Design Photocatalysts to Boost Carrier Dynamics in Plastics Photoconversion into Fuels.

Solar-driven plastics conversion into valuable fuels has attracted broad attention in recent years, which has enormous potential for plastics recycling in the future. However, it usually encounters low conversion efficiency, where one of the reasons is attributed to the poor carrier dynamics in the photocatalytic process. In this Perspective, we critically review the developed strategies, involving defect engineering, doping engineering, heterojunction engineering, and composite construction, for boosted carrier separation efficiency. In addition, we provide an outlook for more potential strategies to engineer catalysts for promoted carrier dynamics. Finally, we also propose prospects for the future research direction of plastics photoconversion into fuels.

Progress and Perspective for "Green" Strategies of Catalytic Plastics Conversion into Fuels by Regulating Half-Reactions.

Nowadays, plastic waste threatens public health and the natural ecosystems of our lives. It is highly beneficial to recycle plastic waste in order to maximize the reuse of its contained carbon sources for the development of other valuable products. Unfortunately, traditional techniques usually require significant energy consumption and result in the generation of hazardous waste. Herein, the up-to-date developments on the "green" strategies under mild conditions including electrocatalysis, photocatalysis, and photoelectrocatalysis of plastic wastes are presented. During the oxidation of plastics in these "green" strategies, corresponding reduction reactions usually exist, which affect the property of catalytic plastics conversion. Particularly, we mainly focus on how to design the corresponding half reactions, such as the water reduction, carbon dioxide reduction, and nitrate reduction. Finally, we provide forward-looking insight into the enhancement of these "green" strategies, the extension of more half reactions into other organic catalysis, a comprehensive exploration of the underlying mechanisms through in situ studies and theoretical analysis and the problems for practical applications that needs to be solved.

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.

PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS: We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning-/deep learning-based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS: The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION: We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

Perceptions and Utilization of Online Peer Support Among Informal Dementia Caregivers: Survey Study.

Background: Informal dementia caregivers are those who care for a person living with dementia and do not receive payment (eg, family members, friends, or other unpaid caregivers). These informal caregivers are subject to substantial mental, physical, and financial burdens. Online communities enable these caregivers to exchange caregiving strategies and communicate experiences with other caregivers whom they generally do not know in real life. Research has demonstrated the benefits of peer support in online communities, but this research is limited, focusing merely on caregivers who are already online community users. Objective: We aimed to investigate the perceptions and utilization of online peer support through a survey. Methods: Following the Andersen and Newman Framework of Health Services Utilization and using REDCap (Research Electronic Data Capture), we designed and administered a survey to investigate the perceptions and utilization of online peer support among informal dementia caregivers. Specifically, we collected types of information that influence whether an informal dementia caregiver accesses online peer support: predisposing factors, which refer to the sociocultural characteristics of caregivers, relationships between caregivers and people living with dementia, and belief in the value of online peer support; enabling factors, which refer to the logistic aspects of accessing online peer support (eg, eHealth literacy and access to high-speed internet); and need factors, which are the most immediate causes of seeking online peer support. We also collected data on caregivers' experiences with accessing online communities. We distributed the survey link on November 14, 2022, within two online locations: the Alzheimer's Association website (as an advertisement) and ALZConnected (an online community organized by the Alzheimer's Association). We collected all responses on February 23, 2023, and conducted a regression analysis to identifyn factors that were associated with accessing online peer support. Results: We collected responses from 172 dementia caregivers. Of these participants, 140 (81.4%) completed the entire survey. These caregivers were aged 19 to 87 (mean 54, SD 13.5) years, and a majority were female (123/140, 87.9%) and White (126/140, 90%). Our findings show that the behavior of accessing any online community was significantly associated with participants' belief in the value of online peer support (P=.006). Moreover, of the 40 non-online community caregivers, 33 (83%) had a belief score above 24-the score that was assigned when a neutral option was selected for each belief question. The most common reasons for not accessing any online community were having no time to do so (14/140, 10%) and having insufficient online information-searching skills (9/140, 6.4%). Conclusions: Our findings suggest that online peer support is valuable, but practical strategies are needed to assist informal dementia caregivers who have limited time or online information-searching skills.

