Relationship between olfactory and gustatory functions: The Iwaki health promotion project 2019.

OBJECTIVE: Olfactory and gustatory functions are important sensory aspects in humans. Although they are believed to influence each other, their interrelationship is not well understood. In this study, we aimed to investigate the relationship between the olfactory and gustatory functions based on the results of a large-scale epidemiological study (Iwaki Health Promotion Project) of the general local population. METHODS: We analyzed 565 participants who underwent taste and olfactory tests in the 2019 Iwaki Project. Gustatory function was tested for four taste qualities (sweet, sour, salty, and bitter) using whole-mouth taste tests. Olfactory function was tested using the University of Pennsylvania Smell Identification Test modified for Japanese (UPSIT-J). We evaluated sex-related differences between olfactory and gustatory functions and the effects of various factors on olfactory identification using multivariate analysis. Furthermore, we compared the percentage of accurate UPSIT-J responses between the normal and hypogeusia groups. We also analyzed the effects of taste and olfactory functions on eating. RESULTS: Olfactory and gustatory functions were lower in men than in women. Among the four taste qualities, salty taste was the most closely associated with olfactory identification ability, with lower olfactory scores of salty taste in the hypogeusia group than in the normal group. Moreover, the hyposmia group had higher daily salt intake than the normal olfaction group in women. CONCLUSION: These results suggest that olfactory identification tests may be useful in predicting elevated salt cognitive thresholds, leading to a reduction in salt intake, which may contribute to hypertension prevention.

Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.

Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model's insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73-4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62-4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.

Development of the Integrated Computer Simulation Model of the Intracellular, Transmembrane, and Extracellular Domain of Platelet Integrin α IIb ß 3 (Platelet Membrane Glycoprotein: GPIIb-IIIa).

Background The structure and functions of the extracellular domain of platelet integrin α IIb ß 3 (platelet membrane glycoprotein: GPIIb-IIIa) change substantially upon platelet activation. However, the stability of the integrated model of extracellular/transmembrane/intracellular domains of integrin α IIb ß 3 with the inactive state of the extracellular domain has not been clarified. Methods The integrated model of integrin α IIb ß 3 was developed by combining the extracellular domain adopted from the crystal structure and the transmembrane and intracellular domain obtained by Nuclear Magnetic Resonace (NMR). The transmembrane domain was settled into the phosphatidylcholine (2-oleoyl-1-palmitoyl-sn-glycerol-3-phosphocholine (POPC)) lipid bilayer model. The position coordinates and velocity vectors of all atoms and water molecules around them were calculated by molecular dynamic (MD) simulation with the use of Chemistry at Harvard Macromolecular Mechanics force field in every 2 × 10 -15 seconds. Results The root-mean-square deviations (RMSDs) of atoms constructing the integrated α IIb ß 3 model apparently stabilized at approximately 23 Å after 200 ns of calculation. However, minor fluctuation persisted during the entire calculation period of 650 ns. The RMSDs of both α IIb and ß 3 showed similar trends before 200 ns. The RMSD of ß 3 apparently stabilized approximately at 15 Å at 400 ns with persisting minor fluctuation afterward, while the structural fluctuation in α IIb persisted throughout the 650 ns calculation period. Conclusion In conclusion, the integrated model of the intracellular, transmembrane, and extracellular domain of integrin α IIb ß 3 suggested persisting fluctuation even after convergence of MD calculation.

Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases.

Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.

Long-term follow-up of task-specific tremor after thalamotomy: a retrospective observational study.

OBJECTIVE: This study aimed to report the long-term results of thalamotomy in 23 patients with task-specific tremor. METHODS: Data of 23 patients with task-specific tremor who underwent ventralis intermedius nucleus and posterior part of ventro-oral nucleus thalamotomy at the Tokyo Women's Medical University Hospital between 2010 and 2022 were retrospectively analyzed. To evaluate neurological conditions, the severity of task-specific tremor was divided into 0 (no tremor), 1 (slightly tremulous), 2 (moderately tremulous), 3 (accomplishing tasks with great difficulty), and 4 (unable to complete tasks). We also used the subscores "handwriting" (0-4) and "spiral drawing" (0-4) of the Clinical Rating Scales for Tremor. Evaluation scales were presented as medians and interquartile ranges. RESULTS: The severities of task-specific tremor were 3.0 (3.0-4.0) preoperatively and 0.0 (0.0-0.0, p < 0.0001) at the last available evaluation. The writing and spiral drawing of the Clinical Rating Scales for Tremor significantly improved from 3.0 (3.0-4.0) and 3.0 (2.0-3.0) preoperatively, respectively, to 0.0 (0.0-0.0, p < 0.0001) and 0.0 (0.0-0.0, p < 0.0001) at the last available evaluation, respectively. The mean clinical follow-up period was 62.7 ± 26.0 months. Seven (30.4%) patients had focal hand dystonia, which newly developed on the ipsilateral side of the tremor at 2-45 months after the surgery. No serious complications were observed. INTERPRETATION: Thalamotomy significantly improves task-specific tremor with high long-term efficacy, and long-term follow-up is important because focal hand dystonia can develop postoperatively.

