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Hyperpolarization techniques significantly enhance the sensitivity of magnetic resonance (MR) and thus present fascinating new directions for research and applications with in vivo MR imaging and spectroscopy (MRI/S). Hyperpolarized 13C MRI/S, in particular, enables real-time non-invasive assessment of metabolic processes and holds great promise for a diverse range of clinical applications spanning fields like oncology, neurology, and cardiology, with a potential for improving early diagnosis of disease, patient stratification, and therapy response assessment. Despite its potential, technical challenges remain for achieving clinical translation. This paper provides an overview of the discussions that took place at the international workshop "New Horizons in Hyperpolarized 13C MRI," in March 2023 at the Bavarian Academy of Sciences and Humanities, Munich, Germany. The workshop covered new developments, as well as future directions, in topics including polarization techniques (particularly focusing on parahydrogen-based methods), novel probes, considerations related to data acquisition and analysis, and emerging clinical applications in oncology and other fields.

Introduction: Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population.Methods: To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23–May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12–June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort.Results: In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747–0.822) performed better than gradient boosting (0.774, 95% CI 0.739–0.811) and extra trees classifier (0.72, 95% CI 0.677–0.762). There was no significant difference between the constructed models.Conclusion: Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.

Abstract Despite extensive research on avian vocal learning, we still lack a general understanding of how and when this ability evolved in birds. As the closest living relatives of the earliest Passeriformes, the New Zealand wrens (Acanthisitti) hold a key phylogenetic position for furthering our understanding of the evolution of vocal learning because they share a common ancestor with two vocal learners: oscines and parrots. However, the vocal learning abilities of New Zealand wrens remain unexplored. Here, we test for the presence of prerequisite behaviors for vocal learning in one of the two extant species of New Zealand wrens, the rifleman (Acanthisitta chloris). We detect the presence of unique individual vocal signatures and show how these signatures are shaped by social proximity, as demonstrated by group vocal signatures and strong acoustic similarities among distantly related individuals in close social proximity. Further, we reveal that rifleman calls share similar phenotypic variance ratios to those previously reported in the learned vocalizations of the zebra finch, Taeniopygia guttata. Together these findings provide strong evidence that riflemen vocally converge, and though the mechanism still remains to be determined, they may also suggest that this vocal convergence is the result of rudimentary vocal learning abilities.

Introduction: The return of spontaneous circulation after cardiac arrest (RACA) score is a well-validated model for estimating the probability of return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest (OHCA) by incorporating several variables, including gender, age, arrest aetiology, witness status, arrest location, initial cardiac rhythms, bystander cardiopulmonary resuscitation (CPR), and emergency medical services (EMS) arrival time. The RACA score was initially designed for comparisons between different EMS systems by standardising ROSC rates. End-tidal carbon dioxide (EtCO2) is a quality indicator of CPR. We aimed to improve the performance of the RACA score by adding minimum EtCO2 measured during CPR to develop the EtCO2 + RACA score for OHCA patients transported to an emergency department (ED). Methods: This was a retrospective analysis using prospectively collected data for OHCA patients resuscitated at an ED during 2015–2020. Adult patients with advanced airways inserted and available EtCO2 measurements were included. We used the EtCO2 values recorded in the ED for analysis. The primary outcome was ROSC. In the derivation cohort, we used multivariable logistic regression to develop the model. In the temporally split validation cohort, we assessed the discriminative performance of the EtCO2 + RACA score by the area under the receiver operating characteristic curve (AUC) and compared it with the RACA score using the DeLong test. Results: There were 530 and 228 patients in the derivation and validation cohorts, respectively. The median measurements of EtCO2 were 8.0 times (interquartile range [IQR] 3.0-12.0 times), with the median minimum EtCO2 of 15.5 millimeters of mercury (mm Hg) (IQR 8.0-26.0 mm Hg). The median RACA score was 36.4% (IQR 28.9-48.0%), and a total of 393 patients (51.8%) achieved ROSC. The EtCO2 + RACA score was validated with good discriminative performance (AUC, 0.82, 95% CI 0.77-0.88), outperforming the RACA score (AUC, 0.71, 95% CI 0.65-0.78) (DeLong test: P < 0.001). Conclusion: The EtCO2 + RACA score may facilitate the decision-making process regarding allocations of medical resources in EDs for OHCA resuscitation.

Abstract Type 2 innate lymphoid cells (ILC2s) perform vital functions in orchestrating humoral immune responses, facilitating tissue remodelling, and ensuring tissue homeostasis. Additionally, in a role that has garnered considerably less attention, ILC2s can also enhance Th1-related cytolytic T lymphocyte immune responses against tumours. Studies have thus far generally failed to address the mystery of how one ILC2 cell-type can participate in a multiplicity of functions. Here we utilized single cell RNA sequencing analysis to create the first comprehensive atlas of naïve and tumour-associated lung ILC2s and discover multiple unique subtypes of ILC2s equipped with developmental gene programs that become skewed during tumour expansion favouring inflammation, antigen processing, immunological memory and Th1-related anti-tumour CTL responses. The discovery of these new subtypes of ILC2s challenges current paradigms of ILC2 biology and provides an explanation for their diversity of function.

Abstract Background To date, most countries lifted the restriction requirement and coexisted with SARS-CoV-2. Thus, dietary behavior for preventing SARS-CoV-2 infection becomes an interesting issue on a daily basis. Coffee consumption is connected with reduced COVID-19 risk and correlated to COVID-19 severity. However, the mechanisms of coffee for the reduction of COVID-19 risk are still unclear. Results Here, we identified that coffee can inhibit multiple variants of the SARS-CoV-2 infection by restraining the binding of the SARS-CoV-2 spike protein to human angiotensin-converting enzyme 2 (ACE2), and reducing transmembrane serine protease 2 (TMPRSS2) and cathepsin L (CTSL) activity. Then, we used the method of "Here" (HRMS-exploring-recombination-examining) and found that isochlorogenic acid A, B, and C of coffee ingredients showed their potential to inhibit SARS-CoV-2 infection (inhibitory efficiency 43–54%). In addition, decaffeinated coffee still preserves inhibitory activity against SARS-CoV-2. Finally, in a human trial of 64 subjects, we identified that coffee consumption (approximately 1–2 cups/day) is sufficient to inhibit infection of multiple variants of SARS-CoV-2 entry, suggesting coffee could be a dietary strategy to prevent SARS-CoV2 infection. Conclusions This study verified moderate coffee consumption, including decaffeination, can provide a new guideline for the prevention of SARS-CoV-2. Based on the results, we also suggest a coffee-drinking plan for people to prevent infection in the post-COVID-19 era.

The continuous emergence of food additives and contaminants in edibles, especially confectioneries, demands advanced detection methods to ensure public health and safety. Vanillin (VAL) and sulfadiazine (SD) are of paramount concern due to their extensive application in various products. While VAL is favored for its flavoring attributes, SD, a common antibiotic, can inadvertently contaminate food items. For accurate and swift detection of these compounds, we introduced an innovative electrochemical sensor using cobalt oxide nanostructures. Notably, synthesizing these nanostructures through a green approach using glucose and starch is a significant advancement, offering both environmental benefits and enhanced material properties. The novelty of the material lies in its eco-friendly synthesis route and superior electrocatalytic performance. Preliminary results indicate a promising limit of detection (LOD) VAL= 0.003 µM & SD= 0.0055 µM and a broad linear range 0.02–209 µM emphasizing its potential for real-world food quality monitoring. This work, therefore, provides a crucial intersection of sustainable material synthesis and effective food contaminant detection, heralding a new era in food safety evaluation.