FAIR Header Reference genome: a TRUSTworthy standard.

The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability and Technology. The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.

The Reactome Pathway Knowledgebase 2024.

The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a broad range of normal and disease-related biological processes. Processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Here we review progress towards annotation of the entire human proteome, targeted annotation of disease-causing genetic variants of proteins and of small-molecule drugs in a pathway context, and towards supporting explicit annotation of cell- and tissue-specific pathways. Finally, we briefly discuss issues involved in making Reactome more fully interoperable with other related resources such as the Gene Ontology and maintaining the resulting community resource network.

Diffusion magnetic resonance imaging of cerebrospinal fluid dynamics: Current techniques and future advancements.

Cerebrospinal fluid (CSF) plays a critical role in metabolic waste clearance from the brain, requiring its circulation throughout various brain pathways, including the ventricular system, subarachnoid spaces, para-arterial spaces, interstitial spaces, and para-venous spaces. The complexity of CSF circulation has posed a challenge in obtaining noninvasive measurements of CSF dynamics. The assessment of CSF dynamics throughout its various circulatory pathways is possible using diffusion magnetic resonance imaging (MRI) with optimized sensitivity to incoherent water movement across the brain. This review presents an overview of both established and emerging diffusion MRI techniques designed to measure CSF dynamics and their potential clinical applications. The discussion offers insights into the optimization of diffusion MRI acquisition parameters to enhance the sensitivity and specificity of diffusion metrics on underlying CSF dynamics. Lastly, we emphasize the importance of cautious interpretations of diffusion-based imaging, especially when differentiating between tissue- and fluid-related changes or elucidating structural versus functional alterations.

Paravascular fluid dynamics reveal arterial stiffness assessed using dynamic diffusion-weighted imaging.

Paravascular cerebrospinal fluid (pCSF) surrounding the cerebral arteries within the glymphatic system is pulsatile and moves in synchrony with the pressure waves of the vessel wall. Whether such pulsatile pCSF can infer pulse wave propagation-a property tightly related to arterial stiffness-is unknown and has never been explored. Our recently developed imaging technique, dynamic diffusion-weighted imaging (dynDWI), captures the pulsatile pCSF dynamics in vivo and can explore this question. In this work, we evaluated the time shifts between pCSF waves and finger pulse waves, where pCSF waves were measured by dynDWI and finger pulse waves were measured by the scanner's built-in finger pulse oximeter. We hypothesized that the time shifts reflect brain-finger pulse wave travel time and are sensitive to arterial stiffness. We applied the framework to 36 participants aged 18-82 years to study the age effect of travel time, as well as its associations with cognitive function within the older participants (N = 15, age > 60 years). Our results revealed a strong and consistent correlation between pCSF pulse and finger pulse (mean CorrCoeff = 0.66), supporting arterial pulsation as a major driver for pCSF dynamics. The time delay between pCSF and finger pulses (TimeDelay) was significantly lower (i.e., faster pulse propagation) with advanced age (Pearson's r = -0.44, p = 0.007). Shorter TimeDelay was further associated with worse cognitive function in the older participants. Overall, our study demonstrated pCSF as a viable pathway for measuring intracranial pulses and encouraged future studies to investigate its relevance with cerebrovascular functions.

Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration.

BACKGROUND: Early detection of clinical deterioration among hospitalized patients is a clinical priority for patient safety and quality of care. Current automated approaches for identifying these patients perform poorly at identifying imminent events. OBJECTIVE: Develop a machine learning algorithm using pager messages sent between clinical team members to predict imminent clinical deterioration. DESIGN: We conducted a large observational study using long short-term memory machine learning models on the content and frequency of clinical pages. PARTICIPANTS: We included all hospitalizations between January 1, 2018 and December 31, 2020 at Vanderbilt University Medical Center that included at least one page message to physicians. Exclusion criteria included patients receiving palliative care, hospitalizations with a planned intensive care stay, and hospitalizations in the top 2% longest length of stay. MAIN MEASURES: Model classification performance to identify in-hospital cardiac arrest, transfer to intensive care, or Rapid Response activation in the next 3-, 6-, and 12-hours. We compared model performance against three common early warning scores: Modified Early Warning Score, National Early Warning Score, and the Epic Deterioration Index. KEY RESULTS: There were 87,783 patients (mean [SD] age 54.0 [18.8] years; 45,835 [52.2%] women) who experienced 136,778 hospitalizations. 6214 hospitalized patients experienced a deterioration event. The machine learning model accurately identified 62% of deterioration events within 3-hours prior to the event and 47% of events within 12-hours. Across each time horizon, the model surpassed performance of the best early warning score including area under the receiver operating characteristic curve at 6-hours (0.856 vs. 0.781), sensitivity at 6-hours (0.590 vs. 0.505), specificity at 6-hours (0.900 vs. 0.878), and F-score at 6-hours (0.291 vs. 0.220). CONCLUSIONS: Machine learning applied to the content and frequency of clinical pages improves prediction of imminent deterioration. Using clinical pages to monitor patient acuity supports improved detection of imminent deterioration without requiring changes to clinical workflow or nursing documentation.

