Structural insights into ClpP protease side exit pore-opening by a pH drop coupled with substrate hydrolysis.

The ClpP serine peptidase is a tetradecameric degradation molecular machine involved in many physiological processes. It becomes a competent ATP-dependent protease when coupled with Clp-ATPases. Small chemical compounds, acyldepsipeptides (ADEPs), are known to cause the dysregulation and activation of ClpP without ATPases and have potential as novel antibiotics. Previously, structural studies of ClpP from various species revealed its structural details, conformational changes, and activation mechanism. Although product release through side exit pores has been proposed, the detailed driving force for product release remains elusive. Herein, we report crystal structures of ClpP from Bacillus subtilis (BsClpP) in unforeseen ADEP-bound states. Cryo-electron microscopy structures of BsClpP revealed various conformational states under different pH conditions. To understand the conformational change required for product release, we investigated the relationship between substrate hydrolysis and the pH-lowering process. The production of hydrolyzed peptides from acidic and basic substrates by proteinase K and BsClpP lowered the pH values. Our data, together with those of previous findings, provide insight into the molecular mechanism of product release by the ClpP self-compartmentalizing protease.

PHGDH preserves one-carbon cycle to confer metabolic plasticity in chemoresistant gastric cancer during nutrient stress.

Molecular classification of gastric cancer (GC) identified a subgroup of patients showing chemoresistance and poor prognosis, termed SEM (Stem-like/Epithelial-to-mesenchymal transition/Mesenchymal) type in this study. Here, we show that SEM-type GC exhibits a distinct metabolic profile characterized by high glutaminase (GLS) levels. Unexpectedly, SEM-type GC cells are resistant to glutaminolysis inhibition. We show that under glutamine starvation, SEM-type GC cells up-regulate the 3 phosphoglycerate dehydrogenase (PHGDH)-mediated mitochondrial folate cycle pathway to produce NADPH as a reactive oxygen species scavenger for survival. This metabolic plasticity is associated with globally open chromatin structure in SEM-type GC cells, with ATF4/CEBPB identified as transcriptional drivers of the PHGDH-driven salvage pathway. Single-nucleus transcriptome analysis of patient-derived SEM-type GC organoids revealed intratumoral heterogeneity, with stemness-high subpopulations displaying high GLS expression, a resistance to GLS inhibition, and ATF4/CEBPB activation. Notably, coinhibition of GLS and PHGDH successfully eliminated stemness-high cancer cells. Together, these results provide insight into the metabolic plasticity of aggressive GC cells and suggest a treatment strategy for chemoresistant GC patients.

Effect of Feature Shape and Dimension of a Confinement Geometry on Selectivity of Electrocatalytic CO2 Reduction.

The local confinement effect, which can generate a high concentration of hydroxide ions and reaction intermediates near the catalyst surface, is an important strategy for converting CO2 into multi-carbon products in electrocatalytic CO2 reduction. Therefore, understanding how the shape and dimension of the confinement geometry affect the product selectivity is crucial. In this study, we report for the first time the effect of the shape (degree of confinement) and dimension of the confined space on the product selectivity without changing the intrinsic property of Cu. We demonstrate that geometry influences the outcomes of products, such as CH4 , C2 H4 , and EtOH, in different ways: the selectivity of CH4 and EtOH is affected by shape, while the selectivity of C2 H4 is influenced by dimension of geometry predominantly. These phenomena are demonstrated, both experimentally and through simulation, to be induced by the local confinement effect within the confined structure. Our geometry model could serve as basis for designing the confined structures tailored for the production of specific products.

Identification of entomopathogenic fungus Metarhizium rileyi infested in fall armyworm in the cornfield of Korea, and evaluation of its virulence.

The fall armyworm (FAW) Spodoptera frugiperda is an important invasive pest in Africa and Asia. It is a polyphagous pest with at least 353 recorded host plant species, including corn. Chemical control of this pest is unsuccessful because of a developed resistance and harmful effects on the environment. Entomopathogenic fungi are potential biological control agents for FAW. In this study, the native strain of Metarhizium rileyi (KNU-Ye-1), collected from a cornfield at Yeongcheon, Korea, was identified by morphological and molecular characterization. The susceptibility of the fourth-instar larvae of FAW to the native strain M. rileyi was examined in the laboratory. The results showed that the Korean strain of M. rileyi (KNU-Ye-1) was highly virulent to FAW larvae, causing 89% mortality 7 days posttreatment. Therefore, M. rileyi (KNU-Ye-1) identified in this study is highly valuable for the biological control of FAW in the field.

Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks.

Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.

