Sacroiliitis in inflammatory bowel disease on abdominal computed tomography: prevalence, misses, and associated factors

OBJECTIVE

To evaluate the prevalence and rate of a missed diagnosis of sacroiliitis on abdominal computed tomography (CT) in patients with inflammatory bowel disease (IBD). Factors associated with sacroiliitis were also assessed.

METHOD

This retrospective study included 210 patients with IBD (mean age 31.1 years) who underwent abdominal CT. Based on a validated abdominal CT scoring tool, bilateral sacroiliac (SI) joints on abdominal CT in the whole study population were retrospectively reviewed. Subsequently, patients were classified into the 'patients with sacroiliitis' group and the 'patients without sacroiliitis' group. Univariate and multivariate regression analyses were used to clarify the factors associated with sacroiliitis.

RESULTS

Sacroiliitis was identified in 26 out of 210 patients (12.4%). However, sacroiliitis was recognized on the primary reading in only five of these 26 patients (19.2%) and was missed on the initial report in the remaining 21 patients (80.8%). Among the 21 patients, 20 (95.2%) were finally diagnosed with axial spondyloarthritis (axSpA). There was a higher prevalence of female sex (p = 0.04), upper gastrointestinal involvement (p = 0.04), and back pain (p < 0.01) in patients with sacroiliitis than in those without sacroiliitis. However, on multivariate analysis, back pain was the only factor associated with sacroiliitis (p = 0.01).

CONCLUSION

Physicians should carefully evaluate SI joints on abdominal CT in patients with IBD to enable early detection of sacroiliitis, potentially leading to an early diagnosis of axSpA. In addition, if patients with IBD present with back pain, the possibility of sacroiliitis should be considered.

Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach

BACKGROUND

The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clinical settings. In this study, we aimed to develop a data-driven shortened version of the ISI that accurately predicts the severity level of insomnia disorder.

METHODS

We collected a sample of 800 responses from the EMBRAIN survey system. Based on the responses, seven items were grouped based on the similarity of their response using exploratory factor analysis (EFA). The most representative item within each group was selected by using eXtreme Gradient Boosting (XGBoost).

RESULTS

Based on the selected three key items, maintenance of sleep, interference with daily function, and concerns about sleep problems, we developed a data-driven shortened questionnaire of ISI, ISI-3 m (machine learning). ISI-3 m achieved the highest coefficient of determination (R 2 = 0.910 ) for the ISI score prediction task and the accuracy of 0.965, precision of 0.841, and recall of 0.838 for the multiclass-classification task, outperforming four previous versions of the shortened ISI.

CONCLUSION

As ISI-3 m is a highly accurate shortened version of the ISI, it allows clinicians to efficiently screen for insomnia and observe variations in the condition throughout the treatment process. Furthermore, the framework based on the combination of EFA and XGBoost developed in this study can be utilized to develop data-driven shortened versions of the other questionnaires.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data

BACKGROUND

Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown.

METHODS

We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method.

FINDINGS

The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms.

INTERPRETATION

Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively.

FUNDING

Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations

Target-mediated drug disposition (TMDD) is a phenomenon characterized by a drug's high-affinity binding to a target molecule, which significantly influences its pharmacokinetic profile within an organism. The comprehensive TMDD model delineates this interaction, yet it may become overly complex and computationally demanding in the absence of specific concentration data for the target or its complexes. Consequently, simplified TMDD models employing quasi-steady state approximations (QSSAs) have been introduced; however, the precise conditions under which these models yield accurate results require further elucidation. Here, we establish the validity of three simplified TMDD models: the Michaelis-Menten model reduced with the standard QSSA (mTMDD), the QSS model reduced with the total QSSA (qTMDD), and a first-order approximation of the total QSSA (pTMDD). Specifically, we find that mTMDD is applicable only when initial drug concentrations substantially exceed total target concentrations, while qTMDD can be used for all drug concentrations. Notably, pTMDD offers a simpler and faster alternative to qTMDD, with broader applicability than mTMDD. These findings are confirmed with antibody-drug conjugate real-world data. Our findings provide a framework for selecting appropriate simplified TMDD models while ensuring accuracy, potentially enhancing drug development and facilitating safer, more personalized treatments.

A robust ultrasensitive transcriptional switch in noisy cellular environments

Ultrasensitive transcriptional switches enable sharp transitions between transcriptional on and off states and are essential for cells to respond to environmental cues with high fidelity. However, conventional switches, which rely on direct repressor-DNA binding, are extremely noise-sensitive, leading to unintended changes in gene expression. Here, through model simulations and analysis, we discovered that an alternative design combining three indirect transcriptional repression mechanisms, sequestration, blocking, and displacement, can generate a noise-resilient ultrasensitive switch. Although sequestration alone can generate an ultrasensitive switch, it remains sensitive to noise because the unintended transcriptional state induced by noise persists for long periods. However, by jointly utilizing blocking and displacement, these noise-induced transitions can be rapidly restored to the original transcriptional state. Because this transcriptional switch is effective in noisy cellular contexts, it goes beyond previous synthetic transcriptional switches, making it particularly valuable for robust synthetic system design. Our findings also provide insights into the evolution of robust ultrasensitive switches in cells. Specifically, the concurrent use of seemingly redundant indirect repression mechanisms in diverse biological systems appears to be a strategy to achieve noise-resilience of ultrasensitive switches.

