viral transfection. This innovative approach fulfills many of these needs and offers an alternative that surpasses the deficiencies associated with traditional cancer cell lines. These CR cells (CRCs) can be reprogrammed to maintain a highly proliferative state and reproduce the genomic and histological characteristics of the parental tissue. Therefore, CR technology may be a clinically relevant model to test and predict drug sensitivity, conduct gene profile analysis and xenograft research, and undertake personalized medicine. This review discusses studies that have applied CR technology to conduct breast cancer research.

Several oncogenic viruses are associated with approximately 20% of human cancers. Experimental models are crucial for studying the pathogenicity and biological aspects of oncogenic viruses and their potential mechanisms in tumorigenesis. Current cell models have considerable limitations such as: their low yield, genetic and epigenetic modification, and reduction in tumor heterogeneity during long propagation. Cancer cell lines are limited and not appropriate for studying the viral life cycle, for example, natural viral life cycles of HPV and EBV, and their persistence and latency in epithelial cells are poorly understood, since these processes are highly related to epithelial differentiation. Therefore, there is an urgent need of reliable human physiological cell models to study viral life cycle and cancer initiation. Conditional cell reprogramming (CCR) is a rapid and robust cell culture system, where the cells can be established from minimally invasive or noninvasive specimens and their lineage functions preserved during the long-term culture. These CR cells retain their ability to differentiate at air–liquid interface (ALI). Here, we recapitulated the applications of CR and ALI approaches in modeling host–virus interactions and viral-mediated tumorigenesis.

The use of advanced preclinical models has become increasingly important in drug development. This is particularly relevant in bladder cancer, where the global burden of disease is quite high based on prevalence and a relatively high rate of lethality. Predictive tools to select patients who will be responsive to invasive or morbid therapies (chemotherapy, radiotherapy, immunotherapy, and/or surgery) are largely absent. Patient-derived and clinically relevant models including patient-derived xenografts (PDX), organoids, and conditional reprogramming (CR) of cell cultures efficiently generate numerous models and are being used in both basic and translational cancer biology. These CR cells (CRCs) can be reprogrammed to maintain a highly proliferative state and reproduce the genomic and histological characteristics of the parental tissue. Therefore, CR technology may be a clinically relevant model to test and predict drug sensitivity, conduct gene profile analysis and xenograft research, and undertake personalized medicine. This review discusses studies that have utilized CR technology to conduct bladder cancer research.

NBI-921352 (formerly XEN901) is a novel sodium channel inhibitor designed to specifically target NaV1.6 channels. Such a molecule provides a precision-medicine approach to target SCN8A-related epilepsy syndromes (SCN8A-RES), where gain-of-function (GoF) mutations lead to excess NaV1.6 sodium current, or other indications where NaV1.6 mediated hyper-excitability contributes to disease (Gardella and Møller, 2019; Johannesen et al., 2019; Veeramah et al., 2012). NBI-921352 is a potent inhibitor of NaV1.6 (IC500.051 µM), with exquisite selectivity over other sodium channel isoforms (selectivity ratios of 756 X for NaV1.1, 134 X for NaV1.2, 276 X for NaV1.7, and >583 Xfor NaV1.3, NaV1.4, and NaV1.5). NBI-921352is a state-dependent inhibitor, preferentially inhibiting inactivatedchannels. The state dependence leads to potent stabilization of inactivation, inhibiting NaV1.6 currents, including resurgent and persistent NaV1.6 currents, while sparing the closed/rested channels. The isoform-selective profile of NBI-921352 led to a robust inhibition of action-potential firing in glutamatergic excitatory pyramidal neurons, while sparing fast-spiking inhibitory interneurons, where NaV1.1 predominates. Oral administration of NBI-921352 prevented electrically induced seizures in a Scn8a GoF mouse,as well as in wild-type mouse and ratseizure models. NBI-921352 was effective in preventing seizures at lower brain and plasma concentrations than commonly prescribed sodium channel inhibitor anti-seizure medicines (ASMs) carbamazepine, phenytoin, and lacosamide. NBI-921352 waswell tolerated at higher multiples of the effective plasma and brain concentrations than those ASMs. NBI-921352 is entering phase II proof-of-concept trials for the treatment of SCN8A-developmental epileptic encephalopathy (SCN8A-DEE) and adult focal-onset seizures.

