Total organic carbon measurements reveal major gaps in petrochemical emissions reporting.

Anthropogenic organic carbon emissions reporting has been largely limited to subsets of chemically speciated volatile organic compounds. However, new aircraft-based measurements revealed total gas-phase organic carbon emissions that exceed oil sands industry-reported values by 1900% to over 6300%, the bulk of which was due to unaccounted-for intermediate-volatility and semivolatile organic compounds. Measured facility-wide emissions represented approximately 1% of extracted petroleum, resulting in total organic carbon emissions equivalent to that from all other sources across Canada combined. These real-world observations demonstrate total organic carbon measurements as a means of detecting unknown or underreported carbon emissions regardless of chemical features. Because reporting gaps may include hazardous, reactive, or secondary air pollutants, fully constraining the impact of anthropogenic emissions necessitates routine, comprehensive total organic carbon monitoring as an inherent check on mass closure.

Chronic activation of tubulin tyrosination in HCM mice and human iPSC-engineered heart tissues improves heart function.

Rationale: Hypertrophic cardiomyopathy (HCM) is the most common cardiac genetic disorder caused by sarcomeric gene variants and associated with left ventricular (LV) hypertrophy and diastolic dysfunction. The role of the microtubule network has recently gained interest with the findings that -α-tubulin detyrosination (dTyr-tub) is markedly elevated in heart failure. Acute reduction of dTyr-tub by inhibition of the detyrosinase (VASH/SVBP complex) or activation of the tyrosinase (tubulin tyrosine ligase, TTL) markedly improved contractility and reduced stiffness in human failing cardiomyocytes, and thus poses a new perspective for HCM treatment. Objective: In this study, we tested the impact of chronic tubulin tyrosination in a HCM mouse model ( Mybpc3 -knock-in; KI), in human HCM cardiomyocytes and in SVBP-deficient human engineered heart tissues (EHTs). Methods and Results: AAV9-mediated TTL transfer was applied in neonatal wild-type (WT) rodents and 3-week-old KI mice and in HCM human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes. We show that i) TTL for 6 weeks dose-dependently reduced dTyr-tub and improved contractility without affecting cytosolic calcium transients in WT cardiomyocytes; ii) TTL for 12 weeks improved diastolic filling, cardiac output and stroke volume and reduced stiffness in KI mice; iii) TTL for 10 days normalized cell hypertrophy in HCM hiPSC-cardiomyocytes; iv) TTL induced a marked transcription and translation of several tubulins and modulated mRNA or protein levels of components of mitochondria, Z-disc, ribosome, intercalated disc, lysosome and cytoskeleton in KI mice; v) SVBP-deficient EHTs exhibited reduced dTyr-tub levels, higher force and faster relaxation than TTL-deficient and WT EHTs. RNA-seq and mass spectrometry analysis revealed distinct enrichment of cardiomyocyte components and pathways in SVBP-KO vs. TTL-KO EHTs. Conclusion: This study provides the first proof-of-concept that chronic activation of tubulin tyrosination in HCM mice and in human EHTs improves heart function and holds promise for targeting the non-sarcomeric cytoskeleton in heart disease.