Effects of Anti-Cancer Drug Sensitivity-Related Genetic Differences on Therapeutic Approaches in Refractory Papillary Thyroid Cancer.

Thyroid cancer (TC) includes tumors of follicular cells; it ranges from well differentiated TC (WDTC) with generally favorable prognosis to clinically aggressive poorly differentiated TC (PDTC) and undifferentiated TC (UTC). Papillary thyroid cancer (PTC) is a WDTC and the most common type of thyroid cancer that comprises almost 70-80% of all TC. PTC can present as a solid, cystic, or uneven mass that originates from normal thyroid tissue. Prognosis of PTC is excellent, with an overall 10-year survival rate >90%. However, more than 30% of patients with PTC advance to recurrence or metastasis despite anti-cancer therapy; consequently, systemic therapy is limited, which necessitates expansion of improved clinical approaches. We strived to elucidate genetic distinctions due to patient-derived anti-cancer drug-sensitive or -resistant PTC, which can support in progress novel therapies. Patients with histologically proven PTC were evaluated. PTC cells were gained from drug-sensitive and -resistant patients and were compared using mRNA-Seq. We aimed to assess the in vitro and in vivo synergistic anti-cancer effects of a novel combination therapy in patient-derived refractory PTC. This combination therapy acts synergistically to promote tumor suppression compared with either agent alone. Therefore, genetically altered combination therapy might be a novel therapeutic approach for refractory PTC.

Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network.

Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the diagnosis and prognosis of diseases. In this study, we propose a deep learning-based method to segment lung areas in chest X-rays. The novel aspect of the proposed method is the self-attention module, where the outputs of the channel and spatial attention modules are combined to generate attention maps, with each highlighting those regions of feature maps that correspond to "what" and "where" to attend in the learning process, respectively. Thereafter, the attention maps are multiplied element-wise with the input feature map, and the intermediate results are added to the input feature map again for residual learning. Using X-ray images collected from public datasets for training and evaluation, we applied the proposed attention modules to U-Net for segmentation of lung areas and conducted experiments while changing the locations of the attention modules in the baseline network. The experimental results showed that our method achieved comparable or better performance than the existing medical image segmentation networks in terms of Dice score when the proposed attention modules were placed in lower layers of both the contracting and expanding paths of U-Net.

Pharmacokinetic/Pharmacodynamic Modeling To Predict the Antiplatelet Effect of the Ticagrelor-Loaded Self-Microemulsifying Drug Delivery System in Rats.

Ticagrelor (TCG) has been used as an antiplatelet agent for acute coronary syndrome patients. The aim of this research was to establish a population pharmacokinetic/pharmacodynamic (PK/PD) model of TCG and to apply the model for predicting the PD response of the TCG-loaded self-microemulsifying drug delivery system (TCG-SME) in rats. Pure TCG and TCG-SME (2, 5, and 10 mg/kg of TCG) were orally administered to male Sprague-Dawley rats. Plasma samples were collected at scheduled time-points and then analyzed for TCG plasma concentrations and antiplatelet effects. The inhibition of platelet aggregation of TCG was measured as a PD response. The PK profiles of pure TCG and TCG-SME could be well-explained with a two-compartment PK model. The accuracy of the PK model was assessed with a goodness-of-fit plot and conditional weight residual error (CWRES). Also, the visual predictive check was investigated based on the predictions. A population PK/PD model for pure TCG was established as an indirect response Emax model linked to the two-compartment PK model of pure TCG. The PK/PD model proposed a suitable fitting to link the plasma concentration of TCG simultaneously with platelet aggregation. Based on the PK data of TCG-SME, as well as the established PK/PD model of pure TCG, the PD profiles of TCG-SME were simulated. TCG-SME was more effective in inducing the antiplatelet effect than pure TCG at equivalent doses of TCG. The accuracy of the simulation was verified by comparing the simulated PD profile with the profile observed in rats. The observations were close to the model simulations. In addition, the values of CWRES were almost within ±2. In conclusion, the PK/PD modeling approach can provide a way for predicting mathematically the PD responses from PK profiles of other TCG formulations and a conceptual prediction for future clinical assessment.

ID1-Mediated BMP Signaling Pathway Potentiates Glucagon-Like Peptide-1 Secretion in Response to Nutrient Replenishment.

