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Board Certified Psychiatric Pharmacists (BCPPs) practice in a variety of inpatient and outpatient health care settings as part of collaborative, multidisciplinary teams. The American Association of Psychiatric Pharmacists (AAPP) has promoted the expansion of psychiatric pharmacy through the development of psychotropic stewardship programs (PSPs). Based on the standards developed during the creation and expansion of antimicrobial stewardship programs, psychotropic stewardship promotes the safe and appropriate use of psychotropic medications. AAPP envisions every patient with a psychiatric diagnosis will have their medication treatment plan reviewed, optimized, and managed by a psychotropic stewardship team with a psychiatric pharmacist as a co-leader. Because of variations in practice site resources, patient populations, and provider collaboration, the creation and implementation of PSPs should be based on site-specific needs and opportunities. Initial patient identification could prioritize those prescribed multiple medications, high-risk psychotropics, or comorbid medical diagnoses. However, every patient prescribed a psychotropic medication should have the opportunity to work with a PSP. Incremental implementation may be required during the planning stages of stewardship teams. Use of clinical practice-related core outcomes will allow for the optimization of program resources, increased recognition, and improved patient outcomes. PSPs should be patient-focused and integrate patients' preferences and access to recommended treatment options. The eventual goal of PSP implementation is official recognition by key regulatory agencies as a standard of care for patients who receive a diagnosis of a psychiatric or substance use disorder.

The patient's voice, which we define as the words the patient uses found in notes and messages and other sources, and their preferences for care and its outcomes, is too small a part of the electronic health record (EHR). To address this shortcoming will require innovation, research, funding, perhaps architectural changes to commercial EHRs, and that we address barriers that have resulted in this state, including clinician burden and financial drivers for care. Advantages to greater patient voice may accrue to many groups of EHR users and to patients themselves. For clinicians, the patient's voice, including symptoms, is invaluable in identifying new serious illness that cannot be detected by screening tests, and as an aid to accurate diagnosis. Informaticians benefit from greater patient voice in the EHR because it provides clues not found elsewhere that aid diagnostic decision support, predictive analytics, and machine learning. Patients benefit when their treatment priorities and care outcomes considered in treatment decisions. What patient voice there is in the EHR today can be found in locations not usually used by researchers. Increasing the patient voice needs be accomplished in equitable ways available to people with less access to technology and whose primary language is not well supported by EHR tools and portals. Use of direct quotations, while carrying potential for harm, permits the voice to be recorded unfiltered. If you are a researcher or innovator, collaborate with patient groups and clinicians to create new ways to capture the patient voice, and to leverage it for good.

Peroxy acyl nitrates (PANs) are photochemical pollutants with implications for health and atmospheric oxidation capacity. PANs are formed via the oxidation of non-methane volatile organic compounds (NMVOCs) in the presence of nitrogen oxide radicals (NOx = NO + NO2). While urban environments are large sources of PANs, in situ observations in urban areas are limited. Here we use satellite measurements of PANs from the Tropospheric Emission Spectrometer (TES) and the Suomi National Polar-orbiting Partnership (S-NPP) Cross-track Infrared Sounder (CrIS) to evaluate the spatiotemporal variability of PANs over and around Mexico City. Monthly mean maxima in PANs over the Mexico City Metropolitan Area (MCMA) occur during spring months (March–May). This time of year coincides with a peak in local photochemistry and more frequent air stagnation. Local fire activity also typically peaks between February and May, which leads to strong interannual variability of PANs over the MCMA. We use S-NPP CrIS data to probe the spatial outflow pattern of PANs produced within urban Mexico City during the month with the largest mixing ratios of PANs (April). Peak outflow in April occurs to the northeast of the city and over the mountains south of the city. Outflow to the northwest appears infrequently. Using observations during 2018 versus 2019, we also show that PANs were not significantly reduced during a year, with a significant decrease in NOx over Mexico City. Our analysis demonstrates that the space-based observations provided by CrIS and TES can increase understanding of the spatiotemporal variability of PANs over and surrounding Mexico City.

