Although histone deacetylase 6 (HDAC6) is considered a therapeutic target for Alzheimer’s disease (AD), its role in cholinergic dysfunction in AD patients remains unclear. This study investigated the effects of (E)-3-(2-(4-fluorostyryl)thiazol-4-yl)-N-hydroxypropanamide (4-FHA), a new synthetic HDAC6 inhibitor, on cognitive and memory impairments in a scopolamine-induced-AD mouse model. Behaviorally, 4-FHA improved scopolamine-induced memory impairments in the Y-maze, passive avoidance, and Morris water maze tests. In addition, 4-FHA ameliorated scopolamine-induced cognitive impairments in the novel object recognition and place recognition tests. Furthermore, 4-FHA increased acetylation of α-tubulin (a major HDAC6 substrate); the expression of BDNF; and the phosphorylation of ERK 1/2, CREB, and ChAT in the hippocampus of scopolamine-treated mice. In summary, according to our data 4-FHA, an HDAC6 inhibitor, improved the cognitive and memory deficits of the AD mouse model by normalizing BDNF signaling and synaptic transmission, suggesting that 4-FHA might be a potential therapeutic candidate for AD.

Although histone deacetylase 6 (HDAC6) is considered a therapeutic target for Alzheimer’s disease (AD), its role in cholinergic dysfunction in AD patients remains unclear. This study investigated the effects of (E)-3-(2-(4-fluorostyryl)thiazol-4-yl)-N-hydroxypropanamide (4-FHA), a new synthetic HDAC6 inhibitor, on cognitive and memory impairments in a scopolamine-induced-AD mouse model. Behaviorally, 4-FHA improved scopolamine-induced memory impairments in the Y-maze, passive avoidance, and Morris water maze tests. In addition, 4-FHA ameliorated scopolamine-induced cognitive impairments in the novel object recognition and place recognition tests. Furthermore, 4-FHA increased acetylation of α-tubulin (a major HDAC6 substrate); the expression of BDNF; and the phosphorylation of ERK 1/2, CREB, and ChAT in the hippocampus of scopolamine-treated mice. In summary, according to our data 4-FHA, an HDAC6 inhibitor, improved the cognitive and memory deficits of the AD mouse model by normalizing BDNF signaling and synaptic transmission, suggesting that 4-FHA might be a potential therapeutic candidate for AD.

Although histone deacetylase 6 (HDAC6) is considered a therapeutic target for Alzheimer’s disease (AD), its role in cholinergic dysfunction in AD patients remains unclear. This study investigated the effects of (E)-3-(2-(4-fluorostyryl)thiazol-4-yl)-N-hydroxypropanamide (4-FHA), a new synthetic HDAC6 inhibitor, on cognitive and memory impairments in a scopolamine-induced-AD mouse model. Behaviorally, 4-FHA improved scopolamine-induced memory impairments in the Y-maze, passive avoidance, and Morris water maze tests. In addition, 4-FHA ameliorated scopolamine-induced cognitive impairments in the novel object recognition and place recognition tests. Furthermore, 4-FHA increased acetylation of α-tubulin (a major HDAC6 substrate); the expression of BDNF; and the phosphorylation of ERK 1/2, CREB, and ChAT in the hippocampus of scopolamine-treated mice. In summary, according to our data 4-FHA, an HDAC6 inhibitor, improved the cognitive and memory deficits of the AD mouse model by normalizing BDNF signaling and synaptic transmission, suggesting that 4-FHA might be a potential therapeutic candidate for AD.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

Purpose: Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables. Methods: The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation cohorts. Five machine learning techniques, including gradient boosting machine (GBM), support vector machine, random forest, decision tree, and neural network, were used to build predictive models, and the area under the receiver operating characteristic curve (AUC) was compared to select the best model. Finally, the model was validated using an independent cohort from Seoul National University Hospital. Results: A total of 1,003 patients were included in the study: 287, 71, and 645 in the training, internal validation, and external validation cohorts, respectively. Overall, 36.3% of the patients achieved pCR. Among the five machine learning models, the GBM showed the highest AUC for pCR prediction (AUC, 0.903; 95% confidence interval [CI], 0.833–0.972).External validation confirmed an AUC of 0.833 (95% CI, 0.800–0.865). Conclusion Commonly available clinical and demographic variables were used to develop a machine learning model for predicting pCR following NAC. External validation of the model demonstrated good discrimination power, indicating that routinely collected variables were sufficient to build a good prediction model.