Biolistics-mediated transformation of hornworts and its application to study pyrenoid protein localization.

Hornworts are a deeply diverged lineage of bryophytes that are sister to mosses and liverworts. Hornworts have an array of unique features that can be leveraged to illuminate not only the early evolution of land plants, but also alternative paths for nitrogen and carbon assimilation via cyanobacterial symbiosis and a pyrenoid-based CO2-concentrating mechanism (CCM), respectively. Despite this, hornworts are one of the few plant lineages with limited available genetic tools. Here we report an efficient biolistics method for generating transient-expression and stable transgenic lines in the model hornwort, Anthoceros agrestis. An average of 569 (± 268) cells showed transient expression per bombardment, with green fluorescent protein expression observed within 48-72 hours. A total of 81 stably transformed lines were recovered across three separate experiments, averaging six lines per bombardment. We followed the same method to transiently transform nine additional hornwort species, and obtained stable transformants from one. This method was further used to verify the localization of Rubisco and Rubisco activase in pyrenoids, which are central proteins for CCM function. Together, our biolistics approach offers key advantages over existing methods as it enables rapid transient expression and can be applied to widely diverse hornwort species.

Factors Associated With Suicide Risk Behavior Outcomes Among Black Middle School Adolescents.

OBJECTIVE: Early adolescent self-identifying Black youth present with high rates of suicidality. This study assesses associations among 7 explanatory variables (sex, weapon carrying, weight perceptions, grades, grade level, bullying at school, and cyberbullying) and suicide ideation, planning, and attempts, and identifies associations with suicidality as a composite measure among a sample of Black middle school adolescents (BMSA) in the United States. METHOD: Data were extracted from the 2019 Middle School Youth Risk Behavior Survey. Descriptive statistics were calculated, and associations between suicide ideation, planning, and attempts were explored. A network graphical representation of polychoric and tetrachoric correlations across suicidality outcomes and explanatory variables was performed. A multivariable, multinomial logistic regression model estimated associations among explanatory variables and suicide ideation (with and without planning) and planning (with and without ideation). Both suicide attempt and a composite suicidality metric were explored using independent logistic models with multiple imputation. RESULTS: The sample included 7,643 self-identifying BMSA. Our findings indicate that approximately 28% reported suicidality. Prevalence rates were found of about 1 in 4 BMSA for suicidal ideation, 1 in 6 for suicidal planning, and 1 in 10 for attempting suicide. Female BMSA, victims of cyberbullying, and BMSA carrying a weapon experienced significantly higher odds of all suicidality outcome measures. CONCLUSION: Practical implications for identifying and screening suicidality among BMSA to guide suicide prevention efforts are necessary. Findings validate the need for informed cultural approaches to suicide screening and prevention efforts to reduce suicide risk outcomes, especially when identifiable environmental factors are strongly associated with those outcomes. DIVERSITY AND INCLUSION STATEMENT: One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list.

A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort.

Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are not always available, complete, or homogeneous, and clinically driven metrics to assess medical practices are not logistically feasible at the population level, particularly in non-centralized healthcare delivery systems and/or for those who only have access to claims data. Data-driven approaches that incorporate all available information are needed to explore coding specificity practices. Using N = 487,775 hospitalization records of individuals diagnosed with dementia and discharged in 2022 from a large all-payor administrative claims dataset, we fitted logistic regression models using patient and facility characteristics to explain the coding specificity of principal and secondary diagnoses of dementia. A two-step approach was produced to allow for the flexible clustering of patient-level outcomes. Model outcomes were then used within a Poisson binomial model to identify facilities that over- or under-specify dementia diagnoses against healthcare industry standards across hospitalizations. The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727). The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map. This study's findings provide healthcare facilities with a benchmark for assessing coding specificity practices and developing quality enhancements to align with healthcare industry standards, ultimately contributing to better patient care and healthcare system reliability.

Estimating potential cardiovascular health benefits of improved population level control of LDL cholesterol through a twice-yearly siRNA-based approach: A simulation study of a health-system level intervention.

