Journal of Current Science and Technology

Assessing Carbon Sequestration and Biodiversity: A Comprehensive Study of Trees at Dhurakij Pundit University

Pattanant Petchchedchoo, Nuchar Sathidpong, Nit Petcharaks — Tue, 25 Mar 2025 00:00:00 +0700

The objectives of this study were: i) to develop a comprehensive tree database for Dhurakij Pundit University (DPU), involving counting and measuring the diameter at breast height (DBS) of 1.3 meters and had a stem circumference of at least 12 cm at breast height, ii) to evaluate CO₂ sequestration within the university campus by estimating the woody biomass of each tree using allometric equations and calculating carbon storage, CO₂ sequestration potential, and oxygen (O₂) production for each tree. Additionally, the Importance Value Index (IVI) and the Shannon-Weiner Species Diversity (SWSD) Index were calculated to identify the dominant tree species and assess overall species diversity. Data collection was conducted over a two-month period, from September to November 2023. The results indicated that within the 13.23-hectare campus, there were 191 distinct species represented by a total of 2,577 trees, with a tree density of 194.79 trees/ha and a total tree basal area of 107.91 m². The estimated carbon storage was 23.41 tC/ha, with CO₂ sequestration reaching 85.82 tCO₂/ha and oxygen production at 62.414 tO₂/ha. DPU's CO₂ sequestration rate of 4.29 tCO₂/ha/year reflected its dense tree population relative to other universities. The SWSD Index (H') of 3.7730 indicated high species diversity. Pterocarpus indicus exhibited the highest CO₂sequestration, while Ficus annulata had the highest IVI at 47.3091. This comprehensive database serves as a basis for future assessments of carbon sequestration on campus.

Diversity of Cellulolytic Bacteria Isolated from Goat Feces in Rayong Province, Eastern Thailand, and Characterization of Their Endoglucanase Activity

Parima Boontanom, Nuttida Phaparat, Aiya Chantarasiri — Tue, 25 Mar 2025 00:00:00 +0700

Bacterial cellulases are crucial for breaking down cellulose, which is essential for various industries. These bacteria are found in the rumen of herbivores including domestic goats. Goat feces show potential as a source of cellulase-producing bacteria, but studies on these bacteria isolated from goat feces in Thailand remain limited. This study isolated and genetically identified cellulase-producing bacteria from goat feces in eastern Thailand. The cellulases produced by the most effective cellulase-producing bacterium were also characterized enzymatically. A total of 30 cellulase-producing bacteria were isolated and classified using PCR-RFLP analysis of the 16S rRNA gene. Thirteen different RFLP patterns were obtained through MspI-AluI digestion, belonging to nine bacterial genera: Acinetobacter, Bacillus, Corynebacterium, Enterococcus, Escherichia, Exiguobacterium, Providencia, Pseudomonas, and Staphylococcus (Mammaliicoccus). The predominant genera of the isolated cellulase-producing bacteria were Escherichia, Exiguobacterium, and Corynebacterium. Several of the isolated bacterial species had limited prior evidence of cellulase production. Bacillus sp. strain FMJ 1105 showed the highest cellulase activity using the CMC agar method and produced CMCase (endoglucanase) activity of 2.67 ± 0.06 U/mL. The optimum temperature and pH for CMCase activity were determined to be 50°C and pH 7.0, with a stability range of 25-70°C and pH 6.0-8.0 over 24 h of incubation. This study provides new insights into cellulase-producing bacteria isolated from goat feces in Thailand, contributing to the understanding of their enzymatic potential.

Pharmacological Effects of Thunbergia laurifolia Lindl. Extract on Gastric Ulceration in Rats

Tue, 25 Mar 2025 00:00:00 +0700

Thunbergia laurifolia Lindl. (TL), a medicinal plant renowned for its anti-inflammatory properties, has attracted interest in its potential antiulcerogenic effects. Gastric ulcers, typically resulting from an imbalance between aggressive factors such as gastric acid and the protective defenses of the gastric mucosa, are frequently aggravated by inflammatory processes. This study aimed to investigate the anti-ulcerogenic potential of TL extract in animal models. Male Sprague Dawley rats were selected for the study. The anti-ulcerogenic effects were evaluated using four distinct in vivo models: ethanol/hydrochloric acid administration, restraint water immersion stress, indomethacin administration, and pylorus ligation. Administration of TL extract at doses of 100, 200, and 400 mg/kg significantly reduced gastric ulcer formation induced by ethanol/hydrochloric acid as well as restraint water immersion stress. At doses of 200 and 400 mg/kg, the extract significantly reduced indomethacin-induced ulcer formation. Additionally, at a dose of 400 mg/kg, the extract significantly reduced both gastric secretion rate and total acidity in the pylorus ligation model. The findings of this study indicate that TL extract exhibits noteworthy anti-ulcerogenic activity in rats, suggesting its potential therapeutic value in treating gastric ulcers.

