Method For Subclassification Research Articles

Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.

This study addresses the pervasive and debilitating impact of Alzheimer's disease (AD) on individuals and society, emphasizing the crucial need for timely diagnosis. We present a multistage convolutional neural network (CNN)-based framework for AD detection and sub-classification using brain magnetic resonance imaging (MRI). After preprocessing, a 26-layer CNN model was designed to differentiate between healthy individuals and patients with dementia. After detecting dementia, the 26-layer CNN model was reutilized using the concept of transfer learning to further subclassify dementia into mild, moderate, and severe dementia. Leveraging the frozen weights of the developed CNN on correlated medical images facilitated the transfer learning process for sub-classifying dementia classes. An online AD dataset is used to verify the performance of the proposed multistage CNN-based framework. The proposed approach yielded a noteworthy accuracy of 98.24% in identifying dementia classes, whereas it achieved 99.70% accuracy in dementia subclassification. Another dataset was used to further validate the proposed framework, resulting in 100% performance. Comparative evaluations against pre-trained models and the current literature were also conducted, highlighting the usefulness and superiority of the proposed framework and presenting it as a robust and effective AD detection and subclassification method.

PT1 Subclassification Predicts Progression-Free Survival in En Bloc Resection of Bladder Tumor Specimens.

The pathologic diagnosis of pT1 substage in conventional transurethral resection of bladder tumor specimens is inaccurate and has low interobserver reproducibility owing to fragmentation and cauterization of the specimens. En bloc resection of bladder tumor is a novel surgical procedure that improves diagnostic feasibility and accuracy in the pathologic diagnosis of bladder cancer, including depth and extent of invasion. To examine the prognostic value of multiple pT1 subclassification methods, using only en bloc resection specimens. We examined 106 patients with T1 bladder cancer who underwent en bloc resection. The pT1 substages were assigned by 3 different subclassification methods by using the muscularis mucosae or stalk of the papillary lesion as diagnostic landmarks or millimetric depth of invasion. Intergroup differences in progression-free survival and recurrence-free survival rates were analyzed. The prognostic values of clinicopathologic factors for progression/recurrence were analyzed by using multivariate analysis. The pT1 substage was evaluable in all cases. Tumors with invasion into/beyond the muscularis mucosae and those beyond the stalk of the papillary lesion were associated with worse progression-free survival (P = .002 and P = .01, respectively). Notably, no patient with invasion confined to the stalk had disease progression during the 23-month median follow-up period. Only the pT1 subclassification method using the muscularis mucosae was an independent prognosticator of progression in multivariate analysis (P = .03). Precise pathologic subclassification of invasion using en bloc resection specimens may enable accurate prognosis and assessment in patients with bladder cancer with suspicious shallow invasion.

Research on the Composition Analysis and Identification of Ancient Glass Products

Chinese glass has a history of thousands of years, and ancient glass is valuable material evidence of early cultural exchanges and trade between China and the West. With the continuous development of economy and technology, Chinese scholars have made deeper research on glass composition, but at present, most of the works in China focus on the chemical point of view of ancient glass composition, detection technology and cultural exchanges, and less on the statistical law of chemical composition, identification and classification. In this paper, the relationship between weathering, ornamentation, colour and type of glass surface is analyzed by correspondence analysis. Then the Mann-Whitney test is used to test the content of each chemical substance of different types of glass before and after weathering, and the statistical law of the chemical composition content of the surface of cultural relics samples with or without weathering is expounded. Then, the classification criteria of high-potassium glass and lead-barium glass were obtained by using a decision tree and random forest model, and the sub-classification method of four types of glass was given by using K-means clustering algorithm based on standardized data, and each type of glass was divided into two categories. Finally, the neural network is used to analyze and identify the unknown glass. It provides a reference for the further study and identification of the chemical composition of ancient glass.

