Network structure of REM sleep behavior disorder symptoms in iRBD patients.

OBJECTIVE: Employing the REM Sleep Behavior Disorder Questionnaire-Hong Kong (RBDQ-HK) to investigate symptoms and their severity in rapid eye movement (REM) sleep behavior disorder (RBD) patients, this study delves into the construct of RBD through the RBDQ-HK and its links to depression and sleep quality. METHODS: Data from the RBDQ-HK, the Geriatric Depression Scale (GDS), and the Pittsburgh Sleep Quality Index (PSQI) were compiled from individuals with isolated RBD (iRBD) confirmed by polysomnography. We constructed a network analysis of the RBDQ-HK, measured the centrality of each symptom (node), conducted Exploratory Graph Analysis (EGA) to unveil the dimension structure of the questionnaire, and calculated bridge expected influence (BEI) to identifying critical bridge. Multivariate linear regression was also employed to discover relationships between RBDQ-HK dimensions and variables such as PSQI and GDS. RESULTS: In our cohort of 455 iRBD patients (299 males), the items in the RBDQ-HK were divided into three dimensions: dream, movement, and SRI/violence. The symptoms identified as most central to RBD were 'shouting or yelling in sleep', 'dream-enacting movements', and 'talking during sleep'. The highest (BEI) was 'violent and aggressive dreams', which has the potential to bridge three dimensions within the symptom network. Depression was significantly correlated with the movement and dream dimensions of RBD, and sleep quality was predominantly related to the dream dimension score. CONCLUSION: Our findings verify that the principal symptoms of the RBDQ-HK align with the established diagnostic criteria and reveal a three-dimensional structure within RBD symptoms. The relationships between the RBD symptoms, depression, and sleep quality need to be identified for the effective management of RBD patients.

Physical and psychological distress amongst patients undergoing neoadjuvant chemotherapy for advanced ovarian cancer.

OBJECTIVE: This study compares baseline clinical characteristics, physical function testing, and patient-reported outcomes for patients undergoing primary cytoreductive surgery versus neoadjuvant chemotherapy, with the goal of better understanding unique patient needs at diagnosis. METHODS: Patients with suspected advanced stage (IIIC/IV) epithelial ovarian cancer undergoing either primary cytoreductive surgery or neoadjuvant chemotherapy were enrolled in a single-institution, non-randomized prospective behavioral intervention trial of prehabilitation. Baseline clinical characteristics were abstracted. Physical function was evaluated using the Short Physical Performance Battery, Fried Frailty Index, gait speed, and grip strength. Patient-reported outcomes were evaluated using Patient-Reported Outcomes Measurement Information System metrics and the Perceived Stress Scale. RESULTS: There were no significant differences in demographics or clinical characteristics between cohorts at enrollment, with the exception of performance status, clinical stage, and albumin. While gait speed and grip strength were lower amongst neoadjuvant chemotherapy patients, there were no significant differences in physical function using the Short Physical Performance Battery and Fried Frailty Index. Patients in the neoadjuvant chemotherapy cohort reported decreased perception of physical function and increased fatigue on Patient-Reported Outcomes Measurement Information System metrics. A larger proportion of patients in the neoadjuvant cohort reported severe levels of emotional distress and anxiety, as well as greater perceived stress at diagnosis. CONCLUSIONS: Our findings suggest that patients undergoing neoadjuvant chemotherapy for advanced ovarian cancer present with increased psychosocial distress and decreased perception of physical function at diagnosis and may benefit most from early introduction of supportive care.

Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.

BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed. OBJECTIVE: This study aims to propose a clinically interpretable ensemble approach for the timely and accurate prediction of CA within 24 hours, regardless of patient heterogeneity, including variations across different populations and ICU subtypes. Additionally, we conducted patient-independent evaluations to emphasize the model's generalization performance and analyzed interpretable results that can be readily adopted by clinicians in real-time. METHODS: Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and the eICU-Collaborative Research Database (eICU-CRD). To address the problem of underperformance, we constructed our framework using feature sets based on vital signs, multiresolution statistical analysis, and the Gini index, with a 12-hour window to capture the unique characteristics of CA. We extracted 3 types of features from each database to compare the performance of CA prediction between high-risk patient groups from MIMIC-IV and patients without CA from eICU-CRD. After feature extraction, we developed a tabular network (TabNet) model using feature screening with cost-sensitive learning. To assess real-time CA prediction performance, we used 10-fold leave-one-patient-out cross-validation and a cross-data set method. We evaluated MIMIC-IV and eICU-CRD across different cohort populations and subtypes of ICU within each database. Finally, external validation using the eICU-CRD and MIMIC-IV databases was conducted to assess the model's generalization ability. The decision mask of the proposed method was used to capture the interpretability of the model. RESULTS: The proposed method outperformed conventional approaches across different cohort populations in both MIMIC-IV and eICU-CRD. Additionally, it achieved higher accuracy than baseline models for various ICU subtypes within both databases. The interpretable prediction results can enhance clinicians' understanding of CA prediction by serving as a statistical comparison between non-CA and CA groups. Next, we tested the eICU-CRD and MIMIC-IV data sets using models trained on MIMIC-IV and eICU-CRD, respectively, to evaluate generalization ability. The results demonstrated superior performance compared with baseline models. CONCLUSIONS: Our novel framework for learning unique features provides stable predictive power across different ICU environments. Most of the interpretable global information reveals statistical differences between CA and non-CA groups, demonstrating its utility as an indicator for clinical decisions. Consequently, the proposed CA prediction system is a clinically validated algorithm that enables clinicians to intervene early based on CA prediction information and can be applied to clinical trials in digital health.

Supramolecular Metal-Organic Framework for the High Stability of Aqueous Rechargeable Zinc Batteries.

Aqueous rechargeable Zn batteries (AZBs) are considered to be promising next-generation battery systems. However, the growth of Zn dendrites and water-induced side reactions have hindered their practical application, especially with regard to long-term cyclability. To address these challenges, we introduce a supramolecular metal-organic framework (SMOF) coating layer using an α-cyclodextrin-based MOF (α-CD-MOF-K) and a polymeric binder. The plate-like α-CD-MOF-K particles, combined with the polymeric binder create dense and homogeneous Zn2+ ion conductive pore channels that can vertically transport Zn2+ ions through the cavity while restricting the contact of water molecules. Molecular dynamics (MD) simulation verifies that Zn2+ ions can reversibly migrate through the pores of α-CD-MOF-K by partial dehydration. The uniform Zn deposition/dissolution promotes a smooth solid-electrolyte interface layer on the Zn metal anode and effectively suppresses side reactions with free water molecules. The α-CD-MOF-K@Zn symmetric cell exhibits stable cycling and a small polarization voltage of 70 mV for 800 h at 5 mA cm-2, and the α-CD-MOF-K@Zn|α-MnO2 full cell shows only 0.12% capacity decay per cycle at a rate of 1 A g-1.

Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.

Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.

Impact of the restraint decision tree for physical restraint use in South Korean neurointensive care units.

BACKGROUND: Nurses in neurointensive care units (NCUs) commonly use physical restraint (PR) to prevent adverse events like unplanned removal of devices (URDs) or falls. However, PR use should be based on evidenced decisions as it has drawbacks. Unfortunately, there is a lack of research-based PR protocol to support decision-making for nurses, especially for neurocritical patients. AIM: This study developed a restraint decision tree for neurocritical patients (RDT-N) to assist nurses in making PR decisions. We assessed its effectiveness in reducing PR use and adverse events. STUDY DESIGN: This study employed a baseline and post-intervention test design at a NCU with 19 beds and 45 nurses in a tertiary hospital in a metropolitan city in South Korea. Two-hundred and thirty-seven adult patients were admitted during the study period. During the intervention, nurses were trained on the RDT-N. PR use and adverse events between the baseline and post-intervention periods were compared. RESULTS: Post-intervention, total number of restrained patients decreased (20.7%-16.3%; χ2 = 7.68, p = .006), and the average number of PR applied per restrained patient decreased (2.42-1.71; t = 5.74, p < .001). The most frequently used PR type changed from extremity cuff to mitten (χ2 = 397.62, p < .001). No falls occurred during the study periods. On the other hand, URDs at baseline were 18.67 cases per 1000 patient days in the high-risk group and 5.78 cases per 1000 patient days in the moderate-risk group; however, no URD cases were reported post-intervention. CONCLUSIONS: The RDT-N effectively reduced PR use and adverse events. Its application can enhance patient-centred care based on individual condition and potential risks in NCUs. RELEVANCE TO CLINICAL PRACTICE: Nurses can use the RDT-N to assess the need for PR in caring for neurocritical patients, reducing PR use and adverse events.

Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network.

Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of variables for sleep stage classification. However, undergoing full polysomnography, which requires many sensors that are directly connected to the heaviness of the setup and the discomfort of sleep, brings a significant burden. In this study, sleep stage classification was performed using the single dimension of nasal pressure, dramatically decreasing the complexity of the process. In turn, such improvements could increase the much needed clinical applicability. Specifically, we propose a deep learning structure consisting of multi-kernel convolutional neural networks and bidirectional long short-term memory for sleep stage classification. Sleep stages of 25 healthy subjects were classified into 3-class (wake, rapid eye movement (REM), and non-REM) and 4-class (wake, REM, light, and deep sleep) based on nasal pressure. Following a leave-one-subject-out cross-validation, in the 3-class the accuracy was 0.704, the F1-score was 0.490, and the kappa value was 0.283 for the overall metrics. In the 4-class, the accuracy was 0.604, the F1-score was 0.349, and the kappa value was 0.217 for the overall metrics. This was higher than the four comparative models, including the class-wise F1-score. This result demonstrates the possibility of a sleep stage classification model only using easily applicable and highly practical nasal pressure recordings. This is also likely to be used with interventions that could help treat sleep-related diseases.

Palbociclib in adults aged 70 years and older with advanced breast cancer: A phase 2 multicenter trial (Alliance A171601).

INTRODUCTION: Palbociclib is a widely used treatment for advanced breast cancer in older adults. However, the existing evidence regarding its safety and tolerability in this age group is inconsistent and limited to retrospective subgroup or pooled analyses. MATERIALS AND METHODS: We conducted a prospective single-arm multicenter phase 2 study to evaluate the safety and tolerability of palbociclib in participants aged 70 years or older with advanced hormone receptor-positive breast cancer. Participants were given palbociclib in combination with their physician's choice of endocrine therapy (letrozole or fulvestrant). The primary endpoint was the incidence of grade 3+ adverse events (AEs) by six months. Secondary endpoints included AE-related dose delays, dose reductions, early discontinuations, and hospitalizations. Additionally, we compared these endpoints by age groups (70-74 and ≥ 75 years). RESULTS: Of the 90 participants (median age 74 years [70-87]) enrolled, 75.6% (95% confidence interval [CI], 65.4-84.0) had grade 3+ AEs by six months. The most frequent grade 3+ AEs were neutropenia (61%), fatigue (4%), and nausea (3%). Febrile neutropenia was uncommon (1.1%). Due to AEs, 36% had dose delays, 34% had dose reductions, 10% had early discontinuations, and 10% had hospitalizations. Compared to those aged 70-74 years, participants aged ≥75 years had higher rates of early discontinuations (5.9% vs 15.9%, a difference of 9.5% [95% CI 3.5%-22.5%]). DISCUSSION: Palbociclib has an overall favorable safety profile in adults aged ≥70 with advanced breast cancer. However, adults ≥75 years had a trend toward higher rates of AE-related early discontinuations compared to those 70-74 years. Further research is needed to evaluate tolerability and improve the delivery of palbociclib in older adults. CLINICALTRIALS: gov:NCT03633331.

IL-7-primed bystander CD8 tumor-infiltrating lymphocytes optimize the antitumor efficacy of T cell engager immunotherapy.