Verification of Concurrent Go Programs using Timed Trace Theory

Denduang Pradubsuwun — Tue, 25 Mar 2025 00:00:00 +0700

The Go programming language, or Go, plays a critical role in developing concurrent programs because it provides features such as goroutines and channels that support program concurrency. Even though concurrency makes programs efficient, verification is required to ensure their correctness. This paper proposes a novel approach to verifying concurrent Go programs using timed trace theory. The proposed approach is specifically designed to verify concurrent systems. Verifying a Go program using timed trace theory is divided into two tasks: modeling and verification. Modeling involves transforming a Go program into time Petri nets using a proposed algorithm. Verification involves checking the conformance between the Go program and its specification. This can be done automatically by the timed trace theoretic verification tool, which supports a partial order reduction technique to mitigate the state explosion problem. We demonstrate the verification of the Philosopher problem using both the total order method and the partial order reduction method. Experiments with the Go program of the Philosopher problem demonstrate the effectiveness of the proposed method.

Study of Traditional Chinese Medicine Syndrome Differentiation in post-COVID patients with Respiratory Symptoms in Thailand

Tue, 25 Mar 2025 00:00:00 +0700

Post-COVID-19 has a significant impact on recovered individuals. Traditional Chinese Medicine (TCM) offers a framework for understanding and treatment, but its utility in post-COVID cases remains under investigation. This study aimed to investigate the TCM syndromes in patients experiencing post-COVID with respiratory symptoms and explore alternative clinically practical assessment tools (spirometry and Fatigue Severity Scale [FSS]) for differentiating TCM patterns. A cross-sectional study was conducted involving 150 subjects with post-COVID respiratory symptoms in Thailand. TCM differentiation was diagnosed by three certified TCM practitioners, and spirometry was conducted by a physical therapist who holds a spirometry certification. Both spirometry and FSS were used for evaluation. Lung and Spleen Qi Deficiency Syndrome (FPQX) was the dominant TCM pattern (72%), followed by Deficiency of Both Qi and Yin Syndrome (QYLX) (28%). Spirometry revealed a statistically significant, but small decrease in lung function (FEV₁/FVC) within the FPQX group compared to the QYLX group. However, both groups remained within the normal range. FSS scores did not differ significantly between the groups. This study suggests that FPQX is prevalent in post-COVID patients in Thailand. It also indicates that spirometry might not be sensitive enough to capture the full extent of lung dysfunction in post-COVID respiratory symptoms. While the FSS confirmed the presence of fatigue in both groups, it may not be the best tool for differentiating between specific TCM syndromes. Future research should explore more sensitive biomarkers for TCM diagnosis and consider potential neurological contributions to post-COVID respiratory symptoms.

Analysis of The Prevalence and Risk Factors for Malalignment of The Lower Limbs in Rice Farmers in Indonesia

Agung Kristanto, A. Miftahul Khair Malkab, Farid Ma’ruf, Choirul Bariyah — Tue, 25 Mar 2025 00:00:00 +0700

