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All Journal Nusantara Science and Technology Proceedings Magna Neurologica
Umam, Hamid Faqih
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Economics, Econometrics & Finance Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Public Health

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A Systematic Review and Meta-Analysis: The Long-Term Effects of Oral N-Acetylcysteine in Chronic Obstructive Pulmonary Disease Nasikhatussoraya, Nova; Umam, Hamid Faqih; Rizkani, Ainingtyas Marda; Santoso, Hidayat
Nusantara Science and Technology Proceedings The 4th International Conference on Community Medicine and Medical Sciences
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4404

Abstract

Chronic obstructive pulmonary disease (COPD) is one of the world's most prevalent causes of morbidity and mortality. About 5.6% of Indonesians have COPD. Most COPD patients have mucus hypersecretion and inadequate mucus clearance, leading to airway obstruction. N-acetylcysteine (NAC) is a mucolytic drug with antioxidant and anti-inflammatory effects. Long-term oral NAC use in COPD remains controversial. We searched four databases to analyze the effects of oral NAC in COPD over a minimum six-month treatment. The data was analyzed through Review Manager 5.4. Eight randomized controlled trials, comprising 3.187 patients, were selected for inclusion in the study. NAC had a lower risk of exacerbations (RR 0.78, 95% CI 0.66-0.93; p=0.005), whether at high dose (1200mg/day) or low dose (600mg/day). The risk is higher at the high dose (RR 0.90, p=0.04) than at the low dose (RR 0.70, p=0.0002). NAC did not affect FEV1 (mean difference 4.71, 95% CI -3.20-12.61; p=0.24), and adverse events were the same as with placebo (RR 1.14, 95% CI 0.78-1.67; p=0.51). Long-term oral NAC reduces the risk of COPD exacerbations at any dose and was well tolerated.
Automated Prediction of Large Vessel Occlusion Using Artificial Intelligence in Non-Contrast Computed Tomography: A Systematic Review and Meta-Analysis Umam, Hamid Faqih; Mustofa, Aila; David Noor Umam; Shabrina Nur Zidny; Dyah Pranani; Retnaningsih
Magna Neurologica Vol. 3 No. 2 (2025): July
Publisher : Department of Neurology Faculty of Medicine Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/magnaneurologica.v3i2.1704

Abstract

Background: Acute ischemic stroke due to large vessel occlusion (LVO) requires rapid identification. Reducing the time to diagnosis and treatment of stroke patients is an important goal to improve clinical outcomes. Non-contrast computed tomography (NCCT) is widely used in clinical practice for suspected stroke patients. Automated analysis using artificial intelligence in NCCT may be a solution to accelerate the early detection of LVO. Objective: To determine the accuracy of artificial intelligence in NCCT to predict LVO. Methods: A systematic literature search was conducted based on the PRISMA flow chart in four databases (PubMed, ProQuest, ScienceDirect, Cochrane Library) until June 2024. Data extraction was performed to evaluate the accuracy of predicting LVO. Quality assessment was performed using QUADAS-2. All data were analyzed using Review Manager 5.4 and MetaDTA 2.0. Results: Five studies involving 4.862 patients were enrolled. The quality of all the studies was high and had a low risk of bias. All studies used different software. Artificial intelligence in NCCT had fairly good accuracy with a sensitivity and specificity of 0.83 (95% CI; 0.78-0.87) and 0.73 (95% CI; 0.52-0.87). NCCT plus clinical status (NIHSS, stroke onset) in two studies slightly improved overall accuracy with a sensitivity and specificity of 0.85 (95% CI; 0.80-0.89) and 0.74 (95% CI; 0.54-0.88). Two studies reported that machine learning took less than two minutes. Conclusion: Artificial intelligence in NCCT was reasonably accurate and took a short time to predict LVO. There are still opportunities for machine learning to improve performance. Further research is still needed.
Co-Authors David Noor Umam Dyah Pranani Mustofa, Aila Nova Nasikhatussoraya, Nova Retnaningsih Rizkani, Ainingtyas Marda Santoso, Hidayat Shabrina Nur Zidny