The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis

Chronic kidney disease (CKD) is recognized as an irreversible reduction of functional nephrons and leads to an increased risk of various pathological conditions, including cardiovascular disease and neurological disorders, such as coronary artery calcification, hypertension, and stroke. In addition, CKD patients have impaired immunity against bacteria and viruses. Conversely, kidney transplantation (KT) is performed for patients with end-stage renal disease as a renal replacement therapy. Although kidney function is almost normalized by KT, immunosuppressive therapy is essential to maintain kidney allograft function and to prevent rejection. However, these patients are more susceptible to infection due to the immunosuppressive therapy required to maintain kidney allograft function. Thus, both CKD and KT present disadvantages in terms of suppression of immune function. Periodontal disease is defined as a chronic infection and inflammation of oral and periodontal tissues. Periodontal disease is characterized by the destruction of connective tissues of the periodontium and alveolar bone, which may lead to not only local symptoms but also systemic diseases, such as cardiovascular diseases, diabetes, liver disease, chronic obstructive pulmonary disease, and several types of cancer. In addition, the prevalence and severity of periodontal disease are significantly associated with mortality. Many researchers pay special attention to the pathological roles and clinical impact of periodontal disease in patients with CKD or KT. In this review, we provide information regarding important modulators of periodontal disease to better understand the relationship between periodontal disease and CKD and/or KT. Furthermore; we evaluate the impact of periodontal disease on various pathological conditions in patients with CKD and KT. Moreover, pathogens of periodontal disease common to CKD and KT are also discussed. Finally, we examine the importance of periodontal care in these patients. Thus, this review provides a comprehensive overview of the pathological roles and clinical significance of periodontal disease in patients with CKD and KT.

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The Impact of Deceased Donor Liver Extraction Time on Early Allograft Function in Adult Liver Transplant Recipients. “Vite fait, bien fait.”

Transplantation Journal ◽

10.1097/tp.0000000000002724 ◽

2019 ◽

Vol 103 (8) ◽

pp. e218-e219 ◽

Cited By ~ 1

Author(s):

Raffaele Brustia ◽

Emmanuel Boleslawski ◽

Jerome Danion ◽

Eric Savier ◽

Benoit Barrou ◽

...

Keyword(s):

Liver Transplant ◽

Deceased Donor ◽

Extraction Time ◽

Transplant Recipients ◽

Adult Liver ◽

Donor Liver ◽

Allograft Function ◽

Liver Transplant Recipients ◽

The Impact

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Associations of Perfusate Biomarkers and Pump Parameters With Delayed Graft Function and Deceased Donor Kidney Allograft Function

American Journal of Transplantation ◽

10.1111/ajt.13655 ◽

2016 ◽

Vol 16 (5) ◽

pp. 1526-1539 ◽

Cited By ~ 41

Author(s):

C. R. Parikh ◽

I. E. Hall ◽

R. S. Bhangoo ◽

J. Ficek ◽

P. L. Abt ◽

...

Keyword(s):

Graft Function ◽

Deceased Donor ◽

Delayed Graft Function ◽

Donor Kidney ◽

Kidney Allograft ◽

Deceased Donor Kidney ◽

Allograft Function

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Machine Learning Analysis of Lifeguard Flag Decisions and Recorded Rescues

10.5194/nhess-2019-142 ◽

2019 ◽

Author(s):

Chris Houser ◽

Jacob Lehner ◽

Phil Wernette

Keyword(s):

Machine Learning ◽

The United States ◽

Machine Learning Techniques ◽

Public Health Issue ◽

Effective Strategies ◽

Wave Forcing ◽

Learning Techniques ◽

Yellow Flag ◽

The Impact ◽

Learning Analysis

Abstract. Rips currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags and signs are important and to varying degrees they are effective strategies to minimize risk. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag does not accurately reflect the potential risk, which may increase the potential for rescues or drownings. In this study, machine learning used to determine the potential for error in the flags used at Pensacola Beach, and the impact of that error on the number of rescues. A decision tree analysis suggests that the wrong flag was flown on ~ 35 % of days between 2004 and 2008 (n = 396/1125), and that those differences account for only 17 % of all rescue days and ~ 60 % of the total number of rescues. Further analysis reveals that the largest number of rescue days and total number of rescues is associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model would suggest a green flag would be more appropriate based on the wind and wave forcing. Regardless whether this is a result of the lifeguards being overly cautious or the rip and surf hazard is associated with weak rips forced by a transverse-bar and rip morphology, the results suggest that beach users are discounting the lifeguard warnings if it isn't consistent with how they perceive the surf hazard. Results suggest that machine learning techniques have the potential to support lifeguard and thereby reduce the number of rescues and drownings.

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The Impact of Deceased Donor Liver Extraction Time on Early Allograft Function in Adult Liver Transplant Recipients

Transplantation Journal ◽

10.1097/tp.0000000000002380 ◽

2018 ◽

Vol 102 (11) ◽

pp. e466-e471 ◽

Cited By ~ 4

Author(s):

Dieter Adelmann ◽

Garrett R. Roll ◽

Rishi Kothari ◽

Shareef Syed ◽

Lyle J. Burdine ◽

...

Keyword(s):

Liver Transplant ◽

Deceased Donor ◽

Extraction Time ◽

Transplant Recipients ◽

Adult Liver ◽

Donor Liver ◽

Allograft Function ◽

Liver Transplant Recipients ◽

The Impact

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Vitamin D and Folate as Predictors of MMSE in Alzheimer’s Disease: A Machine Learning Analysis

Diagnostics ◽

10.3390/diagnostics11060940 ◽

2021 ◽

Vol 11 (6) ◽

pp. 940

Author(s):

Giuseppe Murdaca ◽

Sara Banchero ◽

Alessandro Tonacci ◽

Alessio Nencioni ◽

Fiammetta Monacelli ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Vitamin D ◽

Folic Acid ◽

Machine Learning Approach ◽

Low Levels ◽

The Impact ◽

Learning Analysis

Vitamin D (VD) and micronutrients, including folic acid, are able to modulate both the innate and the adaptive immune responses. Low VD and folic acid levels appear to promote cognitive decline as in Alzheimer’s disease (AD). A machine learning approach was applied to analyze the impact of various compounds, drawn from the blood of AD patients, including VD and folic acid levels, on the Mini-Mental State Exam (MMSE) in a cohort of 108 patients with AD. The first analysis was aimed at predicting the MMSE at recruitment, whereas a second investigation sought to predict the MMSE after a 4 year follow-up. The simultaneous presence of low levels of VD and folic acid allow to predict MMSE, suggestive of poorer cognitive function. Such results suggest that the low levels of VD and folic acid could be associated with more severe cases of cognitive impairment in AD. It could be hypothesized that simultaneous supplementation of VD and folic acid could slow down the progression of cerebral degeneration at least in a subset of AD individuals.

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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