Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees.

In Heliyon

BACKGROUND AND OBJECTIVE : A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis.

MATERIAL AND METHOD : This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease.

RESULTS : Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients.

CONCLUSIONS : This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Huyut Mehmet Tahir, Huyut Zübeyir

2023-Mar

Artificial intelligence, CHAID decision Trees, COVID-19, Coagulation tests, Ferritin, Immunological tests, Machine learning, Mortality risk biomarkers

Radiology Radiology

Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential

ArXiv Preprint

The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on using ChatGPT to translate radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest CT lung cancer screening scans and 76 brain MRI metastases screening scans were collected in the first half of February for this study. According to the evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.1 in the five-point system with 0.07 places of information missing and 0.11 places of misinformation. In terms of the suggestions provided by ChatGPT, they are general relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with a newly released large model GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential.

Qing Lyu, Josh Tan, Mike E. Zapadka, Janardhana Ponnatapuram, Chuang Niu, Ge Wang, Christopher T. Whitlow

2023-03-16

General General

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.

In The Plant journal : for cell and molecular biology

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model, (ii) a Domain2vec encoding-based multiple layer perceptron (MLP) model, and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared to existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and datasets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

Zheng Jingyan, Yang Xiaodi, Huang Yan, Yang Shiping, Wuchty Stefan, Zhang Ziding

2023-Mar-14

Arabidopsis thaliana, GO annotation, deep learning, domain, prediction, protein-protein interaction

Ophthalmology Ophthalmology

The Association of Retinal age gap with metabolic syndrome and inflammation.

In Journal of diabetes

BACKGROUND : Metabolic syndrome (MetS) is a clustering of cardiometabolic components, posing tremendous burdens in the aging society. Retinal age gap has been proposed as a robust biomarker associated with mortality and Parkinson's disease. Although MetS and chronic inflammation could accelerate the aging process and increase the risk of mortality, the association of the retinal age gap with MetS and inflammation has not been examined yet.

METHODS : Retinal age gap (retina-predicted age minus chronological age) was calculated using a deep learning model. MetS was defined as the presence of three or more of the following: central obesity, hypertension, dyslipidemia, hypertriglyceridemia, and hyperglycemia. Inflammation index was defined as a high-sensitivity C-reactive protein level above 3.0 mg/L. Logistic regression models were used to examine the associations of retinal age gaps with MetS and inflammation.

RESULTS : We found that retinal age gap was significantly associated with MetS and inflammation. Specifically, compared to participants with retinal age gaps in the lowest quartile, the risk of MetS was significantly increased by 10% and 14% for participants with retinal age gaps in the third and fourth quartile (odds ratio [OR]:1.10; 95% confidence interval [CI], 1.01,1.21;, p = .030; OR: 1.14, 95% CI, 1.03,1.26; p = .012, respectively). Similar trends were identified for the risk of inflammation and combined MetS and inflammation.

CONCLUSION : We found that retinal age gaps were significantly associated with MetS as well as inflammation. Given the noninvasive and cost-effective nature and the efficacy of the retinal age gap, it has great potential to be used as a screening tool for MetS in large populations.

Zhu Zhuoting, Liu Dan, Chen Ruiye, Hu Wenyi, Liao Huan, Kiburg Katerina, Ha Jason, He Shuang, Shang Xianwen, Huang Yu, Wang Wei, Yu Honghua, Yang Xiaohong, He Mingguang

2023-Mar-14

inflammation, metabolic syndrome, retinal age gap

Cardiology Cardiology

Identification of key immune-related genes and immune infiltration in diabetic nephropathy based on machine learning algorithms.

In IET systems biology

BACKGROUND : Diabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology.

METHODS : The GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls.

RESULTS : In total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people.

CONCLUSION : Immune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.

Sun Yue, Dai Weiran, He Wenwen

2023-Mar-14

CIBERSORT, biomarker, diabetic nephropathy, immune infiltration

General General

Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm.

In Financial innovation

The study aims to investigate the financial technology (FinTech) factors influencing Chinese banking performance. Financial expectations and global realities may be changed by FinTech's multidimensional scope, which is lacking in the traditional financial sector. The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization. The future of FinTech will be shaped by technologies like the Internet of Things, blockchain, and artificial intelligence. The involvement of these platforms in financial services is a major concern for global business growth. FinTech is becoming more popular with customers because of such benefits. FinTech has driven a fundamental change within the financial services industry, placing the client at the center of everything. Protection has become a primary focus since data are a component of FinTech transactions. The task of consolidating research reports for consensus is very manual, as there is no standardized format. Although existing research has proposed certain methods, they have certain drawbacks in FinTech payment systems (including cryptocurrencies), credit markets (including peer-to-peer lending), and insurance systems. This paper implements blockchain-based financial technology for the banking sector to overcome these transition issues. In this study, we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors' algorithm. The chaotic improved foraging optimization algorithm is used to optimize the proposed method. The rolling window autoregressive lag modeling approach analyzes FinTech growth. The proposed algorithm is compared with existing approaches to demonstrate its efficiency. The findings showed that it achieved 91% accuracy, 90% privacy, 96% robustness, and 25% cyber-risk performance. Compared with traditional approaches, the recommended strategy will be more convenient, safe, and effective in the transition period.

Rjoub Husam, Adebayo Tomiwa Sunday, Kirikkaleli Dervis

2023

Adaptive neural fuzzy based KNN algorithm, Blockchain technology, Economic growth, FinTech, Rolling window autoregressive lag modelling