The dynamic relationships between well-being, behavioral restrictions, and health behaviors during the COVID-19 pandemic: A large-scale intensive longitudinal network study.

The behavioral restrictions disrupting daily life during the COVID-19 pandemic have profoundly impacted well-being, and health behaviors have been advocated to prevent decline. To understand how processes related to fluctuation in well-being unfold within individuals, analyses on the within-person level are required. In this preregistered intensive longitudinal study, 1,709 individuals from the Norwegian adult population provided data daily over 40 consecutive days during the pandemic. The responses were modeled in a multilevel vector autoregressive model to estimate within-person networks, across and within-day, and a between-person network. All three networks revealed productivity, relatedness, and optimism as positively associated. Social distancing was contemporaneously negatively associated with productivity and relatedness. Among behavioral factors, being physically active predicted lower relatedness across days but displayed positive associations with relatedness, productivity, and optimism contemporaneously. Alcohol consumption predicted lower productivity across and within-day, although revealing a positive association with optimism within-day. Being social online and feeling related to others displayed a temporal negative bidirectional relationship. In contrast, being social online was positively associated with optimism, productivity, and relatedness contemporaneously. Our study emphasizes the dynamic nature of well-being and its complex associations with behavioral factors during the pandemic. The study shed light on opposing associations of behavioral factors at the within- and between-person level.

Novel Approaches Toward Studying Change: Implications for Understanding and Treating Psychopathology.

In this article, the authors critically evaluate contemporary models of psychopathology and therapies, underscoring the limitations of traditional symptom-based classification approaches in mental health. The authors introduce a paradigm shift in the field, toward a process-oriented and dynamic systems approach to psychotherapy that offers deeper insights into the complex interplay of symptoms and individual experiences in psychopathology. These approaches offer a more personalized and effective understanding and treatment of mental health issues, moving beyond static and 1-dimensional views. The authors discuss the implications for clinical practice, emphasizing improved assessment, diagnosis, and tailored treatment strategies.

The role of the correlated motion(s) of the chromophore in photoswitching of green and red forms of the photoconvertible fluorescent protein mSAASoti.

Wild-type SAASoti and its monomeric variant mSAASoti can undergo phototransformations, including reversible photoswitching of the green form to a nonfluorescent state and irreversible green-to-red photoconversion. In this study, we extend the photochemistry of mSAASoti variants to enable reversible photoswitching of the red form. This result is achieved by rational and site-saturated mutagenesis of the M163 and F177 residues. In the case of mSAASoti it is M163T substitution that leads to the fastest switching and the most photostable variant, and reversible photoswitching can be observed for both green and red forms when expressed in eukaryotic cells. We obtained a 13-fold increase in the switching efficiency with the maximum switching contrast of the green form and the appearance of comparable switching of the red form for the C21N/M163T mSAASoti variant. The crystal structure of the C21N mSAASoti in its green on-state was obtained for the first time at 3.0 Å resolution, and it is in good agreement with previously calculated 3D-model. Dynamic network analysis reveals that efficient photoswitching occurs if motions of the 66H residue and phenyl fragment of chromophore are correlated and these moieties belong to the same community.

Triggered by worry: A dynamic network analysis of COVID-19 pandemic-related anxiety and parental stress.

BACKGROUND: Major disruptions to daily life routines made families and parents particularly vulnerable to psychological distress during the COVID-19 lockdowns. However, the specific psychopathological processes related to within-person variation and maintenance of anxiety symptomatology and parental distress components in the parental population have been largely unexplored in the literature. METHODS: In this preregistered intensive longitudinal study, a multilevel dynamic network was used to model within-person interactions between anxiety symptomatology, psychopathological processes, parental distress, and protective lifestyle components in a sample of 495 parents-each responding to daily assessments over a 40-day period. A total of 30,195 observations were collected across the subjects. RESULTS: Extensive worry, threat monitoring, and uncontrollability of worry were identified as overreaching psychopathological processes related to the aggravation of other symptoms of anxiety and parental distress. A strong association was found between parental stress and parental burnout. Anger toward one's child was associated with both parental stress and parental burnout. Protective factors showed the lowest strength centrality, with few and weak connections to other symptoms and processes in the network. LIMITATIONS: Associations may exist between the study variables on a different time scale; hence, different time lags should be used in future research. CONCLUSIONS: Accessible, low-cost interventions that address worry, threat monitoring, and the uncontrollability of worry could serve as potential targets for reducing the symptom burden of anxiety and distress in the parental population.