Adult human cardiomyocyte mechanics in osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is an extracellular matrix disorder characterized by defects in collagen-1 transport or synthesis, resulting in bone abnormalities. Although reduced collagen in OI hearts has been associated with reduced myocardial stiffness and left ventricular remodeling, its impact on cardiomyocyte (CM) function has not been studied. Here, we explore the tissue-level and CM-level properties of a heart from a deceased organ donor with OI type I. Proteomics and histology confirmed strikingly low expression of collagen 1. Trabecular stretch confirmed low stiffness on the tissue level. However, CMs retained normal viscoelastic properties as revealed by nanoindentation. Interestingly, OI CMs were hypercontractile relative to nonfailing controls after 24 h of culture. In response to 48 h of culture on surfaces with physiological (10 kPa) and pathological (50 kPa) stiffness, OI CMs demonstrated a greater reduction in contractility than nonfailing CMs, suggesting that OI CMs may have an impaired stress response. Levels of detyrosinated α-tubulin, known to be responsive to extracellular stiffness, were reduced in OI CMs. Together these data confirm multiple CM-level adaptations to low stiffness that extend our understanding of OI in the heart and how CMs respond to extracellular stiffness.NEW & NOTEWORTHY In a rare donation of a heart from an individual with osteogenesis imperfecta (OI), we explored cardiomyocyte (CM) adaptations to low stiffness. This represents the first assessment of cardiomyocyte mechanics in OI. The data reveal the hypercontractility of OI CMs with rapid rundown when exposed to acute stiffness challenges, extending our understanding of OI. These data demonstrate that the impact of OI on myocardial mechanics includes cardiomyocyte adaptations beyond known direct effects on the extracellular matrix.

Brentuximab vedotin plus AVD for Hodgkin lymphoma: incidence and management of peripheral neuropathy in a multisite cohort.

Brentuximab vedotin (BV) in combination with doxorubicin, vinblastine, and dacarbazine (AVD) is increasingly used for frontline treatment of stage III/IV classical Hodgkin lymphoma (cHL). Peripheral neuropathy (PN) was the most common and treatment-limiting side effect seen in clinical trials but has not been studied in a nontrial setting, in which clinicians may have different strategies for managing it. We conducted a multisite retrospective study to characterize PN in patients who received BV + AVD for newly diagnosed cHL. One hundred fifty-three patients from 10 US institutions were eligible. Thirty-four patients (22%) had at least 1 ineligibility criteria for ECHELON-1, including stage, performance status, and comorbidities. PN was reported by 80% of patients during treatment; 39% experienced grade (G) 1, 31% G2, and 10% G3. In total, BV was modified in 44% of patients because of PN leading to BV discontinuation in 23%, dose reduction in 17%, and temporary hold in 4%. With a median follow-up of 24 months, PN resolution was documented in 36% and improvement in 33% at the last follow-up. Two-year progression-free survival (PFS) for the advanced-stage patients was 82.7% (95% confidence interval [CI], 0.76-0.90) and overall survival was 97.4% (95% CI, 0.94-1.00). Patients who discontinued BV because of PN did not have inferior PFS. In the nontrial setting, BV + AVD was associated with a high incidence of PN. In our cohort, which includes patients who would not have been eligible for the pivotal ECHELON-1 trial, BV discontinuation rates were higher than previously reported, but 2-year outcomes remain comparable.

Text Message Reminders for Long-Acting Injectable Antipsychotics in Patients with Schizophrenia Spectrum Disorders.

BACKGROUND: Individuals with schizophrenia spectrum disorders have a chronic disease process that is difficult to manage. Medication nonadherence increases the risk for relapse and subsequent rehospitalization. Long-acting injectable (LAI) antipsychotics have greater effectiveness in promoting medication adherence. AIMS: To determine whether text message reminders for LAI antipsychotic administration improve medication adherence. METHODS: The setting is a community mental health clinic in the west Texas region. Reminders deliver upon scheduling the appointment 3 weeks, 3 days, and 3 hr before the medication is due. This project aimed to determine the effectiveness of text reminders for LAI compliance in patients with schizophrenia spectrum disorders. Primary outcome measures include compliance percentage and target day variability. After exclusion criteria, there was a sample size of 49 patients. RESULTS: This pre- and post-intervention study utilized descriptive statistics and nonparametric analysis. Pre-intervention metrics outline 84.39% compliance with 3.55 target day variability. Post-intervention data resulted in a significant increase in compliance percentage to 91.24% (p = .014) and a decrease in target day variability to 1.33 days (p < .05). CONCLUSION: Text message reminders may be an effective intervention in increasing LAI compliance for individuals with schizophrenia spectrum disorders.

