Eumitrin L (1), a new dimeric xanthone, was isolated from the crude acetone extract of the lichen U. baileyi, together with 13 known compounds, including one dimeric xanthone (2), five depsidones (3-7), two depsides (8-9), one dibenzofuran (10) and four mono phenolics (11-14). Spectroscopic analyses (1D, 2D NMR), compared to related references, were employed for structural elucidation, while DP4 probability and ECD spectrum helped identify the absolute configuration of 1. Bioactivity evaluation of the isolated compounds revealed 6, 7, 8, 11 as active tyrosinase inhibitors, and 3, 10, 13, 14 as significant antioxidants. Among them, protocetraric acid (6) (IC50 37.86 μM) and usnic acid (10) (IC50 15.92 μM) were found as the most active components. Molecular docking simulation on these two compounds further confirmed their notable activities, based on effective H-bond formation and excellent binding energy with the tyrosinase protein (2Y9X), and DPPH proteins (2CDU and 3RP8), respectively.

Abstract Dementia, particularly Alzheimer's disease (AD), is a growing concern in aging populations, with mild cognitive impairment (MCI) frequently progressing to AD. Current diagnostic methods rely on clinical assessments and MRI-based visual scoring systems such as MTA, ERICA, and GCA, requiring expert evaluation and leading to delays. This study presents an AI-based diagnostic framework utilizing deep learning models to predict visual scores and classify dementia stages using brain MRI and clinical measures such as TMSE and MoCA. ResNet18 was trained separately for MTA, ERICA, and GCA scoring, while DenseNet121 was applied for MRI-based dementia classification. Results indicate that models integrating AI-predicted Visual Scores with clinical data achieved up to 75.24% accuracy, outperforming MRI-only models (63.44%). Notably, the inclusion of MoCA unexpectedly reduced classification accuracy, suggesting potential biases in its application. The AI system offers a promising tool for early dementia screening, particularly in areas with limited access to neurologists and radiologists, such as rural Thailand. Future studies will focus on refining model generalizability across diverse populations and improving prediction robustness in real-world clinical settings.

L-shape spermine-based cationic lipids with a lysine spacer and both identical and non-identical hydrophobic tails were successfully synthesized. Liposomes prepared from these lipids, either alone or in combination with DOPE, demonstrated DNA-binding capability, as confirmed by gel electrophoresis assays. The physicochemical properties, such as size, zeta-potential, and stability of the lipoplexes formed from the cationic lipids were investigated. The liposomes efficiently condensed DNA into compact structures at an N/P ratio of approximately 5-10. Interestingly, liposomes without DOPE exhibited higher transfection efficiency than those containing DOPE, with the cationic lipid featuring a spermine polar head bonded to a lysine spacer and C12-C16 hydrocarbon tails (Sper-Lys-C12,16) achieved the greatest transfection efficiency, as revealed by fluorescence microscopy and flow cytometry. Notably, this formulation maintained high transfection efficiencies even in serum concentrations up to 40%, outperforming the commercial standard Lipofectamine 3000. Additionally, these liposomes exhibited low cytotoxicity, highlighting their potential as safe and effective gene delivery agents.

ABSTRACTThis paper examines the causal effects of Thailand's 2017 large‐scale unconditional cash transfer programme (‘the state welfare card programme’) that covered over 20% of the country's population. The study uses regression discontinuity methods and data from the 2019 nationally representative household socio‐economic survey. Results show that this programme had no significant impact on consumption expenditure and food expenditure, as intended. However, the programme caused a significant increase in non‐consumption expenditure, especially items paid for in cash. Also, the programme significantly increased in‐kind consumption expenditure, demonstrating the payment modality of the programme. Given a large evidence base showing the poverty‐reducing effects of cash transfer programmes in developing countries, the findings point to the need to revamp the programme at both design and implementation stages.

A low signal relative to background noise (signal-to-noise ratio: SNR) signifies a challenge in distinguishing the signal from the background noise. Herein, estimates for the simultaneous confidence intervals (SCIs) for the differences between the SNRs of several log-normal (LN) distributions are presented. These are based on the fiducial generalized confidence interval (FGCI), large sample (LS), method of variance estimates recovery (MOVER), and Bayesian (BS) approaches. By using a Monte Carlo simulation study with RStudio programming, all SCIs are compared based on their coverage probabilities and average lengths. The results indicate that LS approach provided shorter average lengths compared to the MOVER approach, the former approach is the most effective for estimating the SCIs for the differences among the SNRs of several LN distributions. Furthermore, these methods were also used to compare the equality of three price-earning ratios: the SET50, SET100, and sSET indexes. In conclusion, the LS approach proved to be the best method and is thus recommended for estimating the SCIs for the differences between the SNRs of multiple LN distributions.

The search for a potent α-glucosidase inhibitor from the fungus Biscogniauxia capnodes SWUF15-40 yielded eighteen compounds. A comprehensive analysis from NMR and MS data revealed three new α-pyrones, biscogniapyrones A-C (1-3), two new isocoumarins (5 and 6), and thirteen known compounds. The configurations were assigned from calculated 13C NMR chemical shifts and ECD spectra, together with 1H NMR analysis of Mosher esters. Several compounds exhibited effective inhibitory activity against α-glucosidase with IC50 values in the range of 0.041-0.257mM, which are lower than the positive control, acarbose (IC50 0.713mM). The proposed non-competitive mode of inhibition was deduced from Lineweaver-Burk plots together with Km and Vmax values. In silico dockings of the strongest inhibitor, compound 3 were studied. Three out of the five determined allosteric sites of the enzyme model were favorable, with closed free binding energies of roughly - 4.00kcal/mol. The binding interactions observed between 3 and amino acids in the pocket sites were hydrogen bonding and hydrophobic interactions. These findings, therefore, provide opportunities for drug development processes to be carried out.

ABSTRACT Though there has been much research conducted on religious beliefs and participation in religious activities, most has been conducted in Western countries where the research subjects follow monotheistic religions such Christianity, Judaism, or Islam. Not much research involving religious beliefs, religious participation, and religious donations has been conducted in the case of Buddhism, whose principles and teachings do not involve belief in God. We used the national-representative sample on conditions of society and culture and mental health survey organized by the National Statistical Office of Thailand. With observation more than 50,000 Thai Buddhists with regard to their religious activities, religious beliefs, and faith, including charitable cash donations related to religious and non-religious activities, our econometrics results show that Thai people with a more devout belief in Buddhism make religious donations in greater amounts than do people who are less devout. This finding might be used as motivation for Buddhist temples to foster and enhance Buddhist beliefs in their congregations, thus contributing to an increase in religious participation and a boost in religious donations.

Floods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experience such disruptions annually. Current methods of assistance and relief during flooding rely on field surveys conducted manually by personnel, a process constrained by its time-intensive nature. Moreover, existing applications or platforms do not support the classification and inspection of oil palm plantations affected by floods during harvesting. This research aims to develop a method and application for inspecting oil palm plantations impacted by floods during harvesting. The approach utilizes deep learning and geographic information systems (GIS) to classify and analyze flood-affected areas and determine the ripeness of oil palm bunches on trees, enabling accurate and rapid identification of flood-affected areas. The study results demonstrate that the proposed method achieves a flood classification accuracy ranging from 96.80% to 98.29% and ripeness classification accuracy for oil palm bunches on trees ranging from 97.60% to 99.75%. These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. Additionally, the developed application serves as a valuable tool for flood management, facilitating timely assistance and relief for farmers impacted by flooding.