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This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.

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Pathogenic Escherichia coli (E. coli) strains are distinguished by their diverse virulence factors, which contribute to a wide spectrum of diseases. These pathogens evolve through the horizontal transfer of virulence factors, resulting in the emergence of hybrid pathotypes with complex and heterogeneous characteristics. Recognizing their profound impact on public health, this study introduces the PIP-eco pipeline, a comprehensive analytical tool designed for the precise identification and characterization of E. coli pathotypes. This PIP-eco pipeline advances beyond traditional molecular techniques by facilitating detailed analysis of both single and hybrid pathotypes. It integrates targeted marker gene analysis, virulence factor-based phylogenetic analysis, and pathogenicity islands (PAIs) profiling to elucidate the genetic diversity of E. coli pathotypes and support their accurate classification. This integrative approach enables PIP-eco to uncover connections among various E. coli pathotypes, highlight shared virulence factors, and provide insights into their evolutionary trajectories. By utilizing experimentally validated marker genes, the pipeline ensures robust identification of pathotypes, particularly those of hybrid pathotypes. Additionally, PAI analysis offers comprehensive genetic investigations, revealing strain-specific variations and potential virulence mechanisms. As a result, the PIP-eco pipeline emerges as a useful tool for dissecting the evolutionary dynamics of E. coli and characterizing complex pathotypes, addressing the critical need for accurate detection and understanding of hybrid pathotypes.

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Introduction: The predominant hybrid pathogenic E. coli, enterohemorrhagic E. coli (EHEC), combines characteristics of Shiga toxin-producing E. coli (STEC) and enteropathogenic E. coli (EPEC), contributing to global outbreaks with severe symptoms including fatal consequences. Since EHEC infection was designated as a notifiable disease in 2000 in South Korea, around 2000 cases have been reported, averaging approximately 90 cases annually. Aim: In this work, genome-based characteristic analysis and cell-based assay of hybrid STEC/aEPEC strains isolated from livestock feces, animal source foods, and water in South Korea was performed. Methods: To identify the virulence and antimicrobial resistance genes, determining the phylogenetic position of hybrid STEC/aEPEC strains isolated in South Korea, a combination of real-time PCR and whole-genome sequencing (WGS) was used. Additionally, to assess the virulence of the hybrid strains and compare them with genomic characterization, we performed a cell cytotoxicity and invasion assays. Results: The hybrid STEC/aEPEC strains harbored stx and eae genes, encoding Shiga toxins and E. coli attachment/effacement related protein of STEC and EPEC, respectively. Furthermore, all hybrid strains harbored plasmid-carried enterohemolysin(ehxCABD), a key virulence factor in prevalent pathogenic E. coli infections, such as diarrheal disease and hemolytic-uremic syndrome (HUS). Genome-wide phylogenetic analysis revealed a close association between all hybrid strains and specific EPEC strains, suggesting the potential acquisition of Stx phages during STEC/aEPEC hybrid formation. Some hybrid strains showed cytotoxic activity against HeLa cells and invasive properties against epithelial cells. Notably, all STEC/aEPEC hybrids with sequence type (ST) 1,034 (n = 11) exhibited higher invasiveness than those with E2348/69. This highlights the importance of investigating potential correlations between STs and virulence characteristics of E. coli hybrid strains. Conclusion: Through genome-based characterization, we confirmed that the hybrid STEC/aEPEC strains are likely EPEC strains that have acquired STEC virulence genes via phage. Furthermore, our results emphasize the potential increased danger to humans posed by hybrid STEC/aEPEC strains isolated in South Korea, containing both stx and eaeA, compared to STEC or EPEC alone.

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Metal-metal contacts, though not yet widely realized, may provide exciting opportunities to serve as tunable and functional interfaces in single-molecule devices. One of the simplest components which might facilitate such binding interactions is the ferrocene group. Notably, direct bonds between the ferrocene iron center and metals such as Pd or Co have been demonstrated in molecular complexes comprising coordinating ligands attached to the cyclopentadienyl rings. Here, we demonstrate that ferrocene-based single-molecule devices with Fe-Au interfacial contact geometries form at room temperature in the absence of supporting coordinating ligands. Applying a photoredox reaction, we propose that ferrocene only functions effectively as a contact group when oxidized, binding to gold through a formal Fe3+ center. This observation is further supported by a series of control measurements and density functional theory calculations. Our findings extend the scope of junction contact chemistries beyond those involving main group elements, lay the foundation for light switchable ferrocene-based single-molecule devices, and highlight new potential mechanistic function(s) of unsubstituted ferrocenium groups in synthetic processes.

