Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment.

Introduction: This study bridges traditional remedies and modern pharmacology by exploring the synergy between natural compounds and Ceritinib in treating Non-Small Cell Lung Cancer (NSCLC), aiming to enhance efficacy and reduce toxicities. Methods: Using a combined approach of computational analysis, machine learning, and experimental procedures, we identified and analyzed PD173074, Isoquercitrin, and Rhapontin as potential inhibitors of fibroblast growth factor receptor 3 (FGFR3). Machine learning algorithms guided the initial selection, followed by Quantitative Structure-Activity Relationship (QSAR) modeling and molecular dynamics simulations to evaluate the interaction dynamics and stability of Rhapontin. Physicochemical assessments further verified its drug-like properties and specificity. Results: Our experiments demonstrate that Rhapontin, when combined with Ceritinib, significantly suppresses tumor activity in NSCLC while sparing healthy cells. The molecular simulations and physicochemical evaluations confirm Rhapontin's stability and favorable interaction with FGFR3, highlighting its potential as an effective adjunct in NSCLC therapy. Discussion: The integration of natural compounds with established cancer therapies offers a promising avenue for enhancing treatment outcomes in NSCLC. By combining the ancient wisdom of natural remedies with the precision of modern science, this study contributes to evolving cancer treatment paradigms, potentially mitigating the side effects associated with current therapies.

Establishment of a headspace-thermal desorption and gas chromatography-mass spectrometry method (HS-TD-GC-MS) for simultaneous detection of 51 volatile organic compounds in human urine: Application in occupational exposure assessment.

Volatile organic compounds (VOCs) are a group of ubiquitous environment pollutants especially released into the workplace. Assessment of VOCs exposure in occupational populations is therefore a crucial issue for occupational health. However, simultaneous biomonitoring of a variety of VOCs is less studied. In this study, a simple and sensitive method was developed for the simultaneous determination of 51 prototype VOCs in urine by headspace-thermal desorption coupled to gas chromatography-mass spectrometry (HS-TD-GC-MS). The urinary sample was pretreated with only adding 0.50 g of sodium chloride to 2 mL of urine and 51 VOCs should be determined with limits of detection (LODs) between 13.6 ng/L and 24.5 ng/L. The method linearity ranged from 0.005 to 10 µg/L with correlation coefficients (r) of 0.991 to 0.999. The precision for intraday and inter-day, measured by the variation coefficient (CV) at three levels of concentration, was below 15 %, except for 4-isopropyl toluene, dichloromethane, and trichloromethane at low concentration. For medium and high levels, recoveries of all target VOCs were within the standard range, but 1,1-dichloropropene and styrene, which were slightly under 80 % at low levels. In addition, the proposed method has been used to determine urine samples collected in three times (before, during and after working) from 152 workers at four different factories. 41 types of prototype VOCs were detected in workers urine. Significant differences (Kruskal-Wallis chi-squared = 117.18, df = 1, P < 0.05) in the concentration levels of VOCs between the exposed and unexposed groups were observed, but not between the three sampling times (Kruskal-Wallis chi-squared = 3.39, df = 2, P = 0.183). The present study provides an alternative method for biomonitoring and assessing mixed exposures to VOCs in occupational populations.

Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.

BACKGROUND: Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias. METHODS: In this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. We performed subgroup analysis based on age, sex, and race. We assessed the discrimination and calibration of models using the area under receiver operating characteristic (AUROC) and standardised mortality ratio (SMR) with 95% CIs. FINDINGS: Using the development dataset (n=50 366) and predictive model building process, the XGBoost algorithm performed the best in all types of validations compared with other machine learning algorithms and clinical scores (internal validation with 5037 patients from 14 US hospitals, AUROC=0·866 [95% CI 0·851-0·880]; external validation in the US population with 20 541 patients from 169 hospitals, AUROC=0·838 [0·829-0·847]; external validation in European population with 2411 patients from one hospital, AUROC=0·833 [0·812-0·853]; temporal validation with 4311 patients from one hospital, AUROC=0·884 [0·869-0·897]). In the external validation set (US population), the median AUROCs of bias evaluations covering eight subgroups were above 0·81, and the overall SMR was 0·99 (0·96-1·03). The top ten risk predictors were the minimum Glasgow Coma Scale score, total urine output, average respiratory rate, mechanical ventilation use, best state of activity, Charlson Comorbidity Index score, geriatric nutritional risk index, code status, age, and maximum blood urea nitrogen. A simplified model containing only the top 20 features (ELDER-ICU-20) had similar predictive performance to the full model. INTERPRETATION: The ELDER-ICU model reliably predicts the risk of in-hospital mortality using routinely collected clinical features. The predictions could inform clinicians about patients who are at elevated risk of deterioration. Prospective validation of this model in clinical practice and a process for continuous performance monitoring and model recalibration are needed. FUNDING: National Institutes of Health, National Natural Science Foundation of China, National Special Health Science Program, Health Science and Technology Plan of Zhejiang Province, Fundamental Research Funds for the Central Universities, Drug Clinical Evaluate Research of Chinese Pharmaceutical Association, and National Key R&D Program of China.

Assessment of respiratory system compliance under pressure control ventilation without an inspiratory pause maneuver.