A Case Report on the Effectiveness of Virtual Monitoring of Postdischarge COVID-19 Positive Patients in a Rural Hospital Setting: A Retrospective Review.

Objective: The pandemic has pushed hospital system to re-evaluate the ways they provide care. West Tennessee Healthcare (WTH) developed a remote patient monitoring (RPM) program to monitor positive COVID-19 patients after being discharged from the hospital for any worsening symptomatology and preemptively mitigate the potential of readmission. Methods: We sought to compare the readmission rates of individuals placed on our remote monitoring protocol with individuals not included in the program. We selected remotely monitored individuals discharged from WTH from October 2020 to December 2020 and compared these data points with a control group. Results: We analyzed 1,351 patients with 241 patients receiving no RPM intervention, 969 patients receiving standard monitoring, and 141 patients enrolled in our 24-h remote monitoring. Our lowest all cause readmission rate was 4.96% (p = 0.37) in our 24-h remote monitoring group. We also collected 641 surveys from the monitored patients with two statistically significant answers. Discussion: The low readmission rate noted in our 24-h remotely monitored cohort signifies a potential opportunity that a program of this nature can create for a health care system struggling during a resource-limited time to continue to provide quality care. Conclusion: The program allowed the allocation of hospital resources for individuals with more acute states and monitored less critical patients without using personal protective equipment. The novel program was able to offer an avenue to improve resource utilization and provide care for a health system in a rural area. Further investigation is needed; however, significant opportunities can be seen with data obtained during the study.

Rovibrational Polaritons in Gas-Phase Methane.

Polaritonic states arise when a bright optical transition of a molecular ensemble is resonantly matched to an optical cavity mode frequency. Here, we lay the groundwork to study the behavior of polaritons in clean, isolated systems by establishing a new platform for vibrational strong coupling in gas-phase molecules. We access the strong coupling regime in an intracavity cryogenic buffer gas cell optimized for the preparation of simultaneously cold and dense ensembles and report a proof-of-principle demonstration in gas-phase methane. We strongly cavity-couple individual rovibrational transitions and probe a range of coupling strengths and detunings. We reproduce our findings with classical cavity transmission simulations in the presence of strong intracavity absorbers. This infrastructure will provide a new testbed for benchmark studies of cavity-altered chemistry.

Understanding the Degradation of Methylenediammonium and Its Role in Phase-Stabilizing Formamidinium Lead Triiodide.

Formamidinium lead triiodide (FAPbI3) is the leading candidate for single-junction metal-halide perovskite photovoltaics, despite the metastability of this phase. To enhance its ambient-phase stability and produce world-record photovoltaic efficiencies, methylenediammonium dichloride (MDACl2) has been used as an additive in FAPbI3. MDA2+ has been reported as incorporated into the perovskite lattice alongside Cl-. However, the precise function and role of MDA2+ remain uncertain. Here, we grow FAPbI3 single crystals from a solution containing MDACl2 (FAPbI3-M). We demonstrate that FAPbI3-M crystals are stable against transformation to the photoinactive δ-phase for more than one year under ambient conditions. Critically, we reveal that MDA2+ is not the direct cause of the enhanced material stability. Instead, MDA2+ degrades rapidly to produce ammonium and methaniminium, which subsequently oligomerizes to yield hexamethylenetetramine (HMTA). FAPbI3 crystals grown from a solution containing HMTA (FAPbI3-H) replicate the enhanced α-phase stability of FAPbI3-M. However, we further determine that HMTA is unstable in the perovskite precursor solution, where reaction with FA+ is possible, leading instead to the formation of tetrahydrotriazinium (THTZ-H+). By a combination of liquid- and solid-state NMR techniques, we show that THTZ-H+ is selectively incorporated into the bulk of both FAPbI3-M and FAPbI3-H at ∼0.5 mol % and infer that this addition is responsible for the improved α-phase stability.