Rice farming processes include extended periods of physical labor and interaction between humans and machines. Extended exposure to agricultural activities has the potential for developing misalignment in the lower limbs. This misalignment may heighten the likelihood of harm to the lower limbs and result in physical disability. Nevertheless, the prevalence and variables related to the misalignment of the lower limbs have not been documented so far. The objective of current research was to examine the prevalence and risk factors of lower limb misalignment in individuals engaged in rice farming. A cross-sectional investigation of 400 rice farmers was carried out. Lower limb alignment assessment included: pelvic tilt angle, limb length equality, femoral torsion, quadriceps (Q) angle, tibiofemoral angle, genu recurvatum, rearfoot angle, and medial longitudinal arch angle. Participant characteristics and the prevalence of lower limb malalignment were analyzed using descriptive statistics. The risk factors were identified using logistic regression analysis. The highest prevalence of lower limb malalignment was pelvic tilt angle (28.50%), followed by the femoral antetorsion angle (24.00%), tibiofemoral angle (21.50%), foot pronation (17.75%), limb length inequality (14.25%), genu recurvatum angle (12.75%), Q angle (7.50%), and tibial torsion angle (6.00%). Being underweight was a significant risk factor for experiencing an abnormal pelvic tilt angle. The main risk factors for abnormal genu recuvartum angle were shown to be overweight and increased years of agricultural experience. Simultaneously, being of the female gender was correlated to disparities in limb length, abnormal femoral antetorsion angle, abnormal tibiofemoral angle, and abnormal foot pronation. Aging was identified as a notable risk factor for limb length discrepancy, abnormal tibiofemoral angle, and abnormal genu recurvatum angle. Lower limb screening is intended to aid in the detection of foot and knee misalignment in rice farmers. Consequently, this might result in the early prevention of musculoskeletal problems from such misalignment.

Comparison of myxovirus resistance gene 2 expression among adult and juvenile SLE Iraqi patients

Sahar Aldhahir, Izzat A. M. Al-Rayahi, Salwa S. Muhsin — Tue, 25 Mar 2025 00:00:00 +0700

Abstract

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple systems in the body. The disease may also impact juveniles (jSLE), but are less frequent. SLE is characterized by the excessive production of auto-antibodies, and genetic background that is not fully comprehended. The human Mxyovirus resistance gene (MX2), classified as an immune regulatory gene, has not yet been researched in juvenile-onset-SLE. There are only a few studies on its connection to rheumatic disease in adults. The current study aimed to investigate its expression and its correlation with disease activity in different ages. This study included 50 patients with SLE, 25 adults and 25 juveniles who all met the ACR criteria, and a control group of 30 healthy individuals, Total RNA was obtained by PBMCs from fresh whole blood samples of all participants to detect human (MX2) levels through quantitative real-time polymerase chain reaction (qRT-PCR), and disease activity was assessed using the systemic erythematosus disease activity index score 2000. The human (MX2) gene was overexpressed in all percipients involved, and there were no statistical differences between the studied group in expression rate p=0.76, There was a non-significant p=0.092 weak negative correlation r=-0.351 between the fold of gene expression and the SLEDAI-2k score. The SLEDAI-2k of (jSLE) and (aSLE) indicated a statistically significant difference between the two groups (p=0.04). Understanding the genes linked to SLE is extremely important, as MX2 could potentially serve as a diagnostic marker, and may also lead to further research opportunities.

Optimization of Solvent Mixture for Solubilizing Cannabis Extract Using I-Optimal Design

Natawat Chankana, Chaowalit Monton, Ornchuma Naksuriya, Thanapat Songsak — Tue, 25 Mar 2025 00:00:00 +0700

Cannabis extract is a poorly water-soluble but alcohol-soluble substance. The development of a cannabis-based oral spray using a high ethanol concentration could cause oromucosal irritation. Finding a suitable solvent mixture to dissolve cannabis extract is challenging. This work aimed to evaluate the solubilization characteristics of cannabis extract in different solvent mixtures commonly used in oral spray formulations. A 0.1 g sample of cannabis extract, obtained through supercritical carbon dioxide fluid extraction and containing 14.09% cannabidiol and 34.19% ∆⁹-tetrahydrocannabinol, was dissolved in 1 g of a solvent mixture. The mixture of three solvents (ethanol, propylene glycol, and water) was varied using an I-optimal design. Results showed that the solvent system promoted a clear solution with maximum recovery of cannabidiol and ∆⁹-tetrahydrocannabinol, composed of 53% ethanol, 35% propylene glycol, and 12% water. This optimized solvent mixture was further verified, and a minor percentage error of less than 10% was achieved, indicating that the software-generated prediction was accurate and reliable. In conclusion, this work clarifies the effect of the solvent mixture on the solubilization characteristics of cannabis extract, which is valuable for the development of a cannabis-based oral spray for the treatment of several ailments.

