Best practice and reproducible science are required to advance artificial intelligence in real-world applications.

Drug-induced liver injury (DILI) is one of the most significant concerns in medical practice but yet it still cannot be fully recapitulated with existing in vivo, in vitro and in silico approaches. To address this challenge, Chen et al. [ 1] developed a deep learning-based DILI prediction model based on chemical structure information alone. The reported model yielded an outstanding prediction performance (i.e. 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913 for AUC, accuracy, recall, precision, F1-score and specificity, respectively, on a test set), far outperforming all publicly available and similar in silico DILI models. This extraordinary model performance is counter-intuitive to what we know about the underlying biology of DILI and the principles and hypothesis behind this type of in silico approach. In this Letter to the Editor, we raise awareness of several issues concerning data curation, model validation and comparison practices, and data and model reproducibility.

PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.

MOTIVATION: Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level. RESULTS: Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota. AVAILABILITY AND IMPLEMENTATION: PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.

Reduced prosocial motivation and effort in adolescents with conduct problems and callous-unemotional traits.

BACKGROUND: Prosocial behaviours - acts that benefit others - are of crucial importance for many species including humans. However, adolescents with conduct problems (CP), unlike their typically developing (TD) peers, demonstrate markedly reduced engagement in prosocial behaviours. This pattern is particularly pronounced in adolescents with CP and high levels of callous-unemotional traits (CP/HCU) who are at increased risk of developing psychopathy in adulthood. While a substantial amount of research has investigated the cognitive-affective mechanisms thought to underlie antisocial behaviour, much less is known about the mechanisms that could explain reduced prosocial behaviours in adolescents with CP. METHODS: Here we examined the willingness to exert effort to benefit oneself (self) and another person (other, prosocial condition) in children with CP/HCU, CP and lower levels of CU traits (CP/LCU) and their TD peers. The task captured both prosocial choices, and actual effort exerted following prosocial choices, in adolescent boys aged 11-16 (27 CP/HCU; 34 CP/LCU; 33 TD). We used computational modelling to reveal the mechanistic processes involved when choosing prosocial acts. RESULTS: We found that both CP/HCU and CP/LCU groups were more averse to initiating effortful prosocial acts than TD adolescents - both at a cognitive and at a behavioural level. Strikingly, even if they chose to initiate a prosocial act, the CP/HCU group exerted less effort following this prosocial choice than other groups. CONCLUSIONS: Our findings indicate that reduced exertion of effort to benefit others may be an important factor that differentiates adolescents with CP/HCU from their peers with CP/LCU. They offer new insights into what might drive low prosocial behaviour in adolescents with CP, including vulnerabilities that may particularly characterise those with high levels of CU traits.

TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes.

Animal studies are required for the evaluation of candidate drugs to ensure patient and volunteer safety. Toxicogenomics is often applied in these studies to gain understanding of the underlying mechanisms of toxicity, which is usually focused on critical organs such as the liver or kidney in young male rats. There is a strong ethical reason to reduce, refine and replace animal use (the 3Rs), where the mapping of data between organs, sexes and ages could reduce the cost and time of drug development. Herein, we proposed a generative adversarial network (GAN)-based framework entitled TransOrGAN that allowed the molecular mapping of gene expression profiles in different rodent organ systems and across sex and age groups. We carried out a proof-of-concept study based on rat RNA-seq data from 288 samples in 9 different organs of both sexes and 4 developmental stages. First, we demonstrated that TransOrGAN could infer transcriptomic profiles between any 2 of the 9 organs studied, yielding an average cosine similarity of 0.984 between synthetic transcriptomic profiles and their corresponding real profiles. Second, we found that TransOrGAN could infer transcriptomic profiles observed in females from males, with an average cosine similarity of 0.984. Third, we found that TransOrGAN could infer transcriptomic profiles in juvenile, adult, and aged animals from adolescent animals with an average cosine similarity of 0.981, 0.983, and 0.989, respectively. Altogether, TransOrGAN is an innovative approach to infer transcriptomic profiles between ages, sexes, and organ systems, offering the opportunity to reduce animal usage and to provide an integrated assessment of toxicity in the whole organism irrespective of sex or age.

US regulations to curb alleged cancer causes are ineffectual and compromised by scientific, constitutional and ethical violations.

