Displaying publications 41 - 60 of 68 in total

Abstract:
Sort:
  1. Khandakar A, Chowdhury MEH, Ibne Reaz MB, Md Ali SH, Hasan MA, Kiranyaz S, et al.
    Comput Biol Med, 2021 10;137:104838.
    PMID: 34534794 DOI: 10.1016/j.compbiomed.2021.104838
    Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.
  2. Mahmud S, Ibtehaz N, Khandakar A, Tahir AM, Rahman T, Islam KR, et al.
    Sensors (Basel), 2022 Jan 25;22(3).
    PMID: 35161664 DOI: 10.3390/s22030919
    Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.
  3. Ang GY, Yu CY, Johari James R, Ahmad A, Abdul Rahman T, Mohd Nor F, et al.
    Ann Hum Biol, 2018 Mar;45(2):166-169.
    PMID: 29447003 DOI: 10.1080/03014460.2018.1440004
    BACKGROUND: CYP3A5 is the predominant sub-family of biotransformation enzymes in the liver and the genetic variations in CYP3A5 are an important determinant of inter-individual and inter-ethnic differences in CYP3A-mediated drug disposition and response.

    AIM: This study aims to investigate the genetic polymorphisms of CYP3A5 among the Orang Asli in Peninsular Malaysia using a next generation sequencing platform.

    METHODS: Genomic DNAs were extracted from blood samples of the three main Orang Asli tribes and whole-genome sequencing was performed.

    RESULTS: A total of 61 single nucleotide polymorphisms were identified and all the SNPs were located in introns except rs15524, which is in the 3'UTR, and 11 of these polymorphisms were novel. Two allelic variants and three genotypes were identified in the Orang Asli. The major allelic variant was the non-functional CYP3A5*3 (66.4%). The percentages of Orang Asli with CYP3A5*3/*3 (47.2%) and CYP3A5*1/*3 (38.1%) genotypes are more than twice the percentage of Orang Asli with CYP3A5*1/*1 (14.8%) genotype. Almost half of the Orang Asli harboured CYP3A5 non-expressor genotype (CYP3A5*3/*3).

    CONCLUSIONS: The predominance of the CYP3A5 non-expressor genotype among the Orang Asli was unravelled and the findings in this study may serve as a guide for the optimisation of pharmacotherapy for the Orang Asli community.

  4. Haque F, Ibne Reaz MB, Chowdhury MEH, Md Ali SH, Ashrif A Bakar A, Rahman T, et al.
    Comput Biol Med, 2021 12;139:104954.
    PMID: 34715551 DOI: 10.1016/j.compbiomed.2021.104954
    BACKGROUND: Diabetic Sensorimotor polyneuropathy (DSPN) is one of the major indelible complications in diabetic patients. Michigan neuropathy screening instrumentation (MNSI) is one of the most common screening techniques used for DSPN, however, it does not provide any direct severity grading system.

    METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.

    RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.

    CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.

  5. Yu CY, Ang GY, Subramaniam V, Johari James R, Ahmad A, Abdul Rahman T, et al.
    Genet Test Mol Biomarkers, 2017 Jul;21(7):409-415.
    PMID: 28525288 DOI: 10.1089/gtmb.2016.0235
    AIMS: CYP2D6 is one of the major enzymes in the cytochrome P450 monooxygenase system. It metabolizes ∼25% of prescribed drugs and hence, the genetic diversity of a CYP2D6 gene has continued to be of great interest to the medical and pharmaceutical industries. This study was designed to perform a systematic analysis of the CYP2D6 gene in six subtribes of the Malaysian Orang Asli.

    METHODS: Genomic DNAs were extracted from the blood samples followed by whole-genome sequencing. The reads were aligned to the reference human genome hg19 and variants in the CYP2D6 gene were analyzed. CYP2D6*5 and duplication of CYP2D6 were analyzed using previously established methods.

    RESULTS: A total of 72 single nucleotide polymorphisms were identified. CYP2D6*1, *2, *4, *5, *10,*41, and duplication of the gene were found in the Orang Asli, whereby CYP2D6*2 and *41 alleles are reported for the first time in the Malaysian population.