Beyond microtubules: The cellular environment at the endoplasmic reticulum attracts proteins to the nucleus, enabling nuclear transport

All proteins are translated in the cytoplasm, yet many, including transcription factors, play vital roles in the nucleus. While previous research has concentrated on molecular motors for the transport of these proteins to the nucleus, recent observations reveal perinuclear accumulation even in the absence of an energy source, hinting at alternative mechanisms. Here, we propose that structural properties of the cellular environment, specifically the endoplasmic reticulum (ER), can promote molecular transport to the perinucleus without requiring additional energy expenditure. Specifically, physical interaction between proteins and the ER impedes their diffusion and leads to their accumulation near the nucleus. This result explains why larger proteins, more frequently interacting with the ER membrane, tend to accumulate at the perinucleus. Interestingly, such diffusion in a heterogeneous environment follows Chapman's law rather than the popular Fick's law. Our findings suggest a novel protein transport mechanism arising solely from characteristics of the intracellular environment.

Noisy Delay Denoises Biochemical Oscillators

Genetic oscillations are generated by delayed transcriptional negative feedback loops, wherein repressor proteins inhibit their own synthesis after a temporal production delay. This delay is distributed because it arises from a sequence of noisy processes, including transcription, translocation, translation, and folding. Because the delay determines repression timing and, therefore, oscillation period, it has been commonly believed that delay noise weakens oscillatory dynamics. Here, we demonstrate that noisy delay can surprisingly denoise genetic oscillators. Specifically, moderate delay noise improves the signal-to-noise ratio and sharpens oscillation peaks, all without impacting period and amplitude. We show that this denoising phenomenon occurs in a variety of well-studied genetic oscillators, and we use queueing theory to uncover the universal mechanisms that produce it.

Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction

The transduction time between signal initiation and final response provides valuable information on the underlying signaling pathway, including its speed and precision. Furthermore, multi-modality in a transduction-time distribution indicates that the response is regulated by multiple pathways with different transduction speeds. Here, we developed a method called density physics-informed neural networks (Density-PINNs) to infer the transduction-time distribution from measurable final stress response time traces. We applied Density-PINNs to single-cell gene expression data from sixteen promoters regulated by unknown pathways in response to antibiotic stresses. We found that promoters with slower signaling initiation and transduction exhibit larger cell-to-cell heterogeneity in response intensity. However, this heterogeneity was greatly reduced when the response was regulated by slow and fast pathways together. This suggests a strategy for identifying effective signaling pathways for consistent cellular responses to disease treatments. Density-PINNs can also be applied to understand other time delay systems, including infectious diseases.

Circadian regulation of sinoatrial nodal cell pacemaking function: Dissecting the roles of autonomic control, body temperature, and local circadian rhythmicity

Strong circadian (~24h) rhythms in heart rate (HR) are critical for flexible regulation of cardiac pacemaking function throughout the day. While this circadian flexibility in HR is sustained in diverse conditions, it declines with age, accompanied by reduced maximal HR performance. The intricate regulation of circadian HR involves the orchestration of the autonomic nervous system (ANS), circadian rhythms of body temperature (CRBT), and local circadian rhythmicity (LCR), which has not been fully understood. Here, we developed a mathematical model describing ANS, CRBT, and LCR in sinoatrial nodal cells (SANC) that accurately captures distinct circadian patterns in adult and aged mice. Our model underscores how the alliance among ANS, CRBT, and LCR achieves circadian flexibility to cover a wide range of firing rates in SANC, performance to achieve maximal firing rates, while preserving robustness to generate rhythmic firing patterns irrespective of external conditions. Specifically, while ANS dominates in promoting SANC flexibility and performance, CRBT and LCR act as primary and secondary boosters, respectively, to further enhance SANC flexibility and performance. Disruption of this alliance with age results in impaired SANC flexibility and performance, but not robustness. This unexpected outcome is primarily attributed to the age-related reduction in parasympathetic activities, which maintains SANC robustness while compromising flexibility. Our work sheds light on the critical alliance of ANS, CRBT, and LCR in regulating time-of-day cardiac pacemaking function and dysfunction, offering insights into novel therapeutic targets for the prevention and treatment of cardiac arrhythmias.

Imputing missing sleep data from wearables with neural networks in real-world settings

Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.

Inferring delays in partially observed gene regulation processes

MOTIVATION

Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality.

RESULTS

We develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components.

AVAILABILITY AND IMPLEMENTATION

Our code is implemented in R and is freely available with a simple example data at https://github.com/Mathbiomed/SimMCMC.

Position paper on olfactory dysfunction: 2023

BACKGROUND

Since publication of the original Position Paper on Olfactory Dysfunction in 2017 (PPOD-17), the personal and societal burden of olfactory disorders has come sharply into focus through the lens of the COVID-19 pandemic. Clinicians, scientists and the public are now more aware of the importance of olfaction, and the impact of its dysfunction on quality of life, nutrition, social relationships and mental health. Accordingly, new basic, translational and clinical research has resulted in significant progress since the PPOD-17. In this updated document, we present and discuss currently available evidence for the diagnosis and management of olfactory dysfunction. Major updates to the current version include, amongst others: new recommendations on olfactory related terminology; new imaging recommendations; new sections on qualitative OD and COVID-19 OD; updated management section. Recommendations were agreed by all co-authors using a modified Delphi process.

CONCLUSIONS

We have provided an overview of current evidence and expert-agreed recommendations for the definition, investigation, and management of OD. As for our original Position Paper, we hope that this updated document will encourage clinicians and researchers to adopt a common language, and in so doing, increase the methodological quality, consistency, and generalisability of work in this field.