The new single-pixel TROPESS (TRopospheric Ozone and its Precursors from Earth System Sounding) profile retrievals of carbon monoxide (CO) from the Cross-track Infrared Sounder (CrIS) are evaluated using vertical profiles of in situ observations from the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) aircraft program and from the Atmospheric Tomography Mission (ATom) campaigns. The TROPESS optimal estimation retrievals are produced using the MUSES (MUlti-SpEctra, MUlti-SpEcies, MUlti-Sensors) algorithm, which has heritage from retrieval algorithms developed for the EOS/Aura Tropospheric Emission Spectrometer (TES). TROPESS products provide retrieval diagnostics and error covariance matrices that propagate instrument noise as well as the uncertainties from sequential retrievals of parameters such as temperature and water vapor that are required to estimate the carbon monoxide profiles. The validation approach used here evaluates biases in column and profile values as well as the validity of the retrieval error estimates using the mean and variance of the compared satellite and aircraft observations. CrIS–NOAA GML comparisons had biases of 0.6 % for partial column average volume mixing ratios (VMRs) and (2.3, 0.9, −4.5) % for VMRs at (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 9 % to 14 %. CrIS–ATom comparisons had biases of −0.04 % for partial column and (2.2, 0.5, −3.0) % for (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 6 % to 10 %. The reported observational errors for TROPESS/CrIS CO profiles have the expected behavior with respect to the vertical pattern in standard deviation of the comparisons. These comparison results give us confidence in the use of TROPESS/CrIS CO profiles and error characterization for continuing the multi-decadal record of satellite CO observations.

Dry eye disease (DED) affects up to half of the world's population and causes blur, eye pain, decreased quality of life. Unfortunately, many people do not receive proper treatment due to current challenges associated with DED diagnoses. To address this issue, point-of-care (PoC) clinical tear film diagnostics are beginning to be used for rapid in-office testing. However, these diagnostics also have significant obstacles, including challenges associated with tear sampling. We previously demonstrated how soft contact lenses (SCLs) can be used to capture and concentrate tear proteins on the eye. Given the low tear volume associated with DED subjects, SCL sampling holds great potential to improve DED PoC diagnostics and facilitate prompt treatment for patients. Here we show the detection of new and established DED protein biomarkers using our SCL sampling method. Our analysis revealed associations between DED tear proteins and the immune system, ferroptosis, salivary secretion, and calcium binding proteins. We also identified correlations between clinical DED testing and proteomics, emphasizing the power and importance of translational medicine research. These experiments lay the foundation for both SCL sampled tear protein biomarker discovery as well as improved diagnostic testing to improve DED patient care.

Soft contact lenses (SCLs) have recently been introduced as an alternative method for human tear protein sampling. However, SCLs are available in a variety of chemical compositions which affect protein binding specificity. Here we analyzed 8 different SCL materials to identify an optimal lens for tear protein sampling. Polymer contamination, mass spectrometry (MS) sample preparation method, total protein capture, individual protein specificity, and SCL cost were all assessed. Using a filter-aided sample prep (FASP) method with 4M guanidine for protein removal, only etafilcon A and verofilcon A did not have significant polymer contamination. Polymer was successfully removed using phosphate buffered saline (PBS) with S-Trap columns for all SCL materials, though yielding a slightly lower number of protein identifications per sample. Minor quantitative differences were observed between SCL materials. However, we also saw significant intersubject variation in protein abundance. Of all the assessed SCL materials, verofilcon A lenses yielded the most total protein while comfilcon A and senofilcon A had the least protein variability. As a newly released daily disposable modality (Precision 1, Alcon), verofilcon A has one of the longest predictable production schedules and one of the lowest costs per lens, making it beneficial for large-scale experiments and diagnostics. Furthermore, we demonstrate how protein binding bias with SCL tear sampling is useful for intra-experiment normalization. Overall, these experiments have led us to optimize our previous protocol for SCL tear protein sampling, highlighting important differences between SCL materials and identifying etafilcon A and verofilcon A as optimal materials for tear protein sampling.Abstract Figure