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Hybrid strains Escherichia coli acquires genetic characteristics from multiple pathotypes and is speculated to be more virulent; however, understanding their pathogenicity is elusive. Here, we performed genome-based characterization of the hybrid of enteropathogenic (EPEC) and enterotoxigenic E. coli (ETEC), the strains that cause diarrhea and mortality in children. The virulence genes in the strains isolated from different sources in the South Korea were identified, and their phylogenetic positions were analyzed. The EPEC/ETEC hybrid strains harbored eae and est encoding E. coli attaching and effacing lesions and heat-stable enterotoxins of EPEC and ETEC, respectively. Genome-wide phylogeny revealed that all hybrids (n = 6) were closely related to EPEC strains, implying the potential acquisition of ETEC virulence genes during ETEC/EPEC hybrid emergence. The hybrids represented diverse serotypes (O153:H19 (n = 3), O49:H10 (n = 2), and O71:H19 (n = 1)) and sequence types (ST546, n = 4; ST785, n = 2). Furthermore, heat-stable toxin-encoding plasmids possessing estA and various other virulence genes and transporters, including nleH2, hlyA, hlyB, hlyC, hlyD, espC, espP, phage endopeptidase Rz, and phage holin, were identified. These findings provide insights into understanding the pathogenicity of EPEC/ETEC hybrid strains and may aid in comparative studies, virulence characterization, and understanding evolutionary biology.

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Detecting and identifying the origins of foodborne pathogen outbreaks is a challenging. The Next-Generation Sequencing (NGS) panel method offers a potential solution by enabling efficient screening and identification of various bacteria in one reaction. In this study, new NGS panel primer sets that target 18 specific virulence factor genes from six target pathogens (Bacillus cereus, Yersinia enterocolitica, Staphylococcus aureus, Vibrio cholerae, Vibrio parahaemolyticus, and Vibrio vulnificus) were developed and optimized. The primer sets were validated for specificity and selectivity through singleplex PCR, confirming the expected amplicon size. Crosscheck and multiplex PCR showed no interference in the primer set or pathogenic DNA mixture. The NGS panel analysis of spiked water samples detected all 18 target genes in a single reaction, with pathogen concentrations ranging from 108 to 105 colony-forming units (CFUs) per target pathogen. Notably, the total sequence read counts from the virulence factor genes showed a positive association with the CFUs per target pathogen. However, the method exhibited relatively low sensitivity and occasional false positive results at low pathogen concentrations of 105 CFUs. To validate the detection and identification results, two sets of quantitative real-time PCR (qPCR) analyses were independently performed on the same spiked water samples, yielding almost the same efficiency and specificity compared to the NGS panel analysis. Comparative statistical analysis and Spearman correlation analysis further supported the similarity of the results by showing a negative association between the NGS panel sequence read counts and qPCR cycle threshold (Ct) values. To enhance NGS panel analysis for better detection, optimization of primer sets and real-time NGS sequencing technology are essential. Nonetheless, this study provides valuable insights into applying NGS panel analysis for multiple foodborne pathogen detection, emphasizing its potential in ensuring food safety.

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The global emergence of hybrid diarrheagenic E. coli strains incorporating genetic markers from different pathotypes is a public health concern. Hybrids of Shiga toxin-producing and enterotoxigenic E. coli (STEC/ETEC) are associated with diarrhea and hemolytic uremic syndrome (HUS) in humans. In this study, we identified and characterized STEC/ETEC hybrid strains isolated from livestock feces (cattle and pigs) and animal food sources (beef, pork, and meat patties) in South Korea between 2016 and 2020. The strains were positive for genes from STEC and ETEC, such as stx (encodes Shiga toxins, Stxs) and est (encodes heat-stable enterotoxins, ST), respectively. The strains belong to diverse serogroups (O100, O168, O8, O155, O2, O141, O148, and O174) and sequence types (ST446, ST1021, ST21, ST74, ST785, ST670, ST1780, ST1782, ST10, and ST726). Genome-wide phylogenetic analysis revealed that these hybrids were closely related to certain ETEC and STEC strains, implying the potential acquisition of Stx-phage and/or ETEC virulence genes during the emergence of STEC/ETEC hybrids. Particularly, STEC/ETEC strains isolated from livestock feces and animal source foods mostly exhibited close relatedness with ETEC strains. These findings allow further exploration of the pathogenicity and virulence of STEC/ETEC hybrid strains and may serve as a data source for future comparative studies in evolutionary biology.

