Flea-Borne Typhus as a COVID-19 Mimic: A Report of Four Cases.

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.

Effects of COVID-19 hospitalization rates on the incidence of hospital-acquired Candida central line-associated bloodstream infection.

BACKGROUND: An increase in central line-associated bloodstream infections (CLABSIs) has been reported during the Coronavirus (COVID-19) pandemic; however, few studies have documented causative pathogens, particularly Candida species associated with candidemia. METHODS: This was a retrospective study based on the National Health Care Safety Network surveillance definitions of CLABSI caused by Candida species during pre-COVID-19 (October 2017 to February 2020) and COVID-19 (March 2020 to December 2021) periods within a local community hospital. Candida CLABSI incidence per 1,000 central line days was compared between periods using the χ2 test and correlated with COVID-19 inpatient hospitalization rates using Pearson correlation. RESULTS: Overall CLABSI (0.68 vs 1.98 per 1,000, P = .004) and Candida CLABSI incidence (0.06 vs 0.77 per 1,000, P = .003) significantly increased from pre-COVID-19 to COVID-19 periods. There was a significant correlation between COVID-19 ICU hospitalizations and CLABSIs (R = 0.18, P = .048), but not acute care hospitalizations and CLABSIs (R = 0.065, P = .250). Conversely, there was a significant association between COVID-19 acute care hospitalizations and Candida CLABSIs (R = 0.50, P < .001), but not COVID-19 ICU hospitalizations and Candida CLABSIs (R = 0.01, P = .631). CONCLUSIONS: During the COVID-19 pandemic, our facility experienced a significant increase in Candida CLABSI and a significant correlation of Candida CLABSIs with acute care COVID-19 hospitalizations.

Corrigendum: Spatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode array.

[This corrects the article DOI: 10.3389/fncel.2023.1287089.].

Spatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode array.

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.

An electronic health record cohort of Veterans with amyotrophic lateral sclerosis.

Objective: To assemble and characterize an electronic health record (EHR) dataset for a large cohort of US military Veterans diagnosed with ALS (Amyotrophic Lateral Sclerosis). Methods: An EHR dataset for 19,662 Veterans diagnosed with ALS between January 1, 2000 to December 31, 2020 was compiled from the Veterans Health Administration (VHA) EHR database by a query for ICD9 diagnosis (335.20) or ICD10 diagnosis (G12.21) for Amyotrophic Lateral Sclerosis. Results: The cohort is predominantly male (98.94%) and white (72.37%) with a median age at disease onset of 68 years and median survival from the date of diagnosis of 590 days. With the designation of ALS as a compensable illness in 2009, there was a subsequent increase in the number of Veterans diagnosed per year in the VHA, but no change in median survival. The cohort included a greater-than-expected proportion of individuals whose branch of service at the time of separation was the Army. Conclusions: The composition of the cohort reflects the VHA population who are at greatest risk for ALS. The greater than expected proportion of individuals whose branch of service at the time of separation was the Army suggests the possibility of a branch-specific risk factor for ALS.

Comparative-Effectiveness of Oral Beta-Lactams and Fluoroquinolones for Stepdown Therapy in Patients with Enterobacterales Bloodstream Infections: A Retrospective Cohort Study.

