Accelerated Discovery of Solvation Structure Engineering for Stable Aqueous Rechargeable Zinc Batteries via Physics-Guided Bayesian Active Learning.

Aqueous rechargeable zinc batteries, despite advantages like safety and performance, struggle with water-based side reactions such as hydrogen evolution and corrosion. Regulating the solvation structure of Zn is essential for stability. Introducing n-hexane, a nonpolar alkane, modifies Zn coordination and stabilizes the Zn anode-electrolyte interface.

Best practices and pragmatic approaches for patient-reported outcomes and quality of life measures in cancer clinical trials.

Patient-reported outcomes (PROs) are often collected in cancer clinical trials. Data obtained from trials with PROs are essential in evaluating participant experiences relating to symptoms, financial toxicity, or health-related quality of life. Although most features of clinical trial design, implementation, and analyses apply to trials with PROs, several considerations are unique.

From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models.

Protein language models (pLMs) are powerful predictors of protein structure and function, learning through unsupervised training on millions of protein sequences. pLMs are thought to capture common motifs in protein sequences, but the specifics of pLM features are not well understood. Identifying these features would not only shed light on how pLMs work, but potentially uncover novel protein biology-studying the model to study the biology.

Hildegard of Bingen's Embryology: Enabling Women's Reproductive Power without Seed.

This paper examines Hildegard of Bingen's embryological theory within the intricate landscape of medieval medicine and religion. It situates Hildegard's understanding of conception within ongoing historical debates surrounding the roles of male and female reproductive substances, particularly the concept of seed or semen. By analyzing her conceptual framework against prevailing classical and medieval reproductive theories, the research illuminates Hildegard's distinctive contribution to understanding conception as a holistic bodily process.

Hospital frailty risk score using electronic medical records and geriatric syndromes in an acute-care hospital.

Background: This study investigated the relationship between the Hospital Frailty Risk Score (HFRS) and geriatric syndromes in acute-care hospitals.

Methods: A cross-sectional analysis was performed on 8,205 inpatients aged ≥ 65 years from November 1, 2016, to October 31, 2021. HFRS was determined using ICD-10 codes in the electronic medical records.

A pilot human study using ketamine to treat disorders of consciousness.

Post-comatose disorders of consciousness (DoC) represent persistent neurological conditions with limited therapeutic options and a poor prognosis. Recent works advocate for exploring the effects of psychedelics to enhance brain complexity in DoC and ameliorate their consciousness. We investigated sub-anesthetic concentration of the atypical psychedelic ketamine for treating post-comatose prolonged DoC through a double-blind, placebo-controlled, cross-over trial involving three adult patients.

Racial and ethnic disparities in young adult mental health: Exploring the individual and conjoint effects of ACEs and campus climate.

Objectives: Adverse childhood experiences (ACEs) have been linked to posttraumatic stress symptoms (PTSS), but the role of contextual factors remains underexplored. Therefore, this study examined the moderating effect of campus climate on associations between ACEs and PTSS and whether effects differ based on racial/ethnic identity.

Participants: University students taking Psychology courses at a large public university in the Northeastern United States ( = 419).

A Multinational Comparison Study of the Patient-Reported Outcomes Measurement Information System Anxiety, Depression, and Anger Item Bank in the General Population.

Objectives: This study aimed to compared Patient-Reported Outcomes Measurement Information System (PROMIS) anxiety, depression, and anger item bank among Korean, US and Dutch general population.

Methods: Between December 2021 and January 2022, we surveyed representative Korean participants (N = 2699). Then we compared the mean T-scores of PROMIS anxiety, depression, and anger full items bank among Korean, US (N = 1696) and the Dutch (N = 1002) populations.

Machine learning optimal ordering in global routing problems in semiconductors.

In this work, we propose a new method for ordering nets during the process of layer assignment in global routing problems. The global routing problems that we focus on in this work are based on routing problems that occur in the design of substrates in multilayered semiconductor packages. The proposed new method is based on machine learning techniques and we show that the proposed method supersedes conventional net ordering techniques based on heuristic score functions.

Characteristics and predictors of major occupational injuries in Korean farmers.

Farmers are exposed to various risks due to the nature of the agricultural environment, and occupational injuries occur consistently. Hence, this study aimed to investigate the characteristics and predictors of major non-fatal occupational injuries among farmers by analysing incidents of non-fatal occupational injuries resulting in at least 1 day off work using the Korean Agricultural Workers' Occupational Disease and Injury Survey data. Multivariate logistic regression was performed to identify the predictors of agricultural occupational injuries, and the results indicated that the risk for non-fatal injuries was higher among older individuals, individuals with pre-existing physical limitations and individuals who use agricultural machinery.

Identification of meaningful individual-level change thresholds for worsening on the patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE®).

Background: We derived meaningful individual-level change thresholds for worsening in selected patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE®) items and their composite scores.

Methods: We used two data sources, the PRO-TECT trial (Alliance AFT-39) that collected PRO-CTCAE data from adults with advanced cancer at 26 United States (U.S.

Artificial intelligence and machine learning in disorders of consciousness.

Purpose Of Review: As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions.

Recent Findings: Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data.

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

[This corrects the article DOI: .].

Diamidocarbene-derived palladium and nickel-sulfur clusters.

Novel palladium and nickel-sulfur clusters were synthesized using a diamidocarbene-derived carbon disulfide ligand. Structural characterization revealed a tetranuclear metal-sulfur cluster geometry with each metal center exhibiting square-planar coordination. The ligand was redox-active, accommodating oxidation states ranging from 0 to -2.

Differential Urinary Microbiome and Its Metabolic Footprint in Bladder Cancer Patients Following BCG Treatment.

Recent studies have identified a urinary microbiome, dispelling the myth of urine sterility. Intravesical bacillus Calmette-Guérin (BCG) therapy is the preferred treatment for intermediate to high-risk non-muscle-invasive bladder cancer (BCa), although resistance occurs in 30-50% of cases. Progression to muscle-invasive cancer necessitates radical cystectomy.

Statistical properties of items and summary scores from the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) in a diverse cancer sample.

Background/aims: The Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) was developed to capture symptomatic adverse events from the patient perspective. We aim to describe statistical properties of PRO-CTCAE items and summary scores and to provide evidence for recommendations regarding PRO-CTCAE administration and reporting.

Methods: Using data from the PRO-CTCAE validation study (NCT02158637), prevalence, means, and standard deviations of PRO-CTCAE items, composite scores, and mean and maximum scores across attributes (frequency, severity, and/or interference) of symptomatic adverse events were calculated.

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.

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.

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.

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.

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.