The Impact of Surgical Approach on 90-Day Prosthetic Joint Infection After Total Hip Replacement — A Population-Based, Propensity Score-Matched Cohort Study

BackgroundProsthetic joint infection (PJI) is a devastating complication of total hip arthroplasty (THA). This study aimed to determine if the anterior approach (AP) influenced the incidence of early PJI in THA compared to posterior approach (PP). MethodsRecord linkage was performed between state-wide hospitalization data and a national joint replacement registry to identify unilateral THA performed via the AP or PP. Complete data on 12,605 AP and 25,569 PP THAs were obtained. Propensity score matching (PSM) was undertaken to match covariates between the approaches. Outcomes were the 90-day PJI hospital readmission rate(using narrow and broad definitions) and 90-day PJI revision rate (defined as component removal or exchange). ResultsThe raw PJI readmission rate for AP was lower than PP (0.8% versus 1.1%, respectively). In the PSM analysis, there was no statistically significant difference in PJI readmission rate between approaches using narrow or broad definition of PJI readmission. In terms of revision for infection, both methods showed AP had a significantly lower rate than PP, with an adjusted odds ratio (OR) of 0.47 (95% confidence interval (CI) 0.30, 0.75) for the 1:1 nearest neighbor method and 0.50 (95% CI 0.32, 0.77) for the subclassification method. ConclusionAfter addressing known confounders, there was no significant difference in the 90-day hospital readmission rate for hip PJI between approaches. There was a significantly reduced 90-day PJI revision rate for AP. The difference in revision may reflect differences in the surgical management of PJI between hip approaches rather than a difference in the underlying rate of infection.

IMPACT OF RAMIPRIL/AMLODIPINE DOSE COMBINATIONS OF SINGLE-PILL VERSUS FREE EQUIVALENT COMBINATIONS ON PERSISTENCE AND MAJOR ADVERSE CARDIOVASCULAR EVENTS: A REAL-WORLD ANALYSIS

Objective: Guidelines recommend use of single-pill combination (SPC) for management of hypertensive patients. Very few studies compared SPCs and free equivalent combinations (FEC) focusing on short-term persistence and major adverse cardiovascular events (MACE) outcomes. This study assessed the impact of different dose combinations of ramipril/amlodipine (R/A) SPC versus FEC on treatment persistence and MACE in patients with hypertension over a 5-year period. Design and method: This real-world, retrospective, observational cohort study was performed using National Health Insurance Fund of Hungary database. The study included hypertensive patients aged > = 18 years who initiated on R/A SPC or FEC, of different dose combinations: R/A 5/5 mg, 5/10 mg, 10/5 mg and 10/10 mg, between 01 April 2012 and 31 December 2018. Primary endpoint was the long-term persistence rate, applying 90-days grace period, between R/A SPC and FEC. Secondary endpoint was the rate of MACE (composite of non-fatal myocardial infarction, stroke, hospitalization for heart failure and all-cause death). Analyses were performed using Cox-proportional hazard model and propensity score sub-classification method to adjust the imbalances in baseline characteristics. Results: Overall, 173,206 patients with hypertension were included (SPC group, N = 104,882 [61%], and FEC group, N = 68,324 [39%]); R/A 5/5 mg representing the largest proportion (N = 107,992 [62%]). In SPC and FEC groups, mean (SD) age was 61.31 (13.75) and 66.08 (13.10) years and proportion of male was 45.4% and 43.1%, respectively. As shown in Table 1, the rate of non-persistence was significantly lower with all R/A SPCs compared to FECs from Month 1 to Month 24 (P < 0.001). Thereafter, the rate of non-persistence remained significantly lower in the SPC groups except in the R/A 5/5 mg dose. MACE was significantly reduced with all R/A SPCs compared to FECs (HR 0.68–0.73; P < = 0.002). There was no impact of age and gender on the association between the treatment and persistence rate or MACE outcomes. Conclusions: Our results confirm the beneficial effects of SPCs compared to FECs in achieving long-term improvement on persistence rate and MACE outcomes in patients with hypertension and support current guidelines recommending a widespread use of SPCs.

A novel molecular classification method for osteosarcoma based on tumor cell differentiation trajectories

Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients. However, the highly complex cell origin involved in osteosarcoma (OS) limits the utility of traditional bulk RNA sequencing for OS subclassification. Single-cell RNA sequencing (scRNA-seq) holds great promise for identifying cell heterogeneity. However, this technique has rarely been used in the study of tumor subclassification. By analyzing scRNA-seq data for six conventional OS and nine cancellous bone (CB) samples, we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell (CSC)-like subset, which allowed us to classify OS samples into three groups. The classification model was further examined using the TARGET dataset. Each subgroup of OS had different prognoses and possible drug sensitivities, and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment. In addition, we verified the classification model through IHC staining in 138 OS samples, revealing a worse prognosis for Group B patients. Furthermore, we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS. These findings provide a novel subclassification method based on scRNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.