The Interplay between T Cells and Cancer: The Basis of Immunotherapy.

Over the past decade, immunotherapy has emerged as one of the most promising approaches to cancer treatment. The use of immune checkpoint inhibitors has resulted in impressive and durable clinical responses in the treatment of various cancers. Additionally, immunotherapy utilizing chimeric antigen receptor (CAR)-engineered T cells has produced robust responses in blood cancers, and T cell receptor (TCR)-engineered T cells are showing promising results in the treatment of solid cancers. Despite these noteworthy advancements in cancer immunotherapy, numerous challenges remain. Some patient populations are unresponsive to immune checkpoint inhibitor therapy, and CAR T cell therapy has yet to show efficacy against solid cancers. In this review, we first discuss the significant role that T cells play in the body's defense against cancer. We then delve into the mechanisms behind the current challenges facing immunotherapy, starting with T cell exhaustion due to immune checkpoint upregulation and changes in the transcriptional and epigenetic landscapes of dysfunctional T cells. We then discuss cancer-cell-intrinsic characteristics, including molecular alterations in cancer cells and the immunosuppressive nature of the tumor microenvironment (TME), which collectively facilitate tumor cell proliferation, survival, metastasis, and immune evasion. Finally, we examine recent advancements in cancer immunotherapy, with a specific emphasis on T-cell-based treatments.

A Large-Scale Multicenter Retrospective Study on Nephrotoxicity Associated With Empiric Broad-Spectrum Antibiotics in Critically Ill Patients.

BACKGROUND: Evidence regarding acute kidney injury associated with concomitant administration of vancomycin and piperacillin-tazobactam is conflicting, particularly in patients in the ICU. RESEARCH QUESTION: Does a difference exist in the association between commonly prescribed empiric antibiotics on ICU admission (vancomycin and piperacillin-tazobactam, vancomycin and cefepime, and vancomycin and meropenem) and acute kidney injury? STUDY DESIGN AND METHODS: This was a retrospective cohort study using data from the eICU Research Institute, which contains records for ICU stays between 2010 and 2015 across 335 hospitals. Patients were enrolled if they received vancomycin and piperacillin-tazobactam, vancomycin and cefepime, or vancomycin and meropenem exclusively. Patients initially admitted to the ED were included. Patients with hospital stay duration of < 1 h, receiving dialysis, or with missing data were excluded. Acute kidney injury was defined as Kidney Disease: Improving Global Outcomes stage 2 or 3 based on serum creatinine component. Propensity score matching was used to match patients in the control (vancomycin and meropenem or vancomycin and cefepime) and treatment (vancomycin and piperacillin-tazobactam) groups, and ORs were calculated. Sensitivity analyses were performed to study the effect of longer courses of combination therapy and patients with renal insufficiency on admission. RESULTS: Thirty-five thousand six hundred fifty-four patients met inclusion criteria (vancomycin and piperacillin-tazobactam, n = 27,459; vancomycin and cefepime, n = 6,371; vancomycin and meropenem, n = 1,824). Vancomycin and piperacillin-tazobactam was associated with a higher risk of acute kidney injury and initiation of dialysis when compared with that of both vancomycin and cefepime (Acute kidney injury: OR, 1.37 [95% CI, 1.25-1.49]; dialysis: OR, 1.28 [95% CI, 1.14-1.45]) and vancomycin and meropenem (Acute kidney injury: OR, 1.27 [95%, 1.06-1.52]; dialysis: OR, 1.56 [95% CI, 1.23-2.00]). The odds of acute kidney injury developing was especially pronounced in patients without renal insufficiency receiving a longer duration of vancomycin and piperacillin-tazobactam therapy compared with vancomycin and meropenem therapy. INTERPRETATION: VPT is associated with a higher risk of acute kidney injury than both vancomycin and cefepime and vancomycin and meropenem in patients in the ICU, especially for patients with normal initial kidney function requiring longer durations of therapy. Clinicians should consider vancomycin and meropenem or vancomycin and cefepime to reduce the risk of nephrotoxicity for patients in the ICU.