Machine-learning-based prediction of fractional flow reserve after percutaneous coronary intervention.

BACKGROUND AND AIMS: Post-percutaneous coronary intervention (PCI) fractional flow reserve (FFR) reflects residual atherosclerotic burden and is associated with future events. How much post-PCI FFR can be predicted based on baseline basic information and the clinical relevance have not been investigated. METHODS: We compiled a multicenter registry of patients undergoing pre- and post-PCI FFR. Machine-learning (ML) algorithms were designed to predict post-PCI FFR levels from baseline demographics, quantitative coronary angiography, and pre-PCI FFR. FFR deviation was defined as actual minus ML-predicted post-PCI FFR levels, and its association with incident target vessel failure (TVF) was evaluated. RESULTS: Median (IQR) pre- and post-PCI FFR values were 0.71 (0.61, 0.77) and 0.88 (0.84, 0.93), respectively. The Spearman correlation coefficient of the actual and predicted post-PCI FFR was 0.54 (95% CI: 0.52, 0.57). FFR deviation was non-linearly associated with incident TVF (HR [95% CI] with Q3 as reference: 1.65 [1.14, 2.39] in Q1, 1.42 [0.98, 2.08] in Q2, 0.81 [0.53, 1.26] in Q4, and 1.04 [0.69, 1.56] in Q5). A model with polynomial function of continuous FFR deviation indicated increasing TVF risk for FFR deviation ≤0 but plateau risk with FFR deviation >0. CONCLUSIONS: An ML-based algorithm using baseline data moderately predicted post-PCI FFR. The deviation of post-PCI FFR from the predicted value was associated with higher vessel-oriented event.

MRP1-Dependent Extracellular Release of Glutathione Induces Cardiomyocyte Ferroptosis After Ischemia-Reperfusion.

BACKGROUND: The membrane components of cardiomyocytes are rich in polyunsaturated fatty acids, which are easily oxidized. Thus, an efficient glutathione-based lipid redox system is essential for maintaining cellular functions. However, the relationship between disruption of the redox system during ischemia-reperfusion (IR), oxidized lipid production, and consequent cell death (ferroptosis) remains unclear. We investigated the mechanisms underlying the disruption of the glutathione-mediated reduction system related to ferroptosis during IR and developed intervention strategies to suppress ferroptosis. METHODS: In vivo fluctuations of both intra- and extracellular metabolite levels during IR were explored via microdialysis and tissue metabolome analysis. Oxidized phosphatidylcholines were assessed using liquid chromatography high-resolution mass spectrometry. The areas at risk following IR were assessed using triphenyl-tetrazolium chloride/Evans blue stain. RESULTS: Metabolomic analysis combined with microdialysis revealed a significant release of glutathione from the ischemic region into extracellular spaces during ischemia and after reperfusion. The release of glutathione into extracellular spaces and a concomitant decrease in intracellular glutathione concentrations were also observed during anoxia-reperfusion in an in vitro cardiomyocyte model. This extracellular glutathione release was prevented by chemical inhibition or genetic suppression of glutathione transporters, mainly MRP1 (multidrug resistance protein 1). Treatment with MRP1 inhibitor reduced the intracellular reactive oxygen species levels and lipid peroxidation, thereby inhibiting cell death. Subsequent in vivo evaluation of endogenously oxidized phospholipids following IR demonstrated the involvement of ferroptosis, as levels of multiple oxidized phosphatidylcholines were significantly elevated in the ischemic region 12 hours after reperfusion. Inhibition of the MRP1 transporter also alleviated intracellular glutathione depletion in vivo and significantly reduced the generation of oxidized phosphatidylcholines. Administration of MRP1 inhibitors significantly attenuated infarct size after IR injury. CONCLUSIONS: Glutathione was released continuously during IR, primarily in an MRP1-dependent manner, and induced ferroptosis. Suppression of glutathione release attenuated ferroptosis and reduced myocardial infarct size following IR.

Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study.