Research on Glass Classification Based on K-means Clustering and Decision Tree

Ancient glass is very vulnerable to weather due to the influence of the burial environment, and its chemical composition proportion changes, which makes it impossible to correctly judge its category. In this paper, mathematical models are established based on decision tree and k-means clustering, respectively, to analyze the classification rules of high potassium glass and lead barium glass, and to find out the classification method for sub classification of the two types of glass. Finally, the mode of distinguishing the types of class cultural relics is established, which has guided significance for the identification of class cultural relics.

ASO Author Reflections: Comprehensive Comparison of N Staging Subclassification Methods for Non-Small-Cell Lung Cancer.

ASO Author Reflections: Comprehensive Comparison of N Staging Subclassification Methods for Non-Small-Cell Lung Cancer.

Comparison of statistical methodologies used to estimate the treatment effect on time-to-event outcomes in observational studies

ABSTRACT The use of real-world data became more and more popular in the pharmaceutical industry. The impact of real-world evidence is now well emphasized by the regulatory authorities. Indeed, the analysis of this type of data can play a key role for treatment efficacy and safety. The aim of this work is to assess various methods and give guidance on the comparisons of drugs, mostly with respect to time-to-event data, in non-randomized studies with potentially confounding variables. For that purpose, several statistical methodologies are compared based on simulation studies. These methodologies belong to family classes of methods that are widely used for this type of problem: regression, matching, weighting and subclassification methods. The evaluation criteria used to compare methods performances are the relative bias, the mean square error, the coverage probability and the width of the confidence interval. In this paper, we consider different scenarios of dataset features in order to study the effect of the sample size, the number of covariates and the magnitude of the treatment effect on the statistical methodologies performances. These statistical analyses are conducted within a proportional hazard model framework. Furthermore, we highlight the advantage of using techniques to identify relevant covariates for time-to-event outcomes by comparing two variable selection methods under a frequentist and a Bayesian inference. Based on simulation results, recommendations on each of the family of methods are provided to guide decision making.

Prognostic significance of the N1 classification pattern: a meta-analysis of different subclassification methods.

The number of positive lymph node stations has been viewed as a subclassification in the N1 category in the new revision of tumour node metastasis (TNM) staging. However, the survival curve of these patients overlapped with that of some patients in the N2 categories. Our study focused on the prognostic significance of different subclassifications for N1 patients. We systematically searched PubMed, Ovid, Web of Science and the Cochrane Library on the topic of N1 lymph node dissection. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were used to assess the prognostic significance of N1 metastases. I2 statistics was used to evaluate heterogeneity among the studies: If significant heterogeneity existed (P ≤ 0.10; I2 >50%), a random effect model was adopted. After a careful investigation, a total of 17 articles were included in the analysis. The results showed that patients with non-small-cell lung cancer with multistation N1 disease have worse survival compared with those with single-station N1 disease (HR 1.53, 95% CI 1.32-1.77; P < 0.001; I2 = 5.1%). No significant difference was observed between groups when we assessed the number of positive lymph nodes (single or multiple) (HR 1.25, 95% CI 0.96-1.64; P = 0.097; I2 = 72.5%). Patients with positive hilar zone lymph nodes had poorer survival than those limited to the intrapulmonary zone (HR 1.80, 95% CI 1.57-2.07; P < 0.001; I2 = 0%). A subgroup analysis conducted according to the different validated lymph node maps showed a stable result. Our result confirmed the prognostic significance of the N1 subclassification based on station number. Meanwhile, location-based classifications, especially zone-based, were also identified as prognostically significant, which may need further confirmation and validation in the staged population.

Modification of a new subclassification of Grade B postoperative pancreatic fistula: A bicenter retrospective cohort study.

