The neuropharmacological properties of α-pyrrolidinobutiothiophenone, a new synthetic cathinone, in rodents; role of the dopaminergic system.

BACKGROUND AND PURPOSE: α-Pyrrolidinobutiothiophenone (α-PBT) is a chemical derivative of cathinone, a structural analogue of amphetamine. Until now, there have been a few previous neurochemical or neurobehavioural studies on the abuse potential of α-PBT. EXPERIMENTAL APPROACH: We examined the abuse potential of α-PBT by measuring psychomotor, rewarding, and reinforcing properties and methamphetamine-like discriminative stimulus effects in rodents using locomotor activity, conditioned place preference, self-administration, and drug discrimination studies. To clarify the underlying neuropharmacological mechanisms, we measured dopamine levels and neuronal activation in the dorsal striatum. In addition, we investigated the role of the dopamine D1 receptor or D2 receptors in α-PBT-induced hyperlocomotor activity, conditioned place preference, and the methamphetamine-like discriminative stimulus effect of α-PBT in rodents. KEY RESULTS: α-PBT promoted hyperlocomotor activity in mice. α-PBT induced drug-paired place preference in mice and supported self-administration in rats. In a drug discrimination experiment, α-PBT fully substituted for the discriminative stimulus effects of methamphetamine in rats. Furthermore, α-PBT increased dopamine levels and c-Fos expression in the dorsal striatum of mice, which was associated with these behaviours. Finally, pretreatment with the D1 receptor antagonist SCH23390 or the D2 receptors antagonist eticlopride significantly attenuated acute or repeated α-PBT-induced hyperlocomotor activity, place preference, and the methamphetamine-like discriminative stimulus effects in rodents. CONCLUSIONS AND IMPLICATIONS: These findings suggest that α-PBT has abuse potential at the highest dose tested via enhanced dopaminergic transmission in the dorsal striatum of rodents. The results provide scientific evidence for the legal restrictions of the recreational use of α-PBT.

Lactate Levels as a Predictor of Emergency Department Revisits in Infants With Acute Bronchiolitis.

OBJECTIVE: This study aimed to identify predictive biomarkers for unscheduled emergency department (ED) revisits within 24 hours of discharge in infants diagnosed with acute bronchiolitis (AB). METHODS: A retrospective observational study was conducted on infants diagnosed with AB who visited 3 emergency medical centers between January 2020 and December 2022. The study excluded infants with comorbidities, congenital diseases, and prematurity and infants who revisited the ED after 24 hours of discharge. Demographic data, vital signs, and laboratory results were collected from the medical records. Univariable and multivariable logistic regression analyses were performed on factors with P of less than 0.1 in univariable analysis. Receiver operator curve analysis was used to assess the accuracy of lactate measurements in predicting ED revisits within 24 hours of discharge. RESULTS: Out of 172 participants, 100 were in the revisit group and 72 in the discharge group. The revisit group was significantly younger and exhibited higher lactate levels, lower pH values, and higher pCO2 levels compared to the discharge group. Univariable logistic regression identified several factors associated with revisits. Multivariable analysis found that only lactate was a variable correlated with predicting ED revisits (odds ratio, 18.020; 95% confidence interval [CI], 5.764-56.334). The receiver operator curve analysis showed an area under the curve of 0.856, with an optimal lactate cutoff value of 2.15. CONCLUSION: Lactate value in infants diagnosed with AB were identified as a potential indicator of predicting unscheduled ED revisits within 24 hours of discharge. The predictive potential of lactate levels holds promise for enhancing prognosis prediction, reducing health care costs, and alleviating ED overcrowding. However, given the study's limitations, a more comprehensive prospective investigation is recommended to validate these findings.

Prediction of lung cancer using novel biomarkers based on microbiome profiling of bronchoalveolar lavage fluid.