The timing of extubation following placement of mandibular distractors in the setting of Pierre Robin sequence is variable across institutional algorithms. Postoperative maintenance of intubation allows for an improvement in airway dimension and tongue positioning before extubation, theoretically decreasing the impact of postoperative airway edema. Maintenance of intubation, however, is not without risk. The authors analyze their institutional experience with neonatal mandibular distraction followed by immediate extubation to assess feasibility and safety profiles.A 4-year retrospective review of patients diagnosed with Pierre Robin sequence who underwent mandibular distraction within the first 3 months of life was performed. Patients intubated preoperatively were excluded.Fifty-two patients met inclusion criteria. Thirty-eight patients (73 percent) were extubated immediately, whereas 14 patients (27 percent) remained intubated. No differences between these groups were found when comorbidities, cleft pathology, preoperative respiratory support, or grade of view on direct laryngoscopy were analyzed. Case duration greater than 120 minutes, operation start time after 3 pm, and the subjective designation of a difficult airway by the anesthesiologist were associated with maintaining intubation (p0.05). Eight patients (21 percent) in the extubated group required an increase in respiratory support in the postoperative interval. Four of these patients (11 percent) required reintubation. Increased postoperative respiratory support was more likely in patients with certain comorbidities and higher preoperative respiratory support requirements (p0.05).The authors' data suggest that immediate extubation following neonatal mandibular distraction is feasible in patients who are not intubated preoperatively. Careful consideration should be given to patients who require significant respiratory support preoperatively and in those with certain comorbidities.Therapeutic, III.

Introduction The American Association of Psychiatric Pharmacists (AAPP) used multiple modalities to develop and refine 28 attribute statements to describe a best practice model for outpatient psychiatric pharmacists. Before addressing implementation, assessment, and field testing, it was necessary to finalize and confirm the statements and their supporting narratives among stakeholders. The objective of this project was to confirm the attribute statements and supporting justifications for a best practice model for outpatient psychiatric pharmacists providing direct patient care. Methods The 4 phases that resulted in the 28 attribute statements and supporting narratives have been described and published elsewhere. As part of phase 5, the confirmation survey was distributed to pharmacists and resident members of AAPP in November 2021 for 3 weeks. Results The survey respondents (n = 74; 6.1%) were licensed pharmacists for an average of 15.6 years (SD = 12.0) and had been practicing as psychiatric pharmacists for an average of 11.3 years (SD = 10.4). Slightly more than half (54.2%) of the respondents reported practicing in the outpatient setting and three-fourths (74.3%) were Board Certified Psychiatric Pharmacists. For each of the 28 statements, more than 90% of respondents either agreed or agreed with minimal reservations. Discussion Given the high degree of agreement on the proposed practice model statements, they will be used as the basis for the outpatient psychiatric pharmacist best practice model. Next steps in developing this model include establishing implementation guidance, determining appropriate metrics for evaluation of these statements in practice, and establishing appropriate field-testing methods.

Background Care partners are key members of patients’ health care teams, yet little is known about their experiences accessing patient information via electronic portals. Objective To better understand the characteristics and perceptions of care partners who read patients’ electronic visit notes. Patient involvement Focus groups with diverse patients from a community health center provided input into survey development. Methods We contacted patient portal users at 3 geographically distinct sites in the US via email in 2017 for an online survey including open ended questions which we qualitatively analyzed. Results Respondents chose whether to answer as care partners (N = 874) or patients (N = 28,782). Among care partner respondents, 44% were spouses, 43% children/other family members, and 14% friends/neighbors/other. Both care partners and patients reported that access to electronic notes was very important for promoting positive health behaviors, but care partners’ perceptions of importance were consistently more positive than patients’ perceptions of engagement behaviors. Open-ended comments included positive benefits such as: help with remembering the plan for care, coordinating care with other doctors, decreasing stress of care giving, improving efficiency of visits, and supporting patients from a geographical distance. They also offered suggestions for improving electronic portal and note experience for care partners such as having a separate log on for care partners; having doctors avoid judgmental language in their notes; and the ability to prompt needed medical care for patients. Discussion Care partners value electronic access to patients’ health information even more than patients. The majority of care partners were family members, whose feedback is important for improving portal design that effectively engages these care team members. Practical value Patient care in the time of COVID-19 increasingly requires social distancing which may place additional burden on care partners supporting vulnerable patients. Access to patient notes may promote quality of care by keeping care partners informed, and care partner’s input should be used to optimize portal design and electronic access to patient information.

Influenza virus infects millions of people annually and can cause global pandemics. Hemagglutinin (HA) is the primary component of commercial influenza vaccines (CIV), and antibody titer to HA is a primary correlate of protection. Continual antigenic variation of HA requires that CIVs are reformulated yearly. Structural organization of HA complexes have not previously been correlated with induction of broadly reactive antibodies, yet CIV formulations vary in how HA is organized. Using electron microscopy to study four current CIVs, we find structures including: individual HAs, starfish structures with up to 12 HA molecules, and novel spiked-nanodisc structures that display over 50 HA molecules along the complex’s perimeter. CIV containing these spiked nanodiscs elicit the highest levels of heterosubtypic cross-reactive antibodies in female mice. Here, we report that HA structural organization can be an important CIV parameter and can be associated with the induction of cross-reactive antibodies to conserved HA epitopes.

