Ultrathin titania coating for high-temperature stable SiO2/Pt nanocatalysts

The facile synthesis of silica supported platinum nanoparticles with ultrathin titania coating to enhance metal-support interactions suitable for high temperature reactions is reported, as thermal and structure stability of metal nanoparticles is important for catalytic reactions.close8

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fishers exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient usingin vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage (http://www.wang-lab-hkust.com:3838/TMZEP) Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher’s exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage ( http://www.wang-lab-hkust.com:3838/TMZEP ). Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

Point Cloud Resampling by Simulating Electric Charges on Metallic Surfaces

3D point cloud resampling based on computational geometry is still a challenging problem. In this paper, we propose a point cloud resampling algorithm inspired by the physical characteristics of the repulsion forces between point electrons. The points in the point cloud are considered as electrons that reside on a virtual metallic surface. We iteratively update the positions of the points by simulating the electromagnetic forces between them. Intuitively, the input point cloud becomes evenly distributed by the repulsive forces. We further adopt an acceleration and damping terms in our simulation. This system can be viewed as a momentum method in mathematical optimization and thus increases the convergence stability and uniformity performance. The net force of the repulsion forces may contain a normal directional force with respect to the local surface, which can make the point diverge from the surface. To prevent this, we introduce a simple restriction method that limits the repulsion forces between the points to an approximated local plane. This approach mimics the natural phenomenon in which positive electrons cannot escape from the metallic surface. However, this is still an approximation because the surfaces are often curved rather than being strict planes. Therefore, we project the points to the nearest local surface after the movement. In addition, we approximate the net repulsion force using the K-nearest neighbor to accelerate our algorithm. Furthermore, we propose a new measurement criterion that evaluates the uniformity of the resampled point cloud to compare the proposed algorithm with baselines. In experiments, our algorithm demonstrates superior performance in terms of uniformization, convergence, and run-time

Reactivation of Varicella-Zoster Virus in Patients with Lung Cancer Receiving Immune Checkpoint Inhibitors: Retrospective Nationwide Population-Based Cohort Study from South Korea

Background: This study aimed to determine the association between immune checkpoint inhibitors (ICIs) and the risk of herpes zoster (HZ) incidence in patients with lung cancer. Method: We obtained national claims data of 51,021 patients from South Korea with lung cancer between August 2017 and December 2021. The study population was classified into ICI and non-ICI groups based on the prescription of ICIs at least once during the study period. To estimate the effects of ICIs treatment compared with those without ICIs treatment on HZ incidence, we used the Cox proportional hazards model adjusted for sex, age, comorbidities, and concomitant use of immunosuppressive drugs. Stratified analyses based on sex, age, and comorbidities were conducted to identify corresponding risk factors. Results: Of the 51,021 study participants, 897 (1.8%) were prescribed ICIs and 2262 (4.4%) were diagnosed with HZ. Approximately 75.6% of the patients receiving ICIs were male, and the prevalence of diabetes, cardiovascular disease, and chronic lung disease in the ICI group was significantly lower than that in the non-ICIs group. The Kaplan–Meier plot showed that the probability of incidence of HZ in the ICIs group was lower than that in the non-ICIs group. Additionally, treatment with ICIs was associated with a 31% lower incidence of developing HZ when compared to that seen without ICIs treatment (95% confidence interval [CI], 0.48–1.00). This association was stronger in females (hazard ratio [HR], 0.42; 95% CI, 0.19–0.94) and those less than 68 years of age (HR, 0.58; 95% CI, 0.34–0.99). Conclusions: In these real-world data from an Asian population with lung cancer, ICIs treatment might be associated with a reduced risk of HZ compared to that without ICIs treatment

Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm

This paper suggested simulation approaches for quantifying and reducing the effects of National Forest Inventory (NFI) plot location error on aboveground forest biomass and carbon stock estimation using the k-Nearest Neighbor (kNN) algorithm. Additionally, the effects of plot location error in pre-GPS and GPS NFI plots were compared. Two South Korean cities, Sejong and Daejeon, were chosen to represent the study area, for which four Landsat TM images were collected together with two NFI datasets established in both the pre-GPS and GPS eras. The effects of plot location error were investigated in two ways: systematic error simulation, and random error simulation. Systematic error simulation was conducted to determine the effect of plot location error due to mis-registration. All of the NFI plots were successively moved against the satellite image in 360?? directions, and the systematic error patterns were analyzed on the basis of the changes of the Root Mean Square Error (RMSE) of kNN estimation. In the random error simulation, the inherent random location errors in NFI plots were quantified by Monte Carlo simulation. After removal of both the estimated systematic and random location errors from the NFI plots, the RMSE% were reduced by 11.7% and 17.7% for the two pre-GPS-era datasets, and by 5.5% and 8.0% for the two GPS-era datasets. The experimental results showed that the pre-GPS NFI plots were more subject to plot location error than were the GPS NFI plots. This study's findings demonstrate a potential remedy for reducing NFI plot location errors which may improve the accuracy of carbon stock estimation in a practical manner, particularly in the case of pre-GPS NFI data.close

A multimodal screening system for elderly neurological diseases based on deep learning

