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Background and objective: Genetic polymorphism studies in families and twins indicated the heritability of depression. However, the association between genes with genetic polymorphism and depression provides various findings and remains unclear. Therefore, we conducted a systematic review and meta-analysis to determine the genes with their polymorphism associated with the symptomatic depression known as major depressive disorder (MDD). Materials and methods: PubMed and Scopus were searched for relevant studies published before May 22, 2023 (1968–2023), and 62 were selected for this review. The study's bias risk was investigated using the Newcastle–Ottawa scale. Gene functional enrichment analysis was investigated for molecular function (MF) and biological process (BP) and pathways. A meta-analysis of the studied genes that were replicative in the same single nucleotide polymorphism was conducted using a random-effect model. Results: The 49 genes involved in MDD were studied and engaged in several pathways, such as tryptophan metabolism or dopaminergic and serotonergic synapses. Based on gene overlapping in MF and BP, 13 genes with polymorphisms were identified as related to MDD. Most of them were only studied once. Solute carrier family 6 member 4 (SLC6A4) overlapping between MF and BP and brain-derived neurotrophic factor (BDNF) as unique to BP were replicative studied and used in the meta-analysis. The polymorphism of SLC6A4 SS and LS genotypes increased the occurrence of MDD development but not significantly [odd ratio (OR) = 1.39; 95% confidence interval (CI) = 0.87–2.22; P = 0.16 and OR = 1.13; 95% CI = 0.84–1.53; P = 0.42, respectively]. A similar result was observed for BDNF rs6265 GG (OR = 1.26; 95% CI = 0.78–2.06; P = 0.35) and BDNF rs6265 AA genotypes (OR = 1.12; 95% CI = 0.77–1.64; P = 0.56). These studies indicated low bias and significant heterogeneity. Conclusion: At least 13 studied genes with polymorphisms were involved in MDD development according to MF and BP, but not significantly. These results suggest that MDD development risk factors might require genetic and other factors for interaction and induction. [ABSTRACT FROM AUTHOR]

Background and Objectives: Cervical cancer is one of the most common types of frequently found cancers in Thailand. One of the causative agents is the infection of the high-risk human papillomavirus (HPV) type 16 and 18. Traditional medicines are rich sources of bioactive compounds which are a valuable source for the development of novel cancer therapies. In this study, the therapeutic effects of 3 traditional medicines, KerraTM, KSTM, and MinozaTM, were studied on HeLa and CaSki cells. Materials and Methods: The effects of KerraTM, KSTM, and MinozaTM on cancer cells were evaluated through cytotoxicity and cell death assays. The infection assay using HPV-16 pseudovirus was also carried out. Results: All traditional medicines efficiently suppressed cell growths of HeLa and CaSki, with KerraTM being the most potent anticancer agent followed by KSTM and MinozaTM. KerraTM at 158 µg/mL and 261 µg/mL significantly increases the percentage inhibition of the HPV-16 pseudovirus infection in a pre-attachment step in a dose-dependent manner, while KSTM at 261 µg/mL efficiently inhibited viral infection in both pre-attachment and adsorption steps. However, KerraTM, KSTM, and MinozaTM at subtoxic concentrations could not reduce the viral E6 mRNA expressions of HPV-16 and HPV-18. Cell death assay by acridine orange/ethidium bromide showed that KerraTM increased population of dead cells in dose-dependent manner in both CaSki and HeLa. The percentage of secondary necrosis in KerraTM-treated CaSki was higher than that of HeLa cells, while the percentage of late apoptotic cells in HeLa was higher than that of CaSki, indicating that HeLa was more susceptible to KerraTM than CaSki. For KSTM and MinozaTM, these extracts at 250 µg/mL promoted autophagy over cell death. At 500 µg/mL, the percentage of dead cells in KerraTM was higher than that of KSTM and MinozaTM. Conclusions: KerraTM is a potent traditional medicine for promoting cancer cell death. KerraTM is possibly useful in the prevention and treatment of cervical cancer. Further investigation will be carried out to gain a better understanding of the biochemical mechanism and the pharmacological activity underlying this effect. [ABSTRACT FROM AUTHOR]

The current practice of determining histologic grade with a single molecular biomarker can facilitate differential diagnosis but cannot predict the risk of lesion progression. Cancer is caused by complex mechanisms, and no single biomarker can both make accurate diagnoses and predict progression risk. Modelling using multiple biomarkers can be used to derive scores for risk prediction. Mathematical models (MMs) may be capable of making predictions from biomarker data. Therefore, this study aimed to develop MM–based scores for predicting the risk of precancerous cervical lesion progression and identifying precancerous lesions in patients in northern Thailand by evaluating the expression of multiple biomarkers. The MMs (Models 1–5) were developed in the test sample set based on patient age range (five categories) and biomarker levels (cortactin, p16INK4A, and Ki–67 by immunohistochemistry [IHC], and HPV E6/E7 ribonucleic acid (RNA) by in situ hybridization [ISH]). The risk scores for the prediction of cervical lesion progression ("risk biomolecules") ranged from 2.56–2.60 in the normal and low–grade squamous intraepithelial lesion (LSIL) cases and from 3.54–3.62 in cases where precancerous lesions were predicted to progress. In Model 4, 23/86 (26.7%) normal and LSIL cases had biomolecule levels that suggested a risk of progression, while 5/86 (5.8%) cases were identified as precancerous lesions. Additionally, histologic grading with a single molecular biomarker did not identify 23 cases with risk, preventing close patient monitoring. These results suggest that biomarker level–based risk scores are useful for predicting the risk of cervical lesion progression and identifying precancerous lesion development. This multiple biomarker–based strategy may ultimately have utility for predicting cancer progression in other contexts. [ABSTRACT FROM AUTHOR]