A Phase II Trial Evaluating Rapid Mid-Treatment Nodal Shrinkage to Select for Adaptive Deescalation in p16+ Oropharyngeal Cancer Patients Undergoing Definitive Chemoradiation

PURPOSE/OBJECTIVE(S)

The purpose of this study is to determine if rapid mid-treatment nodal shrinkage (RMNS) can identify patients with p16+ oropharyngeal cancer (OPC) who can be safely deescalated with reduced dose chemoradiation therapy (CRT). The primary endpoint was 2-year progression free survival (PFS).

MATERIALS/METHODS

Inclusion criteria were as follows: T1-3, N1, M0 (AJCC 8th edition) p16+ OPC with <10 pack-year smoking history. All patients were initially planned for standard dose CRT (70 Gy) and weekly cisplatin. Patients were evaluated with a CT scan at week 4 for RMNS, defined as >40% nodal volumetric reduction from baseline. If RMNS was achieved, they proceeded to deescalated CRT (60 Gy). If not, they received standard CRT. Biomarker correlates were collected at baseline and week 4 of CRT including plasma TTMV (tumor tissue modified viral) HPV DNA and MRI diffusion weighted imaging (DWI). Univariate logistic regression analyses (UVA) were performed to evaluate predictors of RMNS. Odds ratios with 95% CI are reported, using a p<0.05 for statistical significance with a two-sided test. Wilcoxon rank sum tests were used to evaluate differences between the two groups using p < 0.05, 2-sided) for statistical significance. All statistical procedures were performed using R () with no adjustments for multiple testing.

RESULTS

Thirty-six patients were enrolled: median age: 60 years; 81% male; primary site: 36% base of tongue, 53% tonsil, 11% both; T-stage: 39% T1, 50% T2, 11% T3; N-stage: 100% N1; any smoking history: 58% yes, 42% no; 67% (n = 24) had RMNS and received deescalated CRT while the remaining proceeded to standard CRT. At a median follow-up of 32.4 months, 2-year PFS between the standard and deescalated groups were 91.7% vs 90.9%, respectively (p = 0.97). All patients with recurrence underwent successful salvage treatment with 2-year OS 100% for all patients. On UVA, rapid TTMV HPV DNA clearance (baseline to week 4) (OR 12.0 [1.65-250], p = 0.034), lower MRI diffusivity (ADC) at baseline (OR 0.79 [0.61-0.97], p = 0.042) and week 4 (OR 0.76 [0.60-0.91], p = 0.009), and higher MRI diffusional kurtosis at baseline (OR 1.09 [1.01-1.21], p = 0.051) and week 4 (OR 1.24 [1.09-1.52], p = 0.009) were significantly associated with RMNS. When comparing the deescalated and standard cohorts, the mean baseline and week 4 MRI ADC were significantly lower and week 4 MRI diffusional kurtosis was significantly higher in the deescalated group.

CONCLUSION

In this phase II study, rapid mid-treatment nodal shrinkage appeared to select favorable risk p16+ oropharynx cancer patients for treatment de-escalation. Rapid clearance of TTMV HPV DNA at week 4 as well as MRI DWI biomarkers of low ADC and high diffusional kurtosis values were correlated with RMNS. A larger study is planned to incorporate RMNS and biomarkers for further treatment de-escalation. Additional trial information is available at ClinicalTrials.gov (Identifier: NCT03215719).

Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study

BACKGROUND

Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients' hesitancy to undergo demanding and costly overnight polysomnography tests.

OBJECTIVE

We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test.

METHODS

We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve.

RESULTS

In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age.

CONCLUSIONS

SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.

A real-time, personalized sleep intervention using mathematical modeling and wearable devices

The prevalence of artificial light exposure has enabled us to be active any time of the day or night, leading to the need for high alertness outside of traditional daytime hours. To address this need, we developed a personalized sleep intervention framework that analyzes real-world sleep-wake patterns obtained from wearable devices to maximize alertness during specific target periods. Our framework utilizes a mathematical model that tracks the dynamic sleep pressure and circadian rhythm based on the user's sleep history. In this way, the model accurately predicts real-time alertness, even for shift workers with complex sleep and work schedules (N = 71, t = 13~21 days). This allowed us to discover a new sleep-wake pattern called the adaptive circadian split sleep, which incorporates a main sleep period and a late nap to enable high alertness during both work and non-work periods of shift workers. We further developed a mobile application that integrates this framework to recommend practical, personalized sleep schedules for individual users to maximize their alertness during a targeted activity time based on their desired sleep onset and available sleep duration. This can reduce the risk of errors for those who require high alertness during nontraditional activity times and improve the health and quality of life for those leading shift work-like lifestyles.

A general model-based causal inference method overcomes the curse of synchrony and indirect effect

To identify causation, model-free inference methods, such as Granger Causality, have been widely used due to their flexibility. However, they have difficulty distinguishing synchrony and indirect effects from direct causation, leading to false predictions. To overcome this, model-based inference methods that test the reproducibility of data with a specific mechanistic model to infer causality were developed. However, they can only be applied to systems described by a specific model, greatly limiting their applicability. Here, we address this limitation by deriving an easily testable condition for a general monotonic ODE model to reproduce time-series data. We built a user-friendly computational package, General ODE-Based Inference (GOBI), which is applicable to nearly any monotonic system with positive and negative regulations described by ODE. GOBI successfully inferred positive and negative regulations in various networks at both the molecular and population levels, unlike existing model-free methods. Thus, this accurate and broadly applicable inference method is a powerful tool for understanding complex dynamical systems.

A framework for deriving analytic steady states of biochemical reaction networks

The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.