Background: This study compares treatment failure for patients who received oral beta-lactams (BLs) and fluoroquinolones (FQs) for stepdown treatment of Enterobacterales bloodstream infections (BSIs). Methods: We conducted a single-center, retrospective, age- and sex-matched, cohort study, at a Veterans Affairs (VA) hospital in South Texas. Eligible patients were at least 18 years of age with a monomicrobial BSI treated with a single oral BL or FQ antibiotic. Treatment failure was defined as recurrence or all-cause mortality within 90 days of documented BSI. Bivariate (chi-square, Fisher's Exact, and Wilcoxon Rank Sum) and multivariate (logistic regression) statistical tests were used to compare groups. Results: A total of 130 patients were included in this study, with 65 patients per group. Groups were well balanced with respect to exact age, sex assigned at birth, Caucasian race, source control, intensive care unit admission, and Charlson Comorbidity Index. Importantly, 60% of patients in the BL group had cultures that were resistant to FQs and 71% were prescribed cefpodoxime. Patients in the BL group had higher median (interquartile range [IQR]) Pitt bacteremia scores than those in the FQ group: 2 (1-4) vs. 1 (1-2), p=0.04. Patients in the BL group also had a higher median (IQR) duration of intravenous (IV) antibiotics than those in the FQ group: 5 (3-7) vs. 4 (3-5), p=0.02. Treatment failure was statistically comparable for patients in the BL and FQ groups: 15% vs. 12%, p=0.61. This finding was consistent in a multivariate logistic regression model with group (BL vs. FQ) as the independent variable, treatment failure as the dependent variable, and Pitt bacteremia score and duration of IV antibiotics as covariates (OR: 0.76, 95% CI: 0.27-2.18). One patient in the FQ group experienced Clostridioides difficile infection. Conclusion: This study suggests that BLs may be as effective as FQs for oral stepdown treatment of Enterobacterales BSI without the potential associated risks. Furthermore, in the setting of FQ-resistant Enterobacterales BSI secondary to urinary source, third generation oral cephalosporins (i.e., cefpodoxime) may be reasonable alternatives.

Eczema Monkeypoxicum in a Female Patient With Atopic Dermatitis.

A female patient with atopic dermatitis who had recently received a tattoo presented with severe right ear pain and several vesiculopustular lesions. Over 1 week, she developed approximately 80 widely distributed lesions. Laboratory testing confirmed mpox (previously monkeypox) virus, and no further lesions developed after initiation of oral tecovirimat.

Dose-dependent consequences of sub-chronic fentanyl exposure on neuron and glial co-cultures.

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.

COVID-19 outcomes in patients with cancer: Findings from the University of California health system database.

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 < 0.001). Among the 1781 SARS-CoV-2-positive patients with cancer, BCR/ABL-negative myeloproliferative neoplasms (RR 2.15, 95% CI: 1.25-3.41, p = 0.007), venetoclax (RR 2.96, 95% CI: 1.14-5.66, p = 0.028), and methotrexate (RR 2.72, 95% CI: 1.10-5.19, p = 0.032) were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID-19. CONCLUSIONS: In this large, diverse cohort, cancer was associated with a decreased risk of SARS-CoV-2 positivity. Patients with BCR/ABL-negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS-CoV-2 infection. Mechanistic and comparative studies are needed to validate findings.

Dynamic modeling of hospitalized COVID-19 patients reveals disease state-dependent risk factors.

OBJECTIVE: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. MATERIALS AND METHODS: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression. RESULTS: The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. DISCUSSION: Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. CONCLUSION: Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

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.

Agent-Based Computational Epidemiological Modeling.

The study of epidemics is useful for not only understanding outbreaks and trying to limit their adverse effects, but also because epidemics are related to social phenomena such as government instability, crime, poverty, and inequality. One approach for studying epidemics is to simulate their spread through populations. In this work, we describe an integrated multi-dimensional approach to epidemic simulation, which encompasses: (1) a theoretical framework for simulation and analysis; (2) synthetic population (digital twin) generation; (3) (social contact) network construction methods from synthetic populations, (4) stylized network construction methods; and (5) simulation of the evolution of a virus or disease through a social network. We describe these aspects and end with a short discussion on simulation results that inform public policy.

Immunologic resilience and COVID-19 survival advantage.