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INTRODUCTION: Although there is increasing interest in conducting cancer clinical trials in older adults, the benefit of such trials is unclear. We aimed to quantify the real-world clinical and economic effects of two phase 3 trials (CALGB 9343 and PRIME II) which showed that post-lumpectomy radiation therapy (RT) improves loco-regional recurrence but makes no improvement in overall survival among older women with early-stage breast cancer (ESBC). MATERIALS AND METHODS: We developed a health-transition model to quantify the incremental clinical and economic outcomes between scenarios with vs. without older adult-specific trial results from a societal perspective between 2004 and 2018. The transition probabilities in the model were mainly derived from the 10-year results of CALGB 9343. The total number of the affected patient population in the US and the change in the probability of omitting post-lumpectomy RT due to the CALGB 9343 and PRIME II results were derived from a retrospective analysis of the SEER registry data for patients with ESBC. Sensitivity analyses were conducted to calculate the 95% credible interval (CR) of the incremental clinical and economic outcomes between the two scenarios. RESULTS: Between 2004 and 2018, 32,936 (95% CR: 31,512, 34,357) fewer patients received post-lumpectomy RT among those aged 70 years or older diagnosed with ESBC in the US and there was a decrease cost of $419 M USD (95% CR: -$238 M, -$689 M) in scenarios with vs. without older adult-specific trial results. The difference in projected life years (1083 years, 95% CI: -2542, 7985) and QALYs (866 years, 95% CI: -2561, 7780) were not significant. At a willingness-to-pay threshold of $100 k/QALY, the probability of older adult-specific trial results generating a positive net monetary benefit was 98%. DISCUSSION: The CALGB 9343 and PRIME II trial results were associated with a substantial cost-saving in the US society. Our results suggest that older adult-specific clinical trials that demonstrate no survival benefit of an intervention in older adults could be correlated with a significant monetary benefit. Further case studies are needed for different types of older adult-specific trials to understand the value of older adult-specific trials comprehensively.

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BACKGROUND: Despite increasing focus on conducting cancer clinical trials in older adults, it is unclear whether such evidence influences practice patterns. We aimed to estimate the impact of cumulative evidence from older adult-specific trial results from the CALGB 9343 and PRIME II trials that found post-lumpectomy irradiation has little benefit among older adults with early-stage breast cancer (ESBC). METHODS: Patients diagnosed with ESBC between 2000 and 2018 were identified from the SEER registry data. We examined the incremental immediate effect, incremental average yearly effect, and cumulative effect of a series of CALGB 9343 and PRIME II results on the utilization level of post-lumpectomy irradiation. We conducted difference-in-differences analyses, comparing those aged 70 or older vs. <65 years old. RESULTS: The initial 5-year CALGB 9343 results in 2004 led to a significant immediate (-0.038, 95% CI: -0.064, -0.012) and average yearly decrease (-0.008, 95% CI: -0.013, -0.003) in the probability of irradiation use among those aged 70 or older compared to those below 65 years of age. 11-year CALGB 9343 results in 2010 significantly accelerated the average yearly effect by 1.7 percentage points (95% CI: -0.030, -0.004). The other later results did not significantly change the time trend. The cumulative effect of all results between 2004 and 2018 was -26.3 percentage points (95% CI: -0.29, -0.24). CONCLUSION: Cumulative evidence from older adult-specific trials in ESBC led to decreasing use of irradiation over time among elderly patients. The rate of decrease after the initial results was accelerated by long-term follow-up results.

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OBJECTIVES: With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR. METHODS: We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses. RESULTS: A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%). CONCLUSION: There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR.

