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We report discovery of a new bacterial genus and species of the family Pasteurellaceae by using phylogenetic and metabolic analysis. The bacterium, Emayella augustorita, was isolated from blood cultures of a patient in France diagnosed with an adenocarcinoma of the intestines and who was treated with a biliary prosthesis placement.

Abstract Background Traumatic shock is the leading cause of preventable death with most patients dying within the first six hours from arriving to the hospital. This underscores the importance of prehospital interventions, and growing evidence suggests prehospital transfusion improves survival. Optimizing transfusion triggers in the prehospital setting is key to improving outcomes for patients in hemorrhagic shock. Our objective was to identify factors associated with early in-hospital transfusion requirements available to prehospital clinicians in the field to develop a simple algorithm for prehospital transfusion, particularly for patients with occult shock. Methods We included trauma patients transported by a single critical care transport service to a level I trauma center between 2012 and 2019. We used logistic regression, Fast and Frugal Trees (FFTs), and Bayesian analysis to identify factors associated with early in-hospital blood transfusion as a potential trigger for prehospital transfusion. Results We included 2,157 patients transported from the scene or emergency department (ED) of whom 207 (9.60%) required blood transfusion within four hours of admission. The mean age was 47 (IQR = 28 – 62) and 1,480 (68.6%) patients were male. From 13 clinically relevant factors for early hospital transfusions, four were incorporated into the FFT in following order: 1) SBP, 2) prehospital lactate concentration, 3) Shock Index, 4) AIS of chest (sensitivity = 0.81, specificity = 0.71). The chosen thresholds were similar to conventional ones. Using conventional thresholds resulted in lower model sensitivity. Consistently, prehospital lactate was among most decisive factors of hospital transfusions identified by Bayesian analysis (OR = 2.31; 95% CI 1.55 – 3.37). Conclusions Using an ensemble of frequentist statistics, Bayesian analysis and machine learning, we developed a simple, clinically relevant prehospital algorithm to help identify patients requiring transfusion within 4 h of hospital arrival.

The climate effect from aviation's non‑CO2 emissions such as contrail cirrus, water vapor and nitrogen oxide induced ozone and methane changes depend on emission location and time. Among other approaches, the resulting climate effect can be reduced by lowering cruise flight levels. However, aircraft typically aim to fly at optimum altitudes and perform step climbs with increasing flight length to enhance fuel efficiency and reduce operating cost, what also limits climate effects from CO2 emissions. To account for this and to reduce the overall climate effect of flights, the higher fuel consumption at lower flight altitudes can be compensated by also reducing flight speeds. Therefore, this study analyzes the mitigation potential of flying lower and slower with regard to the overall climate effect along flight trajectories. Specifically, actually flown point profiles are combined with related meteorological parameters to evaluate the effect from reduced cruise altitudes and speeds with an updated set of prototype algorithmic climate change functions. Different case studies show varying effects for individual days during different seasons, and significant mitigation potentials due to flying lower and slower can be observed (up to 9 % on a summer day and 16 % on a winter day). A sensitivity study to explore uncertainties with regard to the quantification of contrail effects is performed as well as an investigation on possible economic consequences in terms of changes in direct operating cost and eco-efficient solutions.

Objectives: Nontuberculous mycobacteria (NTM) bone and joint infections (BJIs) are uncommon. We evaluated the characteristics of BJIs and identified differences according to immune status. Methods: We performed a multicenter retrospective study in France involving patients with documented NTM BJI over a 9-year period. We collected the clinical and microbiological characteristics, management, and clinical outcomes of the patients. Results: Overall, 95 patients were included, of whom 50.5% (48/95) were immunosuppressed. Tenosynovitis was more frequent in the immunocompetent group, and native arthritis more common in the immunosuppressed group. Mycobacerium marinum and M. abscessus complex were significantly more frequent in the immunocompetent group, and M. avium and M. xenopi were significantly more frequent in the immunosuppressed group. The combination of antibiotherapy with surgery tended to be more frequent in the immunocompetent than the immunosuppressed group (63.8% (30/47) vs 47.8% (22/46), respectively); of the latter, 45.7% (21/46) received antimicrobial therapy alone, a higher frequency than in the immunocompetent group (23.4%, 11/47). The median duration of antimicrobial treatment was similar in the two groups (11 months). Mortality was significantly higher in the immunosuppressed group. Conclusions: Although the clinical presentations and the NTM species involved in BJI differed according to immune status, most recovered completely after treatment.

IntroductionClinical reasoning in veterinary medicine is often based on clinicians’ personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures.Materials and methodsDogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features.ResultsA total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year.ConclusionMachine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.