Exploring Radial Asymmetry in MR Diffusion Tensor Imaging and Its Impact on the Interpretation of Glymphatic Mechanisms.

BACKGROUND: Diffusion imaging holds great potential for the non-invasive assessment of the glymphatic system in humans. One technique, diffusion tensor imaging along the perivascular space (DTI-ALPS), has introduced the ALPS-index, a novel metric for evaluating diffusivity within the perivascular space. However, it still needs to be established whether the observed reduction in the ALPS-index reflects axonal changes, a common occurrence in neurodegenerative diseases. PURPOSE: To determine whether axonal alterations can influence change in the ALPS-index. STUDY TYPE: Retrospective. POPULATION: 100 participants (78 cognitively normal and 22 with mild cognitive impairments) aged 50-90 years old. FIELD STRENGTH/SEQUENCE: 3T; diffusion-weighted single-shot spin-echo echo-planar imaging sequence, T1-weighted images (MP-RAGE). ASSESSMENT: The ratio of two radial diffusivities of the diffusion tensor (i.e., λ2/λ3) across major white matter tracts with distinct venous/perivenous anatomy that fulfill (ALPS-tracts) and do not fulfill (control tracts) ALPS-index anatomical assumptions were analyzed. STATISTICAL TESTS: To investigate the correlation between λ2/λ3 and age/cognitive function (RAVLT) while accounting for the effect of age, linear regression was implemented to remove the age effect from each variable. Pearson correlation analysis was conducted on the residuals obtained from the linear regression. Statistical significance was set at p < 0.05. RESULTS: λ2 was ~50% higher than λ3 and demonstrated a consistent pattern across both ALPS and control tracts. Additionally, in both ALPS and control tracts a reduction in the λ2/λ3 ratio was observed with advancing age (r = -0.39, r = -0.29, association and forceps tract, respectively) and decreased memory function (r = 0.24, r = 0.27, association and forceps tract, respectively). DATA CONCLUSIONS: The results unveil a widespread radial asymmetry of white matter tracts that changes with aging and neurodegeration. These findings highlight that the ALPS-index may not solely reflect changes in the diffusivity of the perivascular space but may also incorporate axonal contributions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

A versatile platform for gas-phase molecular polaritonics.

Cavity coupling of gas-phase molecules will enable studies of benchmark chemical processes under strong light-matter interactions with a high level of experimental control and no solvent effects. We recently demonstrated the formation of gas-phase molecular polaritons by strongly coupling bright ν3, J = 3 → 4 rovibrational transitions of methane (CH4) to a Fabry-Pérot optical cavity mode inside a cryogenic buffer gas cell. Here, we further explore the flexible capabilities of this infrastructure. We show that we can greatly increase the collective coupling strength of the molecular ensemble to the cavity by increasing the intracavity CH4 number density. In doing so, we can tune from the single-mode coupling regime to a multimode coupling regime in which many nested polaritonic states arise as the Rabi splitting approaches the cavity mode spacing. We explore polariton formation for cavity geometries of varying length, finesse, and mirror radius of curvature. We also report a proof-of-principle demonstration of rovibrational gas-phase polariton formation at room temperature. This experimental flexibility affords a great degree of control over the properties of molecular polaritons and opens up a wider range of simple molecular processes to future interrogation under strong cavity-coupling. We anticipate that ongoing work in gas-phase polaritonics will facilitate convergence between experimental results and theoretical models of cavity-altered chemistry and physics.

Beyond chest pain: Incremental value of other variables to identify patients for an early ECG.

