Switch-like Gene Expression Modulates Disease Susceptibility.

A fundamental challenge in biomedicine is understanding the mechanisms predisposing individuals to disease. While previous research has suggested that switch-like gene expression is crucial in driving biological variation and disease susceptibility, a systematic analysis across multiple tissues is still lacking. By analyzing transcriptomes from 943 individuals across 27 tissues, we identified 1,013 switch-like genes. We found that only 31 (3.1%) of these genes exhibit switch-like behavior across all tissues. These universally switch-like genes appear to be genetically driven, with large exonic genomic structural variants explaining five (~18%) of them. The remaining switch-like genes exhibit tissue-specific expression patterns. Notably, tissue-specific switch-like genes tend to be switched on or off in unison within individuals, likely under the influence of tissue-specific master regulators, including hormonal signals. Among our most significant findings, we identified hundreds of concordantly switched-off genes in the stomach and vagina that are linked to gastric cancer (41-fold, p<10-4) and vaginal atrophy (44-fold, p<10-4), respectively. Experimental analysis of vaginal tissues revealed that low systemic levels of estrogen lead to a significant reduction in both the epithelial thickness and the expression of the switch-like gene ALOX12. We propose a model wherein the switching off of driver genes in basal and parabasal epithelium suppresses cell proliferation therein, leading to epithelial thinning and, therefore, vaginal atrophy. Our findings underscore the significant biomedical implications of switch-like gene expression and lay the groundwork for potential diagnostic and therapeutic applications.

Temporal scaling theory for bursty time series with clusters of arbitrarily many events.

Long-term temporal correlations in time series in a form of an event sequence have been characterized using an autocorrelation function that often shows a power-law decaying behavior. Such scaling behavior has been mainly accounted for by the heavy-tailed distribution of interevent times, i.e., the time interval between two consecutive events. Yet, little is known about how correlations between consecutive interevent times systematically affect the decaying behavior of the autocorrelation function. Empirical distributions of the burst size, which is the number of events in a cluster of events occurring in a short time window, often show heavy tails, implying that arbitrarily many consecutive interevent times may be correlated with each other. In the present study, we propose a model for generating a time series with arbitrary functional forms of interevent time and burst size distributions. Then, we analytically derive the autocorrelation function for the model time series. In particular, by assuming that the interevent time and burst size are power-law distributed, we derive scaling relations between power-law exponents of the autocorrelation function decay, interevent time distribution, and burst size distribution. These analytical results are confirmed by numerical simulations. Our approach helps to rigorously and analytically understand the effects of correlations between arbitrarily many consecutive interevent times on the decaying behavior of the autocorrelation function.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Evolutionary Game Dynamics with Environmental Feedback in a Network with Two Communities.

Recent developments of eco-evolutionary models have shown that evolving feedbacks between behavioral strategies and the environment of game interactions, leading to changes in the underlying payoff matrix, can impact the underlying population dynamics in various manners. We propose and analyze an eco-evolutionary game dynamics model on a network with two communities such that players interact with other players in the same community and those in the opposite community at different rates. In our model, we consider two-person matrix games with pairwise interactions occurring on individual edges and assume that the environmental state depends on edges rather than on nodes or being globally shared in the population. We analytically determine the equilibria and their stability under a symmetric population structure assumption, and we also numerically study the replicator dynamics of the general model. The model shows rich dynamical behavior, such as multiple transcritical bifurcations, multistability, and anti-synchronous oscillations. Our work offers insights into understanding how the presence of community structure impacts the eco-evolutionary dynamics within and between niches.

Validation of a New Scoring Method to Assess the Efficacy of Rapid Initiation and Titration of Combination Pharmacotherapy for Patients Hospitalized with Acute Decompensated Heart Failure with Reduced and Mildly Reduced Ejection Fraction.

Background: Despite the encouragement of early initiation and titration of guideline-directed medical therapy (GDMT) for the treatment of heart failure (HF), most patients do not receive an adequate type and dose of pharmacotherapy in the real world. Objectives: This study aimed to determine the efficacy of titrating composite GDMT in patients with HF with reduced and mildly reduced ejection fraction and to identify patient conditions that may benefit from titration of GDMT. Methods: This was a two-center, retrospective study of consecutive patients hospitalized with acute decompensated heart failure (ADHF). Patients were classified into two groups according to a scoring scale determined by combination and doses of four types of HF agents (ACEis/ARBs/ARNis, BBs, MRAs, and SGLT2is) at discharge. A score of 5 or greater was defined as titrated GDMT, and a score of 4 or less was regarded as sub-optimal medical therapy (MT). Results: A total of 979 ADHF patients were screened. After 553 patients were excluded based on exclusion criteria, 426 patients (90 patients in the titrated GDMT group and 336 patients in the sub-optimal MT group) were enrolled for the analysis. The median follow-up period was 612 (453-798) days. Following statistical adjustment using the propensity score weighting method, the 2-year composite endpoint (composite of cardiac death and HF rehospitalization) rate was significantly lower in the titrated GDMT group, at 19%, compared with the sub-optimal MT group: 31% (score 3-4 points) and 43% (score 0-2 points). Subgroup analysis indicated a marked benefit of titrated GDMT in particular patient subgroups: age < 80 years, BMI 19.0-24.9, eGFR > 20 mL/min/1.73 m2, and serum potassium level ≤ 5.5 mmol/L. Conclusions: Prompt initiation and dose adjustment of multiple HF medications, with careful monitoring of the patient's physiologic and laboratory values, is a prerequisite for improving the prognosis of patients with heart failure.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis.

