A Retrospective Analysis of BCR-ABL1 Kinase Domain Mutations in the Frontline Drug Intolerant or Resistant Chronic Myeloid Leukemia Patients: An Indian Experience from a High-End Referral Laboratory.

Atreye MajumdarSambit K. MohantyObjective This article identifies and evaluates the frequency of mutations in the BCR-ABL1 kinase domain (KD) of chronic myeloid leukemia (CML) patients who showed suboptimal response to their current tyrosine kinase inhibitor (TKI) regime and assesses their clinical value in further treatment decisions. Materials and Methods Peripheral and/or bone marrow were collected from 791 CML patients. Ribonucleic acid was extracted, reverse transcribed, and Sanger sequencing method was utilized to detect single-nucleotide variants (SNVs) in BCR-ABL1 KD. Results Thirty-eight different SNVs were identified in 29.8% ( n = 236/791) patients. T315I, E255K, and M244V were among the most frequent mutations detected. In addition, one patient harbored a novel L298P mutation. A subset of patients from the abovementioned harbored compound mutations (13.3%, n = 33/236). Follow-up data was available in 28 patients that demonstrated the efficacy of TKIs in correlation with mutation analysis and BCR-ABL1 quantitation. Molecular response was attained in 50% patients following an appropriate TKI shift. A dismal survival rate of 40% was observed in T315I-harboring patients on follow-up. Conclusion This study shows the incidence and pattern of mutations in one of the largest sets of Indian CML patients. In addition, our findings strengthen the prognostic value of KD mutation analysis among strategies to overcome TKI resistance.

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics.

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature. Methods: In this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Furthermore, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories. In this study, we have used GSE181919 dataset, we have extracted 20 primary cancer (HNSCC) samples, and 9 normal tissues samples. The primary cancer samples contained 13 HPV- and 7 HPV+ samples. The models developed in this study have been trained on 80% of the dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes. Results: Artificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV- patients on the validation set. In GO enrichment analysis, it was found that most genes were involved in binding and catalytic activities. Conclusion: A software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/.

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures.

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm.

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021.

As a non-linear phenomenon that varies along with agro-climatic conditions alongside many other factors, Evapotranspiration (ET) process represents a complexity when be assessed especially if there is a data scarcity in the weather data. However, even under such a data scarcity, the accurate estimates of ET values remain necessary for precise irrigation. So, the present study aims to: i) evaluate the performance of six hybrid machine learning (ML) models in estimating the monthly actual ET values under different agro-climatic conditions in China for seven provinces (Shandong, Jiangsu, Zhejiang, Fujian, Jiangxi, Hubei, and Henan), and ii) select the best-developed model based on statistical metrics and reduce errors between predicted and actual ET (AET) values. AET datasets were divided into 78% for model training (from 1958 to 2007) and the remaining was used for testing (from 2008 to 2021). Deep Neural Networks (DNN) was used as a standalone model at first then the stacking method was applied to integrate DNN with data-driven models such as Additive regression (AR), Random Forest (RF), Random Subspace (RSS), M5 Burned Tree (M5P) and Reduced Error Purning Tree (REPTree). Partial Auto-Correlation Function (PACF) was used for selection of the best lags inputs to the developed models. Results have revealed that DNN-based hybrid models held better performance than non-hybrid DNN models, such that the DNN-RF algorithm outperformed others during both training and testing stages, followed by DNN-RSS. This model has acquired the best values of every statistical measure [MAE (10.8, 12.9), RMSE (15.6, 17.4), RAE (31.9%, 41.4%), and RRSE (39.3%, 47.2%)] for training and testing, respectively. In contrast, the DNN model held the worst performance [MAE (14.9, 13.7), RMSE (20.1, 18.2), RAE (43.9%, 43.7%), and RRSE (50.6%, 49.3%)], for training and testing, respectively. Results from the study presented have revealed the capability of DNN-based hybrid models for long-term predictions of the AET values. Moreover, the DNN-RF model has been suggested as the most suitable model to improve future investigation for AET predictions, which could benefit the enhancement of the irrigation process and increase crop yield.

