Acute Pericarditis: Rapid Evidence Review.

Acute pericarditis is defined as inflammation of the pericardium and occurs in approximately 4.4% of patients who present to the emergency department for nonischemic chest pain, with a higher prevalence in men. Although there are numerous etiologies of pericarditis, most episodes are idiopathic and the cause is presumed to be viral. Diagnosis of pericarditis requires at least two of the following criteria: new or worsening pericardial effusion, characteristic pleuritic chest pain, pericardial friction rub, or electrocardiographic changes, including new, widespread ST elevations or PR depressions. Pericardial friction rubs are highly specific but transient, and they have been reported in 18% to 84% of patients with acute pericarditis. Classic electrocardiographic findings include PR-segment depressions; diffuse, concave, upward ST-segment elevations without reciprocal changes; and T-wave inversions. Transthoracic echocardiography should be performed in all patients with acute pericarditis to characterize the size of effusions and evaluate for complications. Nonsteroidal anti-inflammatory drugs are the first-line treatment option. Glucocorticoids should be reserved for patients with contraindications to first-line therapy and those who are pregnant beyond 20 weeks' gestation or have other systemic inflammatory conditions. Colchicine should be used in combination with first- or second-line treatments to reduce the risk of recurrence. Patients with a higher risk of complications should be admitted to the hospital for further workup and treatment.

Predictors of response in PROMIS-global in a chronic low back pain specialty clinic: STarTBack and chronic overlapping pain conditions.

BACKGROUND: Tools, such as the STarTBack Screening Tool (SBT), have been developed to identify risks of progressing to chronic disability in low back pain (LBP) patients in the primary care population. However, less is known about predictors of change in function after treatment in the specialty care population. OBJECTIVE: We pursued a retrospective observational cohort study involving LBP patients seen in a multidisciplinary specialty clinic to assess which features can predict change in function at follow-up. METHODS: The SBT was administered at initial visit, and a variety of patient characteristics were available in the chart including the presence of chronic overlapping pain conditions (COPCs). Patient Reported Outcomes Measurement Information System-10 (PROMIS-10) global physical health (PH) and global mental health (MH) were measured at baseline and at pragmatic time points during follow-up. Linear regression was used to estimate adjusted associations between available features and changes in PROMIS scores. RESULTS: 241 patients were followed for a mean of 17.0 ± 7.5 months. Mean baseline pain was 6.7 (SD 2.1), PROMIS-10 global MH score was 44.8 (SD 9.3), and PH score was 39.4 (SD 8.6). 29.7% were low-risk on the SBT, 41.8% were medium-risk, and 28.5% were high-risk. Mean change in MH and PH scores from baseline to the follow-up questionnaire were 0.86 (SD 8.11) and 2.39 (SD 7.52), respectively. Compared to low-risk patients, high-risk patients had a mean 4.35 points greater improvement in their MH score (p= 0.004) and a mean 3.54 points greater improvement in PH score (p= 0.006). Fewer COPCs also predicted greater improvement in MH and PH. CONCLUSIONS: SBT and the presence of COPC, which can be assessed at initial presentation to a specialty clinic, can predict change in PROMIS following treatment. Effort is needed to identify other factors that can help predict change in function after treatment in the specialty care setting.

Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey.

This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults aged ≥50 years (Korea National Health and Nutrition Examination Survey), we explored 119 factors to detect LBP in individuals who reported experiencing LBP for at least 30 days within the previous 3 months. Our primary model, model 1, employed eXtreme Gradient Boosting (XGBoost) and selected primary factors (PFs) based on their feature importance scores. To extend this, we introduced additional factors, such as lumbar X-ray findings, physical activity, sitting time, and nutrient intake levels, which were available only during specific survey periods, into models 2 to 4. Model performance was evaluated using the area under the curve, with predicted probabilities explained by SHapley Additive exPlanations. Eleven PFs were identified, and model 1 exhibited an enhanced area under the curve .8 (.77-.84, 95% confidence interval). The factors had varying impacts across individuals, underscoring the need for personalized assessment. Hip and knee joint pain were the most significant PFs. High levels of physical activity were found to have a negative association with LBP, whereas a high intake of omega-6 was found to have a positive association. Notably, we identified factor clusters, including hip joint pain and female sex, potentially linked to osteoarthritis. In summary, this study successfully developed effective XGBoost models for LBP detection, thereby providing valuable insight into LBP-related factors. Comprehensive LBP management, particularly in women with osteoarthritis, is crucial given the presence of multiple factors. PERSPECTIVE: This article introduces XGBoost models designed to detect LBP and explores the multifactorial aspects of LBP through the application of SHapley Additive exPlanations and network analysis on the 4 developed models. The utilization of this analytical system has the potential to aid in devising personalized management strategies to address LBP.