BACKGROUND: The risk of severe coronavirus disease 2019 (COVID-19) varies significantly among persons of similar age and is higher in males. Age-independent, sex-biased differences in susceptibility to severe COVID-19 may be ascribable to deficits in a sexually dimorphic protective attribute that we termed immunologic resilience (IR). OBJECTIVE: We sought to examine whether deficits in IR that antedate or are induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection independently predict COVID-19 mortality. METHODS: IR levels were quantified with 2 novel metrics: immune health grades (IHG-I [best] to IHG-IV) to gauge CD8+ and CD4+ T-cell count equilibrium, and blood gene expression signatures. IR metrics were examined in a prospective COVID-19 cohort (n = 522); primary outcome was 30-day mortality. Associations of IR metrics with outcomes in non-COVID-19 cohorts (n = 13,461) provided the framework for linking pre-COVID-19 IR status to IR during COVID-19, as well as to COVID-19 outcomes. RESULTS: IHG-I, tracking high-grade equilibrium between CD8+ and CD4+ T-cell counts, was the most common grade (73%) among healthy adults, particularly in females. SARS-CoV-2 infection was associated with underrepresentation of IHG-I (21%) versus overrepresentation (77%) of IHG-II or IHG-IV, especially in males versus females (P < .01). Presentation with IHG-I was associated with 88% lower mortality, after controlling for age and sex; reduced risk of hospitalization and respiratory failure; lower plasma IL-6 levels; rapid clearance of nasopharyngeal SARS-CoV-2 burden; and gene expression signatures correlating with survival that signify immunocompetence and controlled inflammation. In non-COVID-19 cohorts, IR-preserving metrics were associated with resistance to progressive influenza or HIV infection, as well as lower 9-year mortality in the Framingham Heart Study, especially in females. CONCLUSIONS: Preservation of immunocompetence with controlled inflammation during antigenic challenges is a hallmark of IR and associates with longevity and AIDS resistance. Independent of age, a male-biased proclivity to degrade IR before and/or during SARS-CoV-2 infection predisposes to severe COVID-19.

Dextrocardia con doble salida del ventrículo derecho con comunicación interventricular no relacionada asociada a malposición de grandes vasos / Right ventricular double outlet dextrocardia with unrelated ventricular septal defect associated with great vessel malposition

Resumen En el siguiente reporte se expone un caso complejo en el que coexisten malformaciones cardiacas congénitas de presentación atípica con un doble tracto de salida del ventrículo derecho, en un paciente con múltiples anomalías menores en la exploración física y un cariotipo normal. La atipia de cada una de estas y su coexistencia hacen pensar en posibles alteraciones genéticas que aún son desconocidas. Lo anterior supone un reto terapéutico con el fin de restaurar una fisiología cardiaca compatible con la vida, lo cual se logra en este caso mediante un cerclaje exitoso de la arteria pulmonar.

Abstract The following report presents a complex case in which congenital cardiac malformations of atypical presentation coexist with a double outlet right ventricle, in a patient with multiple minor abnormalities on the physical examination and a normal karyotype. The atypia of these and their coexistence suggest possible genetic alterations that are still unknown. Therefore, a therapeutic challenge in order to restore a cardiac physiology compatible with life is proposed, which is achieved in this case through a successful banding of the pulmonary artery.

Aspergillosis: Epidemiology, Diagnosis, and Treatment.

The spectrum of disease produced by Aspergillus species ranges from allergic syndromes to chronic pulmonary conditions and invasive infections. Invasive aspergillosis is a major cause of morbidity and mortality in immunocompromised patients. Risk factors continue to evolve and include newer biological agents that target the immune system and postinfluenza infection; and it has been observed following COVID-19 infection. Diagnosis remains a challenge but non-culture-based methods are available. Antifungal resistance has emerged. Voriconazole remains the treatment of choice but isavuconazole and posaconazole have similar efficacy with less toxicity. Combination therapy is used with extensive infection and in severe immunosuppression.

Validation of Available Extended-Spectrum-Beta-Lactamase Clinical Scoring Models in Predicting Drug Resistance in Patients with Enteric Gram-Negative Bacteremia Treated at South Texas Veterans Health Care System.