Confined CO in a sandwich structure promotes C-C coupling in electrocatalytic CO2 reduction.

Microenvironment regulation near the catalyst surface plays a critical role in heterogeneous electrocatalytic reactions. The local concentration of reactants and intermediates significantly affects the reaction kinetics and product selectivity. Herein, we propose an innovative strategy of utilizing the spatial confinement effect in a sandwich-structured C/Cu/C assembly to regulate kinetic mass transport during the electrocatalytic CO2 reduction reaction. The sandwich C/Cu/C assembly catalyst was successfully prepared using a simple bidirectional freezing and freeze-drying method. The sandwich structure changes the free diffusion pathway of the CO intermediate within the sandwich interlayer and helps confine CO with locally increased CO concentration near the catalyst surface, which in turn promotes C-C coupling and thus improves the reaction activity and doubles the C2 product selectivity compared to its disordered mixture counterpart. This kinetics regulation in the sandwich structure may provide a new insight into the catalyst design and inspire the understanding of the structure-performance relationship in electrocatalysis.

Interfacial-Assembly-Induced In Situ Transformation from Aligned 1D Nanowires to Quasi-2D Nanofilms.

As the dimensionality of materials generally affects their characteristics, thin films composed of low-dimensional nanomaterials, such as nanowires (NWs) or nanoplates, are of great importance in modern engineering. Among various bottom-up film fabrication strategies, interfacial assembly of nanoscale building blocks holds great promise in constructing large-scale aligned thin films, leading to emergent or enhanced collective properties compared to individual building blocks. As for 1D nanostructures, the interfacial self-assembly causes the morphology orientation, effectively achieving anisotropic electrical, thermal, and optical conduction. However, issues such as defects between each nanoscale building block, crystal orientation, and homogeneity constrain the application of ordered films. The precise control of transdimensional synthesis and the formation mechanism from 1D to 2D are rarely reported. To meet this gap, we introduce an interfacial-assembly-induced interfacial synthesis strategy and successfully synthesize quasi-2D nanofilms via the oriented attachment of 1D NWs on the liquid interface. Theoretical sampling and simulation show that NWs on the liquid interface maintain their lowest interaction energy for the ordered crystal plane (110) orientation and then rearrange and attach to the quasi-2D nanofilm. This quasi-2D nanofilm shows enhanced electric conductivity and unique optical properties compared with its corresponding 1D geometry materials. Uncovering these growth pathways of the 1D-to-2D transition provides opportunities for future material design and synthesis at the interface.

All of Us participant perspectives on the return of value in research.

PURPOSE: To understand participant preferences for receiving specific types of research information, whether information preferences vary across sociodemographic groups, and the types of health providers participants could access to understand returned information. METHODS: All of Us Research Program participants completed a value of returning research information survey. Stratified sampling was implemented to enhance participant diversity and avoid noncoverage. We used weighted multivariable logistic regression to evaluate associations between the most valuable information types, access to providers, and sociodemographic variables. RESULTS: Participants (N = 20,405) were diverse in their race/ethnicity (eg, 52% were White, 18% were Hispanic/Latino or Spanish, 3% were Asian, and 20% were Black or African American). Most participants (78.6%) valued information about their risk of serious genetic diseases with available treatment. Primary care physicians, specialists, and genetic counselors were the top providers that participants could access for help understanding returned information. Information preferences and provider access varied across sociodemographic groups. For example, as income levels increased, the odds of placing value on genetic results indicating risk of serious disease with available treatment increased when compared with the lowest income levels (P value < .001). CONCLUSION: Although genetic information was most valuable to participants, preferences about specific information types varied across sociodemographic groups.

Identifying erroneous height and weight values from adult electronic health records in the All of Us research program.

