Impacts of workplace verbal aggression classified via text mining on workers' mental health

BACKGROUND

Exposure to workplace aggression adversely affects workers' health; however, little is known regarding the impact of specific types of verbal content.

AIMS

We aimed to examine the relationship between exposure to several types of aggressive words at work and the victim's depressive symptoms and sleep disturbance using text mining.

METHODS

We conducted a longitudinal survey with 800 workers in wholesale and retail companies; of which, 500 responded to the follow-up survey. The Centre for Epidemiologic Studies-Depression Scale and Pittsburgh Sleep Quality Index were filled out by the participants, and their responses were analysed by logistic regression to evaluate the risk of depression or sleep problems. We collected exact aggressive words encountered at work over the past year as a dependent variable and classified it into four types using text mining, such as words criticizing one's performance.

RESULTS

The follow-up rate was 63%. Exposure to words threatening one's life showed a significant relationship with the risk of depression (odds ratio [OR] = 13.94, 95% confidence interval [CI] = 1.76-110.56). The exposure to words criticizing one's job performance is significantly related to the risk of sleep disturbance (OR = 5.56, 95% CI = 2.08-14.88).

CONCLUSIONS

These findings suggest that different contents of verbal aggression can have different impacts on workers' health. This indicates that not only overtly threatening and abusive language but also words related to one's performance can be a risk factor for workers, depending on how they are delivered. To mitigate the adverse effects, promoting effective communication and cultivating psychological detachment from work may be beneficial.

A macrocyclic kinase inhibitor overcomes triple resistant mutations in EGFR-positive lung cancer

Brigatinib-based therapy was effective against osimertinib-resistant EGFR C797S mutants and is undergoing clinical studies. However, tumor relapse suggests additional resistance mutations might emerge. Here, we first demonstrated the binding mode of brigatinib to the EGFR-T790M/C797S mutant by crystal structure analysis and predicted brigatinib-resistant mutations through a cell-based assay including N-ethyl-N-nitrosourea (ENU) mutagenesis. We found that clinically reported L718 and G796 compound mutations appeared, consistent with their proximity to the binding site of brigatinib, and brigatinib-resistant quadruple mutants such as EGFR-activating mutation/T790M/C797S/L718M were resistant to all the clinically available EGFR-TKIs. BI-4020, a fourth-generation EGFR inhibitor with a macrocyclic structure, overcomes the quadruple and major EGFR-activating mutants but not the minor mutants, such as L747P or S768I. Molecular dynamics simulation revealed the binding mode and affinity between BI-4020 and EGFR mutants. This study identified potential therapeutic strategies using the new-generation macrocyclic EGFR inhibitor to overcome the emerging ultimate resistance mutants.

Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method

Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have been developed to predict mitochondrial toxicity based on chemical structures. However, they only provide a binary classification of positive or negative results and do not provide the substructures that contribute to a positive decision. Therefore, we developed an artificial intelligence (AI) model to predict mitochondrial toxicity and visualize structural alerts. To construct the model, we used the open-source software library kMoL, which employs a graph neural network approach that allows learning from chemical structure data. We also utilized the integrated gradient method, which enables the visualization of substructures that contribute to positive results. The dataset used to construct the AI model exhibited a significant imbalance, with significantly more negative than positive data. To address this, we employed the bagging method, which resulted in a model with high predictive performance, as evidenced by an F1 score of 0.839. This model can also be used to visualize substructures that contribute to mitochondrial toxicity using the integrated gradient method. Our AI model predicts mitochondrial toxicity based on chemical structures and may contribute to screening mitochondrial toxicity in the early stages of drug discovery.

VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search

Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery.

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations

Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.