IntroductionIn pancreatic islet transplantation, the exact contribution of human leukocyte antigen (HLA) matching to graft survival remains unclear. Islets may be exposed to allogenic rejection but also the recurrence of type 1 diabetes (T1D). We evaluated the HLA-DR matching, including the impact of diabetogenic HLA-DR3 or HLA-DR4 matches.MethodsWe retrospectively examined the HLA profile in 965 transplant recipients and 2327 islet donors. The study population was obtained from patients enrolled in the Collaborative Islet Transplant Registry. We then identified 87 recipients who received a single-islet infusion. Islet-kidney recipients, 2nd islet infusion, and patients with missing data were excluded from the analysis (n=878).ResultsHLA-DR3 and HLA-DR4 were present in 29.7% and 32.6% of T1D recipients and 11.6% and 15.8% of the donors, respectively. We identified 52 T1D islet recipients mismatched for HLA-DR (group A), 11 with 1 or 2 HLA-DR-matches but excluding HLA-DR3 and HLA- DR4 (group B), and 24 matched for HLA-DR3 or HLA-DR4 (group C). Insulin-independence was maintained in a significantly higher percentage of group B recipients from year one through five post-transplantation (pConclusionThis study suggests that matching HLA-DR but excluding the diabetogenic HLA-DR3 and/or 4 is a significant predictor for long-term islet survival.

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm has been applied to multi-year records of observations from NASA's Orbiting Carbon Observatory -2 and -3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling, but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 ppm and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements show root mean squared errors (RMSE) of approximately 0.8 ppm and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 ppm and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of errors in small areas, as well as biases across land-ocean crossings, also show encouraging results, for each sensor and in their agreement. Taken together, our results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Background Reduction mammaplasty is an effective and safe treatment option for adults with symptomatic macromastia, but there are few data regarding outcomes in adolescents. Objectives The purpose of this study was to determine the short-term psychosocial impact, satisfaction, and safety of reduction mammaplasty when performed during adolescence. Methods A retrospective review was performed of a single pediatric plastic surgeon's experience with reduction mammaplasty from 2018 to 2021 in patients aged ≤18 years. Patients completed the preoperative and postoperative “Satisfaction with Breasts” and “Psychosocial Well-being” sections of the BREAST-Q survey. Clinical variables gathered included age, weight, BMI, complication profile, specimen resection weight, and follow-up duration. Results In total, 41 patients met inclusion criteria. The mean converted Rasch scores for BREAST-Q “Satisfaction with Breasts” and “Psychosocial Well-being” increased significantly following reduction mammaplasty (“Satisfaction with Breasts”: preoperative, 24.1 vs postoperative, 92.6; “Psychosocial Well-being”: preoperative, 37.7 vs postoperative, 90.4; P < .001). Obesity (BMI ≥ 30 kg/m2) was associated with lower preoperative “Psychosocial Well-being” scores (obese, 29.7 vs nonobese, 43.3; P < .001) but a greater improvement in score following surgery (obese, +63.9 vs nonobese, +44.9; P < .001). Specimen weight ≥1000 grams was also associated with greater improvement in score on the “Psychosocial Well-being” section (≥1000 grams, +58 vs Conclusions Reduction mammaplasty during adolescence predictably improves both short-term satisfaction with breasts and psychosocial well-being while demonstrating a favorable short-term complication profile. Level of Evidence: 4

