From bench to clinic: the development of VLA1553/IXCHIQ, a live-attenuated chikungunya vaccine.

BACKGROUND: Over the past 20 years, over 5 million cases of chikungunya, a mosquito-transmitted viral disease, have been reported in over 110 countries. Until recently, preventative strategies for chikungunya were largely ineffective, relying on vector control and individual avoidance of mosquito bites. METHODS: This review outlines the preclinical and clinical efficacy and safety data that led to the approval of VLA1553 (IXCHIQ®), a live-attenuated vaccine against chikungunya disease. It also describes the innovative development pathway of VLA1553, based on an immunological surrogate of protection, and discusses ongoing and future post-licensure studies. RESULTS: In mice and non-human primate models, VLA1553 elicited high titres of neutralizing antibodies, conferred protection against wild-type chikungunya virus challenge and raised no safety concerns. A Phase 1 clinical trial of VLA1553 demonstrated 100% seroconversion among 120 healthy participants, with sustained neutralizing antibody titres after 12 months. These results and determination of a surrogate marker of protection led to advancement of VLA1553 directly into Phase 3 clinical development, as agreed with the US Food and Drug Administration (FDA) and the European Medicines Agency. The pivotal Phase 3 trial met its primary immunogenicity endpoint, achieving seroprotective levels based on immuno-bridging in baseline seronegative participants 28 days post-vaccination. These findings enabled submission of a Biologics License Application to the FDA for accelerated approval of VLA1553 in the US for adults aged ≥18 years. Ongoing and planned studies will confirm the clinical efficacy/effectiveness and safety of VLA1553 in adults and younger individuals, and will generate data in chikungunya endemic countries that have the highest unmet need. CONCLUSION: VLA1553 is the first vaccine approved for the prevention of chikungunya disease in adults, following accelerated development based on a serological surrogate marker of protection. VLA1553 adds to strategies to reduce the spread and burden of chikungunya in endemic populations and travellers.

External validation of the IAIHG autoimmune hepatitis response criteria in a multicentric real-world cohort.

Background & Aims: The goal of treatment in autoimmune hepatitis (AIH) is induction of remission to prevent the development of liver fibrosis, cirrhosis, and its related complications. Various definitions of treatment response and remission have been used. The International Autoimmune Hepatitis Group (IAIHG) recently defined consensus criteria for treatment response. We aimed to validate the IAIHG response criteria in our cohort and establish correlations with survival endpoints. Methods: We performed a retrospective, multicentric cohort study in one tertiary and seven secondary care centres in Belgium. Eligible patients were at least 18 years of age at data collection and were diagnosed with AIH by a simplified IAIHG score of ≥6. Complete biochemical response (CBR) was defined according to the IAIHG consensus criteria as normalisation of transaminases and serum IgG within the first 6 months of treatment. The primary endpoint was liver-related survival - defined as freedom from liver-related death or liver transplantation. Secondary endpoints were overall mortality and transplant-free survival. Outcomes were compared between patients attaining CBR and those with insufficient response. Results: Biochemical response status could be determined in 200 patients with AIH: CBR was achieved in 128 (64.0%) individuals. Patients not achieving CBR more frequently presented with cirrhosis on initial histology (22.2% vs. 10.9%, p = 0.036). Liver-related mortality or liver transplantation as a primary outcome occurred in 26 patients (13.0%). Patients achieving CBR exhibited superior liver-related (hazard ratio 0.118; 95% CI 0.052-0.267; p <0.0001) and overall (hazard ratio 0.253; 95% CI 0.111-0.572; p = 0.0003) survival. Conclusions: We externally validated the IAIHG consensus criteria for CBR and confirmed their correlation with survival endpoints in a multicentric, real-world cohort. Patients with AIH achieving CBR as an intermediate endpoint have significantly superior liver-related and overall survival. Impacts and Implications: Corticosteroids remain the cornerstone of treatment to induce remission of disease activity in autoimmune hepatitis (AIH), and the majority of patients require long-term corticosteroid treatment to achieve sustained remission. Definitions of response to treatment have varied over the years, and consistently used intermediate endpoints are needed to facilitate advancements in non-corticosteroid treatment for autoimmune hepatitis. The International Autoimmune Hepatitis Group (IAIHG) defined consensus criteria on endpoints in the treatment of AIH, for which further external validation is needed. Here, we demonstrate the usefulness of the IAIHG consensus criteria and corroborate their correlation to primary endpoints, such as liver-related survival and native liver survival in a multicentric, real-world setting. The design of future studies can rely on the IAIHG consensus criteria as intermediate endpoints.

