A review of big data technology and its application in cancer care.

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

A qualitative study on inner experience of self-management behavior among elderly patients with type 2 diabetes in rural areas.

BACKGROUND: As a chronic metabolic disease, diabetes poses a serious threat to human health and has become a major public health problem in China and worldwide. In 2020, 30% of Chinese people (aged ≥ 60 years) reported having diabetes mellitus. Moreover, individuals with diabetes living in rural areas face a significantly higher mortality risk compared to those in urban areas. In this study, we explored the inner experience of self-management behaviors in elderly patients with type 2 diabetes in rural areas to inform targeted interventions. METHODS: A phenomenological research design was used to explore the inner experience of self-management in rural elderly diabetes. Ten elderly diabetic patients were sampled from December 2022 to March 2023 in rural areas of Yangcheng County, Jincheng City, ShanXi Province, China. The seven-step Colaizzi phenomenological was used to analyze the interview data and generate themes. RESULTS: Four themes emerged: "Insufficient self-management cognition", "Negative self-management attitude", "Slack self-management behavior", and "No time for self-management". CONCLUSION: The level of self-management among elderly patients with type 2 diabetes in rural areas is low. Healthcare professionals should develop targeted interventions aimed at enhancing their cognitive levels, modifying their coping styles, and improving their self-management abilities to improve their quality of life.

Modulation of Electrokinetic Potentials Using Graphene-Based Surfaces and Variable Substrate Charge Density.

Enhanced electrokinetic phenomena, manifested through the observation of a large streaming potential (Vs), were obtained in microchannels with single-layer graphene (SLG)-coated and few-layer graphene (FLG)-coated surfaces. In comparison to silicon microchannels, the Vs obtained for a given pressure difference along the channel (ΔP) was higher by 75% for the graphene-based channels, with larger values in the SLG case. Computational modeling was used to correlate the surface charge density, tuned through plasma processing, and related zeta potential to measured Vs. The implications related to deploying lower dimensional material surfaces for modulating electrokinetic flows were investigated.

Optical Second Harmonic Generation of Low-Dimensional Semiconductor Materials.

In recent years, the phenomenon of optical second harmonic generation (SHG) has attracted significant attention as a pivotal nonlinear optical effect in research. Notably, in low-dimensional materials (LDMs), SHG detection has become an instrumental tool for elucidating nonlinear optical properties due to their pronounced second-order susceptibility and distinct electronic structure. This review offers an exhaustive overview of the generation process and experimental configurations for SHG in such materials. It underscores the latest advancements in harnessing SHG as a sensitive probe for investigating the nonlinear optical attributes of these materials, with a particular focus on its pivotal role in unveiling electronic structures, bandgap characteristics, and crystal symmetry. By analyzing SHG signals, researchers can glean invaluable insights into the microscopic properties of these materials. Furthermore, this paper delves into the applications of optical SHG in imaging and time-resolved experiments. Finally, future directions and challenges toward the improvement in the NLO in LDMs are discussed to provide an outlook in this rapidly developing field, offering crucial perspectives for the design and optimization of pertinent devices.

Non-contact, highly sensitive sugar concentration detection based on Co3Sn2S2 Weyl semimetal thin film sensor by terahertz wave.

Blood sugar is an important biomedical parameter of diabetic patients. The current blood sugar testing is based on an invasive method, which is not very friendly for patients who require long-term monitoring, while the non-invasive method is still in the developing stage. In this paper, we design a non-invasive and highly sensitive terahertz wave detector with Co3Sn2S2 semimetal thin film to test sugar concentration. As different concentrations have inconsistent responses to terahertz wave, we can deduce the concentration of the sugar solution to realize real-time highly sensitive detection of blood sugar concentration. This novel method can be further expanded to 6 G edge intelligence for non-invasive and real-time monitoring of blood sugar, and promote the development of 6 G technology.

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5.

