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Fang J, Wu J, Hong G, Zheng L, Yu L, Liu X, Lin P, Yu Z, Chen D, Lin Q, Jing C, Zhang Q, Wang C, Zhao J, Yuan X, Wu C, Zhang Z, Guo M, Zhang J, Zheng J, Lei A, Zhang T, Lan Q, Kong L, Wang X, Wang Z, Ma Q. Cancer screening in hospitalized ischemic stroke patients: a multicenter study focused on multiparametric analysis to improve management of occult cancers. EPMA J 2024; 15:53-66. [PMID: 38463627 PMCID: PMC10923752 DOI: 10.1007/s13167-024-00354-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/02/2024] [Indexed: 03/12/2024]
Abstract
Background/aims The reciprocal promotion of cancer and stroke occurs due to changes in shared risk factors, such as metabolic pathways and molecular targets, creating a "vicious cycle." Cancer plays a direct or indirect role in the pathogenesis of ischemic stroke (IS), along with the reactive medical approach used in the treatment and clinical management of IS patients, resulting in clinical challenges associated with occult cancer in these patients. The lack of reliable and simple tools hinders the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) approach. Therefore, we conducted a multicenter study that focused on multiparametric analysis to facilitate early diagnosis of occult cancer and personalized treatment for stroke associated with cancer. Methods Admission routine clinical examination indicators of IS patients were retrospectively collated from the electronic medical records. The training dataset comprised 136 IS patients with concurrent cancer, matched at a 1:1 ratio with a control group. The risk of occult cancer in IS patients was assessed through logistic regression and five alternative machine-learning models. Subsequently, select the model with the highest predictive efficacy to create a nomogram, which is a quantitative tool for predicting diagnosis in clinical practice. Internal validation employed a ten-fold cross-validation, while external validation involved 239 IS patients from six centers. Validation encompassed receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and comparison with models from prior research. Results The ultimate prediction model was based on logistic regression and incorporated the following variables: regions of ischemic lesions, multiple vascular territories, hypertension, D-dimer, fibrinogen (FIB), and hemoglobin (Hb). The area under the ROC curve (AUC) for the nomogram was 0.871 in the training dataset and 0.834 in the external test dataset. Both calibration curves and DCA underscored the nomogram's strong performance. Conclusions The nomogram enables early occult cancer diagnosis in hospitalized IS patients and helps to accurately identify the cause of IS, while the promotion of IS stratification makes personalized treatment feasible. The online nomogram based on routine clinical examination indicators of IS patients offered a cost-effective platform for secondary care in the framework of PPPM. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00354-8.
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Affiliation(s)
- Jie Fang
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
- School of Medicine, Xiamen University, Xiamen, China
| | - Jielong Wu
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Ganji Hong
- Cerebrovascular Interventional Department, Zhangzhou Hospital of Fujian Province, Zhangzhou, China
| | - Liangcheng Zheng
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Lu Yu
- Department of Neurology, Changxing People’s Hospital, Huzhou, China
| | - Xiuping Liu
- Department of Neurology, The Jilin Center Hospital, Jilin, China
| | - Pan Lin
- Department of Neurology, The Second Hospital of Longyan City, Longyan, China
| | - Zhenzhen Yu
- Department of Neurology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Dan Chen
- Department of Neurology, Xiamen Haicang Hospital, Xiamen, China
| | - Qing Lin
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Chuya Jing
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Qiuhong Zhang
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Chen Wang
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Jiedong Zhao
- School of Clinical Medicine, Fujian Medical University, Fuzhou, China
| | - Xiaodong Yuan
- Department of Gynecology of Xiamen Maternal and Child Health Care Hospital, Xiamen, China
| | - Chunfang Wu
- Department of Neurology, Huaihe Hospital, Henan University, Huaihe, China
| | - Zhaojie Zhang
- Department of Neurology, Kaifeng Hospital of Traditional Chinese Medicine, Kaifeng, China
| | - Mingwei Guo
- Department of Neurology, First Affiliated Hospital of Gannan Medical University, Gannan, China
| | - Junde Zhang