Spatially coordinated collective phosphorylation filters spatiotemporal noises for precise circadian timekeeping

The circadian (∼24h) clock is based on a negative-feedback loop centered around the PERIOD protein (PER), translated in the cytoplasm and then enters the nucleus to repress its own transcription at the right time of day. Such precise nucleus entry is mysterious because thousands of PER molecules transit through crowded cytoplasm and arrive at the perinucleus across several hours. To understand this, we developed a mathematical model describing the complex spatiotemporal dynamics of PER as a single random time delay. We find that the spatially coordinated bistable phosphoswitch of PER, which triggers the phosphorylation of accumulated PER at the perinucleus, leads to the synchronous and precise nuclear entry of PER. This leads to robust circadian rhythms even when PER arrival times are heterogeneous and perturbed due to changes in cell crowdedness, cell size, and transcriptional activator levels. This shows how the circadian clock compensates for spatiotemporal noise.

Chemotherapy delivery time affects treatment outcomes of female patients with diffuse large B cell lymphoma

BACKGROUNDChronotherapy is a drug intervention at specific times of the day to optimize efficacy and minimize adverse effects. Its value in hematologic malignancy remains to be explored, in particular in adult patients.METHODSWe performed chronotherapeutic analysis using 2 cohorts of patients with diffuse large B cell lymphoma (DLBCL) undergoing chemotherapy with a dichotomized schedule (morning or afternoon). The effect of a morning or afternoon schedule of rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) on survival and drug tolerability was evaluated in a survival cohort (n = 210) and an adverse event cohort (n = 129), respectively. Analysis of about 14,000 healthy individuals followed to identify the circadian variation in hematologic parameters.RESULTSBoth progression-free survival (PFS) and overall survival (OS) of female, but not male, patients were significantly shorter when patients received chemotherapy mostly in the morning (PFS HR 0.357, P = 0.033; and OS HR 0.141, P = 0.032). The dose intensity was reduced in female patients treated in the morning (cyclophosphamide 10%, P = 0.002; doxorubicin 8%, P = 0.002; and rituximab 7%, P = 0.003). This was mainly attributable to infection and neutropenic fever: female patients treated in the morning had a higher incidence of infections (16.7% vs. 2.4%) and febrile neutropenia (20.8% vs. 9.8%) as compared with those treated in the afternoon. The sex-specific chronotherapeutic effects can be explained by the larger daily fluctuation of circulating leukocytes and neutrophils in female than in male patients.CONCLUSIONIn female DLBCL patients, R-CHOP treatment in the afternoon can reduce toxicity while it improves efficacy and survival outcome.FUNDINGNational Research Foundation of Korea (NRF) grant funded by the Korean government (grant number NRF-2021R1A4A2001553), Institute for Basic Science IBS-R029-C3, and Human Frontiers Science Program Organization Grant RGY0063/2017.

Can Acupuncture be a Part of the Treatment for Breast Cancer-Related Lymphedema? A Systematic Review of the Safety and Proposed Model for Care

Acupuncture is a potential therapy for breast cancer-related lymphedema (BCRL). Despite a recent meta-analysis on efficacy, data on acupuncture safety in BCRL are lacking. Current clinical guidelines recommend avoiding needling in the upper extremity affected by lymph node dissection. We undertook a systematic review focusing on acupuncture safety and treatment protocols in clinical trials for BCRL. Literature searches were conducted in PubMed, Ovid, CINAHL, and Cochrane library. Eight clinical trials on acupuncture for BCRL were analyzed. The Standards of Acupuncture intervention (STRICTA 2010) and Cochrane risk of bias (RoB2 2019) were applied to assess methods for acupuncture interventions within Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Quantity and severity of adverse events (AE) were reviewed. A total of 189 subjects participated in 8 clinical trials with 2965 acupuncture treatments. No serious adverse events (SAE) were reported regardless of treatment laterality or protocol, with only a single grade 2 skin infection in 2,965 total treatments (0.034%), including 1,165 bilateral and 225 ipsilateral treatments. Our comprehensive review of clinical trials of acupuncture for BCRL demonstrated no significant adverse events in 2,965 treatments, including 1,390 in the affected limb. An approach for routine integration of acupuncture into BCRL maintenance therapy is proposed.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m $$ {K}_{\mathrm{m}} $$ ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T $$ {E}_{\mathrm{T}} $$ ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a $$ {F}_{\mathrm{a}} $$ ) is also assumed to be one because F a $$ {F}_{\mathrm{a}} $$ is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a $$ {F}_{\mathrm{a}} $$ was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases

Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration–time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.

Combined multiple transcriptional repression mechanisms generate ultrasensitivity and oscillations

Transcriptional repression can occur via various mechanisms, such as blocking, sequestration and displacement. For instance, the repressors can hold the activators to prevent binding with DNA or can bind to the DNA-bound activators to block their transcriptional activity. Although the transcription can be completely suppressed with a single mechanism, multiple repression mechanisms are used together to inhibit transcriptional activators in many systems, such as circadian clocks and NF-κB oscillators. This raises the question of what advantages arise if seemingly redundant repression mechanisms are combined. Here, by deriving equations describing the multiple repression mechanisms, we find that their combination can synergistically generate a sharply ultrasensitive transcription response and thus strong oscillations. This rationalizes why the multiple repression mechanisms are used together in various biological oscillators. The critical role of such combined transcriptional repression for strong oscillations is further supported by our analysis of formerly identified mutations disrupting the transcriptional repression of the mammalian circadian clock. The hitherto unrecognized source of the ultrasensitivity, the combined transcriptional repressions, can lead to robust synthetic oscillators with a previously unachievable simple design.

