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El objetivo de este estudio descriptivo fue estudiar la relación entre autoeficacia, desesperanza aprendida e incapacidad funcional en pacientes con diagnóstico de artritis reumatoide y así determinar el papel de los factores psicológicos en la adaptación del paciente a la enfermedad. Se aplicó el Cuestionarios de Actividades de la Vida Diaria, el Indice de Actitudes Reumatoides y la Escala de Autoeficacia para la Artritis a 79 pacientes en tratamiento en la unidad de reumatología de la Clínica Bolivariana de Medellín (Colombia). Se encontró que la autoeficacia percibida se asocia negativamente con la desesperanza, el dolor y la incapacidad funcional. A su vez, estas tres variables se relacionaron entre sí de forma positiva. De lo anterior se puede concluir que una adecuada autoeficacia permitiría asumir de manera más favorable la enfermedad y adaptarse a los cambios que ella conlleva. La desesperanza se asoció de manera significativa con el dolor, y éste con la incapacidad, por lo que se puede inferir, una relación indirecta con la desesperanza. Los resultados confirman el peso significativo que tienen las variables psicológicas estudiadas sobre la artritis reumatoide y que tenerlas en cuenta podría modificar potencialmente la experiencia de la enfermedad.

En el presente estudio se examinó la especificidad y la sensibilidad de los anticuerpos antipéptidos citrulinados cíclicos (CCP) en pacientes latinoamericanas con artritis reumatoidea (AR), así como su relación con la actividad de la enfermedad, manifestaciones extraarticulares (MEA), síntesis de citocinas (IL-4, IL-10, IL-12, TNF-? e IFN-?) y factor reumatoideo (FR) IgM e IgA, y con el polimorfismo del HLA-DRB1. Se examinaron 79 pacientes con AR (69 con AR establecida y 10 con AR temprana sin previo tratamiento), 56 pacientes con espondilitis anquilosante (EA), 25 con lupus eritematoso sistémico (LES), 50 con síndrome de Sjögren primario (SSp) y diez individuos sanos. De las 69 pacientes con AR establecida, 36 fueron reevaluadas 2 años después. La actividad de la AR se examinó según los criterios del Colegio Americano de Reumatología. Los anticuerpos anti-CCP2, el FR y los niveles de citocinas se determinaron mediante inmunoensayo, y la genotipificación del HLA se llevó a cabo por reacción en cadena de la polimerasa utilizando mezclas de iniciadores específicos. Los anticuerpos anti-CCP se observaron en 96% de los pacientes con AR en la primera evaluación y en 86% en la segunda (p=0,12), sin modificación significativa en los valores (131±58,7 vs. 130,6±67,1 UI). Su sensibilidad y especificidad global fue de 94% y 92%, respectivamente, pero cuando sólo se consideraron los niveles altos (>60 UI) fueron de 84% y 95%, respectivamente. La razón de probabilidades (RP) positiva fue de 12 y la RP negativa de 0,06. El valor predictivo (VP) positivo fue de 87% y el VP negativo de 96%. Los anticuerpos anti-CCP se observaron en 12% de los pacientes con LES y con SSp, en 2% de los de EA y en 10% de los controles sanos. En los pacientes con AR no se asociaron con la actividad de la enfermedad, MEA y alelos del HLA-DRB1. Tampoco se observaron correlaciones significativas entre sus valores y los niveles de citocinas. En conclusión, los anticuerpos anti-CCP tienen un interés diagnóstico para la AR en nuestra población, pero su utilidad en el seguimiento clínico es limitada y su síntesis es independiente del HLA-DRB1 y no se correlacionan con niveles de citocinas Th1/Th2.

