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Article: Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories.

Li, Wei / Lin, Shuye / He, Yuqi / Wang, Jinghui / Pan, Yuanming

2023 Volume 19, Issue 1, Page(s) 264–269

Abstract: Introduction: Colorectal cancer (CRC) is the third most common cancer. Precise prediction of CRC patients' overall survival (OS) probability could offer advice on its treatment. Neural network (NN) is the first-class algorithm, but a consensus on which ... ...

Abstract	Introduction: Colorectal cancer (CRC) is the third most common cancer. Precise prediction of CRC patients' overall survival (OS) probability could offer advice on its treatment. Neural network (NN) is the first-class algorithm, but a consensus on which NN survival models are better has not been established yet. A predictive model on CRC using Asian data is also lacking. Methods: We conducted 8 NN survival models of CRC ( Results: DeepSurv performed best with a C-index value of 0.8300 in the training cohort and 0.7681 in the test cohort. Conclusions: The deep learning survival model for CRC patients (DeepCRC) could predict CRC's OS accurately.
Language	English
Publishing date	2023-01-13
Publishing country	Poland
Document type	Journal Article
ZDB-ID	2203781-0
ISSN	1734-1922
ISSN	1734-1922
DOI	10.5114/aoms/156477
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network.

Li, Shanshan / Cai, Siyu / Huang, Jinghong / Li, Zongcheng / Shi, Zhengyu / Zhang, Kai / Jiao, Juan / Li, Wei / Pan, Yuanming

Frontiers in endocrinology

2024 Volume 15, Page(s) 1293953

Abstract: Background: The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.: Methods: We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) ... ...

Abstract	Background: The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet. Methods: We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients' overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model's performance. Results: 6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software. Conclusion: Patients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.
MeSH term(s)	Male ; Humans ; Transurethral Resection of Prostate ; Prostatic Neoplasms/surgery ; Prostatic Neoplasms/pathology ; Prognosis ; Biopsy, Large-Core Needle ; Neural Networks, Computer
Language	English
Publishing date	2024-03-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2592084-4
ISSN	1664-2392
ISSN	1664-2392
DOI	10.3389/fendo.2024.1293953
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Article ; Online: Facile synthesis of rose-like composites of zeolites and layered double hydroxides: Growth mechanism and enhanced properties.

Xie, Xiu-Zhen / Xu, Liang / Pan, Yuanming / Mi, Jin-Xiao

Chemosphere

2022 Volume 309, Issue Pt 2, Page(s) 136741

Abstract: Excellent performances of various materials often depend on high specific surface areas. Therefore, increase of specific surface areas is one of the most important means to improve the properties and performances of materials. Herein, we report a facile ... ...

Abstract	Excellent performances of various materials often depend on high specific surface areas. Therefore, increase of specific surface areas is one of the most important means to improve the properties and performances of materials. Herein, we report a facile strategy to prepare novel composite materials of zeolites and hydrotalcite-like layered double hydroxides, with high specific surface areas. The composites with a rose-like morphology were synthesized hydrothermally by adding synthetic zeolites to the raw materials used for the formation of hydrotalcite. The resultant composites were shown to contain two distinct layered double hydroxides with different Mg/Al molar ratios. Nitrogen (N
Language	English
Publishing date	2022-10-06
Publishing country	England
Document type	Journal Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2022.136741
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Article ; Online: Altered gut microbiome composition in nontreated plaque psoriasis patients.

Wen, Chunmiao / Pan, Yuanming / Gao, Ming / Wang, Jianlei / Huang, Kun / Tu, Ping

Microbial pathogenesis

2023 Volume 175, Page(s) 105970

Abstract: Recent studies have demonstrated that dysbiosis of the gut microbiota is associated with psoriasis, but these studies showed some conflicting results. Our study examined differences in microbiome composition associated in people with psoriasis and those ... ...

