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Incidence and Survivability of Acute Lymphocytic Leukemia Patients in the United States: Analysis of SEER Data Set from 2000-2019

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Journal of Cancer Therapy 2024年第4期15卷 141-163页

作者： Ishan Ghosh Sudipto Mukherjee Department of Biology Rockhurst University Kansas City Missouri USA Hematology and Medical Oncology Cleveland Clinic Cleveland Ohio USA

The main goal of this research is to assess the impact of race, age at diagnosis, sex, and phenotype on the incidence and survivability of acute lymphocytic leukemia (ALL) among patients in the United States. By taking these factors into account, the study aims to explore how existing cancer registry data can aid in the early detection and effective treatment of ALL in patients. Our hypothesis was that statistically significant correlations exist between race, age at which patients were diagnosed, sex, and phenotype of the ALL patients, and their rate of incidence and survivability data were evaluated using SEER*Stat statistical software from National Cancer Institute. Analysis of the incidence data revealed that a higher prevalence of ALL was among the Caucasian population. The majority of ALL cases (59%) occurred in patients aged between 0 to 19 years at the time of diagnosis, and 56% of the affected individuals were male. The B-cell phenotype was predominantly associated with ALL cases (73%). When analyzing survivability data, it was observed that the 5-year survival rates slightly exceeded the 10-year survival rates for the respective demographics. Survivability rates of African Americans patients were the lowest compared to Caucasian, Asian, Pacific Islanders, Alaskan Native, Native Americans and others. Survivability rates progressively decreased for older patients. Moreover, this study investigated the typical treatment methods applied to ALL patients, mainly comprising chemotherapy, with occasional supplementation of radiation therapy as required. The study demonstrated the considerable efficacy of chemotherapy in enhancing patients’ chances of survival, while those who remained untreated faced a less favorable prognosis from the disease. Although a significant amount of data and information exists, this study can help doctors in the future by diagnosing patients with certain characteristics. It will further assist the health care professionals in screening

关键词： Acute Lymphocytic Leukemia Survivability Incidence Demography SEER Data Set

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Exploring emerald global geochemical provenance through fingerprinting and machine learning methods

Artificial Intelligence in Geosciences

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Artificial Intelligence in Geosciences 2024年第1期5卷 202-219页

作者： Raquel Alonso-Perez James M.D.Day D.Graham Pearson Yan Luo Manuel A.Palacios Raju Sudhakar Aaron Palke Earth and Planetary Science Department Harvard UniversityCambridgeMA02138USA Scripps Institution of Oceanography University of California San DiegoLa JollaCA92037USA university of Alberta EdmontonABT6G 2R3Canada Eigenvector Research Inc.Manson WA98831USA rockhurst university Kansas CityMO64110USA Gemological Institute of America CarlsbadCA92008USA

Emeralds-the green colored variety of beryl-occur as gem-quality specimens in over fifty deposits *** digital traceability methods for emerald have limitations,sample-based approaches offer robust alterna-tives,particularly for determining the geographic origin of *** factors make emerald suitable for provenance studies and hence for developing models for origin ***,the diverse elemental chemistry of emerald at minor(<1 wt%)and trace levels(<1 to 100’s ppmw)exhibits unique inter-element fractionations between global ***,minimally destructive techniques,including laser ablation inductively coupled plasma mass spectrometry(LA-ICP-MS),enable measurement of these diagnostic elemental ***,when applied to extensive datasets,machine learning(ML)techniques enable the creation of predictive models and statistical discrimination with adequate characterization of the *** study em-ploys a carefully selected dataset comprising more than 1000 LA-ICP-MS analyses of gem-quality emeralds,enriched with new *** dataset represents the largest available for global emerald *** con-ducted unsupervised exploratory analysis using Principal Component Analysis(PCA).For machine learning-based classification,we employed Support Vector Machine Classification(SVM-C),achieving an initial accu-racy rate of 79%.This was enhanced to 96.8%through the use of hierarchical SVM-C with PCA filters as our modeling *** ML models were trained using the concentrations of eight statistically significant ele-ments(Li,V,Cr,Fe,Sc,Ga,Rb,Cs).By leveraging high-quality LA-ICP-MS data and ML techniques,accurate identification of the geographical origin of emerald becomes *** models are important for accurate provenance of emerald,and from a geochemical perspective,for understanding the formation environments of beryl-bearing pegmatites and shales.

关键词： Beryl Provenance LA-ICP-MS Machine learning Multivariate analysis Trace elements

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