Time-keeping and decision-making in living cells: Part I

To survive and reproduce, a cell must process information from its environment and its own internal state and respond accordingly, in terms of metabolic activity, gene expression, movement, growth, division and differentiation. These signal–response decisions are made by complex networks of interacting genes and proteins, which function as biochemical switches and clocks, and other recognizable information-processing circuitry. This theme issue of Interface Focus (in two parts) brings together articles on time-keeping and decision-making in living cells—work that uses precise mathematical modelling of underlying molecular regulatory networks to understand important features of cell physiology. Part I focuses on time-keeping: mechanisms and dynamics of biological oscillators and modes of synchronization and entrainment of oscillators, with special attention to circadian clocks.

Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number

Identifying the sources of cell-to-cell variability in signaling dynamics is essential to understand drug response variability and develop effective therapeutics. However, it is challenging because not all signaling intermediate reactions can be experimentally measured simultaneously. This can be overcome by replacing them with a single random time delay, but the resulting process is non-Markovian, making it difficult to infer cell-to-cell heterogeneity in reaction rates and time delays. To address this, we developed an efficient and scalable moment-based Bayesian inference method (MBI) with a user-friendly computational package that infers cell-to-cell heterogeneity in the non-Markovian signaling process. We applied MBI to single-cell expression profiles from promoters responding to antibiotics and discovered a major source of cell-to-cell variability in antibiotic stress response: the number of rate-limiting steps in signaling cascades. This knowledge can help identify effective therapies that destroy all pathogenic or cancer cells, and the approach can be applied to precision medicine.

Modelling of plant circadian clock for characterizing hypocotyl growth under different light quality conditions

To meet the ever-increasing global food demand, the food production rate needs to be increased significantly in the near future. Speed breeding is considered as a promising agricultural technology solution to achieve the zero-hunger vision as specified in the United Nations Sustainable Development Goal 2. In speed breeding, the photoperiod of the artificial light has been manipulated to enhance crop productivity. In particular, regulating the photoperiod of different light qualities rather than solely white light can further improve speed breading. However, identifying the optimal light quality and the associated photoperiod simultaneously remains a challenging open problem due to complex interactions between multiple photoreceptors and proteins controlling plant growth. To tackle this, we develop a first comprehensive model describing the profound effect of multiple light qualities with different photoperiods on plant growth (i.e. hypocotyl growth). The model predicts that hypocotyls elongated more under red light compared to both red and blue light. Drawing similar findings from previous related studies, we propose that this might result from the competitive binding of red and blue light receptors, primarily Phytochrome B (phyB) and Cryptochrome 1 (cry1) for the core photomorphogenic regulator, CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1). This prediction is validated through an experimental study on Arabidopsis thaliana. Our work proposes a potential molecular mechanism underlying plant growth under different light qualities and ultimately suggests an optimal breeding protocol that takes into account light quality.

Beyond the Michaelis-Menten: Bayesian Inference for Enzyme Kinetic Analysis

Although the Michaelis-Menten (MM) rate law has been widely used to estimate enzyme kinetic parameters, it works only under the condition of extremely low enzyme concentration. Furthermore, even when this condition is satisfied, parameter estimation is often imprecise due to the parameter identifiability issue. To overcome these limitations of the canonical approach to enzyme kinetics, we developed a Bayesian approach based on a modified form of the MM rate law, which is derived with the total quasi-steady state approximation. Here, we illustrate how to perform the Bayesian inference for the progress curve assay with our user-friendly computational R package. We also describe an optimal experimental design for the progress curve assay, with which enzyme kinetic parameters can be accurately and precisely estimated from minimal measurements of the progress curves.

Prognostic implication of left atrial strain in patients undergoing totally thoracoscopic ablation of atrial fibrillation

Background

Atrial fibrillation (AF) is a common form of arrhythmia and associated with poor quality of life. Totally thoracoscopic ablation (TTA) is a novel minimally invasive strategy for symptomatic atrial fibrillation (AF) refractory to other therapy. However, some of patients undergoing TTA are still exposed to a risk of AF recurrence.

Purpose

The aim of this study is to investigate prognostic factors related with AF recurrence after TTA, and to determine the prognostic implication of left atrial (LA) strain in this population.

Methods

This was a prospective observational study. Between February 2012 and March 2015, left atrial appendage (LAA) was harvested from patients who underwent TTA in our Medical Center. Degree of LAA fibrosis was expressed as the percentage of area of positive collagen staining in the total area of the image of specimen. All echocardiographic parameters were measured in preoperative echocardiography. The primary outcome was any recurrence of AF detected in 12- lead electrocardiogram or holter monitoring during 5 years of follow-up.

Results

Out of 150 patients who underwent TTA during the study period, 129 were eligible for analysis with appropriate surgery, LAA specimen, and echocardiographic images. A mean age was 54.4±8.8 years, and 123 patients (95.3%) were male. Twenty four patients (18.6%) had paroxysmal AF and a mean CHA2DS2 VASc score was 1.1±1.2. A median value of peak longitudinal LA strain (reservoir strain) was 15.2% (IQR 12.1–19.2), and the median value of LAA fibrosis was 38.5% (IQR 33.0–44.7). Among clinical and echocardiographic variables, peak longitudinal LA strain (p&lt;0.001) and left ventricular ejection fraction (p=0.044) were significantly associated with degree of LAA fibrosis (Figure). Of 129 patients, 47 (36.4%) experienced recurrent AF during the median 3.9 years of follow-up. In a multivariable Cox regression analysis using clinical, echocardiographic and operative parameters, peak longitudinal LA stain was the only predictor of recurrent AF (adjusted HR 0.89, 95% CI 0.81–0.98, p=0.024; Table).