Transcribed enhancer sequences are required for maize p1 paramutation.

Paramutation is a transfer of heritable silencing states between interacting endogenous alleles or between endogenous alleles and homologous transgenes. Prior results demonstrated that paramutation occurs at the P1-rr (red pericarp and red cob) allele of the maize p1 (pericarp color 1) gene when exposed to a transgene containing a 1.2-kb enhancer fragment (P1.2) of P1-rr. The paramutable P1-rr allele undergoes transcriptional silencing resulting in a paramutant light-pigmented P1-rr' state. To define more precisely the sequences required to elicit paramutation, the P1.2 fragment was further subdivided, and the fragments transformed into maize plants and crossed with P1-rr. Analysis of the progeny plants showed that the sequences required for paramutation are located within a ∼600-bp segment of P1.2 and that this segment overlaps with a previously identified enhancer that is present in 4 direct repeats in P1-rr. The paramutagenic segment is transcribed in both the expressed P1-rr and the silenced P1-rr'. Transcription is sensitive to α-amanitin, indicating that RNA polymerase II mediates most of the transcription of this sequence. Although transcription within the paramutagenic sequence was similar in all tested genotypes, small RNAs were more abundant in the silenced P1-rr' epiallele relative to the expressed P1-rr allele. In agreement with prior results indicating the association of RNA-mediated DNA methylation in p1 paramutation, DNA blot analyses detected increased cytosine methylation of the paramutant P1-rr' sequences homologous to the transgenic P1.2 subfragments. Together these results demonstrate that the P1-rr enhancer repeats mediate p1 paramutation.

Robust negative longitudinal magnetoresistance and spin-orbit torque in sputtered Pt3Sn and Pt3SnxFe1-x topological semimetal.

Contrary to topological insulators, topological semimetals possess a nontrivial chiral anomaly that leads to negative magnetoresistance and are hosts to both conductive bulk states and topological surface states with intriguing transport properties for spintronics. Here, we fabricate highly-ordered metallic Pt3Sn and Pt3SnxFe1-x thin films via sputtering technology. Systematic angular dependence (both in-plane and out-of-plane) study of magnetoresistance presents surprisingly robust quadratic and linear negative longitudinal magnetoresistance features for Pt3Sn and Pt3SnxFe1-x, respectively. We attribute the anomalous negative longitudinal magnetoresistance to the type-II Dirac semimetal phase (pristine Pt3Sn) and/or the formation of tunable Weyl semimetal phases through symmetry breaking processes, such as magnetic-atom doping, as confirmed by first-principles calculations. Furthermore, Pt3Sn and Pt3SnxFe1-x show the promising performance for facilitating the development of advanced spin-orbit torque devices. These results extend our understanding of chiral anomaly of topological semimetals and can pave the way for exploring novel topological materials for spintronic devices.

Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems.

Decision-making in spine surgery is complex due to patients' heterogeneity and complexity of spinal pathologies and the various surgical options applied to a given pathology. Artificial intelligence/machine learning algorithms provide an opportunity to improve patient selection, surgical planning, and outcomes. The purpose of this article is to present the experience and applications of in spine surgery at 2 large academic health care systems.

Statistical information about reward timing is insufficient for promoting optimal persistence decisions.

