Perineural invasion(PNI),a particularly insidious form of tumor metastasis distinct from hematogenous or lymphatic spread,has the capacity to extend well beyond the primary tumor site,infiltrating distant regions devoi...Perineural invasion(PNI),a particularly insidious form of tumor metastasis distinct from hematogenous or lymphatic spread,has the capacity to extend well beyond the primary tumor site,infiltrating distant regions devoid of lymphatic or vascular structures.PNI often heralds a decrease in patient survival rates and is recognized as an indicator of an unfavorable prognosis across a variety of cancers.Despite its clinical significance,the underlying molecular mechanisms of PNI remain elusive,complicating the development of specific and efficacious diagnostic and therapeutic strategies.In the realm of cancer research,non-coding RNAs(ncRNAs)have attracted considerable attention due to their multifaceted roles and cancer-specific expression profiles,positioning them as promising candidates for applications in cancer diagnostics,prognostics,and treatment.Among the various types of ncRNAs,microRNAs(miRNAs),long non-coding RNAs(lncRNAs),and circular RNAs(circRNAs)have emerged as influential players in PNI.Their involvement is increasingly recognized as a contributing factor to tumor progression and therapeutic resistance.Our study synthesizes and explores the diverse functions and mechanisms of ncRNAs in relation to PNI in cancer.This comprehensive review aims to shed light on cutting-edge perspectives that could pave the way for innovative diagnostic and therapeutic approaches to address the challenges posed by PNI in oncology.展开更多
BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new ...BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis(UC).METHODS We obtained gene expression profiles of circRNAs,miRNAs,and mRNAs in UC from the Gene Expression Omnibus dataset.The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions.Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs.We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram,whose efficacy was tested with the C-index,receiver operating characteristic curve(ROC),and decision curve analysis(DCA).RESULTS A circRNA-miRNA-mRNA regulatory network was obtained,containing 12 circRNAs,three miRNAs,and 38 mRNAs.Two optimal prognostic-related differentially expressed circRNAs,hsa_circ_0085323 and hsa_circ_0036906,were included to construct a predictive nomogram.The model showed good discrimination,with a C-index of 1(>0.9,high accuracy).ROC and DCA suggested that the nomogram had a beneficial diagnostic ability.CONCLUSION This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC.The circRNa-miRNA-mRNA network in UC could be more clinically significant.展开更多
In this paper, a t/(t+1)-diagnosable system is studied, which can locate a set S with |S|≤t+1 containing all faulty units only if the system has at most t faulty units. On the basis of the characterization of the t/(...In this paper, a t/(t+1)-diagnosable system is studied, which can locate a set S with |S|≤t+1 containing all faulty units only if the system has at most t faulty units. On the basis of the characterization of the t/(t+1)-diagnosable system, a necessary and sufficient condition is presented to judge whether a system is t/(t+1)-diagnosable. Meanwhile, this paper exposes some new and important properties of the t/(t+1)-diagnosable system to present the t/(t+1)-diagnosability of some networks. Furthermore, the following results for the t/(t+1)-diagnosability of some special networks are obtained: a hypercube network of n -dimensions is (3n-5)/(3n-4)-diagnosable, a star network of n -dimensions is (3n-5)/(3n-4)-diagnosable (n≥5) and a 2D-mesh (3D-mesh) with n 2(n 3) units is 8/9-diagnosable (11/12-diagnosable). This paper shows that in general, the t/(t+1)-diagnosability of a system is not only larger than its t/t -diagnosability , but also its classic diagnosability, specially the t/(t+1)-diagnosability of the hypercube network of n -dimensions is about 3 times as large as its classic t -diagnosability and about 1.5 times as large as its t/t -diagnosability.展开更多
BACKGROUND Hepatic perivascular epithelioid cell neoplasms(PEComas)are rare.Diagnostic and treatment experience with hepatic PEComa remains insufficient.CASE SUMMARY Three hepatic PEComa cases are reported in this pap...BACKGROUND Hepatic perivascular epithelioid cell neoplasms(PEComas)are rare.Diagnostic and treatment experience with hepatic PEComa remains insufficient.CASE SUMMARY Three hepatic PEComa cases are reported in this paper:One case of primary malignant hepatic PEComa,one case of benign hepatic PEComa,and one case of hepatic PEComa with an ovarian mature cystic teratoma.During preoperative imaging and pathological assessment of intraoperative frozen samples,patients were diagnosed with hepatocellular carcinoma(HCC),while postoperative pathology and immunohistochemistry subsequently revealed hepatic PEComa.Patients with hepatic PEComa which is misdiagnosed as HCC often require a wider surgical resection.It is easy to mistake them for distant metastases of hepatic PEComa and misdiagnosed as HCC,especially when it’s combined with tumors in other organs.Three patients eventually underwent partial hepatectomy.After 1-4 years of follow-up,none of the patients experienced recurrence or metastases.CONCLUSION A clear preoperative diagnosis of hepatic PEComa can reduce the scope of resection and prevent unnecessary injuries during surgery.展开更多
