A randomized, double-blind, placebo-controlled phase II study of olanzapine-based prophylactic antiemetic therapy for delayed and persistent nausea and vomiting in patients with HER2-positive or HER2-low breast cancer treated with trastuzumab deruxtecan: ERICA study (WJOG14320B)

BACKGROUND

Nausea and vomiting are common adverse events associated with trastuzumab deruxtecan (T-DXd). We evaluated the efficacy of an olanzapine-based triplet regimen for preventing nausea and vomiting in patients receiving their first cycle T-DXd.

PATIENTS AND METHODS

This multi-institutional, randomized, double-blind, placebo-controlled (ERICA) phase II study enrolled patients with human epidermal growth factor receptor 2-positive/human epidermal growth factor receptor 2-low metastatic breast cancer receiving their first cycle of T-DXd. Patients were randomized to olanzapine 5 mg or placebo once daily (1 : 1 ratio) from day 1 to day 6, plus a 5-hydroxytryptamine type 3 receptor antagonist and dexamethasone 6.6 mg intravenously or 8 mg orally on day 1. The total observation period was 504 h (21 days) from the first T-DXd administration. The primary endpoint was complete response (CR), defined as no emetic events and no rescue medications, in the delayed phase (24-120 h after T-DXd), with the type I error rate of 0.2 (one-sided) for the comparison. Secondary endpoints included no nausea rate in the delayed and persistent phases (120-504 h), adverse event by Common Terminology Criteria for Adverse Events (CTCAE) and patient-reported outcomes version of the CTCAE (PRO-CTCAE).

RESULTS

In total, 168 patients were enrolled at 43 sites in Japan (November 2021-September 2023) with 162 patients (olanzapine, n = 80; placebo, n = 82) included in the per protocol set. The primary endpoint was met as the delayed phase CR rate was significantly greater with olanzapine than placebo (70.0% versus 56.1%, P = 0.047). Efficacy was maintained in the persistent phase (63.9% versus 44.4%). No nausea rate was also greater with olanzapine (delayed phase: 57.5% versus 37.8%; persistent phase: 51.4% versus 31.9%). CR rates in the delayed phase favored olanzapine across subgroups. Appetite loss was also decreased with olanzapine. Hyperglycemia and somnolence were mostly of low-grade severity.

CONCLUSION

Olanzapine 5 mg for 6 days with 5-hydroxytryptamine type 3 receptor antagonist and dexamethasone appears effective for T-DXd-treated patients to prevent delayed and persistent nausea and vomiting.

Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data

BACKGROUND

Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real-world settings.

METHODS AND RESULTS

ML-based models were developed to predict in-hospital mortality in 527 patients with ICH using raw brain imaging data from brain computed tomography and clinical data. The models' performances were evaluated using the area under the receiver operating characteristic curves and calibration plots, comparing them with traditional risk scores such as the ICH score and ICH grading scale. Kaplan-Meier curves were used to examine the post-ICH survival rates, stratified by ML-based risk assessment. The net benefit of ML-based models was evaluated using decision curve analysis. The area under the receiver operating characteristic curves were 0.91 (95% CI, 0.86-0.95) for the ICH score, 0.93 (95% CI, 0.89-0.97) for the ICH grading scale, 0.83 (95% CI, 0.71-0.91) for the ML-based model fitted with raw image data only, and 0.87 (95% CI, 0.76-0.93) for the ML-based model fitted using clinical data without specialist expertise. The area under the receiver operating characteristic curve increased significantly to 0.97 (95% CI, 0.94-0.99) when the ML model was fitted using clinical and image data assessed by specialists. All ML-based models demonstrated good calibration, and the survival rates showed significant differences between risk groups. Decision curve analysis indicated the highest net benefit when utilizing the findings assessed by specialists.

CONCLUSIONS

ML-based prediction models exhibit satisfactory performance in predicting post-ICH in-hospital mortality when utilizing raw imaging data or nonspecialist input. Nevertheless, incorporating specialist expertise notably improves performance.

Extravasation associated with cancer drug therapy: multidisciplinary guideline of the Japanese Society of Cancer Nursing, Japanese Society of Medical Oncology, and Japanese Society of Pharmaceutical Oncology

BACKGROUND

Extravasation (EV), or the leakage of anticancer drugs into perivascular and subcutaneous tissues during intravenous administration, can cause serious conditions that may require surgical intervention. Therefore, updated guidelines for EV based on systematic review are needed. Additionally, classifications for anticancer drugs that cause EV are not standardized across the current guidelines, and some novel drugs have not been classified. Therefore, this study aimed to formulate guidelines using evidence-based information for shared decision making on prevention, early detection, treatment, and care for EV in Japan and provide additional classification for tissue injury based on systematic review.

