Licochalcone C (LCC; PubChem CID:9840805), a chalcone compound originating from the root of Glycyrrhiza inflata, has shown anticancer activity against skin cancer, esophageal squamous cell carcinoma, and oral squamous cell carcinoma. However, the therapeutic potential of LCC in treating colorectal cancer (CRC) and its underlying molecular mechanisms remain unclear. Chemotherapy for CRC is challenging because of the development of drug resistance. In this study, we examined the antiproliferative activity of LCC in human colorectal carcinoma HCT116 cells, oxaliplatin (Ox) sensitive and Ox-resistant HCT116 cells (HCT116-OxR). LCC significantly and selectively inhibited the growth of HCT116 and HCT116-OxR cells. An in vitro kinase assay showed that LCC inhibited the kinase activities of EGFR and AKT. Molecular docking simulations using AutoDock Vina indicated that LCC could be in ATP-binding pockets. Decreased phosphorylation of EGFR and AKT was observed in the LCC-treated cells. In addition, LCC induced cell cycle arrest by modulating the expression of cell cycle regulators p21, p27, cyclin B1, and cdc2. LCC treatment induced ROS generation in CRC cells, and the ROS induction was accompanied by the phosphorylation of JNK and p38 kinases. Moreover, LCC dysregulated mitochondrial membrane potential (MMP), and the disruption of MMP resulted in the release of cytochrome c into the cytoplasm and activation of caspases to execute apoptosis. Overall, LCC showed anticancer activity against both Ox-sensitive and Ox-resistant CRC cells by targeting EGFR and AKT, inducing ROS generation and disrupting MMP. Thus, LCC may be potential therapeutic agents for the treatment of Ox-resistant CRC cells.

This study offers a comprehensive overview of brain organoids for researchers. It combines expert opinions with technical summaries on organoid definitions, characteristics, culture methods, and quality control. This approach aims to enhance the utilization of brain organoids in research. Brain organoids, as three-dimensional human cell models mimicking the nervous system, hold immense promise for studying the human brain. They offer advantages over traditional methods, replicating anatomical structures, physiological features, and complex neuronal networks. Additionally, brain organoids can model nervous system development and interactions between cell types and the microenvironment. By providing a foundation for utilizing the most human-relevant tissue models, this work empowers researchers to overcome limitations of two-dimensional cultures and conduct advanced disease modeling research.

Background: A decline in masticatory function may indicate brain dysfunction related to dementia, but the relationship between masticatory function and dementia risk remains unclear. This study aimed to investigate whether masticatory function is associated with the risk of cognitive decline and dementia. Methods: Data were obtained from the nationwide prospective cohort study of randomly sampled community-dwelling Koreans aged ≥ 60 years. The 5,064 non-demented participants, whose number of chewing cycles per bite was assessed by clinical interview, were followed for 8 years with biennial assessments of cognitive performance and clinical diagnoses of all-cause dementia and Alzheimer’s disease (AD). Structural brain magnetic resonance imaging was collected from a subset of cohort participants and their spouses for imaging analyses. Results: Males who chewed ≥ 30 cycles/bite had faster decline in global cognition and memory function and were at higher risk for incident all-cause dementia (hazard ratio [HR], 2.91; 95% confidence interval [CI], 1.18–7.18) and AD (HR, 3.22; 95% CI, 1.14–9.11) compared to males with less than 10 cycles/bite. Additionally, increased chewing cycles in males were associated with reduced brain volume, particularly in regions involved in compensatory cognitive control of mastication. There was no significant association between chewing cycles and the risk of dementia or brain volume in females. Conclusion Older men who frequently chew their meals could be considered a notable population at risk for dementia who should be carefully assessed for their cognitive trajectories.

Purpose: This study aimed to compare the short-term outcomes and safety profiles of androgen-deprivation therapy (ADT)+abiraterone/prednisone with those of ADT+docetaxel in patients with de novo metastatic hormone-sensitive prostate cancer (mHSPC). Materials and Methods: A web-based database system was established to collect prospective cohort data for patients with mHSPC in Korea. From May 2019 to November 2022, 928 patients with mHSPC from 15 institutions were enrolled. Among these patients, data from 122 patients who received ADT+abiraterone/prednisone or ADT+docetaxel as the primary systemic treatment for mHSPC were collected. The patients were divided into two groups: ADT+abiraterone/prednisone group (n=102) and ADT+docetaxel group (n=20). We compared the demographic characteristics, medical histories, baseline cancer status, initial laboratory tests, metastatic burden, oncological outcomes for mHSPC, progression after mHSPC treatment, adverse effects, follow-up, and survival data between the two groups. Results: No significant differences in the demographic characteristics, medical histories, metastatic burden, and baseline cancer status were observed between the two groups. The ADT+abiraterone/prednisone group had a lower prostate-specific antigen (PSA) progression rate (7.8% vs. 30.0%; p=0.011) and lower systemic treatment discontinuation rate (22.5% vs. 45.0%; p=0.037). No significant differences in adverse effects, oncological outcomes, and total follow-up period were observed between the two groups. Conclusions ADT+abiraterone/prednisone had lower PSA progression and systemic treatment discontinuation rates than ADT+docetaxel. In conclusion, further studies involving larger, double-blinded randomized trials with extended follow-up periods are necessary.