El objetivo de este estudio descriptivo transversal fue estudiar las relaciones entre variables sociodemográficas, incapacidad funcional, dolor y desesperanza aprendida en pacientes con artritis reumatoide. Se utilizó una muestra de 124 pacientes (110 mujeres y 14 hombres) diagnosticados con artritis reumatoide según los criterios del American College of Rheumatology. Como instrumento de medida de la discapacidad funcional se utilizó el Activities of Daily Living (HAQ-ADL), para evaluar el dolor se aplicó el Pain Severity Scale MOS y para la desesperanza aprendida el Arthritis Helplessness Index (AHÍ). Los resultados indicaron que los factores sociodemográficos son mejores predictores de desesperanza que el dolor y la incapacidad funcional, hecho que presenta evidentes discrepancias con investigaciones que plantean lo contrario, es decir una fuerte relación entre incapacidad funcional, dolor y desesperanza. Probablemente estas diferencias dependan de factores culturales que diferencian claramente la sociedad norteamericana de la colombiana con respecto al rol, la forma de atención y al cuidado de los enfermos crónicos. Esto nos permite entender más coherentemente una enfermedad crónica multifactorial, como es la artritis reumatoide, mediante un modelo bio-psico-social-ambiental y cultural.

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO2-capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO2-capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO2-capture property prediction and design optimization.

Organophosphorus nerve agents (OPNA) are hazardous environmental exposures to the civilian population and have been historically weaponized as chemical warfare agents (CWA). OPNA exposure can lead to several neurological, sensory, and motor symptoms that can manifest into chronic neurological illnesses later in life. There is still a large need for technological advancement to better understand changes in brain function following OPNA exposure. The human-relevant in vitro multi-electrode array (MEA) system, which combines the MEA technology with human stem cell technology, has the potential to monitor the acute, sub-chronic, and chronic consequences of OPNA exposure on brain activity. However, the application of this system to assess OPNA hazards and risks to human brain function remains to be investigated. In a concentration-response study, we have employed a human-relevant MEA system to monitor and detect changes in the electrical activity of engineered neural networks to increasing concentrations of the sarin surrogate 4-nitrophenyl isopropyl methylphosphonate (NIMP). We report a biphasic response in the spiking (but not bursting) activity of neurons exposed to low (i.e., 0.4 and 4 μM) versus high concentrations (i.e., 40 and 100 μM) of NIMP, which was monitored during the exposure period and up to 6 days post-exposure. Regardless of the NIMP concentration, at a network level, communication or coordination of neuronal activity decreased as early as 60 min and persisted at 24 h of NIMP exposure. Once NIMP was removed, coordinated activity was no different than control (0 μM of NIMP). Interestingly, only in the high concentration of NIMP did coordination of activity at a network level begin to decrease again at 2 days post-exposure and persisted on day 6 post-exposure. Notably, cell viability was not affected during or after NIMP exposure. Also, while the catalytic activity of AChE decreased during NIMP exposure, its activity recovered once NIMP was removed. Gene expression analysis suggests that human iPSC-derived neurons and primary human astrocytes resulted in altered genes related to the cell’s interaction with the extracellular environment, its intracellular calcium signaling pathways, and inflammation, which could have contributed to how neurons communicated at a network level.

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects’ functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve the important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs, despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods, and provide insights for future research in individualized parcellations.

Flea-borne typhus (FBT), due to Rickettsia typhi and R. felis, is an infection causing fever, headache, rash, hepatitis, thrombocytopenia, and diverse organ manifestations. Cough occurs in about 30% of patients with FBT, and chest X-ray abnormalities are seen in 17%. Severe pulmonary manifestations have also been reported in FBT, including adult respiratory distress syndrome and pulmonary embolism. Because of these pulmonary manifestations, FBT can mimic Coronavirus Illness 2019 (COVID-19), a febrile illness with prominent respiratory involvement. Flea-borne typhus and COVID-19 may also have similar laboratory abnormalities, including elevated ferritin, C-reactive protein, and D-dimer. However, elevated transaminase levels, rash, and thrombocytopenia are more common in FBT. Herein, we present four cases of patients with FBT who were initially suspected to have COVID-19. These cases illustrate the problem of availability bias, in which the clinician thinks a particular common condition (COVID-19 in this case) is more prevalent than it actually is.