In early December 2019,a new virus named“2019 novel coronavirus(2019-nCoV)”appeared in Wuhan,China.The disease quickly spread worldwide,resulting in the COVID-19 pandemic.In the currentwork,we will propose a novel f...In early December 2019,a new virus named“2019 novel coronavirus(2019-nCoV)”appeared in Wuhan,China.The disease quickly spread worldwide,resulting in the COVID-19 pandemic.In the currentwork,we will propose a novel fuzzy softmodal(i.e.,fuzzy-soft expert system)for early detection of COVID-19.Themain construction of the fuzzy-soft expert systemconsists of five portions.The exploratory study includes sixty patients(i.e.,fortymales and twenty females)with symptoms similar to COVID-19 in(Nanjing Chest Hospital,Department of Respiratory,China).The proposed fuzzy-soft expert systemdepended on five symptoms of COVID-19(i.e.,shortness of breath,sore throat,cough,fever,and age).We will use the algorithm proposed by Kong et al.to detect these patients who may suffer from COVID-19.In this way,the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not.Finally,we present the comparison between the present system and the fuzzy expert system.展开更多
Rationale and Objectives: Cystic lung disease may be accurately diagnosed by imaging interpretation of specialist radiologists, without other information. We hypothesized that with minimal training non-specialists cou...Rationale and Objectives: Cystic lung disease may be accurately diagnosed by imaging interpretation of specialist radiologists, without other information. We hypothesized that with minimal training non-specialists could perform similarly to specialist physicians in the diagnosis of cystic lung disease. Methods: 72 cystic lung disease cases and 25 cystic lung disease mimics were obtained from three sources: 1) a prospective acquired diffuse lung disease registry, 2) a retrospective search of medical records and 3) teaching files. Cases were anonymized, randomized and interpreted by 7 diffuse lung disease specialists and 15 non-specialist radiologists and pulmonologists. Clinical information other than age and sex was not provided. Prior to interpretation, non-specialists viewed a short PDF training document explaining cystic lung disease interpretation. Results: Correct first choice diagnosis of 85%-88% may be achieved by high-performing specialist readers and 71%-80% by non-specialists and lower-performing specialists, with mean accuracies in the diagnosis of LAM (91%, p Conclusion: With specific but limited training, non-specialist physicians can diagnose cystic lung diseases from CT appearance alone with similar accuracy to specialists, correctly identifying approximately 75% of cases.展开更多
Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-...Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population.Which reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people.To solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class compactness.Therefore,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period.Meanwhile,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation efficiency.The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference models.The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.展开更多
基金Foundation of Henan Educational Committee,Grant Number 22A310024Natural Science Foundation for Young Teachers’Basic Research of Zhengzhou University,Grant Number JC202035025.
文摘Perineural invasion(PNI),a particularly insidious form of tumor metastasis distinct from hematogenous or lymphatic spread,has the capacity to extend well beyond the primary tumor site,infiltrating distant regions devoid of lymphatic or vascular structures.PNI often heralds a decrease in patient survival rates and is recognized as an indicator of an unfavorable prognosis across a variety of cancers.Despite its clinical significance,the underlying molecular mechanisms of PNI remain elusive,complicating the development of specific and efficacious diagnostic and therapeutic strategies.In the realm of cancer research,non-coding RNAs(ncRNAs)have attracted considerable attention due to their multifaceted roles and cancer-specific expression profiles,positioning them as promising candidates for applications in cancer diagnostics,prognostics,and treatment.Among the various types of ncRNAs,microRNAs(miRNAs),long non-coding RNAs(lncRNAs),and circular RNAs(circRNAs)have emerged as influential players in PNI.Their involvement is increasingly recognized as a contributing factor to tumor progression and therapeutic resistance.Our study synthesizes and explores the diverse functions and mechanisms of ncRNAs in relation to PNI in cancer.This comprehensive review aims to shed light on cutting-edge perspectives that could pave the way for innovative diagnostic and therapeutic approaches to address the challenges posed by PNI in oncology.
基金Supported by the National Natural Science Foundation of China,No.81774093,No.81904009,No.81974546 and No.82174182Key R&D Project of Hubei Province,No.2020BCB001.
文摘BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis(UC).METHODS We obtained gene expression profiles of circRNAs,miRNAs,and mRNAs in UC from the Gene Expression Omnibus dataset.The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions.Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs.We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram,whose efficacy was tested with the C-index,receiver operating characteristic curve(ROC),and decision curve analysis(DCA).RESULTS A circRNA-miRNA-mRNA regulatory network was obtained,containing 12 circRNAs,three miRNAs,and 38 mRNAs.Two optimal prognostic-related differentially expressed circRNAs,hsa_circ_0085323 and hsa_circ_0036906,were included to construct a predictive nomogram.The model showed good discrimination,with a C-index of 1(>0.9,high accuracy).ROC and DCA suggested that the nomogram had a beneficial diagnostic ability.CONCLUSION This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC.The circRNa-miRNA-mRNA network in UC could be more clinically significant.
