[Association between congenital hypothyroidism and in-hospital adverse outcomes in very low birth weight infants]

Objective: To investigate the association between congenital hypothyroidism (CH) and the adverse outcomes during hospitalization in very low birth weight infants (VLBWI). Methods: This prospective, multicenter observational cohort study was conducted based on the data from the Sino-northern Neonatal Network (SNN). Data of 5 818 VLBWI with birth weight <1 500 g and gestational age between 24-<37 weeks that were admitted to the 37 neonatal intensive care units from January 1st, 2019 to December 31st, 2022 were collected and analyzed. Thyroid function was first screened at 7 to 10 days after birth, followed by weekly tests within the first 4 weeks, and retested at 36 weeks of corrected gestational age or before discharge. The VLBWI were assigned to the CH group or non-CH group. Chi-square test, Fisher exact probability method, Wilcoxon rank sum test, univariate and multivariate Logistic regression were used to analyze the relationship between CH and poor prognosis during hospitalization in VLBWI. Results: A total of 5 818 eligible VLBWI were enrolled, with 2 982 (51.3%) males and the gestational age of 30 (29, 31) weeks. The incidence of CH was 5.5% (319 VLBWI). Among the CH group, only 121 VLBWI (37.9%) were diagnosed at the first screening. Univariate Logistic regression analysis showed that CH was associated with increased incidence of extrauterine growth retardation (EUGR) (OR=1.31(1.04-1.64), P<0.05) and retinopathy of prematurity (ROP) of stage Ⅲ and above (OR=1.74(1.11-2.75), P<0.05). However, multivariate Logistic regression analysis showed no significant correlation between CH and EUGR, moderate to severe bronchopulmonary dysplasia, grade Ⅲ to Ⅳ intraventricular hemorrhage, neonatal necrotizing enterocolitis in stage Ⅱ or above, and ROP in stage Ⅲ or above (OR=1.04 (0.81-1.33), 0.79 (0.54-1.15), 1.15 (0.58-2.26), 1.43 (0.81-2.53), 1.12 (0.70-1.80), all P>0.05). Conclusion: There is no significant correlation between CH and in-hospital adverse outcomes, possibly due to timely diagnosis and active replacement therapy.

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging regarding to computational efficiency and risk integration. For determining mortality-related genes, we propose an information fusion system based on a fuzzy system to fuse the numerous deep-learning-based risk scores, consider the significance of features related to time-varying effects and risk stratifications, and interpret the directional relationship and interaction between outcome and predictors. Fuzzy rules were implemented to integrate the considerations mentioned above by merging all the risk score models to achieve advanced risk estimation. The genomic data of head and neck squamous cell carcinoma (HNSCC) were used to evaluate the performance of the proposed computational approach. The results indicated that the proposed computational approach exhibited optimal ability to identify mortality risk-related genes in HNSCC patients. The results also suggest that HNSCC mortality is associated with cancer inflammatory response, the interleukin-17A signaling pathway, stellate cell activation, and the extracellular-regulated protein kinase five signaling pathway, which might offer new therapeutic targets HNSCC through immunologic or antiangiogenic mechanisms. The proposed information fusion system can promote the determination of high-risk genes related to cancer mortality. This study contributes a valid cancer mortality risk estimate that can identify mortality-related genes.

Exploring the association of metal mixture in blood to the kidney function and tumor necrosis factor alpha using machine learning methods

This research aimed to approach relationships between metal mixture in blood and kidney function, tumor necrosis factor alpha (TNF-α) by machine learning. Metals levels were measured by Inductively Couple Plasma Mass Spectrometry in blood from 421 participants. We applied K Nearest Neighbor (KNN), Naive Bayes classifier (NB), Support Vector Machines (SVM), random forest (RF), Gradient Boosting Decision Tree (GBDT), Categorical boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Whale Optimization-based XGBoost (WXGBoost) to identify the effect of plasma metals, TNF-α, and estimated glomerular filtration rate (eGFR by CKD-EPI equation). We conducted not only toxic metals, lead (Pb), arsenic (As), cadmium (Cd) but also included trace essential metals, selenium (Se), copper (Cu), zinc (Zn), cobalt (Co), to predict the interaction of TNF-α, TNF-α/white blood count, and eGFR. The high average TNF-α level group was observed among subjects with higher Pb, As, Cd, Cu, and Zn levels in blood. No associations were shown between the low and high TNF-α level group in blood Se and Co levels. Those with lower eGFR group had high Pb, As, Cd, Co, Cu, and Zn levels. The crucial predictor of TNF-α level in metals was blood Pb, and then Cd, As, Cu, Se, Zn and Co. The machine learning revealed that As was the major role among predictors of eGFR after feature selection. The levels of kidney function and TNF-α were modified by co-exposure metals. We were able to acquire highest accuracy of over 85% in the multi-metals exposure model. The higher Pb and Zn levels had strongest interaction with declined eGFR. In addition, As and Cd had synergistic with prediction model of TNF-α. We explored the potential of machine learning approaches for predicting health outcomes with multi-metal exposure. XGBoost model added SHAP could give an explicit explanation of individualized and precision risk prediction and insight of the interaction of key features in the multi-metal exposure.