Effect of Resampling Techniques on Machine Learning Models for Classifying Road Accident Severity in Thailand

Teerawat Simmachan , Pichit Boonkrong — Tue, 25 Mar 2025 00:00:00 +0700

Road traffic accidents (RTAs) pose a significant global challenge, particularly in Thailand. This study investigates the impact of resampling techniques on machine learning (ML) models for classifying road accident severity in Thailand, utilizing data from 31,817 road traffic accidents collected between January 1, 2021, and December 31, 2022. The primary challenge addressed is class imbalance, where fatal accidents represent a small fraction of the dataset. Three popular ML models, including Random Forest (RF), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), were evaluated with four resampling techniques: Imbalanced (IB), Under-sampling (US), Over-sampling (OS), and Combined Sampling (CS). These resampling approaches generated 12 ML models, whose performance was evaluated under three different train/test split ratios: 70/30, 80/20, and 90/10. Compared to the IB approach, the results demonstrate that all US, OS and CS techniques significantly improved model performance, particularly in terms of F1 score, G-mean, and balanced accuracy. Among the models, RF-CS, KNN-OS, and XGB-CS exhibited the best classification performance. Although these evaluation metrics improved over the imbalanced scheme, KNN’s overall performance in detecting fatal accidents was weaker compared to RF and XGB. Specifically, KNN struggled more with the imbalanced dataset, even after applying resampling techniques. These findings suggest that choosing the appropriate resampling techniques is crucial for enhancing model performance in classifying accident severity.

Future Growing Seasons: Bias Correction with SVR and QDM for Indonesian Temperature Projection under RCP 2.6 and RCP 8.5

Tue, 25 Mar 2025 00:00:00 +0700

Human activities have significantly contributed to greenhouse gas (GHG) emissions, escalating global temperatures and exacerbating extreme weather events, which pose serious agricultural threats by disrupting crop growth. Climate researchers employ Representative Concentration Pathways (RCPs) to forecast future GHG scenarios. Downscaling techniques have improved predictions of Growing Season Lengths (GSL) predictions and mean temperatures (T_mean), both of which are crucial for agricultural planning. This study evaluated Support Vector Regression (SVR) and Quantile Delta Mapping (QDM) for projecting T_mean and its impact on GSL under RCP 2.6 and RCP 8.5. Bias correction was applied to historical T_mean data using both methods, based on ERA5 data. SVR showed a lower Root Mean Square Error (RMSE) (0.6 vs 1.1) and a slightly higher correlation (0.6 vs. 0.5) than QDM. However, QDM was chosen for T_mean projection due to its superior data homogeneity and better alignment of standard deviation with observed values. Projections indicated a significant T_mean increase after 2026 under both RCPs, with T_mean under RCP 8.5 expected to exceed 30°C between 2050 and 2100, necessitating heat-resistant crop varieties. Greater increases in GSL under RCP 2.6 underscored the need for effective mitigation strategies. This study emphasizes adaptive farming practices and recommends integrating quantile-based and machine learning methods into future climate projections to enhance agricultural resilience.

Parametric and Nonparametric Estimation of Population Mean in Poisson-Xgamma Distribution with Applications to Count Data

Patarawan Sangnawakij — Tue, 25 Mar 2025 00:00:00 +0700

This study proposes new estimators and confidence intervals for the population mean of the Poisson-Xgamma distribution, which are useful for overdispersed count data analysis. We prove that the proposed estimators using maximum likelihood and method of moments estimation are consistent and establish the variance of the estimators. Moreover, the confidence intervals are constructed based on large-sample theory and bootstrap method. The former method utilizes the properties of the maximum likelihood and moment estimators, the likelihood ratio, and the asymptotic normality property of the log-transformed maximum likelihood estimator. Percentile bootstrap and bias-corrected and accelerated confidence intervals are considered. The performance of the estimators is investigated through simulations in terms of bias, mean squared error, coverage probability, and length of interval. According to the simulations, the log-transformed maximum likelihood estimation-based confidence interval for the mean provides excellent and better coverage rates than the other competitive methods. Furthermore, two real data sets are used to demonstrate our estimators and perform a comparison that supports the findings obtained from the simulation study.