BACKGROUND: Chest pain (CP) is the hallmark symptom for acute coronary syndrome (ACS) but is not reported in 20-30% of patients, especially women, elderly, non-white patients, presenting to the emergency department (ED) with an ST-segment elevation myocardial infarction (STEMI). METHODS: We used a retrospective 5-year adult ED sample of 279,132 patients to explore using CP alone to predict ACS, then we incrementally added other ACS chief complaints, age, and sex in a series of multivariable logistic regression models. We evaluated each model's identification of ACS and STEMI. RESULTS: Using CP alone would recommend ECGs for 8% of patients (sensitivity, 61%; specificity, 92%) but missed 28.4% of STEMIs. The model with all variables identified ECGs for 22% of patients (sensitivity, 82%; specificity, 78%) but missed 14.7% of STEMIs. The model with CP and other ACS chief complaints had the highest sensitivity (93%) and specificity (55%), identified 45.1% of patients for ECG, and only missed 4.4% of STEMIs. CONCLUSION: CP alone had highest specificity but lacked sensitivity. Adding other ACS chief complaints increased sensitivity but identified 2.2-fold more patients for ECGs. Achieving an ECG in 10 min for patients with ACS to identify all STEMIs will be challenging without introducing more complex risk calculation into clinical care.

A Multilevel Primary Care Intervention to Improve Follow-Up of Overdue Abnormal Cancer Screening Test Results: A Cluster Randomized Clinical Trial.

Importance: Realizing the benefits of cancer screening requires testing of eligible individuals and processes to ensure follow-up of abnormal results. Objective: To test interventions to improve timely follow-up of overdue abnormal breast, cervical, colorectal, and lung cancer screening results. Design, Setting, and Participants: Pragmatic, cluster randomized clinical trial conducted at 44 primary care practices within 3 health networks in the US enrolling patients with at least 1 abnormal cancer screening test result not yet followed up between August 24, 2020, and December 13, 2021. Intervention: Automated algorithms developed using data from electronic health records (EHRs) recommended follow-up actions and times for abnormal screening results. Primary care practices were randomized in a 1:1:1:1 ratio to (1) usual care, (2) EHR reminders, (3) EHR reminders and outreach (a patient letter was sent at week 2 and a phone call at week 4), or (4) EHR reminders, outreach, and navigation (a patient letter was sent at week 2 and a navigator outreach phone call at week 4). Patients, physicians, and practices were unblinded to treatment assignment. Main Outcomes and Measures: The primary outcome was completion of recommended follow-up within 120 days of study enrollment. The secondary outcomes included completion of recommended follow-up within 240 days of enrollment and completion of recommended follow-up within 120 days and 240 days for specific cancer types and levels of risk. Results: Among 11 980 patients (median age, 60 years [IQR, 52-69 years]; 64.8% were women; 83.3% were White; and 15.4% were insured through Medicaid) with an abnormal cancer screening test result for colorectal cancer (8245 patients [69%]), cervical cancer (2596 patients [22%]), breast cancer (1005 patients [8%]), or lung cancer (134 patients [1%]) and abnormal test results categorized as low risk (6082 patients [51%]), medium risk (3712 patients [31%]), or high risk (2186 patients [18%]), the adjusted proportion who completed recommended follow-up within 120 days was 31.4% in the EHR reminders, outreach, and navigation group (n = 3455), 31.0% in the EHR reminders and outreach group (n = 2569), 22.7% in the EHR reminders group (n = 3254), and 22.9% in the usual care group (n = 2702) (adjusted absolute difference for comparison of EHR reminders, outreach, and navigation group vs usual care, 8.5% [95% CI, 4.8%-12.0%], P < .001). The secondary outcomes showed similar results for completion of recommended follow-up within 240 days and by subgroups for cancer type and level of risk for the abnormal screening result. Conclusions and Relevance: A multilevel primary care intervention that included EHR reminders and patient outreach with or without patient navigation improved timely follow-up of overdue abnormal cancer screening test results for breast, cervical, colorectal, and lung cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT03979495.

Cefepime vs Piperacillin-Tazobactam in Adults Hospitalized With Acute Infection: The ACORN Randomized Clinical Trial.