Prevalence of Carbapenem Resistance Genes among Acinetobacter baumannii Isolated from a Teaching Hospital in Taiwan

The problem of antibiotic-resistant strains has become a global public issue; antibiotic resistance not only limits the choice of treatments but also increases morbidity, mortality and treatment costs. The multi-drug resistant Acinetobacter baumannii is occurring simultaneously in hospitals and has become a major public health issue worldwide. Although many medical units have begun to control the use of antibiotics and paid attention to the issue of drug resistance, understanding the transmission pathways of clinical drug-resistant bacteria and drug-resistant mechanisms can be effective in real-time control and prevent the outbreak of antibiotic-resistant pathogens. In this study, a total of 154 isolates of Acinetobacter baumannii obtained from Chia-Yi Christian Hospital in Taiwan were collected for specific resistance genotyping analysis. Ten genes related to drug resistance, including blaOXA-51-like, blaOXA-23-like, blaOXA-58-like, blaOXA-24-like, blaOXA-143-like, tnpA, ISAba1, blaPER-1, blaNDM and blaADC, and the repetitive element (ERIC2) were selected for genotyping analysis. The results revealed that 135 A. baumannii isolates (87.6%) carried the blaOXA-51-like gene, 4.5% of the isolates harbored the blaOXA-23-like gene, and 3.2% of the isolates carried the blaOXA-58-like gene. However, neither the blaOXA-24-like nor blaOXA-143-like genes were detected in the isolates. Analysis of ESBL-producing strains revealed that blaNDM was not found in the test strains, but 38.3% of the test isolates carried blaPER-1. In addition, blaADC, tnpA and ISAba1genes were found in 64.9%, 74% and 93% of the isolates, respectively. Among the carbapenem-resistant strains of A. baumannii, 68% of the isolates presenting a higher antibiotic resistance carried both tnpA and ISAba1 genes. Analysis of the relationship between their phenotypes (antibiotic resistant and biofilm formation) and genotypes (antibiotic-resistant genes and biofilm-related genes) studied indicated that the bap, ompA, ISAba1and blaOXA-51 genes influenced biofilm formation and antibiotic resistance patterns based on the statistical results of a hierarchical clustering dendrogram. The analysis of the antibiotic-resistant mechanism provides valuable information for the screening, identification, diagnosis, treatment and control of clinical antibiotic-resistant pathogens, and is an important reference pointer to prevent strains from producing resistance.

Application of deep learning to predict the low serum albumin in new hemodialysis patients

BACKGROUND

Serum albumin level is a crucial nutritional indicator for patients on dialysis. Approximately one-third of patients on hemodialysis (HD) have protein malnutrition. Therefore, the serum albumin level of patients on HD is strongly correlated with mortality.

METHODS

In study, the data sets were obtained from the longitudinal electronic health records of the largest HD center in Taiwan from July 2011 to December 2015, included 1,567 new patients on HD who met the inclusion criteria. Multivariate logistic regression was performed to evaluate the association of clinical factors with low serum albumin, and the grasshopper optimization algorithm (GOA) was used for feature selection. The quantile g-computation method was used to calculate the weight ratio of each factor. Machine learning and deep learning (DL) methods were used to predict the low serum albumin. The area under the curve (AUC) and accuracy were calculated to determine the model performance.

RESULTS

Age, gender, hypertension, hemoglobin, iron, ferritin, sodium, potassium, calcium, creatinine, alkaline phosphatase, and triglyceride levels were significantly associated with low serum albumin. The AUC and accuracy of the GOA quantile g-computation weight model combined with the Bi-LSTM method were 98% and 95%, respectively.

CONCLUSION

The GOA method was able to rapidly identify the optimal combination of factors associated with serum albumin in patients on HD, and the quantile g-computation with DL methods could determine the most effective GOA quantile g-computation weight prediction model. The serum albumin status of patients on HD can be predicted by the proposed model and accordingly provide patients with better a prognostic care and treatment.

