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Polycythaemia Vera among Sudanese Patients with Special Emphasis on JAK2 Mutations

Ibrahim IK, Hassan R, Ali EW, Omer A

Asian Pac J Cancer Prev, 2019 Jan 25;20(1):41-44.
PMID: 30677867

Background: In recent years, a somatic point mutation in the Janus Kinase 2 (JAK2) gene (1849 G→T, V617F)
has been reported to occur in over 90% of patients with polycythemia vera (PV). Another JAK2 mutation in exon 12
had been described and shown capable of activating erythropoietin signaling pathways. Objective: In this study, we
aimed to determine the frequency of Jak2 mutations (JAK2V617F and JAK2 exon 12) as well as their relationships
with hematological parameters in Sudanese patients with myeloproliferative disorders (MPD). A comparison with
findings of published studies from other geographic regions was included. Materials and Methods: From each of
a total of 83 polycythaemia patients, six milliliters (ml) of venous blood were collected and processed for molecular
analysis and measurement of serum erythropoietin level by enzyme-linked immunoassay (ELISA). The JAK2 V617F
mutation was determined using an allele-specific competitive blocker (ACB) -PCR assay and High Resolution Melting
(HRM) analysis was applied for the JAK2 exon 12 mutation. Results: According to patients’ history and the results
for EPO levels, nine (10.7 %) out of 83 patients were found to have secondary polycythaemia and 74 (89.3%) PV. The
overall frequency of the 2 JAK2 mutations was 94.6% in our Sudanese PV patients, JAK2V617F being found in 91%
and JAK2 exon 12 mutations in 8.1%.Conclusion: In summary JAK2 V617F and JAK2 exon 12 mutations are very
common in Sudanese PC cases.
Fulltext The influence of covalent and non-covalent functionalization of GNP based nanofluids on its thermophysical, rheological and suspension stability properties

Hussein OA, Habib K, Saidur R, Muhsan AS, Shahabuddin S, Alawi OA

RSC Adv, 2019 Nov 25;9(66):38576-38589.
PMID: 35540235 DOI: 10.1039/c9ra07811h

Covalent functionalization (CF-GNPs) and non-covalent functionalization (NCF-GNPs) approaches were applied to prepare graphene nanoplatelets (GNPs). The impact of using four surfactants (SDS, CTAB, Tween-80, and Triton X-100) was studied with four test times (15, 30, 60, and 90 min) and four weight concentrations. The stable thermal conductivity and viscosity were measured as a function of temperature. Fourier transform infrared spectroscopy (FTIR), thermo-gravimetric analysis (TGA), X-ray diffraction (XRD) and Raman spectroscopy verified the fundamental efficient and stable CF. Several techniques, such as dispersion of particle size, FESEM, FETEM, EDX, zeta potential, and UV-vis spectrophotometry, were employed to characterize both the dispersion stability and morphology of functionalized materials. At ultrasonic test time, the highest stability of nanofluids was achieved at 60 min. As a result, the thermal conductivity displayed by CF-GNPs was higher than NCF-GNPs and distilled water. In conclusion, the improvement in thermal conductivity and stability displayed by CF-GNPs was higher than those of NCF-GNPs, while the lowest viscosity was 8% higher than distilled water, and the best thermal conductivity improvement was recorded at 29.2%.
Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

Yaseen ZM, Melini Wan Mohtar WH, Homod RZ, Alawi OA, Abba SI, Oudah AY, et al.

Chemosphere, 2024 Jan 29;352:141329.
PMID: 38296204 DOI: 10.1016/j.chemosphere.2024.141329

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.
Reliability, validity and responsiveness of the Kurdish version of the questionnaire Disability of the Arm, Shoulder and Hand in patients with carpal tunnel syndrome

Rebwar AH, Omer AR, Jalal AH

Med J Malaysia, 2024 Mar;79(Suppl 1):47-52.
PMID: 38555885

INTRODUCTION: The Disabilities of Arm, Shoulder and Hand (DASH) questionnaire predicts the amount of the patient's inabilities and symptoms to evaluate the impacts of upper limb conditions in the patient's daily-life activities. This study aims to test the psychometric properties of DASH in Kurdish patients with carpal tunnel syndrome.
MATERIALS AND METHODS: 93 patients with diagnosed carpal tunnel syndrome subjected to complete the self-report DASH-KU and patient rated wrist\hand evaluation PRWHEKU questionnaire during two consecutive assessments with a 24-hour interval before any intervention.
RESULTS: DASH-KU questionnaire had excellent internal consistency (Cronbach's alpha = 0.99) and test-retest reliability (intra-class correlation coefficient =0.99). A strong correlation between the DASH-KU score and the PRWHE tool (r=0.792) demonstrated acceptable construct validity of DASH-KU. Bland-Altman plot showed good agreement between the two assessments of DASH-KU, and no floor (3%) nor ceiling effects (0%) were observed. Factor analysis showed that the DASH-KU scale had a high acceptable adequacy (adequacy index = 0.700) and a significant sphericity (p<0.001). The analysis showed a major factor that accounted for 40% of the observed variance with an eigenvalue of 13.14. In addition, five items model also explained 81.23% of the DASH-KU scale variance. However, the responsiveness of DASH-KU was suboptimum, which can be linked to the short 24-hour interval between measurements.
CONCLUSION: The DASH-KU scale is a reliable, valid, and responsive instrument for assessing disabilities in patients with carpal tunnel syndrome.
Daily Scale Air Quality Index Forecasting using Bidirectional Recurrent Neural Networks: Case Study of Delhi, India

Pande CB, Kushwaha NL, Alawi OA, Sammen SS, Sidek LM, Yaseen ZM, et al.

Environ Pollut, 2024 Apr 27.
PMID: 38685551 DOI: 10.1016/j.envpol.2024.124040

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum determination coefficient (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.