To propose a modified subclassification of grade B postoperative pancreatic fistula (POPF) based on management approaches in Chinese patients. Data of consecutive pancreatoduodenectomy at two hospitals in China from 2013 to 2018 were collected, and outcomes were compared across different groups of POPF. Subclassification of B-POPF was made based on intervention to B1: non-interventional subclass and B2: interventional subclass. A total of 142 of 522 patients had biochemical leaks (BLs) (27.2%), and POPFs developed in 106 of 522 patients (20.3%), with 81 B-POPFs (15.5%) and 25 C-POPFs (4.8%). BL did not differ from the non-fistula condition in almost all outcomes. The differences of outcomes among the non-fistula/BL, B-POPF and C-POPF groups were significant. The prevalence of subclass B1 and B2 was 56.8% (46/81) and 43.2% (35/81), respectively. Compared to the B1 group, patients in the B2 group had worse outcomes, such as post-pancreatectomy hemorrhage (15.2% vs 34.3%, P=.045), biliary fistula (13.0% vs 34.3%, P=.023), postoperative hospital stay (32 vs 39days, P=.011), and cost ($US28601.0 vs $US39314.5, P<.001). The recently reported B-POPF subclassification method was modified in Chinese patients according to the intervention, and is more practical, simpler and fits Chinese patients.

경향점수를 활용한 인과효과 추정 방법 비교1) : 대응, 가중, 층화, 이중경향점수 보정

This study aimed to introduce the double propensity score adjustment method and compare it with matching, weighting, and subclassification methods using estimated propensity scores. Based on empirical data analysis, the study emphasized the recognition of the estmand (ATT vs ATE) a researcher wants to investigate, considering the data structure, the sample sizes of the treatment and comparison groups, and the distributions of covariates between the groups to draw a valid and meaningful inference with respect to the effect of a treatment on the population of interest. To do so, we compared the results of four different propensity score utilization methods by analyzing the effects of private education on mathematic achievement with Gyeonggi educational longitudinal research data(GEPS2012). The results showed that matching was relatively limited to use when the sample size of the comparison group was not large enough or the distributions of propensity score between the treament and comparison groups were not sufficiently overlapped. On the other hand, the double propensity score adjustment method could be useful to estimate the average treatment effect for the treated when research targeted a special population and the treatment and comparison groups were different in various aspects.

Treatment Effect Decomposition and Bootstrap Hypothesis Testing in Observational Studies

Causal inference with observational data has drawn attention across various fields. These observational studies typically use matching methods which find matched pairs with similar covariate values. However, matching methods may not directly achieve covariate balance, a measure of matching effectiveness. As an alternative, the Balance Optimization Subset Selection (BOSS) framework, which seeks optimal covariate balance directly, has been proposed. This paper extends BOSS by estimating and decomposing a treatment effect as a combination of heterogeneous treatment effects from a partitioned set. Our method differs from the traditional propensity score subclassification method in that we find a subset in each subclass using BOSS instead of using the stratum determined by the propensity score. Then, by conducting a bootstrap hypothesis test on each component, we check the statistical significance of these treatment effects. These methods are applied to a dataset from the National Supported Work Demonstration (NSW) program which was conducted in the 1970s. By examining the statistical significance, we show that the program was not significantly effective to a specific subgroup composed of those who were already employed. This differs from the combined estimate—the NSW program was effective when considering all the individuals. Lastly, we provide results that are obtained when these steps are repeated with sub-samples.

Color electron microprobe cathodoluminescence of Bishunpur meteorite compared with the traditional optical microscopy method

Abstract Cathodoluminescence (CL) imaging is an outstanding method for sub classification of Unequilibrated Ordinary Chondrites (UOC) - petrological type 3. CL can be obtained by several electron beam apparatuses. The traditional method uses an electron gun coupled to an optical microscope (OM). Although many scanning electron microscopes (SEM) and electron microprobes (EPMA) have been equipped with a cathodoluminescence, this technique was not fully explored. Images obtained by the two methods differ due to a different kind of signal acquisition. While in the CL-OM optical photography true colors are obtained, in the CL-EPMA the results are grayscale monochromatic electronic signals. L-RGB filters were used in the CL-EPMA analysis in order to obtain color data. The aim of this work is to compare cathodoluminescence data obtained from both techniques, optical microscope and electron microprobe, on the Bishunpur meteorite classified as LL 3.1 chondrite. The present study allows concluding that 20 KeV and 7 nA is the best analytical condition at EPMA in order to test the equivalence between CL-EPMA and CL-OM colour results. Moreover, the color index revealed to be a method for aiding the study of the thermal metamorphism, but it is not definitive for the meteorite classification.

