AIM:To present a critical discussion of the efficacy of the faecal pyruvate kinase isoenzyme type M2(faecal M2-PK) test for colorectal cancer(CRC) screening based on the currently available studies.METHODS:A literatur...AIM:To present a critical discussion of the efficacy of the faecal pyruvate kinase isoenzyme type M2(faecal M2-PK) test for colorectal cancer(CRC) screening based on the currently available studies.METHODS:A literature search in PubMed and Embase was conducted using the following search terms:fecal Tumor M2-PK,faecal Tumour M2-PK,fecal M2-PK,faecal M2-PK,fecal pyruvate kinase,faecal pyruvate kinase,pyruvate kinase stool and M2-PK stool.RESULTS:Stool samples from 704 patients with CRC and from 11 412 healthy subjects have been investigated for faecal M2-PK concentrations in seventeen independent studies.The mean faecal M2-PK sensitivity was 80.3%;the specificity was 95.2%.Four studies compared faecal M2-PK head-to-head with guaiacbased faecal occult blood test(gFOBT).Faecal M2PK demonstrated a sensitivity of 81.1%,whereas the gFOBT detected only 36.9% of the CRCs.Eight independent studies investigated the sensitivity of faecal M2-PK for adenoma(n = 554),with the following sensitivities:adenoma < 1 cm in diameter:25%;adenoma > 1 cm:44%;adenoma of unspecified diameter:51%.In a direct comparison with gFOBT of adenoma > 1 cm in diameter,47% tested positive with the faecal M2-PK test,whereas the gFOBT detected only 27%.CONCLUSION:We recommend faecal M2-PK as a routine test for CRC screening.Faecal M2-PK closes a gap in clinical practice because it detects bleeding and nonbleeding tumors and adenoma with high sensitivity and specificity.展开更多
Charcot-Marie-Tooth （CMT） disease is the most common hereditary neuropathy, with a population prevalence of 1 in 2500. CMT disease type 1A （CMT1A）, accounting for ~70% of CMT1 cases and ~ 50% of all CMT cases, is ...Charcot-Marie-Tooth （CMT） disease is the most common hereditary neuropathy, with a population prevalence of 1 in 2500. CMT disease type 1A （CMT1A）, accounting for ~70% of CMT1 cases and ~ 50% of all CMT cases, is transmitted in an autosomal dominant manner. CMT1A maps to chromo- some 17pl 1.2 and is caused, in the majority of cases, by a 1.4- Mb tandem duplication that includes the peripheral myelin protein22 （PMP22） gene （Li et al., 2013）. The disease usually presents in the first 20 years of age, causing difficulty in walking or running, distal symmetrical muscle weakness and wasting, and sensory loss （van Paassen et al., 2014）.展开更多
The global prevalence of diabetes is steadily increasing,with a high percentage of patients unaware of their disease status.Screening for diabetes is of great significance in preventive medicine and may benefit from d...The global prevalence of diabetes is steadily increasing,with a high percentage of patients unaware of their disease status.Screening for diabetes is of great significance in preventive medicine and may benefit from deep learning technology.In traditional Chinese medicine,specific features on the ocular surface have been explored as diagnostic indicators for systemic diseases.Here we explore the feasibility of using features from the entire ocular surface to construct deep learning models for risk assessment and detection of type 2 diabetes(T2DM).We performed an observational,multicenter study using ophthalmic images of the ocular surface to develop a deep convolutional network,OcularSurfaceNet.The deep learning system was trained and validated with a multicenter dataset of 416580 images from 67151 participants and tested independently using an additional 91422 images from 12544 participants,and can be used to identify individuals at high risk of T2DM with areas under the receiver operating characteristic curve(AUROC)of 0.89-0.92 and T2DM with AUROC of 0.70-0.82.Our study demonstrated a qualitative relationship between ocular surface images and T2DM risk level,which provided new insights for the potential utility of ocular surface images in T2DM screening.Overall,our findings suggest that the deep learning framework using ocular surface images can serve as an opportunistic screening toolkit for noninvasive and low-cost large-scale screening of the general population in risk assessment and early identification of T2DM patients.展开更多