基金Supported by the National Natural Science Foundation of China(No.61862003,61761006)the Natural Science Foundation of Guangxi of China(No.2018GXNSFDA281052)
文摘In this paper, a t/(t+1)-diagnosable system is studied, which can locate a set S with |S|≤t+1 containing all faulty units only if the system has at most t faulty units. On the basis of the characterization of the t/(t+1)-diagnosable system, a necessary and sufficient condition is presented to judge whether a system is t/(t+1)-diagnosable. Meanwhile, this paper exposes some new and important properties of the t/(t+1)-diagnosable system to present the t/(t+1)-diagnosability of some networks. Furthermore, the following results for the t/(t+1)-diagnosability of some special networks are obtained: a hypercube network of n -dimensions is (3n-5)/(3n-4)-diagnosable, a star network of n -dimensions is (3n-5)/(3n-4)-diagnosable (n≥5) and a 2D-mesh (3D-mesh) with n 2(n 3) units is 8/9-diagnosable (11/12-diagnosable). This paper shows that in general, the t/(t+1)-diagnosability of a system is not only larger than its t/t -diagnosability , but also its classic diagnosability, specially the t/(t+1)-diagnosability of the hypercube network of n -dimensions is about 3 times as large as its classic t -diagnosability and about 1.5 times as large as its t/t -diagnosability.
文摘BACKGROUND Hepatic perivascular epithelioid cell neoplasms(PEComas)are rare.Diagnostic and treatment experience with hepatic PEComa remains insufficient.CASE SUMMARY Three hepatic PEComa cases are reported in this paper:One case of primary malignant hepatic PEComa,one case of benign hepatic PEComa,and one case of hepatic PEComa with an ovarian mature cystic teratoma.During preoperative imaging and pathological assessment of intraoperative frozen samples,patients were diagnosed with hepatocellular carcinoma(HCC),while postoperative pathology and immunohistochemistry subsequently revealed hepatic PEComa.Patients with hepatic PEComa which is misdiagnosed as HCC often require a wider surgical resection.It is easy to mistake them for distant metastases of hepatic PEComa and misdiagnosed as HCC,especially when it’s combined with tumors in other organs.Three patients eventually underwent partial hepatectomy.After 1-4 years of follow-up,none of the patients experienced recurrence or metastases.CONCLUSION A clear preoperative diagnosis of hepatic PEComa can reduce the scope of resection and prevent unnecessary injuries during surgery.
文摘In early December 2019,a new virus named“2019 novel coronavirus(2019-nCoV)”appeared in Wuhan,China.The disease quickly spread worldwide,resulting in the COVID-19 pandemic.In the currentwork,we will propose a novel fuzzy softmodal(i.e.,fuzzy-soft expert system)for early detection of COVID-19.Themain construction of the fuzzy-soft expert systemconsists of five portions.The exploratory study includes sixty patients(i.e.,fortymales and twenty females)with symptoms similar to COVID-19 in(Nanjing Chest Hospital,Department of Respiratory,China).The proposed fuzzy-soft expert systemdepended on five symptoms of COVID-19(i.e.,shortness of breath,sore throat,cough,fever,and age).We will use the algorithm proposed by Kong et al.to detect these patients who may suffer from COVID-19.In this way,the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not.Finally,we present the comparison between the present system and the fuzzy expert system.
文摘Rationale and Objectives: Cystic lung disease may be accurately diagnosed by imaging interpretation of specialist radiologists, without other information. We hypothesized that with minimal training non-specialists could perform similarly to specialist physicians in the diagnosis of cystic lung disease. Methods: 72 cystic lung disease cases and 25 cystic lung disease mimics were obtained from three sources: 1) a prospective acquired diffuse lung disease registry, 2) a retrospective search of medical records and 3) teaching files. Cases were anonymized, randomized and interpreted by 7 diffuse lung disease specialists and 15 non-specialist radiologists and pulmonologists. Clinical information other than age and sex was not provided. Prior to interpretation, non-specialists viewed a short PDF training document explaining cystic lung disease interpretation. Results: Correct first choice diagnosis of 85%-88% may be achieved by high-performing specialist readers and 71%-80% by non-specialists and lower-performing specialists, with mean accuracies in the diagnosis of LAM (91%, p Conclusion: With specific but limited training, non-specialist physicians can diagnose cystic lung diseases from CT appearance alone with similar accuracy to specialists, correctly identifying approximately 75% of cases.
基金This work was supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the National Nature Science Foundation of China under Grant Numbers 62272236,61502096,61304205,61773219,61502240in part,by the Public Welfare Fund Project of Zhejiang Province Grant Numbers LGG20E050001.
文摘Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population.Which reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people.To solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class compactness.Therefore,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period.Meanwhile,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation efficiency.The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference models.The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.