DNA methylation signature (SAM40) identifies subgroups of the Luminal A breast cancer samples with distinct survival.

Breast cancer patients with Luminal A disease generally have a good prognosis, but among this patient group are patients with good prognosis that are currently overtreated with adjuvant chemotherapy, and also patients that have a bad prognosis and should be given more aggressive treatment. There is no available method for subclassification of this patient group. Here we present a DNA methylation signature (SAM40) that segregates Luminal A patients based on prognosis, and identify one good prognosis group and one bad prognosis group. The prognostic impact of SAM40 was validated in four independent patient cohorts. Being able to subdivide the Luminal A patients may give the two-sided benefit of identifying one subgroup that may benefit from a more aggressive treatment than what is given today, and importantly, identifying a subgroup that may benefit from less treatment.

Subclassification of high-risk clinically organ-confined prostate cancer for early cancer-specific mortality after radical prostatectomy

High-risk clinically localized prostate cancer is seen in a highly heterogeneous population with a wide variation of clinical aggressiveness and a novel subclassification for the better prediction of clinical outcomes is needed. The aim of this study is to validate a modified D'Amico risk criteria for substratification of high-risk prostate cancer with regard to the prediction of biochemical recurrence, clinical progression-free survival or prostate cancer-specific mortality after radical prostatectomy. We conducted a retrospective multicenter cohort study including 461 clinically organ-confined (cT1-2), D'Amico high-risk prostate cancer patients who underwent radical prostatectomy with pelvic lymph node dissection. The modified criteria subclassified D'Amico high-risk patients into high-risk (n=189, single high-risk parameter and two low-risk parameters) and very high-risk (n=272, at least one more intermediate or high-risk parameter in addition to the qualifying high-risk parameter) groups. Biochemical recurrence-free survival, clinical progression-free survival, prostate cancer-specific mortality and overall survival were analyzed. The very high-risk group, compared with high-risk group, had significantly poorer biochemical recurrence (5- and 10-year biochemical recurrence-free rates: 52.8 vs 73.9% and 42.1 vs 61.7%, respectively, P <0.0001), clinical progression-free survival (5- and 10-year survivals: 91.8 vs 98.2% and 80.5 vs 98.2%, respectively, P=0.0013) and prostate cancer-specific mortality (5- and 10-year mortalities: 2.5 vs 0.0% and 6.7 vs 0.0%, respectively, P =0.0124). D'Amico high-risk patients can achieve very favorable outcomes unless they are classified as very high risk. Our novel subclassification method is very simple and useful for better patient counseling and decision-making in the pretreatment setting.

Examination of subclassification method of the 7.5-arc-second Japan Engineering Geomorphologic Classification Map using remote sensing data

Examination of subclassification method of the 7.5-arc-second Japan Engineering Geomorphologic Classification Map using remote sensing data

Genotype–phenotype correlation in Chinese patients with pulmonary mixed type adenocarcinoma: Relationship between histologic subtypes, TITF-1/SP-A expressions and EGFR mutations

This study aimed to explore the association between adenocarcinoma-related morphological and molecular characteristics and EGFR mutations in Chinese lung adenocarcinomas. A total of 139 consecutively resected pulmonary adenocarcinoma patients were screened for EGFR mutations by the amplification refractory mutation system assay. For the resected specimens, histologic subtypes were sub-classified using either the 2004 WHO classification or the 2011 IASLC/ATS/ERS classification. Meanwhile, TITF-1 (thyroid transcription factor 1) and SP-A (surfactant-associated protein A) immunohistochemistry staining was also detected. The results were correlated with EGFR mutations and clinicopathological features mentioned above. Both sub-classification methods reflected differences in frequencies of EGFR mutations in lung adenocarcinoma subtypes. In summary, mixed non-mucinous bronchioloalveolar carcinoma (BAC) or papillary components and papillary predominant adenocarcinoma showed a higher frequency of EGFR mutations than mucinous BAC components; Also, EGFR mutations were significantly more common in tumors with TITF-1 or SP-A expressions than in those without (p=0.002, p=0.026), especially the sensitivity of TITF-1 (96.9%) and the negative predictive value of TITF-1 (88.2%). Our data further showed significant genotype–phenotype correlations between EGFR mutations and adenocarcinoma-related morphological and molecular characteristics, and patients with special histologic and IHC staining features might have higher EGFR mutation rates. In addition, this study, for the first time, indicated the significant relationship between SPA IHC and EGFR mutations, which needed confirmation by further research.