Overall mortality risk analysis for rectal cancer using deep learning-based fuzzy systems

Colorectal cancer is a leading cause of cancer mortality worldwide, with an increasing incidence rate in developing countries. Integration of genetic information with cancer therapy guidance has shown promise in cancer treatment, indicating its potential as an essential tool in translation oncology. However, the high-throughput analysis and variability of genomic data poses a major challenge to conventional analytic approaches. In this study, we propose an advanced analytic approach, named "Fuzzy-based RNNCoxPH," incorporated fuzzy logic, recurrent neural networks (RNNs), and Cox proportional hazards regression (CoxPH) for detecting missense variants associated with high-risk of all-cause mortality in rectum adenocarcinoma. The test data set was downloaded from "Rectum adenocarcinoma, TCGA-READ" the Genomic Data Commons (GDC) portal. In this study, four model-based risk score models were derived using RNN, CoxPH, RNNCoxPH_Addition, and RNNCoxPH_{Multiplication}. The RNNCoxPH_Addition and RNNCoxPH_{Multiplication} models were obtained as the sum and product of the RNN risk degree matrix and the CoxPH risk degree matrix, respectively. Moreover, the fuzzy logic system was used to calculate the survival risk values of missense variants and classified their membership grade to improve the identification of high-risk gene variation locations associated with cancer mortality. The four models were integrated to develop an advanced risk estimation model. There were 20 028 variants associated with survival status, amongst 17 638 variants were associated with survival and 2390 variants associated with mortality. The proposed Fuzzy-based RNNCoxPH model obtained a balanced accuracy of 93.7%, which was significantly higher than that of the other four test methods. In particular, the CoxPH model is commonly used in medical researches and the XGBoost model is famous for its high accuracy in machine learning. The results suggest that the Fuzzy-based RNNCoxPH model exhibits a higher efficacy in identifying and classifying the missense variants related to mortality risk in rectum adenocarcinoma.

Dimensionality reduction approach for many-objective epistasis analysis

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

Fuzzy-Based Multiobjective Multifactor Dimensionality Reduction for Epistasis Analysis

Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality. However, the binary classification does not reflect the uncertainty of the H and L classification. In this study, we proposed an empirical fuzzy MOMDR (EFMOMDR) to address the limitations of binary classification using the degree of membership through an empirical fuzzy approach. The EFMOMDR can simultaneously consider two incorporated fuzzy-based measures, including correct classification rate and likelihood rate, and does not require parameter tuning. Simulation studies revealed that EFMOMDR has higher 7.14% detection success rates than MOMDR, indicating that the limitations of binary classification of MOMDR have been successfully improved by empirical fuzzy. Moreover, EFMOMDR was used to analyze coronary artery disease in the Wellcome Trust Case Control Consortium dataset.

[Two cases of Wolfram syndrome]

Two adolescents with T1DM participated in the Shanghai Children and Adolescent DM Eye study (SCADE) 2017-2018. The previous T1DM history of the 2 children were 12 years and 4 years respectively. The history of optic atrophy were 8 years and 4 years respectively. The ophthalmic examination showed that there were slight posterior capsule cataract and atrophy of optic atrophy in both patients. Optical Coherance Tomographic examination showed that the choroidal thickness of the inferior inner ring of the optic nerve and RNFL layer thickness decreased markedly, perfusion density and vascular density of the optic disc area and macular decreased markedly, electrophysiological examination showed that amplitude decreased markedly, and visual field examination showed binocular extinction or tubular vision field. Now the 2 adolescents were in closely followed-up. We suggest that children with T1DM whose best corrected visual acuity decreased recently to 0.6 or lower, in addition to routine ophthalmic examination, choroidal thickness, retinal nerve fiber layer thickness, retinal blood flow, ERG and visual field examination are recommended to distinguish wolfram syndrome from T1DM. Genetic sequencing examination of WFS-1 is also recommended if possible.

DeepBarcoding: Deep Learning for Species Classification Using DNA Barcoding

DNA barcodes with short sequence fragments are used for species identification. Because of advances in sequencing technologies, DNA barcodes have gradually been emphasized. DNA sequences from different organisms are easily and rapidly acquired. Therefore, DNA sequence analysis tools play an increasingly crucial role in species identification. This study proposed deep barcoding, a deep learning framework for species classification by using DNA barcodes. Deep barcoding uses raw sequence data as the input to represent one-hot encoding as a one-dimensional image and uses a deep convolutional neural network with a fully connected deep neural network for sequence analysis. It can achieve an average accuracy of >90 percent for both simulation and real datasets. Although deep learning yields outstanding performance for species classification with DNA sequences, its application remains a challenge. The deep barcoding model can be a potential tool for species classification and can elucidate DNA barcode-based species identification.

