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oncology Oncology

Translational research and innovation in modern transplant practice: Paradigms from Greece and around the world.

In World journal of transplantation ; h5-index 22.0

The continuous clinical and technological advances, together with the social, health and economic challenges that the global population faces, have created an environment where the evolution of the field of transplantation is essentially necessary. The goal of this special issue is to provide a picture of the current status of transplantation in Greece as well as in many other countries in Europe and around the world. Authors from Greece and several other countries provide us with valuable insight into their respective areas of transplant expertise, with a main focus on the field of translational research and innovation. The papers that are part of this Special Issue "Translational Research and Innovation and the current status of Transplantation in Greece" have presented innovative and meaningful approaches in modern transplant research and practice. They provide us with a clear overview of the current landscape in transplantation, including liver transplantation in the context of a major pandemic, the evolution of living donor kidney transplantation or the evolution of the effect of hepatitis C virus infection in transplantation, while at the same time explore more recent challen ges, such as the issue of frailty in the transplant candidate and the changes brought by newer treatments, such as immunotherapy, in transplant oncology. Additionally, they offer us a glimpse of the effect that technological innovations, such as virtual reality, can have on transplantation, both in terms of clinical and educational aspects. Just as critical is the fact that this Special Issue emphasizes the multidisciplinary, collaborative efforts currently taking place that link transplant research and innovation with other cutting-edge disciplines such as bioengineering, advanced information technology and artificial intelligence. In this Special Issue, in addition to the clinical and research evolution of the field of transplantation, we are witnessing the importance of interdisciplinary collaboration in medicine.

Tsoulfas Georgios, Boletis Ioannis, Papalois Vassilios

2023-Feb-18

Artificial intelligence, Bioengineering, Immunosuppression, Immunotherapy, Liver transplantation, Living donor kidney transplantation, Non-alcoholic fatty liver disease, Pandemic, Translational research, Transplant oncology

General General

The role of the mass vaccination programme in combating the COVID-19 pandemic: An LSTM-based analysis of COVID-19 confirmed cases.

In Heliyon

The COVID-19 virus has impacted all facets of our lives. As a global response to this threat, vaccination programmes have been initiated and administered in numerous nations. The question remains, however, as to whether mass vaccination programmes result in a decrease in the number of confirmed COVID-19 cases. In this study, we aim to predict the future number of COVID-19 confirmed cases for the top ten countries with the highest number of vaccinations in the world. A well-known Deep Learning method for time series analysis, namely, the Long Short-Term Memory (LSTM) networks, is applied as the prediction method. Using three evaluation metrics, i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), we found that the model built by using LSTM networks could give a good prediction of the future number and trend of COVID-19 confirmed cases in the considered countries. Two different scenarios are employed, namely: 'All Time', which includes all historical data; and 'Before Vaccination', which excludes data collected after the mass vaccination programme began. The average MAPE scores for the 'All Time' and 'Before Vaccination' scenarios are 5.977% and 10.388%, respectively. Overall, the results show that the mass vaccination programme has a positive impact on decreasing and controlling the spread of the COVID-19 disease in those countries, as evidenced by decreasing future trends after the programme was implemented.

Hansun Seng, Charles Vincent, Gherman Tatiana

2023-Mar

COVID-19, Confirmed cases, Deep learning, LSTM, Mass vaccination

General General

Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task

ArXiv Preprint

Daily progress notes are common types in the electronic health record (EHR) where healthcare providers document the patient's daily progress and treatment plans. The EHR is designed to document all the care provided to patients, but it also enables note bloat with extraneous information that distracts from the diagnoses and treatment plans. Applications of natural language processing (NLP) in the EHR is a growing field with the majority of methods in information extraction. Few tasks use NLP methods for downstream diagnostic decision support. We introduced the 2022 National NLP Clinical Challenge (N2C2) Track 3: Progress Note Understanding - Assessment and Plan Reasoning as one step towards a new suite of tasks. The Assessment and Plan Reasoning task focuses on the most critical components of progress notes, Assessment and Plan subsections where health problems and diagnoses are contained. The goal of the task was to develop and evaluate NLP systems that automatically predict causal relations between the overall status of the patient contained in the Assessment section and its relation to each component of the Plan section which contains the diagnoses and treatment plans. The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes. We present the results of 2022 n2c2 Track 3 and provide a description of the data, evaluation, participation and system performance.

Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M Churpek, Ozlem Uzuner, Majid Afshar

2023-03-14

General General

Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer.