There is an unmet need for biomarkers for the diagnosis of lung cancer and decision criteria for lung biopsy. We comparatively investigated the lung microbiomes of patients with lung cancer and benign lung diseases. Patients who underwent bronchoscopy at Chungnam National University Hospital between June 2021 and June 2022 were enrolled. Bronchoalveolar lavage fluid (BALF) was collected from 24 patients each with lung cancer and benign lung diseases. The samples were analyzed using 16S rRNA-based metagenomic sequencing. We found that alpha diversity and the beta diversity distribution (P = 0.001) differed significantly between patients with benign lung diseases and those with lung cancer. Firmicutes was the most abundant phylum in patients with lung cancer (33.39% ± 17.439), whereas Bacteroidota was the most abundant phylum in patients with benign lung disease (31.132% ± 22.505), respectively. In differential abundance analysis, the most differentially abundant microbiota taxon was unclassified_SAR202_clade, belonging to the phylum Chloroflexi. The established prediction model distinguished patients with benign lung disease from those with lung cancer with a high accuracy (micro area under the curve [AUC] = 0.98 and macro AUC = 0.99). The BALF microbiome may be a novel biomarker for the detection of lung cancer.

Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data.

Although diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we developed a novel diabetes prediction model that incorporates these complex data types. We applied advanced techniques of data imputation (bidirectional recurrent imputation for time series; BRITS) and feature selection (the least absolute shrinkage and selection operator; LASSO). Additionally, we utilized self-supervised algorithms and transfer learning to address the common issues with medical datasets, such as irregular data collection and sparsity. We also proposed a novel approach for discrete time-series data preprocessing, utilizing both shifting and rolling time windows and modifying time resolution. Our study evaluated the performance of a progressive self-transfer network for predicting diabetes, which demonstrated a significant improvement in metrics compared to non-progressive and single self-transfer prediction tasks, particularly in AUC, recall, and F1 score. These findings suggest that the proposed approach can mitigate accumulated errors and reflect temporal information, making it an effective tool for accurate diagnosis and disease management. In summary, our study highlights the importance of considering the complexities of multivariate, multi-instance, and time-series data in diabetes prediction.

A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer.

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer time-to-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multi-scale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2D CNN models of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5 × 5 and 6 × 6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.

Intracellular Adhesion Molecule-1 Improves Responsiveness to Immune Checkpoint Inhibitor by Activating CD8+ T Cells.

Immune checkpoint inhibitor (ICI) clinically benefits cancer treatment. However, the ICI responses are only achieved in a subset of patients, and the underlying mechanisms of the limited response remain unclear. 160 patients with non-small cell lung cancer treated with anti-programmed cell death protein-1 (anti-PD-1) or anti-programmed death ligand-1 (anti-PD-L1) are analyzed to understand the early determinants of response to ICI. It is observed that high levels of intracellular adhesion molecule-1 (ICAM-1) in tumors and plasma of patients are associated with prolonged survival. Further reverse translational studies using murine syngeneic tumor models reveal that soluble ICAM-1 (sICAM-1) is a key molecule that increases the efficacy of anti-PD-1 via activation of cytotoxic T cells. Moreover, chemokine (CXC motif) ligand 13 (CXCL13) in tumors and plasma is correlated with the level of ICAM-1 and ICI efficacy, suggesting that CXCL13 might be involved in the ICAM-1-mediated anti-tumor pathway. Using sICAM-1 alone and in combination with anti-PD-1 enhances anti-tumor efficacy in anti-PD-1-responsive tumors in murine models. Notably, combinatorial therapy with sICAM-1 and anti-PD-1 converts anti-PD-1-resistant tumors to responsive ones in a preclinical study. These findings provide a new immunotherapeutic strategy for treating cancers using ICAM-1.

Intra-person multi-task learning method for chronic-disease prediction.

In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imputation for time series (BRITS) and the least absolute shrinkage and selection operator (LASSO); (2) a convolutional neural network and long short-term memory (CNN-LSTM) for single-task models; and (3) a novel intra-person multi-task learning CNN-LSTM framework developed to predict multiple chronic diseases simultaneously. Our multi-task learning method between correlated chronic diseases produced a more stable and accurate system than single-task models and other baseline recurrent networks. Furthermore, the proposed model was tested using different time steps to illustrate its flexibility and generalization across multiple time steps.

Bifidobacterial carbohydrate/nucleoside metabolism enhances oxidative phosphorylation in white adipose tissue to protect against diet-induced obesity.

