A comparative study of in vitro dose-response estimation under extreme observations.

Quantifying drug potency, which requires an accurate estimation of dose-response relationship, is essential for drug development in biomedical research and life sciences. However, the standard estimation procedure of the median-effect equation to describe the dose-response curve is vulnerable to extreme observations in common experimental data. To facilitate appropriate statistical inference, many powerful estimation tools have been developed in R, including various dose-response packages based on the nonlinear least squares method with different optimization strategies. Recently, beta regression-based methods have also been introduced in estimation of the median-effect equation. In theory, they can overcome nonnormality, heteroscedasticity, and asymmetry and accommodate flexible robust frameworks and coefficients penalization. To identify a reliable estimation method(s) to estimate dose-response curves even with extreme observations, we conducted a comparative study to review 14 different tools in R and examine their robustness and efficiency via Monte Carlo simulation under a list of comprehensive scenarios. The simulation results demonstrate that penalized beta regression using the mgcv package outperforms other methods in terms of stable, accurate estimation, and reliable uncertainty quantification.

REAP-2: An interactive quantitative tool for robust and efficient dose-response curve estimation.

REAP-2 is an interactive dose-response curve estimation tool for Robust and Efficient Assessment of drug Potency. It provides user-friendly dose-response curve estimation for in vitro studies and conducts statistical testing for model comparisons with a redesigned user interface. We also make a major update of the underlying estimation method with penalized beta regression, which demonstrates great reliability and accuracy in dose estimation and uncertainty quantification. In this note, we describe the method and implementation of REAP-2 with a highlight on potency estimation and drug comparison.

Robust and Efficient Assessment of Potency (REAP) as a quantitative tool for dose-response curve estimation.

The median-effect equation has been widely used to describe the dose-response relationship and identify compounds that activate or inhibit specific disease targets in contemporary drug discovery. However, the experimental data often contain extreme responses, which may significantly impair the estimation accuracy and impede valid quantitative assessment in the standard estimation procedure. To improve the quantitative estimation of the dose-response relationship, we introduce a novel approach based on robust beta regression. Substantive simulation studies under various scenarios demonstrate solid evidence that the proposed approach consistently provides robust estimation for the median-effect equation, particularly when there are extreme outcome observations. Moreover, simulation studies illustrate that the proposed approach also provides a narrower confidence interval, suggesting a higher power in statistical testing. Finally, to efficiently and conveniently perform common lab data analyses, we develop a freely accessible web-based analytic tool to facilitate the quantitative implementation of the proposed approach for the scientific community.

Finding a new drug which is both safe and efficient is an expensive and time-consuming endeavour. In particular, establishing the 'dose-effect relationship' ­ how beneficial a drug is at different dosages ­ can be challenging. Predicting this curve requires gathering experimental data by exposing and recording how cells respond to various levels of the drug. However, extreme values are often observed at low and high dosages, potentially introducing errors that are hard to correct in the prediction process. Yet, these extreme observations are sometimes genuine so researchers cannot just ignore them. To improve dose-effect estimation, Zhou, Liu, Fang et al. developed a new general-purpose approach. It uses advanced statistical modelling to account for extremes in lab data. This strategy outperformed other methods when dealing with these observations while also providing higher efficiency in data analysis with more uniform data in experiments. To facilitate implementation, Zhou, Liu, Fang et al. set up a user-friendly tool baptized 'REAP'; this free online resource allows scientists without advanced statistical experience to harness the new approach and to perform dose-effect analysis more easily and accurately. This could boost research across many different disciplines that examine the effects of chemicals on cells.

Investigation and Analysis of Infection among Inpatients in a Tertiary Hospital in Shanghai.

Objectives: This study is aimed at obtaining information about the prevalence of nosocomial infections (NIs) and the use of antibiotics in hospitalized patients and providing relevant references for further understanding, preventing, and controlling NIs. Methods: The medical records of adult patients admitted to a hospital in Shanghai from November to December 2021 were analyzed. The patients were divided into the NI group, community-acquired infection (CAI) group, and uninfected or healed group according to their infection status. The survey results were summarized and analyzed. Results: A total of 1485 patients were investigated, including 115 patients in the NI group, 172 patients in the CAI group, and 1198 patients in the uninfected or healed group. In the NI group, the main infection site was intra-abdominal tissue (49.6%), followed by lower respiratory tract (unrelated to application of catheters) (13%). There were 73 pathogens detected in the samples submitted from the NI group, mainly including 8 cases (11%) of Escherichia coli, 9 cases (12%) of Klebsiella pneumoniae, and 40 cases (55%) of negative microbiological test results. Thirteen of 115 patients with NIs had infections with drug-resistant bacteria, including 9 cases (69.2%) of CRE (carbapenem-resistant Enterobacteriaceae), 2 cases (15.38%) of VRE (vancomycin-resistant Enterococcus), 1 case (7.69%) of MRSA (methicillin-resistant Staphylococcus aureus), and 1 case (7.69%) of CRAB (carbapenem-resistant Acinetobacter baumannii). In terms of medication, single drug use accounted for the majority of the NI group (66.3%) and CAI group (60.6%); both groups had less frequent quadruple drugs. In the uninfected or healed group, single drug occupied 92.0%, and dual drug use accounted for 8.0%. Cefoperazone/sulbactam was the most commonly used antibacterial drug in the NI group (18.0%) and CAI group (17.6%), and piperacillin/tazobactam accounted for 14.0% and 17.6% in the two groups, respectively. In the uninfected or healed group, cefuroxime accounted for 59.8%, followed by cefoperazone/sulbactam (13.3%). Conclusion: This study provides a scientific basis for effective control of NIs. Strict implementation of aseptic techniques, reduction of invasive operations, and rational use of anti-infective drugs can minimize the incidence of nosocomial infection to ultimately achieve effective prevention and control of NIs.