Aerodynamic optimization of athlete posture using virtual skeleton methodology and computational fluid dynamics.

An athlete's posture has a significant impact on aerodynamic drag. Although aerodynamic drag in different sports has been studied extensively, most studies have analysed only a limited number of positions, and no generalized methods for optimization are available. In this work, we present a methodology to perform athlete posture optimization with respect to aerodynamic drag reduction. The method combines the virtual skeleton methodology to adjust the athlete's posture, CFD simulations to evaluate the drag for a given posture, and efficient global optimization to find the optimum position. We demonstrate the method by optimizing the time trial position for a cyclist. The cyclist position was parameterized with 6 design parameters, and the optimization required 41 CFD simulations to converge. The optimal posture yielded a reduction in drag of 17 % compared to the initial posture (disregarding bicycle drag). The method has potential to make posture optimization more accessible across a wide range of sports, and lead to insight into the aerodynamic influence of posture in general.

Enhancing mixed traffic safety assessment: A novel safety metric combined with a comprehensive behavioral modeling framework.

In the context of future traffic systems, where automated vehicles (AVs) coexist with human-driven vehicles (HVs), ensuring road safety is of utmost importance. Existing safety assessment methods, however, are inadequate for the complex scenarios presented by mixed traffic conditions. These methods often fail to distinguish sufficiently between AVs and HVs, leading to inaccuracies in safety evaluations. To address these issues, this paper highlights the shortcomings of current surrogate safety measures (SSMs) in mixed traffic contexts and introduces a novel SSM, the Weighted Combination of Spacing and Speed Difference Rates (WS2DR). We propose a comparative analysis method to validate the effectiveness of WS2DR and to establish its safety threshold. Experiment results reveal that WS2DR outperforms traditional metrics such as time-to-collision and deceleration rate to avoid crashes, in terms of adaptability to both homogeneous and heterogeneous traffic environments and the detection of risk levels across a wider range of traffic conditions. Additionally, the paper presents a sophisticated mixed traffic modeling approach that accounts for different characteristics of AVs and HVs, incorporating factors such as errors of estimating the motion of other vehicles and the extended reaction time of HVs, as well as the perceptual and cooperative-active control capabilities of AVs. The results of the comparison analysis underscore the critical importance of considering the differences between AVs and HVs in modeling for accurate safety evaluations of mixed traffic. Simulation experiments confirm the positive impact on safety with increased AV penetration rates, emphasizing the necessity of employing refined modeling and safety assessment metrics to capture the full benefits of AV integration.

Transcriptomic Point of Departure (tPOD) of androstenedione in zebrafish embryos as a potential surrogate for chronic endpoints.