INTRODUCTION: Electronic Health Records (EHR) are a useful data source for research, but their usability is hindered by measurement errors. This study investigated an automatic error detection algorithm for adult height and weight measurements in EHR for the All of Us Research Program (All of Us). METHODS: We developed reference charts for adult heights and weights that were stratified on participant sex. Our analysis included 4,076,534 height and 5,207,328 wt measurements from âˆ¼ 150,000 participants. Errors were identified using modified standard deviation scores, differences from their expected values, and significant changes between consecutive measurements. We evaluated our method with chart-reviewed heights (8,092) and weights (9,039) from 250 randomly selected participants and compared it with the current cleaning algorithm in All of Us. RESULTS: The proposed algorithm classified 1.4 % of height and 1.5 % of weight errors in the full cohort. Sensitivity was 90.4 % (95 % CI: 79.0-96.8 %) for heights and 65.9 % (95 % CI: 56.9-74.1 %) for weights. Precision was 73.4 % (95 % CI: 60.9-83.7 %) for heights and 62.9 (95 % CI: 54.0-71.1 %) for weights. In comparison, the current cleaning algorithm has inferior performance in sensitivity (55.8 %) and precision (16.5 %) for height errors while having higher precision (94.0 %) and lower sensitivity (61.9 %) for weight errors. DISCUSSION: Our proposed algorithm outperformed in detecting height errors compared to weights. It can serve as a valuable addition to the current All of Us cleaning algorithm for identifying erroneous height values.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Background: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. Objective: We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. Methods: PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. Results: The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). Conclusions: PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Stress-induced ordering evolution of 1D segmented heteronanostructures and their chemical post-transformations.

Investigations of one-dimensional segmented heteronanostructures (1D-SHs) have recently attracted much attention due to their potentials for applications resulting from their structure and synergistic effects between compositions and interfaces. Unfortunately, developing a simple, versatile and controlled synthetic method to fabricate 1D-SHs is still a challenge. Here we demonstrate a stress-induced axial ordering mechanism to describe the synthesis of 1D-SHs by a general under-stoichiometric reaction strategy. Using the continuum phase-field simulations, we elaborate a three-stage evolution process of the regular segment alternations. This strategy, accompanied by easy chemical post-transformations, enables to synthesize 25 1D-SHs, including 17 nanowire-nanowire and 8 nanowire-nanotube nanostructures with 13 elements (Ag, Te, Cu, Pt, Pb, Cd, Sb, Se, Bi, Rh, Ir, Ru, Zn) involved. This ordering evolution-driven synthesis will help to investigate the ordering reconstruction and potential applications of 1D-SHs.

Nitrogen Doping-Roused Synergistic Active Sites in Perovskite Enabling Highly Selective CO2 Photoreduction into CH4.

The intricate protonation process in carbon dioxide reduction usually makes the product unpredictable. Thus, it is significant to control the reactive intermediates to manipulate the reaction steps. Here, we propose that the synergistic La-Ti active sites in the N-La2Ti2O7 nanosheets enable the highly selective carbon dioxide photoreduction into methane. In the photoreduction of CO2 over N-La2Ti2O7 nanosheets, in situ Fourier transform infrared spectra are utilized to monitor the *CH3O intermediate, pivotal for methane production, whereas such monitoring is not conducted for La2Ti2O7 nanosheets. Also, theoretical calculations testify to the increased charge densities on the Ti and La atoms and the regulated formation energy barrier of *CO and *CH3O intermediates by the constructed synergistic active sites. Accordingly, the methane formation rate of 7.97 µL h-1 exhibited by the N-La2Ti2O7 nanosheets, along with an electron selectivity of 96.6%, exceeds that of most previously reported catalysts under similar conditions.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening.