BACKGROUND AND AIMS: Inclisiran, an siRNA therapy, consistently reduces low-density lipoprotein cholesterol (LDL-C) with twice-yearly dosing. Potential cardiovascular benefits of implementing inclisiran at a population level, added to statins, were evaluated through simulation. METHODS: For each participant in the ORION-10 and ORION-11 trials comparing inclisiran with placebo, baseline 10-year cardiovascular risk was estimated using the SMART equation. The time-adjusted LDL-C difference from baseline observed 90-540 days after baseline was assumed to persist and used to estimate potential reduction in 10-year cardiovascular risk. Impact on 500,000 ORION-like individuals was simulated with Monte-Carlo. RESULTS: Mean baseline LDL-C and predicted 10-year major vascular risk among patients randomized to inclisiran (n = 1288) versus placebo (n = 1264) were 2.66 mmol/L versus 2.60 mmol/L and 24.9% versus 24.6%, respectively. Placebo-corrected time-adjusted absolute reduction in LDL-C with inclisiran was -1.32 mmol/L (95% CI -1.37 to -1.26; p < 0.001), which predicted a 10-year cardiovascular risk of 18.1% with inclisiran versus 24.7% with placebo (absolute difference [95% CI], -6.99% [-7.33 to -6.66]; p < 0.001) NNT 15. Extrapolating to 500,000 inclisiran-treated individuals, the model predicted large population shifts towards lower quintiles of risk with fewer remaining in high-risk categories; 3350 to 471 (≥80% risk), 11,793 to 3332 (60-<80% risk), 52,142 to 22,665 (40-<60% risk), 197,752 to 141,014 (20-<40% risk), and more moving into the lowest risk category (<20%) from 234,963 to 332,518. CONCLUSIONS: Meaningful gains in population health might be achieved over 10 years by implementing at-scale approaches capable of providing substantial and sustained reductions in LDL-C beyond those achievable with statins.

A Novel Method for Assessing Risk-Adjusted Diagnostic Coding Specificity for Depression Using a U.S. Cohort of over One Million Patients.

Depression is a prevalent and debilitating mental health condition that poses significant challenges for healthcare providers, researchers, and policymakers. The diagnostic coding specificity of depression is crucial for improving patient care, resource allocation, and health outcomes. We propose a novel approach to assess risk-adjusted coding specificity for individuals diagnosed with depression using a vast cohort of over one million inpatient hospitalizations in the United States. Considering various clinical, demographic, and socioeconomic characteristics, we develop a risk-adjusted model that assesses diagnostic coding specificity. Results demonstrate that risk-adjustment is necessary and useful to explain variability in the coding specificity of principal (AUC = 0.76) and secondary (AUC = 0.69) diagnoses. Our approach combines a multivariate logistic regression at the patient hospitalization level to extract risk-adjusted probabilities of specificity with a Poisson Binomial approach at the facility level. This method can be used to identify healthcare facilities that over- and under-specify diagnostic coding when compared to peer-defined standards of practice.

Difference in the physical and mental health of informal caregivers pre- and post-COVID-19 National Emergency Declaration in the United States.

Background: The burden of informal caregiving represents a chronic stressor for the informal caregivers (ICs). The study investigates differences in the physical and mental health of ICs and that of non-informal caregivers before and during COVID-19. Methods: We used data from the 2019/2020 Behavioral Risk Factor Surveillance System (BRFSS) to investigate differences in the rates of days of poor physical and mental health among ICs compared to non-informal caregivers before and after the COVID-19 National Emergency Declaration (NED). A propensity score model simulated a pseudo experimental design, comparing ICs ("treated") with non-informal caregivers ("control"). A difference-in-difference regression model estimated the incidence rate ratios for days of poor physical and mental health as a function of IC status and time of care provision. Results: A total of 44,583 respondents were identified with valid responses on informal caregiving status and key sociodemographic characteristics. Of those, 6.24% (n = 3073) were ICs, matched against 15,365 non-informal caregivers. In the matched sample (n = 18,848), the incidence rate for days of poor physical health among ICs was 17% (p = 0.003) higher compared to non-informal caregivers. The incidence rate for days of poor physical health was 23% (p < 0.001) lower in the post-NED compared to the pre-NED periods. The incidence rate for days of poor mental health was 44% (p < 0.001) higher among ICs compared to non-informal caregivers and 22% higher among respondents who took the survey post-NED compared to those who answered during the pre-NED period. No statistically significant differences were found between the two groups in their incidence rates for days of poor physical and mental health from the pre-to the post-NED period. Conclusion: These findings suggest a need to balance between the benefits conferred by public health restrictions versus the mental health burden that may result among certain groups, including ICs, who experience higher negative mental health outcomes.