The transcriptomic Point of Departure (tPOD) is increasingly used in ecotoxicology to derive quantitative endpoints from RNA sequencing studies. Utilizing transcriptomic data in zebrafish embryos as a New Approach Methodology (NAM) is beneficial due to its acknowledgment as an alternative to animal testing under EU Directive 2010/63/EU. Transcriptomic profiles are available in zebrafish for various modes of action (MoA). The limited literature available suggest that tPOD values from Fish Embryo Toxicity (FET) tests align with, but are generally lower than, No Observed Effect Concentrations (NOEC) from long-term chronic fish toxicity tests. In studies with the androgenic hormone androstenedione in a Fish Sexual Development Test (FSDT), a significant shift in the sex ratio towards males was noted at all test concentrations, making it impossible to determine a NOEC (NOEC <4.34 µg/L). To avoid additional animal testing in a repetition of the FSDT and adhere to the 3Rs principle (replacement, reduction, and refinement), a modified zebrafish FET (zFET) was conducted aiming to determine a regulatory acceptable effect threshold. This involved lower concentration ranges (20 to 6105 ng/L), overlapping with the masculinization-observed concentrations in the FSDT. The tPOD analysis in zFET showed consistent results with previous FSDT findings, observing strong expression changes in androgen-dependent genes at higher concentrations but not at lower ones, demonstrating a concentration-response relationship. The tPOD values for androstenedione were determined as 24 ng/L (10th percentile), 60 ng/L (20th gene), and 69 ng/L (1st peak). The 10th percentile tPOD value in zFET was 200 times lower than the lowest concentration in the FSDT. Comparing the tPOD values to literature suggests their potential to inform on the NOEC range in FSDT tests.

The private-legal nature of the application of the methods of assisted reproductive technologies in Ukraine.

OBJECTIVE: Aim: Analyze the legislation, judicial practice of Ukraine and EU countries, scientific views on surrogacy, as well as the procedure for legal regulation and registration of the procedure of surrogacy. PATIENTS AND METHODS: Materials and Methods: The following materials were used to write the scientific work: the practice of a number of countries was analyzed; scientific works have been studied; some methods of assisted reproductive technologies are described; the practice of the European Court of Human Rights is analyzed. When conducting the research, a methodology was used that embodies an interdisciplinary approach, which allows for a systematic analysis of theoretical and practical aspects of legal relations arising from the provision of medical services. CONCLUSION: Conclusions: At the legislative level, the provision of medical services (surrogate motherhood services) is partially regulated, therefore the basis of the legal relationship between the performers (surrogate mother) and the customers (genetic parents) is the contract concluded and signed by the parties on the provision of surrogate motherhood services. A contract in defined legal relations is a source of law. This contract is bilateral, paid and consensual.

Percentage area reduction at week 4 as a prognostic indicator of complete healing in patients treated with standard of care: a post hoc analysis.

Early indicators of healing provide valuable information on the potential benefit of treatment. In patients with hard-to-heal (chronic) diabetic foot ulcers (DFUs), timely intervention is critical. Ulcers that fail to show measurable progress within four weeks of treatment are considered recalcitrant. These ulcers increase the risk of soft tissue infection, osteomyelitis and lower extremity amputation. A prognostic indicator or surrogate marker allows for rapid evaluation of treatment efficacy and safety. An inverse correlation between a percentage area reduction (PAR) of ≤50% at week 4 and complete healing by week 12 has been previously established; however, the data were derived from a standard of care (SoC) arm of clinical trials that are over a decade old. In this post hoc analysis, data from a large multicentre prospective randomised controlled trial were reviewed to assess PAR at week 4 as a prognostic indicator in patients treated with SoC. Overall, 65.4% (17/26) of patients with PAR >50% at week 4 achieved complete closure at week 12. The receiver operating characteristic (ROC) curve for area reduction by week 4 showed strong discrimination for predicting non-healing (area under the ROC curve: 0.92; p<0.001; positive predictive value: 70.6%; negative predictive value: 87.2%). These findings are consistent with previous studies and support the use of four-week PAR as a prognostic indicator.

Antenna optimization using machine learning with reduced-dimensionality surrogates.