Abstract	Recent studies have demonstrated that dysbiosis of the gut microbiota is associated with psoriasis, but these studies showed some conflicting results. Our study examined differences in microbiome composition associated in people with psoriasis and those without. Comparing individuals with their healthy partners was a second strategy. We explored the fecal microbiota among 32 nontreated plaque psoriasis patients, 15 healthy controls and 17 healthy couples by metagenomic gene sequencing. The relative levels of intestinal microbiota of the psoriasis cohort differed from those in healthy controls and these patients' partners. However, there was no microbial diversity among these three cohorts. On the level of the phylum, Firmicutes and Bacteroidetes' relative abundances were reversed. Escherichia coli was significantly enriched in the psoriasis group compared with the healthy people and the healthy spouses. Gene functional analysis indicated that Ribosome (ko03010) was upregulated, Flagellar assembly (ko02040) and Bacterial chemotaxis (ko02030) were downregulated in the psoriasis cohort compared with the healthy individuals and the healthy spouses. The microbiota in severe psoriasis patients differed from those with milder conditions. These findings strongly support the association between intestinal flora and psoriasis. It is necessary to perform more meaningful experiments to identify whether the differences of gut microbiota are the cause or consequences of psoriasis in future.
MeSH term(s)	Humans ; Gastrointestinal Microbiome/genetics ; Psoriasis ; Microbiota/genetics ; Bacteria/genetics ; Bacteroidetes ; Feces/microbiology ; Dysbiosis/microbiology ; RNA, Ribosomal, 16S/genetics
Chemical Substances	RNA, Ribosomal, 16S
Language	English
Publishing date	2023-01-06
Publishing country	England
Document type	Journal Article
ZDB-ID	632772-2
ISSN	1096-1208 ; 0882-4010
ISSN (online)	1096-1208
ISSN	0882-4010
DOI	10.1016/j.micpath.2023.105970
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Article: Electron Paramagnetic Resonance and Synchrotron X-ray Absorption Spectroscopy for Highly Sensitive Characterization of Calcium Arsenates

Lin, Jinru / Wiens, Eli / Chen, Ning / Nilges, Mark J. / Chen, Weifeng / Pan, Yuanming

Environmental science & technology. 2022 Apr. 19, v. 56, no. 9

2022

Abstract: Calcium arsenates such as pharmacolite (CaHAsO₄·2H₂O), haidingerite (CaHAsO₄·H₂O), and weilite (CaHAsO₄) are important sinks for arsenic in mine tailings as well as other natural and contaminated sites and are useful for reducing the mobility and ... ...

Abstract	Calcium arsenates such as pharmacolite (CaHAsO₄·2H₂O), haidingerite (CaHAsO₄·H₂O), and weilite (CaHAsO₄) are important sinks for arsenic in mine tailings as well as other natural and contaminated sites and are useful for reducing the mobility and bioavailability of this toxic metalloid in the environment. However, calcium arsenates usually occur in trace amounts dominated by other phases, making their detection, identification, and quantification challenging. In this contribution, pharmacolite, haidingerite, and weilite are shown to exhibit subtle but distinct postedge differences in As K-edge X-ray absorption near-edge structure (XANES) spectra and feature characteristic [AsO₃]²–, [AsO₄]²–, and [AsO₄]⁴– radicals, all derived from the diamagnetic [HAsO₄]²– precursor during γ-ray irradiation, in electron paramagnetic resonance (EPR) spectra. In particular, the ⁷⁵As (nuclear spin I = 3/2 and natural isotope abundance = 100%) hyperfine coupling constants of the [AsO₃]²– radicals in pharmacolite and haidingerite as well as other minerals (e.g., calcite and gypsum) are clearly distinct, allowing the unambiguous identification of calcium arsenates by the EPR technique readily at ∼0.1 wt %. Similarly, linear combination fittings of As K-edge XANES spectra demonstrate that pharmacolite and haidingerite at ∼0.1 wt % each in gypsum-rich mixtures can be detected and quantified as well. Therefore, a combination of the EPR and XANES techniques is a powerful approach for the highly sensitive characterization of calcium arsenates in the quest for the safe management and remediation of arsenic contamination. This work demonstrates the highly sensitive characterization of calcium arsenates by integrated electron paramagnetic resonance and synchrotron X-ray absorption spectroscopy.
Keywords	X-ray absorption spectroscopy ; arsenic ; bioavailability ; calcite ; calcium ; electron paramagnetic resonance spectroscopy ; gypsum ; irradiation ; isotopes ; remediation ; toxicity
Language	English
Dates of publication	2022-0419
Size	p. 5563-5571.
Publishing place	American Chemical Society
Document type	Article
ISSN	1520-5851
DOI	10.1021/acs.est.2c00255
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Electron Paramagnetic Resonance and Synchrotron X-ray Absorption Spectroscopy for Highly Sensitive Characterization of Calcium Arsenates.

Lin, Jinru / Wiens, Eli / Chen, Ning / Nilges, Mark J / Chen, Weifeng / Pan, Yuanming

Environmental science & technology

2022 Volume 56, Issue 9, Page(s) 5563–5571

Abstract: Calcium arsenates such as pharmacolite ( ... ...