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INTRODUCTION: Conducting older adult-specific clinical trials can help overcome the lack of clinical evidence for older adults due to their underrepresentation in clinical trials. Understanding factors contributing to the successful completion of such trials can help trial sponsors and researchers prioritize studies and optimize study design. We aimed to develop a model that predicts trial failure among older adult-specific cancer clinical trials using trial-level factors. MATERIALS AND METHODS: We identified phase 2-4 interventional cancer clinical trials that ended between 2008 and 2019 and had the minimum age limit of 60 years old or older using Aggregate Analysis of ClinicalTrials.gov data. We defined trial failure as closed early for reasons other than interim results or toxicity or completed with a sample of <85% of the targeted size. Candidate trial-level predictors were identified from a literature review. We evaluated eight types of machine learning algorithms to find the best model. Model fitting and testing were performed using 5-fold nested cross-validation. We evaluated the model performance using the area under receiver operating characteristic curve (AUROC). RESULTS: Of 209 older adult-specific clinical trials, 87 were failed trials per the definition of trial failure. The model with the highest AUROC in the validation set was the least absolute shrinkage and selection operator (AUROC in the test set = 0.70; 95% confidence interval [CI]: 0.53, 0.86). Trial-level factors included in the best model were the study sponsor, the number of participating centers, the number of modalities, the level of restriction on performance score, study location, the number of arms, life expectancy restriction, and the number of target size. Among these factors, the number of centers (odds ratio [OR] = 0.83, 95% CI: 0.71, 0.94), study being in non-US only vs. US only (OR = 0.32, 95% CI: 0.12, 0.82), and life expectancy restriction (OR = 2.17, 95% CI: 1.04, 4.73) were significantly associated with the trial failure. DISCUSSION: We identified trial-level factors predictive of trial failure among older adult-specific clinical trials and developed a prediction model that can help estimate the risk of failure before a study is conducted. The study findings could aid in the design and prioritization of future older adult-specific clinical trials.

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These days, bacterial detection methods have some limitations in sensitivity, specificity, and multiple detection. To overcome these, novel detection and identification method is necessary to be developed. Recently, NGS panel method has been suggested to screen, detect, and even identify specific foodborne pathogens in one reaction. In this study, new NGS panel primer sets were developed to target 13 specific virulence factor genes from five types of pathogenic Escherichia coli, Listeria monocytogenes, and Salmonella enterica serovar Typhimurium, respectively. Evaluation of the primer sets using singleplex PCR, crosscheck PCR and multiplex PCR revealed high specificity and selectivity without interference of primers or genomic DNAs. Subsequent NGS panel analysis with six artificially contaminated food samples using those primer sets showed that all target genes were multi-detected in one reaction at 108-105 CFU of target strains. However, a few false-positive results were shown at 106-105 CFU. To validate this NGS panel analysis, three sets of qPCR analyses were independently performed with the same contaminated food samples, showing the similar specificity and selectivity for detection and identification. While this NGS panel still has some issues for detection and identification of specific foodborne pathogens, it has much more advantages, especially multiple detection and identification in one reaction, and it could be improved by further optimized NGS panel primer sets and even by application of a new real-time NGS sequencing technology. Therefore, this study suggests the efficiency and usability of NGS panel for rapid determination of origin strain in various foodborne outbreaks in one reaction.

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Lettuce is one of the most consumed vegetables worldwide. However, it has potential risks associated with pathogenic bacterial contamination because it is usually consumed raw. In this study, we investigated the changes in the bacterial community on lettuce (Lactuca sativa L.) in Chungcheong-do, South Korea, and the prevalence of foodborne pathogens on lettuce in different seasons using 16S rRNA gene-based sequencing. Our data revealed that the Shannon diversity index showed the same tendency in term of the number of OTUs, with the index being greatest for summer samples in comparison to other seasons. Moreover, the microbial communities were significantly different between the four seasons. The relative abundance of Actinobacteriota varied according to the season. Family Micrococcaceae was most dominant in all samples except summer, and Rhizobiaceae was predominant in the microbiome of the summer sample. At the genus level, the relative abundance of Bacillus was greatest in spring samples, whereas Pseudomonas was greatest in winter samples. Potential pathogens, such as Staphylococcus and Clostridium, were detected with low relative abundance in all lettuce samples. We also performed metagenome shotgun sequencing analysis on the selected summer and winter samples, which were expected to be contaminated with foodborne pathogens, to support 16S rRNA gene-based sequencing dataset. Moreover, we could detect seasonal biomarkers and microbial association networks of microbiota on lettuce samples. Our results suggest that seasonal characteristics of lettuce microbial communities, which include diverse potential pathogens, can be used as basic data for food safety management to predict and prevent future outbreaks.