- Department of Neurology, First Affiliated Hospital of Gannan Medical University, Gannan, China
| | - Jingjing Zheng
- Department of Neurology, Ningde Municipal Hospital of Ningde Normal University, Ningde, China
| | - Aidi Lei
- Department of Neurology, The Fifth Hospital of Xiamen, Xiamen, China
| | - Tengkun Zhang
- Department of Neurology, The Fifth Hospital of Xiamen, Xiamen, China
| | - Quan Lan
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | | | - Xinrui Wang
- NHC Key Laboratory of Technical Evaluation of Fertility Regulation for Non-Human Primate (Fujian Maternity and Child Health Hospital), No. 19 Jinjishan Road, Jin’an District, Fuzhou, 350013 China
- Medical Research Center, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Maternityand Child Health Hospital, Fujian Medical University, Fuzhou, China
| | - Zhanxiang Wang
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- Department of Neurosurgery and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
| | - Qilin Ma
- Department of Neurology and Department of Neuroscience, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, 55 Zhenhai Road, Xiamen, 361003 China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Xiamen, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
- School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
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Yang HR, Zahan MN, Yoon Y, Kim K, Hwang DH, Kim WH, Rho IR, Kim E, Kang C. Unveiling the Potent Fibrino(geno)lytic, Anticoagulant, and Antithrombotic Effects of Papain, a Cysteine Protease from Carica papaya Latex Using κ-Carrageenan Rat Tail Thrombosis Model. Int J Mol Sci 2023; 24:16770. [PMID: 38069092 PMCID: PMC10706441 DOI: 10.3390/ijms242316770] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
While fibrinolytic enzymes and thrombolytic agents offer assistance in treating cardiovascular diseases, the existing options are associated with a range of adverse effects. In our previous research, we successfully identified ficin, a naturally occurring cysteine protease that possesses unique fibrin and fibrinogenolytic enzymes, making it suitable for both preventing and treating cardiovascular disorders linked to thrombosis. Papain is a prominent cysteine protease derived from the latex of Carica papaya. The potential role of papain in preventing fibrino(geno)lytic, anticoagulant, and antithrombotic activities has not yet been investigated. Therefore, we examined how papain influences fibrinogen and the process of blood coagulation. Papain is highly stable at pH 4-11 and 37-60 °C via azocasein assay. In addition, SDS gel separation electrophoresis, zymography, and fibrin plate assays were used to determine fibrinogen and fibrinolysis activity. Papain has a molecular weight of around 37 kDa, and is highly effective in degrading fibrin, with a molecular weight of over 75 kDa. Furthermore, papain-based hemostatic performance was confirmed in blood coagulation tests, a blood clot lysis assay, and a κ-carrageenan rat tail thrombosis model, highlighting its strong efficacy in blood coagulation. Papain shows dose-dependent blood clot lysis activity, cleaves fibrinogen chains of Aα, Bβ, and γ-bands, and significantly extends prothrombin time (PT) and activated partial thromboplastin time (aPTT). Moreover, the mean length of the infarcted regions in the tails of Sprague-Dawley rats with κ-carrageenan was shorter in rats administered 10 U/kg of papain than in streptokinase-treated rats. Thus, papain, a cysteine protease, has distinct fibrin and fibrinogenolytic properties, suggesting its potential for preventing or treating cardiovascular issues and thrombosis-related diseases.
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Affiliation(s)
- Hye Ryeon Yang
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
| | - Most Nusrat Zahan
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
| | - Yewon Yoon
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
| | - Kyuri Kim
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
| | - Du Hyeon Hwang
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
| | - Woo Hyun Kim
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
- Institute of Animal Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea
| | - Il Rae Rho
- Institutes of Agriculture and Life Science, Gyeongsang National University, Jinju 52828, Republic of Korea;
| | - Euikyung Kim
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
- Institute of Animal Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea
| | - Changkeun Kang
- Department of Basic Veterinary Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea; (H.R.Y.); (M.N.Z.); (Y.Y.); (K.K.); (D.H.H.); (W.H.K.); (E.K.)