MATERIALS AND METHODS

The members of the Japanese Society of Cancer Nursing (JSCN), Japanese Society of Medical Oncology (JSMO), and Japanese Society of Pharmaceutical Oncology (JASPO) were surveyed about significant clinical challenges related to EV, and 17 clinical questions (CQs) were formulated. PubMed and ICHUSHI Web were searched using the Patient, Intervention, Comparison, and Outcomes terms listed in each CQ as key words. For the classification of new drugs, articles published through February 2021 were selected using the search terms 'extravasation', 'injection-site reaction', 'adverse events', and the names of individual drugs as key words.

RESULTS

Recommendations based on the results of randomized controlled trials (RCTs) were made with regard to the selection of central venous (CV) devices (CQ2, CQ3a, CQ3b, and CQ3c), regular replacement of peripheral venous catheters (CQ5), and use of fosaprepitant (CQ7). These CQs are novel and were not mentioned in previous guidelines. Warm compression monotherapy (CQ10b) and local injection of steroids (CQ12) are discouraged for the management of EV. Ten new drugs were classified for EV tissue injury.

CONCLUSIONS

This study provides updated guidelines for the prevention and treatment of EV, which can be used to help health care providers and patients and their families practice better EV management.

Short-term outcomes of delta-shaped anastomosis versus functional end-to-end anastomosis using linear staplers for colon cancer

BACKGROUND

Several methods are used for reconstruction in colon cancer surgery, including hand-sewn or stapled anastomosis. However, few reports have compared short-term outcomes among reconstruction methods. This study compared short-term outcomes between delta-shaped anastomosis (Delta) and functional end-to-end anastomosis (FEEA).

METHODS

We retrospectively reviewed 1314 consecutive patients who underwent colorectal surgery with FEEA or Delta reconstruction between January 2016 and December 2023. Patients were divided into two groups according to reconstruction by FEEA (F group; n = 1242) or Delta (D group; n = 72). Propensity score matching was applied to minimize the possibility of selection bias and to balance covariates that could affect postoperative complications. Short-term outcomes were compared between groups.

RESULTS

Postoperative complications occurred in 215 patients (17.3%) in F group and 8 patients (11.1%) in D group. Before matching, transverse colon cancer was more frequent (p = 0.002), clinical N-positive status was less frequent (44.1% versus 16.7%, p < 0.001), distant metastasis was less frequent (11.7% versus 1.4%, p = 0.003), and laparoscopic approach was more frequent (87.8% versus 100%, p < 0.001) in D group. After matching, no differences in any clinical factor were evident between groups. Blood loss was significantly lower (28 mL versus 10 mL, p = 0.002) in D group, but operation time and postoperative complication rates were similar between groups.

CONCLUSIONS

Delta and FEEA were both considered safe as reconstruction methods. Further studies are needed to clarify appropriate case selection for Delta and FEEA.

Anti-edematous effects of epinastine, cetirizine and its enantiomers in λ-carrageenan-induced edema in rat hind paw

Urticaria is induced by the histamine released from mast cells which develops wheals (edema) as a visual feature. In clinical practice, second-generation histamine H1 -receptor blockers are routinely used as the first-line symptomatic treatment for urticaria. Nevertheless, not much research has directly examined the second-generation histamine H1-receptor blockers' ability to reduce edema. In this study, we directly evaluated the anti-edematous activities of three second-generation histamine H1-receptor blockers available in the market (epinastine hydrochloride, cetirizine hydrochloride, and levocetirizine hydrochloride) using a λ-carrageenan-induced footpad edema model. One hour before the induction of edema with 1% λ -carrageenan injection, all second-generation histamine H1 -receptor blockers (5, 10, 50 and 100 mg/kg) were subcutaneously administered to rats. At 0.5 and 3 hours after λ -carrageenan administration, the edema volume was evaluated using a Plethysmometer. Epinastine hydrochloride significantly suppressed the edema growth in a dose-dependent manner. Cetirizine hydrochloride showed a slight anti-edematous effect, while levocetirizine significantly inhibited the development of edema in a dose-dependent manner. On the other hand, dextrocetirizine did not prevent edema from growing. In summary, second-generation histamine H1 -receptor blockers, at least those examined in this study, may be able to reduce the clinical symptoms of urticaria associated with edema. Levocetirizine hydrochloride is also anticipated to have stronger anti-edematous effects than cetirizine hydrochloride because levocetirizine is responsible for cetirizine's anti-edematous activity.