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Water electrolysis technology is required to overcome the intermittency of renewable energy sources. Among various water electrolysis methods, the proton exchange membrane water electrolysis (PEMWE) cell has the advantages of a fast response and high current density. However, high capital costs have hindered the commercialization of PEMWE; therefore, it is important to lower the price of bipolar plates, which make PEMWE expensive. In addition, since the flow field inscribed in the bipolar plate significantly influences the performance, it is necessary to design the enhanced pattern. A three-dimensional two-phase flow model was used to analyze the two-phase flow and electrochemical reactions of the PEMWE anode. In order to compare the experimental results with the simulation, experiments were conducted according to the flow rate, and the results were in good agreement. First, as a result of comparing the performance of the channel and PTL (porous transport layer) flow fields, the channel flow field showed better performance than the PTL flow field. For the channel flow field, the higher the ratio of the channel width-to-rib width and the permeability of PTL, the performance got better. In the case of the PTL flow field, with the increased capillary pressure, the performance improved even if the PTL permeability decreased. Next, the direction of gravity affected the performance only when the channel flow field was used, and the X+ and Z+ directions were optimal for the performance. Finally, increasing the inlet flow rate could reduce the difference in performance between the channel and PTL flow fields, but the pressure drop gradually increased.

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Salmonella infections represent an important public health problem. In 2018, a multistate outbreak of S. enterica subsp. enterica serovar Thompson infection associated with contaminated chocolate cakes in schools was reported in South Korea. In this study, we sequenced the 37 S. Thompson strains isolated from chocolate cakes, egg whites, preserves, and cookware associated with the outbreak. In addition, we analyze the genomic sequences of 61 S. Thompson strains (37 chocolate cake-related outbreak strains, 4 strains isolated from outbreaks in South Korea and 20 strains available in the National Center for Biotechnology Information) to assess the genomic characteristics of outbreak-related strains by comparative genomics and phylogenetic analysis. The results showed that identically classified clusters divided strains into two clusters, sub-clusters A & I (with strains from 2018 in South Korea) and sub-clusters B & II (with strains from 2014 to 2015 in South Korea). S. Thompson isolated from South Korea were accurately distinguished from publicly-available strains. Unlike other S. Thompson genomes, those of chocolate cake outbreak-related strains had three Salmonella phages (SEN8, vB SosS Oslo, and SI7) integrated into their chromosome. Comparative genomics revealed several genes responsible for the specific genomic features of chocolate cake outbreak-related strains and three bacteriophages that may contribute to the pathogenicity of other S. Thompson strains.

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OBJECTIVES: Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS: We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS: We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS: The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

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OBJECTIVES: This study aimed to provide recommendations for identifying and implementing real option value (ROV) calculations in value assessment. METHODS: We identified the primary mechanisms through which ROV can be created based on a theoretical framework for ROV, assessed approaches for predicting future innovations and improvements in health, and described the steps for estimating ROV in a cost-effectiveness analysis framework. RESULTS: The 3 primary mechanisms by which ROV can be created are when a current treatment (1) prolongs survival to increase the proportion of patients who can receive future innovations, (2) slows disease progression to increase patients' eligibility for future innovations, and (3) directly affects the efficacy of future innovations. We provide 5 recommendations for implementing ROV in value assessment. First, the decision to quantify ROV should be based on a qualitative evaluation of whether the treatment can enable greater benefits from future innovations. Second, ROV should be quantified in the same value assessment framework (eg, cost-effectiveness analysis using quality-adjusted life-year) as the conventional value. Third, method for quantifying ROV should consider data availability, rate of innovation, and sources of future health improvements. Fourth, ROV estimate should be presented alongside the conventional value as a separate element due to its inherently large uncertainty. Finally, generalizability of ROV estimate should be evaluated, and local data should be used when available. CONCLUSIONS: ROV can arise from a variety of mechanisms that should be considered before investing in an ROV analysis. Calculating ROV includes exploring different approaches for forecasting future innovations and future improvements in health.