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter.

BACKGROUND: The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. OBJECTIVE: The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. METHODS: Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. RESULTS: A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. CONCLUSIONS: The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.

Computational insights into ligand-induced G protein and ß-arrestin signaling of the dopamine D1 receptor.

The dopamine D1 receptor (D1R), is a class A G protein coupled-receptor (GPCR) which has been a promising drug target for psychiatric and neurological disorders such as Parkinson's disease (PD). Previous studies have suggested that therapeutic effects can be realized by targeting the ß-arrestin signaling pathway of dopamine receptors, while overactivation of the G protein-dependent pathways leads to side effects, such as dyskinesias. Therefore, it is highly desirable to develop a D1R ligand that selectively regulates the ß-arrestin pathway. Currently, most D1R agonists are signaling-balanced and stimulate both G protein and ß-arrestin pathways, with a few reports of G protein biased ligands. However, identification and characterization of ß-arrestin biased D1R agonists has been a challenge thus far. In this study, we implemented Gaussian accelerated molecular dynamics (GaMD) simulations to provide valuable computational insights into the possible underlying molecular mechanism of the different signaling properties of two catechol and two non-catechol D1R agonists that are either G protein biased or signaling-balanced. Dynamic network analysis further identified critical residues in the allosteric signaling network of D1R for each ligand at different conformational or binding states. Some of these residues are crucial for G protein or arrestin signals of GPCRs based on previous studies. Finally, we provided a molecular design strategy which can be utilized by medicinal chemists to develop potential ß-arrestin biased D1R ligands. The proposed hypotheses are experimentally testable and can guide the development of safer and more effective medications for a variety of CNS disorders.

Comparative network analysis reveals the dynamics of organic acid diversity during fruit ripening in peach (Prunus persica L. Batsch).

BACKGROUND: Organic acids are important components that determine the fruit flavor of peach (Prunus persica L. Batsch). However, the dynamics of organic acid diversity during fruit ripening and the key genes that modulate the organic acids metabolism remain largely unknown in this kind of fruit tree which yield ranks sixth in the world. RESULTS: In this study, we used 3D transcriptome data containing three dimensions of information, namely time, phenotype and gene expression, from 5 different varieties of peach to construct gene co-expression networks throughout fruit ripening of peach. With the network inferred, the time-ordered network comparative analysis was performed to select high-acid specific gene co-expression network and then clarify the regulatory factors controlling organic acid accumulation. As a result, network modules related to organic acid synthesis and metabolism under high-acid and low-acid comparison conditions were identified for our following research. In addition, we obtained 20 candidate genes as regulatory factors related to organic acid metabolism in peach. CONCLUSIONS: The study provides new insights into the dynamics of organic acid accumulation during fruit ripening, complements the results of classical co-expression network analysis and establishes a foundation for key genes discovery from time-series multiple species transcriptome data.

Dynamic networks of P300-related process.

P300 as an effective biomarker to index attention and memory has been widely used for brain-computer interface, cognitive evaluation, and clinical diagnosis. To evoke clear P300, an oddball paradigm consisting of two types of stimuli, i.e., infrequent target stimuli and frequent standard stimuli, is usually used. However, to simply and quickly explore the P300-related process, previous studies predominately focused on the target condition but ignored the fusion of target and standard conditions, as well as the difference of brain networks between them. Therefore, in this study, we used the hidden Markov model to investigate the fused multi-conditional electroencephalogram dataset of P300, aiming to effectively identify the underlying brain networks and explore the difference between conditions. Specifically, the inferred networks, including their transition sequences and spatial distributions, were scrutinized first. Then, we found that the difference between target and standard conditions was mainly concentrated in two phases. One was the stimulation phase that mainly related to the cortical activities of the postcentral gyrus and superior parietal lobule, and the other corresponded to the response phase that involved the activities of superior and medial frontal gyri. This might be attributed to distinct cognitive functions, as the stimulation phase is associated with visual information integration whereas the response phase involves stimulus discrimination and behavior control. Taken together, the current work explored dynamic networks underlying the P300-related process and provided a complementary understanding of distinct P300 conditions, which may contribute to the design of P300-related brain-machine systems.