Solenoid connectors play important role in electronic stability system design, with the features of small size, low cost, fast response time and high reliability. The main production process challenge for solenoid connectors is the accurate detection of defects, which is closely related to safe driving. Both faultless and defective products have similar color and shape at the defect location, making proper inspection challenging. To address these issues, we proposed a defect detection model called PO-YOLOv5 to achieve accurate defect detection for solenoid connectors. First, an additional prediction head was added to enable the model to acquire more semantic information to detect larger-scale defective features. Second, we introduced dynamic convolution to learn complementary connections between the four dimensions of the convolution kernel by utilizing its multidimensional attention mechanism. Replacing conventional convolution with dynamic convolution enhances the detection accuracy of the model and reduces the inference time. Finally, we validated PO-YOLOv5 versus the state-of-the-art object detection methods on the same solenoid connectors dataset. Experiments revealed that our proposed approach exhibited higher accuracy. The mAP (mean Average Precision) result of PO-YOLOv5 was found to be about 90.1%. Compared with the original YOLOv5, PO-YOLOv5 exhibited improved precision by about 3%.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models.

BACKGROUND: Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets. METHODS: We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent. RESULTS: We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment. CONCLUSIONS: Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

The antioxidant, anti-inflammatory and analgesic activity effect of ethyl acetate extract from the flowers of Syringa pubescens Turcz.

ETHNOPHARMACOLOGICAL RELEVANCE: Syringa Pubescens Turcz. (SP), a member of the Oleaceae family, is a species of plant known as Syringa. Flowers, as the medicinal part, are commonly used in the treatment of hepatitis and tonsillitis. AIM OF THE STUDY: The research was the first to assess the antioxidant and anti-inflammatory potential of different parts of SP flowers (SPF) in vitro. The most promising fraction was ethyl acetate fraction of SP flower (SPFEA). The antioxidant, anti-inflammatory and analgesic activities of SPFEA were further studied, and the chemical components were identified. METHODS: HPLC was used to identify the major components in various fraction of SPF. DPPH and ABTS + radical scavenging assays as well as FRAP test and ß-carotene bleaching test were employed to assess the antioxidant potential of SPF fraction in vitro. The inhibitory effect on NO production in LPS-treated RAW264.7 cells and heat-induced protein denaturation test were used to evaluate the anti-inflammatory potential of SPF fraction. Further analysis of the biological activity of SPFEA was performed. Acute toxicity test was conducted to assess the toxicity of SPFEA. The anti-inflammatory effect was assessed by utilizing xylene induced ear edema model, carrageenan-induced foot edema model and peritonitis model in vivo. The analgesic effect of SPFEA was evaluated using hot plate test, tail immersion test, formaldehyde test as well as acetic acid-induced abdominal writhing pain experiment in vivo. In carrageenan induced foot edema model, ELISA kits were employed to measure levels of inflammation factors (NO, TNF-α, IL-6, COX-2, IL-1ß) in foot tissue as well as MDA, CAT, SOD, GSH-PX levels in liver tissue. RESULTS: HPLC results showed that there were significant differences in bioactive substances among different fractions of SPF, and SPFEA was rich in bioacitve components. Compared with other fractions of SPF, SPFEA exhibited better antioxidant and anti-inflammatory abilities. The 3000 mg/kg SPFEA group in mice had no obvious side effects. The xylene-induced ear edema model, carrageenan-induced foot edema and peritonitis models demonstrated that the SPFEA had significant anti-inflammatory effect. Moreover, inflammation factors including NO, TNF-α, IL-6, COX-2, IL-1ß were significantly reduced in SPFEA groups in foot tissue induced by carrageenan. Additionally, SPFEA effectively decreased liver tissue oxidative stress levels (MDA, SOD, GSH-PX and CAT). The bioactivities of SPFEA demonstrated a clear dose-dependent relationship. The results of the hot plate test, tail immersion test, formaldehyde test and acetic acid-induced abdominal writhing pain experiments indicated the SPFEA possessed an excellent analgesic effect, and this effect was in dose-dependent manner. CONCLUSION: The study provides a scientific foundation for understanding the pharmacological action of SPFEA. It has been indicated that SPFEA has excellent antioxidant, analgesic and anti-inflammatory effects.

SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.

Green space and its types can attenuate the associations of PM2.5 and its components with prediabetes and diabetes-- a multicenter cross-sectional study from eastern China.

BACKGROUND: The effect of fine particulate matter (PM2.5) components on prediabetes and diabetes is of concern, but the evidence is limited and the specific role of different green space types remains unclear. This study aims to investigate the relationship of PM2.5 and its components with prediabetes and diabetes as well as the potential health benefits of different types and combinations of green spaces. METHODS: A multicenter cross-sectional study was conducted in eastern China by using a multi-stage random sampling method. Health screening and questionnaires for 98,091 participants were performed during 2017-2020. PM2.5 and its five components were estimated by the inverse distance weighted method, and green space was reflected by the Normalized Difference Vegetation Index (NDVI), percentages of tree or grass cover. Multivariate logistic regression and quantile g-computing were used to explore the associations of PM2.5 and five components with prediabetes and diabetes and to elucidate the potential moderating role of green space and corresponding type combinations in these associations. RESULTS: Each interquartile range (IQR) increment of PM2.5 was associated with both prediabetes (odds ratio [OR]: 1.15, 95%CI [confidence interval]: 1.10-1.20) and diabetes (OR: 1.18, 95% CI: 1.11-1.25), respectively. All five components of PM2.5 were related to prediabetes and diabetes. The ORs of PM2.5 on diabetes were 1.49 (1.35-1.63) in the low tree group and 0.90 (0.82-0.98) in the high tree group, respectively. In the high tree-high grass group, the harmful impacts of PM2.5 and five components were significantly lower than in the other groups. CONCLUSION: Our study suggested that PM2.5 and its components were associated with the increased risk of prediabetes and diabetes, which could be diminished by green space. Furthermore, the coexistence of high levels of tree and grass cover provided greater benefits. These findings had critical implications for diabetes prevention and green space-based planning for healthy city.

Sleep Disturbances and Emotional and Behavioral Difficulties Among Preschool-Aged Children.

Importance: Sleep disturbances and mental health problems are highly comorbid and bidirectionally correlated across childhood. The association between the natural history of sleep disturbances and the transition of mental health problems has not been quantified. Objective: To examine the association between the natural history of sleep disturbances and resolved and incident emotional and behavioral difficulties (EBDs). Design, Setting, and Participants: This cohort study used data from the Shanghai Children's Health, Education and Lifestyle Evaluation-Preschool (SCHEDULE-P), a prospective and population-based longitudinal cohort study of children enrolled in preschools in Shanghai, China, from November 10 to 24, 2016. A total of 20 324 children aged 3 to 4 years were recruited from the junior class of 191 kindergartens, of whom 17 233 (84.8%) participated in the 2-year follow-up. A multilevel regression model was used to evaluate the association between the development of sleep disturbances and the occurrence of resolved and incident EBDs. The data analysis spanned from August 4, 2021, to October 31, 2023. Exposures: Sleep disturbances were assessed using the Children's Sleep Habit Questionnaire; EBDs were assessed using the Strengths and Difficulties Questionnaire. Main Outcomes and Measures: Occurrence of incident and resolved EBDs at the 2-year follow-up. Results: The cohort included 17 182 participants, with a mean (SD) age of 3.73 (0.29) years at enrollment; 52.0% were boys. The prevalence of EBDs at school entry and graduation years was 27.8% and 18.7%, respectively, while the prevalence of sleep disturbances was 41.3% and 31.5%, respectively. Among those with EBDs at the entry year, 35.0% maintained stability in the graduation year, while sleep disturbances were stable in 50.0% of those with sleep disturbances. After controlling for confounding factors, the odds ratio (OR) for resolved EBDs was lower in the incident sleep disturbance (ISD) group (OR, 0.50 [95% CI, 0.41-0.62]; P < .001) and stable sleep disturbance (SSD) group (OR, 0.47 [95% CI, 0.40-0.56]; P < .001) compared with the group with no sleep disturbances. The ORs for incident EBDs among the ISD group (OR, 2.58 [95% CI, 2.22-3.01]; P < .001) and SSD group (OR, 2.29, [95% CI, 1.98-2.64]; P < .001) were higher than among the group with no sleep disturbances. Conclusions and Relevance: In this prospective cohort study, the natural history of sleep disturbances among preschool-aged children was associated with both resolved and incident EBDs. Routine screening and precise intervention for sleep disturbances may benefit the psychosocial well-being of this population.