Suicide prevention requires risk identification, appropriate intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening with validated instruments or history and physical exam. In the last decade, statistical risk models have been studied and more recently deployed to augment clinical judgment. Models have generally been found to be low precision or problematic at scale due to low incidence. Few have been tested in clinical practice, and none have been tested in clinical trials to our knowledge. Methods: We report the results of a pragmatic randomized controlled trial (RCT) in three outpatient adult Neurology clinic settings. This two-arm trial compared the effectiveness of Interruptive and Non-Interruptive Clinical Decision Support (CDS) to prompt further screening of suicidal ideation for those predicted to be high risk using a real-time, validated statistical risk model of suicide attempt risk, with the decision to screen as the primary end point. Secondary outcomes included rates of suicidal ideation and attempts in both arms. Manual chart review of every trial encounter was used to determine if suicide risk assessment was subsequently documented. Results: From August 16, 2022, through February 16, 2023, our study randomized 596 patient encounters across 561 patients for providers to receive either Interruptive or Non-Interruptive CDS in a 1:1 ratio. Adjusting for provider cluster effects, Interruptive CDS led to significantly higher numbers of decisions to screen (42%=121/289 encounters) compared to Non-Interruptive CDS (4%=12/307) (odds ratio=17.7, p-value <0.001). Secondarily, no documented episodes of suicidal ideation or attempts occurred in either arm. While the proportion of documented assessments among those noting the decision to screen was higher for providers in the Non-Interruptive arm (92%=11/12) than in the Interruptive arm (52%=63/121), the interruptive CDS was associated with more frequent documentation of suicide risk assessment (63/289 encounters compared to 11/307, p-value<0.001). Conclusions: In this pragmatic RCT of real-time predictive CDS to guide suicide risk assessment, Interruptive CDS led to higher numbers of decisions to screen and documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared to standard of care are indicated to measure effectiveness in reducing suicidal self-harm. ClinicalTrials.gov Identifier: NCT05312437.

Measuring social determinants of health in the All of Us Research Program.

To accelerate medical breakthroughs, the All of Us Research Program aims to collect data from over one million participants. This report outlines processes used to construct the All of Us Social Determinants of Health (SDOH) survey and presents the psychometric characteristics of SDOH survey measures in All of Us. A consensus process was used to select SDOH measures, prioritizing concepts validated in diverse populations and other national cohort surveys. Survey item non-response was calculated, and Cronbach's alpha was used to analyze psychometric properties of scales. Multivariable logistic regression models were used to examine associations between demographic categories and item non-response. Twenty-nine percent (N = 117,783) of eligible All of Us participants submitted SDOH survey data for these analyses. Most scales had less than 5% incalculable scores due to item non-response. Patterns of item non-response were seen by racial identity, educational attainment, income level, survey language, and age. Internal consistency reliability was greater than 0.80 for almost all scales and most demographic groups. The SDOH survey demonstrated good to excellent reliability across several measures and within multiple populations underrepresented in biomedical research. Bias due to survey non-response and item non-response will be monitored and addressed as the survey is fielded more completely.

Light-Driven C-C Coupling for Targeted Synthesis of CH3 COOH with Nearly 100 % Selectivity from CO2.

Targeted synthesis of acetic acid (CH3 COOH) from CO2 photoreduction under mild conditions mainly limits by the kinetic challenge of the C-C coupling. Herein, we utilized doping engineering to build charge-asymmetrical metal pair sites for boosted C-C coupling, enhancing the activity and selectivity of CO2 photoreduction towards CH3 COOH. As a prototype, the Pd doped Co3 O4 atomic layers are synthesized, where the established charge-asymmetrical cobalt pair sites are verified by X-ray photoelectron spectroscopy and X-ray absorption near edge spectroscopy spectra. Theoretical calculations not only reveal the charge-asymmetrical cobalt pair sites caused by Pd atom doping, but also manifest the promoted C-C coupling of double *COOH intermediates through shortening of the coupled C-C bond distance from 1.54 to 1.52 Å and lowering their formation energy barrier from 0.77 to 0.33 eV. Importantly, the decreased reaction energy barrier from the protonation of two*COOH into *CO intermediates for the Pd-Co3 O4 atomic layer slab is 0.49 eV, higher than that of the Co3 O4 atomic layer slab (0.41 eV). Therefore, the Pd-Co3 O4 atomic layers exhibit the CH3 COOH evolution rate of ca. 13.8 µmol g-1 h-1 with near 100% selectivity, both of which outperform all previously reported single photocatalysts for CO2 photoreduction towards CH3 COOH under similar conditions.

Resident operative time as an independent predictor of early post-operative cataract surgery outcomes and supervising attending surgeon impact: a retrospective case series.

