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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

Introduction Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. Methods We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). Results The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Discussion Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git .

Chen, Xiaoyi↗
DOE OSTI2022

Gait Characterization and Analysis of Hereditary Amyloidosis Associated with Transthyretin Patients: A Case Series

Hereditary amyloidosis associated with transthyretin (ATTRv), is a rare autosomal dominant disease characterized by length-dependent symmetric polyneuropathy that has gait impairment as one of its consequences. The gait pattern of V30M ATTRv amyloidosis patients has been described as similar to that of diabetic neuropathy, associated with steppage, but has never been quantitatively characterized. In this study we aim to characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding and potential for supporting diagnosis and disease progression evaluation. We present a case series in which we conducted two gait analyses, 18 months apart, of five V30M ATTRv amyloidosis patients using a 12-camera, marker based, optical system as well as six force platforms. Linear kinematics, ground reaction forces, and angular kinematics results are analyzed for all patients. All patients, except one, showed a delayed toe-off in the second assessment, as well as excessive pelvic rotation, hip extension and external transverse rotation and knee flexion (in stance and swing phases), along with reduced vertical and mediolateral ground reaction forces. The described gait anomalies are not clinically quantified; thus, gait analysis may contribute to the assessment of possible disease progression along with the clinical evaluation.

Vilas-Boas, Maria do Carmo (ORCID:000000025012789X↗
DOE OSTI2021

Association Between Patient Education Videos and Knowledge of Radiation Treatment

Patient knowledge of radiation therapy (RT) before consult is typically limited, with many having misconceptions or fears. There exists a need to improve patient education in RT. Our purpose was to study the impact of patient education videos on patient-reported knowledge of RT, anxiety/fear, and satisfaction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

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

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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cryo-EM confirms a common fibril fold in the heart of four patients with ATTRwt amyloidosis

ATTR amyloidosis results from the conversion of transthyretin into amyloid fibrils that deposit in tissues causing organ failure and death. This conversion is facilitated by mutations in ATTRv amyloidosis, or aging in ATTRwt amyloidosis. ATTRv amyloidosis exhibits extreme phenotypic variability, whereas ATTRwt amyloidosis presentation is consistent and predictable. Previously, we found unique structural variabilities in cardiac amyloid fibrils from polyneuropathic ATTRv-I84S patients. In contrast, cardiac fibrils from five genotypically different patients with cardiomyopathy or mixed phenotypes are structurally homogeneous. To understand fibril structure’s impact on phenotype, it is necessary to study the fibrils from multiple patients sharing genotype and phenotype. Here we show the cryo-electron microscopy structures of fibrils extracted from four cardiomyopathic ATTRwt amyloidosis patients. Our study confirms that they share identical conformations with minimal structural variability, consistent with their homogenous clinical presentation. Our study contributes to the understanding of ATTR amyloidosis biopathology and calls for further studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Six Machine-Learning Methods for Predicting Hospital-Stay Duration for Patients with Sepsis: A Comparative Study

Sepsis is a life-threatening medical condition that, if not treated promptly, can result in tissue damage, organ failure, and death. According to the Centers for Disease Control, about 270,000 individuals die of sepsis in the US each year. Further, sepsis expenditures accounted for 13% of total US hospital costs in 2013, totaling more than $24 billion. Our project objectives were to determine if Machine Learning algorithms could reliably predict hospital stay duration for patients with sepsis. The data set we used has been de-identified and is freely available through the BupaR package. The data includes 1050 cases, 15214 events, and 16 types of actions related to sepsis patient care. First, we used process mining to determine how long each patient was in the hospital. Using BupaR’s functions, we created several process model graphs. These process models depict the movement of patients at a hospital and provide duration data for each patent case. Second, we identified outlier data and created two dataset versions: one with and one without outliers. We then applied the following analysis methods: Linear Regression, Random Forest, K-Nearest Neighbors, Neural Networks, XGBoost, and lightGBM. We compared the model validations for the six machine learning models using the same data-splitting method. We found that the XGBoost model had the best prediction accuracy of 73.9 percent for cases with outliers, and 79 percent for cases without outliers. We also found that the lightGBM model had the lowest mean absolute error between prediction and actual duration in days with 3.66 days for the case with outliers, and 2.4 days for the case without outliers. These two models outperformed the other four models. This work will be enhanced in the future by exploring new prediction algorithms and comparing them with the results of this study.