A Novel Stratification Method in Linkage Studies to Address Inter- and Intra-Family Heterogeneity in Autism

Most genome linkage scans for autism spectrum disorders (ASDs) have failed to be replicated. Recently, a new ASD phenotypic sub-classification method was developed which employed cluster analyses of severity scores from the Autism Diagnostic Interview-Revised (ADI-R). Here, we performed linkage analysis for each of the four identified ADI-R stratified subgroups. Additional stratification was also applied to reduce intra-family heterogeneity and to investigate the impact of gender. For the purpose of replication, two independent sets of single nucleotide polymorphism markers for 392 families were used in our study. This deep subject stratification protocol resulted in 16 distinct group-specific datasets for linkage analysis. No locus reached significance for the combined non-stratified cohort. However, study-wide significant (P = 0.02) linkage scores were reached for chromosomes 22q11 (LOD = 4.43) and 13q21 (LOD = 4.37) for two subsets representing the most severely language impaired individuals with ASD. Notably, 13q21 has been previously linked to autism with language impairment, and 22q11 has been separately associated with either autism or language disorders. Linkage analysis on chromosome 5p15 for a combination of two stratified female-containing subgroups demonstrated suggestive linkage (LOD = 3.5), which replicates previous linkage result for female-containing pedigrees. A trend was also found for the association of previously reported 5p14-p15 SNPs in the same female-containing cohort. This study demonstrates a novel and effective method to address the heterogeneity in genetic studies of ASD. Moreover, the linkage results for the stratified subgroups provide evidence at the gene scan level for both inter- and intra-family heterogeneity as well as for gender-specific loci.

A novel method for finding non-small cell lung cancer diagnosis biomarkers

BackgroundOne of the most common causes of worldwide cancer premature death is non-small cell lung carcinoma (NSCLC) with a very low survival rate of 8%-15%. Since patients with an early stage diagnosis can have up to four times the survival rate, discovering cost-effective biological markers that can be used to improve the diagnosis and prognosis of the disease is an important clinical challenge.In the last few years, significant progress has been made to address this challenge with identified biomarkers ranging from 5-gene signatures to 133-gene signatures. However, A typical molecular sub-classification method for lung carcinomas would have a low predictive accuracy of 68%-71% because datasets of gene-expression profiles typically have tens of thousands of genes for just few hundreds of patients. This type of datasets create many technical challenges impacting the accuracy of the diagnostic prediction.ResultsWe discovered that a small set of nine gene-signatures (JAG1, MET, CDH5, ABCC3, DSP, ABCD3, PECAM1, MAPRE2 and PDF5) from the dataset of 12,600 gene-expression profiles of NSCLC acts like an inference basis for NSCLC lung carcinoma and hence can be used as genetic markers. This very small and previously unknown set of biological markers gives an almost perfect predictive accuracy (99.75%) for the diagnosis of the disease the sub-type of cancer. Furthermore, we present a novel method that finds genetic markers for sub-classification of NSCLC. We use generalized Lorenz curves and Gini ratios to overcome many challenges arose from datasets of gene-expression profiles. Our method discovers novel genetic changes that occur in lung tumors using gene-expression profiles.ConclusionsWhile proteins encoded by some of these gene-signatures (e.g., JAG1 and MAPRE2) have been showed to involve in the signal transduction of cells and proliferation control of normal cells, specific functions of proteins encoded by other gene-signatures have not yet been determined. Hence, this work opens new questions for structural and molecular biologists about the role of these gene-signatures for the disease.