Bispecific T cell engagers (TCEs) show promising clinical efficacy in blood tumors, but their application to solid tumors remains challenging. Here, we show that Fc-fused IL-7 (rhIL-7-hyFc) changes the intratumoral CD8 T cell landscape, enhancing the efficacy of TCE immunotherapy. rhIL-7-hyFc induces a dramatic increase in CD8 tumor-infiltrating lymphocytes (TILs) in various solid tumors, but the majority of these cells are PD-1-negative tumor non-responsive bystander T cells. However, they are non-exhausted and central memory-phenotype CD8 T cells with high T cell receptor (TCR)-recall capacity that can be triggered by tumor antigen-specific TCEs to acquire tumoricidal activity. Single-cell transcriptome analysis reveals that rhIL-7-hyFc-induced bystander CD8 TILs transform into cycling transitional T cells by TCE redirection with decreased memory markers and increased cytotoxic molecules. Notably, TCE treatment has no major effect on tumor-reactive CD8 TILs. Our results suggest that rhIL-7-hyFc treatment promotes the antitumor efficacy of TCE immunotherapy by increasing TCE-sensitive bystander CD8 TILs in solid tumors.

Optimization of alert notifications in electronic patient-reported outcome (ePRO) remote symptom monitoring systems (AFT-39).

PURPOSE: Clinical benefits result from electronic patient-reported outcome (ePRO) systems that enable remote symptom monitoring. Although clinically useful, real-time alert notifications for severe or worsening symptoms can overburden nurses. Thus, we aimed to algorithmically identify likely non-urgent alerts that could be suppressed. METHODS: We evaluated alerts from the PRO-TECT trial (Alliance AFT-39) in which oncology practices implemented remote symptom monitoring. Patients completed weekly at-home ePRO symptom surveys, and nurses received real-time alert notifications for severe or worsening symptoms. During parts of the trial, patients and nurses each indicated whether alerts were urgent or could wait until the next visit. We developed an algorithm for suppressing alerts based on patient assessment of urgency and model-based predictions of nurse assessment of urgency. RESULTS: 593 patients participated (median age = 64 years, 61% female, 80% white, 10% reported never using computers/tablets/smartphones). Patients completed 91% of expected weekly surveys. 34% of surveys generated an alert, and 59% of alerts prompted immediate nurse actions. Patients considered 10% of alerts urgent. Of the remaining cases, nurses considered alerts urgent more often when patients reported any worsening symptom compared to the prior week (33% of alerts with versus 26% without any worsening symptom, p = 0.009). The algorithm identified 38% of alerts as likely non-urgent that could be suppressed with acceptable discrimination (sensitivity = 80%, 95% CI [76%, 84%]; specificity = 52%, 95% CI [49%, 55%]). CONCLUSION: An algorithm can identify remote symptom monitoring alerts likely to be considered non-urgent by nurses, and may assist in fostering nurse acceptance and implementation feasibility of ePRO systems.

Genomic Analysis of Monkeypox Virus During the 2023 Epidemic in Korea.

We aimed to characterize the genomes of monkeypox virus isolates from the Far East, providing insights into viral transmission and evolution. Genomic analysis was conducted on 8 isolates obtained from patients with monkeypox virus disease in the Republic of Korea between May 2022 and early 2023. These isolates were classified into Clade IIb. Distinct lineages, including B.1.1, A.2.1, and B.1.3, were observed in 2022 and 2023 isolates, with only the B.1.3 lineage detected in six isolates of 2023. These genetic features were specific to Far East isolates (the Republic of Korea, Japan, and Taiwan), distinguishing them from the diverse lineages found in the Americas, Europe, Africa, and Oceania. In early 2023, the prevalence of the B.1.3 lineage of monkeypox virus identified in six patients with no overseas travel history is considered as an indicator of the potential initiation of local transmission in the Republic of Korea.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.

Minimal effect of long-term clonazepam on cognitive function in patients with isolated rapid eye movement sleep behavior disorder.