Glucagon-like peptide-1 (GLP-1) is a well-known incretin hormone secreted from enteroendocrinal L cells in response to nutrients, such as glucose and dietary fat, and controls glycemic homeostasis. However, the detailed intracellular mechanisms of how L cells control GLP-1 secretion in response to nutrients still remain unclear. Here, we report that bone morphogenetic protein (BMP) signaling pathway plays a pivotal role to control GLP-1 secretion in response to nutrient replenishment in well-established mouse enteroendocrinal L cells (GLUTag cells). Nutrient starvation dramatically reduced cellular respiration and GLP-1 secretion in GLUTag cells. Transcriptome analysis revealed that nutrient starvation remarkably reduced gene expressions involved in BMP signaling pathway, whereas nutrient replenishment rescued BMP signaling to potentiate GLP-1 secretion. Transient knockdown of inhibitor of DNA binding (ID)1, a well-known target gene of BMP signaling, remarkably reduced GLP-1 secretion. Consistently, LDN193189, an inhibitor of BMP signaling, markedly reduced GLP-1 secretion in L cells. In contrast, BMP4 treatment activated BMP signaling pathway and potentiated GLP-1 secretion in response to nutrient replenishment. Altogether, we demonstrated that BMP signaling pathway is a novel molecular mechanism to control GLP-1 secretion in response to cellular nutrient status. Selective activation of BMP signaling would be a potent therapeutic strategy to stimulate GLP-1 secretion in order to restore glycemic homeostasis.

Bioanalytical Method Development and Validation of Veratraldehyde and Its Metabolite Veratric Acid in Rat Plasma: An Application for a Pharmacokinetic Study.

A simple, sensitive, and rapid UHPLC-MS/MS method was developed for the simultaneous determination of veratraldehyde and its metabolite veratric acid in rat plasma. Cinnamaldehyde was used as an internal standard (IS) and the one-step protein precipitation method with 0.2% formic acid in acetonitrile (mobile phase B) was used for the sample extraction. Reversed C18 column (YMC-Triart C18 column, 50 mm × 2.0 mm, 1.9 µm) was used for chromatographic separation and was maintained at 30 °C. The total run time was 4.5 min and the electrospray ionization in positive mode was used with the transition m/z 167.07 → 139.00 for veratraldehyde, m/z 183.07 → 139.00 for veratric acid, and m/z 133.00 → 55.00 for IS. The developed method exhibited good linearity (r2 ≥ 0.9977), and the lower limits of quantification ranged from 3 to 10 ng/mL for the two analytes. Intra-day precision and accuracy parameters met the criteria (within ±15%) during the validation. The bioanalytical method was applied for the determination of veratraldehyde and veratric acid in rat plasma after oral and percutaneous administration of 300 and 600 mg/kg veratraldehyde. Using the analytical methods established in this study, we can confirm the absorption and metabolism of veratraldehyde in rats for various routes.

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces.

BACKGROUND: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. METHODS: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. RESULTS: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. CONCLUSIONS: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions.

Identifying prescription patterns with a topic model of diseases and medications.

Wide variance exists among individuals and institutions for treating patients with medicine. This paper analyzes prescription patterns using a topic model with more than four million prescriptions. Specifically, we propose the disease-medicine pattern model (DMPM) to extract patterns from a large collection of insurance data by considering disease codes joined with prescribed medicines. We analyzed insurance prescription data from 2011 with DMPM and found prescription patterns that could not be identified by traditional simple disease classification, such as the International Classification of Diseases (ICD). We analyzed the identified prescription patterns from multiple aspects. First, we found that our model better explain unseen prescriptions than other probabilistic models. Second, we analyzed the similarities of the extracted patterns to identify their characteristics. Third, we compared the identified patterns from DMPM to the known disease categorization, ICD. This comparison showed what additional information can be provided by the data-oriented bottom-up patterns in contrast to the knowledge-based top-down categorization. The comparison results showed that the bottom-up categorization allowed for the identification of (1) diverse treatment options for the same disease symptoms, and (2) diverse disease cases sharing the same prescription options. Additionally, the extracted bottom-up patterns revealed treatment differences based on basic patient information better than the top-down categorization. We conclude that this data-oriented analysis will be an effective alternative method for analyzing the complex interwoven disease-prescription relationship.

Identifying key hospital service quality factors in online health communities.

BACKGROUND: The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. OBJECTIVE: As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. METHODS: We defined social media-based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea's two biggest online portals were used to test the effectiveness of detection of social media-based key quality factors for hospitals. RESULTS: To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media-based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). CONCLUSIONS: These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies.

Investigating the congruence of crowdsourced information with official government data: the case of pediatric clinics.