Development of Rat Caries-Induced Pulpitis Model for Vital Pulp Therapy

Rodent animal models for vital pulp therapy are commonly used in dental research because their tooth anatomy and cellular processes are similar to the anatomy and processes in humans. However, most studies have been conducted using uninfected sound teeth, which makes it difficult to adequately assess the inflammatory shift after vital pulp therapy. In the present study, we aimed to establish a caries-induced pulpitis model based on the conventional rat caries model and then evaluate inflammatory changes during the wound-healing process after pulp capping in a model of reversible pulpitis induced by carious infection. To establish the caries-induced pulpitis model, the pulpal inflammatory status was investigated at different stages of caries progression by immunostaining targeted to specific inflammatory biomarkers. Immunohistochemical staining revealed that both Toll-like receptor 2 and proliferating cell nuclear antigen were expressed in moderate and severe caries-stimulated pulp, indicating that an immune reaction occurred at both stages of caries progression. M2 macrophages were predominant in moderate caries-stimulated pulp, whereas M1 macrophages were predominant in the severe caries-stimulated pulp. Pulp capping in teeth with moderate caries (i.e., teeth with reversible pulpitis) led to complete tertiary dentin formation within 28 d after treatment. Impaired wound healing was observed in teeth with severe caries (i.e., teeth with irreversible pulpitis). During the wound-healing process in reversible pulpitis after pulp capping, M2 macrophages were predominant at all time points; their proliferative capacity was upregulated in the early stage of wound healing compared with healthy pulp. In conclusion, we successfully established a caries-induced pulpitis model for studies of vital pulp therapy. M2 macrophages have an important role in the early stages of the wound-healing process in reversible pulpitis.

Novel Functional Peptide for Next-Generation Vital Pulp Therapy

Although vital pulp therapy should be performed by promoting the wound-healing capacity of dental pulp, existing pulp-capping materials were not developed with a focus on the pulpal repair process. In previous investigations of wound healing in dental pulp, we found that organic dentin matrix components (DMCs) were degraded by matrix metalloproteinase-20, and DMC degradation products containing protein S100A7 (S100A7) and protein S100A8 (S100A8) promoted the pulpal wound-healing process. However, the direct use of recombinant proteins as pulp-capping materials may cause clinical problems or lead to high medical costs. Thus, we hypothesized that functional peptides derived from recombinant proteins could solve the problems associated with direct use of such proteins. In this study, we identified functional peptides derived from the protein S100 family and investigated their effects on dental pulp tissue. We first performed amino acid sequence alignments of protein S100 family members from several mammalian sources, then identified candidate peptides. Next, we used a peptide array method that involved human dental pulp stem cells (hDPSCs) to evaluate the mineralization-inducing ability of each peptide. Our results supported the selection of 4 candidate functional peptides derived from proteins S100A8 and S100A9. Direct pulp-capping experiments in a rat model demonstrated that 1 S100A8-derived peptide induced greater tertiary dentin formation compared with the other peptides. To investigate the mechanism underlying this induction effect, we performed liquid chromatography-tandem mass spectrometry analysis using hDPSCs and the S100A8-derived peptide; the results suggested that this peptide promotes tertiary dentin formation by inhibiting inflammatory responses. In addition, this peptide was located in a hairpin region on the surface of S100A8 and could function by direct interaction with other molecules. In summary, this study demonstrated that a S100A8-derived functional peptide promoted wound healing in dental pulp; our findings provide insights for the development of next-generation biological vital pulp therapies.

Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis

Distinguishing oncogenic mutations from variants of unknown significance (VUS) is critical for precision cancer medicine. Here, computational modeling of 71,756 RET variants for positive selection together with functional assays of 110 representative variants identified a three-dimensional cluster of VUSs carried by multiple human cancers that cause amino acid substitutions in the calmodulin-like motif (CaLM) of RET. Molecular dynamics simulations indicated that CaLM mutations decrease interactions between Ca2+ and its surrounding residues and induce conformational distortion of the RET cysteine-rich domain containing the CaLM. RET-CaLM mutations caused ligand-independent constitutive activation of RET kinase by homodimerization mediated by illegitimate disulfide bond formation. RET-CaLM mutants possessed oncogenic and tumorigenic activities that could be suppressed by tyrosine kinase inhibitors targeting RET. This study identifies calcium-binding ablating mutations as a novel type of oncogenic mutation of RET and indicates that in silico-driven annotation of VUSs of druggable oncogenes is a promising strategy to identify targetable driver mutations.

SIGNIFICANCE

Comprehensive proteogenomic and in silico analyses of a vast number of VUSs identify a novel set of oncogenic and druggable mutations in the well-characterized RET oncogene.

Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics

Compared to all-atom molecular dynamics (AA-MD) simulations, coarse-grained (CG) MD simulations can significantly reduce calculation costs. However, existing CG-MD methods are unsuitable for sampling structures that depart significantly from the initial structure without any biased force. In this study, we developed a new adaptive CG elastic network model (ENM), in which the dynamic cross-correlation coefficient based on short-time AA-MD of at most ns order is considered. By applying Bayesian optimization to search for a suitable parameter among the vast parameter space of adaptive CG-ENM, we succeeded in reducing the searching cost to approximately 10% of those for random sampling and exhaustive sampling. To evaluate the performance of adaptive CG-ENM, we applied the new methodology to adenylate kinase (ADK) and glutamine binding protein (GBP) in the apo state. The results showed that the structural ensembles explored by adaptive CG-ENM could be considerably more diverse than those by conventional ENMs with enhanced sampling such as temperature replica exchange MD and long-time AA-MD of 1 μs. In particular, some of the structures sampled by adaptive ENM are relatively close to the holo-type structures of ADK and GBP. Furthermore, as a challenging task, to demonstrate the advantages of the CG model with lower calculation cost, we applied our new methodology to a larger biomolecule, integrin (αV) in the inactive state. Then, we sampled various structural ensembles, including extended structures that are apparently different from inactive ones.

Characteristics of gut microbiome, organic acid profiles and viral antibody indexes of healthy Japanese with live Lacticaseibacillus detected in stool

To estimate the health-promoting effects of Lacticaseibacillus paracasei (previously Lactobacillus casei) strain Shirota (LcS) that reached the lower gastrointestinal tract alive, we investigated the characteristics of gut microbiome, organic acid profiles, defecatory symptoms and serum viral antibody indexes of healthy Japanese adults between the group in whom live LcS was detected or not from stool. The β-diversity index of the gut microbiome constituted a significant difference between the live-LcS-detected-group (LLD) and the live-LcS-not-detected-group (LLnD). In the LLD, the Bifidobacteriaceae, Lactobacillaceae, and Coriobacteriaceae counts were significantly higher, and the succinate concentration was significantly lower than that in the LLnD. The serum herpes simplex virus (HSV) immunoglobulin (Ig)M antibody index in the LLD tended to be lower than that of the LLnD in HSV IgG-positive subjects. Of the LLD, those in the fermented milk products containing LcS (FML)-high-frequency-group (FML-HF) and those in the FML-low-frequency-group (FML-LF) had different gut microbiome and organic acid profiles. However, the pattern of differences between FML-HF and FML-LF was dissimilar those between LLD and LLnD. In contrast, among subjects with FML-LF, those in the group with LLD in stool (LF-LLD) and those in the LLnD in stool (LF-LLnD) showed a similar pattern of differences in their gut microbiome and organic acid profiles as those in the LLnD and LLD. The LLD and LF-LLD commonly had lower caloric and carbohydrate intakes from the diet than their respective control groups. In this study, we found that the presence of live LcS in stool is associated with a healthy gut environment and inhibition of the reactivation of latently infected viruses in the host. However, these health-promoting effects on the host were not related to the frequency of FML intake. Furthermore, dysbiosis of the gut microbiome and diet including caloric intake was related to the viability of ingested LcS in the gut.

Large-Scale Prospective Genome-Wide Association Study of Oxaliplatin in Stage II/III Colon Cancer and Neuropathy

IMPORTANCE

The severity of oxaliplatin (L-OHP)-induced peripheral sensory neuropathy (PSN) exhibits substantial interpatient variability, and some patients suffer from long-term, persisting PSN.

OBJECTIVE

To identify single-nucleotide polymorphisms (SNPs) predicting L-OHP-induced PSN using a genome-wide association study (GWAS) approach.

DESIGN, SETTING, PARTICIPANTS

A large prospective GWAS including 1,379 patients with stage II/III colon cancer who received L-OHP-based adjuvant chemotherapy (mFOLFOX6/CAPOX) under the phase II (JOIN/JFMC41) or the phase III (ACHIVE/JFMC47) trial.

MAIN OUTCOMES AND MEASURES

First, GWAS comparison of worst grade PSN (grade 0/1 vs. 2/3) was performed. Next, to minimize the impact of ambiguity in PSN grading, extreme PSN phenotypes were selected and analyzed by GWAS. SNPs that could predict time to recovery from PSN were also evaluated. In addition, SNPs associated with L-OHP-induced allergic reactions (AR) and time to disease recurrence were explored.

RESULTS

No SNPs exceeded the genome-wide significance (p < 5.0 × 10^-8) in either GWAS comparison of worst grade PSN, extreme PSN phenotypes, or time to recovery from PSN. Association study focusing on AR or time to disease recurrence also failed to reveal any significant SNPs.

CONCLUSION AND RELEVANCE

Our results highlight the challenges of utilizing SNPs for predicting susceptibility to L-OHP-induced PSN in daily clinical practice.

Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations

Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have serious limitations in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and the generated molecules possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design. The code is available at https://github.com/clinfo/SBMolGen.

POS0603 ANALYSIS OF FACTORS ASSOCIATED WITH THE EFFECTIVENESS OF ABATACEPT IN THE ORIGAMI STUDY

Background:

The ORIGAMI study is a multicenter, observational study to evaluate the effectiveness, safety, and patient-reported outcomes of abatacept (ABA) in Japanese patients with csDMARD-resistant, Simplified Disease Activity Index (SDAI)-moderate, biologic-naïve rheumatoid arthritis (RA). ABA has shown better effectiveness/efficacy in RA patients with anti-cyclic citrullinated peptide antibody (ACPA) positive (1) and high ACPA titer (2) compared to ACPA negative and low ACPA titer, respectively. However, more accurate predictors of effectiveness in clinical practice are needed than ACPA status.

Objectives:

This post-hoc analysis is aimed to determine the association between ACPA and ABA effectiveness (disease activity and physical function) or retention rate and to investigate other factors associated with the effectiveness of ABA in patients enrolled in the ORIGAMI study.

Methods:

Of the 279 patients in the effectiveness analysis set of the ORIGAMI study, 270 patients with baseline ACPA measurement were analyzed. The patients were divided into the ACPA-positive group (ACPA +ve, ≥4.5 U/mL at baseline) and the ACPA-negative group (ACPA –ve, <4.5 U/mL). Patients’ characteristics, changes in disease activity and physical function (Japanese Health Assessment Questionnaire; J-HAQ) through 52 weeks, and retention rates of ABA at week 52 were evaluated. Baseline characteristics and use of concomitant drugs were analyzed as independent variables by multiple regression analysis using a standard linear model adjusted by SDAI at week 0 to identify factors associated with SDAI change at week 52. In addition, the interaction effects among ACPA status, RF status, and the factor that was significantly associated with SDAI change in multiple regression analysis on changes in SDAI were explored.

Results:

The numbers of ACPA +ve and –ve patients were 226 and 44, respectively. ACPA values (mean ± SD, U/mL) were 280.3 ± 376.8 and 0.9 ± 0.7, and rheumatoid factor (RF) values were 174.8 ± 302.6 and 20.9 ± 61.7 in the ACPA +ve and –ve groups, respectively. Mean (95% confidence interval) changes in SDAI at week 52 were −11.3 (−12.4 to −10.3) and −8.0 (−10.5 to −5.5), and those in J-HAQ were −0.27 (−0.34 to −0.20) and −0.16 (−0.34 to 0.01) in the ACPA +ve and –ve groups, respectively. In the Kaplan–Meier analysis, the retention rates of ABA at week 52 in the ACPA +ve and –ve groups were 72.1% and 58.7%, (discontinuation for any reason), and 91.6% and 75.7% (discontinuation because of lack of effectiveness), respectively. In a multiple regression analysis, the duration of disease (< 1 year) was associated with the change in SDAI at week 52. With respect to SDAI changes, the estimated difference of ACPA +ve and disease duration (< 1 year), ACPA +ve and disease duration (≥1 year), and ACPA –ve and disease duration (< 1 year), versus ACPA −ve and disease duration (≥ 1 year), were −4.26 (p = 0.022), −0.82 (p = 0.618), and −0.93 (p = 0.716), respectively (Fig. 1). The estimated difference of ACPA +ve and RF +ve, ACPA +ve and RF –ve, and ACPA –ve and RF +ve, versus ACPA –ve and RF –ve, were −2.48 (p = 0.060), −2.77 (p = 0.107), and −5.48 (p = 0.087), respectively.

Conclusion:

A higher retention rate as well as better effectiveness of ABA on disease activity and physical function in ACPA +ve group versus ACPA –ve group were shown in the simple subgroup analysis. ABA effectiveness on the SDAI change was significantly better in patients with disease duration <1 year and ACPA +ve compared to those with ACPA −ve and disease duration ≥ 1 year.

References:

[1]Harrold LR et al. J Rheumatol 2018;45(1):32–39.

[2]Sokolove J et al. Ann Rheum Dis 2016;75(4):709–714.