Objective. The measurement of the static compliance of the respiratory system (Cstat) during mechanical ventilation requires zero end-inspiratory flow. An inspiratory pause maneuver is needed if the zero end-inspiratory flow condition cannot be satisfied under normal ventilation.Approach. We propose a method to measure the quasi-static respiratory compliance (Cqstat) under pressure control ventilation mode without the inspiratory pause maneuver. First, a screening strategy was applied to filter out breaths affected strongly by spontaneous breathing efforts or artifacts. Then, we performed a virtual extrapolation of the flow-time waveform when the end-inspiratory flow was not zero, to allow for the calculation ofCqstatfor each kept cycle. Finally, the outputCqstatwas obtained as the average of the smallest 40Cqstatmeasurements. The proposed method was validated against the gold standardCstatmeasured from real clinical settings and compared with two reported algorithms. The gold standardCstatwas obtained by applying an end-inspiratory pause maneuver in the volume-control ventilation mode.Main results. Sixty-nine measurements from 36 patients were analyzed. The Bland-Altman analysis showed that the bias of agreement forCqstatversus the gold standard measurement was -0.267 ml/cmH2O (95% limits of agreement was -4.279 to 4.844 ml/cmH2O). The linear regression analysis indicated a strong correlation (R2 = 0.90) between theCqstatand gold standard.Significance. The results showed that theCqstatcan be accurately estimated from continuous ventilator waveforms, including spontaneous breathing without an inspiratory pause maneuver. This method promises to provide continuous measurements compliant with mechanical ventilation.

Dynamic programming for solving a simulated clinical scenario of sepsis resuscitation.

BACKGROUND: A major challenge in clinical research is population heterogeneity and we need to consider both historical response and current condition of an individual in considering medical decision making. The idea of precise medicine cannot be fully accounted for in traditional randomized controlled trials. Reinforcement learning (RL) is developing rapidly and has found its way into various fields including clinical medicine in which RL is employed to find an optimal treatment strategy. The key idea of RL is to optimize the treatment policy depending on the current state and previous treatment history, which is consistent with the idea behind dynamic programming (DP). DP is a prototype of RL and can be implemented when the system dynamics can be fully quantified. METHODS: The present article aims to illustrate how to perform DP algorithm in a clinical scenario of Sepsis resuscitation. The state transition dynamics are constructed in the framework of Markov Decision Process. The state space is defined by mean arterial pressure (MAP) and lactate; the action space is comprised of fluid administration and vasopressor. The implementation of policy evaluation, policy improvement and iteration are explained with R code. RESULTS: the DP algorithm was able to find the optimal treatment policy depending on the current states and previous conditions. The iteration process converged at finite steps. We defined several functions such as nextStep(), policyEval() and policy_iteration() to implement the DP algorithm. CONCLUSIONS: This article illustrates how DP can be used to solve a clinical problem. We show that DP is a potential useful tool to tailor treatment strategy to patients with different conditions/states. Potential audience of the paper are those who are interested in using DP for solving clinical problems with dynamic changing states.

A global priority list of the TOp TEn resistant Microorganisms (TOTEM) study at intensive care: a prioritization exercise based on multi-criteria decision analysis.

The World Health Organization (WHO) proposed a global priority pathogen list (PPL) of multidrug-resistant (MDR) bacteria. Our current objective was to provide global expert ranking of the most serious MDR bacteria present at intensive care units (ICU) that have become a threat in clinical practice. A proposal addressing a PPL for ICU, arising from the WHO Global PPL, was developed. Based on the supporting data, the pathogens were grouped in three priority tiers: critical, high, and medium. A multi-criteria decision analysis (MCDA) was used to identify the priority tiers. After MCDA, mortality, treatability, and cost of therapy were of highest concern (scores of 19/20, 19/20, and 15/20, respectively) while dealing with PPL, followed by healthcare burden and resistance prevalence. Carbapenem-resistant (CR) Acinetobacter baumannii, carbapenemase-expressing Klebsiella pneumoniae (KPC), and MDR Pseudomonas aeruginosa were identified as critical organisms. High-risk organisms were represented by CR Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and extended-spectrum beta-lactamase (ESBL) Enterobacteriaceae. Finally, ESBL Serratia marcescens, vancomycin-resistant Enterococci, and TMP-SMX-resistant Stenotrophomonas maltophilia were identified as medium priority. We conclude that education, investigation, funding, and development of new antimicrobials for ICU organisms should focus on carbapenem-resistant Gram-negative organisms.

Quality of critical care clinical practice guidelines: Assessment with AGREE II instrument.

STUDY OBJECTIVE: Clinical practice guidelines (CPGs) are cornerstones for the management of critically ill patients. Numerous CPGs have been generated in critical care medicine, but their qualities have never been systematically appraised. The aim of the present study was to systematically assess the quality of critical care CPGs. DESIGN: A systematic electronic search was performed in PubMed and Scopus. All critical care CPGs were included for analysis. SETTING: Not applicable. PATIENTS: Not applicable. INTERVENTION: None. MEASUREMENTS: The Appraisal of guidelines for research & evaluation II (AGREE II) instrument was employed to appraise the quality. CPGs were assessed independently by three raters and intraclass correlation coefficient to represent the agreement among raters. MAIN RESULTS: A total of 89 CPGs were included for quantitative analysis. The results showed that domain 1 (scope and purpose) had the highest scores (0.93, IQR: 0.89-0.98) and domain 2 (stakeholder involvement) had the lowest scores (0.37, IQR: 0.30-0.46). The overall score was 0.83 (IQR: 0.67-0.83). Publication year was not associated with scaled scores in each domain. Domain 2 (stakeholder involvement) was significantly associated with the number of societies (coefficient: 0.702, p = 0.033). Also, greater number of societies were associated with higher scaled scores of domain 3 (coefficient: 0.768, p = 0.027), 4 (coefficient: 0.730, p = 0.029) and 5 (coefficient: 0.995, p = 0.023). CONCLUSIONS: The study showed that the reporting quality of critical care CPGs were suboptimal. The reporting quality varied across the six domains, with the highest quality in domain 1 and lowest quality in domain 2. Strenuous efforts need to be made to improve the reporting of critical care CPGs.

Decision tree modeling using R.

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.