Multiobjective optimization-driven primer design mechanism: towards user-specified parameters of PCR primer

Primers are critical for polymerase chain reaction (PCR) and influence PCR experimental outcomes. Designing numerous combinations of forward and reverse primers involves various primer constraints, posing a computational challenge. Most PCR primer design methods limit parameters because the available algorithms use general fitness functions. This study designed new fitness functions based on user-specified parameters and used the functions in a primer design approach based on the multiobjective particle swarm optimization (MOPSO) algorithm to address the challenge of primer design with user-specified parameters. Multicriteria evaluation was conducted simultaneously based on primer constraints. The fitness functions were evaluated using 7425 DNA sequences and compared with a predominant primer design approach based on optimization algorithms. Each DNA sequence was run 100 times to calculate the difference between the user-specified parameters and primer constraint values. The algorithms based on fitness functions with user-specified parameters outperformed the algorithms based on general fitness functions for 11 primer constraints. Moreover, MOPSO exhibited superior implementation in all experiments. Practical gel electrophoresis was conducted to verify the PCR experiments and established that MOPSO effectively designs primers based on user-specified parameters.

Effects of radial stress on piezoelectric ceramic tubes and transducers

Static analysis is performed for fiber windings to quantitatively control the radial stress at the outer radius of the piezoelectric ceramic tube. The radial stress is verified both experimentally and theoretically, and the dependence of the resonant and material properties of the piezoelectric ceramic tubes on the radial stress is clarified. The resonance frequencies and dielectric loss remain relatively stable, but the relative permittivity and the short circuit elastic constant decrease with the radial stress. The variations of the increased bandwidth and decreased electromechanical coupling coefficient (k₃₁), piezoelectric constant (d₃₁ and g₃₁), and mechanical quality factor (Q_m) are associated with the height-to-radius ratio. The properties of three cylindrical transducers applied with various radial stress show similar change tendencies, and a difference of 0.34 MPa radial stress results in a variation of approximately 13 in the bandwidth, 14 in Q_m, 15 in k₃₁, d₃₁, and g₃₁, and 16 in the amplitude of the first pulse. These results suggest that the consistency of the radial stress is essential, and it should be relatively small. These findings guide the design and preparation of the enhanced transducer.

Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis

Introduction:

Mortality is a major primary endpoint for long-term hemodialysis (HD) patients. The clinical status of HD patients generally relies on longitudinal clinical observations such as monthly laboratory examinations and physical examinations.

Methods:

A total of 829 HD patients who met the inclusion criteria were analyzed. All patients were tracked from January 2009 to December 2013. Taken together, this study performed full-adjusted-Cox proportional hazards (CoxPH), stepwise-CoxPH, random survival forest (RSF)-CoxPH, and whale optimization algorithm (WOA)-CoxPH model for the all-cause mortality risk assessment in HD patients. The model performance between proposed selections of CoxPH models were evaluated using concordance index.

Results:

The WOA-CoxPH model obtained the highest concordance index compared with RSF-CoxPH and typical selection CoxPH model. The eight significant parameters obtained from the WOA-CoxPH model, including age, diabetes mellitus (DM), hemoglobin (Hb), albumin, creatinine (Cr), potassium (K), Kt/V, and cardiothoracic ratio, have also showed significant survival difference between low- and high-risk characteristics in single-factor analysis. By integrating the risk characteristics of each single factor, patients who obtained seven or more risk characteristics of eight selected parameters were dichotomized as high-risk subgroup, and remaining is considered as low-risk subgroup. The integrated low- and high-risk subgroup showed greater discrepancy compared with each single risk factor selected by WOA-CoxPH model.

Conclusion:

The study findings revealed WOA-CoxPH model could provide better risk assessment performance compared with RSF-CoxPH and typical selection CoxPH model in the HD patients. In summary, patients who had seven or more risk characteristics of eight selected parameters were at potentially increased risk of all-cause mortality in HD population.

Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%-91%), Intersection over Union (IoU, 96% vs. 74%-95%), and loss value (30% vs. 44%-32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%-96%) but a better IoU (94% vs. 89%-93%) and loss value (11% vs. 13%-11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.