BACKGROUND: The authors sought to determine if resident operative time in cataract extraction and intraocular lens insertion (CE/IOL) affects early visual outcomes and post-operative recovery. They further sought to investigate if attending surgeons can reduce resident operative time. METHODS: This retrospective, chart-review, case series at single Veterans Affairs Hospital (VA Tennessee Valley Healthcare System) studied resident cataract surgeries between March 1, 2018 and March 31, 2020. Following power analysis, 420 eyes of 400 patients from all resident cataract surgeries were included. Eyes with attending as primary surgeon, laser-assisted cataract surgery, or concurrent secondary procedures were excluded. Linear mixed effect models were used to study the association between operative time and visual outcomes while adjusting for covariates including cumulative dissipated energy, preoperative factors, and intraoperative complications. RESULTS: Longer operative time was statistically associated with worse post-operative-day 1 (POD1) pinhole visual acuity (PH-VA) adjusting for cumulative dissipated energy and other operative factors (p = 0.049). Although resident physicians were the primary surgeons, the operative times were different between the ten supervising attending surgeons in the study (p < 0.001). CONCLUSION: The results suggest that increased resident operative time is a significant, independent risk factor for decreased POD1 PH-VA. Increased resident operative time is not associated with worsened long term visual outcomes. Attending surgeons may be able to reduce resident operative time, which is associated with improved early visual outcomes.

Selective Photoreduction of CO2 to CH4 Triggered by Metal-Vacancy Pair Sites.

Selectively achieving the photoreduction of carbon dioxide (CO2) to methane (CH4) remains a significant challenge, which primarily arises from the complexity of the protonation process. In this work, we designed metal-vacancy pair sites in defective metal oxide semiconductors, which anchor the reactive intermediates with a bridged linkage for the selective protonation to produce CH4. As an example, oxygen-deficient Nb2O5 nanosheets are synthesized, in which the niobium-oxygen vacancy pair sites are demonstrated by X-ray photoelectron spectroscopy and electron paramagnetic resonance spectra. In situ Fourier transform infrared spectroscopy monitors the *CH3O intermediate, a key intermediate for CH4 production, during the CO2 photoreduction in oxygen-deficient Nb2O5 nanosheets. Importantly, the built metal-vacancy pair sites regulate the *CH3O formation step as a spontaneous process, making the reduction of CO2 to CH4 the preferred method. Therefore, the oxygen-deficient Nb2O5 nanosheets exhibit a CH4 formation rate of 19.14 µmol g-1 h-1, with an electron selectivity of ∼94.1%.

Association between F2-Isoprostane Metabolites and Weight Change in Older Women: A Longitudinal Analysis.

INTRODUCTION: Theoretically, some metabolic traits may predispose older individuals to weight loss during aging, leading to increased all-cause mortality and many serious health issues. Biomarkers to robustly predict progressive weight loss during aging are, however, lacking. We prospectively assessed if urinary levels of F2-isoprostanes and their peroxisomal ß-oxidation metabolite, 2,3-dinor-5,6-dihydro-15-F2t-isoprostane (F2-IsoP-M), were associated with subsequent weight loss in middle-aged and older women. METHODS: Included in the analysis were 2,066 women aged 40-70 years, a subset of a prospective cohort study. F2-isoprostanes (F2-IsoPs) and its ß-oxidation metabolite, F2-IsoP-M, were measured in urine using gas chromatography-mass spectrometry. Measurements of anthropometry and exposures to major determinants of body weight were performed at baseline and repeated thrice over 15-year follow-up. The longitudinal associations of F2-IsoP-M and the F2-IsoP-M to its parent compound, F2-IsoP, ratio (MPR) with repeatedly measured weight changes were examined using linear mixed-effect models. RESULTS: After adjusting for time-varying covariates: energy intake, physical activity, and comorbidity index, among others, levels of F2-IsoP-M and the MPR were both inversely associated with percentage of weight change. Weight in the highest quartile of these two biomarkers was 1.33% (95% CI = -2.41, -0.24) and 1.09% (95% CI = -2.16, -0.02) lower than those in the lowest quartile group, with p for trend of 0.01 and 0.03, respectively. The inverse association was consistently seen across follow-up periods, although appearing stronger with prolonged follow-up. There was no association between the parent compound, F2-IsoPs, and weight change. CONCLUSION: This study demonstrates the first piece of evidence to associate F2-IsoP metabolism, peroxisomal ß-oxidation, with weight loss in older women. Further investigations into the role of lipid peroxidation and peroxisomal ß-oxidation in weight change among older individuals are warranted.