Abstract Background Diabetic Foot Infection (DFI) guidelines recommend empiric methicillin-resistant Staphylococcus aureus (MRSA)-targeted therapy in settings where there is high prevalence of MRSA infections or in cases of severe infection; however, they do not provide recommendations for de-escalation. This approach has the potential to increase unnecessary use of broad-spectrum antibiotics; therefore, additional strategies are needed to optimize appropriate antibiotic use. This study evaluates the effect of MRSA nasal PCR testing on MRSA-targeted antibiotic use and clinical outcomes in patients with DFI. Methods This was a retrospective quasi-experimental study of patients admitted to South Texas Veterans Health Care System for DFI, with or without osteomyelitis (OM), who had an MRSA nasal PCR and culture data. Eligible patients were identified from the Corporate Data Warehouse and reviewed via electronic health record. Patients were allocated into two groups: PRE (5/1/2019-4/30/2020) and POST (12/1/2020-11/30/2021) protocol implementation for de-escalation or avoidance of MRSA-targeted antibiotics. The primary outcome was median (interquartile range [IQR]) hours of empiric inpatient MRSA-targeted antibiotic therapy. A Wilcoxon Rank Sum test was used to assess the difference between the groups for the primary outcome. Secondary outcomes included the proportion of patients needing MRSA coverage added back for MRSA after de-escalation, hospital readmission, length of hospital stay (LOS), patient mortality, and acute kidney injury. Results A total of 151 patients were included (83 PRE; 68 POST). Most patients were male (98% PRE; 97% POST) with a median age of 64 (IQR, 56–72) years. Incidence of MRSA in DFI in the cohort was 14.7% overall (12% PRE and 17.6% POST). MRSA was detected via nasal PCR in 12% of patients 15.7% PRE and 7.4% POST). After protocol implementation, there was a significant decrease in empiric MRSA-targeted antibiotic therapy use, from a median of 72 (IQR, 27–120) hours in the PRE group, to 24 (IQR, 12–72) hours in the POST group (p

While there is a growing appreciation of three-dimensional (3D) neural tissues (i.e., hydrogel-based, organoids, and spheroids), shown to improve cellular health and network activity to mirror brain-like activity in vivo, functional assessment using current electrophysiology techniques (e.g., planar multi-electrode arrays or patch clamp) has been technically challenging and limited to surface measurements at the bottom or top of the 3D tissue. As next-generation MEAs, specifically 3D MEAs, are being developed to increase the spatial precision across all three dimensions (X, Y, Z), development of improved computational analytical tools to discern region-specific changes within the Z dimension of the 3D tissue is needed. In the present study, we introduce a novel computational analytical pipeline to analyze 3D neural network activity recorded from a “bottom-up” 3D MEA integrated with a 3D hydrogel-based tissue containing human iPSC-derived neurons and primary astrocytes. Over a period of ~6.5 weeks, we describe the development and maturation of 3D neural activity (i.e., features of spiking and bursting activity) within cross sections of the 3D tissue, based on the vertical position of the electrode on the 3D MEA probe, in addition to network activity (identified using synchrony analysis) within and between cross sections. Then, using the sequential addition of postsynaptic receptor antagonists, bicuculline (BIC), 2-amino-5-phosphonovaleric acid (AP-5), and 6-cyano-5-nitroquinoxaline-2,3-dione (CNQX), we demonstrate that networks within and between cross sections of the 3D hydrogel-based tissue show a preference for GABA and/or glutamate synaptic transmission, suggesting differences in the network composition throughout the neural tissue. The ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue is a critical bottleneck; here we demonstrate a computational pipeline that can be implemented in studies to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.