Progression Risk Score Estimation Based on Immunostaining Data in Oral Cancer Using Unsupervised Hierarchical Clustering Analysis: A Retrospective Study in Taiwan

This study aimed to investigate whether the progression risk score (PRS) developed from cytoplasmic immunohistochemistry (IHC) biomarkers is available and applicable for assessing risk and prognosis in oral cancer patients. Participants in this retrospective case-control study were diagnosed between 2012 and 2014 and subsequently underwent surgical intervention. The specimens from surgery were stained by IHC for 16 cytoplasmic target markers. We evaluated the results of IHC staining, clinical and pathological features, progression-free survival (PFS), and overall survival (OS) of 102 oral cancer patients using a novel estimation approach with unsupervised hierarchical clustering analysis. Patients were stratified into high-risk (52) and low-risk (50) groups, according to their PRS; a metric consisting of cytoplasmic PLK1, PhosphoMet, SGK2, and SHC1 expression. Moreover, PRS could be extended for use in the Cox proportional hazard regression model to estimate survival outcomes with associated clinical parameters. Our study findings revealed that the high-risk patients had a significantly increased risk in cancer progression compared with low-risk patients (hazard ratio (HR) = 2.20, 95% confidence interval (CI) = 1.10-2.42, p = 0.026). After considering the influences of demographics, risk behaviors, and tumor characteristics, risk estimation with PRS provided distinct PFS groups for patients with oral cancer (p = 0.017, p = 0.019, and p = 0.020). Our findings support that PRS could serve as an ideal biomarker for clinical use in risk stratification and progression assessment in oral cancer.

Effect of clinical factors on trajectory of functional performance in patients undergoing hemodialysis

Purpose

This study aimed to investigate the association between clinical factors and temporary changes in functional performance in patients undergoing hemodialysis.

Methods

This was a retrospective, longitudinal observational study conducted from 2015 to 2017. Eight-two patients undergoing hemodialysis in the outpatient clinic were enrolled. Functional performance was measured using the Karnofsky Performance Status (KPS) scale. Collected data for analysis included demographics, laboratory parameters, and KPS scale scores. All participants were grouped into a high KPS cluster and a low KPS cluster based on dynamic changes in KPS scales from 2015 to 2017.

Results

Participants in the high KPS cluster demonstrated an approximate trend, and those in the low KPS cluster demonstrated a low pattern. By stepwise selection model analysis, age (OR 1.12, 95% CI 1.03–1.23, p = 0.011), serum BUN (OR 1.08, 95% CI 1.02–1.16, p = 0.015), calcium levels (OR 3.24, 95% CI 1.2–8.73, p = 0.02), and beta-2-microglobulin (OR > 1.0, CI >1.00-<1.01, p = 0.031) showed risk for the low KPS cluster. Male sex (OR 0.20, 95% CI 0.04–0.96, p = 0.045) and albumin level (OR 0.02, 95% CI 0–0.4, p = 0.009) showed a low risk for the low KPS cluster.

Conclusions

A different trajectory pattern was observed between the high and low KPS clusters in a 3-year period. Risk factors for the low KPS cluster were age, serum BUN, calcium, and beta-2-microglobulin levels. Male sex and serum albumin levels reduced the risk for the low KPS cluster.

Coherent spin qubit transport in silicon

A fault-tolerant quantum processor may be configured using stationary qubits interacting only with their nearest neighbours, but at the cost of significant overheads in physical qubits per logical qubit. Such overheads could be reduced by coherently transporting qubits across the chip, allowing connectivity beyond immediate neighbours. Here we demonstrate high-fidelity coherent transport of an electron spin qubit between quantum dots in isotopically-enriched silicon. We observe qubit precession in the inter-site tunnelling regime and assess the impact of qubit transport using Ramsey interferometry and quantum state tomography techniques. We report a polarization transfer fidelity of 99.97% and an average coherent transfer fidelity of 99.4%. Our results provide key elements for high-fidelity, on-chip quantum information distribution, as long envisaged, reinforcing the scaling prospects of silicon-based spin qubits.

Application of the Interaction between Tissue Immunohistochemistry Staining and Clinicopathological Factors for Evaluating the Risk of Oral Cancer Progression by Hierarchical Clustering Analysis: A Case-Control Study in a Taiwanese Population

The aim of this single-center case-control study is to investigate the feasibility and accuracy of oral cancer protein risk stratification (OCPRS) to analyze the risk of cancer progression. All patients diagnosed with oral cancer in Taiwan, between 2012 and 2014, and who underwent surgical intervention were selected for the study. The tissue was further processed for immunohistochemistry (IHC) for 21 target proteins. Analyses were performed using the results of IHC staining, clinicopathological characteristics, and survival outcomes. Novel stratifications with a hierarchical clustering approach and combinations were applied using the Cox proportional hazard regression model. Of the 163 participants recruited, 102 patients were analyzed, and OCPRS successfully identified patients with different progression-free survival (PFS) profiles in high-risk (53 subjects) versus low-risk (49 subjects) groups (p = 0.012). OCPRS was composed of cytoplasmic PLK1, phosphoMet, and SGK2 IHC staining. After controlling for the influence of clinicopathological features, high-risk patients were 2.33 times more likely to experience cancer progression than low-risk patients (p = 0.020). In the multivariate model, patients with extranodal extension (HR = 2.66, p = 0.045) demonstrated a significantly increased risk for disease progression. Risk stratification with OCPRS provided distinct PFS groups for patients with oral cancer after surgical intervention. OCPRS appears suitable for routine clinical use for progression and prognosis estimation.