Direct comparison of anti-inflammatory effects of 14-, 15-, and 16-membered macrolide antibiotics in experimental inflammation model induced by carrageenan in rats

Some macrolide antibiotics, which share a basic lactone ring structure, also exhibit anti-inflammatory actions in addition to their antibacterial activities. However, no study has directly compared anti-inflammatory effects on acute inflammation among macrolide antibiotics with the distinct size of the lactone ring. In this study, we evaluated and compared the anti-inflammatory activities of four 14-membered macrolides (erythromycin, clarithromycin, roxithromycin, oleandomycin), one 15-membered macrolide (azithromycin), and three 16-membered macrolides (midecamycin, josamycin, leucomycin) using a rat carrageenan-induced footpad edema model. All macrolide antibiotics were intraperitoneally administered to rats one hour before the induction of inflammatory edema with 1% λ -carrageenan. The anti-inflammatory effects on acute inflammation were evaluated by changing the edema volume. All 14-membered and 15-membered macrolide antibiotics significantly suppressed the development of edema. Conversely, none of the 16-membered macrolide antibiotics inhibited the growth of edema. In conclusion, compared to 16-membered macrolide antibiotics, 14-membered and 15-membered macrolide antibiotics have stronger anti-inflammatory effects. Further research should be done to determine why different lactone ring sizes should have distinct anti-inflammatory effects.

Development of Machine Learning Prediction Models for Self-Extubation After Delirium Using Emergency Department Data

Delirium is common in the emergency department, and once it develops, there is a risk of self-extubation of drains and tubes, so it is critical to predict delirium before it occurs. Machine learning was used to create two prediction models in this study: one for predicting the occurrence of delirium and one for predicting self-extubation after delirium. Each model showed high discriminative performance, indicating the possibility of selecting high-risk cases. Visualization of predictors using Shapley additive explanation (SHAP), a machine learning interpretability method, showed that the predictors of delirium were different from those of self-extubation after delirium. Data-driven decisions, rather than empirical decisions, on whether or not to use physical restraints or other actions that cause patient suffering will result in improved value in medical care.

Predictive Performance of Machine Learning-Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study

BACKGROUND

Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be examined.

OBJECTIVE

This study aims to examine how much data-driven models with machine learning improve predictive performance for poststroke outcomes compared with conventional stroke prognostic scores and to elucidate how explanatory variables in machine learning-based models differ from the items of the stroke prognostic scores.

METHODS

We used data from 10,513 patients who were registered in a multicenter prospective stroke registry in Japan between 2007 and 2017. The outcomes were poor functional outcome (modified Rankin Scale score >2) and death at 3 months after stroke. Machine learning-based models were developed using all variables with regularization methods, random forests, or boosted trees. We selected 3 stroke prognostic scores, namely, ASTRAL (Acute Stroke Registry and Analysis of Lausanne), PLAN (preadmission comorbidities, level of consciousness, age, neurologic deficit), and iScore (Ischemic Stroke Predictive Risk Score) for comparison. Item-based regression models were developed using the items of these 3 scores. The model performance was assessed in terms of discrimination and calibration. To compare the predictive performance of the data-driven model with that of the item-based model, we performed internal validation after random splits of identical populations into 80% of patients as a training set and 20% of patients as a test set; the models were developed in the training set and were validated in the test set. We evaluated the contribution of each variable to the models and compared the predictors used in the machine learning-based models with the items of the stroke prognostic scores.

RESULTS

The mean age of the study patients was 73.0 (SD 12.5) years, and 59.1% (6209/10,513) of them were men. The area under the receiver operating characteristic curves and the area under the precision-recall curves for predicting poststroke outcomes were higher for machine learning-based models than for item-based models in identical populations after random splits. Machine learning-based models also performed better than item-based models in terms of the Brier score. Machine learning-based models used different explanatory variables, such as laboratory data, from the items of the conventional stroke prognostic scores. Including these data in the machine learning-based models as explanatory variables improved performance in predicting outcomes after stroke, especially poststroke death.

CONCLUSIONS

Machine learning-based models performed better in predicting poststroke outcomes than regression models using the items of conventional stroke prognostic scores, although they required additional variables, such as laboratory data, to attain improved performance. Further studies are warranted to validate the usefulness of machine learning in clinical settings.

