FunHoMic: A Marie Skłodowska-Curie Innovative Training Network for the study of the Fungus-Host-Microbiota interplay

Introduction The FunHoMic project is a Marie Skłodowska-Curie Innovative Training Network comprising 13 PhD students, 8 academic partners and 3 industry partnersaimingto understand the interplay between fungi, hostsand microbiota to improve prevention and treatment of fungal infections. Importance About 2 billion people suffer fungal infections, which have a mortality rate close to that of malaria or breast cancer. Candida albicans has a high clinical and economic burden, making it of particular interest to the FunHoMic project. 70% of women experience at least one episode of vulvovaginal candidiasis (“thrush”) during their lifetime; 8% suffer recurring infections. C. albicans may live as a commensal but can cause symptoms when the fungus-host-microbiota equilibrium is disrupted. Infections by C. albicans have a significant clinical impact, with fatalities in severe cases. Many factors are associated with C. albicans infections; intensive care, neutropenic and diabetic patients are most at risk of systemic infection. Rising antifungal drug resistance has led to certain C. albicans infections having no treatment option. Aim The FunHoMic consortium combines projectson fungal pathogenesis, immunology, microbial ecology and’omics technologies to understand and exploit interactions between fungus, host and microbiota. Identification of novel bio markers on the fungal side such as genetic polymorphisms or on the host side such asmicrobiota profiles, metabolites and/or immune markers can lead to patient classification based on relative risk of infection. This could be the beginning of personalised management for fungal infections using preventive or therapeutic interventions like new antifungals, immune modulators or Live Biotherapeutic Products (LBPs). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the MarieSklodowska-Curie grant agreement No 812969.

Effect of problem-based learning on severity classification agreement by triage nurses

Background Differences in the classification results among triage nurses in the emergency room can be improved by training or applying an algorithm. This study aimed to confirm whether the agreement among triage nurses could be improved through learner-led problem-based learning. Methods This study had a single-group time series design to investigate the effect of problem-based learning led by triage nurses on the agreement of Korean Triage and Acuity Scale classification results for patients who visited the emergency department. We extracted 300 patients each in May and August 2018 before learning began and 300 patients each in May and August 2019 after learning. Results After problem-based learning was applied, the self-efficacy of triage nurses for emergency patient classification increased statistically significantly compared to before learning (7.88 ± 0.96, p < .001), and the weighted kappa coefficient was also found to be almost perfectly agreement (0.835, p < .001). Conclusions In this study, problem-based learning improved the inter-rater agreement of Korean Triage and Acuity Scale classification results and self-efficacy of triage nurses. Therefore, problem-based learning can contribute to patient safety in the emergency department by enhancing the expertise of triage nurses and increasing the accuracy of triage classification.

Comparison of D-Dimer Level on Mild, Moderate and Severe COVID-19 at Immanuel Hospital Bandung City October 1 st - December 31 st, 2020

Background: Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). In COVID-19 there is a process of thrombosis and coagulopathy, which are systemic inflammation and endothelial disorders resulting in hypercoagulability. D-dimer is a laboratory test that can determine coagulation activation (hypercoagulability) and fibrinolysis. D-dimer can be used as a guide of anticoagulant and thrombolytic therapy and prognostic parameter in COVID-19 patients, where its value runs parallel to the severity of the disease and is associated with increased mortality. Materials and methods: The whole sampling was taken from 59 medical records of inpatients confirmed with COVID-19 through the RT-PCR examination at Immanuel Hospital, Bandung City for the period of October 1 st - December 31 st, 2020. The value of d-dimer was taken from whole blood, tested with sandwich ELISA method with cut-off value (normal value) &lt; 0.5μg/ml. COVID-19 patient classification was based on the COVID-19 guideline from Indonesian Ministry of Health. The research method was observational analytical with cross-sectional design. Statistical test used was Kruskal-Wallis test with Mann-Whitney advanced test (α = 0.05). Results: The mean d-dimer value in patients with mild, moderate, and severe COVID-19 was 0.3034 µg/mL; 0.5138 µg/mL; and 1.1751 µg/mL. The results of Kruskal-Wallis test showed a very significant difference in mean d-dimer values in mild, moderate, and severe COVID-19 patients. Mann-Whitney test showed that there was a very significant difference in the mean d-dimer value between mild and severe COVID-19 patients, also between moderate and severe COVID-19 patients with a value of p = 0.000 (p &lt;0.01). But there was no difference in the mean d-dimer value between mild and moderate COVID-19 patients, p = 0.454 (p&gt; 0.05). Conclusion: There is a very significant difference in d-dimer values between mild, moderate and severe COVID-19 patients. The d-dimer value increases with the severity of COVID-19. Keywords: COVID-19, mild COVID-19, moderate COVID-19, severe COVID-19, d-dimer Disclosures No relevant conflicts of interest to declare.