Predicting Player Churn in the Gaming Industry: A Machine Learning Framework for Enhanced Retention Strategies

Tue, 25 Mar 2025 00:00:00 +0700

This research presents a prediction of gaming player churn along with a thorough analysis. It employs predictive modeling techniques utilizing machine learning approaches to predict player churn (customer attrition) on gaming platforms. Using real-world gaming data from player demographics, in-game purchases, social interactions, and historical gaming behavior, this study proposes a new framework that integrates data preprocessing, segmentation, and predictive modeling to determine which players will churn. Additionally, it uses Logistic Regression and Random Forest, a powerful ensemble learning algorithm, to estimate player churn within a limited time horizon. We found that this approach accurately identified potential churners through a thorough exploration and understanding of the dataset. This predictive model provides insight into the key factors influencing player attrition, allowing game developers to take countermeasures to prevent churn risks and improve player retention strategies. In addition, Power BI insights highlight the key factors influencing player churn. These findings provide actionable recommendations for game developers to mitigate churn risks and enhance player retention strategies. This study contributes to predicting player turnover in the gaming industry, providing a valuable tool for fostering sustainable growth and profitability.

Development of Spray-Dried Co-crystals of Piperine and Succinic Acid for Solubility Enhancement

Tue, 25 Mar 2025 00:00:00 +0700

Piperine (PIP) is an amide alkaloid that belongs to the biopharmaceutical classification system II and shows poor aqueous solubility, which limits its therapeutic efficacy. To counter this issue, the objective of the current research was to screen and prepare co-crystals of piperine using succinic acid as a co-former in different molar ratios. Since spray drying is a well-known scale-up technology for co-crystallization, it has been used in the current research for co-crystal preparation. This study aimed at enhancing solubility and dissolution rate by preparing piperine co-crystals. Equimolar ratios of piperine and succinic acid were used to prepare co-crystals via spray drying. The developed spray-dried co-crystals were characterized by in silico modeling, solubility studies, in vitro dissolution studies, Powder X-ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC), Fourier Transform Infrared Spectroscopy (FT-IR) and Scanning Electron Microscopy (SEM). PXRD studies of spray-dried co-crystals showed improved crystallinity, SEM studies revealed a distinct morphology compared to parent components, and the DSC thermogram indicated enhanced thermal stability. The solubility of piperine at pH 7.4 (19.2 ± 1.23 µg/mL) and pH 1.2 (35.2 ± 0.16 µg/mL) increased by almost 2-3 times which was 49.5 ± 0.89 µg/mL at pH 7.4 and 96.4 ± 1.62 µg/mL at pH 1.2. Increasing the concentration of succinic acid improves the solubility of PIP. Moreover, compared to pure piperine, the dissolution rate of piperine co-crystals increased by almost threefold. This increase may be due to the strong hydrogen bonding of piperine and succinic acid in co-crystals. The stability of piperine was also improved as the co-crystals remained stable under accelerated temperature and humidity conditions. Our findings conclude that prepared spray-dried co-crystals have the potential to improve the solubility and dissolution profile of piperine. Hence, co-crystals can be a suitable approach for improving the physicochemical properties of BCS Class II drugs.

Neuroprotective Potential of Thai Cinnamon (Cinnamomum bejolghota) Bark Extracts against Oxidative Stress-Induced Neurotoxicity in SH-SY5Y Neuroblastoma Cells

Tue, 25 Mar 2025 00:00:00 +0700

Neurodegenerative diseases are associated with oxidative stress, which may be alleviated by antioxidant-rich plant compounds such as cinnamon. This study investigates the neuroprotective effects of Thai cinnamon bark (Cinnamomum bejolghota) extracts, in both powder and essential oil forms, against H₂O₂-induced neurotoxicity in SH-SY5Y neuroblastoma cells. The extracts were analyzed for their phenolic and flavonoid content as well as their antioxidant activities using DPPH, FRAP, and ABTS assays. Neurotoxicity and neuroprotection were assessed via MTT, LDH, TBARs, and ROS assays. The powder extract exhibited a high phenolic content (1,275.25 ± 24.07 mg GAE/g) and demonstrated stronger antioxidant activity across all assays compared to the oil form. DPPH and ABTS assays revealed IC50 values of 195.80 µg/mL and 35.55 µg/mL, respectively, for the powder extract, while the FRAP assay confirmed its dose-dependent scavenging effects (p ≤ 0.01). Conversely, the oil extract exhibited low flavonoid content (3.05 ± 3.82 mg QE/g), weaker antioxidant activity, and no significant effects at any tested concentration (p ≤ 0.01). MTT assays confirmed the safety of powder concentrations (100–1,000 µg/mL) for neurons, whereas oil concentrations as low as 80 µg/mL reduced cell survival to 68.85%. Both extracts displayed neuroprotective properties against oxidative stress, with the powder extract demonstrating superior efficacy. At 1,000 µg/mL, the powder extract significantly reduced LDH activity, ROS levels, and lipid peroxidation (p ≤ 0.01). These findings suggest that Thai cinnamon powder, when incorporated into food or beverages, may help prevent neurodegeneration caused by oxidative stress. Further studies are needed to optimize its therapeutic potential.

