The aim of this prospective, long-term study was to define the flow cytometric characteristics of plasma cell CD56 expression as well as determine the clinical characteristics of 204 multiple myeloma (MM) patients and 26 plasma cell leukemia (PCL) patients with regard to CD56 expression. CD56 expression intensity was determined by measurement of antigen molecules on the cell defined as Antibodies Bound per Cell (ABC) and calculation of Relative Fluorescence Intensity (RFI). CD56 expression was found in 66% of MM and 54% of PCL cases. The RFI values for individual MM patients ranged from 7.6 to 27.4 while ABC values on MM plasma cells from 2255 to 58469. There was a correlation between the proportion of all bone marrow CD38(++)/CD138(+) cells with CD56 expression and ABC and RFI indices. With regard to CD56 expression positive patients, the CD56(-) MM patients presented lower frequency of osteolysis (p = 0.01). The median survival was 48 months in CD56(+) patients and 43 months (p = 0.84) in CD56(-) cases. In conclusion, CD56 expression carries no distinct adverse prognosis and the lack of CD56 expression does not define a unique clinicopathological or prognostic entity in MM. A remarkable heterogeneity of CD56 expression intensity in CD56(+) patients imposes the necessity of determining CD56 expression intensity in candidates to antibody-based therapy.

Clinicopathological correlates of plasma cell CD56 (NCAM) expression in multiple myeloma

Background
Extraosseous plasmacytoma, so called extramedullary plasmacytoma (EMP) is relatively rare in China. The aim was investigate the clinicopathologic features of EMP and the role of Immunophenotype and genotype detection in diagnosis of EMP.

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Extraosseous (extramedullary) plasmacytomas: a clinicopathologic and immunophenotypic study of 32 Chinese cases

ABSTRACT In this research, the mean monthly groundwater level with a range of 3.78 m in Qoşaçay plain, Iran, is forecast. Regarding three different layers of gated recurrent unit (GRU) structures and a hybrid of variational mode decomposition with gated recurrent unit (VMD-GRU), deep learning-based neural network models are developed. As the base model for performance comparison, the general single-long short-term memory-layer network model is developed. In all models, the module of sequence-to-one is used because of the lack of meteorological variables recorded in the study area. For modeling, 216 monthly datasets of the mean monthly water table depth of 33 different monitoring piezometers in the period April 2002–March 2020 are utilized. To boost the performance of the models and reduce the overfitting problem, an algorithm tuning process using different types of hyperparameter accompanied by a trial-and-error procedure is applied. Based on performance evaluation metrics, the total learnable parameters value and especially the model grading process, the new double-GRU model coupled with multiplication layer (×) (GRU2× model) is chosen as the best model. Under the optimal hyperparameters, the GRU2× model results in an R 2 of 0.86, a root mean square error (RMSE) of 0.18 m, a corrected Akaike’s information criterion (AICc) of −280.75, a running time for model training of 87 s and a total grade (TG) of 6.21 in the validation stage; and the hybrid VMD-GRU model yields an RMSE of 0.16 m, an R 2 of 0.92, an AICc of −310.52, a running time of 185 s and a TG of 3.34.

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2022.2104928?needAccess=true

Time series-based groundwater level forecasting using gated recurrent unit deep neural networks

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.

https://www.mdpi.com/2073-4441/15/4/694/pdf?version=1676012925

Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study

With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.

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Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

Groundwater level prediction using machine learning models: A comprehensive review

Monoclonal gammopathy of undetermined significance does not overexpress cluster of differentiation (CD) 56, but plasma cell myeloma frequently overexpressed it. However, plasma cell leukemia and extramedullary plasmacytoma usually down-regulate CD56 expression. Plasmacytoma, especially 'solitary plasmacytoma of bone', is difficult to diagnose as plasma cell neoplasm, because it occasionally appears similar to other bone tumors, both clinically and pathologically, and is rarely accompanied by monoclonal protein in the serum or urine. The present case was a patient with an osteolytic 'small round cell tumor' of the iliac bone, which also invaded the femora. An immunohistopathological finding of CD56 expression played a key role in making a diagnosis. The definitive diagnosis of plasmacytoma was made based on the electron microscopic findings. The plasma cells which infiltrated her sternum showed the same restriction to kappa light chain expression in their cytoplasms as that of the iliac bone tumor cells, but did not express CD56. Locally infiltrating osteolytic bone tumors should be examined for surface immunoglobulin light chains as well as CD56 expression when plasmacytoma is suspected.

Kim Sungwon

Papers

Groundwater level prediction using machine learning models: A comprehensive review

CD56-positive Small Round Cell Tumor: Osseous Plasmacytoma Manifested in Osteolytic Tumors of the Iliac Bone and Femora