BACKGROUND: Comparisons of the gut microbiome of lean and obese humans have revealed that obesity is associated with the gut microbiome plus changes in numerous environmental factors, including high-fat diet (HFD). Here, we report that two species of Bifidobacterium are crucial to controlling metabolic parameters in the Korean population. RESULTS: Based on gut microbial analysis from 99 Korean individuals, we observed the abundance of Bifidobacterium longum and Bifidobacterium bifidum was markedly reduced in individuals with increased visceral adipose tissue (VAT), body mass index (BMI), blood triglyceride (TG), and fatty liver. Bacterial transcriptomic analysis revealed that carbohydrate/nucleoside metabolic processes of Bifidobacterium longum and Bifidobacterium bifidum were associated with protecting against diet-induced obesity. Oral treatment of specific commercial Bifidobacterium longum and Bifidobacterium bifidum enhanced bile acid signaling contributing to potentiate oxidative phosphorylation (OXPHOS) in adipose tissues, leading to reduction of body weight gain and improvement in hepatic steatosis and glucose homeostasis. Bifidobacterium longum or Bifidobacterium bifidum manipulated intestinal sterol biosynthetic processes to protect against diet-induced obesity in germ-free mice. CONCLUSIONS: Our findings support the notion that treatment of carbohydrate/nucleoside metabolic processes-enriched Bifidobacterium longum and Bifidobacterium bifidum would be a novel therapeutic strategy for reprograming the host metabolic homeostasis to protect against metabolic syndromes, including diet-induced obesity. Video Abstract.

Gut microbiome alterations in preclinical Alzheimer's disease.

BACKGROUND: Although some human studies have reported gut microbiome changes in individuals with Alzheimer's disease (AD) dementia or mild cognitive impairment (MCI), gut microbiome alterations in preclinical AD, i.e., cerebral amyloidosis without cognitive impairment, is largely unknown. OBJECTIVE: We aimed to identify gut microbial alterations associated with preclinical AD by comparing cognitively normal (CN) older adults with cerebral Aß deposition (Aß+ CN) and those without cerebral Aß deposition (Aß- CN). METHODS: Seventy-eight CN older participants (18 Aß+ CN and 60 Aß- CN) were included, and all participants underwent clinical assessment and Pittsburg compound B-positron emission tomography. The V3-V4 region of the 16S rRNA gene of genomic DNA extracted from feces was amplified and sequenced to establish the microbial community. RESULTS: Generalized linear model analysis revealed that the genera Megamonas (B = 3.399, q<0.001), Serratia (B = 3.044, q = 0.005), Leptotrichia (B = 5.862, q = 0.024) and Clostridium (family Clostridiaceae) (B = 0.788, q = 0.034) were more abundant in the Aß+ CN group than the Aß- CN group. In contrast, genera CF231 (B = -3.237, q< 0.001), Victivallis (B = -3.447, q = 0.004) Enterococcus (B = -2.044, q = 0.042), Mitsuokella (B = -2.119, q = 0.042) and Clostridium (family Erysipelotrichaceae) (B = -2.222, q = 0.043) were decreased in Aß+ CN compared to Aß- CN. Notably, the classification model including the differently abundant genera could effectively distinguish Aß+ CN from Aß- CN (AUC = 0.823). CONCLUSION: Our findings suggest that specific alterations of gut bacterial taxa are related to preclinical AD, which means these changes may precede cognitive decline. Therefore, examining changes in the microbiome may be helpful in preclinical AD screening.

Whole-Transcriptome Sequencing Reveals Characteristics of Cancer Microbiome in Korean Patients with GI Tract Cancer: Fusobacterium nucleatum as a Therapeutic Target.