STUDY OBJECTIVES: Despite its widespread use in patients with isolated rapid eye movement sleep behavior disorder (iRBD), the cognitive effect of clonazepam is uncertain. This study aimed to investigate the effect of cumulative clonazepam on cognitive function in patients with iRBD. METHODS: Demographic characteristics, baseline cognitive test, and most recent cognitive test information were collected retrospectively. Based on cumulative clonazepam doses, patients were classified into 4 subgroups: group 1, < 365 mg (1 mg × 1 year); group 2, 365 mg to < 1,095 mg (1 mg × 3 years); group 3, 1,095 mg to < 2,190 mg (1 mg × 6 years); and group 4, 2,190 mg or more. Cognitive test scores were calculated as z scores adjusted for age, education, and sex. RESULTS: This study included 101 patients with iRBD (63 males). Groups 1, 2, 3, and 4 had 14, 20, 32, and 35 patients, respectively. In within-group comparisons, follow-up Digit Span Backward test and the Trail Making Test A scores decreased in group 3, and follow-up Trail Making Test A and the Trail Making Test B scores decreased significantly in group 4. In the multiple regression analysis to determine influential factors on cognitive decline, cumulative clonazepam dose did not show a significant correlation with any cognitive domain. Follow-up cognitive function showed significant correlation only with baseline cognitive function. CONCLUSIONS: Memory and executive functions tended to decline in patients with iRBD. However, there was no significant effect of cumulative clonazepam. There was no evidence that long-term use of clonazepam was related to cognitive decline in patients with iRBD. CITATION: Lee M, Kim TK, Hong JK, Yoon I-Y. Minimal effect of long-term clonazepam on cognitive function in patients with isolated rapid eye movement sleep behavior disorder. J Clin Sleep Med. 2024;20(7):1173-1182.

Extending venous thromboembolism secondary prevention with apixaban in cancer patients. The EVE trial.

BACKGROUND: Cancer-associated venous thromboembolism (VTE) management guideline recommendations include continued therapeutic anticoagulation while active cancer persists. The Federal Drug Administration label for apixaban for secondary VTE prevention includes a dose reduction to 2.5 mg twice daily after 6 months of treatment. OBJECTIVES: The study's purpose was to determine whether this dose reduction is advisable for cancer-associated VTE. METHODS: A randomized, double-blind trial compared apixaban 2.5 mg with 5 mg twice daily for 12 months among cancer patients with VTE who had completed 6 to 12 months of anticoagulation therapy. The primary outcome was combined major bleeding plus clinically relevant nonmajor bleeding. RESULTS: Of 370 patients recruited, 360 were included in the intention-to-treat analyses. Major plus clinically relevant nonmajor bleeding occurred in 16 of 179 patients (8.9%) in the apixaban 2.5 mg group compared with 22 of 181 patients (12.2%) in the 5 mg group (hazard ratio [HR], 0.72; 95% CI, 0.38-1.37; P = .39). Major bleeding occurred in 2.8% of the apixaban 2.5 mg group and in 2.2% of the 5 mg group (HR, 1.26; 95% CI, 0.34-4.66; P = .73). Recurrent VTE or arterial thrombosis occurred in 9 of 179 patients (5.0%) in the apixaban 2.5 mg group and 9 of 181 patients (5.0%) in the 5 mg group (HR, 1.0; 95% CI, 0.40-2.53; P = 1.00). All-cause mortality rates were similar between groups, 13% vs 12% (HR, 1.14; 95% CI, 0.63-2.04; P = .67). CONCLUSION: For secondary prevention of cancer-associated VTE, apixaban 2.5 mg compared with 5 mg twice daily did not lower combined bleeding events (EVE trial NCT03080883).

rhIL-7-hyFc and hIL-2/TCB2c combination promotes an immune-stimulatory tumor microenvironment that improves antitumor efficacy of checkpoint inhibitors.