Surveillance of Microbiological Environment of Operation Theaters.

Introduction Control of infections in the operation theater (OT) is of utmost importance. Microbiological surveillance is an effective tool for identifying and controlling infections. The purpose of this study was to investigate the prevalence rate of microorganisms in OTs, to identify the type of microorganisms, and to detect contamination of various surfaces and air of OT. Methods OTs were properly cleaned with soap and water. All surfaces were disinfected, followed by fumigation with quaternary ammonium compounds. OTs were kept closed overnight. In the morning, they were opened, and samples were collected, taking all aseptic precautions. The settle plate method was used for air sampling, and the swab method was used for surface sampling. Samples were collected from four surfaces of OTs, i.e., floor, wall, table, and light, and samples of the OT air were also collected and immediately transported to the microbiology laboratory of the institution in sterile conditions. Result A total of 1640 swab samples were taken from eight OTs, out of which 487 (29.7%) were found positive for bacterial growth. Most of them were non-pathological microorganisms such as aerobic spore-forming Bacilli and Micrococcus. Among various OTs, septic OT showed the highest bacterial growth (82 positive cultures out of 200). In the surface sampling of various OTs, aerobic spore-forming Bacilli (221/487) was the most common isolate, followed by coagulase-negative Staphylococci (74/487), and Micrococcus (67/487). General surgery, septic, and emergency OTs had maximum air bioload (97, 93, and 91 colony-forming unit (CFU)/M3, respectively). Conclusion In surface sampling of OTs, it was found that septic OT and general surgery OT were most contaminated where the patient load was high. Among all the surfaces, OT walls and tables were most contaminated with pathogenic microorganisms. The average air bioload of all OTs was ranged between 79 and 97 CFU/M3.

Survey of acaricides resistance status of Rhipiciphalus (Boophilus) microplus collected from selected places of Bihar, an eastern state of India.

Monitoring acaricide resistance in field ticks and use of suitable managemental practices are essential for controlling tick populations infesting animals. In the present study, the acaricide resistance status in Rhipicephalus (Boophilus) microplus ticks infesting cattle and buffaloes of five districts located in the eastern Indian state, Bihar were characterized using three data sets (AIT, Biochemical assays and gene sequences). Adult immersion test (AIT) was adopted using seven field isolates and their resistance factor (RF) was determined. Six isolates (DNP, MUZ, BEG, VSH, DRB and SUL) were found resistant to both deltamethrin and diazinon and except VSH all were resistant to cypermethrin. One isolate (PTN) was susceptible with a RF below 1.5. To understand the possible mode of resistance development, targeted enzymes and gene sequences of the para sodium channel and achetylcholinesterase 2 (AChE2) were analyzed. The esterase, monooxygenase and glutathione-S-transferase (GST) activity of reference susceptible IVRI-I line was determined as 2.47 ± 0.007 nmol/min/mg protein, 0.089 ± 0.0016 nmol/mg of protein and 0.0439 ± 0.0003 nmol/mg/min respectively, which increased significantly in the resistant field isolates. However, except esterases, the fold increase of monooxygenase (1.14-2.27 times) and GST (0.82-1.53 times) activities were not very high. A cytosine (C) to adenine (A) nucleotide substitution (CTC to ATC) at position 190 in domain II S4-5 linker region was detected only in one isolate (SUL) having RF of 34.9 and in the reference deltamethrin resistant line (IVRI-IV). However, the T2134A mutation was not detected in domain IIIS6 transmembrane segment of resistant isolates and also in reference IVRI-IV line despite of varying degree of resistance. The flumethrin specific G215T and the recently identified T170C mutations were also absent in domain II sequences under study. Four novel amino acid substitutions in AChE2 gene of field isolates and in organophosphate (OP) resistant reference IVRI-III line were identified which can possibly have a role in resistance development.