In Frontiers in oncology

Endometrial cancer (EC) is women's fourth most common malignant tumor. Neddylation plays a significant role in many diseases; however, the effect of neddylation and neddylation-related genes (NRGs) on EC is rarely reported. In this study, we first used MLN4924 to affect the activation of neddylation in different cell lines (Ishikawa and HEC-1-A) and determined the critical role of neddylation-related pathways for EC progression. Subsequently, we screened 17 prognostic NRGs based on expression files of the TCGA-UCEC cohort. Based on unsupervised consensus clustering analysis, patients with EC were classified into two neddylation patterns (C1 and C2). In terms of prognosis, substantial differences were observed between the two patterns. Compared with C2, C1 exhibited low levels of immune infiltration and promoted tumor progression. More importantly, based on the expression of 17 prognostic NRGs, we transformed nine machine-learning algorithms into 89 combinations. The random forest (RSF) was selected to construct the neddylation-related risk score according to the average C-index of different cohorts. Notably, our score had important clinical implications for EC. Patients with high scores have poor prognoses and a cold tumor state. In conclusion, neddylation-related patterns and scores can distinguish tumor microenvironment (TME) and prognosis and guide personalized treatment in patients with EC.

Li Yi, Niu Jiang-Hua, Wang Yan

2023

TME, endometrial cancer, machine learning, neddylation, prognosis

General General

Variation of Female Pronucleus Reveals Oocyte or Embryo Chromosomal Copy Number Variations.

In Advanced genetics (Hoboken, N.J.)

The characteristics of the human pronuclei (PNs), which exist 16-22 h after fertilization, appear to serve as good indicators to evaluate the quality of human oocyte and embryo, and may reflect the status of female and male chromosome composition. Here, a quantitative PN measurement method that is generated by applying expert experience combined with deep learning from large annotated datasets is reported. After mathematic reconstruction of PNs, significant differences are obtained in chromosome-normal rate and chromosomal small errors such as copy number variants by comparing the size of the reconstructive female PN. After integrating the whole procedure of PN dynamics and adjusting for errors that occur during PN identification, the results are robust. Notably, all positive prediction results are obtained from the female propositus population. Thus, the size of female PNs may mirror the internal quality of the chromosomal integrity of the oocyte. Embryos that develop from zygotes with larger female PNs may have a reduced risk of copy number variations.

Yang Jingwei, Wang Yikang, Li Chong, Han Wei, Liu Weiwei, Xiong Shun, Zhang Qi, Tong Keya, Huang Guoning, Zhang Xiaodong

2023-Mar

artificial intelligence, expert experience deep learning, mathematical models, pre‐implantation genetic tests, pronuclei identification

General General

The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia.

In Frontiers in digital health

OBJECTIVE : Chest radiographs are frequently used to diagnose community-acquired pneumonia (CAP) for children in the acute care setting. Natural language processing (NLP)-based tools may be incorporated into the electronic health record and combined with other clinical data to develop meaningful clinical decision support tools for this common pediatric infection. We sought to develop and internally validate NLP algorithms to identify pediatric chest radiograph (CXR) reports with pneumonia.

MATERIALS AND METHODS : We performed a retrospective study of encounters for patients from six pediatric hospitals over a 3-year period. We utilized six NLP techniques: word embedding, support vector machines, extreme gradient boosting (XGBoost), light gradient boosting machines Naïve Bayes and logistic regression. We evaluated their performance of each model from a validation sample of 1,350 chest radiographs developed as a stratified random sample of 35% admitted and 65% discharged patients when both using expert consensus and diagnosis codes.

RESULTS : Of 172,662 encounters in the derivation sample, 15.6% had a discharge diagnosis of pneumonia in a primary or secondary position. The median patient age in the derivation sample was 3.7 years (interquartile range, 1.4-9.5 years). In the validation sample, 185/1350 (13.8%) and 205/1350 (15.3%) were classified as pneumonia by content experts and by diagnosis codes, respectively. Compared to content experts, Naïve Bayes had the highest sensitivity (93.5%) and XGBoost had the highest F1 score (72.4). Compared to a diagnosis code of pneumonia, the highest sensitivity was again with the Naïve Bayes (80.1%), and the highest F1 score was with the support vector machine (53.0%).

CONCLUSION : NLP algorithms can accurately identify pediatric pneumonia from radiography reports. Following external validation and implementation into the electronic health record, these algorithms can facilitate clinical decision support and inform large database research.

Rixe Nancy, Frisch Adam, Wang Zhendong, Martin Judith M, Suresh Srinivasan, Florin Todd A, Ramgopal Sriram

2023

chest radiograph, clinical decision support, machine learning, natural language processing, pediatric, pneumonia