Comparing the Robustness of ResNet, Swin-Transformer, and MLP-Mixer under Unique Distribution Shifts in Fundus Images

BACKGROUND

Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness. Consequently, numerous deep learning models have been developed for the early detection of DR. Safety-critical applications employed in medical diagnosis must be robust to distribution shifts. Previous studies have focused on model performance under distribution shifts using natural image datasets such as ImageNet, CIFAR-10, and SVHN. However, there is a lack of research specifically investigating the performance using medical image datasets. To address this gap, we investigated trends under distribution shifts using fundus image datasets.

METHODS

We used the EyePACS dataset for DR diagnosis, introduced noise specific to fundus images, and evaluated the performance of ResNet, Swin-Transformer, and MLP-Mixer models under a distribution shift. The discriminative ability was evaluated using the Area Under the Receiver Operating Characteristic curve (ROC-AUC), while the calibration ability was evaluated using the monotonic sweep calibration error (ECE sweep).

RESULTS

Swin-Transformer exhibited a higher ROC-AUC than ResNet under all types of noise and displayed a smaller reduction in the ROC-AUC due to noise. ECE sweep did not show a consistent trend across different model architectures.

CONCLUSIONS

Swin-Transformer consistently demonstrated superior discrimination compared to ResNet. This trend persisted even under unique distribution shifts in the fundus images.

Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study

BACKGROUND

Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications.

OBJECTIVE

The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model.

METHODS

The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method.

RESULTS

A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance.

CONCLUSIONS

The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.

Online Adaptive Radiotherapy for Pharyngeal Cancer: Dose-Volume Histogram Analysis between Adapted and Scheduled Plan

PURPOSE/OBJECTIVE(S)

The present study aimed to evaluate whether online adapted plan with artificial intelligence (AI) driven work flow could be used in clinical settings with variable changes of the targets and organs at risk (OARs) for pharyngeal cancer.

MATERIALS/METHODS

Ten patients with pharyngeal cancer who underwent chemoradiotherapy at our institution between January and July 2020 were included for the analysis. All patients had been previously aligned daily with cone-beam computed tomography (CBCT) and treated by O-ring Linac. A simulated treatment was performed on the treatment emulator. Weekly fractions, once in every 4-5 fractions, were simulated in the treatment emulator for each patient using their previous on-treatment CBCTs. The dataset was divided into three groups according to the treatment period (1st-2nd week, 20 CBCTs), middle (3rd-4th week, 20 CBCTs), and late (5th-7th week, 30 CBCTs) period. In the present study, all of reference plan generation in treatment emulator were created on the initial plans of two-step method using 12 equidistant field IMRT. The prescribed dose was 70 Gy in 35 fractions and normalized to the dose of 68.6 Gy (98% dose) to 95% of the planning target volume (PTV). The adaptation process on treatment emulator includes auto-segmentation of daily anatomy, calculation of the dose in scheduled plans using the same monitor units and optimization and calculation of the dose in adapted plan. Dose-volume histogram (DVH) parameters between adapted and scheduled plans in terms of PTV (D98%, D95%, D50% and D2%), spinal cord (Dmax and D1cc), brain stem (Dmax), ipsilateral and contralateral parotid glands (Dmedian and Dmean) were evaluated in each period.

RESULTS

D98% of PTV of adapted plan was significantly higher than that of scheduled plan in early and middle period (p = 0.02 and <0.01, respectively). D95% of PTV of adapted plan was significantly higher than that of scheduled plan in all periods (p<0.01). D2% of PTV of adapted plan was significantly lower than that of scheduled plan in all periods (p = 0.04, 0.04 and 0.02 in each period, respectively). There was not significant difference in D50% of PTV between adapted and scheduled plan in all periods. In terms of OARs, Dmax of spinal cord of adapted plan was significantly lower than that of scheduled plan in all periods (p<0.01). Similarly, D1cc of spinal cord of adapted plan was lower than that of scheduled plan. Dmean of ipsilateral and contralateral parotid glands of adapted plan were lower than those of scheduled plan in the late period (p<0.01 and 0.03, respectively).

CONCLUSION

The present study revealed that adapted plan with AI driven work flow could create dosimetrically better plans for pharyngeal cancer compared to scheduled plan. It was suggested that online adaptive radiotherapy could be necessary to maintain PTV coverage while reducing the dose to OARs in all periods for pharyngeal cancer.

