Geriatric assessment for the practicing clinician: The why, what, and how.

Older adults with cancer heterogeneously experience health care, treatment, and symptoms. Geriatric assessment (GA) offers a comprehensive evaluation of an older individual's health status and can predict cancer-related outcomes in individuals with solid tumors and those with hematologic malignancies. In the last decade, randomized controlled trials have demonstrated the benefits of GA and GA management (GAM), which uses GA information to provide tailored intervention strategies to address GA impairments (e.g., implementing physical therapy for impaired physical function). Multiple phase 3 clinical trials in older adults with solid tumors and hematologic malignancies have demonstrated that GAM improves treatment completion, quality of life, communication, and advance care planning while reducing treatment-related toxicity, falls, and polypharmacy. Nonetheless, implementation and uptake of GAM remain challenging. Various strategies have been proposed, including the use of GA screening tools, to identify patients most likely to benefit from GAM, the systematic engagement of the oncology workforce in the delivery of GAM, and the integration of technologies like telemedicine and mobile health to enhance the availability of GA and GAM interventions. Health inequities in minoritized groups persist, and systematic GA implementation has the potential to capture social determinants of health that are relevant to equitable care. Caregivers play an important role in cancer care and experience burden themselves. GA can guide dyadic supportive care interventions, ultimately helping both patients and caregivers achieve optimal health.

Towards fair and clinically relevant polygenic predictions.

Lennon et al. recently proposed a clinical polygenic score (PGS) pipeline as part of the Electronic Medical Records and Genomics (eMERGE) network initiative. In this spotlight article we discuss the broader context for the use of PGS in preventive medicine and highlight key limitations and challenges facing their inclusion in prediction models.

Simulating institutional heterogeneity in sustainability science.

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches- integrated assessment modeling, engineering-economic optimization, and agent-based modeling-with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.

Carbon reduction and water saving potentials for growing corrugated boxes for express delivery services in China.

Corrugated packaging for express grew by 90 times to 16.5 Mt y-1 in China, where 81% of recent global express delivery growth occurred. However, the environmental impacts of production, usage, disposal, and recycling of corrugated boxes under the entire supply chain remain unclear. Here, we estimate the magnitudes, drivers, and mitigation potentials of cradle-to-grave life-cycle carbon footprint (CF) and three colors of water footprints (WFs) for corrugated cardboard packaging in China. Over 2007 to 2021, CF, blue and gray WFs per unit package decreased by 45%, 60%, and 84%, respectively, while green WF increased by 23% with growing imports of virgin pulp and China's waste ban. National total CF and WFs were 21 to 102 folded with the scale effects. Only a combination of the supply chain reconstruction, lighter single-piece packaging, and increased recycling rate can possibly reduce the environmental footprints by 24 to 44% by 2035.

Policies, projections, and the social cost of carbon: Results from the DICE-2023 model.

The present study examines the assumptions, modeling structure, and results of DICE-2023, the revised Dynamic Integrated Model of Climate and the Economy (DICE), updated to 2023. The revision contains major changes in the treatment of risk, the carbon and climate modules, the treatment of nonindustrial greenhouse gases, discount rates, as well as updates on all the major components. Noteworthy changes are a significant reduction in the target for the optimal (cost-beneficial) temperature path, a lower cost of reaching the 2 °C target, an analysis of the impact of the Paris Accord, and a major increase in the estimated social cost of carbon.

Global decarbonization potential of CO2 mineralization in concrete materials.