Abstract	Calcium arsenates such as pharmacolite (CaHAsO
MeSH term(s)	Arsenates/chemistry ; Arsenic/chemistry ; Calcium/chemistry ; Calcium Compounds ; Calcium Sulfate/chemistry ; Electron Spin Resonance Spectroscopy ; Synchrotrons ; X-Ray Absorption Spectroscopy
Chemical Substances	Arsenates ; Calcium Compounds ; calcium arsenate (95OX15I8ZU) ; Arsenic (N712M78A8G) ; Calcium (SY7Q814VUP) ; Calcium Sulfate (WAT0DDB505)
Language	English
Publishing date	2022-04-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1520-5851
ISSN (online)	1520-5851
DOI	10.1021/acs.est.2c00255
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Article ; Online: Desmarais et al. Reply.

Desmarais, Jacques K / Erba, Alessandro / Pan, Yuanming / Civalleri, Bartolomeo / Tse, John S

Physical review letters

2022 Volume 128, Issue 9, Page(s) 99702

Language	English
Publishing date	2022-04-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.128.099702
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Article: Eukaryotic translation initiation factor 4A1 in the pathogenesis and treatment of cancers.

Huang, Jinghong / Zhang, Lei / Yang, Rui / Yao, Lixia / Gou, Jinming / Cao, Dongdong / Pan, Zeming / Li, Dongmei / Pan, Yuanming / Zhang, Wei

Frontiers in molecular biosciences

2023 Volume 10, Page(s) 1289650

Abstract: Abnormal translate regulation is an important phenomenon in cancer initiation and progression. Eukaryotic translation initiation factor 4A1 (eIF4A1) protein is an ATP-dependent Ribonucleic Acid (RNA) helicase, which is essential for translation and has ... ...

Abstract	Abnormal translate regulation is an important phenomenon in cancer initiation and progression. Eukaryotic translation initiation factor 4A1 (eIF4A1) protein is an ATP-dependent Ribonucleic Acid (RNA) helicase, which is essential for translation and has bidirectional RNA unwinders function. In this review, we discuss the levels of expression, regulatory mechanisms and protein functions of eIF4A1 in different human tumors. eIF4A1 is often involved as a target of microRNAs or long non-coding RNAs during the epithelial-mesenchymal transition, associating with the proliferation and metastasis of tumor cells. eIF4A1 protein exhibits the promising biomarker for rapid diagnosis of pre-cancer lesions, histological phenotypes, clinical staging diagnosis and outcome prediction, which provides a novel strategy for precise medical care and target therapy for patients with tumors at the same time, relevant small molecule inhibitors have also been applied in clinical practice, providing reliable theoretical support and clinical basis for the development of this gene target.
Language	English
Publishing date	2023-11-09
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2814330-9
ISSN	2296-889X
ISSN	2296-889X
DOI	10.3389/fmolb.2023.1289650
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Article: Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.

Li, Wei / Zhang, Minghang / Cai, Siyu / Wu, Liangliang / Li, Chao / He, Yuqi / Yang, Guibin / Wang, Jinghui / Pan, Yuanming

BioData mining

2023 Volume 16, Issue 1, Page(s) 21

Abstract: Backgrounds: The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural ... ...

Abstract	Backgrounds: The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients. Methods: In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance. Results: The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428). Conclusions: GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.
Language	English
Publishing date	2023-07-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2438773-3
ISSN	1756-0381
ISSN	1756-0381
DOI	10.1186/s13040-023-00335-z
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Article ; Online: A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.

Li, Wei / Zhang, Minghang / Cai, Siyu / Li, Siqi / Yang, Biao / Zhou, Shijie / Pan, Yuanming / Xu, Shaofa

Translational cancer research

2023 Volume 12, Issue 10, Page(s) 2887–2897

Abstract: Background: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use.: Methods: In the population-based cohort study, we ... ...

Abstract	Background: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. Methods: In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM. Results: The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model's performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030-0.7045] in the training cohort and 0.7076 (95% CI: 0.7067-0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy. Conclusions: Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.
Language	English
Publishing date	2023-09-22
Publishing country	China
Document type	Journal Article
ZDB-ID	2901601-0
ISSN	2219-6803 ; 2218-676X
ISSN (online)	2219-6803
ISSN	2218-676X
DOI	10.21037/tcr-23-422
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