Data Documentation Initiative-Lifecycle (DDI-L) introduced a robust metadata model to support the capture of questionnaire content and flow, and encouraged through support for versioning and provenancing, objects such as BasedOn for the reuse of existing question items. However, the dearth of questionnaire banks including both question text and response domains has meant that an ecosystem to support the development of DDI ready Computer Assisted Interviewing (CAI) tools has been limited. Archives hold the information in PDFs associated with surveys but extracting that in an efficient manner into DDI-Lifecycle is a significant challenge. While CLOSER Discovery has been championing the provision of high-quality questionnaire metadata in DDI-Lifecycle, this has primarily been done manually. More automated methods need to be explored to ensure scalable metadata annotation and uplift. This paper presents initial results in engineering a machine learning (ML) pipeline to automate the extraction of questions from survey questionnaires as PDFs. Using CLOSER Discovery as a ‘training and test dataset’, a number of machine learning approaches have been explored to classify parsed text from questionnaires to be output as valid DDI items for inclusion in a DDI-L compliant repository. The developed ML pipeline adopts a continuous build and integrate approach, with processes in place to keep track of various combinations of the structured DDI-L input metadata, ML models and model parameters against the defined evaluation metrics, thus enabling reproducibility and comparative analysis of the experiments. Tangible outputs include a map of the various metadata and model parameters with the corresponding evaluation metrics’ values, which enable model tuning as well as transparent management of data and experiments.

Objectives: People living with human immunodeficiency virus in Canada can face criminal charges for human immunodeficiency virus non-disclosure before sex, unless a condom is used and their viral load is

Objectives: We assessed the prevalence and correlates of sexual concerns and associated distress among women living with HIV in Canada. Methods: We analyzed cross-sectional survey data from the Canadian HIV Women’s Sexual and Reproductive Health Cohort Study (2017–2018). Self-identified women living with HIV were asked about sexual concerns post-HIV diagnosis and associated distress (none, mild, moderate, severe). Five areas of concern were assessed, including difficulties related to sexual self-esteem, sexual function, relationships, and emotional and behavioral aspects of sex. Logistic regression analyses identified correlates of reporting any sexual concerns and severe distress about these concerns. Results: Of 906 participants (median age 48, Q1–Q3 = 41–55), 596 (65.8%) reported sexual concerns post-HIV diagnosis. We found a high prevalence of concerns related to relationships (43.3%), sexual self-esteem (49.4%), and emotional aspects of sex (45.4%), relative to sexual functioning (38.4%) and behavioral aspects (33.7%). Of those with sexual concerns, 36.7% reported severe distress. Reports of severe distress were the highest for relationship difficulties (32.5%), relative to other areas of concern (21.4%–22.8%). In adjusted analyses, women reporting sexual dissatisfaction and high HIV-related stigma had significantly higher odds of reporting sexual concerns. Conversely, those reporting higher resilience, better mental health, African, Caribbean, and Black identity, and sex as somewhat unimportant, not at all important, or neutral to their lives had lower adjusted odds. Factors associated with severe distress about sexual concerns included older age, body dissatisfaction, sexual dissatisfaction, and high HIV-related stigma, while better mental health and getting support from someone living with HIV were protective. While 84.4% of women had discussed with a provider how viral load impacts transmission risk, only 40.6% had conversations about sexual wellbeing. Conclusion: More attention to women’s sexual wellbeing within social and relational contexts is critical to ensure the sexual rights of women living with HIV are upheld.

Introduction People living with HIV (PLHIV) are increasingly at risk of age-related comorbidities such as diabetes mellitus (DM). While DM is associated with elevated mortality and morbidity, understanding of DM among PLHIV is limited. We assessed the incidence of DM among people living with and without HIV in British Columbia (BC), Canada, during 2001–2013.Methods We used longitudinal data from a population-based cohort study linking clinical data and administrative health data. We included PLHIV who were antiretroviral therapy (ART) naïve at baseline, and 1:5 age-sex-matched persons without HIV. All participants had ≥5 years of historic data pre-baseline and ≥1 year(s) of follow-up. DM was identified using the BC Ministry of Health’s definitions applied to hospitalisation, physician billing and drug dispensation datasets. Incident DM was identified using a 5-year run-in period. In addition to unadjusted incidence rates (IRs), we estimated adjusted incidence rate ratios (IRR) using Poisson regression and assessed annual trends in DM IRs per 1000 person years (PYs) between 2001 and 2013.Results A total of 129 PLHIV and 636 individuals without HIV developed DM over 17 529 PYs and 88,672 PYs, respectively. The unadjusted IRs of DM per 1000 PYs were 7.4 (95% CI 6.2 to 8.8) among PLHIV and 7.2 (95% CI 6.6 to 7.8) for individuals without HIV. After adjustment for confounding, HIV serostatus was not associated with DM incidence (adjusted IRR: 1.03, 95% CI 0.83 to 1.27). DM incidence did not increase over time among PLHIV (Kendall trend test: p=0.9369), but it increased among persons without HIV between 2001 and 2013 (p=0.0136).Conclusions After adjustment, HIV serostatus was not associated with incidence of DM, between 2001 and 2013. Future studies should investigate the impact of ART on mitigating the potential risk of DM among PLHIV.

