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This study was aimed to evaluate whether the dose–response relationship of the sodium glucose co-transporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM)—canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin—can be explained in a unified manner based on their ability to promote urinary glucose excretion (UGE). Information on HbA1c reduction at various doses of each SGLT2i was collected from literatures on randomized controlled trials and was normalized based on the daily UGE data from phase I studies. After normalizing doses, the dose–response relationship of HbA1c reduction of most of SGLT2is was represented by a unified nonlinear mixed-effect model, with the estimated maximum HbA1c (%) reduction (E max ) of 0.796 points, whereas covariate analysis showed that canagliflozin had a 1.33-fold higher E max than those of the other drugs. Other covariates included baseline HbA1c levels, body weight, disease duration, prior treatment, and renal function. Findings from this study would influence drug selection and adjustment in clinical practice. As with SGLT2is, in cases where the efficacy cannot be easily evaluated but an appropriate pharmacodynamic marker was assessed in early clinical trials, similar approaches for other drug classes can guide strategic and evidence-based dose selection in phase III trials.

The expanded polystyrene (EPS) façade has been widely used to save building energy, but it has also caused many severe facade fire accidents worldwide. Especially for aged buildings, the naturally weathered exterior surface layer can further increase the facade fire risk and the fire spread rate (FSR). In this work, a series of real-scale EPS External Thermal Insulation Composite System (ETICS) façades are tested via the JIS A 1310 standard. The EPS thickness varies from 100 to 300 mm, density changes from 15 kg/m3 to 30 kg/m3, and heat release rate (HRR) of window spilled flame ranges from 600 kW to 1100 kW. Tests showed that the surface cement layer was quickly damaged by a spilled flame that provided negligible fire resistance for the internal flammable EPS panel. The measured upward FSR increases with the rising of HRR and with the decreasing EPS thickness like the thermally thin material. An empirical correlation of instantaneous upward FRS is proposed, FSR = 0.22Φ + 3.45 [cm/min], where Φ is a modified fire propagation index derived from the experimental temperature distribution. In addition, a simple prediction method for FSR is proposed for the façade fire and verified by the experimental data. This work provides a useful method to quantify the upward façade fire propagation, which also helps evaluate the fire risk and hazard of EPS ETICS façade prior to the costly large-scale tests and installation.Graphic Abstract

As Parkinson's disease (PD) progresses, there are multiple biomarker changes, and sex and genetic variants may influence the rate of progression. Data‐driven, long‐term disease progression model analysis may provide precise knowledge of the relationships between these risk factors and progression and would allow for the selection of appropriate diagnosis and treatment according to disease progression. To construct a long‐term disease progression model of PD based on multiple biomarkers and evaluate the effects of sex and leucine‐rich repeat kinase 2 (LRRK2) mutations, a technique derived from the nonlinear mixed‐effects model (Statistical Restoration of Fragmented Time course [SReFT]) was applied to datasets of patients provided by the Parkinson's Progression Markers Initiative. Four biomarkers, including the Unified PD Rating Scale, were used, and a covariate analysis was performed to investigate the effects of sex and LRRK2‐related mutations. A model of disease progression over ~30 years was successfully developed using patient data with a median of 6 years. Covariate analysis suggested that female sex and LRRK2 G2019S mutations were associated with 21.6% and 25.4% significantly slower progression, respectively. LRRK2 rs76904798 mutation also tended to delay disease progression by 10.4% but the difference was not significant. In conclusion, a long‐term PD progression model was successfully constructed using SReFT from relatively short‐term individual patient observations and depicted nonlinear changes in relevant biomarkers and their covariates, including sex and genetic variants.