Conclusions

Peak longitudinal LA strain was associated with LAA fibrosis, and was a significant predictor of recurrent AF after TTA

Funding Acknowledgement

Type of funding sources: None.

Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities

Biochemical systems consist of numerous elementary reactions governed by the law of mass action. However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation. To facilitate the framework, we provide a user-friendly open-source computational package, ASSISTER, that automatically performs the present framework.

Inferring causality in biological oscillators

MOTIVATION

Fundamental to biological study is identifying regulatory interactions. The recent surge in time-series data collection in biology provides a unique opportunity to infer regulations computationally. However, when components oscillate, model-free inference methods, while easily implemented, struggle to distinguish periodic synchrony and causality. Alternatively, model-based methods test the reproducibility of time series given a specific model but require inefficient simulations and have limited applicability.

RESULTS

We develop an inference method based on a general model of molecular, neuronal and ecological oscillatory systems that merges the advantages of both model-based and model-free methods, namely accuracy, broad applicability and usability. Our method successfully infers the positive and negative regulations within various oscillatory networks, e.g. the repressilator and a network of cofactors at the pS2 promoter, outperforming popular inference methods.

AVAILABILITY AND IMPLEMENTATION

We provide a computational package, ION (Inferring Oscillatory Networks), that users can easily apply to noisy, oscillatory time series to uncover the mechanisms by which diverse systems generate oscillations. Accompanying MATLAB code under a BSD-style license and examples are available at https://github.com/Mathbiomed/ION. Additionally, the code is available under a CC-BY 4.0 License at https://doi.org/10.6084/m9.figshare.16431408.v1.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hierarchical Bayesian models of transcriptional and translational regulation processes with delays

MOTIVATION

Simultaneous recordings of gene network dynamics across large populations have revealed that cell characteristics vary considerably even in clonal lines. Inferring the variability of parameters that determine gene dynamics is key to understanding cellular behavior. However, this is complicated by the fact that the outcomes and effects of many reactions are not observable directly. Unobserved reactions can be replaced with time delays to reduce model dimensionality and simplify inference. However, the resulting models are non-Markovian, and require the development of new inference techniques.

RESULTS

We propose a non-Markovian, hierarchical Bayesian inference framework for quantifying the variability of cellular processes within and across cells in a population. We illustrate our approach using a delayed birth-death process. In general, a distributed delay model, rather than a popular fixed delay model, is needed for inference, even if only mean reaction delays are of interest. Using in silico and experimental data we show that the proposed hierarchical framework is robust and leads to improved estimates compared to its non-hierarchical counterpart. We apply our method to data obtained using time-lapse microscopy and infer the parameters that describe the dynamics of protein production at the single cell and population level. The mean delays in protein production are larger than previously reported, have a coefficient of variation of around 0.2 across the population, and are not strongly correlated with protein production or growth rates.

AVAILABILITY AND IMPLEMENTATION

Accompanying code in Python is available at https://github.com/mvcortez/Bayesian-Inference.

CONTACT

kresimir.josic@gmail.com or jaekkim@kaist.ac.kr or cbskust@korea.ac.kr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Viral stimulation modulates endotype-related ACE2 expression in eosinophilic chronic rhinosinusitis

BACKGROUND

Angiotensin-converting enzyme 2 (ACE2), a receptor targeted by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is highly expressed in the nasal mucosa. Chronic rhinosinusitis (CRS) shows diverse endotypes and is aggravated by viral infection. Whether viral stimulation and CRS endotype influence ACE2 expression remains unclear. We investigated the expression of ACE2 and the transmembrane protease, serine 2 (TMPRSS2), which mediate the entry of SARS-CoV-2 into cells, and assessed polyinosinic:polycytidylic acid (poly[I:C])-induced changes based on CRS endotype.

METHODOLOGY

ACE2 and TMPRSS2 expression was evaluated based on CRS phenotype, endotype, and tissue type. Correlations between ACE2/TMPRSS2 expression and inflammatory mediators in nasal polyps (NP) were examined. Air-liquid interface culture experiments were performed to assess the effects of major cytokines or poly(I:C) stimulation on ACE2/TMPRSS2 expression in primary epithelial cells from healthy nasal mucosa, eosinophilic NP (ENP), and non-eosinophilic NP (NENP).

RESULTS

In primary nasal epithelial cells, interleukin (IL)-13 decreased ACE2 expression but increased TMPRSS2. Eosinophilic CRS showed lower ACE2 expression than non-eosinophilic CRS, regardless of CRS phenotype. CRS endotype was an independent factor associated with ACE2/TMPRSS2 expression in NP. Serum and tissue eosinophilic marker levels were inversely correlated with ACE2 expression, whereas tissue neutrophilic marker levels and ACE2 expression were positively correlated in NP. ACE2 expression was suppressed in ENP tissues; however, a combination of poly(I:C) and IL-13 induced ACE2/TMPRSS2 upregulation in ENP.

CONCLUSIONS

ENP tissues have lower ACE2 expression than NENP; however, viral stimulation promotes ACE2/TMPRSS2 upregulation in ENP.

Subepithelial neutrophil infiltration as a predictor of the surgical outcome of chronic rhinosinusitis with nasal polyps

BACKGROUND

Neutrophils present as major inflammatory cells in refractory chronic rhinosinusitis with nasal polyps (CRSwNP), regardless of the endotype. However, their role in the pathophysiology of CRSwNP remains poorly understood. We investigated factors predicting the surgical outcomes of CRSwNP patients with focus on neutrophilic localization.

