MyMedR

Displaying publications 21 - 24 of 24 in total

Abstract:

Sort:

Fulltext An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Mandala S, Rizal A, Adiwijaya, Nurmaini S, Suci Amini S, Almayda Sudarisman G, et al.

PLoS One, 2024;19(4):e0297551.
PMID: 38593145 DOI: 10.1371/journal.pone.0297551

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.
Fulltext Prostate cancer management in Southeast Asian countries: a survey of clinical practice patterns

Chiong E, Saad M, Hamid ARAH, Ong-Cornel AB, Lojanapiwat B, Pripatnanont C, et al.

Ther Adv Med Oncol, 2024;16:17588359231216582.
PMID: 38249332 DOI: 10.1177/17588359231216582

BACKGROUND: Prostate cancer (PC) has a serious public health impact, and its incidence is rising due to the aging population. There is limited evidence and consensus to guide the management of PC in Southeast Asia (SEA). We present real-world data on clinical practice patterns in SEA for advanced PC care.
METHOD: A paper-based survey was used to identify clinical practice patterns and obtain consensus among the panelists. The survey included the demographics of the panelists, the use of clinical guidelines, and clinical practice patterns in the management of advanced PC in SEA.
RESULTS: Most panelists (81%) voted prostate-specific antigen (PSA) as the most effective test for early PC diagnosis and risk stratification. Nearly 44% of panelists agreed that prostate-specific membrane antigen positron emission tomography-computed tomography imaging for PC diagnostic and staging information aids local and systemic therapy decisions. The majority of the panel preferred abiraterone acetate (67%) or docetaxel (44%) as first-line therapy for symptomatic mCRPC patients. Abiraterone acetate (50%) is preferred over docetaxel as a first-line treatment in metastatic castration-sensitive prostate cancer patients with high-volume disease. However, the panel did not support the use of abiraterone acetate in non-metastatic castration-resistant prostate cancer (nmCRPC) patients. Apalutamide (75%) is the preferred treatment option for patients with nmCRPC. The cost and availability of modern treatments and technologies are important factors influencing therapeutic decisions. All panelists supported the use of generic versions of approved therapies.
CONCLUSION: The survey results reflect real-world management of advanced PC in a SEA country. These findings could be used to guide local clinical practices and highlight the financial challenges of modern healthcare.
Fulltext Approach in inputs & outputs selection of Data Envelopment Analysis (DEA) efficiency measurement in hospitals: A systematic review

Zubir MZ, Noor AA, Mohd Rizal AM, Harith AA, Abas MI, Zakaria Z, et al.

PLoS One, 2024;19(8):e0293694.
PMID: 39141630 DOI: 10.1371/journal.pone.0293694

The efficiency and productivity evaluation process commonly employs Data Envelopment Analysis (DEA) as a performance tool in numerous fields, such as the healthcare industry (hospitals). Therefore, this review examined various hospital-based DEA articles involving input and output variable selection approaches and the recent DEA developments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilised to extract 89 English articles containing empirical data between 2014 and 2022 from various databases (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar). Furthermore, the DEA model parameters were determined using information from previous studies, while the approaches were identified narratively. This review grouped the approaches into four sections: literature review, data availability, systematic method, and expert judgement. An independent single strategy or a combination with other methods was then applied to these approaches. Consequently, the focus of this review on various methodologies employed in hospitals could limit its findings. Alternative approaches or techniques could be utilised to determine the input and output variables for a DEA analysis in a distinct area or based on different perspectives. The DEA application trend was also significantly similar to that of previous studies. Meanwhile, insufficient data was observed to support the usability of any DEA model in terms of fitting all model parameters. Therefore, several recommendations and methodological principles for DEA were proposed after analysing the existing literature.
Long-term evaluation of osteoarthritis sheep knee, treated with TGF-β3 and BMP-6 induced multipotent stem cells

Ude CC, Shamsul BS, Ng MH, Chen HC, Ohnmar H, Amaramalar SN, et al.

Exp Gerontol, 2018 04;104:43-51.
PMID: 29421350 DOI: 10.1016/j.exger.2018.01.020

BACKGROUND: Hyaline articular cartilage, which protects the bones of diarthrodial joints from forces associated with load bearing, frictions, and impacts has very limited capacities for self-repair. Over the years, the trend of treatments has shifted to regenerations and researchers have been on the quest for a lasting regeneration. We evaluated the treatment of osteoarthritis by chondrogenically induced ADSCs and BMSCs for a long time functional recovery.
METHODS: Osteoarthritis was induced at the right knee of sheep by complete resection of ACL and medial meniscus. Stem cells from sheep were induced to chondrogenic lineage. Test sheep received 5 mls single doses of 2 × 107 autologous PKH26-labelled ADSCs or BMSCs, while controls received basal medium. Functional recovery of the knees was evaluated via electromyography.
RESULTS: Induced ADSCs had 625, 255, 393, 908, 409, 157 and 1062 folds increases of collagen I, collagen II, aggrecan, SOX9, cartilage oligomeric protein, chondroadherin and fibromodullin compare to uninduced cells, while BMSCs had 702, 657, 321, 276, 337, 233 and 1163 respectively; p = .001. Immunocytochemistry was positive for these chondrogenic markers. 12 months post-treatment, controls scored 4 in most regions using ICRS, while the treated had 8; P = .001. Regenerated cartilages were positive to PKH26 and demonstrated the presence of condensing cartilages on haematoxylin and eosin; and Safranin O. OA degenerations caused significant amplitude shift from right to left hind limb. After treatments, controls persisted with significant decreases; while treated samples regained balance.
CONCLUSIONS: Both ADSCs and BMSCs had increased chondrogenic gene expressions using TGF-β3 and BMP-6. The treated knees had improved cartilage scores; PKH26 can provide elongated tracking, while EMG results revealed improved joint recoveries. These could be suitable therapies for osteoarthritis.