The Association of eHealth Literacy Skills and mHealth Application Use Among US Adults With Obesity: Analysis of Health Information National Trends Survey Data.

BACKGROUND: Physical inactivity and a poor diet are modifiable behaviors that contribute to obesity. Obesity is a well-recognized risk factor for chronic diseases, including diabetes. Mobile health (mHealth) apps can play an important adjuvant role in preventing and treating chronic diseases and promoting positive health behavior change among people with obesity, and eHealth literacy skills have the potential to impact mHealth app use. OBJECTIVE: The purpose of this study was to explore the associations between the 2 dimensions, access and application, of eHealth literacy skills and mHealth app use among US adults (≥18 years of age) with obesity (BMI ≥30 kg/m2). METHODS: Data were obtained from February to June 2020 using the Health Information National Trends Survey 5. A total of 1079 respondents met the inclusion criteria of adults with obesity and owners of smartphones. Individual associations between mHealth app use and sociodemographic variables were explored using weighted chi-square and 2-tailed t tests. A multivariable weighted logistic regression model was fitted, and adjusted odds ratios (ORs) of using mHealth apps with corresponding 95% CIs were reported across multiple sociodemographic variables. An Ising model-weighted network visualization was produced. A receiver operating characteristic curve was calculated, and the area under the curve was reported with the corresponding Delong 95% CI. RESULTS: A majority of respondents were female (550/923, 59.6%) or non-Hispanic White (543/923, 58.8%). Individuals in households earning less than US $50,000 comprised 41.4% (382/923) of the sample. All sociodemographic variables were found to be univariately significant at the 5% level, except employment and region. Results from the multivariable weighted logistic regression model showed that the adjusted odds of using an mHealth app are 3.13 (95% CI 1.69-5.80) and 2.99 (95% CI 1.67-5.37) times higher among those with an access eHealth literacy skill of using an electronic device to look for health or medical information for themselves and an application eHealth literacy skill of using electronic communications with a doctor or doctor's office, respectively. Several sociodemographic variables were found to be significant, such as education, where adjusted ORs comparing subgroups to the lowest educational attainment were substantial (ORs ≥7.77). The network visualization demonstrated that all eHealth literacy skills and the mHealth app use variable were positively associated to varying degrees. CONCLUSIONS: This work provides an initial understanding of mHealth app use and eHealth literacy skills among people with obesity, identifying people with obesity subpopulations who are at risk of a digital health divide. Future studies should identify equitable solutions for people with obesity (as well as other groups) and their use of mHealth apps.

Observation of a single protein by ultrafast X-ray diffraction.

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes. This was first demonstrated on biological samples a decade ago on the giant mimivirus. Since then, a large collaboration has been pushing the limit of the smallest sample that can be imaged. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that of Escherichia coli GroEL which at 14 nm in diameter is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale.

Factors Associated with Suicide Risk Behavior Outcomes Among Black High School Adolescents.

There is an urgent need for youth suicide research, particularly among Black adolescents, for whom there have been significant increases over time. This study examines associations between eight covariates and suicide ideation, planning, and attempts among a national sample of Black high school adolescents to inform prevention efforts. Utilizing the 2019 National Youth Risk Behavior Survey data, a sample of N = 6225 Black high school participants was analyzed. A weighted classification tree and network analysis were used to visualize data features, and weighted multinomial ordered logistic regression analyses with multiple imputation pooled using Rubin's rules were performed. Suicide ideation rates of 16% were found in the sample and a higher reported rate of suicide attempts (56%) for those who have reported both ideation and planning. Interpersonal and multilevel factors, including suffering from bullying/cyberbullying, carrying a weapon, or being a sexual minority, were significantly associated with all three suicide behaviors. Findings support an interrelated and multilevel nature of suicide risk factors. Prevention programs for Black adolescents should consider the intersectionality of identities and experiences.

Twelve new metagenome-assembled genomes from non-axenic culture of Griffithsia monilis (Rhodophyta).

We report 12 metagenome-assembled genomes from a non-axenic culture of the red alga Griffithsia monilis Harvey, some of which are distantly related to publicly available genomes.

Collectives for Care: Perspectives of Providers and Community Members in Transgender Health Care in a Southern U.S. City.