[Auxiliary pathological diagnosis algorithm based on color moments for frozen-section of thyroid cancer]

Objective: To develop a color-moment based model for frozen-section diagnosis of thyroid lesions, and to evaluate the model's value in the frozen-section diagnosis of thyroid cancer. Methods: In this study, 550 frozen thyroid pathological slides, including malignant and non-malignant cases, were collected from Taizhou Central Hospital (Taizhou University Hospital), China, between June 2018 and January 2020. The 550 digitalized frozen-section slides of thyroid were divided into training set (190 slides), validation set (48 slides), test set A (60 slides) and test set B (252 slides). The tumor regions on the slides of malignant cases in the training and validation sets were labeled by pathologists. The labeling information was then used to train the thyroid frozen-section diagnosis models based on the voting method and those based on the color moment. Finally, the performance of two pathological slide diagnosis models was evaluated using the test set A and test set B, respectively. Result: The classification accuracy of the thyroid frozen-section diagnosis model based on the voting method was 90.0% and 83.7%, using test sets A and B, respectively, while that based on color moments was 91.6% and 90.9%, respectively. For actual frozen-section diagnosis of thyroid cancer, the model developed in this study had higher accuracy and stability. Conclusion: This study proposes a color-moment based frozen-section diagnosis model, which is more accurate than other classification models for frozen-section diagnoses of thyroid cancer.

Associations between Circulating Markers of Cholesterol Homeostasis and Macrovascular Events among Patients Undergoing Hemodialysis

Current strategies targeting serum cholesterol bring limited benefits to mortality and macrovascular events prevention among hemodialysis patients. Direct measurements and analysis on circulating markers of cholesterol homeostasis could be promising solutions to this bottleneck. We prospectively enrolled 90 maintenance hemodialysis patients and 9 healthy controls in 2019 for 1 year. We measured circulating desmosterol and lathosterol as markers for cholesterol synthesis and campesterol and sitosterol for cholesterol absorption. At baseline, hemodialysis patients showed higher levels of campesterol (p = 0.023) compared to healthy controls. During follow-up, we identified 14 (15.4%) patients who experienced macrovascular events. Comparisons of cholesterol homeostasis markers between cohorts with and without macrovascular events showed no significant differences in markers of cholesterol synthesis or absorption. Using logistic regression analysis, the odds ratio was not statistically significant for the prediction of macrovascular events after full-adjusting for age, sex, diabetes, serum albumin, cholesterol, and triglyceride. We concluded that hemodialysis patients demonstrated higher level of cholesterols absorption, indicated by circulating campesterol compared to healthy subjects. Markers for cholesterol homeostasis were not significantly associated with macrovascular events during a 1-year follow-up. Our results shed light on the novel therapeutic target of modulating cholesterol absorption in HD patients.

Identification of mortality-risk-related missense variant for renal clear cell carcinoma using deep learning

Introduction:

Kidney renal clear cell carcinoma (KIRCC) is a highly heterogeneous and lethal cancer that can arise in patients with renal disease. DeepSurv combines a deep feed-forward neural network with a Cox proportional hazards function and could provide optimized survival results compared with convenient survival analysis.

Methods:

This study used an improved DeepSurv algorithm to identify the candidate genes to be targeted for treatment on the basis of the overall mortality status of KIRCC subjects. All the somatic mutation missense variants of KIRCC subjects were abstracted from TCGA-KIRC database.

Results:

The improved DeepSurv model (95.1%) achieved greater balanced accuracy compared with the DeepSurv model (75%), and identified 610 high-risk variants associated with overall mortality. The results of gene differential expression analysis also indicated nine KIRCC mortality-risk-related pathways, namely the tRNA charging pathway, the D-myo-inositol-5-phosphate metabolism pathway, the DNA double-strand break repair by nonhomologous end-joining pathway, the superpathway of inositol phosphate compounds, the 3-phosphoinositide degradation pathway, the production of nitric oxide and reactive oxygen species in macrophages pathway, the synaptic long-term depression pathway, the sperm motility pathway, and the role of JAK2 in hormone-like cytokine signaling pathway. The biological findings in this study indicate the KIRCC mortality-risk-related pathways were more likely to be associated with cancer cell growth, cancer cell differentiation, and immune response inhibition.