The 1958 Delaney amendment to the Federal Food Drug and Cosmetics Act prohibited food additives causing cancer in animals by appropriate tests. Regulators responded by adopting chronic lifetime cancer tests in rodents, soon challenged as inappropriate, for they led to very inconsistent results depending on the subjective choice of animals, test design and conduct, and interpretive assumptions. Presently, decades of discussions and trials have come to conclude it is impossible to translate chronic animal data into verifiable prospects of cancer hazards and risks in humans. Such conclusion poses an existential crisis for official agencies in the US and abroad, which for some 65 years have used animal tests to justify massive regulations of alleged human cancer hazards, with aggregated costs of $trillions and without provable evidence of public health advantages. This article addresses suitable remedies for the US and potentially worldwide, by critically exploring the practices of regulatory agencies vis-á-vis essential criteria for validating scientific evidence. According to this analysis, regulations of alleged cancer hazards and risks have been and continue to be structured around arbitrary default assumptions at odds with basic scientific and legal tests of reliable evidence. Such practices raise a manifold ethical predicament for being incompatible with basic premises of the US Constitution, and with the ensuing public expectations of testable truth and transparency from government agencies. Potential remedies in the US include amendments to the US Administrative Procedures Act, preferably requiring agencies to justify regulations compliant with the Daubert opinion of the Daubert ruling of the US Supreme Court, which codifies the criteria defining reliable scientific evidence. International reverberations are bound to follow what remedial actions may be taken in the US, the origin of current world regulatory procedures to control alleged cancer causing agents.

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the International Council for Harmonization (ICH) guidelines. Building on this in silico approach, here we describe DeepAmes, a high performance and robust model developed with a novel deep learning (DL) approach for potential utility in regulatory science. DeepAmes was developed with a large and consistent Ames dataset (>10,000 compounds) and was compared with other five standard Machine Learning (ML) methods. Using a test set of 1,543 compounds, DeepAmes was the best performer in predicting the outcome of Ames assay. In addition, DeepAmes yielded the best and most stable performance up to when compounds were >30% outside of the applicability domain (AD). Regarding the potential for regulatory application, a revised version of DeepAmes with a much-improved sensitivity of 0.87 from 0.47. In conclusion, DeepAmes provides a DL-powered Ames test predictive model for predicting the results of Ames tests; with its defined AD and clear context of use, DeepAmes has potential for utility in regulatory application.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

Parenting boys with conduct problems and callous-unemotional traits: parent and child perspectives.

Parenting children with conduct problems (CP) is challenging, yet very little research has examined parenting using both quantitative and qualitative methods, from the perspective of the child and their parent/caregiver, and separately for those with high vs. low levels of callous-unemotional traits (HCU vs. LCU). One hundred and forty-six boys aged 11-16 [Typically developing (TD) n = 31; CP/HCU n = 35; CP/LCU n = 35] and their parents/caregivers completed the Alabama Parenting Questionnaire and provided a written qualitative statement describing their respective experiences of parenting/being parented. Parents/caregivers of CP/HCU boys reported more difficulty with child monitoring and supervision than parents of TD boys. This was echoed in qualitative reports of parents of CP/HCU boys reporting concerns regarding their child's safety. Parents/caregivers of both groups of CP boys reported more inconsistent discipline than parents of TD boys. Parental qualitative descriptions of challenging behavior in CP/HCU boys, and difficulties with setting boundaries and motivating CP/LCU boys, provided further insight to the potential triggers for inconsistent discipline. Qualitative reports from boys with CP indicated that they understood the parenting challenges their parents/caregivers faced. These findings replicate and extend previous work on the associations between parenting and CP. Children with CP/HCU and CP/LCU show some commonalities and differences in their parenting experiences and CP children and their parents/caregivers do not necessarily share all the same perceptions or concerns. CP interventions often involve parent/family engagement and this research highlights the continued importance of examining both parent and child perspectives.

Toward Clinical Implementation of Next-Generation Sequencing-Based Genetic Testing in Rare Diseases: Where Are We?