- Institute of Animal Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea
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Ischemic stroke of unclear aetiology: a case-by-case analysis and call for a multi-professional predictive, preventive and personalised approach. EPMA J 2022; 13:535-545. [PMID: 36415625 PMCID: PMC9670046 DOI: 10.1007/s13167-022-00307-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/04/2022] [Indexed: 11/18/2022]
Abstract
Due to the reactive medical approach applied to disease management, stroke has reached an epidemic scale worldwide. In 2019, the global stroke prevalence was 101.5 million people, wherefrom 77.2 million (about 76%) suffered from ischemic stroke; 20.7 and 8.4 million suffered from intracerebral and subarachnoid haemorrhage, respectively. Globally in the year 2019 — 3.3, 2.9 and 0.4 million individuals died of ischemic stroke, intracerebral and subarachnoid haemorrhage, respectively. During the last three decades, the absolute number of cases increased substantially. The current prevalence of stroke is 110 million patients worldwide with more than 60% below the age of 70 years. Prognoses by the World Stroke Organisation are pessimistic: globally, it is predicted that 1 in 4 adults over the age of 25 will suffer stroke in their lifetime. Although age is the best known contributing factor, over 16% of all strokes occur in teenagers and young adults aged 15–49 years and the incidence trend in this population is increasing. The corresponding socio-economic burden of stroke, which is the leading cause of disability, is enormous. Global costs of stroke are estimated at 721 billion US dollars, which is 0.66% of the global GDP. Clinically manifested strokes are only the “tip of the iceberg”: it is estimated that the total number of stroke patients is about 14 times greater than the currently applied reactive medical approach is capable to identify and manage. Specifically, lacunar stroke (LS), which is characteristic for silent brain infarction, represents up to 30% of all ischemic strokes. Silent LS, which is diagnosed mainly by routine health check-up and autopsy in individuals without stroke history, has a reported prevalence of silent brain infarction up to 55% in the investigated populations. To this end, silent brain infarction is an independent predictor of ischemic stroke. Further, small vessel disease and silent lacunar brain infarction are considered strong contributors to cognitive impairments, dementia, depression and suicide, amongst others in the general population. In sub-populations such as diabetes mellitus type 2, proliferative diabetic retinopathy is an independent predictor of ischemic stroke. According to various statistical sources, cryptogenic strokes account for 15 to 40% of the entire stroke incidence. The question to consider here is, whether a cryptogenic stroke is fully referable to unidentifiable aetiology or rather to underestimated risks. Considering the latter, translational research might be of great clinical utility to realise innovative predictive and preventive approaches, potentially benefiting high risk individuals and society at large. In this position paper, the consortium has combined multi-professional expertise to provide clear statements towards the paradigm change from reactive to predictive, preventive and personalised medicine in stroke management, the crucial elements of which are:Consolidation of multi-disciplinary expertise including family medicine, predictive and in-depth diagnostics followed by the targeted primary and secondary (e.g. treated cancer) prevention of silent brain infarction Application of the health risk assessment focused on sub-optimal health conditions to effectively prevent health-to-disease transition Application of AI in medicine, machine learning and treatment algorithms tailored to robust biomarker patterns Application of innovative screening programmes which adequately consider the needs of young populations
Stroke is a severe brain disease which has reached an epidemic scale worldwide: in 2019, the global stroke prevalence was 101.5 million people. The World Stroke Organisation predicted that globally, 1 in 4 adults over the age of 25 will get a stroke in their lifetime. Not only old people but also teenagers and young adults are affected. Current global costs of stroke are estimated at 721 billion US dollars. Due to undiagnosed so-called “silent” brain infarction, the number of affected individuals is about 14 times greater in the population than clinically recorded. If it remains untreated, silent brain infarction may cause many severe and fatal disorders such as dementia, depression and even suicide. In this position paper, the consortium describes how the rudimental approach to treating severely diseased people could be replaced by an innovative predictive and preventive one to protect people against the health-to-disease transition.
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