Affirming and accessible health care may improve health outcomes for trans individuals in the U.S. We explored strategies to improve affirming care for trans individuals from the perspectives of providers and transgender and non-binary community members. Forty members of a collective group of gender-affirming providers (GAP) in the southern U.S. were recruited to participate in a brief online survey. A graphical LASSO undirected network analysis approach visualized associations across outcomes and explanatory variables. Multinomial ordered (or logistic, for binary outcomes) models explored associations between a common set of explanatory variables and outcomes. Strong partial correlations (network) and statistically significant explanatory variables (ordinal and logistic models) were identified. Additionally, we conducted three focus groups (FGs) audio-recorded over Zoom with 11 community members. Four study team members analyzed the transcripts using content analyses. Survey results indicated that higher frequency of attendance at monthly provider meetings, additional training, and provision of training and consultation were associated with greater perceived competence among GAP members. To improve services, FG participants suggested treating patients as experts, increasing diversity and representation among providers, and expanding the GAP group. These results highlight the critical need to design and implement community-identified interventions to improve gender-affirming services and enhance provider training.

Exploring Cytokine Networks in Resistant Hypertension.

BACKGROUND: Controlling high blood pressure (BP) continues to be a major concern because the associated complications can lead to an increased risk of heart, brain, and kidney disease. Those with hypertension, despite lifestyle and diet modifications and pharmacotherapy, defined as resistant hypertension, are at increased risk for further risk for morbidity and mortality. Understanding inflammation in this population may provide novel avenues for treatment. OBJECTIVES: This study aimed to examine a broad range of cytokines in adults with cardiovascular disease and identify specific cytokines associated with resistant hypertension. METHODS: A secondary data analysis was conducted. The parent study included 156 adults with a history of myocardial infarction within the past 3-7 years and with a multiplex plasma analysis yielding a cytokine panel. A network analysis with lasso penalization for sparsity was performed to explore associations between cytokines and BP. Associated network centrality measures by cytokine were produced, and a community graph was extracted. A sensitivity analysis BP was also performed. RESULTS: Cytokines with larger node strength measures were sTNFR2 and CX3. The graphical network highlighted six cytokines strongly associated with resistant hypertension. Cytokines IL-29 and CCL3 were found to be negatively associated with resistant hypertension, whereas CXCL12, MMP3, sCD163, and sIL6Rb were positively associated with resistant hypertension. DISCUSSION: Understanding the network of associations through exploring oxidative stress and vascular inflammation may provide insight into treatment approaches for resistant hypertension.

Group brief cognitive behavioral therapy for suicide prevention compared to dialectal behavior therapy skills group for military service members: a study protocol of a randomized controlled trial.

BACKGROUND: Suicide is a pressing matter for the military. Not only does it pose a health risk, but suicide also compromises operational readiness. Despite provision of suicide prevention clinical best practices, the Department of Defense suffers several challenges (e.g., clinician shortages) limiting the agency's ability to effectively respond to service member suicide. Implementation of evidence-based suicide-specific group therapy is a possible solution to service member well-being needs and system challenges. Service members can also gain coping skills useful beyond managing suicidal thoughts and behaviors. METHODS: This 2-arm non-inferiority randomized controlled trial compares a group therapy format of Brief Cognitive Behavioral Therapy (i.e., G-BCBT) with Dialectical Behavior Therapy (DBT) Skills Group. Both therapies are delivered in-person at a United States Naval Medical Center. Participants (N = 136) are active-duty service members with recent suicidal thoughts or suicidal behavior. Evaluation features electronically delivered questionnaires at baseline, after each treatment session, and at 3- and 6-month follow-up. DISCUSSION: The primary outcome concerns G-BCBT impacts on suicidal ideation. Secondary outcomes of interest are suicide attempt, psychological distress (e.g., symptoms of depression, anxiety), and self-regulatory skills (e.g., emotion regulation). We also examine self-regulatory skills as treatment moderators. Clinical trial strengths and limitations are reviewed. TRIAL REGISTRATION: This study was registered at Clinicaltrials.gov (protocol NCT05401838).

Physical and mental health of informal caregivers before and during the COVID-19 pandemic in the United States.