The Development and Evaluation of an All-Purpose Bolus for Radiotherapy

PURPOSE/OBJECTIVE(S)

The purpose of this study was to develop on a new bolus (HM bolus) which had tissue equivalence, transparency, reusability, and free shaping at approximately 40°C for excellent adhesion, and to evaluate its features could be satisfy ideal bolus conditions for clinical use.

MATERIALS/METHODS

The newly developed HM bolus was controlled to prevent phase separation by adjusting the contents of ethylene propylene rubber, styrene, butadiene rubber, thermoplastic resin, temperature-sensitive adjuster, and silica. The element ratios (wt%) in the HM bolus are H: 10.2%, C: 63.5%, O: 17.1%, and Si: 9.2%. The density was adjusted to 0.96 g cm-3. We evaluated dose characteristics, a vinyl gel sheet bolus (Gel bolus) and HM bolus placed on a water-equivalent phantom were used to obtain the percent depth dose (PDD) of electron (6 MeV, 9 MeV) and photon (4 MV,6 MV) beams. The average dose difference of the HM bolus and Gel bolus was calculated. The Gel bolus, a soft rubber bolus (SR bolus), and HM bolus were placed in adherence to a pelvic phantom. CT images taken after shaping and 1, 2, and 3 weeks after shaping were used to evaluate the adhesion and reproducibility using air gap and dice similarity coefficient (DSC) metrics. The visibility of letters (maximum: 80 pt, minimum: 10 pt) through a plate-shaped bolus and the visibility of markers when each bolus was set up on the pelvic phantom under normal room lighting were evaluated.

RESULTS

The average dose difference for electron beams was 0.16% ± 0.79% and photon beams was 0.06% ± 0.34%, both within 1% of the PDD results. The HM bolus showed the same build-up effect and dose characteristics as the Gel bolus. The mean air gap values for the Gel bolus, SR bolus, and HM bolus were 96.02 ± 43.77 cm3, 34.93 ± 21.44 cm3, and 4.40 ± 1.50 cm3 44, respectively. The mean DSC values for the Gel bolus, SR bolus, and HM bolus were 0.363 ± 0.035, 0.556 ± 0.042, and 0.837±0.018. The HM bolus showed the smallest air gap at all time points and the DSC closest to 1. Excellent adhesion was observed in the CT simulation and during the treatment period. The letter visibility through the HM bolus and Gel bolus was sufficient, and when the HM bolus was set up on the pelvic phantom, the markers that were completely invisible with the SR bolus were visible.

CONCLUSION

We succeeded in developing an all-purpose bolus with unique characteristics for clinical use. The HM bolus had the same build-up effect and dose characteristics as a Gel bolus. Therefore, it can be used for CT simulation and dose calculation. The other advantages of the new bolus are tissue equivalence, transparency, reusability, and free shaping at approximately 40°C, providing excellent adhesion at each setup during the treatment period.

Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer

There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I-IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23-89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.

Anti-Inflammatory Actions of Expectorants in a Rat Carrageenan-Induced Footpad Edema Model

S-Carboxymethyl-L-cysteine (SCMS) exhibits sputum-regulating and anti-inflammatory actions. Previous studies reported the anti-inflammatory effects of SCMS on chronic inflammatory diseases, but no study has examined these effects on acute inflammatory diseases. In this study, we investigated the anti-inflammatory effects of SCMS in a rat carrageenan-induced footpad edema model, which is routinely used as an acute inflammation model. Expectorants were administered to rats with footpad edema induced by subcutaneously administering 1%λ-carrageenan to the footpad of the left posterior limb, and the dose dependency of the anti-inflammatory effects was evaluated. As a result, even when the dose of SCMS was increased to 400 mg/kg, there were no inhibitory effects on edema. Furthermore, we examined the inhibitory effects of other expectorants (ambroxol hydrochloride, N-acetyl-L-cysteine, L-cysteine ethylester hydrochloride, and L-cysteine methylester hydrochloride), which were reported to exhibit anti-inflammatory effects on chronic inflammation, on edema. However, none of these expectorants inhibited edema.