Conclusion:

The results proved that the improved DeepSurv model effectively classified mortality-related high-risk variants and identified the candidate genes. In the context of KIRCC overall mortality, the proposed model effectively recognized mortality-related high-risk variants for KIRCC.

Association of proportion of the HDL-cholesterol subclasses HDL-2b and HDL-3 and macrovascular events among patients undergoing hemodialysis

Altered high-density lipoprotein cholesterol (HDL-C) subclass distribution in hemodialysis (HD) patients is well documented. Aim of this study is to investigate the relationship between HDL-C subclass distribution and macrovascular events in patients undergoing HD. A total of 164 prevalent HD patients and 71 healthy individuals in one hospital-facilitated clinic were enrolled from May 2019 to July 2019 and individual HD patients was follow-up for one year. Macrovascular events (cerebral stroke, coronary heart disease) were recorded in the study period. The HDL-2b, HDL-3 proportions and biochemical parameters were measured. Pearson correlation test and logistic regression analysis were used to examine correlation and odds ratio (OR). 144 HD patients completed one-year follow-up. Cohort with macrovascular events revealed significantly lower HDL-2b and higher HDL-3 subclass proportions compared to those without events. By multivariable adjustment, HDL-3 subclass proportion revealed significantly increase risk for these events (OR 1.17, 95% CI 1.02–1.41, P = 0.044). HDL-2b subclass was significantly higher and HDL-3 subclass was significantly lower in the HD cohort under the hs-CRP level of < 3 mg/L compared to higher hs-CRP level. In conclusion, HDL-2b and HDL-3 subclasses distributions were associated with macrovascular events in HD patients. Proinflammatory status influences the distribution of HDL-2b and HDL-3 subclasses in HD patients.

Effects of melatonin administration to cashmere goats on cashmere production and hair follicle characteristics in two consecutive cashmere growth cycles

The objective of the study was to determine the long-term effects of melatonin treatment on cashmere production and hair follicle populations in cashmere goats and their activity in two consecutive cashmere growth cycles. Twenty-four female Inner Mongolian Cashmere goats were randomly allocated to two groups (n = 12), one of which received melatonin implants, the other being an untreated control group. Melatonin implants were subcutaneously inserted behind the ear at a dose of 2 mg/kg live weight on two occasions -April 30 and June 30, 2016. Hair samples were collected by combing in April of 2017 and 2018, and the weight, staple length, and diameter of the cashmere fibers were measured. Blood and skin samples were collected monthly between April and September 2016, and in April and September in 2017 for the analysis of melatonin concentration and the characteristics of secondary hair follicle populations, respectively. The results indicated that serum melatonin concentration in the treated goats was elevated (P < 0.05) relative to that of the control group, but only during the first growth cycle. Melatonin treatment of cashmere goats in one cashmere growth cycle increased the weight, staple length, and density (all P < 0.05) of the cashmere fibers and decreased fiber diameter (P < 0.01), but did not affect the characteristics of cashmere production in the subsequent annual cycle. Melatonin treatment had no effect on the population of skin secondary hair follicles for two consecutive cycles. However, in the first growth cycle after treatment, it advanced the onset of activity of skin secondary hair follicles by 2 mo (P < 0.05), and it increased the number of follicles that were active (P < 0.05). In summary, the melatonin treatment of cashmere goats in one cashmere growth cycle improved cashmere production for that cycle only, with no residual effects on the subsequent cycle, a technique acceptable to the cashmere goat industry. The enhancement of cashmere production after the treatment of goats with melatonin appears to involve the acceleration of the annual regeneration of skin secondary hair follicles and increased population of active secondary hair follicles in the skin of cashmere goats.

Higher-order clinical risk factor interaction analysis for overall mortality in maintenance hemodialysis patients

BACKGROUND AND AIMS

In Taiwan, approximately 90% of patients with end-stage renal disease receive maintenance hemodialysis. Although studies have reported the survival predictability of multiclinical factors, the higher-order interactions among these factors have rarely been discussed. Conventional statistical approaches such as regression analysis are inadequate for detecting higher-order interactions. Therefore, this study integrated receiver operating characteristic, logistic regression, and balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction (MDR-ER) analyses to examine the impact of interaction effects between multiclinical factors on overall mortality in patients on maintenance hemodialysis.

METERIALS AND METHODS

In total, 781 patients who received outpatient hemodialysis dialysis three times per week before 1 January 2009 were included; their baseline clinical factor and mortality outcome data were retrospectively collected using an approved data protocol (201800595B0).

RESULTS

Consistent with conventional statistical approaches, the higher-order interaction model could indicate the impact of potential risk combination unique to patients on maintenance hemodialysis on the survival outcome, as described previously. Moreover, the MDR-based higher-order interaction model facilitated higher-order interaction effect detection among multiclinical factors and could determine more detailed mortality risk characteristics combinations.