Disclosure of Interests:

Kenta Misaki Speakers bureau: Eisai Co., Ltd., AbbVie GK, Eli Lilly Japan K.K., Ono Pharmaceutical Co., Ltd., Grant/research support from: Ono Pharmaceutical Co., Ltd., Eiichi Tanaka Speakers bureau: AbbVie GK, Asahi Kasei Pharma Corporation, Astellas Pharma Inc, Ayumi Pharmaceutical Corporation, Chugai Pharmaceutical Co., Ltd., Eisai Co., Ltd., Eli Lilly Japan K.K., GlaxoSmithKline K.K., Kyowa Pharma Chemical Co., Ltd., Janssen Pharmaceutical K.K., Mochida Pharmaceutical Co., Ltd., Pfizer, Takeda Pharmaceutical Co., Ltd, and Teijin Pharma Ltd., Eisuke Inoue Speakers bureau: Pfizer Japan, Bristol-Myers Squibb K.K., Katsuki Tsuritani Employee of: Bristol-Myers Squibb K.K., Shigeru Matsumoto Employee of: Ono Pharmaceutical Co., Ltd., Hisashi Yamanaka Consultant of: Bristol-Myers Squibb K.K., masayoshi harigai Speakers bureau: AbbVie GK, Ayumi Pharmaceutical Corporation, Bristol-Myers Squibb K.K., Chugai Pharmaceutical Co., Ltd., Eisai Co., Ltd., Eli Lilly Japan K.K., Pfizer Japan Inc., and Takeda Pharmaceutical Co., Ltd., Consultant of: AbbVie GK, Bristol-Myers Squibb K.K., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., and Gilead Sciences Inc., Grant/research support from: AbbVie GK, and Asahi Kasei Corp., Astellas Pharma Inc., Ayumi Pharmaceutical Corporation, Bristol-Myers Squibb K.K., Chugai Pharmaceutical Co., Ltd. Daiichi-Sankyo, Inc., Eisai Co., Ltd., Mitsubishi Tanabe Pharma Corporation., Nippon Kayaku Co., Ltd., Taisho Pharmaceutical Co., Ltd., and Takeda Pharmaceutical Co., Ltd.

Microsecond-timescale MD simulation of EGFR minor mutation predicts the structural flexibility of EGFR kinase core that reflects EGFR inhibitor sensitivity

Approximately 15–30% of patients with lung cancer harbor mutations in the EGFR gene. Major EGFR mutations (>90% of EGFR-mutated lung cancer) are highly sensitive to EGFR tyrosine kinase inhibitors (TKIs). Many uncommon EGFR mutations have been identified, but little is known regarding their characteristics, activation, and sensitivity to various EGFR-TKIs, including allosteric inhibitors. We encountered a case harboring an EGFR-L747P mutation, originally misdiagnosed with EGFR-del19 mutation using a routine diagnostic EGFR mutation test, which was resistant to EGFR-TKI gefitinib. Using this minor mutation and common EGFR-activating mutations, we performed the binding free energy calculations and microsecond-timescale molecular dynamic (MD) simulations, revealing that the L747P mutation considerably stabilizes the active conformation through a salt-bridge formation between K745 and E762. We further revealed why several EGFR inhibitors, including the allosteric inhibitor, were ineffective. Our computational structural analysis strategy would be beneficial for future drug development targeting the EGFR minor mutations.

Neck Location on the Outer Convexity is a Predictor of Incomplete Occlusion in Treatment with the Pipeline Embolization Device: Clinical and Angiographic Outcomes

BACKGROUND AND PURPOSE

With the increasing use of the Pipeline Embolization Device for the treatment of aneurysms, predictors of clinical and angiographic outcomes are needed. This study aimed to identify predictors of incomplete occlusion at last angiographic follow-up.

MATERIALS AND METHODS

In our retrospective, single-center cohort study, 105 ICA aneurysms in 89 subjects were treated with Pipeline Embolization Devices. Patients were followed per standardized protocol. Clinical and angiographic outcomes were analyzed. We introduced a new morphologic classification based on the included angle of the parent artery against the neck location: outer convexity type (included angle,  <160°), inner convexity type (included angle,  >200°), and lateral wall type (160° ≤ included angle  ≤200°). This classification reflects the metal coverage rate and flow dynamics.