Remarkable progress has occurred over the past two decades in identifying microbiomes affecting the human body in numerous ways. The microbiome is linked to gastrointestinal (GI) tract cancer. The purpose of this study was to determine if there is a common microbiome among GI tract cancers and how the microbiome affects the disease. To ensure ethnic consistency, Korean patients with GI tract cancer were selected. Fusobacterium nucleatum is an enriched bacteria in all cancer tissues. F. nucleatum is a Gram-negative obligate anaerobe that promotes colorectal cancer. Through Gene Set Enrichment Analysis (GSEA) and Differentially Expressed Genes (DEG) analyses, the upregulation of the G2M checkpoint pathway was identified in the F. nucleatum-high group. Cell viability and G2M checkpoint pathway genes were examined in MC 38 cells treated with F. nucleatum. F. nucleatum upregulated the expression of G2M checkpoint pathway genes and the cell proliferation of MC 38 cells. F. nucleatum facilitated cancer's use of G2M checkpoint pathways and F. nucleatum could be a therapeutic target in Korean GI tract cancer.

Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer.

Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence of metastasis, is often inaccurate in predicting NSCLC recurrence. These limitations make it difficult for clinicians to tailor treatments to individual patients. Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This will aid clinicians in successfully identifying patients at high risk of recurrence and improve treatment planning.

Staphylococcus epidermidis WF2R11 Suppresses PM2.5-Mediated Activation of the Aryl Hydrocarbon Receptor in HaCaT Keratinocytes.

The skin supports a diverse microbiome whose imbalance is related to skin inflammation and diseases. Exposure to fine particulate matter (PM2.5), a major air pollutant, can adversely affect the skin microbiota equilibrium. In this study, the effect and mechanism of PM2.5 exposure in HaCaT keratinocytes were investigated. PM2.5 stimulated the aryl hydrocarbon receptor (AhR) to produce reactive oxygen species (ROS) in HaCaT cells, leading to mitochondrial dysfunction and intrinsic mitochondrial apoptosis. We observed that the culture medium derived from a particular skin microbe, Staphylococcus epidermidis WF2R11, remarkably reduced oxidative stress in HaCaT cells caused by PM2.5-mediated activation of the AhR pathway. Staphylococcus epidermidis WF2R11 also exhibited inhibition of ROS-induced inflammatory cytokine secretion. Herein, we demonstrated that S. epidermidis WF2R11 could act as a suppressor of AhRs, affect cell proliferation, and inhibit apoptosis. Our results highlight the importance of the clinical application of skin microbiome interventions in the treatment of inflammatory skin diseases.

Integrated metagenomics and metabolomics analysis illustrates the systemic impact of the gut microbiota on host metabolism after bariatric surgery.

AIM: To explore how bariatric surgery (BS) modified the obesity-associated gut microbiome, the host metabolome, and their interactions in obese Korean patients. MATERIALS AND METHODS: Stool and fasting blood samples were obtained before and 1, 3, 6, and 12 months after BS from 52 patients enrolled in the Korean Obesity Surgical Treatment Study. We analysed the gut microbiome by 16S rRNA gene sequencing and the serum metabolome, including bile acids, by nuclear magnetic resonance spectroscopy and ultrahigh-performance liquid chromatography/triple quadrupole mass spectrometry. RESULTS: Stool metagenomics showed that 27 microbiota were enriched and 14 microbiota were reduced after BS, whereas the abundances and diversity of observed features were increased. The levels of branched-chain amino acids and metabolites of energy metabolism in serum were decreased after surgery, whereas the levels of metabolites related to microbial metabolism, including dimethyl sulphone, glycine, and secondary bile acids, were increased in the serum samples. In addition, we found notable mutual associations among metabolites and gut microbiome changes attributed to BS. CONCLUSIONS: Changes in the gut microbiome community and systemic levels of amino acids and sugars were directly derived from anatomical changes in the gastrointestinal tract after BS. We hypothesized that the observed increases in microbiome-related serum metabolites were a result of complex and indirect changes derived from BS. Ethnic-specific environmental or genetic factors could affect Korean-specific postmetabolic modification in obese patients who undergo BS.

Bifidobacterium Strain-Specific Enhances the Efficacy of Cancer Therapeutics in Tumor-Bearing Mice.