Next-generation sequencing (NGS) technologies have changed the landscape of genetic testing in rare diseases. However, the rapid evolution of NGS technologies has outpaced its clinical adoption. Here, we re-evaluate the critical steps in the clinical application of NGS-based genetic testing from an informatics perspective. We suggest a 'fit-for-purpose' triage of current NGS technologies. We also point out potential shortcomings in the clinical management of genetic variants and offer ideas for potential improvement. We specifically emphasize the importance of ensuring the accuracy and reproducibility of NGS-based genetic testing in the context of rare disease diagnosis. We highlight the role of artificial intelligence (AI) in enhancing understanding and prioritization of variance in the clinical setting and propose deep learning frameworks for further investigation.

Key Characteristics of Human Hepatotoxicants as a Basis for Identification and Characterization of the Causes of Liver Toxicity.

Hazard identification regarding adverse effects on the liver is a critical step in safety evaluations of drugs and other chemicals. Current testing paradigms for hepatotoxicity rely heavily on preclinical studies in animals and human data (epidemiology and clinical trials). Mechanistic understanding of the molecular and cellular pathways that may cause or exacerbate hepatotoxicity is well advanced and holds promise for identification of hepatotoxicants. One of the challenges in translating mechanistic evidence into robust decisions about potential hepatotoxicity is the lack of a systematic approach to integrate these data to help identify liver toxicity hazards. Recently, marked improvements were achieved in the practice of hazard identification of carcinogens, female and male reproductive toxicants, and endocrine disrupting chemicals using the key characteristics approach. Here, we describe the methods by which key characteristics of human hepatotoxicants were identified and provide examples for how they could be used to systematically identify, organize, and use mechanistic data when identifying hepatotoxicants.

DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation.

Drug-induced liver injury (DILI) is the most frequently reported single cause of safety-related withdrawal of marketed drugs. It is essential to identify drugs with DILI potential at the early stages of drug development. In this study, we describe a deep learning-powered DILI (DeepDILI) prediction model created by combining model-level representation generated by conventional machine learning (ML) algorithms with a deep learning framework based on Mold2 descriptors. We conducted a comprehensive evaluation of the proposed DeepDILI model performance by posing several critical questions: (1) Could the DILI potential of newly approved drugs be predicted by accumulated knowledge of early approved ones? (2) is model-level representation more informative than molecule-based representation for DILI prediction? and (3) could improved model explainability be established? For question 1, we developed the DeepDILI model using drugs approved before 1997 to predict the DILI potential of those approved thereafter. As a result, the DeepDILI model outperformed the five conventional ML algorithms and two state-of-the-art ensemble methods with a Matthews correlation coefficient (MCC) value of 0.331. For question 2, we demonstrated that the DeepDILI model's performance was significantly improved (i.e., a MCC improvement of 25.86% in test set) compared with deep neural networks based on molecule-based representation. For question 3, we found 21 chemical descriptors that were enriched, suggesting a strong association with DILI outcome. Furthermore, we found that the DeepDILI model has more discrimination power to identify the DILI potential of drugs belonging to the World Health Organization therapeutic category of 'alimentary tract and metabolism'. Moreover, the DeepDILI model based on Mold2 descriptors outperformed the ones with Mol2vec and MACCS descriptors. Finally, the DeepDILI model was applied to the recent real-world problem of predicting any DILI concern for potential COVID-19 treatments from repositioning drug candidates. Altogether, this developed DeepDILI model could serve as a promising tool for screening for DILI risk of compounds in the preclinical setting, and the DeepDILI model is publicly available through https://github.com/TingLi2016/DeepDILI.

Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment?

In vitro toxicogenomics (TGx) has the potential to replace or supplement animal studies. However, TGx studies often suffer from a limited sample size and cell types. Meanwhile, transcriptomic data have been generated for tens of thousands of compounds using cancer cell lines mainly for drug efficacy screening. Here, we asked the question of whether these types of transcriptomic data can be used to support toxicity assessment. We compared transcriptomic profiles from three cancer lines (HL60, MCF7, and PC3) from the CMap data set with those using primary hepatocytes or in vivo repeated dose studies from the Open TG-GATEs database by using our previously reported pair ranking (PRank) method. We observed an encouraging similarity between HL60 and human primary hepatocytes (PRank score = 0.70), suggesting the two cellular assays could be potentially interchangeable. When the analysis was limited to drug-induced liver injury (DILI)-related compounds or genes, the cancer cell lines exhibited promise in DILI assessment in comparison with conventional TGx systems (i.e., human primary hepatocytes or rat in vivo repeated dose). Also, some toxicity-related pathways, such as PPAR signaling pathways and fatty acid-related pathways, were preserved across various assay systems, indicating the assay transferability is biological process-specific. Furthermore, we established a potential application of transcriptomic profiles of cancer cell lines for studying immune-related biological processes involving some specific cell types. Moreover, if PRank analysis was focused on only landmark genes from L1000 or S1500+, the advantage of cancer cell lines over the TGx studies was limited. In conclusion, repurposing of existing cancer-related transcript profiling data has great potential for toxicity assessment, particularly in predicting DILI.