Influence of Five Drying Methods on Active Compound Contents and Bioactivities of Fresh Flowers from Syringa pubescens Turcz.

The flower of Syringa pubescens Turcz. is used in Chinese folk medicine and also as a flower tea for healthcare. The effects of five drying methods on the active compound contents, the antioxidant abilities, anti-inflammatory properties and enzyme inhibitory activities were evaluated. The plant materials were treated using shade-drying, microwave-drying, sun-drying, infrared-drying and oven-drying. The seven active compounds were simultaneously determined using an HPLC method. Furthermore, the chemical profile was assessed using scanning electron microscopy, ultraviolet spectroscopy and infrared spectroscopy. The antioxidant capacities and protective effects on L02 cells induced with hydrogen peroxide were measured. The anti-inflammatory effects on lipopolysaccharide-induced RAW264.7 cells were investigated. The enzyme inhibitory activities were determined against α-amylase, α-glucosidase cholinesterases and tyrosinase. The results indicated that drying methods had significant influences on the active compound contents and biological properties. Compared with other samples, the OD samples possessed low IC50 values with 0.118 ± 0.004 mg/mL for DPPH radical, 1.538 ± 0.0972 for hydroxyl radical and 0.886 ± 0.199 mg/mL for superoxide radical, while the SHD samples had stronger reducing power compared with other samples. The SHD samples could be effective against H2O2-induced injury on L02 cells by the promoting of T-AOC, GSH-PX, SOD and CAT activities and the reducing of MDA content compared with other samples. Furthermore, SPF samples, especially the SHD sample, could evidently ameliorate inflammation through the inhibition of IL-6, IL-1ß and TNF-α expression. All the studied SPF samples exhibited evidently inhibitory effects on the four enzymes. The IC50 values of inhibitory activity on α-glucosidase and α-amylase from SHD sample were 2.516 ± 0.024 and 0.734 ± 0.034 mg/mL, respectively. SD samples had potential inhibitory effects on cholinesterases and tyrosinase with IC50 values of 3.443 ± 0.060 and 1.732 ± 0.058 mg/mL. In consideration of active compound contents and biological activities, it was recommended that SHD and SD be applied for drying SPF at an industrial scale.

Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data.

Alzheimer disease (AD) is a common neurodegenerative disease with a late onset. It is critical to identify novel blood-based DNA methylation biomarkers to better understand the extent of the molecular pathways affected in AD. Two sets of blood DNA methylation genetic prediction models developed using different reference panels and modelling strategies were leveraged to evaluate associations of genetically predicted DNA methylation levels with AD risk in 111,326 (46,828 proxy) cases and 677,663 controls. A total of 1,168 cytosine-phosphate-guanine (CpG) sites showed a significant association with AD risk at a false discovery rate (FDR) < 0.05. Methylation levels of 196 CpG sites were correlated with expression levels of 130 adjacent genes in blood. Overall, 52 CpG sites of 32 genes showed consistent association directions for the methylation-gene expression-AD risk, including nine genes (CNIH4, THUMPD3, SERPINB9, MTUS1, CISD1, FRAT2, CCDC88B, FES, and SSH2) firstly reported as AD risk genes. Nine of 32 genes were enriched in dementia and AD disease categories (P values ranged from 1.85 × 10-4 to 7.46 × 10-6), and 19 genes in a neurological disease network (score = 54) were also observed. Our findings improve the understanding of genetics and etiology for AD.