Palliative Extubation: A Discussion of Practices and Considerations.

Palliative extubation (PE), also known as compassionate extubation, is a common event in the critical care setting and an important aspect of end-of-life care.1 In a PE, mechanical ventilation is discontinued. Its goal is to honor the patient's preferences, optimize comfort, and allow a natural death when medical interventions, including maintenance of ventilatory support, are not achieving desired outcomes. If not done effectively, PE can cause unintended physical, emotional, psychosocial, or other stress for patients, families, and healthcare staff. Studies show that PE is done with much variability across the globe, and there is limited evidence of best practice. Nevertheless, the practice of PE increased during the coronavirus disease 2019 pandemic due to the surge of dying mechanically ventilated patients. Thus, the importance of effectively conducting a PE has never been more crucial. Some studies have provided guidelines for the process of PE. However, our goal is to provide a comprehensive review of issues to consider before, during, and after a PE. This paper highlights the core palliative skills of communication, planning, symptom assessment and management, and debriefing. Our aim is to better prepare healthcare workers to provide quality palliative care during PEs, most especially when facing future pandemics.

Characteristics and FEA verification of the attraction between like magnetic poles.

The attraction between unequally sized like magnetic poles is characterized herein. Finite element analysis (FEA) simulation has verified that attraction can occur between like poles. Between two unequally sized like poles with various dimensions and alignments, a turning point (TP) appears on the curves of force vs. distance between them, which is caused by the localized demagnetization (LD). The LD plays a role far before the distance between the poles reduces to the TP. The LD area may have a changed polarity, making the attraction possible and not in violation of basic laws of magnetism. Here, the LD levels have been determined using FEA simulation, and the factors affecting the LD have been explored, including the geometry, the linearity of the BH curve, and the alignment of the magnet pairs. Novel devices can be designed with attraction between the centers of such like poles and repulsion when off-center.

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.

BACKGROUND: Photographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional systemic medical conditions. METHODS: We developed a deep learning system (DLS) that takes external eye photographs as input and predicts systemic parameters, such as those related to the liver (albumin, aspartate aminotransferase [AST]); kidney (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [ACR]); bone or mineral (calcium); thyroid (thyroid stimulating hormone); and blood (haemoglobin, white blood cells [WBC], platelets). This DLS was trained using 123 130 images from 38 398 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA, USA. Evaluation focused on nine prespecified systemic parameters and leveraged three validation sets (A, B, C) spanning 25 510 patients with and without diabetes undergoing eye screening in three independent sites in Los Angeles county, CA, and the greater Atlanta area, GA, USA. We compared performance against baseline models incorporating available clinicodemographic variables (eg, age, sex, race and ethnicity, years with diabetes). FINDINGS: Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST >36·0 U/L, calcium <8·6 mg/dL, eGFR <60·0 mL/min/1·73 m2, haemoglobin <11·0 g/dL, platelets <150·0 × 103/µL, ACR ≥300 mg/g, and WBC <4·0 × 103/µL on validation set A (a population resembling the development datasets), with the area under the receiver operating characteristic curve (AUC) of the DLS exceeding that of the baseline by 5·3-19·9% (absolute differences in AUC). On validation sets B and C, with substantial patient population differences compared with the development datasets, the DLS outperformed the baseline for ACR ≥300·0 mg/g and haemoglobin <11·0 g/dL by 7·3-13·2%. INTERPRETATION: We found further evidence that external eye photographs contain biomarkers spanning multiple organ systems. Such biomarkers could enable accessible and non-invasive screening of disease. Further work is needed to understand the translational implications. FUNDING: Google.

Development of a Machine Learning Model for Sonographic Assessment of Gestational Age.