Abstract Background The interaction between cancer diagnoses and COVID‐19 infection and outcomes is unclear. We leveraged a state‐wide, multi‐institutional database to assess cancer‐related risk factors for poor COVID‐19 outcomes. Methods We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) at 17 California medical centers. We identified adults tested for SARS‐CoV‐2 from 2/1/2020–12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS‐CoV‐2 test. Secondary outcomes were SARS‐CoV‐2 positivity and severe COVID‐19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer‐related factors associated with outcomes. Results We identified 409,462 patients undergoing SARS‐CoV‐2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67–0.74, p

Abstract The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Fentanyl is one of the most common opioid analgesics administered to patients undergoing surgery or for chronic pain management. While the side effects of chronic fentanyl abuse are recognized (e.g., addiction, tolerance, impairment of cognitive functions, and inhibit nociception, arousal, and respiration), it remains poorly understood what and how changes in brain activity from chronic fentanyl use influences the respective behavioral outcome. Here, we examined the functional and molecular changes to cortical neural network activity following sub-chronic exposure to two fentanyl concentrations, a low (0.01 μM) and high (10 μM) dose. Primary rat co-cultures, containing cortical neurons, astrocytes, and oligodendrocyte precursor cells, were seeded in wells on either a 6-well multi-electrode array (MEA, for electrophysiology) or a 96-well tissue culture plate (for serial endpoint bulk RNA sequencing analysis). Once networks matured (at 28 days in vitro), co-cultures were treated with 0.01 or 10 μM of fentanyl for 4 days and monitored daily. Only high dose exposure to fentanyl resulted in a decline in features of spiking and bursting activity as early as 30 min post-exposure and sustained for 4 days in cultures. Transcriptomic analysis of the complex cultures after 4 days of fentanyl exposure revealed that both the low and high dose induced gene expression changes involved in synaptic transmission, inflammation, and organization of the extracellular matrix. Collectively, the findings of this in vitro study suggest that while neuroadaptive changes to neural network activity at a systems level was detected only at the high dose of fentanyl, transcriptomic changes were also detected at the low dose conditions, suggesting that fentanyl rapidly elicits changes in plasticity.

Abstract Background Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public health risk. Prior methods have used publicly-available school immunization data for analysis (except for a few, which use private healthcare patient records). School immunization data has limited demographic information—as a result, such analyses are not able to provide demographic characteristics of significant clusters. Further, the resolution of the clusters identified by prior methods is limited since they are typically restricted to disks or well-rounded shapes. Methods We use realistic population models for Minnesota (MN) and Washington (WA) state, which provide a model of activities for all individuals in the population. We combine this with school level immunization data for these two states, to estimate vaccine coverage at the level of census block groups. A scan statistic method defined on networks is used for finding significant clusters of under-immunized block groups, without any restrictions on shape. Further we provide the demographic characteristics of these clusters. Results We find 2 significant under-vaccinated clusters in MN and 3 in WA. These are very irregular in shape, in contrast to the circular disks reported in prior work, which rely on the SatScan approach. Some of the clusters found by our method are not contained in those computed using SatScan, a state-of-the-art software tool used in similar studies in other states. Conclusions The emergence of under-immunized clusters is a growing concern for public health agencies because they can act as reservoirs of infection and increase the risk of infection into the wider population. Higher resolution clusters computed using our network based approach and population models provide new insights on the structure and characteristics of such clusters and enable targeted interventions.

Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons.

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen "on the ground." Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.

Although routinely done, there has been no evaluation of the utility of performing routine cerebrospinal fluid (CSF) examination in patients with active coccidioidomycosis and high complement fixation (IgG) antibody titers or other risk factors for disseminated infection. In our review 100% of patients diagnosed with coccidioidal meningitis had at least one sign or symptom consistent with infection of the central nervous system, headache was present in 100% of those with meningitis, while no patients without signs/symptoms of CNS infection were found to have coccidioidal meningitis, irrespective of antibody titers or other risk factors. Thus routine lumbar puncture may be unnecessary for patients with coccidioidomycosis who lack suggestive clinical symptoms.