The Safety and Efficacy of Hepatic Arterial Infusion Chemotherapy Combined with PD-(L)1 Inhibitors and Molecular Targeted Therapies for the Treatment of Intermediate and Advanced Hepatocellular Carcinoma Unsuitable for Transarterial Chemoembolization.

Objective: To investigate the efficacy and safety of hepatic arterial infusion chemotherapy (HAIC) combined with PD-(L)1 inhibitors and molecular targeted therapies (MTT) for intermediate and advanced HCC that are unsuitable for transarterial chemoembolization (TACE). Methods: We conducted a retrospective analysis of data from patients with TACE-unsuitable HCC who were receiving triple therapy from January 2020 to December 2021 at two medical centers. The primary outcome was overall survival (OS), and the secondary outcomes were progression-free survival (PFS), objective response rates (ORR), disease control rates (DCR), and incidence of adverse events (AEs). Results: A total of 55 patients were enrolled in the study with median treatment periods of 4 and 6 for HAIC and PD-(L)1 inhibitors, respectively. The median OS and PFS were 15.0 and 10.0 months, respectively, with a median follow-up of 11.0 months (range: 4.0-27.5 months). According to the mRECIST criteria, the optimal ORR was 43.6% (24/55) and the DCR was 61.8% (34/55). The incidence of AEs was 58.2%, with grade 3 and above accounting for 20.0%; elevated AST (18.2%), hyperbilirubinemia (16.4%), and thrombocytopenia (16.4%) were most common. There were no treatment-related fatalities and all AEs were effectively managed. Multifactorial analysis showed that NLR > 3.82 (HR 2.380, 95% CI 1.116-2-5.079, P = 0.025), ECOG 1 (HR 2.906, 95% CI 1.373-6.154, P = 0.005), and extrahepatic metastases (HR 8.373, 95% CI 3.492-20.078, P < 0.001) were associated with the median OS. Conclusion: Triple therapy with HAIC, PD-(L)1 inhibitors, and MTT was safe and effective for patients with intermediate and advanced HCC for TACE-unsuitability.

Rapid detection of hydrogen using narrow bandwidth fiber-optic spectral combs with a low limit of detection.

The all-fiber detection solutions are the key technology to detecting hydrogen leakage in time because of the low explosive limit of the hydrogen-air mixture gas. However, most of the fiber-optic-based hydrogen sensing platforms must disrupt their structure to achieve a special architecture for interacting with the hydrogen. Here, we report a promising non-damaged structure of fiber-optic narrow bandwidth spectral combs, that can be developed to determine the refractive change as low as 10-5 using its cut-off cladding resonance mode. Such high performance of response for the refractive index induces a rapid detection of hydrogen after a proper thickness of palladium was deposited on the device. An average response time of hydrogen of 4 min with a low limit of detection of 348 ppm was achieved. It is demonstrated that these narrow bandwidth fiber-optic resonance combs can be used for gas detection after being combined with functional materials.

Band-to-Band Tunneling Leakage Current Characterization and Projection in Carbon Nanotube Transistors.

Carbon nanotube (CNT) transistors demonstrate high mobility but also experience off-state leakage due to the small effective mass and band gap. The lower limit of off-current (IMIN) was measured in electrostatically doped CNT metal-oxide-semiconductor field-effect transistors (MOSFETs) across a range of band gaps (0.37 to 1.19 eV), supply voltages (0.5 to 0.7 V), and extension doping levels (0.2 to 0.8 carriers/nm). A nonequilibrium Green's function (NEGF) model confirms the dependence of IMIN on CNT band gap, supply voltage, and extension doping level. A leakage current design space across CNT band gap, supply voltage, and extension doping is projected based on the validated NEGF model for long-channel CNT MOSFETs to identify the appropriate device design choices. The optimal extension doping and CNT band gap design choice for a target off-current density are identified by including on-current projection in the leakage current design space. An extension doping level >0.5 carrier/nm is required for optimized on-current.

Long Short-Term Memory-Based Twin Support Vector Regression for Probabilistic Load Forecasting.