Thermodynamic integration combined with molecular dynamic simulations to explore the cross-resistance mechanism of isoniazid and ethionamide.

Tuberculosis is an ancient disease of mankind, and its causative bacterium is Mycobacterium tuberculosis. Isoniazid is one of the most effective first-line antituberculosis drugs. As prodrugs, it and its derivative ethionamide act on enoyl-acyl carrier protein reductase (InhA) after being oxidized in bacteria, and kill the bacteria by inhibiting the formation of M. tuberculosis cell walls. However, the S94A mutation of InhA causes M. tuberculosis to develop cross-resistance to isoniazid and ethionamide. This work is dedicated to studying the cross-resistance mechanism of isoniazid and ethionamide through theoretical calculations. First, thermodynamic integral simulations are used to accurately calculate the relative binding energy of two drugs in the mutant and wild-type system. Furthermore, through classic molecular dynamic simulations and molecular mechanics generalized-Born surface area calculation, some key residues are identified and the binding affinity of isoniazid and ethionamide reduced by 9-13 kcal/mol due to S94A mutation. The hydrogen bond between Ala94 and isoniazid (ethionamide) disappeared and the energy contribution of Ala94 decreased after the mutation. In addition, the dynamic network analysis indicated that the mutation of Ser94 also indirectly affected the conformation of key residues such as Met147, Thr196, and Leu97, resulting in a reduction in the energy contribution of these residues. Finally, the binding conformation of isoniazid and ethionamide has also undergone major changes. The obtained results could provide valuable information for the future molecular design to overcome the drug resistance.

Changed dynamic symptom networks after a self-compassion training in patients with somatic symptom disorder: A multiple single-case pilot project.

OBJECTIVE: Pre-to-post mean group differences of intermittently assessed generic outcome variables may not capture all relevant treatment-related changes in individual patients with somatic symptom disorder (SSD). Aim of this multiple single-case observational pilot project was to find out whether the Experience Sampling Method (ESM) and dynamic symptom networks may offer new opportunities in evaluating treatment outcomes for individual patients with SSD. METHODS: Patients with SSD (N = 6 in study 1, N = 7 in study 2) received a self-compassion training in a tertiary care mental health expert center. Using a single-case pre-post treatment observational design, intensive longitudinal data were collected with ESM. A brief questionnaire was presented via the patient's smartphone three times per day for 16 weeks before, during and after the training in study 1, and for 5 weeks before and 5 weeks after the training in study 2. Eleven questions comprised somatic symptoms, functional disability, stress, self-compassion, and acceptance of affect; three personalized questions comprised self-chosen affects and an additional symptom. RESULTS: Sufficient observations for means and network comparison were obtained for 11 and 10 patients, respectively. After the training, self-compassion was significantly increased in 10 patients, functional disability, stress and affect improved in 6 patients, and (although not a treatment goal) somatic symptoms decreased in 6 patients. Dynamic symptom networks significantly changed in 5 patients. CONCLUSION: Patient-specific changes in means and dynamic symptom networks were observed after self-compassion training. In future clinical trials, single-case ESM may offer new opportunities to evaluate treatment outcomes in patients with SSD.

Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation.

In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis with network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike engine. By combining functional dynamics analysis and ensemble-based alanine scanning of the SARS-CoV-2 spike proteins we found that the D614G mutation can improve stability of the spike protein in both closed and open forms, but shifting thermodynamic preferences towards the open mutant form. Our results revealed that the D614G mutation can promote the increased number of stable communities and allosteric hub centers in the open form by reorganizing and enhancing the stability of the S1-S2 inter-domain interactions and restricting mobility of the S1 regions. This study provides atomistic-based view of allosteric communications in the SARS-CoV-2 spike proteins, suggesting that the D614G mutation can exert its primary effect through allosterically induced changes on stability and communications in the residue interaction networks.Communicated by Ramaswamy H. Sarma.

Within- and across-day patterns of interplay between depressive symptoms and related psychopathological processes: a dynamic network approach during the COVID-19 pandemic.