    CONCLUSION: The findings in this study provide insights into the genetic polymorphisms of CYP2D6 in the Orang Asli of Peninsular Malaysia.

  6. Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem SB, et al.
    Comput Biol Med, 2021 May;132:104319.
    PMID: 33799220 DOI: 10.1016/j.compbiomed.2021.104319
    Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
  7. Awuah WA, Kalmanovich J, Mehta A, Huang H, Abdul-Rahman T, Cheng Ng J, et al.
    Curr Top Med Chem, 2023;23(5):389-402.
    PMID: 36593538 DOI: 10.2174/1568026623666230102095836
    Glioblastoma Multiforme (GBM) is a debilitating type of brain cancer with a high mortality rate. Despite current treatment options such as surgery, radiotherapy, and the use of temozolomide and bevacizumab, it is considered incurable. Various methods, such as drug repositioning, have been used to increase the number of available treatments. Drug repositioning is the use of FDA-approved drugs to treat other diseases. This is possible because the drugs used for this purpose have polypharmacological effects. This means that these medications can bind to multiple targets, resulting in multiple mechanisms of action. Antipsychotics are one type of drug used to treat GBM. Antipsychotics are a broad class of drugs that can be further subdivided into typical and atypical classes. Typical antipsychotics include chlorpromazine, trifluoperazine, and pimozide. This class of antipsychotics was developed early on and primarily works on dopamine D2 receptors, though it can also work on others. Olanzapine and Quetiapine are examples of atypical antipsychotics, a category that was created later. These medications have a high affinity for serotonin receptors such as 5- HT2, but they can also act on dopamine and H1 receptors. Antipsychotic medications, in the case of GBM, also have other effects that can affect multiple pathways due to their polypharmacological effects. These include NF-B suppression, cyclin deregulation, and -catenin phosphorylation, among others. This review will delve deeper into the polypharmacological, the multiple effects of antipsychotics in the treatment of GBM, and an outlook for the field's future progression.
  8. Kundu M, Ng JC, Awuah WA, Huang H, Yarlagadda R, Mehta A, et al.
    Postgrad Med J, 2023 May 22;99(1170):240-243.
    PMID: 36892407 DOI: 10.1093/postmj/qgad002
    The tremendous evolution in modern technology has led to a paradigm shift in neurosurgery. The latest advancements such as augmented reality, virtual reality, and mobile applications have been incorporated into neurosurgical practice. NeuroVerse, representing the application of the metaverse in neurosurgery, brings enormous potential to neurology and neurosurgery. Implementation of NeuroVerse could potentially elevate neurosurgical and interventional procedures, enhance medical visits and patient care, and reshape neurosurgical training. However, it is also vital to consider the challenges that may be associated with its implementation, such as privacy issues, cybersecurity breaches, ethical concerns, and widening of existing healthcare inequalities. NeuroVerse adds phenomenal dimensions to the neurosurgical environment for patients, doctors, and trainees, and represents an incomparable advancement in the delivery of medicine. Therefore, more research is needed to encourage widespread use of the metaverse in healthcare, particularly focusing on the areas of morality and credibility. Although the metaverse is expected to expand rapidly during and after the COVID-19 pandemic, it remains to be seen whether it represents an emerging technology that will revolutionize our society and healthcare or simply an immature condition of the future.
  9. Rahman T, Khandakar A, Abir FF, Faisal MAA, Hossain MS, Podder KK, et al.
    Comput Biol Med, 2022 Apr;143:105284.
    PMID: 35180500 DOI: 10.1016/j.compbiomed.2022.105284
    The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20-25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively.
  10. Awuah WA, Ng JC, Bulut HI, Nazir A, Tenkorang PO, Yarlagadda R, et al.
    Int J Surg, 2023 Mar 01;109(3):519-520.
    PMID: 36927835 DOI: 10.1097/JS9.0000000000000025
  11. Awuah WA, Adebusoye FT, Tenkorang PO, Mehta A, Mustapha MJ, Debrah AF, et al.
    Int J Surg, 2023 Mar 01;109(3):227-229.
    PMID: 36906787 DOI: 10.1097/JS9.0000000000000020
  12. Awuah WA, Ng JC, Mehta A, Nansubuga EP, Abdul-Rahman T, Kundu M, et al.
    Postgrad Med J, 2023 Aug 22;99(1175):941-945.
    PMID: 37280156 DOI: 10.1093/postmj/qgad043
    With increasing prevalence and an expected rise in disease burden, cancer is a cause of concern for African healthcare. The cancer burden in Africa is expected to rise to 2.1 million new cases per year and 1.4 million deaths annually by the year 2040. Even though efforts are being made to improve the standard of oncology service delivery in Africa, the current state of cancer care is not yet on par with the rise in the cancer burden. Cutting-edge technologies and innovations are being developed across the globe to augment the battle against cancer; however, many of them are beyond the reach of African countries. Modern oncology innovations targeted to ward Africa would be promising to address the high cancer mortality rates. The innovations should be cost-effective and widely accessible to tackle the rapidly rising mortality rate on the African continent. Though it may seem promising, a multidisciplinary approach is required to overcome the challenges associated with the development and implementation of modern oncology innovations in Africa.
  13. Wireko AA, Ohenewaa Tenkorang P, Tope Adebusoye F, Yaa Asieduwaa O, Mehta A, Fosuah Debrah A, et al.
    Int J Surg, 2023 Feb 01;109(2):88-90.
    PMID: 36799812 DOI: 10.1097/JS9.0000000000000146
  14. Wireko AA, Ohenewaa Tenkorang P, Tope Adebusoye F, Mehta A, Cheng Ng J, Yaa Asieduwaa O, et al.
    Int J Surg, 2023 Feb 01;109(2):91-93.
    PMID: 36799813 DOI: 10.1097/JS9.0000000000000216
  15. Karupaiah T, Mat Daud ZA, Khosla P, Khor BH, Sahathevan S, Kaur D, et al.
    J Ren Nutr, 2023 Nov;33(6S):S73-S79.
    PMID: 37597574 DOI: 10.1053/j.jrn.2023.08.003
    BACKGROUND: Recent surveys highlight gross workforce shortage of dietitians in global kidney health and significant gaps in renal nutrition care, with disparities greater in low/low-middle income countries.

