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Article ; Online: Association between blood heavy metal exposure levels and risk of metabolic dysfunction associated fatty liver disease in adults: 2015-2020 NHANES large cross-sectional study.

Tang, Song / Luo, Simin / Wu, Zhendong / Su, Jiandong

2024 Volume 12, Page(s) 1280163

Abstract: Background: The relationships between heavy metals and fatty liver, especially the threshold values, have not been fully elucidated. The objective of this research was to further investigate the correlation between blood heavy metal exposures and the ... ...

Abstract	Background: The relationships between heavy metals and fatty liver, especially the threshold values, have not been fully elucidated. The objective of this research was to further investigate the correlation between blood heavy metal exposures and the risk of Metabolic dysfunction Associated Fatty Liver Disease (MAFLD) in adults. Methods: Laboratory data on blood metal exposure levels were obtained from National Health and Nutrition Examination Survey (NHANES) data for the period 2015 to 2020 for a cross-sectional study in adults. Associations between blood levels of common heavy metals and the risk of MAFLD in adults were analyzed using multifactorial logistic regression and ranked for heavy metal importance using a random forest model. Finally, thresholds for important heavy metals were calculated using piecewise linear regression model. Results: In a multifactorial logistic regression model, we found that elevated levels of selenium (Se) and manganese (Mn) blood exposure were strongly associated with the risk of MAFLD in adults. The random forest model importance ranking also found that Se and Mn blood exposure levels were in the top two positions of importance for the risk of disease in adults. The restricted cubic spline suggested a non-linear relationship between Se and Mn blood exposure and adult risk of disease. The OR (95% CI) for MAFLD prevalence was 3.936 (2.631-5.887) for every 1 unit increase in Log Mn until serum Mn levels rose to the turning point (Log Mn = 1.10, Mn = 12.61 μg/L). This correlation was not significant ( Conclusion: Blood heavy metals, especially Se and Mn, are significantly associated with MAFLD in adults. They have a non-linear relationship with a clear threshold.
MeSH term(s)	Adult ; Humans ; Cross-Sectional Studies ; Nutrition Surveys ; Metals, Heavy/adverse effects ; Non-alcoholic Fatty Liver Disease ; Selenium
Chemical Substances	Metals, Heavy ; Selenium (H6241UJ22B)
Language	English
Publishing date	2024-02-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2024.1280163
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Book ; Online: UBfuzz

Li, Shaohua / Su, Zhendong

Finding Bugs in Sanitizer Implementations

2024

Abstract: In this paper, we propose a testing framework for validating sanitizer implementations in compilers. Our core components are (1) a program generator specifically designed for producing programs containing undefined behavior (UB), and (2) a novel test ... ...

Abstract	In this paper, we propose a testing framework for validating sanitizer implementations in compilers. Our core components are (1) a program generator specifically designed for producing programs containing undefined behavior (UB), and (2) a novel test oracle for sanitizer testing. The program generator employs Shadow Statement Insertion, a general and effective approach for introducing UB into a valid seed program. The generated UB programs are subsequently utilized for differential testing of multiple sanitizer implementations. Nevertheless, discrepant sanitizer reports may stem from either compiler optimization or sanitizer bugs. To accurately determine if a discrepancy is caused by sanitizer bugs, we introduce a new test oracle called crash-site mapping. We have incorporated our techniques into UBfuzz, a practical tool for testing sanitizers. Over a five-month testing period, UBfuzz successfully found 31 bugs in both GCC and LLVM sanitizers. These bugs reveal the serious false negative problems in sanitizers, where certain UBs in programs went unreported. This research paves the way for further investigation in this crucial area of study. Comment: accepted to ASPLOS 2024
Keywords	Computer Science - Cryptography and Security ; Computer Science - Programming Languages ; Computer Science - Software Engineering
Subject code	005
Publishing date	2024-01-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Effect and Mechanism of Theaflavins on Fluoride Transport and Absorption in Caco-2 Cells.

Fan, Yueqin / Lei, Zhendong / Huang, Jiasheng / Su, Dan / Ni, Dejiang / Chen, Yuqiong

Foods (Basel, Switzerland)

2023 Volume 12, Issue 7

Abstract: This paper investigated the effect and mechanism of theaflavins (TFs) on fluoride ( ... ...

Abstract	This paper investigated the effect and mechanism of theaflavins (TFs) on fluoride (F
Language	English
Publishing date	2023-04-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods12071487
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Article ; Online: Zinc Oxide Nanoclusters Encapsulated in MFI Zeolite as a Highly Stable Adsorbent for the Ultradeep Removal of Hydrogen Sulfide.

Yu, Tao / Zheng, Jinyu / Su, Shikun / Wang, Yundong / Xu, Jianhong / Liu, Zhendong

JACS Au

2024 Volume 4, Issue 3, Page(s) 985–991

Abstract: Often, trace impurities in a feed stream will cause failures in industrial applications. The efficient removal of such a trace impurity from industrial steams, however, is a daunting challenge due to the extremely small driving force for mass transfer. ... ...