We assessed the feasibility of implementing a virtually guided Neuromuscular Electrical Stimulation (NMES) protocol over the tibialis anterior (TA) muscle while collecting heart rate (HR), Numeric Pain Rating Scale (NPRS), and quality of contraction (QoC) data. We investigated if HR, NPRS, and QoC differ ON and OFF the TA motor point and explored potential relationships between heart rate variability (HRV) and the NPRS. Twelve healthy adults participated in this cross-sectional study. Three NMES trials were delivered ON and OFF the TA motor point. HR, QoC, and NPRS data were collected. There was no significant difference in HRV ON and OFF the motor point (p > 0.05). The NPRS was significantly greater OFF the motor point (p < 0.05). The QoC was significantly different between motor point configurations (p < 0.05). There was no correlation between the NPRS and HRV (p > 0.05, r = −0.129). We recommend non-electrical methods of measuring muscle activity for future studies. The NPRS and QoC can be administered virtually. Time-domain HRV measures could increase the validity of the protocol. The variables should be explored further virtually to enhance the protocol before eventual ICU studies.

Gastric cancer (GC) is the second most common cause of death of any cancer-related cases in the world, and is also in the top 5 most common malignancy cancers in general. There are plenty of well-distributed treatments, offering better hygiene, more robust and complete nutrition, and the eradication of pathogens such as Helicobacter pylori. Currently, there is still the need for more treatments, especially those of lower cost, like those coming from already easily available products. Quercetin (QRC) is a natural phenolic compound present in a wide variety of products, e.g., in plants like Hibiscus sabdariffa, onions, grapes, broccoli, and citrus fruits. This product has been shown to have great potential therapeutic effects, and it has also been suggested that it could be useful in combating different types of cancer; however, information regarding the targets or mechanisms that QRC has on cancer cells is still unclear. Therefore, this study aims to identify the targets that QRC has, like anti-cancer treatment for GC using different bio-informatic tools and databases. From MalaCards and SwissTargetPrediction, both QRC and GC molecular targets were defined, and then they were matched with the Venny 2.1.0 platform. From this, 31 genes were gathered, and then they were analyzed using the ShinnyGo0.77 and DAVID-Bioinformatic Resources. Furthermore, StringDB was used to identify the protein—protein interactions, and Citoscape 3.6.0 12 hub genes were obtained. Those hub genes were then subject to Gene Expression Profiling Interactive Analysis and TISIDB. Finally, molecular docking studies were performed using the SwissDock database. The results suggest that, according to the gene ontology data, QRC has a relationship with the regulation of cell death, response to stress, cell motility, response to amyloid-beta, cellular response to reactive oxygen species, and apoptotic processes. Some genes like EGFR were correlated with an abundance of CD8 and Neutrophil infiltration but didn’t show to improve the survival rate. Furthermore, molecular docking results show that QRC can bind to multiple molecules of interest. These results complement some of the currently available information alluding to the effectiveness of plants rich with QRC as part of the treatment used for different kinds of cancer, but it also suggests a plethora of new targets that this molecule has in GC, while at the same time giving a clearer idea of the mechanisms that are affected in GC by QRC. However, as with any other study that primarily uses bioinformatic tools, these final results are to be used for more direct and precise research, especially if experimental protocols are used.

The utilization of food as a therapeutic measure for various ailments has been a prevalent practice throughout history and across different cultures. This is exemplified in societies where substances like Hibiscus sabdariffa have been employed to manage health conditions like hypertension and elevated blood glucose levels. The inherent bioactive compounds found in this plant, namely, delphinidin-3-sambubioside (DS3), quercetin (QRC), and hibiscus acid (HA), have been linked to various health benefits. Despite receiving individual attention, the specific molecular targets for these compounds remain unclear. In this study, computational analysis was conducted using bioinformatics tools such as Swiss Target Prediction, ShinnyGo 0.77, KEGG, and Stringdb to identify the molecular targets, pathways, and hub genes. Supplementary results were obtained through a thorough literature search in PubMed. DS3 analysis revealed potential genetic alterations related to the metabolism of nitrogen and glucose, inflammation, angiogenesis, and cell proliferation, particularly impacting the PI3K-AKT signaling pathway. QRC analysis demonstrated interconnected targets spanning multiple pathways, with some overlap with DS3 analysis and a particular focus on pathways related to cancer. HA analysis revealed distinct targets, especially those associated with pathways related to the nervous system. These findings emphasize the necessity for focused research on the molecular effects of DS3, QRC, and HA, thereby providing valuable insights into potential therapeutic pathways.