CO2 mineralization products are often heralded as having outstanding potentials to reduce CO2-eq. emissions. However, these claims are generally undermined by incomplete consideration of the life cycle climate change impacts, material properties, supply and demand constraints, and economic viability of CO2 mineralization products. We investigate these factors in detail for ten concrete-related CO2 mineralization products to quantify their individual and global CO2-eq. emissions reduction potentials. Our results show that in 2020, 3.9 Gt of carbonatable solid materials were generated globally, with the dominant material being end-of-life cement paste in concrete and mortar (1.4 Gt y-1). All ten of the CO2 mineralization technologies investigated here reduce life cycle CO2-eq. emissions when used to substitute comparable conventional products. In 2020, the global CO2-eq. emissions reduction potential of economically competitive CO2 mineralization technologies was 0.39 Gt CO2-eq., i.e., 15% of that from cement production. This level of CO2-eq. emissions reduction is limited by the supply of end-of-life cement paste. The results also show that it is 2 to 5 times cheaper to reduce CO2-eq. emissions by producing cement from carbonated end-of-life cement paste than carbon capture and storage (CCS), demonstrating its superior decarbonization potential. On the other hand, it is currently much more expensive to reduce CO2-eq. emissions using some CO2 mineralization technologies, like carbonated normal weight aggregate production, than CCS. Technologies and policies that increase recovery of end-of-life cement paste from aged infrastructure are key to unlocking the potential of CO2 mineralization in reducing the CO2-eq. footprint of concrete materials.

Adopting electric school buses in the United States: Health and climate benefits.

Electric school buses have been proposed as an alternative to reduce the health and climate impacts of the current U.S. school bus fleet, of which a substantial share are highly polluting old diesel vehicles. However, the climate and health benefits of electric school buses are not well known. As they are substantially more costly than diesel buses, assessing their benefits is needed to inform policy decisions. We assess the health benefits of electric school buses in the United States from reduced adult mortality and childhood asthma onset risks due to exposure to ambient fine particulate matter (PM2.5). We also evaluate climate benefits from reduced greenhouse-gas emissions. We find that replacing the average diesel bus in the U.S. fleet in 2017 with an electric bus yields $84,200 in total benefits. Climate benefits amount to $40,400/bus, whereas health benefits amount to $43,800/bus due to 4.42*10-3 fewer PM2.5-attributable deaths ($40,000 of total) and 7.42*10-3 fewer PM2.5-attributable new childhood asthma cases ($3,700 of total). However, health benefits of electric buses vary substantially by driving location and model year (MY) of the diesel buses they replace. Replacing old, MY 2005 diesel buses in large cities yields $207,200/bus in health benefits and is likely cost-beneficial, although other policies that accelerate fleet turnover in these areas deserve consideration. Electric school buses driven in rural areas achieve small health benefits from reduced exposure to ambient PM2.5. Further research assessing benefits of reduced exposure to in-cabin air pollution among children riding buses would be valuable to inform policy decisions.

Age-Related Perioperative Neurocognitive Disorders: Experimental Models and Druggable Targets.

With the worldwide increase in life span, surgical patients are becoming older and have a greater propensity for postoperative cognitive impairment, either new onset or through deterioration of an existing condition; in both conditions, knowledge of the patient's preoperative cognitive function and postoperative cognitive trajectory is imperative. We describe the clinical utility of a tablet-based technique for rapid assessment of the memory and attentiveness domains required for executive function. The pathogenic mechanisms for perioperative neurocognitive disorders have been investigated in animal models in which excessive and/or prolonged postoperative neuroinflammation has emerged as a likely contender. The cellular and molecular species involved in postoperative neuroinflammation are the putative targets for future therapeutic interventions that are efficacious and do not interfere with the surgical patient's healing process.

FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network.

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of ~5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.

Implementing personalized pathways for cancer follow-up care in the United States: Proceedings from an American Cancer Society-American Society of Clinical Oncology summit.