METHODS

We employed machine-learning methods such as the decision tree and random forest models to predict the surgical outcomes of CRSwNP. Immunofluorescence analysis was conducted to detect human neutrophil elastase (HNE), Bcl-2, and Ki-67 in NP tissues. We counted the immunofluorescence-positive cells and divided them into three groups based on the infiltrated area, namely, epithelial, subepithelial, and perivascular groups.

RESULTS

On machine learning, the decision tree algorithm demonstrated that the number of subepithelial HNE-positive cells, Lund-Mackay (LM) scores, and endotype (eosinophilic or non-eosinophilic) were the most important predictors of surgical outcomes in CRSwNP patients. Additionally, the random forest algorithm showed that, after ranking the mean decrease in the Gini index or the accuracy of each factor, the top three ranking factors associated with surgical outcomes were the LM score, age, and number of subepithelial HNE-positive cells. In terms of cellular proliferation, immunofluorescence analysis revealed that Ki-67/HNE-double positive and Bcl-2/HNE-double positive cells were significantly increased in the subepithelial area in refractory CRSwNP.

CONCLUSION

Our machine-learning approach and immunofluorescence analysis demonstrated that subepithelial neutrophils in NP tissues had a high expression of Ki-67 and could serve as a cellular biomarker for predicting surgical outcomes in CRSwNP patients.

Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer

AIM

To develop and validate a radiomics-based model for predicting response to neoadjuvant chemotherapy (NAC) using baseline computed tomography (CT) images in patients with muscle-invasive bladder cancer (MIBC).

MATERIALS AND METHODS

A radiomics signature for predicting pathological complete response (pCR) was developed using radiomics features selected by a random forest classifier on baseline CT images, and imaging predictors were identified in the training set (87 patients). By incorporating imaging predictors and radiomics signature, an imaging-based model was constructed using multivariate logistic regression analysis and validated in an independent validation set consisting of 48 patients with CT from outside institutions. The performance and clinical usefulness of the imaging-based model for predicting pCR were evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Using a cut-off determined in the training set, the positive likelihood ratios of the imaging-based model were calculated and compared with imaging and histological predictors.

RESULTS

The radiomics signature was developed based on six stable radiomics features. An imaging-based model incorporating radiomics signature, tumour shape, tumour size, and clinical stage showed good performance for predicting pCR in both the training (AUC, 0.85; 95% confidence interval [CI], 0.78-0.93) and validation (AUC, 0.75; 95% CI, 0.60-0.86) sets, providing a larger net benefit in decision curve analysis. The imaging-based model showed a higher positive likelihood ratio (1.91) for pCR than imaging and histological predictors (1.33-1.63).

CONCLUSIONS

The radiomics-based model using baseline CT images may predict the response of patients with MIBC to NAC.

Timing and clinical outcomes of tracheostomy in patients with COVID-19

In this retrospective multicentre cohort study that included 27 COVID-19 patients who underwent tracheostomy, the mean time between intubation and tracheostomy was 15.8 days and the negative conversion time of COVID-19 was 43.1 days. Eleven patients (40.7%) died of COVID-19 and the use of percutaneous dilatation tracheostomy was significantly associated with in-hospital death. Timely tracheostomy could be performed in COVID-19 patients, regardless of duration of intubation or positivity of COVID-19 test, with an open surgical tracheostomy as a preferable technique.

Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Unsupervised clustering models have been widely used for multimetric phenotyping of complex and heterogeneous diseases such as diabetes and obstructive sleep apnea (OSA) to more precisely characterize the disease beyond simplistic conventional diagnosis standards. However, the number of clusters and key phenotypic features have been subjectively selected, reducing the reliability of the phenotyping results. Here, to minimize such subjective decisions for highly confident phenotyping, we develop a multimetric phenotyping framework by combining supervised and unsupervised machine learning. This clusters 2277 OSA patients to six phenotypes based on their multidimensional polysomnography (PSG) data. Importantly, these new phenotypes show statistically different comorbidity development for OSA-related cardio-neuro-metabolic diseases, unlike the conventional single-metric apnea–hypopnea index-based phenotypes. Furthermore, the key features of highly comorbid phenotypes were identified through supervised learning rather than subjective choice. These results can also be used to automatically phenotype new patients and predict their comorbidity risks solely based on their PSG data. The phenotyping framework based on the combination of unsupervised and supervised machine learning methods can also be applied to other complex, heterogeneous diseases for phenotyping patients and identifying important features for high-risk phenotypes.

Misuse of the Michaelis-Menten rate law for protein interaction networks and its remedy

For over a century, the Michaelis-Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right condition, namely, that the substrate is in large excess over the enzyme-substrate complex. Unfortunately, in circumstances where its validity condition is not satisfied, especially so in protein interaction networks, the MM rate law has frequently been misused. In this review, we illustrate how inappropriate use of the MM rate law distorts the dynamics of the system, provides mistaken estimates of parameter values, and makes false predictions of dynamical features such as ultrasensitivity, bistability, and oscillations. We describe how these problems can be resolved with a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA). Furthermore, we show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost than using the full set of mass-action rate laws. This review describes how to use quasi-steady state approximations in the right context, to prevent drawing erroneous conclusions from in silico simulations.

Reliability of the Korean version of the Richards-Campbell Sleep Questionnaire

BACKGROUND

Sleep disorders are common in critically ill patients. Unfortunately, sleep assessment is challenging in many intensive care units (ICUs). The Richards-Campbell Sleep Questionnaire (RCSQ) is a simple subjective tool that has been validated and used in many countries. This study aimed to evaluate the reliability of the Korean version of the RCSQ (K-RCSQ).