BACKGROUND: Informal caregiving, a common form of social support, can be a chronic stressor with health consequences for caregivers. It is unclear how varying restrictions during the COVID-19 pandemic affected caregivers' physical and mental health. This study explores pre-post March 2020 differences in reported days of poor physical and mental health among informal caregivers. METHODS: Data from the 2019/2020 Behavioral Risk Factor Surveillance System survey were used to match, via propensity scores, informal caregivers who provided care during COVID-19 restrictions to those who provided care before the pandemic. Negative binomial weighted regression models estimated incidence rate ratios (IRRs) and differences by demographics of reporting days of poor physical and mental health. A sensitivity analysis including multiple imputation was also performed. RESULTS: The sample included 9,240 informal caregivers, of whom 861 provided care during the COVID-19 pandemic. The incidence rate for days of poor physical health was 26% lower (p = 0.001) for those who provided care during the COVID-19 pandemic, though the incidence rates for days of poor mental health were not statistically different between groups. Informal caregivers with low educational attainment experienced significantly higher IRRs for days of poor physical and mental health. Younger informal caregivers had a significantly lower IRR for days of poor physical health, but higher IRR for days of poor mental health. CONCLUSIONS: This study contends that the physical and mental health burden associated with informal caregiving in a period of great uncertainty may be heightened among certain populations. Policymakers should consider expanding access to resources through institutional mechanisms for informal caregivers, who may be likely to incur a higher physical and mental health burden during public health emergencies, especially those identified as higher risk.

Marble melancholy: using crossmodal correspondences of shapes, materials, and music to predict music-induced emotions.

Introduction: Music is known to elicit strong emotions in listeners, and, if primed appropriately, can give rise to specific and observable crossmodal correspondences. This study aimed to assess two primary objectives: (1) identifying crossmodal correspondences emerging from music-induced emotions, and (2) examining the predictability of music-induced emotions based on the association of music with visual shapes and materials. Methods: To achieve this, 176 participants were asked to associate visual shapes and materials with the emotion classes of the Geneva Music-Induced Affect Checklist scale (GEMIAC) elicited by a set of musical excerpts in an online experiment. Results: Our findings reveal that music-induced emotions and their underlying core affect (i.e., valence and arousal) can be accurately predicted by the joint information of musical excerpt and features of visual shapes and materials associated with these music-induced emotions. Interestingly, valence and arousal induced by music have higher predictability than discrete GEMIAC emotions. Discussion: These results demonstrate the relevance of crossmodal correspondences in studying music-induced emotions. The potential applications of these findings in the fields of sensory interactions design, multisensory experiences and art, as well as digital and sensory marketing are briefly discussed.

PrEP awareness, willingness, and likelihood to use future HIV prevention methods among undergraduate college students in an ending the HIV epidemic jurisdiction.

OBJECTIVE: Identify factors associated with PrEP awareness, willingness, and future prevention modalities among undergraduate college students. PARTICIPANTS: Undergraduates (N = 701) were recruited from a private university, a public research university, and a private historically Black college and university for an online survey. METHODS: Upon multiple imputations, a multivariate logistic model, a multivariate multinomial model, and independent multivariate ordinal logistic models were used to calculate Rubin's rules-pooled adjusted odds ratios for PrEP awareness, willingness, and future HIV prevention methods. RESULTS: Only 33.4% of students had heard of and 32.4% were willing to take PrEP. PrEP willingness was higher among sexual minority students compared to heterosexual/straight students (OR = 1.65; 95% CI: 1.03-2.63); p = .036). The likelihood to take a future vaccine or antibody prophylaxis treatment was higher than the likelihood to take injectable PrEP or implants. CONCLUSIONS: Interventions to increase PrEP uptake and willingness among undergraduates should emphasize equity in HIV education and include future prevention modalities.

Grafting Rhodobacter sphaeroides with red algae Rubisco to accelerate catalysis and plant growth.

Improving the carboxylation properties of Rubisco has primarily arisen from unforeseen amino acid substitutions remote from the catalytic site. The unpredictability has frustrated rational design efforts to enhance plant Rubisco towards the prized growth-enhancing carboxylation properties of red algae Griffithsia monilis GmRubisco. To address this, we determined the crystal structure of GmRubisco to 1.7 Å. Three structurally divergent domains were identified relative to the red-type bacterial Rhodobacter sphaeroides RsRubisco that, unlike GmRubisco, are expressed in Escherichia coli and plants. Kinetic comparison of 11 RsRubisco chimaeras revealed that incorporating C329A and A332V substitutions from GmRubisco Loop 6 (corresponding to plant residues 328 and 331) into RsRubisco increased the carboxylation rate (kcatc) by 60%, the carboxylation efficiency in air by 22% and the CO2/O2 specificity (Sc/o) by 7%. Plastome transformation of this RsRubisco Loop 6 mutant into tobacco enhanced photosynthesis and growth up to twofold over tobacco producing wild-type RsRubisco. Our findings demonstrate the utility of RsRubisco for the identification and in planta testing of amino acid grafts from algal Rubisco that can enhance the enzyme's carboxylase potential.