Abstract In this paper, we propose a deep-learning-based algorithm for screening neurological diseases. We proposed various examination protocols for screening neurological diseases and collected data by video-recording persons performing these protocols. We converted video data into human landmarks that capture action information with a much smaller data dimension. We also used voice data which are also effective indicators of neurological disorders. We designed a subnetwork for each protocol to extract features from landmarks or voice and a feature aggregator that combines all the information extracted from the protocols to make a final decision. Multitask learning was applied to screen two neurological diseases. To capture meaningful information about these human landmarks and voices, we applied various pre-trained models to extract preliminary features. The spatiotemporal characteristics of landmarks are extracted using a pre-trained graph neural network, and voice features are extracted using a pre-trained time-delay neural network. These extracted high-level features are then passed onto the subnetworks and an additional feature aggregator that are simultaneously trained. We also used various data augmentation techniques to overcome the shortage of data. Using a frame-length staticizer that considers the characteristics of the data, we can capture momentary tremors without wasting information. Finally, we examine the effectiveness of different protocols and different modalities (different body parts and voice) through extensive experiments. The proposed method achieves AUC scores of 0.802 for stroke and 0.780 for Parkinson’s disease, which is effective for a screening system

Associations between missing teeth and the risk of cancer in Korea: a nationwide cohort study

Abstract Background Poor dental health is correlated with an increased risk of cancer. Using a nationwide population cohort database, we investigated which cancer is highly associated with poor dental health and which dental indicator mostly influences cancer risk. Methods This study was conducted using the National Health Checkups (NHC) and National Health Insurance System (NHIS) database in Korea. NHC in Korea includes dental examinations. We retrieved subjects who underwent NHC between 2002 and 2003 and their medical information in NHIS database was followed until December 31,2015. Results Data for 200,170 who participated in the NHC between 2002 and 2003 were analysed. During the maximum follow-up period of 13 years, 15,506 (7.75%) subjects were diagnosed with cancer. The median time to cancer diagnosis after the dental examination was 87 months (range, 51–119 months). The proportion of people with missing teeth was higher in the cancer-diagnosed group than in the non-diagnosed group (26.27% vs. 22.59%, p < 0.001). Among several dental health factors, missing teeth were significantly associated with higher cancer risk. Subjects with missing teeth showed a 12% increased cancer risk compared to those without missing teeth (odds ratio [OR] 1.12, 95% confidence interval [CI], 1.08–1.16). The risk was significantly higher, especially in lung, head and neck, pancreatic, liver, biliary, and esophageal cancers (OR 1.27 [95% CI, 1.14–1.41], 1.32 [95% CI, 1.13–1.55], 1.27 [95% CI, 1.02–1.58], 1.24 [95% CI, 1.1–1.4], 1.28 [95% CI, 1.03–1.6], 1.4 [95% CI, 1.04–1.88], respectively). Conclusions Missing teeth were the most important dental indicator associated with cancer risk. Korean adults with missing teeth should be cautious about the risk of several cancers, particularly head and neck, lung, gastrointestinal, hepatobiliary, and pancreatic cancer

The risk of cardiovascular disease and stroke in survivors of head and neck cancer in Korea

Abstract Background Head and neck cancer (HNCA) survivors have a high risk of developing cardiovascular disease (CVD) or stroke because of sharing risk factors of disease. Therefore, we investigated the risk of CVD or stroke occurrence among HNCA survivors in Korea based on the Health Insurance Review and Assessment (HIRA) Service claims database. Methods We retrieved claims data of patients who were diagnosed with HNCA in 2014‐2015 using ICD‐10 code and followed up data until 2018. Patients with newly diagnosed with CVD or stroke after HNCA diagnosis during the follow‐up period were detected. We analyzed the characteristics of patients with HNCA who were subsequently diagnosed with CVD or stroke. In addition, the risk factors of CVD or stroke occurrence were investigated using Cox proportional hazard regression analysis. Results Among the 8288 patients with HNCA, 477 and 404 patients were diagnosed with new‐onset CVD and stroke, respectively. Patients with hypertension, diabetes mellitus (DM), and hyperlipidemia had a 3.25‐fold higher risk of CVD comparing to patients without any underlying disease (95% confidence index [CI], 2.38‐4.45) Patients with three underlying diseases had a 2.92‐fold higher risk of stroke compared to patients without any underlying disease (95% CI 2.03‐4.21). Conclusions HNCA survivors with hypertension, DM, and hyperlipidemia should be cautious of the risks of CVD and stroke

Elucidation of critical chemical moieties of metallo-β-lactamase inhibitors and prioritisation of target metallo-β-lactamases

AbstractThe urgent demand for effective countermeasures against metallo-β-lactamases (MBLs) necessitates development of novel metallo-β-lactamase inhibitors (MBLIs). This study is dedicated to identifying critical chemical moieties within previously developed MBLIs, and critical MBLs should serve as the target in MBLI evaluations. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a systematic literature analysis was conducted, and the NCBI RefSeq genome database was exploited to access the abundance profile and taxonomic distribution of MBLs and their variant types. Through the implementation of two distinct systematic approaches, we elucidated critical chemical moieties of MBLIs, providing pivotal information for rational drug design. We also prioritised MBLs and their variant types, highlighting the imperative need for comprehensive testing to ensure the potency and efficacy of the newly developed MBLIs. This approach contributes valuable information to advance the field of antimicrobial drug discovery