When deciding how long to keep waiting for delayed rewards that will arrive at an uncertain time, different distributions of possible reward times dictate different optimal strategies for maximizing reward. When reward timing distributions are heavy-tailed (e.g., waiting on hold) there is a point at which waiting is no longer advantageous because the opportunity cost of waiting is too high. Alternatively, when reward timing distributions have more predictable timing (e.g., uniform), it is advantageous to wait as long as necessary for the reward. Although people learn to approximate optimal strategies, little is known about how this learning occurs. One possibility is that people learn a general cognitive representation of the probability distribution that governs reward timing and then infer a strategy from that model of the environment. Another possibility is that they learn an action policy in a way that depends more narrowly on direct task experience, such that general knowledge of the reward timing distribution is insufficient for expressing the optimal strategy. Here, in a series of studies in which participants decided how long to persist for delayed rewards before quitting, we provided participants with information about the reward timing distribution in several ways. Whether the information was provided through counterfactual feedback (Study 1), previous exposure (Studies 2a and 2b), or description (Studies 3a and 3b), it did not obviate the need for direct, feedback-driven learning in a decision context. Therefore, learning when to quit waiting for delayed rewards might depend on task-specific experience, not solely on probabilistic reasoning.

Chronic Asthma Treatment: Common Questions and Answers.

Asthma affects more than 25 million people in the United States, and 62% of adults with asthma do not have adequately controlled symptoms. Asthma severity and level of control should be assessed at diagnosis and evaluated at subsequent visits using validated tools such as the Asthma Control Test or the asthma APGAR (activities, persistent, triggers, asthma medications, response to therapy) tools. Short-acting beta2 agonists are preferred asthma reliever medications. Controller medications consist of inhaled corticosteroids, long-acting beta2 agonists, long-acting muscarinic antagonists, and leukotriene receptor antagonists. Treatment typically begins with inhaled corticosteroids, and additional medications or dosage increases should be added in a stepwise fashion according to guideline-directed therapy recommendations from the National Asthma Education and Prevention Program or the Global Initiative for Asthma when symptoms are inadequately controlled. Single maintenance and reliever therapy combines an inhaled corticosteroid and long-acting beta2 agonist for controller and reliever treatments. This therapy is preferred for adults and adolescents because of its effectiveness in reducing severe exacerbations. Subcutaneous immunotherapy may be considered for those five years and older with mild to moderate allergic asthma; however, sublingual immunotherapy is not recommended. Patients with severe uncontrolled asthma despite appropriate treatment should be reassessed and considered for specialty referral. Biologic agents may be considered for patients with severe allergic and eosinophilic asthma.

Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients.

OBJECTIVE: We applied natural language processing and inference methods to extract social determinants of health (SDoH) information from clinical notes of patients with chronic low back pain (cLBP) to enhance future analyses of the associations between SDoH disparities and cLBP outcomes. MATERIALS AND METHODS: Clinical notes for patients with cLBP were annotated for 7 SDoH domains, as well as depression, anxiety, and pain scores, resulting in 626 notes with at least one annotated entity for 364 patients. We used a 2-tier taxonomy with these 10 first-level classes (domains) and 52 second-level classes. We developed and validated named entity recognition (NER) systems based on both rule-based and machine learning approaches and validated an entailment model. RESULTS: Annotators achieved a high interrater agreement (Cohen's kappa of 95.3% at document level). A rule-based system (cTAKES), RoBERTa NER, and a hybrid model (combining rules and logistic regression) achieved performance of F1 = 47.1%, 84.4%, and 80.3%, respectively, for first-level classes. DISCUSSION: While the hybrid model had a lower F1 performance, it matched or outperformed RoBERTa NER model in terms of recall and had lower computational requirements. Applying an untuned RoBERTa entailment model, we detected many challenging wordings missed by NER systems. Still, the entailment model may be sensitive to hypothesis wording. CONCLUSION: This study developed a corpus of annotated clinical notes covering a broad spectrum of SDoH classes. This corpus provides a basis for training machine learning models and serves as a benchmark for predictive models for NER for SDoH and knowledge extraction from clinical texts.

Risk factors for extended length of stay and non-home discharge in adults treated with multi-level fusion for lumbar degenerative pathology and deformity.