Colorectal cancer (CRC) is among the leading causes of cancer-related death in the world. The development of CRC is associated with smoking, diet, and microbial exposure. Previous studies have shown that dysbiosis of the gut microbiome affects cancer development, because it leads to inflammation and genotoxicity. Supplementation with specific microbiota induces anti-tumor effects by enhancing of anti-tumor immunity. Here, we observed that supplementation with either of two B. breve strains reduces tumor growth in MC38 colon carcinoma-bearing mice. Interestingly, only one B. breve strain boosted the efficacy of cancer therapeutics, including oxaliplatin and PD-1 blockade. Extensive immune profiling and transcriptomic analysis revealed that the boosting B. breve strain augments lymphocyte-mediated anti-cancer immunity. Our results suggest that supplementation with B. breve strains could potentially be used as a strategy to enhance the efficacy of CRC therapeutics.

Spermidine-induced recovery of human dermal structure and barrier function by skin microbiome.

An unbalanced microbial ecosystem on the human skin is closely related to skin diseases and has been associated with inflammation and immune responses. However, little is known about the role of the skin microbiome on skin aging. Here, we report that the Streptococcus species improved the skin structure and barrier function, thereby contributing to anti-aging. Metagenomic analyses showed the abundance of Streptococcus in younger individuals or those having more elastic skin. Particularly, we isolated Streptococcus pneumoniae, Streptococcus infantis, and Streptococcus thermophilus from face of young individuals. Treatment with secretions of S. pneumoniae and S. infantis induced the expression of genes associated with the formation of skin structure and the skin barrier function in human skin cells. The application of culture supernatant including Streptococcal secretions on human skin showed marked improvements on skin phenotypes such as elasticity, hydration, and desquamation. Gene Ontology analysis revealed overlaps in spermidine biosynthetic and glycogen biosynthetic processes. Streptococcus-secreted spermidine contributed to the recovery of skin structure and barrier function through the upregulation of collagen and lipid synthesis in aged cells. Overall, our data suggest the role of skin microbiome into anti-aging and clinical applications.

Practical prediction model of the clinical response to programmed death-ligand 1 inhibitors in advanced gastric cancer.

The identification of predictive biomarkers or models is necessary for the selection of patients who might benefit the most from immunotherapy. Seven histological features (signet ring cell [SRC], fibrous stroma, myxoid stroma, tumor-infiltrating lymphocytes [TILs], necrosis, tertiary lymphoid follicles, and ulceration) detected in surgically resected tissues (N = 44) were used to train a model. The presence of SRC became an optimal decision parameter for pathology alone (AUC = 0.78). Analysis of differentially expressed genes (DEGs) for the prediction of genomic markers showed that C-X-C motif chemokine ligand 11 (CXCL11) was high in responders (P < 0.001). Immunohistochemistry (IHC) was performed to verify its potential as a biomarker. IHC revealed that the expression of CXCL11 was associated with responsiveness (P = 0.003). The response prediction model was trained by integrating the results of the analysis of pathological factors and RNA sequencing (RNA-seq). When trained with the C5.0 decision tree model, the categorical level of the expression of CXCL11, a single variable, was shown to be the best model (AUC = 0.812). The AUC of the model trained with the random forest was 0.944. Survival analysis revealed that the C5.0-trained model (log-rank P = 0.01 for progression-free survival [PFS]; log-rank P = 0.012 for overall survival [OS]) and the random forest-trained model (log-rank P < 0.001 for PFS; log-rank P = 0.001 for OS) predicted prognosis more accurately than the PD-L1 test (log-rank P = 0.031 for PFS; log-rank P = 0.107 for OS).

Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice.

The gut microbiome can influence the development of tumours and the efficacy of cancer therapeutics1-5; however, the multi-omics characteristics of antitumour bacterial strains have not been fully elucidated. In this study, we integrated metagenomics, genomics and transcriptomics of bacteria, and analyses of mouse intestinal transcriptome and serum metabolome data to reveal an additional mechanism by which bacteria determine the efficacy of cancer therapeutics. In gut microbiome analyses of 96 samples from patients with non-small-cell lung cancer, Bifidobacterium bifidum was abundant in patients responsive to therapy. However, when we treated syngeneic mouse tumours with commercial strains of B. bifidum to establish relevance for potential therapeutic uses, only specific B. bifidum strains reduced tumour burden synergistically with PD-1 blockade or oxaliplatin treatment by eliciting an antitumour host immune response. In mice, these strains induced tuning of the immunological background by potentiating the production of interferon-γ, probably through the enhanced biosynthesis of immune-stimulating molecules and metabolites.