New ideas for non-animal approaches to predict repeated-dose systemic toxicity: Report from an EPAA Blue Sky Workshop.

The European Partnership for Alternative Approaches to Animal Testing (EPAA) convened a 'Blue Sky Workshop' on new ideas for non-animal approaches to predict repeated-dose systemic toxicity. The aim of the Workshop was to formulate strategic ideas to improve and increase the applicability, implementation and acceptance of modern non-animal methods to determine systemic toxicity. The Workshop concluded that good progress is being made to assess repeated dose toxicity without animals taking advantage of existing knowledge in toxicology, thresholds of toxicological concern, adverse outcome pathways and read-across workflows. These approaches can be supported by New Approach Methodologies (NAMs) utilising modern molecular technologies and computational methods. Recommendations from the Workshop were based around the needs for better chemical safety assessment: how to strengthen the evidence base for decision making; to develop, standardise and harmonise NAMs for human toxicity; and the improvement in the applicability and acceptance of novel techniques. "Disruptive thinking" is required to reconsider chemical legislation, validation of NAMs and the opportunities to move away from reliance on animal tests. Case study practices and data sharing, ensuring reproducibility of NAMs, were viewed as crucial to the improvement of non-animal test approaches for systemic toxicity.

Opportunities for use of one species for longer-term toxicology testing during drug development: A cross-industry evaluation.

An international expert working group representing 37 organisations (pharmaceutical/biotechnology companies, contract research organisations, academic institutions and regulatory bodies) collaborated in a data sharing exercise to evaluate the utility of two species within regulatory general toxicology studies. Anonymised data on 172 drug candidates (92 small molecules, 46 monoclonal antibodies, 15 recombinant proteins, 13 synthetic peptides and 6 antibody-drug conjugates) were submitted by 18 organisations. The use of one or two species across molecule types, the frequency for reduction to a single species within the package of general toxicology studies, and a comparison of target organ toxicities identified in each species in both short and longer-term studies were determined. Reduction to a single species for longer-term toxicity studies, as used for the development of biologicals (ICHS6(R1) guideline) was only applied for 8/133 drug candidates, but might have been possible for more, regardless of drug modality, as similar target organ toxicity profiles were identified in the short-term studies. However, definition and harmonisation around the criteria for similarity of toxicity profiles is needed to enable wider consideration of these principles. Analysis of a more robust dataset would be required to provide clear, evidence-based recommendations for expansion of these principles to small molecules or other modalities where two species toxicity testing is currently recommended.

Trajectories of cognitive development during adolescence among youth at-risk for schizophrenia.

BACKGROUND: Among adults with schizophrenia, evidence suggests that premorbid deficits in different cognitive domains follow distinct developmental courses during childhood and adolescence. The aim of this study was to delineate trajectories of adolescent cognitive functions prospectively among different groups of youth at-risk for schizophrenia, relative to their typically developing (TD) peers. METHOD: Using linear mixed models adjusted for sex, ethnicity, parental occupation and practice effects, cognitive development between ages 9 and 16 years was compared for youth characterised by a triad of well-replicated developmental antecedents of schizophrenia (ASz; N = 32) and youth with a least one affected relative with schizophrenia or schizoaffective disorder (FHx; N = 29), relative to TD youth (N = 45). Participants completed measures of IQ, scholastic achievement, memory and executive function at three time-points, separated by approximately 24-month intervals. RESULTS: Compared to TD youth, both ASz and FHx youth displayed stable developmental deficits in verbal working memory and inhibition/switching executive functions. ASz youth additionally presented with stable deficits in measures of vocabulary (IQ), word reading, numerical operations, and category fluency executive function, and a slower rate of growth (developmental lag) on spelling from 9 to 16 years than TD peers. Conversely, faster rates of growth relative to TD peers (developmental delay) were observed on visual and verbal memory, and on category fluency executive function (ASz youth only) and on matrix reasoning (IQ) and word reading (FHx youth only). CONCLUSIONS: These differential patterns of deviation from normative adolescent cognitive development among at-risk youth imply potential for cognitive rehabilitation targeting of specific cognitive deficits at different developmental phases.