Importance: Fetal ultrasonography is essential for confirmation of gestational age (GA), and accurate GA assessment is important for providing appropriate care throughout pregnancy and for identifying complications, including fetal growth disorders. Derivation of GA from manual fetal biometry measurements (ie, head, abdomen, and femur) is operator dependent and time-consuming. Objective: To develop artificial intelligence (AI) models to estimate GA with higher accuracy and reliability, leveraging standard biometry images and fly-to ultrasonography videos. Design, Setting, and Participants: To improve GA estimates, this diagnostic study used AI to interpret standard plane ultrasonography images and fly-to ultrasonography videos, which are 5- to 10-second videos that can be automatically recorded as part of the standard of care before the still image is captured. Three AI models were developed and validated: (1) an image model using standard plane images, (2) a video model using fly-to videos, and (3) an ensemble model (combining both image and video models). The models were trained and evaluated on data from the Fetal Age Machine Learning Initiative (FAMLI) cohort, which included participants from 2 study sites at Chapel Hill, North Carolina (US), and Lusaka, Zambia. Participants were eligible to be part of this study if they received routine antenatal care at 1 of these sites, were aged 18 years or older, had a viable intrauterine singleton pregnancy, and could provide written consent. They were not eligible if they had known uterine or fetal abnormality, or had any other conditions that would make participation unsafe or complicate interpretation. Data analysis was performed from January to July 2022. Main Outcomes and Measures: The primary analysis outcome for GA was the mean difference in absolute error between the GA model estimate and the clinical standard estimate, with the ground truth GA extrapolated from the initial GA estimated at an initial examination. Results: Of the total cohort of 3842 participants, data were calculated for a test set of 404 participants with a mean (SD) age of 28.8 (5.6) years at enrollment. All models were statistically superior to standard fetal biometry-based GA estimates derived from images captured by expert sonographers. The ensemble model had the lowest mean absolute error compared with the clinical standard fetal biometry (mean [SD] difference, -1.51 [3.96] days; 95% CI, -1.90 to -1.10 days). All 3 models outperformed standard biometry by a more substantial margin on fetuses that were predicted to be small for their GA. Conclusions and Relevance: These findings suggest that AI models have the potential to empower trained operators to estimate GA with higher accuracy.

Adherence to 24-Hour Movement Recommendations and Health Indicators in Early Adolescence: Cross-Sectional and Longitudinal Associations in the Adolescent Brain Cognitive Development Study.

PURPOSE: Adherence to 24-hour movement guidelines of ≥60 minutes of physical activity, ≤2 hours of screen time, and 9-11 hours of sleep has been shown to benefit cognitive, physical, and psychosocial health in children and young adolescents aged 5-13 years. However, these findings have mostly been based on cross-sectional studies or relatively small samples and the associations between adherence to guidelines and brain structure remain to be evaluated. METHODS: Data from the Adolescent Brain Cognitive Development℠ (ABCD) study of 10,574 early adolescents aged 9-14 years from September 2016 to January 2021 were used to examine whether adherence to 24-hour movement guidelines benefits cognition (general cognitive ability, executive function, and learning/memory assessed by the National Institutes of Health Toolbox neurocognitive battery), body mass index, psychosocial health (internalizing, externalizing, and total problems from the parent-reported Child Behavior Checklist), and magnetic resonance imaging-derived brain morphometric measures at baseline (T1), ∼2 years later (T2), and longitudinally from T1 to T2 (T2-T1). Multivariable linear mixed models were used, with adjustments for sociodemographic confounders. Time elapsed and T1 outcome measures were also controlled for in longitudinal models. RESULTS: Better cognitive scores, fewer behavioral problems, lower adiposity levels, and greater gray matter volumes were observed in those who met both sleep and screen time recommendations compared to those who met none. Longitudinal follow-up further supports these findings; participants who met both recommendations at T1 and T2 evidenced better outcome measures than those who met none. DISCUSSION: These findings support consideration of integrated rather than isolated movement recommendations across the day in early adolescence for better cognitive, physical and psychosocial health. Although the associations between physical activity and health indicators were less consistent in this study, the significant findings from sleep and screen time demonstrate the importance of considering movement recommendations in an integrated rather than isolated manner for adolescent health. It is recommended that movement behaviors be simultaneously targeted for better developmental outcomes.

Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists.

Background The World Health Organization (WHO) recommends chest radiography to facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise remains limited in many regions. Purpose To develop a deep learning system (DLS) to detect active pulmonary TB on chest radiographs and compare its performance to that of radiologists. Materials and Methods A DLS was trained and tested using retrospective chest radiographs (acquired between 1996 and 2020) from 10 countries. To improve generalization, large-scale chest radiograph pretraining, attention pooling, and semisupervised learning ("noisy-student") were incorporated. The DLS was evaluated in a four-country test set (China, India, the United States, and Zambia) and in a mining population in South Africa, with positive TB confirmed with microbiological tests or nucleic acid amplification testing (NAAT). The performance of the DLS was compared with that of 14 radiologists. The authors studied the efficacy of the DLS compared with that of nine radiologists using the Obuchowski-Rockette-Hillis procedure. Given WHO targets of 90% sensitivity and 70% specificity, the operating point of the DLS (0.45) was prespecified to favor sensitivity. Results A total of 165 754 images in 22 284 subjects (mean age, 45 years; 21% female) were used for model development and testing. In the four-country test set (1236 subjects, 17% with active TB), the receiver operating characteristic (ROC) curve of the DLS was higher than those for all nine India-based radiologists, with an area under the ROC curve of 0.89 (95% CI: 0.87, 0.91). Compared with these radiologists, at the prespecified operating point, the DLS sensitivity was higher (88% vs 75%, P < .001) and specificity was noninferior (79% vs 84%, P = .004). Trends were similar within other patient subgroups, in the South Africa data set, and across various TB-specific chest radiograph findings. In simulations, the use of the DLS to identify likely TB-positive chest radiographs for NAAT confirmation reduced the cost by 40%-80% per TB-positive patient detected. Conclusion A deep learning method was found to be noninferior to radiologists for the determination of active tuberculosis on digital chest radiographs. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by van Ginneken in this issue.

A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment.

Background: Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption in low-to-middle-income countries. This study investigated the use of artificial intelligence for fetal ultrasound in under-resourced settings. Methods: Blind sweep ultrasounds, consisting of six freehand ultrasound sweeps, were collected by sonographers in the USA and Zambia, and novice operators in Zambia. We developed artificial intelligence (AI) models that used blind sweeps to predict gestational age (GA) and fetal malpresentation. AI GA estimates and standard fetal biometry estimates were compared to a previously established ground truth, and evaluated for difference in absolute error. Fetal malpresentation (non-cephalic vs cephalic) was compared to sonographer assessment. On-device AI model run-times were benchmarked on Android mobile phones. Results: Here we show that GA estimation accuracy of the AI model is non-inferior to standard fetal biometry estimates (error difference -1.4 ± 4.5 days, 95% CI -1.8, -0.9, n = 406). Non-inferiority is maintained when blind sweeps are acquired by novice operators performing only two of six sweep motion types. Fetal malpresentation AUC-ROC is 0.977 (95% CI, 0.949, 1.00, n = 613), sonographers and novices have similar AUC-ROC. Software run-times on mobile phones for both diagnostic models are less than 3 s after completion of a sweep. Conclusions: The gestational age model is non-inferior to the clinical standard and the fetal malpresentation model has high AUC-ROCs across operators and devices. Our AI models are able to run on-device, without internet connectivity, and provide feedback scores to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings.