BACKGROUND: Health 2.0 is a benefit to society by helping patients acquire knowledge about health care by harnessing collective intelligence. However, any misleading information can directly affect patients' choices of hospitals and drugs, and potentially exacerbate their health condition. OBJECTIVE: This study investigates the congruence between crowdsourced information and official government data in the health care domain and identifies the determinants of low congruence where it exists. In-line with infodemiology, we suggest measures to help the patients in the regions vulnerable to inaccurate health information. METHODS: We text-mined multiple online health communities in South Korea to construct the data for crowdsourced information on public health services (173,748 messages). Kendall tau and Spearman rank order correlation coefficients were used to compute the differences in 2 ranking systems of health care quality: actual government evaluations of 779 hospitals and mining results of geospecific online health communities. Then we estimated the effect of sociodemographic characteristics on the level of congruence by using an ordinary least squares regression. RESULTS: The regression results indicated that the standard deviation of married women's education (P=.046), population density (P=.01), number of doctors per pediatric clinic (P=.048), and birthrate (P=.002) have a significant effect on the congruence of crowdsourced data (adjusted R²=.33). Specifically, (1) the higher the birthrate in a given region, (2) the larger the variance in educational attainment, (3) the higher the population density, and (4) the greater the number of doctors per clinic, the more likely that crowdsourced information from online communities is congruent with official government data. CONCLUSIONS: To investigate the cause of the spread of misleading health information in the online world, we adopted a unique approach by associating mining results on hospitals from geospecific online health communities with the sociodemographic characteristics of corresponding regions. We found that the congruence of crowdsourced information on health care services varied across regions and that these variations could be explained by geospecific demographic factors. This finding can be helpful to governments in reducing the potential risk of misleading online information and the accompanying safety issues.

PHGDH: a novel therapeutic target in cancer.

Serine is a key contributor to the generation of one-carbon units for DNA synthesis during cellular proliferation. In addition, it plays a crucial role in the production of antioxidants that prevent abnormal proliferation and stress in cancer cells. In recent studies, the relationship between cancer metabolism and the serine biosynthesis pathway has been highlighted. In this context, 3-phosphoglycerate dehydrogenase (PHGDH) is notable as a key enzyme that functions as the primary rate-limiting enzyme in the serine biosynthesis pathway, facilitating the conversion of 3-phosphoglycerate to 3-phosphohydroxypyruvate. Elevated PHGDH activity in diverse cancer cells is mediated through genetic amplification, posttranslational modification, increased transcription, and allosteric regulation. Ultimately, these characteristics allow PHGDH to not only influence the growth and progression of cancer but also play an important role in metastasis and drug resistance. Consequently, PHGDH has emerged as a crucial focal point in cancer research. In this review, the structural aspects of PHGDH and its involvement in one-carbon metabolism are investigated, and PHGDH is proposed as a potential therapeutic target in diverse cancers. By elucidating how PHGDH expression promotes cancer growth, the goal of this review is to provide insight into innovative treatment strategies. This paper aims to reveal how PHGDH inhibitors can overcome resistance mechanisms, contributing to the development of effective cancer treatments.

DGAT2 Plays a Crucial Role to Control ESRRAPROX1 Transcriptional Network to Maintain Hepatic Mitochondrial Sustainability.

Background: Diacylglycerol O-acyltransferase 2 (DGAT2) synthesizes triacylglycerol (TG) from diacylglycerol; therefore, DGAT2 is considered as a therapeutic target for steatosis. However, the consequence of inhibiting DGAT2 is not fully investigated due to side effects including lethality and lipotoxicity. In this article, we observed the role of DGAT2 in hepatocarcinoma. Methods: The role of DGAT2 is analyzed via loss-of-function assay. DGAT2 knockdown (KD) and inhibitor treatment on HepG2 cell line was analyzed. Cumulative analysis of cell metabolism with bioinformatic data were assessed, and further compared with different cohorts of liver cancer patients and non-alcoholic fatty liver disease (NAFLD) patients to elucidate how DGAT2 is regulating cancer metabolism. Results: Mitochondrial function is suppressed in DGAT2 KD HepG2 cell along with the decreased lipid droplets. In the aspect of the cancer, DGAT2 KD upregulates cell proliferation. Analyzing transcriptome of NAFLD and hepatocellular carcinoma (HCC) patients highlights negatively correlating expression patterns of 73 lipid-associated genes including DGAT2. Cancer patients with the lower DGAT2 expression face lower survival rate. DGAT2 KD cell and patients' transcriptome show downregulation in estrogen- related receptor alpha (ESRRA) via integrated system for motif activity response analysis (ISMARA), with increased dimerization with corepressor prospero homeobox 1 (PROX1). Conclusion: DGAT2 sustains the stability of mitochondria in hepatoma via suppressing ESRRA-PROX1 transcriptional network and hinders HCC from shifting towards glycolytic metabolism, which lowers cell proliferation.