Aims Ablative therapy, such as focal therapy, cryotherapy or electroporation, aims to treat clinically significant prostate cancer with reduced treatment-related toxicity. Up to a third of patients may require further local salvage treatment after ablative therapy failure. Limited descriptive, but no comparative, evidence exists between different salvage treatment outcomes. The aim of this study was to compare oncological and functional outcomes after salvage robot-assisted radical prostatectomy (SRARP) and salvage radiotherapy (SRT). Materials and methods Data were collected prospectively and retrospectively on 100 consecutive SRARP cases and 100 consecutive SRT cases after ablative therapy failure in a high-volume tertiary centre. Results High-risk patients were over-represented in the SRARP group (66.0%) compared with the SRT group (48.0%) (P = 0.013). The median (interquartile range) follow-up after SRARP was 16.5 (10.0–30.0) months and 37.0 (18.5–64.0) months after SRT. SRT appeared to confer greater biochemical recurrence-free survival at 1, 2 and 3 years compared with SRARP in high-risk patients (year 3: 86.3% versus 66.0%), but biochemical recurrence-free survival was similar for intermediate-risk patients (year 3: 90.0% versus 75.6%). There was no statistical difference in pad-free continence at 12 and 24 months between SRARP (77.2 and 84.7%) and SRT (75.0 and 74.0%) (P = 0.724, 0.114). Erectile function was more likely to be preserved in men who underwent SRT. After SRT, cumulative bowel and urinary Radiation Therapy Oncology Group toxicity grade I were 25.0 and 45.0%, grade II were 11.0 and 11.0% and grade III or IV complications were 4.0 and 5.0%, respectively. Conclusion We report the first comparative analyses of salvage prostatectomy and radiotherapy following ablative therapy. Men with high-risk disease appear to have superior oncological outcomes after SRT; however, treatment allocation does not appear to influence oncological outcomes for men with intermediate-risk disease. Treatment allocation was associated with a different spectrum of toxicity profile. Our data may inform shared decision-making when considering salvage treatment following focal or whole-gland ablative therapy.

Ammonia is a significant precursor of PM2.5 particles and thus contributes to poor air quality in many regions. Furthermore, ammonia concentrations are rising due to the increase of large scale, intensive agricultural activities, which are often accompanied by greater use of fertilizers and concentrated animal feedlots. Ammonia is highly reactive, and thus highly variable and difficult to measure. Satellite based instruments, such as the Atmospheric Infrared Sounder (AIRS), and the Cross-Track Infrared Sounder (CrIS) sensors, have been shown to provide much greater temporal and spatial coverage of ammonia distribution and variability than is possible with in situ networks or aircraft campaigns, but the validation of these data is limited. Here we evaluate ammonia retrievals from AIRS and CrIS against ammonia measurements from aircraft in the California Central Valley and in the Colorado Front Range. The satellite datasets were small and in California were obtained under difficult conditions. We show that the surface values of the retrieved profiles are biased very low in California and slightly high in Colorado, and that the bias appears to be primarily due to smoothing error. We also compare three years of CrIS ammonia against an in situ network in the Magic Valley in Idaho We show that CrIS ammonia captures both the seasonal signal and the spatial variability in the Magic Valley, though it is biased low here also. In summary, analysis adds to the validation record but also points to the need for more validation under different conditions.

OBJECTIVE To determine C-peptide measures and levels associated with positive glycemic control outcomes following islet transplantation (ITx) in type 1 diabetes. RESEARCH DESIGN AND METHODS We evaluated Collaborative Islet Transplant Registry (CITR) Islet-Alone recipients with pre-transplant C-peptide RESULTS Fasting C-peptide was highly predictive for ASHE (ROC-AUC=0.906; optimal cut point=0.070 nmol/L), and the optimal outcome (ROC-AUC=0.845; optimal cut point=0.33 nmol/L). Mixed-meal tolerance test (MMTT) stimulated C-peptide to Glucose ratio (CPGR) outperformed both fasting and stimulated C-peptide for all outcomes except ASHE. Optimal cut point for the optimal outcome was 0.12 nmol/mmol for MMTT stimulated CPGR, and 0.97 nmol/L for MMTT stimulated C-peptide. CONCLUSION Fasting C-peptide reliably predicts ITx primary outcomes. MMTT stimulated CPGR provides marginally better prediction for composite ITx outcomes, including insulin independence. In the absence of a MMTT, a fasting C-peptide ≥0.33 nmol/L is a reassuring measure of optimal islet graft function. C-peptide targets represent excellent and easily determinable means to predict glycemic control outcomes after ITx and should be considered as potential goals of beta cell replacement.