Biological Activities and Gene Expression Effects of Blended Essential Oils in Promoting Dermal Wound Healing

Watcharacha Krongkeha, Natprapas Ritwatthanavanich — Tue, 25 Mar 2025 00:00:00 +0700

Essential oils (EOs) are highly concentrated plant-derived compounds that have been used for centuries for their benefits in medicinal, food-processing, and commercial purposes - notably within the realm of cultural and luxury industries. However, only a single essential oil has been extensively studied for biological activities and wound healing. This study aimed to investigate the synergistic potential of blended essential oils (BEO) composed of tea tree essential oil (TTO), lavender essential oil (LEO), rosemary essential oil (REO), and turmeric essential oil (TEO) for its biological activities and wound healing effects on human dermal fibroblast (HDF) cells. BEO exhibited the strongest antioxidant activity against 2,2 diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radicals with IC₅₀ values of 10.669 and 108.497 mg/mL, respectively. It exhibited the lowest cytotoxicity against Vero cells and also demonstrated promising anti-inflammatory and anti-collagenase activities. The in vitro scratch wound healing assay on human dermal fibroblast cells indicated that BEO significantly increased cell migration rate after 48 h of incubation. The gene expression study revealed that BEO promoted wound healing by upregulating the COL1A1 gene, which is involved in type I collagen synthesis. This study is the first to confirm that the blended essential oil (BEO) has the significant potential for use as wound healing agent.

Production of β-cryptoxanthin at Different Artificial Light Spectra by Three Strains of Microalgae

Tue, 25 Mar 2025 00:00:00 +0700

Microalgae have significant potential for β-cryptoxanthin production. This study aimed to evaluate the effects of white (445, 544 nm), blue (465 nm), and red (660 nm) light-emitting diodes (LEDs) on biomass accumulation, total carotenoid content, and β-cryptoxanthin production in three strains of microalgae: Scenedesmus obliquus, Coelastrum morus, and Chlorococcum sp. Biomass accumulation increased under blue and red LED cultivation, while red LED significantly enhanced carotenoid and β-cryptoxanthin accumulation. β-Cryptoxanthin content in S. obliquus, C. morus, and Chlorococcum sp. cultivated under red LED was 171.92 ± 10.42, 217.35 ± 9.17, and 256.27 ± 8.80 μg/g cell dry weight, respectively. These values represent a 29.43%–33.27% increase compared to cultivation under white and blue LEDs. The antioxidant activity of all microalgal extracts exceeded 85%. These findings highlight the potential of red LED lighting to enhance β-cryptoxanthin production in the investigated microalgae strains.

Survival Rates, Quality of Life and Side Effects of Medical Cannabis in Patients with Palliative Cancer in Thailand

Tue, 25 Mar 2025 00:00:00 +0700

This study aimed to compare clinical outcomes and safety in patients with palliative cancer who used cannabis and morphine across dimensions such as survival rate, hospital admission rate, number of hospital days, quality of life, and side effects. A retrospective cohort observational study was conducted based on medical records from January 1, 2020 – March 30, 2023. Additionally, a prospective cohort study was conducted using the EQ-5D-5L and Edmonton Symptom Assessment System (ESAS) quality of life scales. The study included 98 cancer patients receiving palliative care in six hospitals. The results showed that the median survival time of the cannabis group (CG) was 0.69 years, and the morphine group (MG) was 0.52 years (p = 0.168). The CG had a lower hospital admission rate than the MG, p>0.05. The CG had fewer number of hospital days than the MG, p<0.05. The quality of life (QOL) assessment by ESAS indicated that the CG had a better QOL than the MG, p<0.05. The EQ-5D-5L tool also demonstrated that the QOL score for the CG was higher than that of the MG, p<0.05. The MG had higher levels of liver enzymes, including AST and ALT than those who received cannabis, p<0.05. These findings suggest that medical cannabis may be beneficial for palliative cancer patients.