Extended-spectrum-beta-lactamase (ESBL)-producing Enterobacteriaceae are increasingly common; however, predicting which patients are likely to be infected with an ESBL pathogen is challenging, leading to increased use of carbapenems. To date, five prediction models have been developed to distinguish between patients infected with ESBL pathogens. The aim of this study was to validate and compare each of these models to better inform antimicrobial stewardship. This was a retrospective cohort study of patients with Gram-negative bacteremia treated at the South Texas Veterans Health Care System over 3 months from 2018 to 2019. We evaluated isolate, clinical syndrome, and score variables for the five published prediction models/scores: Italian "Tumbarello," Duke, University of South Carolina (USC), Hopkins clinical decision tree, and modified Hopkins. Each model was assessed using the area under the receiver operating characteristic curve (AUROC) and Pearson correlation. One hundred forty-five patients were included for analysis, of which 20 (13.8%) were infected with an ESBL Escherichiacoli or Klebsiella spp. The most common sources of infection were genitourinary (55.8%) and gastrointestinal/intraabdominal (24.1%), and the most common pathogen was E. coli (75.2%). The prediction model with the strongest discriminatory ability (AUROC) was Tumbarello (0.7556). The correlation between prediction model score and percent ESBL was strongest with the modified Hopkins model (R2 = 0.74). In this veteran population, the modified Hopkins and Duke prediction models were most accurate in discriminating between Gram-negative bacteremia patients when considering both AUROC and correlation. However, given the moderate discriminatory ability, many patients with ESBL Enterobacteriaceae (at least 25%) may still be missed empirically.

Cases and etiologies of suspected COVID-19 reactivation.

Our article outlines a perspective on COVID-19 reactivation with considerations of implored commentary on behalf of the medical community regarding open discourse about this subject. Such a topic is paramount in elucidating parameters that pertain to testing, and subsequent public health population dynamics once uneventful cases pass. We argue that what some may refer to as a reinfection or reactivation of the virus, is actually a result of prolonged shedding of the virus complemented with occasional false positives/negatives and lab errors. This article was written with the perspective of informing in addition to engage discussions that distill salient, evidence-based characterization of COVID-19. We hope to recruit fellow academics in medicine who see trends in their own respective communities about people who re-test, and to explore their clinical outcomes.

Functional and transcriptional characterization of complex neuronal co-cultures.

Brain-on-a-chip systems are designed to simulate brain activity using traditional in vitro cell culture on an engineered platform. It is a noninvasive tool to screen new drugs, evaluate toxicants, and elucidate disease mechanisms. However, successful recapitulation of brain function on these systems is dependent on the complexity of the cell culture. In this study, we increased cellular complexity of traditional (simple) neuronal cultures by co-culturing with astrocytes and oligodendrocyte precursor cells (complex culture). We evaluated and compared neuronal activity (e.g., network formation and maturation), cellular composition in long-term culture, and the transcriptome of the two cultures. Compared to simple cultures, neurons from complex co-cultures exhibited earlier synapse and network development and maturation, which was supported by localized synaptophysin expression, up-regulation of genes involved in mature neuronal processes, and synchronized neural network activity. Also, mature oligodendrocytes and reactive astrocytes were only detected in complex cultures upon transcriptomic analysis of age-matched cultures. Functionally, the GABA antagonist bicuculline had a greater influence on bursting activity in complex versus simple cultures. Collectively, the cellular complexity of brain-on-a-chip systems intrinsically develops cell type-specific phenotypes relevant to the brain while accelerating the maturation of neuronal networks, important features underdeveloped in traditional cultures.

Modeling the temporal network dynamics of neuronal cultures.

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.

Finding Spatial Clusters Susceptible to Epidemic Outbreaks due to Undervaccination.

Geographical clusters of undervaccinated populations have emerged in various parts of the United States in recent years. Public health response involves surveillance and field work, which is very resource intensive. Given that public health resources are often limited, identifying and rank-ordering critical clusters can help prioritize and allocate scarce resources for surveillance and quick intervention. We quantify the criticality of a cluster as the additional number of infections caused if the cluster is underimmunized. We focus on finding clusters that maximize this measure and develop efficient approximation algorithms for finding critical clusters by exploiting structural properties of the problem. Our methods involve solving a more general problem of maximizing a submodular function on a graph with connectivity constraints. We apply our methods to the state of Minnesota, where we find clusters with significantly higher criticality than those obtained by heuristics used in public health.