BACKGROUND: Recombinant human interleukin (rhIL)-7-hyFc (efineptakin alfa; NT-I7) is a potent T-cell amplifier, with two IL-7 molecules fused to IgD/IgG4 elements. rhIL-7-hyFc promotes extensive infiltration of CD8+ T cells into the tumor, concurrently increasing the numbers of intratumoral PD-1+CD8+ T cells. The hIL-2/TCB2 complex (SLC-3010) inhibits tumor growth by preferential activation of CD122 (IL-2Rß)high CD8+ T cells and natural killer cells, over regulatory T cells (Tregs). We investigated the underlying mechanisms of rhIL-7-hyFc and hIL-2/TCB2c antitumor activity and the potential synergistic efficacy, specifically focusing on tumor-specific CD8+ cells within the tumor and the tumor-draining lymph nodes (tdLN). METHODS: MC38 and CT26 tumor-bearing mice were administered with 10 mg/kg rhIL-7-hyFc intramuscularly and 0.9 mg/kg hIL-2/TCB2c intravenously. Anti-PD-1 monoclonal antibody was administered intraperitoneally three times at 3-day intervals at a dose of 5 mg/kg. Tumor volume was measured to assess efficacy. To compare the composition of immune cells between each monotherapy and the combination therapy, we analyzed tumors and tdLNs by flow cytometry. RESULTS: Our data demonstrate that the combination of rhIL-7-hyFc and hIL-2/TCB2c increases efficacy and generates an immune-stimulatory tumor microenvironment (TME). The TME is characterized by an increased infiltration of tumor-specific CD8+ T cells, and a decreased frequency of CD39highTIM-3+ Treg cells. Most importantly, rhIL-7-hyFc increases infiltration of a CD62L+Ly108+ early progenitor population of exhausted CD8+ T cells (TPEX), which may retain long-term proliferation capacity and replenish functional effector CD8+ T cells. hIL-2/TCB2c induces differentiation of CD62L+Ly108+ TPEX rapidly into CD101+ terminally differentiated subsets (terminally exhausted T cell (TEX term)). Our study also demonstrates that rhIL-7-hyFc significantly enhances the proliferation rate of TPEX in the tdLNs, positively correlating with their abundance within the tumor. Moreover, rhIL-7-hyFc and hIL-2/TCB2c can overcome the limited therapeutic effectiveness of PD-1 blockade, culminating in the complete regression of tumors. CONCLUSIONS: rhIL-7-hyFc can expand and maintain the progenitor pool of exhausted CD8+ T cells, whereas hIL-2/TCB2c promotes their differentiation into TEX term. Together, this induces an immune-stimulatory TME that improves the efficacy of checkpoint blockade.

PRO-Angoff method for remote standard setting: establishing clinical thresholds for the upper digestive disease tool.

BACKGROUND: The Upper Digestive Disease (UDD) Tool™ is used to monitor symptom frequency, intensity, and interference across nine symptom domains and includes two Patient-Reported Outcome Measurement Information System (PROMIS) domains assessing physical and mental health. This study aimed to establish cut scores for updated symptom domains through standard setting exercises and evaluate the effectiveness and acceptability of virtual standard setting. METHODS: The extended Angoff method was employed to determine cut scores. Subject matter experts refined performance descriptions for symptom control categories and achieved consensus. Domains were categorized into good, moderate, and poor symptom control. Two cut scores were established, differentiating good vs. moderate and moderate vs. poor. Panelists estimated average scores for 100 borderline patients per item. Cut scores were computed based on the sum of the average ratings for individual questions, converted to 0-100 scale. RESULTS: Performance descriptions were refined. Panelists discussed that interpretation of the scores should take into account the timing of symptoms after surgery and patient populations, and the importance of items asking symptom frequency, severity, and interference with daily life. The good/moderate cut scores ranged from 21.3 to 35.0 (mean 28.6, SD 3.6) across domains, and moderate/poor ranged from 47.5 to 71.3 (mean 54.5, SD 7.0). CONCLUSIONS: Panelists were confident in the virtual standard setting process, expecting valid cut scores. Future studies can further validate the cut scores using patient perspectives and collect patient and physician preferences for displaying contextual items on patient- and physician-facing dashboard.