The mechanistic functions of 3-deoxysappanchalcone (3-DSC), a chalcone compound known to have many pharmacological effects on lung cancer, have not yet been elucidated. In this study, we identified the comprehensive anti-cancer mechanism of 3-DSC, which targets EGFR and MET kinase in drug-resistant lung cancer cells. 3-DSC directly targets both EGFR and MET, thereby inhibiting the growth of drug-resistant lung cancer cells. Mechanistically, 3-DSC induced cell cycle arrest by modulating cell cycle regulatory proteins, including cyclin B1, cdc2, and p27. In addition, concomitant EGFR downstream signaling proteins such as MET, AKT, and ERK were affected by 3-DSC and contributed to the inhibition of cancer cell growth. Furthermore, our results show that 3-DSC increased redox homeostasis disruption, ER stress, mitochondrial depolarization, and caspase activation in gefitinib-resistant lung cancer cells, thereby abrogating cancer cell growth. 3-DSC induced apoptotic cell death which is regulated by Mcl-1, Bax, Apaf-1, and PARP in gefitinib-resistant lung cancer cells. 3-DSC also initiated the activation of caspases, and the pan-caspase inhibitor, Z-VAD-FMK, abrogated 3-DSC induced-apoptosis in lung cancer cells. These data imply that 3-DSC mainly increased mitochondria-associated intrinsic apoptosis in lung cancer cells to reduce lung cancer cell growth. Overall, 3-DSC inhibited the growth of drug-resistant lung cancer cells by simultaneously targeting EGFR and MET, which exerted anti-cancer effects through cell cycle arrest, mitochondrial homeostasis collapse, and increased ROS generation, eventually triggering anticancer mechanisms. 3-DSC could potentially be used as an effective anti-cancer strategy to overcome EGFR and MET target drug-resistant lung cancer.

Objective: To investigate the relationship between reduced glutathione (GSH), a key molecule of the antioxidant defense system in the blood, and glutathione reductase (GR), which reduces oxidized glutathione (glutathione disulfide [GSSG]) to GSH and maintains the redox balance, with the prevalence of Alzheimer’s dementia and cognitive decline. Methods: In all, 20 participants with Alzheimer’s dementia who completed the third follow-up clinical evaluation over 6 years were selected, and 20 participants with normal cognition were selected after age and sex matching. The GSH and GR concentrations were the independent variables. Clinical diagnosis and neurocognitive test scores were the dependent variables indicating cognitive status. Results: The higher the level of GR, the greater the possibility of having normal cognition than of developing Alzheimer’s dementia. Additionally, the higher the level of GR, the higher the neurocognitive test scores. However, this association was not significant for GSH. After 6 years, the conversion rate from normal cognition to cognitive impairment was significantly higher in the lower 50th percentile of the GR group than in the upper 50th percentile. Conclusion The higher the GR, the lower the prevalence of Alzheimer’s dementia and incidence of cognitive impairment and the higher the cognitive test scores. Therefore, GR is a potential protective biomarker against Alzheimer’s dementia and cognitive decline.

Background: Pediatric alopecia areata (AA) can affect the quality of life (QoL) of patients and their family members. Research on the QoL and burden on family members in pediatric AA is limited. Objective: This nationwide multicenter questionnaire study described the QoL and burden of the family members of patients with pediatric AA. Methods: This nationwide multicenter questionnaire study enrolled AA patients between the ages of 5 and 18 years from March 1, 2017 to February 28, 2018. Enrolled patients and their parents completed the modified Children’s Dermatology Life Quality Index (CDLQI) and the modified Dermatitis Family Impact (mDFI). The disease severity was measured using the Severity of Alopecia Tool (SALT) survey scores. Results: A total of 268 patients with AA from 22 hospitals participated in this study. Our study found that the efficacy and satisfaction of previous treatments of AA decreased as the severity of the disease increased. The use of home-based therapies and traditional medicines increased with the increasing severity of the disease, but the efficacy felt by patients was limited. CDLQI and mDFI scores were higher in patients with extensive AA than those with mild to moderate AA. The economic and time burden of the family members also increased as the severity of the disease increased. Conclusion The severity of the AA is indirectly proportional to the QoL of patients and their family members and directly proportional to the burden. Physicians need to understand these characteristics of pediatric AA and provide appropriate intervention to patients and their family members.

Background/Aims: Risk prediction models using a deep neural network (DNN) have not been reported to predict the risk of advanced colorectal neoplasia (ACRN). The aim of this study was to compare DNN models with simple clinical score models to predict the risk of ACRN in colorectal cancer screening. Methods: Databases of screening colonoscopy from Kangbuk Samsung Hospital (n=121,794) and Kyung Hee University Hospital at Gangdong (n=3,728) were used to develop DNN-based prediction models. Two DNN models, the Asian-Pacific Colorectal Screening (APCS) model and the Korean Colorectal Screening (KCS) model, were developed and compared with two simple score models using logistic regression methods to predict the risk of ACRN. The areas under the receiver operating characteristic curves (AUCs) of the models were compared in internal and external validation databases. Results: In the internal validation set, the AUCs of DNN model 1 and the APCS score model were 0.713 and 0.662 (p<0.001), respectively, and the AUCs of DNN model 2 and the KCS score model were 0.730 and 0.667 (p<0.001), respectively. However, in the external validation set, the prediction performances were not significantly different between the two DNN models and the corresponding APCS and KCS score models (both p>0.1). Conclusions Simple score models for the risk prediction of ACRN are as useful as DNN-based models when input variables are limited. However, further studies on this issue are warranted to predict the risk of ACRN in colorectal cancer screening because DNN-based models are currently under improvement.