Living with conduct problem youth: family functioning and parental perceptions of their child.

Parenting children with conduct problems (CP) is challenging, yet very little is known about the impact of the child's behaviour on family functioning or how parents of children with CP perceive their child. The aim of this research was to examine whether families with children with CP and high vs. low levels of callous-unemotional traits (HCU vs. LCU) experience differences in family functioning and parental perceptions. One hundred and one parents/caregivers of boys aged 11-16 [Typically developing (TD) n = 31; CP/HCU n = 35; CP/LCU n = 35] completed the McMaster Family Assessment Device, measuring multiple domains of family functioning. Parents/caregivers also completed a written statement describing their child, used for qualitative analysis. Families with CP/HCU children had poorer affective involvement than TD (p = 0.00; d = - 1.17) and CP/LCU (p = 0.03; d = - 0.62) families. Families with CP/HCU children showed significantly poorer general family functioning (p = 0.04; d = - 0.63) and more poorly defined family roles (p = 0.005; d = - 0.82) than families with TD children. Qualitative analyses indicated that parents/caregivers of CP/HCU children characterised them as having a dichotomous personality and being superficially charming. CP/LCU children were characterised as cheeky and endearing, with parents reporting good rapport. Families with CP/HCU children presented with specific difficulties in affective involvement and parents described challenges which were in line with the child's specific presentation of lack of empathy and shallow affect. These findings may be used to help clinicians identify targets for family interventions.

The relationship between salivary C-reactive protein and cognitive function in children aged 11-14years: Does psychopathology have a moderating effect?

Elevated C-reactive protein (CRP), a non-specific biomarker of systemic bodily inflammation, has been associated with more pronounced cognitive impairments in adults with psychiatric disorders, particularly in the domains of memory and executive function. Whether this association is present in early life (i.e., the time at which the cognitive impairments that characterise these disorders become evident), and is specific to those with emerging psychiatric disorders, has yet to be investigated. To this end, we examined the association between salivary CRP and cognitive function in children aged 11-14years and explored the moderating effect of psychopathology. The study utilised data from an established longitudinal investigation of children recruited from the community (N=107) that had purposively over-sampled individuals experiencing psychopathology (determined using questionnaires). CRP was measured in saliva samples and participants completed assessments of cognition (memory and executive function) and psychopathology (internalising and externalising symptoms and psychotic-like experiences). Linear regression models indicated that higher salivary CRP was associated with poorer letter fluency (ß=-0.24, p=0.006) and scores on the inhibition (ß=-0.28, p=0.004) and inhibition/switching (ß=-0.36, p<0.001) subtests of the colour-word interference test, but not with performance on any of the memory tasks (working, visual, and verbal memory tasks). Results were largely unchanged after adjustment for psychopathology and no significant interactions between CRP and psychopathology were observed on any cognitive measure. Our findings provide preliminary evidence that elevated salivary CRP is associated with poorer cognitive function in early life, but that this association is not moderated by concurrent psychopathology. These findings have implications for early intervention strategies that attempt to ameliorate cognitive deficits associated with emerging psychiatric disorders. Further research is needed to determine whether salivary CRP levels can be used as a valid marker of peripheral inflammation among healthy adolescents.

Facility-Based Delivery during the Ebola Virus Disease Epidemic in Rural Liberia: Analysis from a Cross-Sectional, Population-Based Household Survey.

