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Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of liver metastasis, these interactions play a crucial role in promoting tumor survival and progression. This study leverages multiomics coverage of the microenvironment via liquid chromatography and high-resolution, high-mass accuracy mass spectrometry-based untargeted metabolomics, 13 C-stable isotope tracing, and RNA sequencing to uncover the metabolic impact of co-localized primary hepatocytes and a colon adenocarcinoma cell line, SW480, using a 2D co-culture model. Metabolic profiling revealed disrupted Warburg metabolism with an 80% decrease in glucose consumption and 94% decrease in lactate production by hepatocyte-SW480 co-cultures relative to SW480 control cultures. Decreased glucose consumption was coupled with alterations in glutamine and ketone body metabolism, suggesting a possible fuel switch upon co-culturing. Further, integrated multiomic analysis indicates that disruptions in metabolic pathways, including nucleoside biosynthesis, amino acids, and TCA cycle, correlate with altered SW480 transcriptional profiles and highlight the importance of redox homeostasis in tumor adaptation. Finally, these findings were replicated in 3-dimensional microtissue organoids. Taken together, these studies support a bioinformatic approach to study metabolic crosstalk and discovery of potential therapeutic targets in preclinical models of the tumor microenvironment., Competing Interests: Competing Interest Statement: P.A.C. has served as an external consultant for Pfizer, Inc., Abbott Laboratories, Janssen Research & Development and Juvenescence. A.C. is now an employee of Solventum.

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction., Competing Interests: Declaration of interests The authors declare no competing interests.

Background: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types., Methods: We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data., Results: First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data., Conclusions: We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2024. Published by Elsevier Inc.)

Competing Interests: Competing interests: None declared.

Importance: Coronavirus disease 2019 (COVID-19) has infected more than 8.1 million US residents and killed more than 221 000. There is a dearth of research on epidemiology and clinical outcomes in US patients with COVID-19., Objectives: To characterize patients with COVID-19 treated in US hospitals and to examine risk factors associated with in-hospital mortality., Design, Setting, and Participants: This cohort study was conducted using Premier Healthcare Database, a large geographically diverse all-payer hospital administrative database including 592 acute care hospitals in the United States. Inpatient and hospital-based outpatient visits with a principal or secondary discharge diagnosis of COVID-19 (International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code, U07.1) between April 1 and May 31, 2020, were included., Exposures: Characteristics of patients were reported by inpatient/outpatient and survival status. Risk factors associated with death examined included patient characteristics, acute complications, comorbidities, and medications., Main Outcomes and Measures: In-hospital mortality, intensive care unit (ICU) admission, use of invasive mechanical ventilation, total hospital length of stay (LOS), ICU LOS, acute complications, and treatment patterns., Results: Overall, 64 781 patients with COVID-19 (29 479 [45.5%] outpatients; 35 302 [54.5%] inpatients) were analyzed. The median (interquartile range [IQR]) age was 46 (33-59) years for outpatients and 65 (52-77) years for inpatients; 31 968 (49.3%) were men, 25 841 (39.9%) were White US residents, and 14 340 (22.1%) were Black US residents. In-hospital mortality was 20.3% among inpatients (7164 patients). A total of 5625 inpatients (15.9%) received invasive mechanical ventilation, and 6849 (19.4%) were admitted to the ICU. Median (IQR) inpatient LOS was 6 (3-10) days. Median (IQR) ICU LOS was 5 (2-10) days. Common acute complications among inpatients included acute respiratory failure (19 706 [55.8%]), acute kidney failure (11 971 [33.9%]), and sepsis (11 910 [33.7%]). Older age was the risk factor most strongly associated with death (eg, age ≥80 years vs 18-34 years: odds ratio [OR], 16.20; 95% CI, 11.58-22.67; P < .001). Receipt of statins (OR, 0.60; 95% CI, 0.56-0.65; P < .001), angiotensin-converting enzyme inhibitors (OR, 0.53; 95% CI, 0.46-0.60; P < .001), and calcium channel blockers (OR, 0.73; 95% CI, 0.68-0.79; P < .001) was associated with decreased odds of death. Compared with patients with no hydroxychloroquine or azithromycin, patients with both azithromycin and hydroxychloroquine had increased odds of death (OR, 1.21; 95% CI, 1.11-1.31; P < .001)., Conclusions and Relevance: In this cohort study of patients with COVID-19 infection in US acute care hospitals, COVID-19 was associated with high ICU admission and in-hospital mortality rates. Use of statins, angiotensin-converting enzyme inhibitors, and calcium channel blockers were associated with decreased odds of death. Understanding the potential benefits of unproven treatments will require future randomized trials.

Background: Extensive research suggests that positive patient experience leads to improvement in patient health outcomes. Patient experience is particularly important in ambulatory care, where a patient builds a long-term relationship with a provider to manage his/her chronic illness over the span of years. Despite these known benefits, patient experience and its impact on health outcomes is poorly understood in low- and middle-income countries, where resources may be limited and primary care infrastructure spotty., Objectives: This paper aims to better characterize patient experience in a tertiary teaching hospital in Accra, Ghana., Methods: Forty qualitative interviews were conducted in the Outpatient Medical Clinic at Korle-Bu Teaching Hospital in Accra, Ghana. All interviews were transcribed and a qualitative analysis of central themes was evaluated by the study team., Findings: We found patients eager to share their views on clinical care in an ambulatory clinic in Ghana's largest tertiary care center. Patients voiced desires for decreasing patient wait times, increasing wayfinding resources to navigate the clinic, creating appointment times, and implementing continuity of care with a single physician. The majority of patients also reported feeling actively engaged in their clinical care and emphasized their positive interpersonal interactions with providers., Conclusions: These findings suggest that patients described positive interpersonal experiences with providers at this ambulatory clinic, but identified numerous operational changes that could be made to vastly improve patient experience., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2020 The Author(s).)

Background: Community advisory boards (CAB) are proposed as one mechanism to carry out successful community based participatory research (CBPR), but the presence of CABs may be insufficient to optimize academic-community partnerships., Methods: We conducted semi-structured interviews with minority members of a CAB partnered with a HIV/AIDS research center and identified three themes., Results: First, lack of trust in researchers included two subthemes: researchers' lacked respect for community-based organizations' (CBO's) interests and paid inadequate attention to building trust. Second, power imbalance included three subthemes: CAB members felt like inferior "token" members, felt that a lack of communication led to disempowerment, and held preconceived beliefs of researchers that led to perceived power imbalance. Third, CAB members suggested best practices, including using collaborations to build trust, actively allocating power, and sharing tangible research benefits with CBOs., Conclusions: Our findings indicate that CABs must be founded on trust and instilled with power to meet the tenets of CBPR.