Chen, Lingtao↗
DOE OSTI2022

Longitudinal Trajectories of Memory Performance in Patients with Early-Stage Breast Cancer

Background. While breast cancer and its treatments may affect cognition, the longitudinal trajectories of cognition among those receiving differing cancer treatment types remain poorly understood. Prior research suggests hippocampal-prefrontal cortex network integrity may influence cognition, although how this network predicts performance over time remains unclear. Methods. We conducted a prospective trial including 69 patients with early-stage breast cancer receiving adjuvant therapy and 12 controls. Longitudinal cognitive testing was conducted at four visits: pretreatment-baseline, 6-7 months, 14-15 months, and 23-24 months. Cognitive composite scores of episodic memory, executive functioning, and processing speed were assessed at each timepoint. Baseline structural MRI was obtained in a subset of these participants, and hippocampal and prefrontal cortex regional volumes were extracted. Results. Longitudinal linear mixed modeling revealed significant group by time interactions on memory performance, controlling for age and education. Post hoc analyses revealed this effect was driven by patients treated with chemotherapy or chemotherapy plus hormone therapy, who demonstrated the least improvement in memory scores over time. Treatment group did not significantly influence the relationship between time and processing speed or executive functioning. Neither pretreatment hippocampal nor prefrontal volume differed between groups, and there were no significant group by time by baseline regional volume effects on cognition. Conclusion. Patients with early-stage breast cancer treated with chemotherapy or chemotherapy plus hormone therapy benefit less from practice effects seen in healthy controls on memory tests. Loss of longitudinal practice effect may be a new and clinically relevant measure for capturing patients’ experience of cognitive difficulties after treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Nomogram for Predicting Postoperative Portal Venous Systemic Thrombosis in Patients with Cirrhosis Undergoing Splenectomy and Esophagogastric Devascularization

Objectives. The aim of the study is to develop a nomogram for predicting postoperative portal venous systemic thrombosis (PVST) in patients with cirrhosis undergoing splenectomy and esophagogastric devascularization. Methods. In total, 195 eligible patients were included. Demographic characteristics were collected, and the results of perioperative routine laboratory investigations and ultrasound examinations were also recorded. Blood cell morphological traits, including the red cell volume distribution width (RDW), mean platelet volume, and platelet distribution width, were identified. Univariate and multivariate logistic regressions were implemented for risk factor filtration, and an integrated nomogram was generated and then validated using the bootstrap method. Results. A color Doppler abdominal ultrasound examination on a postoperative day (POD) 7 (38.97%) revealed that 76 patients had PVST. The results of the multivariate logistic regression suggested that a higher RDW on POD3 (RDW3) (odds ratio (OR): 1.188, 95% confidence interval (CI): 1.073–1.326), wider portal vein diameter (OR: 1.387, 95% CI: 1.203–1.642), history of variceal hemorrhage (OR: 3.407, 95% CI: 1.670–7.220), and longer spleen length (OR: 1.015, 95% CI: 1.001–1.029) were independent risk parameters for postoperative PVST. Moreover, the nomogram integrating these four parameters exhibited considerable capability in PVST forecasting. The nomogram’s receiver operating characteristic curve reached 0.83 and achieved a sensitivity and specificity of 0.711 and 0.848, respectively, at its cutoff. The nomogram’s calibration curve demonstrated that it was well calibrated. Conclusion. The nomogram exhibited excellent performance in PVST prediction and might assist surgeons in identifying vulnerable patients and administering timely prophylaxis.

Chen, Miao↗
DOE OSTI2024

Proteomics identifies complement protein signatures in patients with alcohol-associated hepatitis

Diagnostic challenges continue to impede development of effective therapies for successful management of alcohol-associated hepatitis (AH), creating an unmet need to identify noninvasive biomarkers for AH. In murine models, complement contributes to ethanol-induced liver injury. Therefore, we hypothesized that complement proteins could be rational diagnostic/prognostic biomarkers in AH. Here, we performed a comparative analysis of data derived from human hepatic and serum proteome to identify and characterize complement protein signatures in severe AH (sAH). The quantity of multiple complement proteins was perturbed in liver and serum proteome of patients with sAH. Multiple complement proteins differentiated patients with sAH from those with alcohol cirrhosis (AC) or alcohol use disorder (AUD) and healthy controls (HCs). Serum collectin 11 and C1q binding protein were strongly associated with sAH and exhibited good discriminatory performance among patients with sAH, AC, or AUD and HCs. Furthermore, complement component receptor 1-like protein was negatively associated with pro-inflammatory cytokines. Additionally, lower serum MBL associated serine protease 1 and coagulation factor II independently predicted 90-day mortality. In summary, meta-analysis of proteomic profiles from liver and circulation revealed complement protein signatures of sAH, highlighting a complex perturbation of complement and identifying potential diagnostic and prognostic biomarkers for patients with sAH.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Epidemiological surveillance of common respiratory viruses in patients with suspected COVID-19 in Southwest China

Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the peak season of common respiratory viral infections. However, the clinical symptoms of most SARS-CoV-2 infected patients are not significantly different from those of common respiratory viral infections. Therefore, knowing the epidemiological patterns of common respiratory viruses may be valuable to improve the diagnostic and therapeutic efficacy of patients with suspected COVID-19, especially in Southwest China (a mild epidemic area). Methods A total of 2188 patients with clinically suspected of COVID-19 in Southwest China were recruited from January 21 to February 29, 2020. Nasopharyngeal swabs, throat swabs and sputum specimens were collected to detect SARS-CoV-2 by using real-time reverse transcription-polymerase chain reaction (RT-PCR) and other 12 viruses via PCR fragment analysis combined with capillary electrophoresis. Clinical characteristics and laboratory test findings were acquired from electronic medical records. All data were analyzed to unravel the epidemiological patterns. Results Only 1.1% (24/2188) patients with suspected COVID-19 were eventually confirmed to have SARS-CoV-2 infection, and the most frequently observed symptoms were fever (75.0%, 18/24) and cough (20.8%, 5/24). The overall detection rate of other respiratory pathogens was 10.3% (226/2188). Among them, human rhinovirus (3.2%, 71/2188), human parainfluenza viruses (1.6%, 35/2188), influenza B virus (1.2%, 26/2188) and mycoplasma pneumonia (1.2%, 26/2188) were the predominantly detected pathogens in this study. Moreover, the co-infection was observed in 22 specimens. Notably, one COVID-19 case had a coexisting infection with human parainfluenza virus (4.2%, 1/24) and bocavirus was the most common virus tending to occur in co-infection with other respiratory pathogens. Conclusions This study reveals the epidemiological features of common respiratory viruses and their clinical impact during the ongoing outbreak of COVID-19 in a mild epidemic area. The findings highlight the importance of understanding the transmission patterns of the common respiratory virus in COVID-19 regions, which can provide information support for the development of appropriate treatment plans and health policies, while eliminating unnecessary fear and tension.

Si, Yanjun↗
DOE OSTI2022

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

Abstract Background Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. Methods A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. Results Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53–0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47–0.56), invasive ventilation (OR: 0.59 95% CI: 0.55–0.64), AKI (OR: 0.67 95% CI: 0.63–0.72), or ECMO (OR: 0.51 95% CI: 0.36–0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. Conclusions Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease

Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

Background: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Polyunsaturated Fatty Acids Mend Macrophage Transcriptome, Glycome, and Phenotype in the Patients with Neurodegenerative Diseases, Including Alzheimer’s Disease

Macrophages of healthy subjects have a pro-resolution phenotype, upload amyloid-β (Aβ) into endosomes, and degrade Aβ, whereas macrophages of patients with Alzheimer’s disease (AD) generally have a pro-inflammatory phenotype and lack energy for brain clearance of Aβ. To clarify the pathogenesis of sporadic AD and therapeutic effects of polyunsaturated fatty acids (PUFA) with vitamins B and D and antioxidants on monocyte/macrophage (MM) migration in the AD brain, MM transcripts in energy and Aβ degradation, MM glycome, and macrophage clearance of Aβ. We followed for 31.3 months (mean) ten PUFA-supplemented neurodegenerative patients: 3 with subjective cognitive impairment (SCI), 2 with mild cognitive impairment (MCI), 3 MCI/vascular cognitive impairment, 2 with dementia with Lewy bodies, and 7 non-supplemented caregivers. We examined: monocyte migration in the brain and a blood-brain barrier model by immunochemistry and electron microscopy; macrophage transcriptome by RNAseq; macrophage glycome by N-glycan profiling and LTQ-Orbitrap mass spectrometry; and macrophage phenotype and phagocytosis by immunofluorescence. MM invade Aβ plaques, upload but do not degrade Aβ, and release Aβ into vessels, which develop cerebrovascular amyloid angiopathy (CAA); PUFA upregulate energy and Aβ degradation enzyme transcripts in macrophages; PUFA enhance sialylated N-glycans in macrophages; PUFA reduce oxidative stress and increase pro-resolution MM phenotype, mitochondrial membrane potential, and Aβ phagocytosis (p < 0.001). Macrophages of SCI, MCI, and AD patients have interrelated defects in the transcriptome, glycome, Aβ phagocytosis, and Aβ degradation. PUFA mend macrophage transcriptome, enrich glycome, enhance Aβ clearance, and benefit the cognition of early-stage AD patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Case report: Fatal Legionella infection diagnosed via by metagenomic next-generation sequencing in a patient with chronic myeloid leukemia