BACKGROUND: In order to understand the intricate patterns of interplay connected to the formation and maintenance of depressive symptomatology, repeated measures investigations focusing on within-person relationships between psychopathological mechanisms and depressive components are required. METHODS: This large-scale preregistered intensive longitudinal study conducted 68,240 observations of 1706 individuals in the general adult population across a 40-day period during the COVID-19 pandemic to identify the detrimental processes involved in depressive states. Daily responses were modeled using multi-level dynamic network analysis to investigate the temporal associations across days, in addition to contemporaneous relationships between depressive components within a daily window. RESULTS: Among the investigated psychopathological mechanisms, helplessness predicted the strongest across-day influence on depressive symptoms, while emotion regulation difficulties displayed more proximal interactions with symptomatology. Helplessness was further involved in the amplification of other theorized psychopathological mechanisms including rumination, the latter of which to a greater extent was susceptible toward being influenced rather than temporally influencing other components of depressive states. Distinctive symptoms of depression behaved differently, with depressed mood and anhedonia most prone to being impacted, while lethargy and worthlessness were more strongly associated with outgoing activity in the network. CONCLUSIONS: The main mechanism predicting the amplifications of detrimental symptomatology was helplessness. Lethargy and worthlessness revealed greater within-person carry-over effects across days, providing preliminary indications that these symptoms may be more strongly associated with pushing individuals toward prolonged depressive state experiences. The psychopathological processes of rumination, helplessness, and emotion regulation only exhibited interactions with the depressed mood and worthlessness component of depression, being unrelated to lethargy and anhedonia. The findings have implications for the impediment of depressive symptomatology during and beyond the pandemic period. They further outline the gaps in the literature concerning the identification of psychopathological processes intertwined with lethargy and anhedonia on the within-person level.

Patterns of Adaptation in Child-Directed and Child Speech in the Emergence of Hebrew Verbs.

Children approach verb learning in ways that are specific to their native language, given the differential typological organization of verb morphology and lexical semantics. Parent-child interaction is the arena where children's socio-cognitive abilities enable them to track predictive relationships between tokens and extract linguistic generalizations from patterns and regularities in the ambient language. The current study examines how the system of Hebrew verbs develops as a network over time in early childhood, and the dynamic role of input-output adaptation in the network's increasing complexity. Focus is on the morphological components of Hebrew verbs in a dense corpus of two parent-child dyads in natural interaction between the ages 1;8-2;2. The 91-hour corpus contained 371,547 word tokens, 62,824 verb tokens, and 1,410 verb types (lemmas) in CDS and CS together. Network analysis was employed to explore the changing distributions and emergent systematicity of the relations between verb roots and verb patterns. Taking the Semitic root and pattern morphological constructs to represent linked nodes in a network, findings show that children's networks change with age in terms of node degree and node centrality, representing linkage level and construct importance respectively; and in terms of network density, as representing network growth potential. We put forward three main hypotheses followed by findings concerning (i) changes in verb usage through development, (ii) CS adaptation, and (iii) CDS adaptation: First, we show that children go through punctuated development, expressed by their using individual constructs for short periods of time, whereas parents' patterns of usage are more coherent. Second, regarding CS adaptation within a dynamic network system relative to time and CDS, we conclude that children are attuned to their immediate experience consisting of current CDS usage as well as previous usage in the immediate past. Finally, we show that parents (unintentionally) adapt to their children's language knowledge in three ways: First, by relating to their children's current usage. Second, by expanding on previous experience, building upon the usage their children have already been exposed to. And third, we show that when parents experience a limited network in the speech of their children, they provide them with more opportunities to expand their system in future interactions.

Improved supervised prediction of aging-related genes via weighted dynamic network analysis.