Background: Atrial septal defect (ASD) increases the risk of adverse cardiovascular outcomes. Despite the potential for risk mitigation through minimally invasive percutaneous closure, ASD remains underdiagnosed due to subtle symptoms and examination findings. To bridge this diagnostic gap, we propose a novel screening strategy aimed at early detection and enhanced diagnosis through the implementation of a convolutional neural network (CNN) to identify ASD from 12-lead electrocardiography (ECG). Methods: ECGs were collected from patients with at least one recorded echocardiogram at 3 hospitals from 2 continents (Keio University Hospital from July 2011 to December 2020, Brigham and Women's Hospital from January 2015 to December 2020, and Dokkyo Medical University Saitama Medical Center from January 2010 and December 2021). ECGs from patients with a diagnosis of ASD were labeled as positive cases while the remainder were labeled as negative. ECGs after the closure of ASD were excluded. After randomly splitting the ECGs into 3 datasets (50% derivation, 20% validation, and 30% test) with no patient overlap, a CNN-based model was trained using the derivation datasets from 2 hospitals and was tested on held-out datasets along with an external validation on the 3rd hospital. All eligible ECGs were used for derivation and validation whereas the earliest ECG for each patient was used for the test and external validation. The discrimination of ASD was assessed by the area under the receiver operating characteristic curve (AUROC). Multiple subgroups were examined to identify any heterogeneity. Findings: A total of 671,201 ECGs from 80,947 patients were collected from the 3 institutions. The AUROC for detecting ASD was 0.85-0.90 across the 3 hospitals. The subgroup analysis showed excellent performance across various characteristics Screening simulation using the model greatly increased sensitivity from 80.6% to 93.7% at specificity 33.6% when compared to using overt ECG abnormalities. Interpretation: A CNN-based model using 12-lead ECG successfully identified the presence of ASD with excellent generalizability across institutions from 2 separate continents. Funding: This work was supported by research grants from JST (JPMJPF2101), JSR corporation, Taiju Life Social Welfare Foundation, Kondou Kinen Medical Foundation, Research fund of Mitsukoshi health and welfare foundation, Tokai University School of Medicine Project Research and Internal Medicine Project Research, Secom Science and Technology Foundation, and Grants from AMED (JP23hma922012 and JP23ym0126813). This work was partially supported by One Brave Idea, co-funded by the American Heart Association and Verily with significant support from AstraZeneca and pillar support from Quest Diagnostics.

Adenocarcinoma with mediastinal lymph node involvement developed from a pure ground grass nodule during 14 years.

A 69-year-old female Japanese patient presented with an abnormal shadow on chest computed tomography (CT). She had received a mastectomy 14 years prior. Under the diagnosis of primary lung cancer, left upper lobectomy was conducted. Pathology showed a lepidic adenocarcinoma with mediastinal lymph node metastases with pT2aN2M0. Upon retrospective analysis, the chest CT at the time of mastectomy depicted a ground-glass nodule (GGN) of less than 20 mm. Over the previous 10.5 years, the concentration of the central part of the GGN increased. Conclusively, a pure GGN developed into lung adenocarcinoma with mediastinal lymph node involvement over 14 years. She had bone metastases 4 years after the lobectomy but has survived for five and a half years after surgery with treatment with osimertinib. Comparison readings of films should be performed throughout the patient's clinical history to detect subtle shadow alterations indicative of tumour progression.

Psychological distress among healthcare providers in oncology during the COVID-19 pandemic in Japan: The mediating role of moral distress and resilience.

Objective: Even though vaccines have become widespread, there is an explosion of infection due to the emergence of new mutant strains, and support for healthcare providers' mental health is necessary. The aims of this study were to explore factors associated with the psychological distress, and to determine the degree of association between moral distress, resilience and psychological distress in order to consider intervention models for psychological distress of healthcare providers involved with cancer patients during the COVID-19 pandemic. Method: We conducted a cross-sectional survey among healthcare providers at the National Cancer Center, Japan. Psychological distress was assessed by the Hospital Anxiety and Depression Scale. We also assessed moral distress using the Moral Distress Thermometer and resilience using the Connor-Davidson Resilience Scale 10 in April and May 2020 which was the first surge of the epidemic period. Results: Five hundred sixty-six of 3,900 healthcare providers (14.5%) responded. Sixty-eight percent (385/566) responders were above the Hospital Anxiety and Depression Scale cutoff. Hierarchical regression analyses indicated that nurses, allied health professionals and office workers/engineers (odds ratio = 4.63; 95% confidence interval 1.90-11.29; p < 0.001, odds ratio = 3.88; 95% confidence interval 1.88-8.00; p < 0.001, odds ratio = 2.10; 95% confidence interval 1.06-4.18; p < 0.05) as well as healthcare providers with low resilience (odds ratio = 0.88; 95% confidence interval 0.85-0.91; p < 0.001) were at risk of psychological distress. Moral distress was not significantly associated with prevalence of psychological distress. Conclusion: During the first surge of the pandemic, a high prevalence of psychological distress was demonstrated among cancer center healthcare providers. The study suggests that mental health care should be available to cancer care providers. Since the COVID-19 pandemic is still going on, in addition to the efforts by our facilities, we should consider interventions that promote resilience and a feasibility study of these interventions.

Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography.

AIMS: Available predictive models for sudden cardiac death (SCD) in heart failure (HF) patients remain suboptimal. We assessed whether the electrocardiography (ECG)-based artificial intelligence (AI) could better predict SCD, and also whether the combination of the ECG-AI index and conventional predictors of SCD would improve the SCD stratification among HF patients. METHODS AND RESULTS: In a prospective observational study, 4 tertiary care hospitals in Tokyo enrolled 2559 patients hospitalized for HF who were successfully discharged after acute decompensation. The ECG data during the index hospitalization were extracted from the hospitals' electronic medical record systems. The association of the ECG-AI index and SCD was evaluated with adjustment for left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and competing risk of non-SCD. The ECG-AI index plus classical predictive guidelines (i.e. LVEF ≤35%, NYHA Class II and III) significantly improved the discriminative value of SCD [receiver operating characteristic area under the curve (ROC-AUC), 0.66 vs. 0.59; P = 0.017; Delong's test] with good calibration (P = 0.11; Hosmer-Lemeshow test) and improved net reclassification [36%; 95% confidence interval (CI), 9-64%; P = 0.009]. The Fine-Gray model considering the competing risk of non-SCD demonstrated that the ECG-AI index was independently associated with SCD (adjusted sub-distributional hazard ratio, 1.25; 95% CI, 1.04-1.49; P = 0.015). An increased proportional risk of SCD vs. non-SCD with an increasing ECG-AI index was also observed (low, 16.7%; intermediate, 18.5%; high, 28.7%; P for trend = 0.023). Similar findings were observed in patients aged ≤75 years with a non-ischaemic aetiology and an LVEF of >35%. CONCLUSION: To improve risk stratification of SCD, ECG-based AI may provide additional values in the management of patients with HF.

Prevalence and associated factors of psychological distress among young adult cancer patients in Japan.

OBJECTIVES: Adolescent and young adult (AYA) cancer patients may be at high risk of experiencing psychological distress because their diagnosis came during a key time of consolidation of identity and social growth. This study aimed (1) to examine the prevalence of psychological distress among AYA cancer patients within a year of diagnosis to long-term survivors and (2) to describe socio-demographic and cancer-related characteristics associated with psychological distress. METHODS: In a cross-sectional web-based survey, patients who scored 5 or more on the Kessler Psychological Distress Scale were assessed for significant psychological distress. Logistic regression examined whether demographics, clinical variables, and social support were associated with psychological distress. RESULTS: A total of 206 young adult cancer patients participated. The median age at the survey was 34.5 years (range: 22-39 years), and 87.4% were female. The prevalence of psychological distress was 55.3%. Psychological distress among patients diagnosed within a year and long-term survivors (≥10 years since diagnosis) was significantly higher than patients 1-4 years since diagnosis. Pain, decrease in income after a cancer diagnosis, experience of negative change in work/school after a cancer diagnosis and poor social support were significantly associated with psychological distress. SIGNIFICANCE OF RESULTS: Over half of young adult patients had significant psychological distress in Japan. Our findings potentially contribute to the intervention components for distress management among AYA cancer survivors.

Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population.

AIMS: Whether the multi-dimensional data of serially measured blood pressure contains information for predicting the future risk of death in elderly individuals in nursing homes is unclear. METHODS: Of the elderly individuals staying in a nursing home, 19,740 and 40,055 individuals with serially measured blood pressure from day 1 to 365 (for AI-long) and 1 to 90 (for AI-short) along with the death information at day 366 to 730 and 91-365 were included. The neural network-based artificial intelligence (AI) was applied to find the relationship between BP time-series and the future risks of death in both populations. RESULTS: AI-long found a significant relationship between the serially measured BP from day 1 to day 365 days and the risk of death occurring 366-730 days with c-statistics of 0.57 (95% CI: 0.51-0.63). AI-short also found a significant relationship between the serially measured BP from day 1 to day 90 and the rate of death occurring 91-365 days with c-statistics of 0.58 (95%CI: 0.52-0.63). CONCLUSION: Our results suggest that neural network-based AI could find the hidden subtle relationship between multi-dimensional data of serially measured BP and the future risk of death in apparently healthy elderly Japanese individuals under nursing care.