Prolonged viral shedding in an immunocompromised Korean patient infected with hMPXV, sub-lineage B.1.3, with acquired drug resistant mutations during tecovirimat treatment.

Following the worldwide surge in mpox (monkeypox) in 2022, cases have persisted in Asia, including South Korea, and sexual contact is presumed as the predominant mode of transmission, with a discernible surge in prevalence among immunocompromised patients. Drugs such as tecovirimat can result in drug-resistant mutations, presenting obstacles to treatment. This study aimed to ascertain the presence of tecovirimat-related resistant mutations through genomic analysis of the monkeypox virus isolated from a reported case involving prolonged viral shedding in South Korea. Here, tecovirimat-resistant mutations, previously identified in the B.1 clade, were observed in the B.1.3 clade, predominant in South Korea. These mutations exhibited diverse patterns across different samples from the same patient and reflected the varied distribution of viral subpopulations in different anatomical regions. The A290V and A288P mutant strains we isolated hold promise for elucidating these mechanisms, enabling a comprehensive analysis of viral pathogenesis, replication strategies, and host interactions. Our findings imply that acquired drug-resistant mutations, may present a challenge to individual patient treatment. Moreover, they have the potential to give rise to transmitted drug-resistant mutations, thereby imposing a burden on the public health system. Consequently, the meticulous genomic surveillance among immunocompromised patients, conducted in this research, assumes paramount importance.

Heterogeneous osteoimmune profiles via single-cell transcriptomics in osteoporotic patients who fail bisphosphonate treatment.

Postmenopausal osteoporosis arises from imbalanced osteoclast and osteoblast activity, and mounting evidence suggests a role for the osteoimmune system in bone homeostasis. Bisphosphonate (BP) is an antiresorptive agent, but its treatment failure rate can be as high as 40%. Here, we performed single-cell RNA sequencing on peripheral immune cells from carefully selected postmenopausal women: non-osteoporotic, osteoporosis improved after BP treatment, and BP-failed cases. We found an increase in myeloid cells in patients with osteoporosis (specifically, T cell receptor+ macrophages). Furthermore, lymphoid lineage cells varied significantly, notably elevated natural killer cells (NKs) in the BP-failed group. Moreover, we provide fruitful lists of biomarkers within the immune cells that exhibit condition-dependent differences. The existence of osteoporotic- and BP-failure-specific cellular information flows was revealed by cell-cell interaction analysis. These findings deepen our insight of the osteoporosis pathology enhancing comprehension of the role of immune heterogeneity in postmenopausal osteoporosis and BP treatment failure.

Toxic dinoflagellate Centrodinium punctatum (Cleve) F.J.R. Taylor: An examination on the responses in growth and toxin contents to drastic changes of temperature and salinity.

To understand environmental effects affecting paralytic shellfish toxin production of Centrodinium punctatum, this study examined the growth responses, and toxin contents and profiles of a C. punctatum culture exposed to drastic changes of temperature (5-30 °C) and salinity (15-40). C. punctatum grew over a temperature range of 15-25 °C, with an optimum of 20 °C., and over a salinity range of 25-40, with optimum salinities of 30-35. This suggests that C. punctatum prefers relatively warm waters and an oceanic habitat for its growth and can adapt to significant changes of salinity levels. When C. punctatum was cultivated at different temperature and salinity levels, the PST profile included four major analogs (STX, neoSTX, GTX1 and GTX4, constituted >80 % of the profile), while low amounts of doSTX and traces of dc-STX and dc-GTX2 were also observed. Interestingly, though overall toxin contents did not change significantly with temperature, increases in the proportion of STX, and decreases in proportions in GTX1 and GTX4 were observed with higher temperatures. Salinity did not affect either toxin contents or profile from 25 to 35. However, the total toxin content dropped to approximately half at salinity 40, suggesting this salinity may induce metabolic changes in C. punctatum.