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM) simulation models using conventional nature-inspired methods entails significant computational costs. Alternatively, surrogate-based techniques show promise but are impeded by dimensionality-related challenges and nonlinearity of antenna outputs. This study introduces an innovative technique for swiftly optimizing antennas. It leverages a machine learning framework with an infill criterion employing predicted enhancement of the merit function, utilizing a particle swarm optimizer as the primary search engine, and employs kriging for constructing the underlying surrogate model. The surrogate model operates within a reduced-dimensionality domain, guided by directions corresponding to maximum antenna response variability identified through fast global sensitivity analysis, tailored explicitly for domain determination. Operating within this reduced domain enables building dependable metamodels at a significantly lower computational cost. To address accuracy loss resulting from dimensionality reduction, the global optimization phase is supplemented by local sensitivity-based parameter adjustment. Extensive comparative experiments involving various planar antennas demonstrate the competitive operation of the presented technique over machine learning algorithms operating in full-dimensionality space and direct EM-driven bio-inspired optimization techniques.

Immunogenicity of mRNA vs. BBV152 vaccine boosters against Omicron subvariants: Final results from Phase B of the PRIBIVAC study, a randomized clinical trial.

BACKGROUND: BBV152 (Covaxin™) is a whole-virion inactivated SARS-CoV-2 vaccine mixed with an immune adjuvant. We aimed to compare immune responses after booster vaccination with heterologous BBV152 versus homologous mRNA vaccine. METHODS: We conducted a randomized, participant-blinded, controlled trial. Fifty mRNA-vaccinated participants were enrolled and randomized to receive an mRNA booster (n = 26) or BBV152 (n = 24). Blood samples were collected pre-vaccination, and at Day 7, 28, 180 and 360 post-booster for analysis of humoral and cellular immune responses. Primary end point was the SARS-CoV-2 anti-spike antibody titer at day 28. RESULTS: Recruitment began in January 2022 and was terminated early due to the BBV152 group meeting pre-specified criteria for futility. At Day 28 post-boost, mean SARS-CoV-2 spike antibody titers were lower with BBV152 (2004 IU/mL; 95 % confidence interval [CI], 1132-3548) vs mRNA (26,669 IU/mL; 95 % CI, 21,330-33,266; p < 0.0001), but comparable levels of spike-specific CD4 and cytotoxic T-cells were observed. Anti-spike antibody titers remained significantly different at Day 180: BBV152 4467 IU/mL (95 % CI, 1959-10,186) vs mRNA 20,749 IU/mL (95 % CI, 12,303-35,075; p = 0.0017). Levels of surrogate virus neutralizing antibodies against ancestral and Omicron subvariants BA.1 and BA.2 were significantly higher among mRNA recipients at Day 180, including after adjusting for intercurrent infection. By Day 360, anti-spike antibody titers and neutralizing antibody levels against Omicron subvariants became similar between vaccine groups. By the end of the study, 16 in each arm (mRNA 64 % and BBV152 69.6 %) had breakthrough infections and time to COVID-19 infection between vaccine groups were similar (p = 0.63). CONCLUSIONS: Wild-type SARS-CoV-2 anti-spike antibody titer and surrogate virus neutralizing test levels against wild-type SARS-CoV-2 and Omicron subvariants BA.1/BA.2/BA.5 were significantly higher at Day 28 and 180 in individuals who received booster vaccination with an mRNA vaccine compared with BBV152. CLINICAL TRIAL REGISTRATION NUMBER: NCT05142319.

Multi-Objective Robust Design Optimization for Crashworthiness Enhancement of Hybrid 2D Triaxially Braided Composite Tube Using Evolutionary Algorithms.

An innovative optimal design framework is developed aiming at enhancing the crashworthiness while ensuring the lightweight design of a hybrid two-dimensional triaxial braided composite (2DTBC) tube, drawing insights from the mesostructure of the composite material. To achieve these goals, we first compile the essential mechanical properties of the 2DTBC using a concentric cylinder model (CCM) and an analytical laminate model. Subsequently, a kriging surrogate model to elucidate the intricate relationship between design variables and macroscopic crashworthiness is developed and validated. Finally, employing multi-objective evolutionary optimization, we identify Pareto optimal solutions, highlighting that reducing the total fiber volume and increasing the glass fiber content in the total fiber volume are crucial for optimal crashworthiness and the lightweight design of the hybrid 2DTBC tube. By integrating advanced predictive modeling techniques with multi-objective evolutionary optimization, the proposed approach not only sheds light on the fundamental principles governing the crashworthiness of hybrid 2DTBC but also provides valuable insights for the design of robust and lightweight composite structures.

Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding.

Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the injection molding process and the challenges associated with collecting process data remain significant obstacles for the application of ML methods. To address this, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidelity numerical simulations. The hybrid modeling approaches include feature learning, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injection-molded part are utilized. While all hybrid approaches outperform the purely data-based model, the fine-tuning approach yields the best result in the simulation setting. The combination of calibrating a physical model (feature learning) and incorporating it implicitly into the training process (physical constraints) outperforms the other approaches in the experimental setting.

A Robust Recurrent Neural Networks-Based Surrogate Model for Thermal History and Melt Pool Characteristics in Directed Energy Deposition.

In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups.

A Facile Synthesis of Red-Shifted Bis-Quinoline (BisQ) Surrogate Base.

Forced intercalation peptide nucleic acids (FIT-PNAs) are DNA mimics that act as RNA sensors. The sensing event occurs due to sequence-specific RNA hybridization, leading to a substantial increase in fluorescence. The fluorophore in the FIT-PNA is termed a surrogate base. This molecule typically replaces a purine in the PNA sequence. BisQ is a surrogate base that connects two quinolines via a monomethine bond. BisQ-based FIT-PNAs have excellent biophysical features that include high brightness and red-shifted emission (λem, max = 613 nm). In this report, we detail two chemical approaches that allow for the facile synthesis of the BisQ PNA monomer. In both cases, the key compound used for the synthesis of BisQ-CH2COOH is the tBu-ester-modified quinoline synthon (compound 5). Subsequently, one method uses the Alloc acid-protected PNA backbone, whereas the other uses the tBu ester-protected PNA backbone. In the latter case, the overall yield for BisQ acid (compound 7) and BisQ PNA monomer syntheses was 61% in six synthetic steps. This is a substantial improvement to the published procedures to date (7% total yield). Lastly, we have prepared an 11-mer FIT-PNA with either BisQ or thiazole orange (TO) and studied their photophysical properties. We find superior photophysical properties for the BisQ FIT-PNA in terms of the brightness and selectivity, highlighting the added value of using this surrogate base for RNA sensing.

Diagnosis of botulism in mammals aided by toxin ELISA and C and D gene RT-PCRs with an emphasis on farm animals.

Animal welfare and ethical considerations limit the use of the mouse lethality bioassay for the detection of botulinum toxins. This study assessed an ELISA and RT-PCRs to detect toxin types C and D in samples from mammals with flaccid paralysis or weakness. DNA/RNA tightly bound to the toxin molecules were used as surrogates for the presence of toxin. In total 391 diverse samples from 101 clinical cases from cattle and 9 other species were examined. Botulism was confirmed by ELISA in samples from 16 cases. Toxin type C or D was identified by RT-PCR in 10 cases, five of which were among the cases confirmed by ELISA. Among the 80 cases with negative samples, sampling was not comprehensive enough in 66 cases (botulism therefore not excluded) but was adequate in 14 cases (no indication of botulism). The last two categories included 16 cases with other laboratory findings explaining the clinical signs. The ELISA that detects botulinum C and D toxins was superior in identifying cases with positive samples compared to the RT-PCRs but the latter were able to identify the toxin type present when positive. Furthermore, the RT-PCRs are available to any laboratory with molecular testing capability. Both laboratory assays can screen a comprehensive range and a large number of samples which is crucial for a successful diagnosis of botulism.

When a Patient Is at Foreseeable Risk of Losing Decisional and Functional Capacity.