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and standard of care during the COVID-19 pandemic. A common partial mitigation is transfer learning by pretraining a "generic network" on a large nonmedical data set and then fine-tuning on a task-specific radiology data set. Purpose To reduce data set size requirements for chest radiography deep learning models by using an advanced machine learning approach (supervised contrastive [SupCon] learning) to generate chest radiography networks. Materials and Methods SupCon helped generate chest radiography networks from 821 544 chest radiographs from India and the United States. The chest radiography networks were used as a starting point for further machine learning model development for 10 prediction tasks (eg, airspace opacity, fracture, tuberculosis, and COVID-19 outcomes) by using five data sets comprising 684 955 chest radiographs from India, the United States, and China. Three model development setups were tested (linear classifier, nonlinear classifier, and fine-tuning the full network) with different data set sizes from eight to 85. Results Across a majority of tasks, compared with transfer learning from a nonmedical data set, SupCon reduced label requirements up to 688-fold and improved the area under the receiver operating characteristic curve (AUC) at matching data set sizes. At the extreme low-data regimen, training small nonlinear models by using only 45 chest radiographs yielded an AUC of 0.95 (noninferior to radiologist performance) in classifying microbiology-confirmed tuberculosis in external validation. At a more moderate data regimen, training small nonlinear models by using only 528 chest radiographs yielded an AUC of 0.75 in predicting severe COVID-19 outcomes. Conclusion Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations. © RSNA, 2022 Online supplemental material is available for this article.

Limited evidence for enhanced working memory performance and effort allocation in the face of loss versus gain incentives: A preregistered (non) replication.

The motivation to avoid losses is often considered a strong drive of human behavior, affecting decisions in the context of risk, temporal delay, and effort provision. However, studies measuring cognitive performance under loss and gain incentives have yielded mixed findings. In a recent study, we found evidence that losses motivated better working memory performance than gains. Since these results deviated from previous studies, we decided to perform a preregistered, high-powered, replication, and extension study. Eighty-six participants performed a working memory task (N-Back) under Gain and Loss conditions. Pupillometry was recorded during task performance, and subjective effort ratings were taken after each task run. Results showed that contrary to our previous findings, losses did not enhance working memory performance, relative to gains, at any N-Back level. Similarly, while pupil diameter was parametrically modulated by working memory load, no differences between the Gain and Loss conditions were found. Participants did believe that they were exerting more effort during Loss versus Gain conditions, regardless of N-Back level. However, this did not translate to performance differences between Gain and Loss conditions.

Transcriptional, Post-Transcriptional, and Post-Translational Mechanisms Rewrite the Tubulin Code During Cardiac Hypertrophy and Failure.

A proliferated and post-translationally modified microtubule network underlies cellular growth in cardiac hypertrophy and contributes to contractile dysfunction in heart failure. Yet how the heart achieves this modified network is poorly understood. Determining how the "tubulin code"-the permutations of tubulin isoforms and post-translational modifications-is rewritten upon cardiac stress may provide new targets to modulate cardiac remodeling. Further, while tubulin can autoregulate its own expression, it is unknown if autoregulation is operant in the heart or tuned in response to stress. Here we use heart failure patient samples and murine models of cardiac remodeling to interrogate transcriptional, autoregulatory, and post-translational mechanisms that contribute to microtubule network remodeling at different stages of heart disease. We find that autoregulation is operant across tubulin isoforms in the heart and leads to an apparent disconnect in tubulin mRNA and protein levels in heart failure. We also find that within 4 h of a hypertrophic stimulus and prior to cardiac growth, microtubule detyrosination is rapidly induced to help stabilize the network. This occurs concomitant with rapid transcriptional and autoregulatory activation of specific tubulin isoforms and microtubule motors. Upon continued hypertrophic stimulation, there is an increase in post-translationally modified microtubule tracks and anterograde motors to support cardiac growth, while total tubulin content increases through progressive transcriptional and autoregulatory induction of tubulin isoforms. Our work provides a new model for how the tubulin code is rapidly rewritten to establish a proliferated, stable microtubule network that drives cardiac remodeling, and provides the first evidence of tunable tubulin autoregulation during pathological progression.