Objective To confirm previous effort to identify type 2 diabetes susceptibility genes in a Northern Chinese population by conducting a new genome scan with both an increased number of type 2 diabetes families and a n...Objective To confirm previous effort to identify type 2 diabetes susceptibility genes in a Northern Chinese population by conducting a new genome scan with both an increased number of type 2 diabetes families and a new set of microsatellite markers within the previously localized regions.Methods A genome scan method was applied. After multiplexed PCR, electrophoreses, genescan and genotyping analysis, we obtained size information for all loci , and then a further study was done by both parametric and non-parametric linkage analysis to investigate the P values and Z values of these loci.Results We surveyed 34 microsatellite markers which distributed within 5 regions along chromosome 1, and a total of 12?000 genotypes were screened. Evidence of linkage with diabetes was identified for 8 of the 34 loci. All P values of the 8 loci were lower than 0.05, and the highest Z value was 2.17. A very interesting finding is that all 5 markers at the p- terminal 1p36.3-1p36.23 region, spanning a long range of 16.9?cM, were identified to have a low P value of less than 0.05, which suggests that this region may contain multiple susceptibility genes. Regions 4 and 5 also confirmed the previous findings, and we narrowed these two regions to a 2.7?cM and 2.5?cM regions, respectively.Conclusions We further confirmed the results gained in the previous genome-wide scan using an increased number of NIDDM families and a new set of microsatellite markers lying within the initially localized regions. The fact that all 5 loci at the p- terminal region displayed a low P value of less than 0.05 suggests that more than 1 susceptibility gene may reside in this region.展开更多
Background: Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or ...Background: Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.Methods: A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi-K-fold cross-validation, and confusion matrices to provide a reliable classification of diabetes-positive and -negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.Results: A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early-onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.Conclusion: The proposed methodology can rapidly predict early-onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.展开更多
文摘AIM:To present a critical discussion of the efficacy of the faecal pyruvate kinase isoenzyme type M2(faecal M2-PK) test for colorectal cancer(CRC) screening based on the currently available studies.METHODS:A literature search in PubMed and Embase was conducted using the following search terms:fecal Tumor M2-PK,faecal Tumour M2-PK,fecal M2-PK,faecal M2-PK,fecal pyruvate kinase,faecal pyruvate kinase,pyruvate kinase stool and M2-PK stool.RESULTS:Stool samples from 704 patients with CRC and from 11 412 healthy subjects have been investigated for faecal M2-PK concentrations in seventeen independent studies.The mean faecal M2-PK sensitivity was 80.3%;the specificity was 95.2%.Four studies compared faecal M2-PK head-to-head with guaiacbased faecal occult blood test(gFOBT).Faecal M2PK demonstrated a sensitivity of 81.1%,whereas the gFOBT detected only 36.9% of the CRCs.Eight independent studies investigated the sensitivity of faecal M2-PK for adenoma(n = 554),with the following sensitivities:adenoma < 1 cm in diameter:25%;adenoma > 1 cm:44%;adenoma of unspecified diameter:51%.In a direct comparison with gFOBT of adenoma > 1 cm in diameter,47% tested positive with the faecal M2-PK test,whereas the gFOBT detected only 27%.CONCLUSION:We recommend faecal M2-PK as a routine test for CRC screening.Faecal M2-PK closes a gap in clinical practice because it detects bleeding and nonbleeding tumors and adenoma with high sensitivity and specificity.