METHODS

This prospective, cross-sectional, observational study was conducted in the ICUs of two hospitals. In total, 52 consenting patients answered questionnaires regarding their previous night's sleep (K-RCSQ) and the noise they experienced (range, 0-100).

RESULTS

The K-RCSQ showed excellent internal consistency of 0.960 by Cronbach's alpha. The mean total score of the K-RCSQ was 41.9±28.9 (range, 0-100). The mean perceived ICU noise score was 40.7±28.1 (range, 0-90). There was a significant linear correlation between noise score and average K-RCSQ score (r=-0.37, P<0.001).

CONCLUSIONS

The K-RCSQ demonstrated excellent reliability (internal consistency). This simple tool may help assess sleep quality in critically ill patients and improve the quality of ICU care.

Montel mirror based collimating analyzer system for high-pressure resonant inelastic X-ray scattering experiments

Resonant inelastic X-ray scattering (RIXS) is increasingly playing a significant role in studying highly correlated systems, especially since it was proven capable of measuring low-energy magnetic excitations. However, despite high expectations for experimental evidence of novel magnetic phases at high pressure, unequivocal low-energy spectral signatures remain obscured by extrinsic scattering from material surrounding the sample in a diamond anvil cell (DAC): pressure media, Be gasket and the diamond anvils themselves. A scattered X-ray collimation based medium-energy resolution (∼100 meV) analyzer system for a RIXS spectrometer at the Ir L₃-absorption edge has been designed and built to remediate these difficulties. Due to the confocal nature of the analyzer system, the majority of extrinsic scattering is rejected, yielding a clean low-energy excitation spectrum of an iridate Sr₂IrO₄ sample in a DAC cell. Furthermore, the energy resolution of different configurations of the collimating and analyzing optics are discussed.

Beyond the Michaelis-Menten: Accurate Prediction of In Vivo Hepatic Clearance for Drugs With Low KM

Clearance (CL) is the major pharmacokinetic parameter for evaluating systemic exposure of drugs in the body and, thus, for developing new drugs. To predict in vivo CL, the ratio between the maximal rate of metabolism and Michaelis‐Menten constant (V_max/K_M estimated from in vitro metabolism study has been widely used. This canonical approach is based on the Michaelis‐Menten equation, which is valid only when the K_M value of a drug is much higher than the hepatic concentration of the enzymes, especially cytochrome P450, involved in its metabolism. Here, we find that such a condition does not hold for many drugs with low K_M, and, thus, the canonical approach leads to considerable error. Importantly, we propose an alternative approach, which incorporates the saturation of drug metabolism when concentration of the enzymes is not sufficiently lower than K_M. This new approach dramatically improves the accuracy of prediction for in vivo CL of high‐affinity drugs with low K_M. This indicates that the proposed approach in this study, rather than the canonical approach, should be used to predict in vivo hepatic CL for high‐affinity drugs, such as midazolam and propafenone.

Impact of inversion symmetry on a quasi-1D S = 1 system

Here, we report the synthesis and magnetic properties of a novel, centrosymmetric, quasi-1D spin chain system La₃VWS₃O₆, with hexagonal crystal structure (P6₃/m, a  =  9.460 76(3), c  =  5.518 09(2) Å). Pure powders were obtained by solid-state reactions from La₂O₃, WO₃ and metal powders of V and W. X-ray powder diffraction, specific heat, magnetization, ¹³⁹La-nuclear magnetic resonance (NMR), and electric resistivity measurements indicate that the compound is a low dimensional magnet with an S  =  1 spin chain that exhibits no sign of magnetic ordering above 2 K. A single ion anisotropy (D/k _B ~ 10 K), caused by magneto-crystalline effects, is probably responsible for a thermodynamic entropy release at lower temperatures, which concurs with ¹³⁹La-NMR data. By detailed comparison with non-centrosymmetric Ba₃V₂S₄O₃, having a very similar magnetic lattice, it is obvious that the presence of crystallographic inversion symmetry has an effect on the behaviour of the magnetic chains.

A Systems Biology Approach Identifies Hidden Regulatory Connections Between the Circadian and Cell-Cycle Checkpoints

Circadian rhythms form a self-sustaining, endogenous, time-keeping system that allows organisms to anticipate daily environmental changes. The core of the clock network consists of interlocking transcriptional-translational feedback loops that ensures that metabolic, behavioral, and physiological processes run on a 24 h timescale. The hierarchical nature of the clock manifests itself in multiple points of control on the daily cell division cycle, which relies on synthesis, degradation, and post-translational modification for progression. This relationship is particularly important for understanding the role of clock components in sensing stress conditions and triggering checkpoint signals that stop cell cycle progression. A case in point is the interplay among the circadian factor PERIOD2 (PER2), the tumor suppressor p53, and the oncogenic mouse double minute-2 homolog protein (MDM2), which is the p53's negative regulator. Under unstressed conditions, PER2 and p53 form a stable complex in the cytosol and, along with MDM2, a trimeric complex in the nucleus. Association of PER2 to the C-terminus end of p53 prevents MDM2-mediated ubiquitylation and degradation of p53 as well as p53's transcriptional activation. Remarkably, when not bound to p53, PER2 acts as substrate for the E3-ligase activity of MDM2; thus, PER2 is degraded in a phosphorylation-independent fashion. Unexpectedly, the phase relationship between PER2 and p53 are opposite; however, a systematic modeling approach, inferred from the oscillatory time course data of PER2 and p53, aided in identifying additional regulatory scenarios that explained, a priori, seemingly conflicting experimental data. Therefore, we advocate for a combined experimental/mathematical approach to elucidating multilevel regulatory cellular processes.