A Novel Approach to Developing Disease and Outcome-Specific Social Risk Indices.

INTRODUCTION: A variety of industry composite indices are employed within health research in risk-adjusted outcome measures and to assess health-related social needs. During the COVID-19 pandemic, the relationships among risk adjustment, clinical outcomes, and composite indices of social risk have become relevant topics for research and healthcare operations. Despite the widespread use of these indices, composite indices are often comprised of correlated variables and therefore may be affected by information duplicity of their underlying risk factors. METHODS: A novel approach is proposed to assign outcome- and disease group-driven weights to social risk variables to form disease and outcome-specific social risk indices and apply the approach to the county-level Centers for Disease Control and Prevention social vulnerability factors for demonstration. The method uses a subset of principal components reweighed through Poisson rate regressions while controlling for county-level patient mix. The analyses use 6,135,302 unique patient encounters from 2021 across seven disease strata. RESULTS: The reweighed index shows reduced root mean squared error in explaining county-level mortality in five of the seven disease strata and equivalent performance in the remaining strata compared with the reduced root mean squared error using the current Centers for Disease Control and Prevention Social Vulnerability Index as a benchmark. CONCLUSIONS: A robust method is provided, designed to overcome challenges with current social risk indices, by accounting for redundancy and assigning more meaningful disease and outcome-specific variable weights.

Risk Adjustment of ICD-10-CM Coded Potential Inpatient Complications Using Administrative Data.

OBJECTIVE: To risk-adjust the Potential Inpatient Complication (PIC) measure set and propose a method to identify large deviations between observed and expected PIC counts. DATA SOURCES: Acute inpatient stays from the Premier Healthcare Database from January 1, 2019 to December 31, 2021. STUDY DESIGN: In 2014, the PIC list was developed to identify a broader set of potential complications that can occur as a result of care decisions. Risk adjustment for 111 PIC measures is performed across 3 age-based strata. Using patient-level risk factors and PIC occurrences, PIC-specific probabilities of occurrence are estimated through multivariate logistic regression models. Poisson Binomial cumulative mass function estimates identify deviations between observed and expected PIC counts across levels of patient-visit aggregation. Area under the curve (AUC) estimates are used to demonstrate PIC predictive performance in an 80:20 derivation-validation split framework. DATA COLLECTION/EXTRACTION METHODS: We used N=3,363,149 administrative hospitalizations between 2019 and 2021 from the Premier Healthcare Database. PRINCIPAL FINDINGS: PIC-specific model predictive performance was strong across PICs and age strata. Average area under the curve estimates across PICs were 0.95 (95% CI: 0.93-0.96), 0.91 (95% CI: 0.90-0.93), and 0.90 (95% CI: 0.89-0.91) for the neonate and infant, pediatric, and adult strata, respectively. CONCLUSIONS: The proposed method provides a consistent quality metric that adjusts for the population's case mix. Age-specific risk stratification further addresses currently ignored heterogeneity in PIC prevalence across age groups. Finally, the proposed aggregation method identifies large PIC-specific deviations between observed and expected counts, flagging areas with a potential need for quality improvements.

The small subunit of Rubisco and its potential as an engineering target.

Rubisco catalyses the first rate-limiting step in CO2 fixation and is responsible for the vast majority of organic carbon present in the biosphere. The function and regulation of Rubisco remain an important research topic and a longstanding engineering target to enhance the efficiency of photosynthesis for agriculture and green biotechnology. The most abundant form of Rubisco (Form I) consists of eight large and eight small subunits, and is found in all plants, algae, cyanobacteria, and most phototrophic and chemolithoautotrophic proteobacteria. Although the active sites of Rubisco are located on the large subunits, expression of the small subunit regulates the size of the Rubisco pool in plants and can influence the overall catalytic efficiency of the Rubisco complex. The small subunit is now receiving increasing attention as a potential engineering target to improve the performance of Rubisco. Here we review our current understanding of the role of the small subunit and our growing capacity to explore its potential to modulate Rubisco catalysis using engineering biology approaches.