Extracellular vesicles (EVs) play important roles in cell-to-cell communication and are potential biomarkers as they carry markers of their derived tissues and disease signatures. However, obtaining pure EV preparations from biofluids is challenging due to contaminants with similar physicochemical properties. Here, we performed a meta-analysis of plasma EV proteomics data deposited in public repositories to refine the protein composition of EVs and investigate potential roles in type 1 diabetes development. With the concept that each purification method yields different proportions of distinct contaminants, we grouped proteins into clusters based on their abundance profiles. This allowed us to separate clusters with classical EV markers, such as CD9, CD40, C63 and CD81, from clusters of well-known contaminants, such as serum albumin, apolipoproteins and components of the complement and coagulation pathways. Two clusters containing a total of 1720 proteins combined were enriched with EV markers and depleted in common contaminants; therefore, they were considered to contain bona fide EV components. As possible origins of plasma EVs, these clusters had markers of tissues such as spleen, liver, brain, lungs, pancreas, and blood/immune cells. These clusters were also enriched in cell surface markers CD antigens, and proteins from cell-to-cell communication and signaling pathways, such as chemokine signaling and antigen presentation. We also show that the EV component and type 1 diabetes biomarker, platelet basic protein (PPBP/CXCL7) regulates apoptosis in both beta and macrophage cell lines. Overall, our meta-analysis refined the composition of plasma EVs, reinforcing a primary function as messengers for cell-to-cell communication and signaling. Furthermore, this analysis identifies optimal avenues to target EVs for development of disease biomarkers.

Influenza virus infects millions of people annually and can cause global pandemics. Hemagglutinin (HA) is the primary component of commercial influenza vaccines (CIV), and antibody to HA is a primary correlate of protection. Persistent antigenic variation of HA requires that CIV be reformulated for new strains yearly. Differences in structural organization of HA has not been correlated with induction of broadly reactive antibodies, and CIV formulations can vary in how HA is organized. Using electron microscopy to study four current CIV, we found that these different formulations contained a variety of structures including: individual HAs, starfish-like structures with up to 12 HA molecules, and novel “spiked nanodisc” structures that displayed over 50 HA molecules along the complex’s perimeter. These spiked nanodiscs uniquely exposed conserved stem epitopes and elicited the highest levels of heterosubtypic cross-reactive antibodies. Overall, we found that HA structural organization can be an important CIV parameter and can be associated with the induction of cross-reactive antibodies to conserved HA epitopes.

ImportanceWith timely collection of SARS-CoV-2 viral genome sequences, it is important to apply efficient data analytics to detect emerging variants at the earliest time.ObjectiveTo evaluate the application of a statistical learning strategy (SLS) to improve early detection of novel SARS-CoV-2 variants using viral sequence data from global surveillance.Design, Setting, and ParticipantsThis case series applied an SLS to viral genomic sequence data collected from 63 686 individuals in Africa and 531 827 individuals in the United States with SARS-CoV-2. Data were collected from January 1, 2020, to December 28, 2021.Main Outcomes and MeasuresThe outcome was an indicator of Omicron variant derived from viral sequences. Centering on a temporally collected outcome, the SLS used the generalized additive model to estimate locally averaged Omicron caseload percentages (OCPs) over time to characterize Omicron expansion and to estimate when OCP exceeded 10%, 25%, 50%, and 75% of the caseload. Additionally, an unsupervised learning technique was applied to visualize Omicron expansions, and temporal and spatial distributions of Omicron cases were investigated.ResultsIn total, there were 2698 cases of Omicron in Africa and 12 141 in the United States. The SLS found that Omicron was detectable in South Africa as early as December 31, 2020. With 10% OCP as a threshold, it may have been possible to declare Omicron a variant of concern as early as November 4, 2021, in South Africa. In the United States, the application of SLS suggested that the first case was detectable on November 21, 2021.Conclusions and RelevanceThe application of SLS demonstrates how the Omicron variant may have emerged and expanded in Africa and the United States. Earlier detection could help the global effort in disease prevention and control. To optimize early detection, efficient data analytics, such as SLS, could assist in the rapid identification of new variants as soon as they emerge, with or without lineages designated, using viral sequence data from global surveillance.

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a

Variability in post-graduate year 5 (PGY5) residents' operative self-efficacy exists; yet the causes of variability have not been explored. Our study aims to determine resident-related and program-dependent factors associated with residents' perceptions of self-efficacy.Following the 2020 American Board of Surgery In-Training Examination, a national survey of self-efficacy in 10 of the most commonly performed Accreditation Council for Graduate Medical Education case-log procedures was completed.A total of 1,145 PGY5 residents completed the survey (response rate 83.8%), representing 296 surgical residency programs. Female sex (odds ratio [OR] 0.46 to 0.67; 95% CI 0.30 to 0.95; p0.05) was associated with decreased self-efficacy for 6 procedures. Residents from institutions with emphasis on autonomy were more likely to report higher self-efficacy for 8 of 10 procedures (OR 1.39 to 3.03; 95% CI 1.03 to 4.51; p0.05). In addition, increased socialization among residents and faculty also correlated with increased self-efficacy in 3 of 10 procedures (OR 1.41 to 2.37; 95% CI 1.03 to 4.69; p0.05). Procedures performed with higher levels of resident responsibility, based on Graduated Levels of Resident Responsibility (GLRR) and Teaching Assistant (TA) scores, were correlated with higher self-efficacy (p0.001).Ensuring that residents receive ample opportunities for GLRR and TA experiences, while implementing programmatic support for resident-dependent factors, may be crucial for building self-efficacy in PGY5 residents. Institutional support of resident "autonomy" and increasing methods of socialization may provide a means of building trust and improving perceptions of self-efficacy. In addition, reevaluating institutional policies that limit opportunities for graduated levels of responsibility, while maintaining patient safety, may lead to increased self-efficacy.