High-throughput viable circulating tumor cell isolation using tapered-slit membrane filter-based chipsets in the differential diagnosis of ovarian tumors.

OBJECTIVE: To evaluate the diagnostic performance of circulating tumor cells (CTCs) using tapered-slit membrane filter (TSF)-based chipsets for the differential diagnosis of adnexal tumors. METHODS: A total of 230 women with indeterminate adnexal tumors were prospectively enrolled. The sensitivity, specificity, and accuracy of the CTC-detecting chipsets were analyzed according to postoperative pathological results and compared with those of cancer antigen (CA)-125 and imaging tests. RESULTS: Eighty-one (40.3%) benign tumors, 31 (15.4%) borderline tumors, and 89 (44.3%) ovarian cancers were pathologically confirmed. The sensitivity, specificity, and accuracy of CTC-detecting chipsets (75.3%, 58.0%, and 67.1%) for differentiating ovarian cancer from benign tumors were similar to CA-125 (78.7%, 53.1%, and 66.5%), but lower than CT/MRI (94.2%, 77.9%, and 86.5%). "CTC or CA125" showed increased sensitivity (91.0%) and "CTC and CA-125" revealed increased specificity (77.8%), comparable to CT/MRI. CTC detection rates in stage I/II and stage III/IV ovarian cancers were 69.6% and 81.4%, respectively. The sensitivity to detect high-grade serous (HGS) cancer from benign tumors (84.6%) was higher than that to detect non-HGS cancers (68.0%). CONCLUSION: Although the diagnostic performance of the TSF platform to differentiate between ovarian cancer and benign tumors did not yield significant results, the combination of CTC and CA-125 showed promising potential in the diagnostic accuracy of ovarian cancer.

Toward Advanced Superomniphobicity: Hierarchical Insights from Serif-T Nanostructures to Microscale Wrinkles.

Developing a superomniphobic surface that exceeds the static and dynamic repellency observed in nature's springtails for various liquids presents a significant challenge in the realm of surface and interface science. However, progress in this field has been particularly limited when dealing with low-surface-tension liquids. This is because dynamic repellency values are typically at least 2 orders of magnitude lower than those observed with water droplets. Our study introduces an innovative hierarchical topography demonstrating exceptional dynamic repellency to low-surface-tension liquids. Inspired by the structural advantages found in springtails, we achieve a static contact angle of >160° and the complete rebound of droplet impact with a Weber number (We) of ∼104 using ethanol. These results surpass all existing benchmarks that have been reported thus far, including those of natural surfaces. The key insight from our research is the vital role of the microscale air pocket size, governed by wrinkle wavelength, in both static and dynamic repellency. Additionally, nanoscale air pockets within serif-T nanostructures prove to be essential for achieving omniphobicity. Our investigations into the wetting dynamics of ethanol droplets further reveal aspects such as the reduction in contact time and the occurrence of a fragmentation phenomenon beyond We ∼ 350, which has not been previously observed.

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments.

BRCA1 mutation promotes sprouting angiogenesis in inflammatory cancer-associated fibroblast of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with inferior outcomes owing to its low treatment response and high invasiveness. Based on abundant cancer-associated fibroblasts (CAFs) and frequent mutation of breast cancer-associated 1 (BRCA1) in TNBC, the characteristics of CAFs in TNBC patients with BRCA1 mutation compared to wild-type were investigated using single-cell analysis. Intriguingly, we observed that characteristics of inflammatory CAFs (iCAFs) were enriched in patients with BRCA1 mutation compared to the wild-type. iCAFs in patients with BRCA1 mutation exhibited outgoing signals to endothelial cells (ECs) clusters, including chemokine (C-X-C motif) ligand (CXCL) and vascular endothelial growth factor (VEGF). During CXCL signaling, the atypical chemokine receptor 1 (ACKR1) mainly interacts with CXCL family members in tumor endothelial cells (TECs). ACKR1-high TECs also showed high expression levels of angiogenesis-related genes, such as ANGPT2, MMP1, and SELE, which might lead to EC migration. Furthermore, iCAFs showed VEGF signals for FLT1 and KDR in TECs, which showed high co-expression with tip cell marker genes, including ZEB1 and MAFF, involved in sprouting angiogenesis. Moreover, BRCA1 mutation patients with relatively abundant iCAFs and tip cell gene expression exhibited a limited response to neoadjuvant chemotherapy, including cisplatin and bevacizumab. Importantly, our study observed the intricate link between iCAFs-mediated angiogenesis and chemoresistance in TNBC with BRCA1 mutation.