A new approach to cancer follow-up care is necessary to meet the needs of cancer survivors while dealing with increasing volume and provider shortages, knowledge gaps, and costs to both health care systems and patients. An approach that triages patients to personalized follow-up care pathways, depending on the type(s) and level(s) of resources needed for patients' long-term care, is in use in the United Kingdom and other countries and has been shown to meet patients' needs, more efficiently use the health care system, and reduce costs. Recognizing that testing and implementing a similar personalized approach to cancer follow-up care in the United States will require a multipronged strategy, the American Cancer Society and the American Society of Clinical Oncology convened a summit in January 2018 to identify the needed steps to move this work from concept to implementation. The summit identified 4 key strategies going forward: 1) developing a candidate model (or models) of care delivery; 2) building the case for implementation by conducting studies modeling the effects of personalized pathways of follow-up care on patient outcomes, workforce and health care resources, and utilization and costs; 3) creating consensus-based guidelines to guide the delivery of personalized care pathways; and 4) identifying and filling research gaps to develop and implement needed care changes. While these national strategies are pursued, oncology and primary care providers can lay the groundwork for implementation by assessing their patients' risk of recurrence and the chronic and late effects of cancer as well as other health care needs and resources available for care and by considering triaging patients accordingly, referring patients to appropriate specialized survivorship clinics as these are developed, helping to support patients who are capable of self-managing their health, setting expectations with patients from diagnosis onward for the need for follow-up in primary care and/or a survivorship clinic, and improving coordination of care between oncology and primary care.

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics.

Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized medicine in the pharmacy field. Protein drugs are crucial in the field of personalized drug therapy and are currently the mainstay, which possess higher target specificity and biological activity than small-molecule chemical drugs, making them efficient in regulating disease-related biological processes, and have significant potential in the development of personalized drugs. Currently, protein drugs are designed and developed for specific protein targets based on patient-specific protein data. However, due to the rapid development of two-dimensional gel electrophoresis and mass spectrometry, it is now widely recognized that a canonical protein actually includes multiple proteoforms, and the differences between these proteoforms will result in varying responses to drugs. The variation in the effects of different proteoforms can be significant and the impact can even alter the intended benefit of a drug, potentially making it harmful instead of lifesaving. As a result, we propose that protein drugs should shift from being targeted through the lens of protein (proteomics) to being targeted through the lens of proteoform (proteoformics). This will enable the development of personalized protein drugs that are better equipped to meet patients' specific needs and disease characteristics. With further development in the field of proteoformics, individualized drug therapy, especially personalized protein drugs aimed at proteoforms as a drug target, will improve the understanding of disease mechanisms, discovery of new drug targets and signaling pathways, provide a theoretical basis for the development of new drugs, aid doctors in conducting health risk assessments and making more cost-effective targeted prevention strategies conducted by artificial intelligence/machine learning, promote technological innovation, and provide more convenient treatment tailored to individualized patient profile, which will benefit the affected individuals and society at large.

Mapping the timescale of suicidal thinking.

This study aims to identify the timescale of suicidal thinking, leveraging real-time monitoring data and a number of different analytic approaches. Participants were 105 adults with past week suicidal thoughts who completed a 42-d real-time monitoring study (total number of observations = 20,255). Participants completed two forms of real-time assessments: traditional real-time assessments (spaced hours apart each day) and high-frequency assessments (spaced 10 min apart over 1 h). We found that suicidal thinking changes rapidly. Both descriptive statistics and Markov-switching models indicated that elevated states of suicidal thinking lasted on average 1 to 3 h. Individuals exhibited heterogeneity in how often and for how long they reported elevated suicidal thinking, and our analyses suggest that different aspects of suicidal thinking operated on different timescales. Continuous-time autoregressive models suggest that current suicidal intent is predictive of future intent levels for 2 to 3 h, while current suicidal desire is predictive of future suicidal desire levels for 20 h. Multiple models found that elevated suicidal intent has on average shorter duration than elevated suicidal desire. Finally, inferences about the within-person dynamics of suicidal thinking on the basis of statistical modeling were shown to depend on the frequency at which data was sampled. For example, traditional real-time assessments estimated the duration of severe suicidal states of suicidal desire as 9.5 h, whereas the high-frequency assessments shifted the estimated duration to 1.4 h.

Cohort bias in predictive risk assessments of future criminal justice system involvement.