Simultaneous poisoning by carbon monoxide (CO) and hydrogen cyanide is the major cause of mortality in fire gas accidents. Here, we report on the invention of an injectable antidote against CO and cyanide (CN⁻) mixed poisoning. The solution contains four compounds: iron(III)porphyrin (FeIIITPPS, F), two methyl-β-cyclodextrin (CD) dimers linked by pyridine (Py3CD, P) and imidazole (Im3CD, I), and a reducing agent (Na₂S₂O₄, S). When these compounds are dissolved in saline, the solution contains two synthetic heme models including a complex of F with P (hemoCD-P) and another one of F with I (hemoCD-I), both in their iron(II) state. hemoCD-P is stable in its iron(II) state and captures CO more strongly than native hemoproteins, while hemoCD-I is readily autoxidized to its iron(III) state to scavenge CN⁻ once injected into blood circulation. The mixed solution (hemoCD-Twins) exhibited remarkable protective effects against acute CO and CN⁻ mixed poisoning in mice (~85% survival vs. 0% controls). In a model using rats, exposure to CO and CN⁻ resulted in a significant decrease in heart rate and blood pressure, which were restored by hemoCD-Twins in association with decreased CO and CN⁻ levels in blood. Pharmacokinetic data revealed a fast urinary excretion of hemoCD-Twins with an elimination half-life of 47 min. Finally, to simulate a fire accident and translate our findings to a real-life scenario, we confirmed that combustion gas from acrylic cloth caused severe toxicity to mice and that injection of hemoCD-Twins significantly improved the survival rate, leading to a rapid recovery from the physical incapacitation.

Individual predictions of long‐term chronic disease progression from data of limited duration provide valuable insights into estimating patient outcomes and therapeutic needs. Statistical Restoration of Fragmented Time course (SReFT) was developed to address this challenge, yet it is computationally too intensive for large‐scale datasets. Although diabetes is a representative chronic disease with significant medical needs, it has been challenging to analyze long‐term changes using large‐scale patient data due to this limitation. In this study, we aimed to develop a new method (SReFT‐machine learning, SReFT‐ML) by applying machine learning to the concept of SReFT, and to confirm its performance using synthetic data and the data from a clinical trial, the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251). SReFT‐ML has successfully analyzed both synthetic and clinical data, and reconstructed biomarker trajectories over a 30‐year period in patients with diabetes. Decreases in diastolic blood pressure and renal function may be important indicators of disease progression. Furthermore, although age and mortality data were not included in the model, survival analysis demonstrated a clear trend of hazard increases in mortality and diabetes‐related outcomes with disease progression. This study introduced machine learning to enhance long‐term disease progression modeling. The resulting model characterized a 30‐year trajectory of disease risk in diabetes. The results provide a clinically meaningful hypothesis that incorporating systemic factors such as renal function and blood pressure, in addition to classic glycemic control, may enhance comprehensive diabetes care. Trial Registration: ClinicalTrials.gov number: NCT00000620 From Study Observations to Disease‐Time Alignment: A Novel ML‐Based Reconstruction of Lifelong Trajectories and Risk Estimation — An Application to Type 2 Diabetes.

Background Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages. Methods Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life ( t 1/2 ), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (V Hyp ) were inferred using the Markov Chain Monte Carlo (MCMC) method. Result Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t 1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred. Conclusion This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.

Wildland fires and wildland–urban interface (WUI) fires quite often become the focus of discussion among fire researchers whenever large outdoor fires are the topic, especially because of their global occurrence and their potential for causing serious damage. But at the same time, large urban fires have been occurring in Japan especially after major earthquakes or with high winds, at densely built urban area consisting of old wooden houses. Normally, fire spread is caused by three mechanisms: flame, radiation, and firebrands, especially in the cases of wildland fires, WUI fires, and large urban fires. And what is common between WUI fires and large urban fires is structure–structure fire spreading. In Japan, “Taika” (large urban fire) is a fire when the total burnout floor area is 33,000 m2 or more. The present paper first discusses the history of large urban fires in Japan, then examines the national standard regulations for fire safety in Japan aimed at mitigating the damage caused by those fires. Finally, an overview of current scientific research in methods of controlling urban fires in Japan is provided. This paper compiles such impotent information on large urban fires in Japan, which is the novelty and merit in this work. Furthermore, the contents in this paper will be contributing to the progress of new project ISO/TR 24188 on “Large outdoor fires and the built environment—Global overview of different approaches for standardization”, at ISO/TC92/WG14 “Large outdoor fires and the built environment”.