Importance: Cefepime and piperacillin-tazobactam are commonly administered to hospitalized adults for empirical treatment of infection. Although piperacillin-tazobactam has been hypothesized to cause acute kidney injury and cefepime has been hypothesized to cause neurological dysfunction, their comparative safety has not been evaluated in a randomized clinical trial. Objective: To determine whether the choice between cefepime and piperacillin-tazobactam affects the risks of acute kidney injury or neurological dysfunction. Design, Setting, and Participants: The Antibiotic Choice on Renal Outcomes (ACORN) randomized clinical trial compared cefepime vs piperacillin-tazobactam in adults for whom a clinician initiated an order for antipseudomonal antibiotics within 12 hours of presentation to the hospital in the emergency department or medical intensive care unit at an academic medical center in the US between November 10, 2021, and October 7, 2022. The final date of follow-up was November 4, 2022. Interventions: Patients were randomized in a 1:1 ratio to cefepime or piperacillin-tazobactam. Main Outcomes and Measures: The primary outcome was the highest stage of acute kidney injury or death by day 14, measured on a 5-level ordinal scale ranging from no acute kidney injury to death. The 2 secondary outcomes were the incidence of major adverse kidney events at day 14 and the number of days alive and free of delirium and coma within 14 days. Results: There were 2511 patients included in the primary analysis (median age, 58 years [IQR, 43-69 years]; 42.7% were female; 16.3% were Non-Hispanic Black; 5.4% were Hispanic; 94.7% were enrolled in the emergency department; and 77.2% were receiving vancomycin at enrollment). The highest stage of acute kidney injury or death was not significantly different between the cefepime group and the piperacillin-tazobactam group; there were 85 patients (n = 1214; 7.0%) in the cefepime group with stage 3 acute kidney injury and 92 (7.6%) who died vs 97 patients (n = 1297; 7.5%) in the piperacillin-tazobactam group with stage 3 acute kidney injury and 78 (6.0%) who died (odds ratio, 0.95 [95% CI, 0.80 to 1.13], P = .56). The incidence of major adverse kidney events at day 14 did not differ between groups (124 patients [10.2%] in the cefepime group vs 114 patients [8.8%] in the piperacillin-tazobactam group; absolute difference, 1.4% [95% CI, -1.0% to 3.8%]). Patients in the cefepime group experienced fewer days alive and free of delirium and coma within 14 days (mean [SD], 11.9 [4.6] days vs 12.2 [4.3] days in the piperacillin-tazobactam group; odds ratio, 0.79 [95% CI, 0.65 to 0.95]). Conclusions and Relevance: Among hospitalized adults in this randomized clinical trial, treatment with piperacillin-tazobactam did not increase the incidence of acute kidney injury or death. Treatment with cefepime resulted in more neurological dysfunction. Trial Registration: ClinicalTrials.gov Identifier: NCT05094154.

Terminations in Primary Care: a Retrospective Observational Study of 16 Primary Care Clinics.

BACKGROUND: The relationship between clinician and patient is the cornerstone of primary care. Breakdown and termination of this relationship are understudied yet important, undesirable outcomes. OBJECTIVE: To better understand the nature and extent of provider and clinic termination of the primary care relationship. DESIGN: Retrospective observational case-control study. SUBJECTS: Adult patients in Eastern Massachusetts who received primary care at hospital- and community-based clinics and health centers participating in a practice-based research network between January 2013 and June 2017. MAIN MEASURES: Formal termination by primary care physician (PCP), reasons for termination, independent predictors of termination based on mixed-effects logistic regression, and documentation of a new PCP after termination. KEY RESULTS: We identified 158,192 patients who received primary care from 182 PCPs across 16 clinics. We found 536 cases of formal termination. Clinics ranged from 4 to 119 terminations per 10,000 patients (intraclass correlation coefficient [ICC]=0.21; 95% CI: 0.18-0.24). Patient age, race/ethnicity, educational attainment, relationship status, employment status, and insurance type were independent predictors of termination (e.g., compared to patients employed full-time, patients unemployed due to disability were more likely to be terminated [adjusted OR:9.26; 95% CI: 6.74-12.74]). The most common cause for termination (38%) was appointment "no-shows" with some PCPs/clinics found to enforce a policy of dismissal following three no-shows. At the time of chart review, 201 patients (38%) had no documentation of a new PCP. Among patients who re-established care within the network, 134 (25%) had a primary care visit within 6 months of termination. CONCLUSIONS: Detailed chart review found that, unlike previous survey-based studies, dismissal was often for missed appointments based on enforcement of no-show policies. Many sociodemographic factors were associated with termination. Variability among clinics highlights the need for further research to better understand circumstances surrounding terminations, with the principal goals of improving patient-provider relationships and providing equitable care.