    OBJECTIVE: This paper narrates ground experiences gained through the Palm Tocotrienols in Chronic Hemodialysis (PaTCH) project on kidney nutrition care scenarios and some Asian low-to-middle-income countries namely Bangladesh, India, and Malaysia.

    METHOD: Core PaTCH investigators from 3 universities (USA and Malaysia) were supported by their postgraduate students (n = 17) with capacity skills in kidney nutrition care methodology and processes. This core team, in turn, built capacity for partnering hospitals as countries differed in their ability to deliver dietitian-related activities for dialysis patients.

    RESULTS: We performed a structural component analyses of PaTCH affiliated and nonaffiliated (Myanmar and Indonesia) countries to identify challenges to kidney nutrition care. Deficits in patient-centered care, empowerment processes and moderating factors to nutrition care optimization characterized country comparisons. Underscoring these factors were some countries lacked trained dietitians whilst for others generalist dietitians or nonclinical nutritionists were providing patient care. Resolution of some challenges in low-to-middle-income countries through coalition networking to facilitate interprofessional collaboration and task sharing is described.

    CONCLUSIONS: We perceive interprofessional collaboration is the way forward to fill gaps in essential dietitian services and regional-based institutional coalitions will facilitate culture-sensitive capacity in building skills. For the long-term an advanced renal nutrition course such as the Global Renal Internet Course for Dietitians is vital to facilitate sustainable kidney nutrition care.