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.

Evaluation of a joint workshop on study design for hospital and community pharmacists: a retrospective cross-sectional survey.

BACKGROUND: Although pharmacists often identify numerous clinical questions, they face several barriers, including the lack of mentors for research activities in clinical settings. Therefore, a workshop for the appropriate selection of a study design, which is a fundamental first step, may be necessary. The purpose of this study was to evaluate the effectiveness of a workshop on study design for hospital and community pharmacists. Moreover, the characteristics of pharmacists with little involvement in research activities were extracted using decision-tree analysis to guide the design of future workshops. METHODS: A workshop was conducted on October 1, 2023. It comprised three parts: lectures, group work, and presentations. Questionnaire-based surveys were conducted with workshop participants regarding their basic information, their background that influenced research activities, their satisfaction, and their knowledge/awareness. For the questions on knowledge/awareness, the same responses were requested before and after the workshop using a five-scale scoring system. Multivariate logistic regression analysis was conducted to identify independent factors influencing research activities. Decision tree analysis was performed to extract low-effort characteristics of the research activities. RESULTS: Of the 40 workshop attendees, the overall satisfaction score for the workshop was 4.38 of 5, and the score for each question was 4 or higher. Significant increases were observed in the scores of knowledge/awareness after the workshop. Moreover, 95% of the pharmacists answered that it would be highly useful to conduct a joint workshop between hospitals and community pharmacists. Although independent influencing factors were not detected in the multivariate logistic regression analysis, the decision tree analysis revealed that pharmacists who were no member of an academic society (85%, 11/13) or members without any certifications or accreditations related to pharmacy practice (80%, 4/5) were the least active in clinical research. In contrast, those belonging to academic societies and holding certifications or accreditations related to pharmacy practice frequently conducted clinical research. CONCLUSION: The present study revealed that a joint workshop on study design may have the potential to change pharmacists' knowledge and awareness of research activities. Moreover, future workshops should be conducted with pharmacists who do not belong to academic societies.

Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis.

Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages-834 NL, 711 MGUS, and 970 MM spectra-and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527-1530 cm-1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.

Anticipating regime shifts by mixing early warning signals from different nodes.

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.

Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network.

AIM: While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear. METHODS: Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter. RESULTS: We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions. CONCLUSION: Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.

Gene communities in co-expression networks across different tissues.

With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.

Effects of Hyperbaric Oxygen Therapy for Clostridioides difficile-associated Colitis: A Retrospective Study.

Objectives: Clostridioides difficile (CD) is an anaerobic spore-forming Gram-positive rod that is a major cause of antibiotic-associated diarrhea. Hyperbaric oxygen therapy (HBO) is a well-established treatment for Clostridium perfringens, but there are no reports that have examined the efficacy of HBO against CD, which is also an anaerobic bacterium. Methods: In this study, we retrospectively examined whether HBO therapy affects the prognosis following CD infections (CDI). This study included 92 inpatients diagnosed with CDI at our hospital between January 2013 and December 2022. Of these, 16 patients received HBO therapy. The indications for HBO therapy were stroke in five patients, ileus in four patients, cancer in two patients, acute peripheral circulatory disturbance in two patients, and others in three patients. The mean observation period was 5.4 years. Results: In the univariate analysis, there was no significant difference in severity, mortality, hospitalization, or overall survival between patients who did and did not receive HBO therapy. However, the HBO group had a significantly lower recurrence rate (0% vs. 22.4%, p=0.0363) and a shorter symptomatic period (6.2 vs. 13.6 days, p=0.0217). Conclusions: HBO may have beneficial effect on CDI by shortening the symptomatic period and preventing recurrence.

Fixation probability in evolutionary dynamics on switching temporal networks.

Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. Research in the past two decades has found various families of amplifiers while suppressors still remain somewhat elusive. It has also been discovered that most networks are amplifiers of selection under the birth-death updating combined with uniform initialization, which is a standard condition assumed widely in the literature. In the present study, we extend the birth-death processes to temporal (i.e., time-varying) networks. For the sake of tractability, we restrict ourselves to switching temporal networks, in which the network structure deterministically alternates between two static networks at constant time intervals or stochastically in a Markovian manner. We show that, in a majority of cases, switching networks are less amplifying than both of the two static networks constituting the switching networks. Furthermore, most small switching networks, i.e., networks on six nodes or less, are suppressors, which contrasts to the case of static networks.

Impact of coronary bifurcation angle on stent malapposition in a randomized comparison between proximal optimization technique followed by side branch dilatation and kissing balloon inflation.

Background: The impact of coronary bifurcation angle (BA) on incomplete stent apposition (ISA) after crossover stenting followed by side branch (SB) intervention has not been established. Methods: A total of 100 crossover stentings randomly treated with proximal optimization technique followed by short balloon dilation in the SB (POT-SBD group, 48 patients) and final kissing balloon technique (KBT group, 52 patients) were analyzed in the PROPOT trial. Major ISA with maximum distance > 400 µm and its location was determined using optical coherence tomography before SB intervention and at the final procedure. The BA was defined as the angle between the distal main vessel and SB. Optimal POT was determined when the difference in stent volume index between the proximal and distal bifurcation was greater than the median value (0.86 mm3/mm) before SB intervention. Result: Major ISA was more frequently observed in the POT-SBD than in the KBT group (35% versus 17%, p < 0.05). In the POT-SBD group, worsening ISA after SBD was prominent at the distal bifurcation. The BA was an independent predictor of major ISA (odds ratio 1.04, 95% confidence interval 1.00-1.07, p < 0.05) with a cut-off value of 59.5° (p < 0.05). However, the cases treated with optimal POT in the short BA (<60°) indicated the lowest incidence of major ISA. In the KBT group, BA had no significant impact. Conclusion: A wide BA has a potential risk for the occurrence of major ISA after POT followed by SBD in coronary bifurcation stenting.

Fixation dynamics on hypergraphs.

Hypergraphs have been a useful tool for analyzing population dynamics such as opinion formation and the public goods game occurring in overlapping groups of individuals. In the present study, we propose and analyze evolutionary dynamics on hypergraphs, in which each node takes one of the two types of different but constant fitness values. For the corresponding dynamics on conventional networks, under the birth-death process and uniform initial conditions, most networks are known to be amplifiers of natural selection; amplifiers by definition enhance the difference in the strength of the two competing types in terms of the probability that the mutant type fixates in the population. In contrast, we provide strong computational evidence that a majority of hypergraphs are suppressors of selection under the same conditions by combining theoretical and numerical analyses. We also show that this suppressing effect is not explained by one-mode projection, which is a standard method for expressing hypergraph data as a conventional network. Our results suggest that the modeling framework for structured populations in addition to the specific network structure is an important determinant of evolutionary dynamics, paving a way to studying fixation dynamics on higher-order networks including hypergraphs.

Biases in inverse Ising estimates of near-critical behavior.

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.

Clinical Efficacy of Pre-Hospital Electrocardiogram Transmission in Patients Undergoing Primary Percutaneous Coronary Intervention for ST-Segment Elevation Myocardial Infarction.

Rapid reperfusion by primary percutaneous coronary intervention (pPCI) is an established strategy for the treatment of patients with ST-segment elevation myocardial infarction (STEMI). Pre-hospital electrocardiogram (PH-ECG) transmission by the emergency medical services (EMS) facilitates timely reperfusion in these patients. However, evidence regarding the clinical benefits of PH-ECG in individual hospitals is limited.This retrospective, observational study investigated the clinical efficacy of PH-ECG in STEMI patients who underwent pPCI. Of a total of 382 consecutive STEMI patients, 237 were enrolled in the study and divided into 2 groups: a PH-ECG group (n = 77) and non-PH-ECG group (n = 160). Door-to-balloon time (D2BT) was significantly shorter in the PH-ECG group (66 [52-80] min), compared to the non-PH-ECG group (70 [57-88] minutes, P = 0.01). The 30-day all-cause mortality rate was 6% in the PH-ECG group, which was significantly lower than that in the non-PH-ECG group (16%) (P = 0.037, hazard ratio [HR]: 0.38, 95% CI: 0.15-0.98). This trend was particularly evident in severely ill patients when stratified by GRACE score.The use of PH-ECG improved the survival rate of STEMI patients undergoing pPCI due to the improved pre-arrival preparation based on the EMS information. Coordination between EMS and PCI-capable institutes is essential for the management of PH-ECG.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyze multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e., within-participant reliability) than across different sets of sessions from different participants (i.e., between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Gene communities in co-expression networks across different tissues.

With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.