PURPOSE: To identify independent risk factors, including the Risk Assessment and Prediction Tool (RAPT) score, associated with extended length of stay (eLOS) and non-home discharge following elective multi-level instrumented spine fusion operations for diagnosis of adult spinal deformity (ASD) and lumbar degenerative pathology. METHODS: Adults who underwent multi-level ([Formula: see text] segments) instrumented spine fusions for ASD and lumbar degenerative pathology at a single institution (2016-2021) were reviewed. Presence of a pre-operative RAPT score was used as an inclusion criterion. Excluded were patients who underwent non-elective operations, revisions, operations for trauma, malignancy, and/or infections. Outcomes were eLOS (> 7 days) and discharge location (home vs. non-home). Predictor variables included demographics, comorbidities, operative information, Surgical Invasiveness Index (SII), and RAPT score. Fisher's exact test was used for univariate analysis, and significant variables were implemented in multivariate binary logistic regression, with generation of 95% percent confidence intervals (CI), odds ratios (OR), and p-values. RESULTS: Included for analysis were 355 patients. Post-operatively, 36.6% (n = 130) had eLOS and 53.2% (n = 189) had a non-home discharge. Risk factors significant for a non-home discharge were older age (> 70 years), SII > 36, pre-op RAPT < 10, DMII, diagnosis of depression or anxiety, and eLOS. Risk factors significant for an eLOS were SII > 20, RAPT < 6, and an ASA score of 3. CONCLUSION: The RAPT score and SII were most important significant predictors of eLOS and non-home discharges following multi-level instrumented fusions for lumbar spinal pathology and deformity. Preoperative optimization of the RAPT's individual components may provide a useful strategy for decreasing LOS and modifying discharge disposition.

Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator.

STUDY DESIGN: A retrospective study at a single academic institution. OBJECTIVE: The purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator. SUMMARY OF BACKGROUND DATA: A total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record. METHODS: Patients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N=587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days). RESULTS: Of 3678 patients analyzed, 51.4% were male (n=1890) and 48.6% were female (n=1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R2 =0.16), the predictions of poisson regression ( R2 =0.29) and LASSO ( R2 =0.29) models were significantly more correlated with observed LOS ( P =0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP ( P =0.135). CONCLUSION: The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

Complex chromosomal rearrangements induced by transposons in maize.

Eukaryotic genomes are large and complex, and gene expression can be affected by multiple regulatory elements and their positions within the dynamic chromatin architecture. Transposable elements are known to play important roles in genome evolution, yet questions remain as to how transposable elements alter genome structure and affect gene expression. Previous studies have shown that genome rearrangements can be induced by Reversed Ends Transposition involving termini of Activator and related transposable elements in maize and other plants. Here, we show that complex alleles can be formed by the rapid and progressive accumulation of Activator-induced duplications and rearrangements. The p1 gene enhancer in maize can induce ectopic expression of the nearby p2 gene in pericarp tissue when placed near it via different structural rearrangements. By screening for p2 expression, we identified and studied 5 cases in which multiple sequential transposition events occurred and increased the p1 enhancer copy number. We see active p2 expression due to multiple copies of the p1 enhancer present near p2 in all 5 cases. The p1 enhancer effects are confirmed by the observation that loss of p2 expression is correlated with transposition-induced excision of the p1 enhancers. We also performed a targeted Chromosome Conformation Capture experiment to test the physical interaction between the p1 enhancer and p2 promoter region. Together, our results show that transposon-induced rearrangements can accumulate rapidly and progressively increase genetic variation important for genomic evolution.

Analyzing excipient-related adverse events in antiseizure drug formulations.

There have been several reports that switching formulations of antiseizure medications (ASMs) has been associated with a deterioration of seizure control, seizure relapse or increased adverse effects. Considering recent findings that excipients, namely purported inactive ingredients, may nevertheless exert biological effects, it is possible that the variation in adverse event profile of antiseizure drugs may be related to differences in excipients. To test our hypothesis, we constructed a new research tool to connect FDA-compiled adverse event reports to the excipient in the medicine. Analysis of adverse events to formulations of five different second-generation antiseizure drugs - gabapentin, lamotrigine, levetiracetam, oxcarbazepine, and topiramate - showed several significant associations between specific excipient in the formulations and an adverse event when compared to the same medicine formulated with other excipients. Epilepsy and seizure adverse events were associated with multiple excipients across gabapentin, lamotrigine, and levetiracetam formulations. A different group of excipients were associated with reports of hypersensitivity reactions including urticaria, rash, erythema, and Stevens-Johnson syndrome. Our study provides a novel approach to analyzing post-market surveillance reports by including excipients. It may be useful to clinicians when evaluating select patient groups that may be refractory to pharmacological treatment or experience worsening adverse events when switching formulations of the same antiseizure medicine.