Non-linear association between body weight and functional outcome after acute ischemic stroke

This study aimed to determine whether body weight is associated with functional outcome after acute ischemic stroke. We measured the body mass index (BMI) and assessed clinical outcomes in patients with acute ischemic stroke. The BMI was categorized into underweight (< 18.5 kg/m²), normal weight (18.5-22.9 kg/m²), overweight (23.0-24.9 kg/m²), and obesity (≥ 25.0 kg/m²). The association between BMI and a poor functional outcome (modified Rankin Scale [mRS] score: 3-6) was evaluated. We included 11,749 patients with acute ischemic stroke (70.3 ± 12.2 years, 36.1% women). The risk of a 3-month poor functional outcome was higher for underweight, lower for overweight, and did not change for obesity in reference to a normal weight even after adjusting for covariates by logistic regression analysis. Restricted cubic splines and SHapley Additive exPlanation values in eXtreme Gradient Boosting model also showed non-linear relationships. Associations between BMI and a poor functional outcome were maintained even after excluding death (mRS score: 3-5) or including mild disability (mRS score: 2-6) as the outcome. The associations were strong in older patients, non-diabetic patients, and patients with mild stroke. Body weight has a non-linear relationship with the risk of a poor functional outcome after acute ischemic stroke.

Probing Superatomic Orbitals of Sc-Doped and Undoped Silver Cluster Anions via Photoelectron Angular Anisotropy

Valence s electrons in alkali- or coinage-metal clusters are conceived to delocalize over the metal frameworks. The electrons occupy so-called superatomic orbitals (SAOs, i.e., 1S, 1P, 1D, 2S, 1F, ...), which provide an essential picture for understanding the size-dependent, unique properties of these metal clusters. While such electronic shells are unambiguously identified in their photoelectron spectra and supported by electronic structure calculations, characterization of SAOs in heteroatom-doped metal clusters has remained elusive as the doping significantly affects its energy levels and even alters the ordering of SAOs. Here, we present a photoelectron imaging study to explore SAOs formed in Sc-doped and undoped silver cluster anions, Ag_NSc^- (N = 15, 16) and Ag_N^- (N = 18, 19). Photoelectron angular distributions from their outermost SAOs are clearly visualized, whose characters are analyzed with the aid of density functional theory calculations. The present methodology enables us to explore not only the quantized energy levels but also the spatial distributions of SAOs formed in various metal cluster anions.

Combination of heat-killed Lactiplantibacillus plantarum YIT 0132 (LP0132) and oral immunotherapy in cow's milk allergy: a randomised controlled trial

Safer and more effective cow milk (CM)-oral immunotherapy that does not induce allergic reactions has not yet been standardised. We sought to explore the efficacy and feasibility of a combination of heat-killed Lactiplantibacillus plantarum YIT 0132 (LP0132) and oral immunotherapy for treating IgE-mediated cow milk allergy (CMA). We conducted a 24-week, double-blind, randomised (1:1), two-arm, parallel-group, placebo-controlled, phase 2 trial of LP0132 intervention for treating IgE-mediated CMA in children aged 1-18 years (n=60) from January 29, 2018 to July 12, 2019 in Tokyo, Japan. Participants were randomly assigned to the LP0132 group receiving citrus juice fermented with LP0132 or to the control group receiving citrus juice without. Both groups received low-dose slow oral immunotherapy with CM. The primary outcome was improved tolerance to CM, proven by the CM challenge test at 24 weeks. Secondary outcomes were changes in serum biomarkers of serum-specific β-lactoglobulin-IgE (sIgE) and β-lactoglobulin-IgG4 (sIgG4). Exploratory outcomes included changes in serum cytokine levels and gut microbiota composition. A total of 61 participants were included. Finally, 31 children were assigned to the LP0132 group and 30 to the control group, respectively. After the intervention, 41.4 and 37.9% of the participants in the LP0132 and control groups, respectively, showed improved tolerance to CM. In serum biomarkers after the intervention, the sIgG4 level was significantly higher, and interleukin (IL)-5 and IL-9 were significantly lower, in the LP0132 group than in the control group. In the gut microbiome, the α-diversity and Lachnospiraceae increased significantly in the LP0132 group, and Lachnospiraceae after the intervention was significantly higher in the LP0132 group than in the control group. In conclusion, low-dose oral immunotherapy with modulating gut microbiota might be a safer and more effective approach for treating cow's milk allergy.

Formation and evolution of carbonaceous asteroid Ryugu: Direct evidence from returned samples

Samples of the carbonaceous asteroid Ryugu were brought to Earth by the Hayabusa2 spacecraft. We analyzed 17 Ryugu samples measuring 1 to 8 millimeters. Carbon dioxide-bearing water inclusions are present within a pyrrhotite crystal, indicating that Ryugu's parent asteroid formed in the outer Solar System. The samples contain low abundances of materials that formed at high temperatures, such as chondrules and calcium- and aluminum-rich inclusions. The samples are rich in phyllosilicates and carbonates, which formed through aqueous alteration reactions at low temperature, high pH, and water/rock ratios of <1 (by mass). Less altered fragments contain olivine, pyroxene, amorphous silicates, calcite, and phosphide. Numerical simulations, based on the mineralogical and physical properties of the samples, indicate that Ryugu's parent body formed ~2 million years after the beginning of Solar System formation.