Legionella is an aerobic, gram-negative, intracellular pathogen and is an important cause of community-acquired pneumonia. Legionella pneumophila is the most common causative agent of Legionella pneumonia. Clinical diagnosis of Legionella pneumonia is challenging due to the lack of specific clinical manifestations and the low positive rates of conventional pathogen detection methods. In this study, we report a case of a patient with chronic myeloid leukemia who developed rigors and high fever after chemotherapy and immunotherapy. Chest computed tomography revealed consolidation in the left lower lobe of the lung and ground-glass opacities in both lower lobes. Multiple blood cultures showed Escherichia coli , Staphylococcus aureus , Bacillus licheniformis , and positive results in the β-D-glucan test (G test). The patient was treated with various sensitive antimicrobial agents, including meropenem plus fluconazole, meropenem plus carpofungin, and vancomycin. Unfortunately, the patient’s condition gradually worsened and eventually resulted in death. On the following day of death, metagenomic next-generation sequencing (mNGS) of 1whole blood revealed L. pneumophila pneumonia with concurrent bloodstream infection (blood mNGS reads 114,302). These findings suggest that when conventional empirical antimicrobial therapy proves ineffective for critically ill patients with pneumonia, the possibility of combined Legionella infection must be considered, and mNGS can provide a diagnostic tool in such cases.

Bu, Chunhong↗
DOE OSTI2023

Host genetics and gut microbiota composition: Baseline gut microbiota composition as a possible prognostic factor for the severity of COVID-19 in patients with familial Mediterranean fever disease

It is known that the gut microbiome of a healthy person affects the process of COVID-19 after getting infected with SARS-CoV-2 virus. It is also believed that colchicine can alleviate the severity of COVID-19. Current investigations aimed to evaluate the associations between the baseline gut microbiota composition of healthy and Familial Mediterranean fever (FMF) - carrier Armenian men populations, and the severity of the COVID-19 disease after their infection with the SARS-CoV-2. The study has a purpose of answering three core questions: i. Do the characteristics of gut microbiome of Armenians affect the course of COVID-19 severity? ii. How does the COVID-19 disease course on go for FMF patients who have been taking colchicine as a medication over the years after getting infected with SARS-CoV-2? iii. Is there an initial gut micribiota structure pattern for non-FMF and FMF patients in the cases when COVID-19 appears in mild form? The gut microbiota composition in non-FMF and FMF patients before the first infection (mild and moderate course of COVID-19) was considered. COVID-19 was diagnosed by SARS-CoV-2 nucleic acid RT-PCR in nasopharyngeal swab and/or sputum. The number of patients with male FMF with mild COVID-19 was approximately two times higher than that of non-FMF male subjects with COVID-19. In addition, an association of COVID-19 disease severity with the baseline gut Prevotella, Clostridium hiranonis, Eubacterium biforme, Veillonellaceae, Coprococcus, and Blautia diversities in the non-FMF and FMF populations were revealed by us, which can be used as risk/prognostic factor for the severity of COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Structural Connectivity-Based Parcellation of the Dopaminergic Midbrain in Healthy Subjects and Schizophrenic Patients

Background and objectives: Functional deregulation of dopaminergic midbrain regions is a core feature of schizophrenia pathophysiology. Anatomical research on primates suggests that these regions may be subdivided into distinct, topographically organized functional territories according to their connectivity to the striatum. The aim of the present work was the reconstruction of dopaminergic midbrain subregions in healthy subjects and schizophrenic patients and the evaluation of their structural connectivity profiles. Materials and Methods: A hypothesis-driven connectivity-based parcellation derived from diffusion tractography was applied on 24 healthy subjects and 30 schizophrenic patients to identify distinct territories within the human dopaminergic midbrain in vivo and non-invasively. Results: We identified a tripartite subdivision of dopaminergic midbrain, including limbic, prefrontal and sensorimotor territories. No significant differences in structural features or connectivity were found between subjects and patients. Conclusions: The parcellation scheme proposed herein may help to achieve detailed characterization of structural and functional anomalies of the dopaminergic midbrain in schizophrenic patients.

ventral tegmental area↗
DOE OSTI2020

Patient rooms transition: Contemporary makes way for the future

Recent advances in lighting research, technology and control systems are creating new opportunities to use lighting in healthcare facilities, particularly patient rooms, to improve everyday experiences for patients, visitors and staff. Healthcare facilities design has undergone shifts in thinking over the past 50 years, including the incorporation of more technology, and moving from an institutional appearance to a residential appearance, with biophilic elements. The Facilities Guidelines Institute defines the term environment of care as “those physical environment features in a health care facility that are created, structured, and maintained to support and enhance the delivery of health care”. Many attributes of the environment of care in existing hospital patient rooms, especially those built prior to 1990, reflect a focus on maximizing the efficiency of delivering medical care, sometimes without much attention to the impact of design on the experience of patients and guests.

Davis, Robert G.↗