The four-skills model of decisional capacity for providing informed consent for medical treatment developed by Appelbaum and Grisso is codified into most state statutes in articulating the legal criteria for establishing capacity. Decisional capacity is traditionally determined at a point in time based on a narrow clinical question; however, there are clinical scenarios in which patients may currently have decisional capacity but their recurrent nonadherence to care places them at foreseeable risk of being acutely incapacitated, both decisionally and functionally, in the near future. There is a gap in terms of how these four skills ought to be adapted when applied to a patient with recurrent altered mental status, especially delirium, because of nonadherence. To describe this clinical situation, we introduce a new risk factor, "foreseeable risk of losing decisional and functional capacity," and discuss the clinical evaluation of a patient who currently has capacity but for whom this risk factor applies. We consider the implications of being at foreseeable risk of losing capacity and how foreseeable risk can be translated into a capacity determination in the present. We also describe interventions that can serve to protect the patient's rights and safety.

Sensitivity Analysis of C. auris, S. cerevisiae, and C. cladosporioides by Irradiation with Far-UVC, UVC, and UVB.

Background: The World Health Organization has published a list of pathogenic fungi with prior-itizing groups and calls for research and development of antifungal measures, with Candida auris belonging to the group with high priority. Methods: The photosensitivity towards short wavelength ultraviolet irradiation (Far-UVC, UVC, and UVB) was investigated and compared to other yeasts (Saccharomyces cerevisiae) and a mold (Cladosporium cladosporioides). The observed 1-log reduction doses were compared to literature values of other representatives of the genus Candida, but also with S. cerevisiae, Aspergillus niger, and A. fumigatus. Results: For the determined 1-log reduction doses, an increase with higher wavelengths was observed. A 1-log reduction dose of 4.3 mJ/cm2 was determined for C. auris when irradiated at 222 nm, a dose of 6.1 mJ/cm2 at 254 nm and a 1-log reduction dose of 51.3 mJ/cm2 was required when irradiated with UVB. Conclusions: It was observed that S. cerevisiae is a possible surrogate for C. auris for irradiation with Far-UVC and UVB due to close 1-log reduction doses. No surrogate suitability was verified for C. cladosporioides in relation to A. niger and A. fumigatus for irradiation with a wavelength of 254 nm and for A. niger at 222 nm.

Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks.

Storm water systems (SWSs) are essential infrastructure providing multiple services including environmental protection and flood prevention. Typically, utility companies rely on computer simulators to properly design, operate, and manage SWSs. However, multiple applications in SWSs are highly time-consuming. Researchers have resorted to cheaper-to-run models, i.e. metamodels, as alternatives of computationally expensive models. With the recent surge in artificial intelligence applications, machine learning has become a key approach for metamodelling urban water networks. Specifically, deep learning methods, such as feed-forward neural networks, have gained importance in this context. However, these methods require generating a sufficiently large database of examples and training their internal parameters. Both processes defeat the purpose of using a metamodel, i.e., saving time. To overcome this issue, this research focuses on the application of inductive biases and transfer learning for creating SWS metamodels which require less data and retain high performance when used elsewhere. In particular, this study proposes an auto-regressive graph neural network metamodel of the Storm Water Management Model (SWMM) from the Environmental Protection Agency (EPA) for estimating hydraulic heads. The results indicate that the proposed metamodel requires a smaller number of examples to reach high accuracy and speed-up, in comparison to fully connected neural networks. Furthermore, the metamodel shows transferability as it can be used to predict hydraulic heads with high accuracy on unseen parts of the network. This work presents a novel approach that benefits both urban drainage practitioners and water network modeling researchers. The proposed metamodel can help practitioners on the planning, operation, and maintenance of their systems by offering an efficient metamodel of SWMM for computationally intensive tasks like optimization and Monte Carlo analyses. Researchers can leverage the current metamodel's structure for developing new surrogate model architectures tailored to their specific needs or start paving the way for more general foundation metamodels of urban drainage systems.

From Then to Now and Beyond: Exploring How Machine Learning Shapes Process Design Problems.