Primary Care Implementation of Genomic Population Health Screening Using a Large Gene Sequencing Panel.

To realize the promise of genomic medicine, harness the power of genomic technologies, and capitalize on the extraordinary pace of research linking genomic variation to disease risks, healthcare systems must embrace and integrate genomics into routine healthcare. We have implemented an innovative pilot program for genomic population health screening for any-health-status adults within the largest health system in Vermont, United States. This program draws on key research and technological advances to safely extract clinical value for genomics in routine health care. The program offers no-cost, non-research DNA sequencing to patients by their primary care providers as a preventive health tool. We partnered with a commercial clinical testing company for two next generation sequencing gene panels comprising 431 genes related to both high and low-penetrance common health risks and carrier status for recessive disorders. Only pathogenic or likely pathogenic variants are reported. Routine written clinical consultation is provided with a concise, clinical "action plan" that presents core messages for primary care provider and patient use and supports clinical management and health education beyond the testing laboratory's reports. Access to genetic counseling is free in most cases. Predefined care pathways and access to genetics experts facilitates the appropriate use of results. This pilot tests the feasibility of routine, ethical, and scalable use of population genomic screening in healthcare despite generally imperfect genomic competency among both the public and health care providers. This article describes the program design, implementation process, guiding philosophies, and insights from 2 years of experience offering testing and returning results in primary care settings. To aid others planning similar programs, we review our barriers, solutions, and perceived gaps in the context of an implementation research framework.

Maintaining One-Lung Ventilation With an Endobronchial Blocker Through a Damaged Left-Sided Double-Lumen Tube: A Case Report.

One-lung ventilation (OLV) can be accomplished utilizing a double-lumen tube (DLT) and an endobronchial blocker (EBB) or intentionally placing a standard endotracheal tube (ETT) into a mainstem bronchus. However, secondary options must be available should the primary method fail. We present a case where an EBB and a fiberoptic bronchoscope (FOB) were successfully passed through a left-sided DLT to reestablish right-lung isolation after the DLT bronchial cuff was surgically damaged. We advocate competency in placing both DLTs and EBBs, as well as having EBBs readily accessible as a secondary isolation method during OLV.

Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain.

Chronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current priority of LBP research is the development of objective, multidimensional assessment tools that subgroup LBP patients based on neurobiological pain mechanisms, to facilitate matching patients with the optimal therapies. Using unsupervised machine learning on full body biomechanics, including kinematics, dynamics, and muscle forces, captured with a marker-less depth camera, this study identified a forward-leaning sit-to-stand strategy (STS) as a discriminating movement biomarker for LBP subjects. A forward-leaning STS strategy, as opposed to a vertical rise strategy seen in the control participants, is less efficient and results in increased spinal loads. Inefficient STS with the subsequent higher spinal loading may be a biomarker of poor motor control in LBP patients as well as a potential source of the ongoing symptomology.

Excision and reinsertion of Ac macrotransposons in maize.

Eukaryotic Macrotransposons (MTns) can be formed by 2 nearby elements flanking a segment of host DNA. The maize Ac transposon can form Ac::MTns, but little is known about Ac::MTn transposition activities. Here, we studied 3 Ac::MTns at the maize p1 locus, each of which is composed of a segment of maize p1 genomic DNA (up to 15 kb) bounded by a fractured Ac element (fAc, 2039 bp), and a full-length Ac element in direct orientation. The resulting Ac::MTns are of 16, 16.5, and 22 kb total length. From these 3 Ac::MTns, we identified 10 independent cases of macrotransposition, and observed similar features of transposition between Ac::MTn and standard Ac/Ds, including characteristic excision footprints and insertion target site duplications. Nine out of the 10 Ac::MTn reinsertion targets were genetically linked to the donor sites, another similarity with Ac/Ds standard transposition. We also identified a MTn-like structure in the maize B73 reference genome and 5 NAM founder lines. The MTn in diverse lines is flanked by target site duplications, confirming the historic occurrence of MTn transposition during genome evolution. Our results show that Ac::MTns are capable of mobilizing segments of DNA long enough to include a typical full-length plant gene and in theory could erode gene colinearity in syntenic regions during plant genome evolution.