Isoprene is the dominant nonmethane organic compound emitted to the atmosphere, where it drives ozone and aerosol production, modulates atmospheric oxidation, and interacts with the global nitrogen cycle. Isoprene emissions are highly variable and uncertain, as is the nonlinear chemistry coupling isoprene and its primary sink, the hydroxyl radical. Space-based isoprene measurements can help close the gap on these uncertainties, and when combined with concurrent formaldehyde data provide a new constraint on atmospheric oxidation regimes. Here, we present a next-generation machine-learning isoprene retrieval for the Cross-track Infrared Sounder (CrIS) that provides improved sensitivity, lower noise, and thus higher space-time resolution than earlier approaches. The Retrieval of Organics from CrIS Radiances (ROCR) isoprene measurements compare well with previous space-based retrievals as well as with the first-ever ground-based isoprene column measurements, with 20%–50% discrepancies that reflect differing sources of systematic uncertainty. An ensemble of sensitivity tests points to the spectral background and isoprene profile specification as the most relevant uncertainty sources in the ROCR framework. We apply the ROCR isoprene algorithm to the full CrIS record from 2012 to 2020, showing that it can resolve fine-scale spatial gradients at daily resolution over the world's isoprene hotspots. Results over North America and Amazonia highlight emergent connections between isoprene abundance and daily-to-interannual variations in temperature and nitrogen oxides.

Extensive mutations in the Omicron spike protein appear to accelerate the transmission of SARS-CoV-2, and rapid infections increase the odds that additional mutants will emerge. To build an investigative framework, we have applied an unsupervised machine learning approach to 4296 Omicron viral genomes collected and deposited to GISAID as of December 14, 2021, and have identified a core haplotype of 28 polymutants (A67V, T95I, G339D, R346K, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S, Q498R, N501Y, Y505H, T547K, D614G, H655Y, N679K, P681H, N764K, K796Y, N856K, Q954H, N69K, L981F) in the spike protein and a separate core haplotype of 17 polymutants in non-spike genes: (K38, A1892) in nsp3, T492 in nsp4, (P132, V247, T280, S284) in 3C-like proteinase, I189 in nsp6, P323 in RNA-dependent RNA polymerase, I42 in Exonuclease, T9 in envelope protein, (D3, Q19, A63) in membrane glycoprotein, and (P13, R203, G204) in nucleocapsid phosphoprotein. Using these core haplotypes as reference, we have identified four newly emerging polymutants (R346, A701, I1081, N1192) in the spike protein (p value = 9.37*10−4, 1.0*10−15, 4.76*10−7 and 1.56*10−4, respectively), and five additional polymutants in non-spike genes (D343G in nucleocapsid phosphoprotein, V1069I in nsp3, V94A in nsp4, F694Y in the RNA-dependent RNA polymerase and L106L/F of ORF3a) that exhibit significant increasing trajectories (all p values −15). In the absence of relevant clinical data for these newly emerging mutations, it is important to monitor them closely. Two emerging mutations may be of particular concern: the N1192S mutation in spike protein locates in an extremely highly conserved region of all human coronaviruses that is integral to the viral fusion process, and the F694Y mutation in the RNA polymerase may induce conformational changes that could impact remdesivir binding.

ImportanceEarlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emerging novel variants based on variant-specific mutation haplotypes and, in turn, be associated with enhanced implementation of risk-stratified public health prevention strategies.ObjectiveTo develop a haplotype-based artificial intelligence (HAI) model for identifying novel variants, including mixture variants (MVs) of known variants and new variants with novel mutations.Design, Setting, and ParticipantsThis cross-sectional study used serially observed viral genomic sequences globally (prior to March 14, 2022) to train and validate the HAI model and used it to identify variants arising from a prospective set of viruses from March 15 to May 18, 2022.Main Outcomes and MeasuresViral sequences, collection dates, and locations were subjected to statistical learning analysis to estimate variant-specific core mutations and haplotype frequencies, which were then used to construct an HAI model to identify novel variants.ResultsThrough training on more than 5 million viral sequences, an HAI model was built, and its identification performance was validated on an independent validation set of more than 5 million viruses. Its identification performance was assessed on a prospective set of 344 901 viruses. In addition to achieving an accuracy of 92.8% (95% CI within 0.1%), the HAI model identified 4 Omicron MVs (Omicron-Alpha, Omicron-Delta, Omicron-Epsilon, and Omicron-Zeta), 2 Delta MVs (Delta-Kappa and Delta-Zeta), and 1 Alpha-Epsilon MV, among which Omicron-Epsilon MVs were most frequent (609/657 MVs [92.7%]). Furthermore, the HAI model found that 1699 Omicron viruses had unidentifiable variants given that these variants acquired novel mutations. Lastly, 524 variant-unassigned and variant-unidentifiable viruses carried 16 novel mutations, 8 of which were increasing in prevalence percentages as of May 2022.Conclusions and RelevanceIn this cross-sectional study, an HAI model found SARS-COV-2 viruses with MV or novel mutations in the global population, which may require closer examination and monitoring. These results suggest that HAI may complement phylogenic variant assignment, providing additional insights into emerging novel variants in the population.

Introduction A 2019 survey identified significant variability of practice characteristics among outpatient psychiatric pharmacists (OPPs). No published model establishes which attributes constitute best practice for OPPs. By developing a consensus for best practice model attributes, OPPs can work toward consistent, effective patient care. This project aimed to develop attribute statements for a best practice model for OPPs providing direct patient care. Methods Board Certified Psychiatric Pharmacists and American Association of Psychiatric Pharmacists (AAPP) members were questioned using a 5-phase (P1-P5) survey and summit approach. The phases were: P1, broad ideation survey; P2, 10-person summit to develop draft statements; P3, survey of the draft statements for acceptance; P4, summit to resolve review feedback; and P5, survey of AAPP membership to confirm the finalized statements. Results P1 survey results generated a list of 143 possible attributes that informed the P2 summit, which were refined to 28 statements. P3 survey results confirmed at least 70% agreement with each statement. The P4 summit evaluated all P3 survey results and made significant modifications to 4 statements. Informal feedback was sought with other stakeholders, and supporting narratives and references were developed to provide clarity regarding the intent of each statement. Finalized statements and supporting narratives were confirmed in the P5 survey. Discussion The 28 attribute statements were developed over 18 months by gathering input and consensus through multiple modalities, including 3 surveys, 2 summit meetings, and numerous informal feedback requests. The agreement on the attribute statements was consistently high across all phases. The final attribute statements are presented elsewhere in this issue.

SARS-CoV-2 is spreading worldwide with continuously evolving variants, some of which occur in the Spike protein and appear to increase viral transmissibility. However, variants that cause severe COVID-19 or lead to other breakthroughs have not been well characterized. To discover such viral variants, we assembled a cohort of 683 COVID-19 patients; 388 inpatients (“cases”) and 295 outpatients (“controls”) from April to August 2020 using electronically captured COVID test request forms and sequenced their viral genomes. To improve the analytical power, we accessed 7137 viral sequences in Washington State to filter out viral single nucleotide variants (SNVs) that did not have significant expansions over the collection period. Applying this filter led to the identification of 53 SNVs that were statistically significant, of which 13 SNVs each had 3 or more variant copies in the discovery cohort. Correlating these selected SNVs with case/control status, eight SNVs were found to significantly associate with inpatient status (q-values p = 2.84 × 10–11). This haplotype appeared in April 2020, peaked in June, and persisted into January 2021. The association was replicated (OR = 5.46, p-value = 4.71 × 10−12) in an independent cohort of 964 COVID-19 patients (June 1, 2020 to March 31, 2021). The haplotype included a synonymous change N73N in endoRNase, and three non-synonymous changes coding residues R203K, R203S and G204R in the nucleocapsid protein. This discovery points to the potential functional role of the nucleocapsid protein in triggering “cytokine storms” and severe COVID-19 that led to hospitalization. The study further emphasizes a need for tracking and analyzing viral sequences in correlations with clinical status.