Developing a Nursing Needs Assessment Scale and Patient Classification System based on Nursing Activities in Comprehensive Nursing Care Units

Purpose: This study was done to develop and validate a scale for assessing nursing needs on comprehensive nursing care units and to derive a patient classification system based on nursing needs.Methods: In this methodological study, the initial items were identified through a review of the literature and surveys from nursing staff regarding the nursing needs on comprehensive nursing care units. Content validity was evaluated by nine nursing staff members from comprehensive nursing care units. To evaluate the concurrent validity and derive a patient classification system, nursing needs scores, perceived nursing needs and perceived patient severity for 216 patient cases were evaluated by five nurses. These data were analyzed using Pearson‘s correlation coefficients, one-way ANOVA with Scheffépost hoc tests and K-means clustering.Results: After evaluating content validity, the developed scale contained 64 activities in two domains: nursing intervention and assistance of daily living. Concurrent validity was verified by analyzing the differences in the nursing needs scores according to each group of perceived nursing needs and severity (p<.001) and by analyzing the correlation between the score of the developed scale and the National Health Insurance Service nursing need assessment scale (r=.68, p<.001). Based on the score of the developed scale, a patient classification system that classified nursing needs into four stages was derived.Conclusion: The developed scale represented nursing activities in comprehensive nursing care units. It also provided specific data regarding the time spent on nursing activities. Therefore, it is expected to contribute toward establishing appropriate nurse staffing strategies to provide quality patient care.

A High Accuracy Electrographic Seizure Classifier Trained Using Semi-Supervised Labeling Applied to a Large Spectrogram Dataset

The objective of this study was to explore using ECoG spectrogram images for training reliable cross-patient electrographic seizure classifiers, and to characterize the classifiers’ test accuracy as a function of amount of training data. ECoG channels in ∼138,000 time-series ECoG records from 113 patients were converted to RGB spectrogram images. Using an unsupervised spectrogram image clustering technique, manual labeling of 138,000 ECoG records (each with up to 4 ECoG channels) was completed in 320 h, which is an estimated 5 times faster than manual labeling without ECoG clustering. For training supervised classifier models, five random folds of data were created; with each fold containing 72, 18, and 23 patients’ data for model training, validation and testing respectively. Five convolutional neural network (CNN) architectures, including two with residual connections, were trained. Cross-patient classification accuracies and F1 scores improved with model complexity, with the shallowest 6-layer model (with ∼1.5 million trainable parameters) producing a class-balanced seizure/non-seizure classification accuracy of 87.9% on ECoG channels and the deepest ResNet50-based model (with ∼23.5 million trainable parameters) producing a classification accuracy of 95.7%. The trained ResNet50-based model additionally had 93.5% agreement in scores with an independent expert labeller. Visual inspection of gradient-based saliency maps confirmed that the models’ classifications were based on relevant portions of the spectrogram images. Further, by repeating training experiments with data from varying number of patients, it was found that ECoG spectrogram images from just 10 patients were sufficient to train ResNet50-based models with 88% cross-patient accuracy, while at least 30 patients’ data was required to produce cross-patient classification accuracies of &gt;90%.

Clinical symptoms, underlying pathogenesis, and the prospect of tailored therapies have all benefited from genetic discoveries in Parkinson's disease

Clinical symptoms, underlying pathogenesis, and the prospect of tailored therapies have all benefited from genetic discoveries in Parkinson's disease.Even as our understanding of disease biology improves, there are still knowledge gaps that must be filled in the future. Reliable biomarkers that uniquely recapitulate pathophysiological aspects are necessary for patient classification and medication response tracking. Genetic testing is essential in 'idiopathic' or 'sporadic' PD patients to identify those who would benefit from genotype-driven treatment. Genotype-dependent segmentation of research participants will broaden the possible usefulness of targeted treatments. Biomarker-assisted clinical trials will benefit tremendously from new adaptable designs. Recent breakthroughs in genotype-driven therapy, on the other hand, should deliver considerable benefits for Parkinson's patients in the medium term and lead to the development of the first disease-modifying drugs.

Calculation of the Optimal Number of Nurses Based on Nursing Intensity by Patient Classification Groups in General Units in South Korea: a Cross-sectional Study

Background: Calculating the accurate number of nursing personnel based on a patient classification system that clearly reflects the nursing needs of patients is a problem directly related to the nursing unit’s budget management, productivity, etc. This study aimed to calculate the total daily nursing workload and the optimal number of nurses per general unit based on the nursing intensity and direct nursing time per inpatient through patient classification.Methods: Three units at one general hospital were investigated. To calculate nursing intensity, patient classification according to nursing needs was performed for over 10 days in each unit in September 2018. The direct and non-direct nursing time and nursing intensity scores were analyzed using descriptive statistics (e.g. frequency, percentage, and average) generated using Microsoft Excel.Results: For the internal medicine unit, the average direct nursing time per patient was 1.0, 1.5, 2.2, and 2.9 hours for Groups 1, 2, 3 and 4, respectively. For the surgical unit, the average direct nursing time per patient was 0.9, 1.4, 2.1, and 2.6 hours for Groups 1, 2, 3, and 4, respectively. For the comprehensive nursing care unit, the average direct nursing time per patient was 0.8, 1.2, 1.7, and 2.2 hours for Groups 1, 2, 3, and 4. The optimal number of nurses was 25 in the internal medicine unit, 37 in the surgical unit, and 22 in the comprehensive nursing unit. There was a shortage of five nurses in the internal medicine unit and nine in the surgical unit.Conclusion: Based on the nursing time according to patient classification groups, this study confirmed that the optimal number of nurses cannot be secured and that the nursing intensity is very high. The results of this study suggest that long-term efforts, such as improving the nursing environment, should be made to secure an optimal number of nurses in various hospital nursing units.