BACKGROUND: The Ebola virus disease (EVD) epidemic has threatened access to basic health services through facility closures, resource diversion, and decreased demand due to community fear and distrust. While modeling studies have attempted to estimate the impact of these disruptions, no studies have yet utilized population-based survey data. METHODS AND FINDINGS: We conducted a two-stage, cluster-sample household survey in Rivercess County, Liberia, in March-April 2015, which included a maternal and reproductive health module. We constructed a retrospective cohort of births beginning 4 y before the first day of survey administration (beginning March 24, 2011). We then fit logistic regression models to estimate associations between our primary outcome, facility-based delivery (FBD), and time period, defined as the pre-EVD period (March 24, 2011-June 14, 2014) or EVD period (June 15, 2014-April 13, 2015). We fit both univariable and multivariable models, adjusted for known predictors of facility delivery, accounting for clustering using linearized standard errors. To strengthen causal inference, we also conducted stratified analyses to assess changes in FBD by whether respondents believed that health facility attendance was an EVD risk factor. A total of 1,298 women from 941 households completed the survey. Median age at the time of survey was 29 y, and over 80% had a primary education or less. There were 686 births reported in the pre-EVD period and 212 in the EVD period. The unadjusted odds ratio of facility-based delivery in the EVD period was 0.66 (95% confidence interval [CI] 0.48-0.90, p-value = 0.010). Adjustment for potential confounders did not change the observed association, either in the principal model (adjusted odds ratio [AOR] = 0.70, 95%CI 0.50-0.98, p = 0.037) or a fully adjusted model (AOR = 0.69, 95%CI 0.50-0.97, p = 0.033). The association was robust in sensitivity analyses. The reduction in FBD during the EVD period was observed among those reporting a belief that health facilities are or may be a source of Ebola transmission (AOR = 0.59, 95%CI 0.36-0.97, p = 0.038), but not those without such a belief (AOR = 0.90, 95%CI 0.59-1.37, p = 0.612). Limitations include the possibility of FBD secular trends coincident with the EVD period, recall errors, and social desirability bias. CONCLUSIONS: We detected a 30% decreased odds of FBD after the start of EVD in a rural Liberian county with relatively few cases. Because health facilities never closed in Rivercess County, this estimate may under-approximate the effect seen in the most heavily affected areas. These are the first population-based survey data to show collateral disruptions to facility-based delivery caused by the West African EVD epidemic, and they reinforce the need to consider the full spectrum of implications caused by public health emergencies.

Update on Clinical Utility of Continuous Glucose Monitoring in Type 1 Diabetes.

Since the early 2000s, continuous glucose monitoring (CGM) technology has advanced to become a standard of care in the treatment of type 1 diabetes. Unfortunately, CGM use is not commonly integrated into practice. This article will review the history, technology, and need for systematic training in CGM. Additionally, it will review recent clinical trial data demonstrating the benefits that CGM offers to all people with type 1 diabetes and the clinicians who care for them.

Skin sensitization induced Langerhans' cell mobilization: variable requirements for tumour necrosis factor-α.

Upon antigen/allergen recognition, epidermal Langerhans' cells (LC) are mobilized and migrate to the local lymph node where they play a major role in initiating or regulating immune responses. It had been proposed that all chemical allergens induce LC migration via common cytokine signals delivered by TNF-α and IL-1ß. Here the dependence of LC migration on TNF-α following treatment of mice with various chemical allergens has been investigated. It was found that under standard conditions the allergens oxazolone, paraphenylene diamine, and trimellitic anhydride, in addition to the skin irritant sodium lauryl sulfate, were unable to trigger LC mobilization in the absence of TNF-α signalling. In contrast, two members of the dinitrohalobenezene family (2,4-dinitrochlorobenzene [DNCB] and 2,4-dinitrofluorobenzene [DNFB]) promoted LC migration independently of TNF-R2 (the sole TNF-α receptor expressed by LC) and TNF-α although the presence of IL-1ß was still required. However, increasing doses of oxazolone overcame the requirement of TNF-α for LC mobilization, whereas lower doses of DNCB were still able to induce LC migration in a TNF-α-independent manner. These novel findings demonstrate unexpected heterogeneity among chemical allergens and furthermore that LC can be induced to migrate from the epidermis via different mechanisms that are either dependent or independent of TNF-α. Although the exact mechanisms with regard to the signals that activate LC have yet to be elucidated, these differences may translate into functional speciation that will likely impact on the extent and quality of allergic sensitization.