BACKGROUND: This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein-protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did not improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. This could be because the dynamic subnetwork was inferred using a naive Induced subgraph approach. Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process. RESULTS: Here, we evaluate whether our existing NP-based dynamic subnetwork will improve upon the dynamic as well as static subnetwork constructed by the Induced approach in the considered task of supervised prediction of aging-related genes. The existing NP-based subnetwork is unweighted, i.e., it gives equal importance to each of the aging-specific PPIs. Because accounting for aging-specific edge weights might be important, we additionally propose a weighted NP-based dynamic aging-specific subnetwork. We demonstrate that a predictive machine learning model trained and tested on the weighted subnetwork yields higher accuracy when predicting aging-related genes than predictive models run on the existing unweighted dynamic or static subnetworks, regardless of whether the existing subnetworks were inferred using NP or the Induced approach. CONCLUSIONS: Our proposed weighted dynamic aging-specific subnetwork and its corresponding predictive model could guide with higher confidence than the existing data and models the discovery of novel aging-related gene candidates for future wet lab validation.

Spatiotemporally specific roles of TLR4, TNF, and IL-17A in murine endotoxin-induced inflammation inferred from analysis of dynamic networks.

BACKGROUND: Bacterial lipopolysaccharide (LPS) induces a multi-organ, Toll-like receptor 4 (TLR4)-dependent acute inflammatory response. METHODS: Using network analysis, we defined the spatiotemporal dynamics of 20, LPS-induced, protein-level inflammatory mediators over 0-48 h in the heart, gut, lung, liver, spleen, kidney, and systemic circulation, in both C57BL/6 (wild-type) and TLR4-null mice. RESULTS: Dynamic Network Analysis suggested that inflammation in the heart is most dependent on TLR4, followed by the liver, kidney, plasma, gut, lung, and spleen, and raises the possibility of non-TLR4 LPS signaling pathways at defined time points in the gut, lung, and spleen. Insights from computational analyses suggest an early role for TLR4-dependent tumor necrosis factor in coordinating multiple signaling pathways in the heart, giving way to later interleukin-17A-possibly derived from pathogenic Th17 cells and effector/memory T cells-in the spleen and blood. CONCLUSIONS: We have derived novel, systems-level insights regarding the spatiotemporal evolution acute inflammation.

Characterizing network dynamics of online hate communities around the COVID-19 pandemic.

Hate speech has long posed a serious problem for the integrity of digital platforms. Although significant progress has been made in identifying hate speech in its various forms, prevailing computational approaches have tended to consider it in isolation from the community-based contexts in which it spreads. In this paper, we propose a dynamic network framework to characterize hate communities, focusing on Twitter conversations related to COVID-19 in the United States and the Philippines. While average hate scores remain fairly consistent over time, hate communities grow increasingly organized in March, then slowly disperse in the succeeding months. This pattern is robust to fluctuations in the number of network clusters and average cluster size. Infodemiological analysis demonstrates that in both countries, the spread of hate speech around COVID-19 features similar reproduction rates as other COVID-19 information on Twitter, with spikes in hate speech generation at time points with highest community-level organization of hate speech. Identity analysis further reveals that hate in the US initially targets political figures, then grows predominantly racially charged; in the Philippines, targets of hate consistently remain political over time. Finally, we demonstrate that higher levels of community hate are consistently associated with smaller, more isolated, and highly hierarchical network clusters across both contexts. This suggests potentially shared structural conditions for the effective spread of hate speech in online communities even when functionally targeting distinct identity groups. Our findings bear theoretical and methodological implications for the scientific study of hate speech and understanding the pandemic's broader societal impacts both online and offline.

Social cybersecurity: an emerging science.

With the rise of online platforms where individuals could gather and spread information came the rise of online cybercrimes aimed at taking advantage of not just single individuals but collectives. In response, researchers and practitioners began trying to understand this digital playground and the way in which individuals who were socially and digitally embedded could be manipulated. What is emerging is a new scientific and engineering discipline-social cybersecurity. This paper defines this emerging area, provides case examples of the research issues and types of tools needed, and lays out a program of research in this area.

Understanding Allogrooming Through a Dynamic Social Network Approach: An Example in a Group of Dairy Cows.