Lactobacillus fermentum promotes adipose tissue oxidative phosphorylation to protect against diet-induced obesity.

The gut microbiota has pivotal roles in metabolic homeostasis and modulation of the intestinal environment. Notably, the administration of Lactobacillus spp. ameliorates diet-induced obesity in humans and mice. However, the mechanisms through which Lactobacillus spp. control host metabolic homeostasis remain unclear. Accordingly, in this study, we evaluated the physiological roles of Lactobacillus fermentum in controlling metabolic homeostasis in diet-induced obesity. Our results demonstrated that L. fermentum-potentiated oxidative phosphorylation in adipose tissue, resulting in increased energy expenditure to protect against diet-induced obesity. Indeed, oral administration of L. fermentum LM1016 markedly ameliorated glucose clearance and fatty liver in high-fat diet-fed mice. Moreover, administration of L. fermentum LM1016 markedly decreased inflammation and increased oxidative phosphorylation in gonadal white adipose tissue, as demonstrated by transcriptome analysis. Finally, metabolome analysis showed that metabolites derived from L. fermentum LM1016-attenuated adipocyte differentiation and inflammation in 3T3-L1 preadipocytes. These pronounced metabolic improvements suggested that the application of L. fermentum LM1016 could have clinical applications for the treatment of metabolic syndromes, such as diet-induced obesity.

Exsolution trends and co-segregation aspects of self-grown catalyst nanoparticles in perovskites.

In perovskites, exsolution of transition metals has been proposed as a smart catalyst design for energy applications. Although there exist transition metals with superior catalytic activity, they are limited by their ability to exsolve under a reducing environment. When a doping element is present in the perovskite, it is often observed that the surface segregation of the doping element is changed by oxygen vacancies. However, the mechanism of co-segregation of doping element with oxygen vacancies is still an open question. Here we report trends in the exsolution of transition metal (Mn, Co, Ni and Fe) on the PrBaMn2O5+δ layered perovskite oxide related to the co-segregation energy. Transmission electron microscopic observations show that easily reducible cations (Mn, Co and Ni) are exsolved from the perovskite depending on the transition metal-perovskite reducibility. In addition, using density functional calculations we reveal that co-segregation of B-site dopant and oxygen vacancies plays a central role in the exsolution.

Anti-Proliferative and Pro-Apoptotic Activities of 4-Methyl-2,6-bis(1-phenylethyl)phenol in Cancer Cells.

It has been found that 4-isopropyl-2,6-bis(1-phenylethyl)phenol (KTH-13), a novel compound isolated from Cordyceps bassiana, is able to suppress tumor cell proliferation by inducing apoptosis. To mass-produce this compound, we established a total synthesis method. Using those conditions, we further synthesized various analogs with structural similarity to KTH-13. In this study, we aimed to test their anti-cancer activity by measuring anti-proliferative and pro-apoptotic activities. Of 8 compounds tested, 4-methyl-2,6-bis(1-phenylethyl)phenol (KTH-13-Me) exhibited the strongest anti-proliferative activity toward MDA-MB 231 cells. KTH-13-Me also similarly suppressed the survival of various cancer cell lines, including C6 glioma, HCT-15, and LoVo cells. Treatment of KTH-13-Me induced several apoptotic signs in C6 glioma cells, such as morphological changes, induction of apoptotic bodies, and nuclear fragmentation and chromatin condensation. Concordantly, early-apoptotic cells were also identified by staining with FITC-Annexin V/PI. Moreover, KTH-13-Me highly enhanced the activation of caspase-3 and caspase-9, and decreased the protein level of Bcl-2. In addition, the phosphorylation levels of Src and STAT3 were diminished in KTH-13-Me-treated C6 cells. Therefore, these results suggest that KTH-13-Me can be developed as a novel anti-cancer drug capable of blocking proliferation, inducing apoptosis, and blocking cell survival signaling in cancer cells.