To characterize and compare various medicines for chronic heart failure (CHF), changes in circulatory physiological parameter during pharmacotherapy were investigated by a model‐based meta‐analysis (MBMA) of circulatory physiology. The clinical data from 61 studies mostly in patients with heart failure with reduced ejection fraction (HFrEF), reporting changes in heart rate, blood pressure, or ventricular volumes after treatment with carvedilol, metoprolol, bisoprolol, bucindolol, enalapril, aliskiren, or felodipine, were analyzed. Seven cardiac and vasculature function indices were estimated without invasive measurements using models based on appropriate assumptions, and their correlations with the mortality were assessed. Estimated myocardial oxygen consumption, a cardiac load index, correlated excellently with the mortality at 3, 6, and 12 months after treatment initiation, and it explained differences in mortality across the different medications. The analysis based on the present models were reasonably consistent with the hypothesis that the treatment of HFrEF with various medications is due to effectively reducing the cardiac load. Assessment of circulatory physiological parameters by using MBMA would be insightful for quantitative understanding of CHF treatment.

In this study, the ethnic ratios (ERs) of oral clearance between Japanese and Western populations were subjected to model‐based meta‐analysis (MBMA) for 81 drugs evaluated in 673 clinical studies. The drugs were classified into eight groups according to the clearance mechanism, and the ER for each group was inferred together with interindividual variability (IIV), interstudy variability (ISV), and inter‐drug variability within a group (IDV) using the Markov chain Monte Carlo (MCMC) method. The ER, IIV, ISV, and IDV were dependent on the clearance mechanism, and, except for particular groups such as drugs metabolized by polymorphic enzymes or their clearance mechanism is not confirmative, the ethnic difference was found to be generally small. The IIV was well‐matched across ethnicities, and the ISV was approximately half of the IIV as the coefficient of variation. To adequately assess ethnic differences in oral clearance without false detections, phase I studies should be designed with full consideration of the mechanism of clearance. This study suggests that the methodology of classifying drugs based on the mechanism that causes ethnic differences and performing MBMA with statistical techniques such as MCMC analysis is helpful for a rational understanding of ethnic differences and for strategic drug development.

The aim of this study was to identify significant factors affecting the effectiveness of exercise training using information of the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) study. Background factors influencing the effect of exercise training were comprehensively surveyed for 2,130 patients by multivariable Cox regression analysis with the stepwise variable selection, and only significant factors were selected that were statistically distinguished from dummy noise factors using the Boruta method. The analysis suggested that the use of beta-blockers, pulse pressure, hemoglobin level, electrocardiography findings, body mass index, and history of stroke at baseline potentially influenced the exercise effect on all-cause death (AD). Therefore, a hypothetical score to estimate the effect of exercise training was constructed based on the analysis. The analysis suggested that the score is useful in identifying patients for whom exercise training may be significantly effective in reducing all-caused death and hospitalization (ADH) as well as AD. Such a subpopulation accounted for approximately 40% of the overall study population. On the other hand, in approximately 45% of patients, the effect of exercise was unclear on either AD or ADH. In the remaining 15% of patients, it was estimated that the effect of exercise might be unclear for ADH and potentially rather increase AD. This study is the first analysis to comprehensively evaluate the effects of various factors on the outcome of exercise training in chronic heart failure, underscoring the need to carefully consider the patient's background before recommending exercise training. However, it should be noted that exercise training can improve many outcomes in a wide variety of diseases. Therefore, given the limitations involved in analyses of a single clinical trial, the characteristics of patients to whom the results of this analysis can be applied need attention, and also further research is necessary on the relationship between the degree of exercise and the outcomes. A new clinical trial would be needed to confirm the factors detected and the appropriateness of the score.