Risk assessment instruments (RAIs) are widely used to aid high-stakes decision-making in criminal justice settings and other areas such as health care and child welfare. These tools, whether using machine learning or simpler algorithms, typically assume a time-invariant relationship between predictors and outcome. Because societies are themselves changing and not just individuals, this assumption may be violated in many behavioral settings, generating what we call cohort bias. Analyzing criminal histories in a cohort-sequential longitudinal study of children, we demonstrate that regardless of model type or predictor sets, a tool trained to predict the likelihood of arrest between the ages of 17 and 24 y on older birth cohorts systematically overpredicts the likelihood of arrest for younger birth cohorts over the period 1995 to 2020. Cohort bias is found for both relative and absolute risks, and it persists for all racial groups and within groups at highest risk for arrest. The results imply that cohort bias is an underappreciated mechanism generating inequality in contacts with the criminal legal system that is distinct from racial bias. Cohort bias is a challenge not only for predictive instruments with respect to crime and justice, but also for RAIs more broadly.

Unproctored online exams provide meaningful assessment of student learning.

In the United States, the onset of COVID-19 triggered a nationwide lockdown, which forced many universities to move their primary assessments from invigilated in-person exams to unproctored online exams. This abrupt change occurred midway through the Spring 2020 semester, providing an unprecedented opportunity to investigate whether online exams can provide meaningful assessments of learning relative to in-person exams on a per-student basis. Here, we present data from nearly 2,000 students across 18 courses at a large Midwestern University. Using a meta-analytic approach in which we treated each course as a separate study, we showed that online exams produced scores that highly resembled those from in-person exams at an individual level despite the online exams being unproctored-as demonstrated by a robust correlation between online and in-person exam scores. Moreover, our data showed that cheating was either not widespread or ineffective at boosting scores, and the strong assessment value of online exams was observed regardless of the type of questions asked on the exam, the course level, academic discipline, or class size. We conclude that online exams, even when unproctored, are a viable assessment tool.

Complementary roles for mechanical and solvent-based recycling in low-carbon, circular polypropylene.

Plastic recycling presents a vexing challenge. Mechanical recycling offers substantial greenhouse gas emissions savings relative to virgin plastic production but suffers from degraded aesthetic and mechanical properties. Polypropylene, one of the most widely used and lowest-cost plastics, features methyl pendants along the polymer backbone, rendering it particularly susceptible to declining properties, performance, and aesthetics across a succession of mechanical recycles. Advanced processes, such as solvent-assisted recycling, promise near-virgin quality outputs at a greater energy and emissions footprint. Mechanical and advanced recycling are often presented as competing options, but real-world plastic waste streams are likely to require preprocessing regardless of whether they are routed to an advanced process. This study quantifies the life-cycle greenhouse gas implications of multiple recycling strategies and proposes a system in which mechanical and solvent-assisted recycling can be leveraged together to boost recycling rates and satisfy demand for a wider range of product applications. Polypropylene can be recovered from mixed-plastic bales produced at material recovery facilities and processed through mechanical recycling, with a varying fraction sent for further upgrading via solvent-assisted recycling to produce material approved for food packaging and other higher-quality applications. The resulting mechanically recycled rigid polypropylene reduces life-cycle greenhouse gas emissions by 80% relative to the same quantity of virgin material, while the upgraded higher-quality material achieves GHG savings of 30%.

Brain imaging and neuropsychological assessment of individuals recovered from a mild to moderate SARS-CoV-2 infection.

As severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections have been shown to affect the central nervous system, the investigation of associated alterations of brain structure and neuropsychological sequelae is crucial to help address future health care needs. Therefore, we performed a comprehensive neuroimaging and neuropsychological assessment of 223 nonvaccinated individuals recovered from a mild to moderate SARS-CoV-2 infection (100 female/123 male, age [years], mean ± SD, 55.54 ± 7.07; median 9.7 mo after infection) in comparison with 223 matched controls (93 female/130 male, 55.74 ± 6.60) within the framework of the Hamburg City Health Study. Primary study outcomes were advanced diffusion MRI measures of white matter microstructure, cortical thickness, white matter hyperintensity load, and neuropsychological test scores. Among all 11 MRI markers tested, significant differences were found in global measures of mean diffusivity (MD) and extracellular free water which were elevated in the white matter of post-SARS-CoV-2 individuals compared to matched controls (free water: 0.148 ± 0.018 vs. 0.142 ± 0.017, P < 0.001; MD [10-3 mm2/s]: 0.747 ± 0.021 vs. 0.740 ± 0.020, P < 0.001). Group classification accuracy based on diffusion imaging markers was up to 80%. Neuropsychological test scores did not significantly differ between groups. Collectively, our findings suggest that subtle changes in white matter extracellular water content last beyond the acute infection with SARS-CoV-2. However, in our sample, a mild to moderate SARS-CoV-2 infection was not associated with neuropsychological deficits, significant changes in cortical structure, or vascular lesions several months after recovery. External validation of our findings and longitudinal follow-up investigations are needed.

Evolutionary stability of cooperation in indirect reciprocity under noisy and private assessment.

Indirect reciprocity is a mechanism that explains large-scale cooperation in humans. In indirect reciprocity, individuals use reputations to choose whether or not to cooperate with a partner and update others' reputations. A major question is how the rules to choose their actions and the rules to update reputations evolve. In the public reputation case where all individuals share the evaluation of others, social norms called Simple Standing (SS) and Stern Judging (SJ) have been known to maintain cooperation. However, in the case of private assessment where individuals independently evaluate others, the mechanism of maintenance of cooperation is still largely unknown. This study theoretically shows for the first time that cooperation by indirect reciprocity can be evolutionarily stable under private assessment. Specifically, we find that SS can be stable, but SJ can never be. This is intuitive because SS can correct interpersonal discrepancies in reputations through its simplicity. On the other hand, SJ is too complicated to avoid an accumulation of errors, which leads to the collapse of cooperation. We conclude that moderate simplicity is a key to stable cooperation under the private assessment. Our result provides a theoretical basis for the evolution of human cooperation.

Diversity of plant DNA in stool is linked to dietary quality, age, and household income.

Eating a varied diet is a central tenet of good nutrition. Here, we develop a molecular tool to quantify human dietary plant diversity by applying DNA metabarcoding with the chloroplast trnL-P6 marker to 1,029 fecal samples from 324 participants across two interventional feeding studies and three observational cohorts. The number of plant taxa per sample (plant metabarcoding richness or pMR) correlated with recorded intakes in interventional diets and with indices calculated from a food frequency questionnaire in typical diets (ρ = 0.40 to 0.63). In adolescents unable to collect validated dietary survey data, trnL metabarcoding detected 111 plant taxa, with 86 consumed by more than one individual and four (wheat, chocolate, corn, and potato family) consumed by >70% of individuals. Adolescent pMR was associated with age and household income, replicating prior epidemiologic findings. Overall, trnL metabarcoding promises an objective and accurate measure of the number and types of plants consumed that is applicable to diverse human populations.

The legacy environmental footprints of manufactured capital.

The foundations of today's societies are provided by manufactured capital accumulation driven by investment decisions through time. Reconceiving how the manufactured assets are harnessed in the production-consumption system is at the heart of the paradigm shifts necessary for long-term sustainability. Our research integrates 50 years of economic and environmental data to provide the global legacy environmental footprint (LEF) and unveil the historical material extractions, greenhouse gas emissions, and health impacts accrued in today's manufactured capital. We show that between 1995 and 2019, global LEF growth outpaced GDP and population growth, and the current high level of national capital stocks has been heavily relying on global supply chains in metals. The LEF shows a larger or growing gap between developed economies (DEs) and less-developed economies (LDEs) while economic returns from global asset supply chains disproportionately flow to DEs, resulting in a double burden for LDEs. Our results show that ensuring best practice in asset production while prioritizing well-being outcomes is essential in addressing global inequalities and protecting the environment. Achieving this requires a paradigm shift in sustainability science and policy, as well as in green finance decision-making, to move beyond the focus on the resource use and emissions of daily operations of the assets and instead take into account the long-term environmental footprints of capital accumulation.