A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method

Risk factor analysis and construction of prediction models of gallbladder carcinoma in patients with gallstones

BackgroundGallbladder carcinoma (GBC) is a biliary tract tumor with a high mortality rate. The objectives of this study were to explore the risk factors of GBC in patients with gallstones and to establish effective screening indicators.MethodsA total of 588 patients from medical centers in two different regions of China were included in this study and defined as the internal test samples and the external validation samples, respectively. We retrospectively reviewed the differences in clinicopathologic data of the internal test samples to find the independent risk factors that affect the occurrence of GBC. Then, we constructed three different combined predictive factors (CPFs) through the weighting method, integral system, and nomogram, respectively, and named them CPF-A, CPF-B, and CPF-C sequentially. Furthermore, we evaluated these indicators through calibration and DCA curves. The ROC curve was used to analyze their diagnostic efficiency. Finally, their diagnostic capabilities were validated in the external validation samples.ResultsIn the internal test samples, the results showed that five factors, namely, age (RR = 3.077, 95% CI: 1.731-5.496), size of gallstones (RR = 13.732, 95% CI: 5.937-31.762), course of gallstones (RR = 2.438, 95% CI: 1.350-4.403), CEA (RR = 9.464, 95% CI: 3.394-26.392), and CA199 (RR = 9.605, 95% CI: 4.512-20.446), were independent risk factors for GBC in patients with gallstones. Then, we established three predictive indicators: CPF-A, CPF-B, and CPF-C. These models were further validated using bootstrapping with 1,000 repetitions. Calibration and decision curve analysis showed that the three models fit well. Meanwhile, multivariate analysis showed that CPF-B and CPF-C were independent risk factors for GBC in patients with gallstones. In addition, the validation results of the external validation samples are essentially consistent with the internal test samples.ConclusionAge (≤58.5 vs. >58.5 years), size of gallstones (≤1.95 vs. >1.95cm), course of gallstones (≤10 vs. >10 years), CEA (≤5 vs. >5 ng/ml), and CA199 (≤37 vs. >37 U/ml) are independent risk factors for GBC in patients with gallstones. When positive indicators were ≥2 among the five independent risk factors or the score of the nomogram was >82.64, the risk of GBC was high in gallstone patients

Hydrochlorothiazide/Losartan Potassium Tablet Prepared by Direct Compression

Hydrochlorothiazide (HCTZ)/losartan potassium (LOS-K) was used as a model drug to prepare compound tablets through the investigation of the compression and mechanical properties of mixed powders to determine the formulation and preparation factors, followed by D-optimal mixture experimental design to optimize the final parameters. The type and amount of lactose monohydrate (SuperTab®14SD, 19.53–26.91%), microcrystalline cellulose (MCC PH102, 32.86–43.31%), pre-gelatinized starch (Starch-1500, 10.96–15.91%), and magnesium stearate (0.7%) were determined according to the compressive work, stress relaxation curves, and Py value. Then, the compression mechanism of the mixed powder was investigated by the Kawakita equation, Shapiro equation, and Heckel analysis, and the mixed powder was classified as a Class-II powder. The compaction pressure (150–300 MPa) and tableting speed (1200–2400 Tab/h) were recommended. A D-optimal mixture experimental design was utilized to select the optimal formulation (No 1, 26.027% lactose monohydrate, 32.811% MCC PH102, and 15.462% pregelatinized starch) according to the drug dissolution rate, using Hyzaar® tablets as a control. Following oral administration in beagle dogs, there were no significant differences in bioavailability between the No. 1 tablet and the Hyzaar® tablet in HCTZ, losartan carboxylic acid (E-3174), and LOS-K (F < F0.05). Thus, formulation and preparation factors were determined according to the combination of the compression and mechanical properties of the mixed powder and quality of tablets, which was demonstrated to be a feasible method in direct powder compression

Stock Market Trading Rules Discovery Based on Biclustering Method

The prediction of stock market’s trend has become a challenging task for a long time, which is affected by a variety of deterministic and stochastic factors. In this paper, a biclustering algorithm is introduced to find the local patterns in the quantized historical data. The local patterns obtained are regarded as the trading rules. Then the trading rules are applied in the short term prediction of the stock price, combined with the minimum-error-rate classification of the Bayes decision theory under the assumption of multivariate normal probability model. In addition, this paper also makes use of the idea of the stream mining to weaken the impact of historical data on the model and update the trading rules dynamically. The experiment is implemented on real datasets and the results prove the effectiveness of the proposed algorithm

A clinical case report of Balamuthia granulomatous amoebic encephalitis in a non-immunocompromised patient and literature review

Abstract Background Balamuthia granulomatous amoebic encephalitis (GAE) is a peculiar parasitic infectious disease of the central nervous system, about 39% of the infected Balamuthia GAE patients were found to be immunocompromised and is extremely rare clinically. The presence of trophozoites in diseased tissue is an important basis for pathological diagnosis of GAE. Balamuthia GAE is a rare and highly fatal infection for which there is no effective treatment plan in clinical practice. Case presentation This paper reports clinical data from a patient with Balamuthia GAE to improve physician understanding of the disease and diagnostic accuracy of imaging and reduce misdiagnosis. A 61-year-old male poultry farmer presented with moderate swelling pain in the right frontoparietal region without obvious inducement three weeks ago. Head computed tomography(CT) and magnetic resonance imaging(MRI) revealed a space-occupying lesion in the right frontal lobe. Intially clinical imaging diagnosed it as a high-grade astrocytoma. The pathological diagnosis of the lesion was inflammatory granulomatous lesions with extensive necrosis, suggesting amoeba infection. The pathogen detected by metagenomic next-generation sequencing (mNGS) is Balamuthia mandrillaris, the final pathological diagnosis was Balamuthia GAE. Conclusion When a head MRI shows irregular or annular enhancement, clinicians should not blindly diagnose common diseases such as brain tumors. Although Balamuthia GAE accounts for only a small proportion of intracranial infections, it should be considered in the differential diagnosis