RESULTS

Imaging data were acquired in 95.3% of aneurysms persistent at 6 months. Complete occlusion was achieved in 70.5%, and incomplete occlusion, in 29.5% at last follow-up. Multivariable regression analysis revealed that 60 years of age or older (OR, 5.70; P = .001), aneurysms with the branching artery from the dome (OR, 10.56; P = .002), fusiform aneurysms (OR, 10.2; P = .009), and outer convexity-type saccular aneurysms (versus inner convexity type: OR, 30.3; P < .001; versus lateral wall type: OR, 9.71; P = .001) were independently associated with a higher rate of incomplete occlusion at the last follow-up. No permanent neurologic deficits or rupture were observed in the follow-up period.

CONCLUSIONS

The aneurysm neck located on the outer convexity is a new, incomplete occlusion predictor, joining older age, fusiform aneurysms, and aneurysms with the branching artery from the dome. No permanent neurologic deficits or rupture was observed in the follow-up, even with incomplete occlusion.

Clinical usefulness of Lipoprotein(a) for the prevalence and severity of peripheral artery disease among patients with acute coronary syndrome

Background

Lipoprotein(a) [LP(a)] is known to be a robust lipid marker associated with cardiovascular events. Though coronary artery disease and peripheral artery disease (PAD) are often coexist, little is known about the relationship between LP(a) and PAD among patients with acute coronary syndrome (ACS).

Purpose

The purpose of this study is to examine if LP(a) is of predictive value for PAD among ACS patients in Japanese population.

Methods

Of consecutive 238 ACS patient who received successful primary PCI, a total of 175 patients were enrolled in the current study. We excluded the patients who received hemodialysis (n=10), required multidisciplinary treatment (n=36) and incomplete data (n=17). PAD was diagnosed as ankle brachial index &lt;0.9. Multiple lipid biomarkers [LP(a), low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), malondialdehyde-modified LDL (MDA-LDL), docosahexaenoic acid and arachidonic acid] were compared between patients with PAD (n=21) and without PAD (n=154). Further, multivariable logistic regression models were used to assess if LP(a) was associated with PAD. In addition, serum LP(a) level were compared between 3 groups according to pattern of PAD [none (n=154), unilateral (n=10) and bilateral PAD (n=11), respectively].

Results

Compared to patients without PAD, those with PAD were older (74.4 vs. 65.4 years, p=0.003), and had a higher prevalence of chronic kidney disease (CKD) (61.9% vs. 20.1%, p&lt;0.001), diabetes mellitus (DM) (66.7% vs. 27.3%, p&lt;0.001). Serum LP(a) level was significantly higher in patients with PAD (36.4 vs. 18.5 mg/dl, p&lt;0.001), whereas LDL-C and MDA-LDL were significantly lower in PAD (92.0 vs. 109.5 mg/dl, p=0.015 and 98.6 vs. 119.5 mg/dl, p=0.046, respectively). After adjusting for LDL-C and MDA-LDL, LP(a) &gt;30 mg/dl was independently associated with a presence of PAD (OR 5.67, 95% CI 2.09–15.4, p=0.0006). When adjusting for CKD and DM in a different model, LP(a) &gt;30 mg/dl was similarly associated with PAD (OR 4.98, 95% CI 1.66–14.9, p=0.004). Serum LP(a) levels were significantly higher in bilateral PAD group compared to none PAD group (Figure).

Conclusion

LP(a) was a useful lipid biomarker for the prevalence and severity of PAD among patients with ACS in Japanese population.

Funding Acknowledgement

Type of funding source: None

E487K-Induced Disorder in Functionally Relevant Dynamics of Mitochondrial Aldehyde Dehydrogenase 2

Mitochondrial aldehyde dehydrogenase 2 (ALDH2), which is a homotetramer assembled by two equivalent dimers, is an important enzyme that metabolizes ethanol-derived acetaldehyde to acetate in a coenzyme-dependent manner. The highly reactive acetaldehyde exhibits a toxic effect, indicating that the proper functioning of ALDH2 is essential to counteract aldehyde-associated diseases. It is known that the catalytic activity of ALDH2 is drastically impaired by a frequently observed mutation, E487K, in a dominant fashion. However, the molecular basis of the inactivation mechanism is elusive because of the complex nature of the dynamic behavior. Here, we performed microsecond-timescale molecular dynamics simulations of the proteins complexed with coenzymes. The E487K mutation elevated the conformational heterogeneity of the dimer interfaces, which are relatively distal from the substituted residue. Dynamic network analyses showed that Glu487 and the dimer interface were dynamically communicated, and the dynamic community further spanned throughout all of the subunits in the wild-type; however, this network was completely rearranged by the E487K mutation. The perturbation of the dynamic properties led to alterations of the global conformational motions and destabilization of the coenzyme binding required for receiving a proton from the catalytic nucleophile. The insights into the dynamic behavior of the dominant negative mutant in this work will provide clues to restore its function.