A probabilistic load forecast that is accurate and reliable is crucial to not only the efficient operation of power systems but also to the efficient use of energy resources. In order to estimate the uncertainties in forecasting models and nonstationary electric load data, this study proposes a probabilistic load forecasting model, namely BFEEMD-LSTM-TWSVRSOA. This model consists of a data filtering method named fast ensemble empirical model decomposition (FEEMD) method, a twin support vector regression (TWSVR) whose features are extracted by deep learning-based long short-term memory (LSTM) networks, and parameters optimized by seeker optimization algorithms (SOAs). We compared the probabilistic forecasting performance of the BFEEMD-LSTM-TWSVRSOA and its point forecasting version with different machine learning and deep learning algorithms on Global Energy Forecasting Competition 2014 (GEFCom2014). The most representative month data of each season, totally four monthly data, collected from the one-year data in GEFCom2014, forming four datasets. Several bootstrap methods are compared in order to determine the best prediction intervals (PIs) for the proposed model. Various forecasting step sizes are also taken into consideration in order to obtain the best satisfactory point forecasting results. Experimental results on these four datasets indicate that the wild bootstrap method and 24-h step size are the best bootstrap method and forecasting step size for the proposed model. The proposed model achieves averaged 46%, 11%, 36%, and 44% better than suboptimal model on these four datasets with respect to point forecasting, and achieves averaged 53%, 48%, 46%, and 51% better than suboptimal model on these four datasets with respect to probabilistic forecasting.

Low-k SiOx/AlOx Nanolaminate Dielectric on Dielectric Achieved by Hybrid Pulsed Chemical Vapor Deposition.

Selective and smooth low-k SiOx/AlOx nanolaminate dielectric on dielectric (DOD) was achieved by a hybrid water-free pulsed CVD process consisting of 50 pulses of ATSB (tris(2-butoxy)aluminum) at 330 °C and a 60 s TBS (tris(tert-butoxy)silanol) exposure at 200 °C. Aniline selective passivation was demonstrated on W surfaces in preference to Si3N4 and SiO2 at 300 °C. At 200 °C, TBS pulsed CVD exhibited no growth on W or SiO2, but its growth was catalyzed by AlOx. Using a two-temperature pulsed CVD process, ∼2.7 nm selective SiOx/AlOx nanolaminate was deposited on Si3N4 in preference to aniline passivated W. Nanoselectivity was confirmed and demonstrated on nanoscale W/SiO2 patterned samples by TEM analysis. For a 1:1 Si:Al ratio, a dielectric constant (k) value of 3.3 was measured. For a 2:1 Si:Al ratio, a dielectric constant (k) value of 2.5 was measured. The k value well below that of Al2O3 and SiO2 is consistent with the formation of a low-density, low-k SiO2/Al2O3 nanolaminate in a purely thermal process. This is the first report of a further thermal CVD process for deposition of a low-k dielectric and the first report for a selective low-k process on the nanoscale.

Splicing transcriptome-wide association study to identify splicing events for pancreatic cancer risk.

A large proportion of the heritability of pancreatic cancer risk remains elusive, and the contribution of specific mRNA splicing events to pancreatic cancer susceptibility has not been systematically evaluated. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies (Enet, LASSO and MCP) to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for pancreatic cancer risk by assessing 8275 pancreatic cancer cases and 6723 controls of European ancestry. Data from 305 subjects of whom the majority are of European descent in the Genotype-Tissue Expression Project (GTEx) were used and both cis-acting and promoter-enhancer interaction regions were considered to build these models. We identified nine splicing events of seven genes (ABO, UQCRC1, STARD3, ETAA1, CELA3B, LGR4 and SFT2D1) that showed an association of genetically predicted expression with pancreatic cancer risk at a false discovery rate ≤0.05. Of these genes, UQCRC1 and LGR4 have not yet been reported to be associated with pancreatic cancer risk. Fine-mapping analyses supported likely causal associations corresponding to six splicing events of three genes (P4HTM, ABO and PGAP3). Our study identified novel genes and splicing events associated with pancreatic cancer risk, which can improve our understanding of the etiology of this deadly malignancy.