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OBJECTIVES: A drug that improves survival and/or disease progression can create real option value (ROV)-the additional health gain from future innovations enabled by a longer survival. ROV can be a relevant consideration for both clinical and payer decision-makers. We aimed to estimate the ex ante ROV for first-line (1L) alectinib in anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancer (NSCLC). METHODS: We developed a Markov model to estimate life-years (LYs) and quality-adjusted life-years (QALYs) gained with 1L alectinib versus 1L crizotinib due to potential future second-line (2L) drugs. Transition probabilities were derived from the phase 3 trial of 1L alectinib and phase 2 trial of 2L brigatinib. We identified drugs being studied in phase 2 and 3 trials in ALK-positive NSCLC at the time of alectinib's 1L approval and projected the likelihood and timing of their arrival and their potential efficacy based on publicly available data. RESULTS: The discounted incremental LYs and QALYs for alectinib increased by 12.9% (95% CR - 2.96%, 34.82%; 1.25 vs. 1.11) and 11.2% (95% CR - 2.14%, 29.29%; 1.03 vs. 0.92), respectively, after accounting for ROV. The incremental ROV of alectinib was sensitive to the projected efficacy of future drugs, uptake level, and the hazard ratio of progression-free survival of alectinib (vs. crizotinib). CONCLUSIONS: Ex ante ROV can be a significant value consideration in therapeutic areas with high levels of expected innovation. The potential efficacy of future drugs and incremental survival with alectinib at the projected time of arrival are important considerations in assessing ROV.

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BACKGROUND: Older adults are underrepresented in cancer clinical trials despite accounting for most of the disease burden. Geriatric assessment (GA) could be used in clinical trials of cancer drugs for older adults to improve the clinical evidence for cancer drug use among older adults. OBJECTIVE: To examine patterns of use of GA in cancer clinical trials. METHODS: We undertook a systematic review of the studies reporting use of GA in a clinical trial setting for all cancer types and published between January 2010 and January 2020. Characteristics of GA use were extracted for each study, along with study phase, cancer type, and participant age (PROSPERO: CRD42020170584). RESULTS: We identified 320 studies and 63 studies met the final inclusion criteria. Among 74 purposes of GA use, the most common was to examine the association between impairments in GA domains and clinical outcomes (28/74, 38%). Among 258 GA domains assessed across 63 studies, physical status (59/258, 23%) and comorbidities (50/258, 19%) were most often evaluated. There was significant heterogeneity in the instruments used to assess physical function (n = 16) and mood disorders (n = 7). Most studies were phase 2 (32/63, 51%). CONCLUSIONS: GA is most often used in clinical trial settings to examine associations between GA-identified deficits and clinical outcomes. Significant heterogeneity exists in the GA instruments used across trials. Comprehensive and consistent incorporation of GA into future cancer clinical trial designs could help collect more older adult-specific clinical information and adjust trial eligibility criteria to increase representation by older adults.

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Coherent tunneling electron transport through molecular wires has been theoretically established as a temperature-independent process. Although several experimental studies have shown counter examples, robust models to describe this temperature dependence have not been thoroughly developed. Here, we demonstrate that dynamic molecular structures lead to temperature-dependent conductance within coherent tunneling regime. Using a custom-built variable-temperature scanning tunneling microscopy break-junction instrument, we find that oligo[n]phenylenes exhibit clear temperature-dependent conductance. Our calculations reveal that thermally activated dihedral rotations allow these molecular wires to have a higher probability of being in a planar conformation. As the tunneling occurs primarily through π-orbitals, enhanced coplanarization substantially increases the time-averaged tunneling probability. These calculations are consistent with the observation that more rotational pivot points in longer molecular wires leads to larger temperature-dependence on conductance. These findings reveal that molecular conductance within coherent and off-resonant electron transport regimes can be controlled by manipulating dynamic molecular structure.

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