For gregarious species such as domestic cattle, the social environment is a very important determinant of their welfare and fitness. Understanding the complexity of cows' relationships can assist the development of management practices that are more integrated with the cows' social behavioral processes. The two aims of this study were: (1) to determine the dynamics of affiliative relationships, as indicated by allogrooming, by means of stochastic actor-oriented modeling, in dairy cows during early lactation; (2) to explore the underlying processes and the individual attributes, such as age, social rank and reproductive state, that could shape network pattern changes in grooming contacts between individual. We observed the allogrooming behavior of a dynamic group of 38 dairy cows for 4 h per day for 30 days. Using stochastic actor-oriented models, we modeled the dynamics of weekly contacts and studied how structural processes (e.g., reciprocity, transitivity, or popularity) and individual attributes (i.e., age, social rank, and reproductive state) influence network changes. We found that cows tended to groom individuals that had previously groomed them, implying a possible cooperation. Cows that groomed more actively did not appear to have a preference for specific individuals in the herd, and in return, tended to be groomed by fewer cows over time. Older individuals groomed more cows than younger ones, indicating that allogrooming could be related to seniority. Cows groomed mainly individuals of similar age, suggesting that familiarity and growing up together enhanced social grooming. Over time, cows with higher social rank were groomed by fewer cows and individuals recently reintroduced to the group groomed more herdmates. The study of social network dynamics can be used to better understand the complexity and non-linearity of cow relationships. Our findings, along with further research, can complement and strengthen the design of improved management practices that are more in line with the natural social behavior of cows.

Probing the Molecular Mechanism of Rifampin Resistance Caused by the Point Mutations S456L and D441V on Mycobacterium tuberculosis RNA Polymerase through Gaussian Accelerated Molecular Dynamics Simulation.

Rifampin is the first-line antituberculosis drug, with Mycobacterium tuberculosis RNA polymerase as the molecular target. Unfortunately, M. tuberculosis strains that are resistant to rifampin have been identified in clinical settings, which limits its therapeutic effects. In clinical isolates, S531L and D516V (in Escherichia coli) are two common mutated codons in the gene rpoB, corresponding to S456L and D441V in M. tuberculosis However, the resistance mechanism at the molecular level is still elusive. In this work, Gaussian accelerated molecular dynamics simulations were performed to uncover the resistance mechanism of rifampin due to S456L and D441V mutations at the atomic level. The binding free energy analysis revealed that the reduction in the ability of two mutants to bind rifampin is mainly due to a decrease in electrostatic interaction, specifically, a decrease in the energy contribution of the R454 residue. R454 acts as an anchor and forms stable hydrogen bond interaction with rifampin, allowing rifampin to be stably incorporated in the center of the binding pocket. However, the disappearance of the hydrogen bond between R454 and the mutated residues increases the flexibility of the side chain of R454. The conformation of R454 changes, and the hydrogen bond interaction between it and rifampin is disrupted. As result, the rifampin molecule moves to the outside of the pocket, and the binding affinity decreases. Overall, these findings can provide useful information for understanding the drug resistance mechanism of rifampin and also can give theoretical guidance for further design of novel inhibitors to overcome the drug resistance.

Unsupervised Clustering Analysis Based on MODS Severity Identifies Four Distinct Organ Dysfunction Patterns in Severely Injured Blunt Trauma Patients.

Purpose: We sought to identify a MODS score parameter that highly correlates with adverse outcomes and then use this parameter to test the hypothesis that multiple severity-based MODS clusters could be identified after blunt trauma. Methods: MOD score across days (D) 2-5 was subjected to Fuzzy C-means Clustering Analysis (FCM) followed by eight Clustering Validity Indices (CVI) to derive organ dysfunction patterns among 376 blunt trauma patients admitted to the intensive care unit (ICU) who survived to discharge. Thirty-one inflammation biomarkers were assayed (Luminex™) in serial blood samples (3 samples within the first 24 h and then daily up to D 5) and were analyzed using Two-Way ANOVA and Dynamic Network analysis (DyNA). Results: The FCM followed by CVI suggested four distinct clusters based on MOD score magnitude between D2 and D5. Distinct patterns of organ dysfunction emerged in each of the four clusters and exhibited statistically significant differences with regards to in-hospital outcomes. Interleukin (IL)-6, MCP-1, IL-10, IL-8, IP-10, sST2, and MIG were elevated differentially over time across the four clusters. DyNA identified remarkable differences in inflammatory network interconnectivity. Conclusion: These results suggest the existence of four distinct organ failure patterns based on MOD score magnitude in blunt trauma patients admitted to the ICU who survive to discharge.