Corporations significantly influence the public and political spheres. In light of this corporate power in society, academics have criticized the lack of legitimization (i.e., the legitimacy gap) and highlighted a potential divergence between corporate resource allocation and the needs and preferences of the public (i.e., the social issues gap). To address these problems, democratizing organizations has been proposed as a potential solution. In line with this, the authors argue that an increase in corporate power outside the economic realm should be counterbalanced by more democratic corporate governance (i.e., an internalization of democracy). While important groundwork exists, academic attention to these topics remains limited. The authors provide an overview of the literature linking corporate power and the claim for democratizing organizations, advocate for legitimization, identify research gaps, and develop a research agenda to inspire and guide future research efforts. The paper's findings suggest the importance of conceptual and empirical research on the micro‐, meso‐, and macro‐levels and the cross‐level effects, which is needed to advance this incipient research area. Future research must (1) clearly define corporate power and discuss how democratizing organizations can contribute to more legitimate organizational decisions, (2) unveil novel approaches for more democratic corporate governance, and (3) understand the organizational‐ and individual‐level factors necessary for the successful realization of democratic organizations. [ABSTRACT FROM AUTHOR]

The utilization of food as a therapeutic measure for various ailments has been a prevalent practice throughout history and across different cultures. This is exemplified in societies where substances like Hibiscus sabdariffa have been employed to manage health conditions like hypertension and elevated blood glucose levels. The inherent bioactive compounds found in this plant, namely, delphinidin-3-sambubioside (DS3), quercetin (QRC), and hibiscus acid (HA), have been linked to various health benefits. Despite receiving individual attention, the specific molecular targets for these compounds remain unclear. In this study, computational analysis was conducted using bioinformatics tools such as Swiss Target Prediction, ShinnyGo 0.77, KEGG, and Stringdb to identify the molecular targets, pathways, and hub genes. Supplementary results were obtained through a thorough literature search in PubMed. DS3 analysis revealed potential genetic alterations related to the metabolism of nitrogen and glucose, inflammation, angiogenesis, and cell proliferation, particularly impacting the PI3K-AKT signaling pathway. QRC analysis demonstrated interconnected targets spanning multiple pathways, with some overlap with DS3 analysis and a particular focus on pathways related to cancer. HA analysis revealed distinct targets, especially those associated with pathways related to the nervous system. These findings emphasize the necessity for focused research on the molecular effects of DS3, QRC, and HA, thereby providing valuable insights into potential therapeutic pathways. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is the second most common cause of death of any cancer-related cases in the world, and is also in the top 5 most common malignancy cancers in general. There are plenty of well-distributed treatments, offering better hygiene, more robust and complete nutrition, and the eradication of pathogens such as Helicobacter pylori. Currently, there is still the need for more treatments, especially those of lower cost, like those coming from already easily available products. Quercetin (QRC) is a natural phenolic compound present in a wide variety of products, e.g., in plants like Hibiscus sabdariffa, onions, grapes, broccoli, and citrus fruits. This product has been shown to have great potential therapeutic effects, and it has also been suggested that it could be useful in combating different types of cancer; however, information regarding the targets or mechanisms that QRC has on cancer cells is still unclear. Therefore, this study aims to identify the targets that QRC has, like anti-cancer treatment for GC using different bio-informatic tools and databases. From MalaCards and SwissTargetPrediction, both QRC and GC molecular targets were defined, and then they were matched with the Venny 2.1.0 platform. From this, 31 genes were gathered, and then they were analyzed using the ShinnyGo0.77 and DAVID-Bioinformatic Resources. Furthermore, StringDB was used to identify the protein—protein interactions, and Citoscape 3.6.0 12 hub genes were obtained. Those hub genes were then subject to Gene Expression Profiling Interactive Analysis and TISIDB. Finally, molecular docking studies were performed using the SwissDock database. The results suggest that, according to the gene ontology data, QRC has a relationship with the regulation of cell death, response to stress, cell motility, response to amyloid-beta, cellular response to reactive oxygen species, and apoptotic processes. Some genes like EGFR were correlated with an abundance of CD8 and Neutrophil infiltration but didn't show to improve the survival rate. Furthermore, molecular docking results show that QRC can bind to multiple molecules of interest. These results complement some of the currently available information alluding to the effectiveness of plants rich with QRC as part of the treatment used for different kinds of cancer, but it also suggests a plethora of new targets that this molecule has in GC, while at the same time giving a clearer idea of the mechanisms that are affected in GC by QRC. However, as with any other study that primarily uses bioinformatic tools, these final results are to be used for more direct and precise research, especially if experimental protocols are used. [ABSTRACT FROM AUTHOR]