Mahidol University's Institutional Repository
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Recent Submissions
Revisiting the theory of scalar cloud: Kerr-Einstein-Maxwell-Dilaton-Axion black hole
(2025-11-01) Senjaya D.; Senjaya D.; Mahidol University
In this work, we present the novel exact scalar cloud solution in the Einstein-Maxwell-Dilaton-Axion (EMDA) theory. Traditionally, the scalar cloud is examined using an approximation approach known as the AAM technique, which operates in the ultralight scalar field and low black hole spin regimes. The objective of this work is to revisit the theory of the scalar cloud of the black hole by first deriving the exact solution of the massive Klein-Gordon equation in the Kerr-EMDA black hole spacetime. We discover that the scalar cloud exact solution is achieved using the Confluent Heun function and its exact spectrum conforms to the polynomial condition of the radial solution. Working with the novel exact solution allows us to investigate the scalar cloud without being constrained by ultralight scalar mass or the slow black hole limit. Finally, we discover that the precise characteristic frequency of the black hole’s scalar cloud accounts for the influence of both the event horizon and the Cauchy horizon.
Cannabinoid/lysophosphatidylinositol-sensing G-protein coupled receptor 55 promotes intestinal tight junction assembly and its mechanistic insights
(2025-01-01) Treveeravoot S.; Sukmak P.; Chatkul P.; Arinno A.; Amonsiriwit S.; Supapol P.; Limwattananon T.; Choksukchalalai N.; Kitti-Udom N.; Chindaduangratn N.; Wachiradejkul W.; Satianrapapong W.; Kwanthongdee J.; Moonwiriyakit A.; Muanprasat C.; Pongkorpsakol P.; Treveeravoot S.; Mahidol University
Intestinal tight junction disruption initiates progression of related diseases including inflammatory bowel disease (IBD) with no FDA-approved drug for tight junction recovery. To demonstrate the effect of pharmacological activation of the cannabinoid/lysophosphatidylinositol-sensing G-protein coupled receptor 55 (GPR55) by its specific synthetic agonist O1602 on intestinal barrier function, tight junction-dependent permeability, and its underlying mechanisms. We show that O1602 treatment increased transepithelial electrical resistance (TER) across intestinal epithelial-like T84 cell monolayers and suppressed 4-kDa FITC-dextran permeability. Neither CB1 inhibitor nor CB2 inhibitor has affected TER increases in response to O1602 treatment. O1602 was ineffective in enhancing intestinal barrier integrity in T84 monolayers treated with GPR55 antagonist or in GPR55 KD T84 monolayers, indicating that GPR55 agonism promotes intestinal barrier function and inhibits tight junction-dependent leak pathway permeability. In fact, O1602 treatment also prevented TNF-α-induced intestinal barrier disruption in IFN-γ-primed T84 and Caco-2BBe monolayers. The effect of O1602 treatment on enhancing TER across T84 cell monolayers was abolished by pre-treatment with inhibitors of PLC, CaMKKβ, AMPK, SIRT-1, ERK, PKA, β-arrestin, and mTOR. In addition, O1602 failed to promote TER increases in SIRT-1 KO T84 monolayers. Our data from western blot analysis, SIRT-1 activity assay, and immunofluorescence staining of tight junction proteins, coherently recapitulates that GPR55 agonism induces intestinal tight junction assembly via PLC/[Ca2+]i/CaMKKβ/AMPK/SIRT-1/ERK-dependent mechanism. Hence, we furnish the first line of evidence supporting that GPR55 is the regulator of tight junction in intestinal epithelial monolayers and may serve as a novel class of therapeutic target for tight junction disruption-associated diseases.