Following the discovery of the least squares method in 1805 by Legendre and later in 1809 by Gauss, surrogate modeling and machine learning have come a long way. From identifying patterns and trends in process data to predictive modeling, optimization, fault detection, reaction network discovery, and process operations, machine learning became an integral part of all aspects of process design and process systems engineering. This is enabled, at the same time necessitated, by the vast amounts of data that are readily available from processes, increased digitalization, automation, increasing computation power, and simulation software that can model complex phenomena that span over several temporal and spatial scales. Although this paper is not a comprehensive review, it gives an overview of the recent history of machine learning models that we use every day and how they shaped process design problems from the recent advances to the exploration of their prospects.

Elevated osmol gaps in patients with alcoholic ketoacidosis.

INTRODUCTION: The use of the osmol gap as a surrogate marker of toxic alcohol poisoning is common. Unfortunately, many patients with alcoholic ketoacidosis have elevated osmol gaps and are misdiagnosed with toxic alcohol poisoning. We aimed to characterize the range of osmol gaps in patients with alcoholic ketoacidosis. METHODS: This was a retrospective poison center study. Data from 24 years were reviewed using the following case definition of alcoholic ketoacidosis: (1) documented alcohol use disorder; (2) presence of urine or serum ketones or an elevated blood beta-hydroxybutyrate concentration; (3) an anion gap ≥14 mmol/L. Potential cases of alcoholic ketoacidosis that failed to fulfill all three criteria were adjudicated by three toxicologists. Exclusion criteria included (1) detectable toxic alcohol concentration, (2) hemodialysis and/or multiple doses of fomepizole, (3) no osmol gap documented, (4) other diagnoses that lead to a metabolic acidosis. Demographics, pH, anion gap, lactate concentration, and osmol gap were extracted. RESULTS: Of 1,493 patients screened, 55 met criteria for alcoholic ketoacidosis. Sixty-four percent were male, and their median age was 52 years. The median osmol gap was 27 [IQR 18-36]. The largest anion gap was 57 mmol/L, and the lowest pH was 6.8. Forty-five (82%) of the patients with alcoholic ketoacidosis had osmol gaps >10; 38 (69%) had osmol gaps >20; 24 (44%) had osmol gaps >30; 11 (20%) had osmol gaps > 40. DISCUSSION: The large range of osmol gaps in patients with alcoholic ketoacidosis often reaches values associated with toxic alcohol poisoning. The study is limited by the potential for transcribing errors and the inability to identify the cause of the osmol gap. CONCLUSIONS: In this retrospective study, patients with alcoholic ketoacidosis had a median osmol gap of 26. Given that alcoholic ketoacidosis is easily and inexpensively treated, proper identification may prevent costly and invasive treatment directed at toxic alcohol poisoning.

Surrogate endpoint meta-regression: Useful statistics for regulators and trialists.

OBJECTIVES: The main purpose of using a surrogate endpoint is to estimate the treatment effect on the true endpoint sooner than with a true endpoint. Based on a meta-regression of historical randomized trials with surrogate and true endpoints, we discuss statistics for applying and evaluating surrogate endpoints. METHODS: We computed statistics from two types of linear meta-regressions for trial-level data: simple random effects and novel random effects with correlations among estimated treatment effects in trials with more than 2 arms. A key statistic is the estimated intercept of the meta-regression line. An intercept that is small or not statistically significant increases confidence when extrapolating to a new treatment because of consistency with a single causal pathway and invariance to labeling of treatments as controls. For a regulator applying the meta-regression to a new treatment, a useful statistic is the 95% prediction interval. For a clinical trialist planning a trial of a new treatment, useful statistics are the surrogate threshold effect proportion, the sample size multiplier adjusted for dropouts, and the novel true endpoint advantage. RESULTS: We illustrate these statistics with surrogate endpoint meta-regressions involving anti-hypertension treatment, breast cancer screening, and colorectal cancer treatment. CONCLUSION: Regulators and trialists should consider using these statistics when applying and evaluating surrogate endpoints.