Abstract	Often, trace impurities in a feed stream will cause failures in industrial applications. The efficient removal of such a trace impurity from industrial steams, however, is a daunting challenge due to the extremely small driving force for mass transfer. The issue lies in an activity-stability dilemma, that is, an ultrafine adsorbent that offers a high exposure of active sites is favorable for capturing species of a low concentration, but free-standing adsorptive species are susceptible to rapidly aggregating in working conditions, thus losing their intrinsic high activity. Confining ultrafine adsorbents in a porous matrix is a feasible solution to address this activity-stability dilemma. We herein demonstrate a proof of concept by encapsulating ZnO nanoclusters into a pure-silica MFI zeolite (ZnO@silicalite-1) for the ultradeep removal of H
Language	English
Publishing date	2024-03-01
Publishing country	United States
Document type	Journal Article
ISSN	2691-3704
ISSN (online)	2691-3704
DOI	10.1021/jacsau.3c00733
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Article: Construction of machine learning models based on transrectal ultrasound combined with contrast-enhanced ultrasound to predict preoperative regional lymph node metastasis of rectal cancer.

Huang, Xuanzhang / Yang, Zhendong / Qin, Wanyue / Li, Xigui / Su, Shitao / Huang, Jianyuan

Heliyon

2024 Volume 10, Issue 4, Page(s) e26433

Abstract: Purpose: Constructing a machine learning model based on transrectal ultrasound (TRUS) combined with contrast-enhanced ultrasound (CEUS) to predict preoperative regional lymph node metastasis (RLNM) of rectal cancer and provide new references for ... ...

Abstract	Purpose: Constructing a machine learning model based on transrectal ultrasound (TRUS) combined with contrast-enhanced ultrasound (CEUS) to predict preoperative regional lymph node metastasis (RLNM) of rectal cancer and provide new references for decision-making. Materials and methods: 233 patients with rectal cancer were enrolled and underwent TRUS and CEUS prior to surgery. Clinicopathological and ultrasound data were collected to analyze the correlation of RLNM status, clinical features and ultrasound parameters. A 75% training set and 25% test set were utilized to construct seven machine learning algorithms. The DeLong test was used to assess the model's diagnostic performance, then chose the best one to predict RLNM of rectal cancer. Results: The diagnostic performance was most dependent on the following: MMT difference (36), length (30), location (29), AUC ratio (27), and PI ratio (24). The prediction accuracy, sensitivity, specificity, precision, and F1 score range of KNN, Bayes, MLP, LR, SVM, RF, and LightGBM were (0.553-0.857), (0.000-0.935), (0.600-1.000), (0.557-0.952), and (0.617-0.852), respectively. The LightGBM model exhibited the optimal accuracy (0.857) and F1 score (0.852). The AUC for machine learning analytics were (0.517-0.941, 95% CI: 0.380-0.986). The LightGBM model exhibited the highest AUC (0.941, 95% CI: 0.843-0.986), though no statistic significant showed in comparison with the SVM, LR, RF, and MLP models ( Conclusion: The LightGBM machine learning model based on TRUS combined with CEUS may help predict RLNM prior to surgery and provide new references for clinical treatment in rectal cancer.
Language	English
Publishing date	2024-02-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e26433
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Article ; Online: Application of artificial intelligence in diagnosis of pulmonary tuberculosis.

Du, Jingli / Su, Yue / Qiao, Juan / Gao, Shang / Dong, Enjun / Wang, Ruilan / Nie, Yanhui / Ji, Jing / Wang, Zhendong / Liang, Jianqin / Gong, Wenping

Chinese medical journal

2024 Volume 137, Issue 5, Page(s) 559–561

MeSH term(s)	Humans ; Artificial Intelligence ; Tuberculosis, Pulmonary/diagnostic imaging ; Diagnosis, Computer-Assisted
Language	English
Publishing date	2024-02-19
Publishing country	China
Document type	Journal Article
ZDB-ID	127089-8
ISSN	2542-5641 ; 0366-6999 ; 1002-0187
ISSN (online)	2542-5641
ISSN	0366-6999 ; 1002-0187
DOI	10.1097/CM9.0000000000003018
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Article ; Online: Preparation of poly(methacrylic acid-co-ethylene glycol dimethacrylate)-functionalized magnetic polydopamine nanoparticles for the extraction of six cannabinoids in wastewater followed by UHPLC-MS/MS.

Chen, Simin / Qie, Yiqi / Hua, Zhendong / Zhang, Haoyue / Wang, Youmei / Di, Bin / Su, Mengxiang

Talanta

2023 Volume 264, Page(s) 124752

Abstract: Phytocannabinoids and their synthetic analogs (natural and synthetic cannabinoids) are illicit drugs that are widely abused worldwide. Wastewater-based epidemiology (WBE) is an objective approach for the estimation of population-level exposure to a wide ... ...