Assessment of a Naloxone Coprescribing Alert for Patients at Risk of Opioid Overdose: A Quality Improvement Project.

BACKGROUND: Patients taking high doses of opioids, or taking opioids in combination with other central nervous system depressants, are at increased risk of opioid overdose. Coprescribing the opioid-reversal agent naloxone is an essential safety measure, recommended by the surgeon general, but the rate of naloxone coprescribing is low. Therefore, we set out to determine whether a targeted clinical decision support alert could increase the rate of naloxone coprescribing. METHODS: We conducted a before-after study from January 2019 to April 2021 at a large academic health system in the Southeast. We developed a targeted point of care decision support notification in the electronic health record to suggest ordering naloxone for patients who have a high risk of opioid overdose based on a high morphine equivalent daily dose (MEDD) ≥90 mg, concomitant benzodiazepine prescription, or a history of opioid use disorder or opioid overdose. We measured the rate of outpatient naloxone prescribing as our primary measure. A multivariable logistic regression model with robust variance to adjust for prescriptions within the same prescriber was implemented to estimate the association between alerts and naloxone coprescribing. RESULTS: The baseline naloxone coprescribing rate in 2019 was 0.28 (95% confidence interval [CI], 0.24-0.31) naloxone prescriptions per 100 opioid prescriptions. After alert implementation, the naloxone coprescribing rate increased to 4.51 (95% CI, 4.33-4.68) naloxone prescriptions per 100 opioid prescriptions (P < .001). The adjusted odds of naloxone coprescribing after alert implementation were approximately 28 times those during the baseline period (95% CI, 15-52). CONCLUSIONS: A targeted decision support alert for patients at risk for opioid overdose significantly increased the rate of naloxone coprescribing and was relatively easy to build.

WormBase: a modern Model Organism Information Resource.

WormBase (https://wormbase.org/) is a mature Model Organism Information Resource supporting researchers using the nematode Caenorhabditis elegans as a model system for studies across a broad range of basic biological processes. Toward this mission, WormBase efforts are arranged in three primary facets: curation, user interface and architecture. In this update, we describe progress in each of these three areas. In particular, we discuss the status of literature curation and recently added data, detail new features of the web interface and options for users wishing to conduct data mining workflows, and discuss our efforts to build a robust and scalable architecture by leveraging commercial cloud offerings. We conclude with a description of WormBase's role as a founding member of the nascent Alliance of Genome Resources.

Non-Interruptive Clinical Decision Support to Improve Perioperative Electronic Positive Patient Identification.

Positive patient identification (PPID) is an integral step to ensure the correct patient identity prior to a healthcare delivery event. Following implementation of a new EHR in November 2017, Vanderbilt University Medical Center (VUMC) experienced frequent and inconsistent failure of barcode scanners which impacted the electronic PPID (ePPID) and blood verification processes. Following multiple iterations of troubleshooting, vendor engagement, and device upgrades, we developed a clinical decision support (CDS) tool as a visual reminder to perform ePPID. If ePPID was initially bypassed, the clinician received a passive alert which remained visible throughout the procedure or until ePPID was completed successfully. We conducted a retrospective observational study using an interrupted time series analysis and analysis of variance pre- and post- CDS intervention. Following CDS intervention, we observed an immediate 20.8% increase in successful ePPID (p < 0.001). The mean success rate of ePPID attempts increased from 62.0% pre-intervention to 94.4% post-intervention (p < 0.001). There were 108 providers who had less than 80.0% success in the six-months prior to CDS intervention, of whom all improved to an average of 95.9% success. Our CDS approach highlights the utility of non-interruptive but continually visible alerts to improve patient safety workflows. By making errors clearly visible to users and their peers, performance improved to only 5.6% of alerts bypassed.

Highly Absorbing Lead-Free Semiconductor Cu2AgBiI6 for Photovoltaic Applications from the Quaternary CuI-AgI-BiI3 Phase Space.

