This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

INTRODUCTIONIgA nephropathy is a disease where the pathogenesis is still poorly understood. Deoxyribonucleic acid (DNA) microarray technique allows tens of thousands of gene expressions to be examined at the same time. Commercial availability of microarray genechips has made this powerful tool accessible for wider utilisation in the study of diseases.

MATERIALS AND METHODSSeven patients with IgA nephropathy, 6 with minimal change nephrotic syndrome (MCNS) as patient controls and 7 normal healthy subjects were screened for the differential expression of genes, genome-wide. The Human Genome U133 Plus 2.0 Arrays (Affymetrix, USA) were used to quantitate the differential expression of 38,500 well-characterised human genes.

RESULTSA total of 7761 gene expressions were identified that have an IgAN/Normal gene expression ratio of 0.06-fold to 5.58-fold. About 35% of the altered gene expressions have no gene title or just a hypothetical protein label such as FLJ30679. Most of the remaining 65% are identified proteins where their importance to IgAN is not immediately apparent at this time. Among the 30 most upregulated and 30 most downregulated genes are Urotensin 2 (upregulated 3.09-fold, P <0.05) and Fatty-acid binding protein 6 (downregulated to 0.12-fold, P <0.05). Retinoic acid receptor alpha (vitamin A receptor) was also found downregulated to 0.41-fold (P <0.005). Taqman realtime polymerase chain reaction (PCR) for urotensin 2 and retinoic acid receptor alpha (RARA) were performed on 20 patients with IgA nephropathy and 11 with Minimal Change Disease and the data correlated with various clinical indices.

CONCLUSIONSThe findings suggest that there may be a therapeutic role for retinoic acid receptor alpha (RARA) in IgA nephropathy and a clinical monitoring role for Urotensin 2 in Minimal Change Disease.

Adult ; Aged ; Case-Control Studies ; Female ; Gene Expression ; Gene Expression Regulation ; Genome-Wide Association Study ; Glomerulonephritis, IGA ; genetics ; metabolism ; pathology ; Humans ; Immunoglobulin A ; genetics ; metabolism ; Male ; Middle Aged ; Nephrosis, Lipoid ; genetics ; metabolism ; pathology ; Oligonucleotide Array Sequence Analysis ; Polymerase Chain Reaction ; Receptors, G-Protein-Coupled ; genetics ; metabolism ; Receptors, Retinoic Acid ; genetics ; metabolism ; Tretinoin ; metabolism

INTRODUCTIONThis paper presents the results of a community survey on urinary abnormalities which covered 1/80th of the population of Singapore in 1975. These findings were compared with the data from the Singapore National Service Registrants in 1974 as well as data from a recent survey in Singapore and that of other Asian and Western countries.

MATERIALS AND METHODSThe study covered 18,000 persons aged 15 years and above, representing a sampling fraction of 1/80th of the population. A total of 16,808 respondents attended the field examination centres, of whom 16,497 had their urine sample tested representing 92.7% of the sample population.

RESULTSIn the dipstick urine testing at the field examination centres, 769 subjects (4.6%) were found to have urinary abnormalities. Two hundred and eighty-two (36.7%) of these 769 subjects were found to have urinary abnormalities based on urine microscopy constituting a prevalence of 1.71%. The prevalence of proteinuria was 0.63% and for both haematuria and proteinuria was 0.73%. The prevalence for hypertension was 0.43% and renal insufficiency was 0.1%.

DISCUSSIONThe consensus is that routine screening for chronic kidney disease (CKD) in the general population is not cost effective as the yield is too low. Whilst, most studies showed that screening of the general population was not cost effective, it has been suggested that screening for targeted groups of subjects could help to identify certain risk groups who may benefit from early intervention to prevent or retard the progression of CKD.

CONCLUSIONThe prevalence of urinary abnormalities in Singapore has remained the same, now and three decades ago.

Adult ; Aged ; Aged, 80 and over ; Female ; Hematuria ; epidemiology ; pathology ; Humans ; Male ; Middle Aged ; Prevalence ; Proteinuria ; epidemiology ; pathology ; Renal Insufficiency, Chronic ; epidemiology ; pathology ; Risk Assessment ; Singapore ; epidemiology ; Urinalysis ; Urinary Tract Infections ; epidemiology ; Young Adult