Abstract	Phytocannabinoids and their synthetic analogs (natural and synthetic cannabinoids) are illicit drugs that are widely abused worldwide. Wastewater-based epidemiology (WBE) is an objective approach for the estimation of population-level exposure to a wide range of substances, especially drugs of abuse. However, the concentrations of cannabinoids in wastewater are extremely low (frequently at the levels of nanograms per liter), and the existing pretreatment procedures for wastewater have the disadvantages of time-consumption or low extraction recoveries. This study aimed to propose a novel poly (methacrylic acid-co-ethylene glycol dimethacrylate)-functionalized polydopamine-coated Fe
MeSH term(s)	Humans ; Tandem Mass Spectrometry/methods ; Chromatography, High Pressure Liquid/methods ; Wastewater ; Solid Phase Extraction/methods ; Magnetic Phenomena ; Cannabinoids
Chemical Substances	polydopamine ; methacrylic acid (1CS02G8656) ; Wastewater ; poly(methacrylic acid-co-ethylene glycol dimethacrylate) ; poly(ethylene glycol)-dimethacrylate ; Cannabinoids
Language	English
Publishing date	2023-05-31
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1500969-5
ISSN	1873-3573 ; 0039-9140
ISSN (online)	1873-3573
ISSN	0039-9140
DOI	10.1016/j.talanta.2023.124752
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Article ; Online: Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.

Lu, Yingjie / Cao, Yuqi / Tang, Xiaohang / Hu, Na / Wang, Zhengyong / Xu, Peng / Hua, Zhendong / Wang, Youmei / Su, Yue / Guo, Yinlong

Talanta

2024 Volume 272, Page(s) 125757

Abstract: Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended ...

Abstract	Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.
MeSH term(s)	Deep Learning ; Mass Spectrometry/methods ; Diagnostic Imaging ; Brain ; Neurotransmitter Agents ; Psychotropic Drugs/pharmacology ; Psychotropic Drugs/analysis
Chemical Substances	Neurotransmitter Agents ; Psychotropic Drugs
Language	English
Publishing date	2024-02-07
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1500969-5
ISSN	1873-3573 ; 0039-9140
ISSN (online)	1873-3573
ISSN	0039-9140
DOI	10.1016/j.talanta.2024.125757
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Book ; Online: Extended Paper

Sotiropoulos, Thodoris / Chaliasos, Stefanos / Su, Zhendong

API-driven Program Synthesis for Testing Static Typing Implementations

2023

Abstract: We introduce a novel approach for testing static typing implementations based on the concept of API-driven program synthesis. The idea is to synthesize type-intensive but small and well-typed programs by leveraging and combining application programming ... ...

Abstract	We introduce a novel approach for testing static typing implementations based on the concept of API-driven program synthesis. The idea is to synthesize type-intensive but small and well-typed programs by leveraging and combining application programming interfaces (APIs) derived from existing software libraries. Our primary insight is backed up by real-world evidence: a significant number of compiler typing bugs are caused by small test cases that employ APIs from the standard library of the language under test. This is attributed to the inherent complexity of the majority of these APIs, which often exercise a wide range of sophisticated type-related features. The main contribution of our approach is the ability to produce small client programs with increased feature coverage, without bearing the burden of generating the corresponding well-formed API definitions from scratch. To validate diverse aspects of static typing procedures (i.e., soundness, precision of type inference), we also enrich our API-driven approach with fault-injection and semantics-preserving modes, along with their corresponding test oracles. We evaluate our implemented tool, Thalia on testing the static typing implementations of the compilers for three popular languages, namely, Scala, Kotlin, and Groovy. Thalia has uncovered 84 typing bugs (77 confirmed and 22 fixed), most of which are triggered by test cases featuring APIs that rely on parametric polymorphism, overloading, and higher-order functions. Our comparison with state-of-the-art shows that Thalia yields test programs with distinct characteristics, offering additional and complementary benefits.
Keywords	Computer Science - Programming Languages ; Computer Science - Software Engineering
Subject code	005
Publishing date	2023-11-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Precise and Generalized Robustness Certification for Neural Networks

Yuan, Yuanyuan / Wang, Shuai / Su, Zhendong

2023

Abstract: The objective of neural network (NN) robustness certification is to determine if a NN changes its predictions when mutations are made to its inputs. While most certification research studies pixel-level or a few geometrical-level and blurring operations ... ...

Abstract	The objective of neural network (NN) robustness certification is to determine if a NN changes its predictions when mutations are made to its inputs. While most certification research studies pixel-level or a few geometrical-level and blurring operations over images, this paper proposes a novel framework, GCERT, which certifies NN robustness under a precise and unified form of diverse semantic-level image mutations. We formulate a comprehensive set of semantic-level image mutations uniformly as certain directions in the latent space of generative models. We identify two key properties, independence and continuity, that convert the latent space into a precise and analysis-friendly input space representation for certification. GCERT can be smoothly integrated with de facto complete, incomplete, or quantitative certification frameworks. With its precise input space representation, GCERT enables for the first time complete NN robustness certification with moderate cost under diverse semantic-level input mutations, such as weather-filter, style transfer, and perceptual changes (e.g., opening/closing eyes). We show that GCERT enables certifying NN robustness under various common and security-sensitive scenarios like autonomous driving. Comment: The extended version of a paper to appear in the Proceedings of the 32nd USENIX Security Symposium, 2023, (USENIX Security '23), 19 pages
Keywords	Computer Science - Cryptography and Security ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-06-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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