Microbiota profiles and antimicrobial resistance genes in sow fecal samples from farms with and without antibiotic use
(2025-11-01) Senawin S.; Bunchasak C.; Rakangthong C.; Kaewtapee C.; Foongladda S.; Surachat K.; Chinli R.; Loongyai W.; Senawin S.; Mahidol University
Objective: Antibiotics have been used in swine production, and they are known to be associated with the gut microbiota and antimicrobial resistance (AMR). This study aimed to evaluate the dynamics of the microbiota and AMR among fecal bacteria in sowsby 16S rRNA gene sequencing and TaqMan array card assays. Methods: A total of 40 healthy multiparous sows were tested in a completely randomized design. Sows were randomly divided into two groups: one was fed a commercial diet with antibiotics for 3 weeks from mating to day 21 of gestation, before the farrowing stage (amoxycillin 300 mg/kg and tiamulin 150 mg/kg: control group, ABO), and the other was fed the same diet without antibiotics (treatment group, NOABO). Results: The ABO group had a higher alpha diversity than the NOABO group (p<0.05). The results re-vealed the highest bacterial abundance in the phylum Firmicutes in sow fe-ces in the ABO group at an average level of 92.01% and 92.32% in the NOABO group. Erysipelotrichaceae, Clostridiaceae, and Terrisporobacter in the ABO group had enriched proportions. On the other hand, Lactobacillales, Bacilli, and Streptococcus were enriched in the NOABO group (p<0.05). In terms of AMR, a comparison of the normal log of resistance gene copies between the ABO and NOABO groups displays that the gene copy number was significantly higher (p<0.05) in the ABO group (59%) than in the NOABO group (41%) especially those of β-lactam, aminoglycosides, quinolones, and macrolides. Conclusion: Our investigations discovered that the core microbiota of withdrawal antibiotics may be related to the gut microbiota and AMR. Therefore, understanding the gut mi-crobiota composition and function in animals could enable strategies for its modulation to improve sows’ gut microbiota and minimize the negative impact of antibiotics.
Whole-genome sequencing and SNP analysis of Thai Cannabis sativa cultivar ‘Hang Kra Rog Phu Phan’ (Cannabaceae)
(2025-01-01) Kamoltham T.; Luangpirom N.; Kuamsab N.; Kummalue T.; Chaiphongpachara T.; Kamoltham T.; Mahidol University
Cannabis (Cannabis sativa) is a herbaceous plant valued for its medicinal and therapeutic uses. In Thailand, the indigenous cultivar ‘Hang Kra Rog Phu Phan’ has long been applied in traditional medicine and is recognized for its high Tetrahydrocannabinol (THC) content. This study provides the first genomic characterization of this traditional Thai cultivar using Whole-Genome Sequencing (WGS). Fresh leaf samples were collected from a licensed cultivation site, and paired-end libraries were prepared with the Illumina TruSeq DNA PCR-Free Kit. Sequencing on the NovaSeq 6000 platform produced 63.8 million raw reads (9.57 Gb), yielding 63.3 million high-quality reads (9.50 Gb) after trimming. Clean reads showed a 94.43% alignment rate to the ‘Pink Pepper’ reference genome, with an average depth of 11.39×. Variant calling identified 23.0 million genomic variants, including 18.5 million SNPs and 4.5 million Indels, with 6.04 million high-confidence SNPs retained after stringent filtering. Phylogenetic and principal component analyses revealed unexpected genomic proximity between ‘Hang Kra Rog Phu Phan’ and the CBD-dominant ‘CBD Shark’ cultivar, highlighting its distinct lineage among high-THC cultivars. These findings provide valuable genomic resources for precise cultivar authentication, marker-assisted breeding, conservation of native Thai germplasm, and functional genomics to advance cannabis-based therapeutics.
Active Stacking-Deep Learning with Strategic Sampling for Small and Imbalanced Chemical Toxicity Prediction
(2025-11-18) Zetta D.N.; Shoombuatong W.; Srisongkram T.; Zetta D.N.; Mahidol University
Major challenges in toxicity prediction include dealing with imbalanced and limited data sets, especially when evaluating the harmful potential of chemicals. These issues often lead to poor predictive model performance. Stacking ensemble learning enhances performance by combining predictions from multiple base models, enabling the stack model to improve overall generalization. Active learning (AL), on the other hand, reduces the need for large-scale data sets by effectively training models using carefully selected samples. One effective approach to address data imbalance is the use of strategic sampling techniques. Hereby, we introduce an active stacking-deep learning framework that integrates deep neural networks (DNNs), including a convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM), and an attention mechanism, with strategic data sampling to tackle challenges posed by imbalanced and limited data, ultimately improving the performance of a chemical risk assessment predictive model. In this study, we focused on thyroid-disrupting chemicals (TDCs) that target thyroid peroxidase, as they are linked to thyroid dysfunction, making it essential to evaluate their risks to human health. Using stacking ensemble learning with strategic sampling within an AL framework, our approach achieved an MCC of 0.51, AUROC of 0.824, and AUPRC of 0.851. Although performance decreased across varying test ratios, our uncertainty-based method demonstrated superior stability under severe class imbalance. While a full-data stacking ensemble trained with strategic sampling performs slightly better in MCC, our method achieves marginally higher AUROC and AUPRC, requiring up to 73.3% less labeled data. Molecular docking further validated our predictions, especially for highly toxic compounds, reinforcing the reliability of our framework in identifying TDCs. These findings highlight how active stacking-deep learning with strategic sampling can transform toxicity prediction, offering a more accurate and data-efficient alternative to traditional chemical risk assessment methods.