Efficient Gene Knockout in Salivary Gland Epithelial Explant Cultures

We have developed methods to achieve efficient CRISPR-Cas9-mediated gene knockout in ex vivo mouse embryonic salivary epithelial explants. Salivary epithelial explants provide a valuable model for characterizing cell signaling, differentiation, and epithelial morphogenesis, but research has been limited by a paucity of efficient gene perturbation methods. Here, we demonstrate highly efficient gene perturbation by transient transduction of guide RNA-expressing lentiviruses into Cas9-expressing salivary epithelial buds isolated from Cas9 transgenic mice. We first show that salivary epithelial explants can be cultured in low-concentration, nonsolidified Matrigel suspensions in 96-well plates, which greatly increases sample throughput compared to conventional cultures embedded in solidified Matrigel. We further show that salivary epithelial explants can grow and branch with FGF7 alone, while supplementing with insulin, transferrin, and selenium (ITS) enhances growth and branching. We then describe an efficient workflow to produce experiment-ready, high-titer lentiviruses within 1 wk after molecular cloning. To track transduced cells, we designed the lentiviral vector to coexpress a nuclear fluorescent reporter with the guide RNA. We routinely achieved 80% transduction efficiency when antibiotic selection was used. Importantly, we detected robust loss of targeted protein products when testing 9 guide RNAs for 3 different genes. Moreover, targeting the β1 integrin gene (Itgb1) inhibited branching morphogenesis, which supports the importance of cell-matrix adhesion in driving branching morphogenesis. In summary, we have established a lentivirus-based method that can efficiently perturb genes of interest in salivary epithelial explants, which will greatly facilitate studies of specific gene functions using this system.

Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow

Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients were categorized into three analysis populations, according to inclusion and exclusion criteria, to develop delirium prediction models. The predictors were then visualized using Shapley additive explanation (SHAP) and fed back to clinical practice. The machine learning-based prediction of delirium in each population exhibited excellent predictive performance. SHAP was used to visualize the body mass index and albumin levels as critical contributors to delirium prediction. In addition, the cutoff value for age, which was previously unknown, was visualized, and the risk threshold for age was raised. By using the SHAP method, we demonstrated that data-driven decision support is possible using electronic medical record data.

Diagnostic model for preschool workers' unwillingness to continue working: Developed using machine-learning techniques

The turnover of kindergarten teachers has drastically increased in the past 10 years. Reducing the turnover rates among preschool workers has become an important issue worldwide. Parents have avoided enrolling children in preschools due to insufficient care, which affects their ability to work. Therefore, this study developed a diagnostic model to understand preschool workers' unwillingness to continue working. A total of 1002 full-time preschool workers were divided into 2 groups. Predictors were drawn from general questionnaires, including those for mental health. We compared 3 algorithms: the least absolute shrinkage and selection operator, eXtreme Gradient Boosting, and logistic regression. Additionally, the SHapley Additive exPlanation was used to visualize the relationship between years of work experience and intention to continue working. The logistic regression model was adopted as the diagnostic model, and the predictors were "not living with children," "human relation problems with boss," "high risk of mental distress," and "work experience." The developed risk score and the optimal cutoff value were 14 points. By using the diagnostic model to determine workers' unwillingness to continue working, supervisors can intervene with workers who are experiencing difficulties at work and can help resolve their problems.

Rapid quasi-periodic oscillations in the relativistic jet of BL Lacertae

Blazars are active galactic nuclei (AGN) with relativistic jets whose non-thermal radiation is extremely variable on various timescales^1-3. This variability seems mostly random, although some quasi-periodic oscillations (QPOs), implying systematic processes, have been reported in blazars and other AGN. QPOs with timescales of days or hours are especially rare⁴ in AGN and their nature is highly debated, explained by emitting plasma moving helically inside the jet⁵, plasma instabilities^6,7 or orbital motion in an accretion disc^7,8. Here we report results of intense optical and γ-ray flux monitoring of BL Lacertae (BL Lac) during a dramatic outburst in 2020 (ref. ⁹). BL Lac, the prototype of a subclass of blazars¹⁰, is powered by a 1.7 × 10⁸ M_Sun (ref. ¹¹) black hole in an elliptical galaxy (distance = 313 megaparsecs (ref. ¹²)). Our observations show QPOs of optical flux and linear polarization, and γ-ray flux, with cycles as short as approximately 13 h during the highest state of the outburst. The QPO properties match the expectations of current-driven kink instabilities⁶ near a recollimation shock about 5 parsecs (pc) from the black hole in the wake of an apparent superluminal feature moving down the jet. Such a kink is apparent in a microwave Very Long Baseline Array (VLBA) image.