Objectives Innovative methods are needed for identification of transgender people in administrative records for health research purposes. This study investigated the feasibility of using transgender-specific healthcare utilisation in a Canadian population-based health records database to develop a computable phenotype (CP) and identify the proportion of transgender people within the HIV-positive population as a public health priority.Design The Comparative Outcomes and Service Utilization Trends (COAST) Study cohort comprises a data linkage between two provincial data sources: The British Columbia (BC) Centre for Excellence in HIV/AIDS Drug Treatment Program, which coordinates HIV treatment dispensation across BC and Population Data BC, a provincial data repository holding individual, longitudinal data for all BC residents (1996–2013).Setting British Columbia, Canada.Participants COAST participants include 13 907 BC residents living with HIV (≥19 years of age) and a 10% random sample comparison group of the HIV-negative general population (514 952 individuals).Primary and secondary outcome measures Healthcare records were used to identify transgender people via a CP algorithm (diagnosis codes+androgen blocker/hormone prescriptions), to examine related diagnoses and prescription concordance and to validate the CP using an independent provider-reported transgender status measure. Demographics and chronic illness burden were also characterised for the transgender sample.Results The best-performing CP identified 137 HIV-negative and 51 HIV-positive transgender people (total 188). In validity analyses, the best-performing CP had low sensitivity (27.5%, 95% CI: 17.8% to 39.8%), high specificity (99.8%, 95% CI: 99.6% to 99.8%), low agreement using Kappa statistics (0.3, 95% CI: 0.2 to 0.5) and moderate positive predictive value (43.2%, 95% CI: 28.7% to 58.9%). There was high concordance between exogenous sex hormone use and transgender-specific diagnoses.Conclusions The development of a validated CP opens up new opportunities for identifying transgender people for inclusion in population-based health research using administrative health data, and offers the potential for much-needed and heretofore unavailable evidence on health status, including HIV status, and the healthcare use and needs of transgender people.

SurePath specimens from women referred to colposcopy were treated with Aptima Transfer Solution (ATS) before testing in Aptima HPV (AHPV) and Aptima HPV 16, 18/45 (AHPV-GT) assays. Untreated SurePath specimens were tested with the cobas HPV test. PreservCyt specimens were assessed for cytology and tested with AHPV. High-grade cervical intraepithelial neoplasia lesions served as the reference standard. Excellent agreement (95.5%; k=0.91) was observed for ATS-treated SurePath specimens between Tigris and Panther systems and between the PreservCyt and ATS-treated SurePath specimens (91.1%, k=0.81) with the AHPV assay on Tigris. Agreement between the AHPV and cobas assays with SurePath specimens was substantial (89.9%, k=0.80). AHPV sensitivity for CIN2+(n=147) was 91.2% for SurePath and PreservCyt. Cobas HPV sensitivity was 93.9% for SurePath specimens. AHPV testing of SurePath specimens was more specific (59.4%) than cobas (54.7%) (p

Rationale: Nav1.1, 1.2, and 1.6 are transmembrane proteins acting as voltage-gated sodium channels implicated in various forms of epilepsy. There is a need for knowing their actual concentration in target tissues during drug development. Methods: Unique peptides for Nav1.1, Nav1.2, and Nav1.6 were selected as quantotropic peptides for each protein and used for their quantification in membranes from stably transfected HEK293 cells and rodent and human brain samples using ultra-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry. Results: Nav 1.1, 1.2, and 1.6 protein expressions in three stably individually transfected HEK293 cell lines were found to be 2.1 ± 0.2, 6.4 ± 1.2, and 4.0 ± 0.6 fmol/µg membrane protein, respectively. In brains, Nav1.2 showed the highest expression, with approximately three times higher (P < 0.003) in rodents than in humans at 3.05 ± 0.57, with 3.35 ± 0.56 in mouse and rat brains and 1.09 ± 0.27 fmol/µg in human brain. Both Nav1.1 and 1.6 expressions were much lower in the brains, with approximately 40% less expression in human Nav1.1 than rodent Nav1.1 at 0.49 ± 0.1 (mouse), 0.43 ± 0.3 (rat), and 0.28 ± 0.04 (humans); whereas Nav1.6 had approximately 60% less expression in humans than rodents at 0.27 ± 0.09 (mouse), 0.26 ± 0.06 (rat), and 0.11 ± 0.02 (humans) fmol/µg membrane proteins. Conclusions: Multiple reaction monitoring was used to quantify sodium channels Nav1.1, 1.2, and 1.6 expressed in stably transfected HEK293 cells and brain tissues from mice, rats, and humans. We found significant differences in the expression of these channels in mouse, rat, and human brains. Nav expression ranking among the three species was Nav1.2 Nav1.1 > Nav1.6, with the human brain expressing much lower concentrations overall compared to rodent brain. [ABSTRACT FROM AUTHOR]