  16. Awuah WA, Ahluwalia A, Ghosh S, Roy S, Tan JK, Adebusoye FT, et al.
    Eur J Med Res, 2023 Nov 16;28(1):529.
    PMID: 37974227 DOI: 10.1186/s40001-023-01504-w
    Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.
  17. Chee Wezen X, Chandran A, Eapen RS, Waters E, Bricio-Moreno L, Tosi T, et al.
    J Chem Inf Model, 2022 May 23;62(10):2586-2599.
    PMID: 35533315 DOI: 10.1021/acs.jcim.2c00300
    Lipoteichoic acid synthase (LtaS) is a key enzyme for the cell wall biosynthesis of Gram-positive bacteria. Gram-positive bacteria that lack lipoteichoic acid (LTA) exhibit impaired cell division and growth defects. Thus, LtaS appears to be an attractive antimicrobial target. The pharmacology around LtaS remains largely unexplored with only two small-molecule LtaS inhibitors reported, namely "compound 1771" and the Congo red dye. Structure-based drug discovery efforts against LtaS remain unattempted due to the lack of an inhibitor-bound structure of LtaS. To address this, we combined the use of a molecular docking technique with molecular dynamics (MD) simulations to model a plausible binding mode of compound 1771 to the extracellular catalytic domain of LtaS (eLtaS). The model was validated using alanine mutagenesis studies combined with isothermal titration calorimetry. Additionally, lead optimization driven by our computational model resulted in an improved version of compound 1771, namely, compound 4 which showed greater affinity for binding to eLtaS than compound 1771 in biophysical assays. Compound 4 reduced LTA production in S. aureus dose-dependently, induced aberrant morphology as seen for LTA-deficient bacteria, and significantly reduced bacteria titers in the lung of mice infected with S. aureus. Analysis of our MD simulation trajectories revealed the possible formation of a transient cryptic pocket in eLtaS. Virtual screening (VS) against the cryptic pocket led to the identification of a new class of inhibitors that could potentiate β-lactams against methicillin-resistant S. aureus. Our overall workflow and data should encourage further drug design campaign against LtaS. Finally, our work reinforces the importance of considering protein conformational flexibility to a successful VS endeavor.
  18. Ahmed S, Rahman T, Ripon MSH, Rashid HU, Kashem T, Md Ali MS, et al.
    Nutrients, 2021 Dec 17;13(12).
    PMID: 34960076 DOI: 10.3390/nu13124521
    Diet is a recognized risk factor and cornerstone for chronic kidney disease (CKD) management; however, a tool to assess dietary intake among Bangladeshi dialysis patients is scarce. This study aims to validate a prototype Bangladeshi Hemodialysis Food Frequency Questionnaire (BDHD-FFQ) against 3-day dietary recall (3DDR) and corresponding serum biomarkers. Nutrients of interest were energy, macronutrients, potassium, phosphate, iron, sodium and calcium. The BDHD-FFQ, comprising 132 food items, was developed from 606 24-h recalls and had undergone face and content validation. Comprehensive facets of relative validity were ascertained using six statistical tests (correlation coefficient, percent difference, paired t-test, cross-quartiles classification, weighted kappa, and Bland-Altman analysis). Overall, the BDHD-FFQ showed acceptable to good correlations (p < 0.05) with 3DDR for the concerned nutrients in unadjusted and energy-adjusted models, but this correlation was diminished when adjusted for other covariates (age, gender, and BMI). Phosphate and potassium intake, estimated by the BDHD-FFQ, also correlated well with the corresponding serum biomarkers (p < 0.01) when compared to 3DDR (p > 0.05). Cross-quartile classification indicated that <10% of patients were incorrectly classified. Weighted kappa statistics showed agreement with all but iron. Bland-Altman analysis showed positive mean differences were observed for all nutrients when compared to 3DDR, whilst energy, carbohydrates, fat, iron, sodium, and potassium had percentage data points within the limit of agreement (mean ± 1.96 SD), above 95%. In summary, the BDHD-FFQ demonstrated an acceptable relative validity for most of the nutrients as four out of the six statistical tests fulfilled the cut-off standard in assessing dietary intake of CKD patients in Bangladesh.
  19. Awuah WA, Roy S, Tan JK, Adebusoye FT, Qiang Z, Ferreira T, et al.
    J Cell Mol Med, 2024 Apr;28(7):e18159.
    PMID: 38494861 DOI: 10.1111/jcmm.18159
    Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.
  20. Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, et al.
    World Neurosurg X, 2024 Jul;23:100301.
    PMID: 38577317 DOI: 10.1016/j.wnsx.2024.100301
    Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links