POS0530 MOLECULAR SIGNATURE IN SUSTAINED CLINICAL REMISSION INDUCED BY TOCILIZUMAB IN PATIENTS WITH RHEUMATOID ARTHRITIS

Background

Clinical remission is a clinical goal in the treatment of rheumatoid arthritis (RA). Sustained, biologics-free and true remission is an unachieved goal of the “treat-to-target” approach in most patients, and the determinants for achievement are still unclear. In our recent prospective study using multiomics analysis, we proposed that a molecular signature in peripheral whole blood can be a predictor for subsequent disease activity or activities of daily living.1 We also showed that tocilizumab (TCZ) induced deep clinical remission associated with gene expression in peripheral CD4+ T cells.2

Objectives

To consolidate and expand our hypothesis, we investigated the significance of molecular signatures in sustained remission in a larger scale cohort.

Methods

To build and validate the diagnostic model, we collected 73 peripheral blood samples from 30 patients with active RA, 30 patients in clinical remission induced by TCZ and 13 healthy controls. We then collected another 23 samples at a point before TCZ was halted due to sustained clinical remission. In total, 96 samples were analyzed by a multiomics platform, which included RNA sequencing and comprehensive proteomics.

Results

We first developed an optimized partial least-squares regression (PLSR) model using data from 5,436 genes and 255 proteins extracted in our previous model.1 The odds ratio in the model clearly reflected the clinical state with high fidelity (Figure 1). In that study, TCZ induced nearly half of the patients with clinical remission into molecular remission, with an odds ratio of less than zero. To clarify the characteristics of the molecular signature at sustained clinical remission under TCZ continuation, 23 samples were applied to the model. The odds ratio was largely the same as that for clinical remission. Next, we investigated the association with disease flare after cessation of TCZ. At some points before cessation, the median odds ratio in patients who experienced disease flare after stopping TCZ tended to be higher than that in patients with sustained remission after stopping TCZ in the transcriptomics model but not in the proteomics model. Thirty-five differentially expressed genes were identified between the two groups under the conditions of a >1.5-fold change and P-value<0.05.

Figure 1.

Odds ratio in the partial least-squares regression model using transcriptomics (A) and proteomics (B) data from rheumatoid arthritis and healthy control groups

Conclusion

Our larger scale study validated the idea in our previous study that TCZ induces molecular remission. A certain substantial gap associated with prognosis after quitting TCZ may exist as a molecular signature of sustained clinical remission induced by TCZ. These multiomics data sets enable us to understand sustained clinical remission at a molecular level.

References

[1]Nat Commun. 9(1):2775, 2018, 2) Sci Rep.11(1):16691, 2021

Graphs:

Acknowledgements

We acknowledge funding by Chugai Pharmaceutical Co., Ltd.

Disclosure of Interests

Nobuhiko Kajio: None declared, Katsuya Suzuki Speakers bureau: AbbVie, AsahiKasei, Astellas, Ayumi, Bristol-Myers Squibb, Chugai, Eisai, Eli Lilly, Gilead, Janssen, Mitsubishi Tanabe, Pfizer, Sanofi, Viatris, Consultant of: AbbVie, Asahi Kasei, Janssen, Pfizer, Grant/research support from: Chugai, Daiichi-Sankyo, Eli Lilly, Mitsubishi Tanabe, Ono, Takeda, Kotaro Matsumoto: None declared, Hiroshi Iijima: None declared, Seiji Nakamura: None declared, Yohei Ishizawa: None declared, Jun Inamo: None declared, Masaru Takeshita: None declared, Keiko Yoshimoto: None declared, Yuko Kaneko Speakers bureau: Chugai, Consultant of: Chugai, Grant/research support from: Chugai, Tsutomu Takeuchi Speakers bureau: Chugai, Consultant of: Chugai, Grant/research support from: Chugai.