Dietary medium-chain fatty acids and risk of incident colorectal cancer in a predominantly low-income population: a report from the Southern Community Cohort Study.

BACKGROUND: No prospective observational study has specifically examined the associations between dietary intakes of medium-chain fatty acids and risk of colorectal cancer. OBJECTIVES: This study examined the association between dietary intakes of medium-chain fatty acids and colorectal cancer risk overall and by racial subgroups in a predominantly low-income United States population. METHODS: This prospective study included 71,599 eligible participants aged 40 to 79 who were enrolled in the Southern Community Cohort Study between 2002 and 2009 in 12 southeastern United States states. Incident colorectal cancer cases were ascertained via linkage to state cancer registries, which was completed through 31 December, 2016. The dietary intakes of medium-chain fatty acids were assessed using a validated 89-item food frequency questionnaire. Multivariable Cox proportional hazards regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between intakes of medium-chain fatty acids and risk for incident colorectal cancer. RESULTS: Among 71,599 participants, 48,008 (67.3%) were Black individuals and 42,260 (59.0%) were female. A total of 868 incident colorectal cancer cases occurred during a median follow-up of 13.7 y. Comparing the highest to the lowest quartile, high intake of dodecanoic acid/lauric acid (C12:0) was associated with reduced risk of colorectal cancer among White participants (HR: 0.52; 95% CI: 0.30, 0.91; P-trend = 0.05), but not in Black individuals (HR: 0.92; 95% CI, 0.68, 1.24; P-trend = 0.80) in multivariable-adjusted models. No associations were found between intakes of hexanoic acid/caproic acid (C6:0), octanoic acid/caprylic acid (C8:0), or decanoic acid/capric acid (C10:0) and risk of incident colorectal cancer overall or within racial subgroups. CONCLUSIONS: In a predominantly low-income United States population, an increased dietary C12:0 intake was associated with a substantially reduced risk of colorectal cancer only among White individuals, but not in Black individuals.

A Pilot Nonrandomized Controlled Trial Examining the Use of Artificial Tears on the Radioactivity of Tears After Radioactive Iodine Treatment for Thyroid Cancer.

Background: Nasolacrimal duct obstruction (NLDO) is an adverse effect of high dose radioactive iodine (RAI) therapy for thyroid carcinoma. There are currently no established preventive measures. This study assesses whether preservative free artificial tears (PFATs) can decrease the 131I sodium iodide (131I) activity in the tears of patients following RAI therapy for thyroid carcinoma, and potentially serve as a preventive measure for RAI-associated NLDO. Methods: This non-randomized prospective pilot clinical trial recruited contact-lens wearing patients undergoing RAI therapy for thyroid cancer to self-administer PFATs into the right eye for four days starting on the day of RAI ingestion. Left eyes were the controls. While wearing contacts, patients self-administered PFATs per the following-Day 1: every 15 minutes for 2 hours, then every 30 minutes until bedtime, day 2: every hour for at least 12 hours, day 3: four times a day, and day 4: two times a day. Contact lenses were changed daily, and all lenses were collected one week later. Levels of 131I activity were measured by a well counter, decay-corrected, and converted to units of becquerel. Statistical analyses were performed to compare the 131I activities of the experimental and control eyes. Results: Sixteen eyes of eight patients treated with an average of 145.7 mCi (range 108-159) of 131I for papillary thyroid cancer were included. On day 1, artificial tears decreased the geometric mean 131I activity by 26% in the experimental eyes (p = 0.008). Artificial tears also decreased the geometric mean area under the curve over four days by 23% (p = 0.002). Conclusions: 131I is present in the tears following RAI therapy for thyroid carcinoma. Frequent PFATs starting on the day of RAI ingestion may decrease the level of 131I in the tears. This finding could have implications for lowering the risk of NLDO. Future multi-center clinical trials are needed to determine whether the use of artificial tears after RAI therapy may decrease the risk of NLDO. Clinical Trial Registration: NCT04327999.