Subcategories of Fibromyalgia: A New Concept.

Fibromyalgia has previously been categorized as primary, secondary, and juvenile fibromyalgia. However, these definitions do not adequately explain the etiopathology of disease, nor do they help direct new specific therapies. Herein, we review the previously known categorizations of fibromyalgia. Based on common patient characteristics and previously studied pathophysiologies, we propose new subcategorizations of fibromyalgia that we have self-narrated, including hormonal fibromyalgia, neuroendocrine fibromyalgia, psychologic fibromyalgia, inflammatory fibromyalgia, and lastly, neuropathic fibromyalgia. Future research needs to be done to verify, add to, and fully describe these self-narrated categories of fibromyalgia that we have proposed.

Rheumatologic Manifestations of Post SARS-CoV-2 Infection: A Case Series.

BACKGROUND: It has been over a year since the first documented case of the COVID-19 virus was recorded. Since then, our understanding of this virus has continually evolved, however, its wide-ranging effects are still unfolding. Similar to previously studied viral infections, severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) has been shown to lead to a degree of autoimmunity in patients who are recovering from its effects. Due to its effects on the innate immune system, such as the toll-like receptors and complement system, a varying degree of proinflammatory markers can become widespread in those who continue to recover from the virus. This case series offers a unique perspective on how COVID-19 has had dramatic effects on those already suffering from inflammatory rheumatic conditions, such as rheumatoid arthritis, systemic lupus erythematosus, or fibromyalgia. As the ever-lasting effects of COVID-19 are still unfolding, this case series is one of few to discuss the development and changes of patients with rheumatic conditions. This study hopes to encourage larger studies to be conducted on the effects of COVID- 19 on autoimmune conditions. CASE PRESENTATION: Seven patients were identified with new manifestations of rheumatic conditions, which included 3 cases of rheumatoid arthritis, 2 cases of polymyalgia rheumatica, 1 case of reactive arthritis, and 1 case of cutaneous lupus. Post-COVID syndrome was also diagnosed in 7 other patients. Patients with rheumatoid arthritis presented with symptoms 4-5 weeks after being diagnosed with COVID-19. Symptoms of polyarticular joint pain, swelling, and morning stiffness were reported in this group. These patients were treated with disease-modifying anti-rheumatic drugs and experienced an improvement in symptoms on follow-up. Two cases of polymyalgia rheumatica were identified in patients that were previously diagnosed with COVID-19 six weeks prior. One patient had no significant past medical history and the other patient had a history of rheumatoid arthritis, which was well controlled. These patients experienced weakness and tenderness in the proximal joints with elevated levels of ESR and CRP. They were treated with prednisone and showed improvement. Reactive arthritis was diagnosed in 1 patient who presented with swelling in both hands and wrists 2 days after being diagnosed with COVID-19. This patient began to experience symptoms of reactive arthritis 2 days after resolution of initial COVID-19 symptoms and this persisted for 3 months. The patient was managed with methylprednisolone injections and NSAIDs, which improved her symptoms. Post-COVID syndrome was identified in 7 patients. All patients were female and had a history of well-controlled fibromyalgia. Patients generally experienced fatigue, headaches, and memory fog, which had variable onset from a few days and up to 4 weeks after being diagnosed with COVID-19. One patient had a complete recovery of her symptoms at follow-up 3 months after the initial presentation. The other 6 patients continued to report symptoms of post-COVID syndrome at follow-up. Patients were managed with lifestyle modifications and their previous fibromyalgia treatment. CONCLUSION: While cases of COVID-19 continue to rise, complications of this disease are still being discovered. Those who initially recover from COVID-19 may experience new-onset rheumatic conditions, worsening of previously diagnosed rheumatic conditions, or post-COVID syndrome. As we continue to learn more about the effects of COVID-19, the awareness of these manifestations will play a key role in the appropriate management of these patients.