Since the emergence of lead halide perovskites for photovoltaic research, there has been mounting effort in the search for alternative compounds with improved or complementary physical, chemical, or optoelectronic properties. Here, we report the discovery of Cu2AgBiI6: a stable, inorganic, lead-free wide-band-gap semiconductor, well suited for use in lead-free tandem photovoltaics. We measure a very high absorption coefficient of 1.0 × 105 cm-1 near the absorption onset, several times that of CH3NH3PbI3. Solution-processed Cu2AgBiI6 thin films show a direct band gap of 2.06(1) eV, an exciton binding energy of 25 meV, a substantial charge-carrier mobility (1.7 cm2 V-1 s-1), a long photoluminescence lifetime (33 ns), and a relatively small Stokes shift between absorption and emission. Crucially, we solve the structure of the first quaternary compound in the phase space among CuI, AgI and BiI3. The structure includes both tetrahedral and octahedral species which are open to compositional tuning and chemical substitution to further enhance properties. Since the proposed double-perovskite Cs2AgBiI6 thin films have not been synthesized to date, Cu2AgBiI6 is a valuable example of a stable Ag+/Bi3+ octahedral motif in a close-packed iodide sublattice that is accessed via the enhanced chemical diversity of the quaternary phase space.

Intrinsic quantum confinement in formamidinium lead triiodide perovskite.

Understanding the electronic energy landscape in metal halide perovskites is essential for further improvements in their promising performance in thin-film photovoltaics. Here, we uncover the presence of above-bandgap oscillatory features in the absorption spectra of formamidinium lead triiodide thin films. We attribute these discrete features to intrinsically occurring quantum confinement effects, for which the related energies change with temperature according to the inverse square of the intrinsic lattice parameter, and with peak index in a quadratic manner. By determining the threshold film thickness at which the amplitude of the peaks is appreciably decreased, and through ab initio simulations of the absorption features, we estimate the length scale of confinement to be 10-20 nm. Such absorption peaks present a new and intriguing quantum electronic phenomenon in a nominally bulk semiconductor, offering intrinsic nanoscale optoelectronic properties without necessitating cumbersome additional processing steps.

Predicting Self-Rated Health Across the Life Course: Health Equity Insights from Machine Learning Models.

BACKGROUND: Self-rated health is a strong predictor of mortality and morbidity. Machine learning techniques may provide insights into which of the multifaceted contributors to self-rated health are key drivers in diverse groups. OBJECTIVE: We used machine learning algorithms to predict self-rated health in diverse groups in the Behavioral Risk Factor Surveillance System (BRFSS), to understand how machine learning algorithms might be used explicitly to examine drivers of self-rated health in diverse populations. DESIGN: We applied three common machine learning algorithms to predict self-rated health in the 2017 BRFSS survey, stratified by age, race/ethnicity, and sex. We replicated our process in the 2016 BRFSS survey. PARTICIPANTS: We analyzed data from 449,492 adult participants of the 2017 BRFSS survey. MAIN MEASURES: We examined area under the curve (AUC) statistics to examine model fit within each group. We used traditional logistic regression to predict self-rated health associated with features identified by machine learning models. KEY RESULTS: Each algorithm, regularized logistic regression (AUC: 0.81), random forest (AUC: 0.80), and support vector machine (AUC: 0.81), provided good model fit in the BRFSS. Predictors of self-rated health were similar by sex and race/ethnicity but differed by age. Socioeconomic features were prominent predictors of self-rated health in mid-life age groups. Income [OR: 1.70 (95% CI: 1.62-1.80)], education [OR: 2.02 (95% CI: 1.89, 2.16)], physical activity [OR: 1.52 (95% CI: 1.46-1.58)], depression [OR: 0.66 (95% CI: 0.63-0.68)], difficulty concentrating [OR: 0.62 (95% CI: 0.58-0.66)], and hypertension [OR: 0.59 (95% CI: 0.57-0.61)] all predicted the odds of excellent or very good self-rated health. CONCLUSIONS: Our analysis of BRFSS data show social determinants of health are prominent predictors of self-rated health in mid-life. Our work may demonstrate promising practices for using machine learning to advance health equity.