Impact of Pediatric Intensive Care Unit Preadmission Huddle on Perceptions of Interprofessional Communication About Patient Safety.

BACKGROUND: Preadmission discussions in the study institution's pediatric intensive care unit are not standardized and admission plans were thought to be disjointed, leading to a perceived lack of organization and preparation for the arrival of a critically ill child. OBJECTIVE: To evaluate the impact of a new, formalized preadmission pediatric intensive care unit interdisciplinary huddle on clinician perceptions of interprofessional communication. The hypothesis was that preadmission huddles would improve unit clinicians' perceptions of interprofessional communication. METHODS: Interprofessional pediatric intensive care unit clinicians (physicians, advanced practice providers, nurses, and respiratory therapists) completed surveys before and 7 months after preadmission interdisciplinary huddle implementation. Huddle compliance and perceptions of interprofessional communication in the unit were evaluated. RESULTS: Of 265 eligible pediatric intensive care unit admissions, 69 huddles (26.0%) occurred. The postintervention survey revealed increased odds (odds ratio [95% CI]) of responding "strongly agree" or "agree" to questions about the opportunity to "communicate effectively with health care team members" (2.42 [1.10-5.34]), "respond to feedback from health care team members" (2.54 [1.23-5.24]), and "convey knowledge to other health care team members" (2.71 [1.31-5.61]) before an admission. DISCUSSION: This study introduced a formalized huddle that improved pediatric intensive care unit clinicians' perceived communication with other health care team members in the preadmission period. CONCLUSIONS: Future studies are needed to determine if this perceived improvement in communication significantly affects health care outcomes of critically ill children or if these results are generalizable to other pediatric intensive care unit settings.

Impact of the Updated Guideline for Pediatric Brain Death Determination on Current Practice.

Background: A guideline to determine pediatric brain death was updated in 2011. It is unknown how pediatric intensivists have accepted and adopted the revised guideline into clinical practice. Methods: We surveyed US pediatric critical care attending physicians July 2013 to September 2013 and February 2020 to May 2020. Brain death testing practices and utilization of the 2011 pediatric and neonatal brain death guideline were assessed. Results: The 2020 respondents found that the revised pediatric brain death guideline were useful in clinical practice (93.7% vs 83.3%, P = .0484) and provided more consistency and clarity (73.2% vs 63.1%, P = .0462) when compared to 2013 respondents. Conclusion: This study demonstrates that with defined criteria, survey participants reported increased clarity and consistency. Findings from our study indicate that in clinical practice there is no significant deviation from the minimum requirements to determine brain death in children as outlined in the 2011 guideline.

Frequency of persistent obesity 5 years after first time diagnosis of status asthmaticus.

BACKGROUND/OBJECTIVES: When a severe asthma exacerbation occurs in an obese pediatric patient, it is unknown if this body type persists in future encounters. Persistent obesity can lead to future asthma exacerbations. The main study objective is to evaluate the persistence of a BMI percentile ≥85th in children 5 years after the first reported diagnosis of status asthmaticus. We hypothesized that a hospital admission for status asthmaticus was associated with persistence of a BMI percentile ≥85th. METHODS: This was a long-term retrospective observational cohort study utilizing TriNetX ® electronic health record (EHR) data. We included subjects aged 2 to 18 years of age with a diagnosis of status asthmaticus. Study population was divided into two groups based on their admission body mass index percentile: (underweight/healthy weight [<85th percentile] and overweight/obese [≥85th percentile]) and evaluated for the following outcomes: age, race, ethnicity, diagnostic codes, and BMI percentiles (initially and 5 years after diagnosis of status asthmaticus). RESULTS: A total of 129 subjects (n%) (76 [58.9%] underweight/healthy weight and 53 [41.1%] overweight/obese) were included. Children that were initially overweight/obese with status asthmaticus had significantly increased odds of continuing to be overweight/obese 5 years after diagnosis compared to children who were underweight/healthy weight at baseline (OR = 7.50 [95% confidence interval, 3.20-17.60; p < 0.001]). CONCLUSIONS: Overweight/obese asthmatic children are at risk of continuing to be obese several years after being diagnosed with status asthmaticus. This study reinforces the notion that when an asthmatic obese child presents with status asthmaticus, a thorough evaluation of nutrition, physical activity, and asthma control should be considered to reduce the risk of persistent obesity and possibly future asthma exacerbations.

Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data.

BACKGROUND: previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software. RESULTS: here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning-based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression. CONCULSION: Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.