文摘Charcot-Marie-Tooth （CMT） disease is the most common hereditary neuropathy, with a population prevalence of 1 in 2500. CMT disease type 1A （CMT1A）, accounting for ~70% of CMT1 cases and ~ 50% of all CMT cases, is transmitted in an autosomal dominant manner. CMT1A maps to chromo- some 17pl 1.2 and is caused, in the majority of cases, by a 1.4- Mb tandem duplication that includes the peripheral myelin protein22 （PMP22） gene （Li et al., 2013）. The disease usually presents in the first 20 years of age, causing difficulty in walking or running, distal symmetrical muscle weakness and wasting, and sensory loss （van Paassen et al., 2014）.
基金supported by the Science and Technology Planning Project of Inner Mongolia Autonomous Region[201802146]National Key Research and Development Program of China[2018YFC1707601,2018YFC1704200]+1 种基金Major Basic and Applied Basic Research Projects of Guangdong Province of China[2019B030302005]Mongolian Medicine Standardization Project of Inner Mongolia Autonomous Region People's Government[2018001].
文摘The global prevalence of diabetes is steadily increasing,with a high percentage of patients unaware of their disease status.Screening for diabetes is of great significance in preventive medicine and may benefit from deep learning technology.In traditional Chinese medicine,specific features on the ocular surface have been explored as diagnostic indicators for systemic diseases.Here we explore the feasibility of using features from the entire ocular surface to construct deep learning models for risk assessment and detection of type 2 diabetes(T2DM).We performed an observational,multicenter study using ophthalmic images of the ocular surface to develop a deep convolutional network,OcularSurfaceNet.The deep learning system was trained and validated with a multicenter dataset of 416580 images from 67151 participants and tested independently using an additional 91422 images from 12544 participants,and can be used to identify individuals at high risk of T2DM with areas under the receiver operating characteristic curve(AUROC)of 0.89-0.92 and T2DM with AUROC of 0.70-0.82.Our study demonstrated a qualitative relationship between ocular surface images and T2DM risk level,which provided new insights for the potential utility of ocular surface images in T2DM screening.Overall,our findings suggest that the deep learning framework using ocular surface images can serve as an opportunistic screening toolkit for noninvasive and low-cost large-scale screening of the general population in risk assessment and early identification of T2DM patients.
基金ThisworkwassupportedbytheNationalNaturalSciencesFoundationofChina (No .398962 0 0 ) theNationalHighTechnologyResearchandDevelopmentProgram (No .10 2 10 0 2 0 2 ) theNationalProgramforKeyBasicResearchProject (No .G19980 5 10 16)
文摘Objective To confirm previous effort to identify type 2 diabetes susceptibility genes in a Northern Chinese population by conducting a new genome scan with both an increased number of type 2 diabetes families and a new set of microsatellite markers within the previously localized regions.Methods A genome scan method was applied. After multiplexed PCR, electrophoreses, genescan and genotyping analysis, we obtained size information for all loci , and then a further study was done by both parametric and non-parametric linkage analysis to investigate the P values and Z values of these loci.Results We surveyed 34 microsatellite markers which distributed within 5 regions along chromosome 1, and a total of 12?000 genotypes were screened. Evidence of linkage with diabetes was identified for 8 of the 34 loci. All P values of the 8 loci were lower than 0.05, and the highest Z value was 2.17. A very interesting finding is that all 5 markers at the p- terminal 1p36.3-1p36.23 region, spanning a long range of 16.9?cM, were identified to have a low P value of less than 0.05, which suggests that this region may contain multiple susceptibility genes. Regions 4 and 5 also confirmed the previous findings, and we narrowed these two regions to a 2.7?cM and 2.5?cM regions, respectively.Conclusions We further confirmed the results gained in the previous genome-wide scan using an increased number of NIDDM families and a new set of microsatellite markers lying within the initially localized regions. The fact that all 5 loci at the p- terminal region displayed a low P value of less than 0.05 suggests that more than 1 susceptibility gene may reside in this region.
文摘Background: Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.Methods: A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi-K-fold cross-validation, and confusion matrices to provide a reliable classification of diabetes-positive and -negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.Results: A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early-onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.Conclusion: The proposed methodology can rapidly predict early-onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.