Alteration in faecal bile acids, gut microbial composition and diversity after laparoscopic sleeve gastrectomy

Background

Laparoscopic sleeve gastrectomy (LSG) is a well established treatment for severe obesity and type 2 diabetes. Although the gut microbiota is linked to the efficacy of LSG, the underlying mechanisms remain elusive. The effect of LSG for morbid obesity on the gut microbiota and bile acids was assessed here.

Methods

Severely obese subjects who were candidates for LSG were included and followed until 6 months after surgery. The composition and abundance of the microbiota and bile acids in faeces were assessed by 16S ribosomal RNA sequencing, quantitative PCR and liquid chromatography–mass spectrometry.

Results

In total, 28 patients with a mean(s.d.) BMI of 44·2(6·6) kg/m2 were enrolled. These patients had achieved excess weight loss of 53·2(19·0) per cent and showed improvement in metabolic diseases by 6 months after LSG, accompanied by an alteration in the faecal microbial community. The increase in α-diversity and abundance of specific taxa, such as Rikenellaceae and Christensenellaceae, was strongly associated with reduced faecal bile acid levels. These changes had a significant positive association with excess weight loss and metabolic alterations. However, the total number of faecal bacteria was lower in patients before (mean(s.d.) 10·26(0·36) log10 cells per g faeces) and after (10·39(0·29) log10 cells per g faeces) operation than in healthy subjects (10·83(0·27) log10 cells per g faeces).

Conclusion

LSG is associated with a reduction in faecal bile acids and greater abundance of specific bacterial taxa and α-diversity that may contribute to the metabolic changes.

Landscape and function of multiple mutations within individual oncogenes

Sporadic reports have described cancer cases in which multiple driver mutations (MMs) occur in the same oncogene^1,2. However, the overall landscape and relevance of MMs remain elusive. Here we carried out a pan-cancer analysis of 60,954 cancer samples, and identified 14 pan-cancer and 6 cancer-type-specific oncogenes in which MMs occur more frequently than expected: 9% of samples with at least one mutation in these genes harboured MMs. In various oncogenes, MMs are preferentially present in cis and show markedly different mutational patterns compared with single mutations in terms of type (missense mutations versus in-frame indels), position and amino-acid substitution, suggesting a cis-acting effect on mutational selection. MMs show an overrepresentation of functionally weak, infrequent mutations, which confer enhanced oncogenicity in combination. Cells with MMs in the PIK3CA and NOTCH1 genes exhibit stronger dependencies on the mutated genes themselves, enhanced downstream signalling activation and/or greater sensitivity to inhibitory drugs than those with single mutations. Together oncogenic MMs are a relatively common driver event, providing the underlying mechanism for clonal selection of suboptimal mutations that are individually rare but collectively account for a substantial proportion of oncogenic mutations.

Incidence of overwork-related mental disorders and suicide in Japan

Background

Mental disorders and suicide resulting from overwork or work-related stress have become major occupational health issues worldwide, particularly in Asian countries. However, no studies have reported incidence rates of mental disorders and suicide by sex, age group and industry, using a national database containing all cases involving compensation in Asian countries.

Aims

The present study examined incidence rates of occupational mental disorders and suicide by sex, age group and industry using a database containing all cases involving compensation for mental disorders and suicide in Japan over a 5-year period.

Methods

Cases involving compensation for mental disorders and suicide in Japan between January 2010 and March 2015 were analysed. Incidence rates over the 5-year study period were calculated by sex, age group and industry.

Results

In total, 1990 cases involving compensation for mental disorders and suicide (619 women and 1371 men) between January 2010 and March 2015 were analysed. The incidence rate involving compensation was higher in employees aged between 30 and 39 years. In men, incidence rates were higher in 'accommodation/eating/drinking services', 'information/communication' and 'scientific research, professional and technical services'. In these industries, incidence rates were particularly high for those aged 29 years or younger.

Conclusions

Our findings highlight the importance of promoting mental health support for younger employees and increasing awareness of their working conditions. Differences in incidence rates by sex, age and industry should be taken into consideration in the development of a national policy and industry- and age-specific preventive measures for overwork-related mental disorders and suicide.