CONCLUSION

Therefore, higher-order clinical risk interaction analysis is a reasonable strategy for detecting non-traditional risk factor interaction effects on survival outcome unique to patients on maintenance hemodialysis and thus clinically achieving whole-scale patient care.

Forecasting outbound student mobility: A machine learning approach

BACKGROUND

A country's ability to become a prominent knowledge economy is tied closely to its ability to acquire skilled people who can compete internationally while resolving challenges of the future. To equip students with competence that can only by gained by being immersed in a foreign environment, outbound mobility is vital.

METHODS

To analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy.

RESULTS

The proposed approach was examined using a dataset of outbound Taiwanese student mobility to ten countries between 1998 and 2018. Without the requirements of any special conditions for the proprieties of the objective function and constraints, the FSDESVR model retained the advantage of FS, SVR, and DE. A comparison of the FSDESVR model and other forecasting models revealed that FSDESVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) results for all the analyzed nations. The experimental results indicate that FSDESVR achieved higher forecasting accuracy than the compared models from the literature.

CONCLUSION

With the recognition of outbound values, key findings of Taiwanese outbound student mobility, and accurate application of the FSDESVR model, education administration units are exposed to a more in-depth view of future student mobility, which enables the implement of a more accurate education curriculum.

Importance of non-medical reasons for dropout in patients on peritoneal dialysis

BACKGROUND

To describe the associated factors for non-medical reasons for dropout in peritoneal dialysis (PD) patients.

METHODS

A retrospective cohort study was performed using registry data of adult patients commencing PD as their initial renal replacement therapy in one hospital-facilitated PD center in Taiwan between 2014 and 2018. The collected data included socio-demographics and relevant medical and PD-related parameters. Kaplan-Meier analysis was used to determine the impact of non-medical reasons and medical reasons on PD dropout.

RESULTS

The analysis included 224 PD patients, of whom 37 dropped out for non-medical reasons and 187 for medical reasons during the study period. There was significant difference between the two cohorts in age (62.3 years vs. 56.1 years, P = 0.010) and PD vintage (median 3.4 years vs. 4.8 years, P = 0.001). Diabetes was more predominant in the cohort for non-medical reasons than in the one for medical reasons (54.1% vs. 27.3% respectively, P = 0.001). In non-medical reason cohort, two leading reasons given for dropping out were lacking of caregivers (n = 12) and losing confidence (n = 10), whereas PD-related peritonitis (n = 101) was the main medical reason for PD dropout. Using Kaplan-Meier curve analysis, patients in the non-medical reason cohort demonstrated higher cumulative dropout rate compared to patients in the medical reason cohort during a 10-year period (P < 0.001).

CONCLUSIONS

The main characteristics of PD dropout patients for non-medical reasons are age, diabetes, patients' perception and caregiver support.

Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study

Background

Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci.

Objective

This study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.

Methods

A hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability.

Results

The proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes.

Conclusions

We systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets.

[Epidemiological characteristics of leptospirosis in Sichuan province, 2004-2018]

Objective: To explore the epidemiological characteristics of leptospirosis in Sichuan province from 2004 to 2018, and provide evidence for the prevention and control of leptospirosis. Methods: The descriptive epidemiology analysis was conducted based on the epidemic data of leptospirosis collected from the national notifiable infectious disease reporting information system (NNIDRIS) and sentinel surveillance system in 11 areas in Sichuan from 2004 to 2018. The ArcGIS 10.2 software was used for mapping. The SaTScan 9.1.1 software was used to analyze spatiotemporal scanning and characteristics of temporal-spatial clusters of leptospirosis. Results: A total of 2 834 cases of leptospirosis, including 41 deaths, were reported in Sichuan from 2004 to 2018, and the reported morbidity rate was 0.23/100 000 and the mortality rate was 0.003/100 000. It revealed that leptospirosis had an overall downward fluctuated trend. The incidence of leptospirosis had obvious seasonality, mainly from the last ten-day of August to the end of September, 1-2 weeks later after rice harvesting time. The reported cases were mainly males, the male to female ratio of the cases was 2.05∶1. The incidence was higher in age group 50-65 years. The majority of reported cases were farmers, accounting for 82.75% (2 345/2 834), followed by students, accounting for 12.74% (361/2 834). However, rare cases in students had been reported since 2011. In recent years, the high-incidence areas were alternating between Mabian, Muchuan counties along the Yangtze River and Yilong county located in the Jialing River basin. According to the spatial-temporal descriptive analyses by SaTScan, there were two clustering areas in the province where most cases occurred (P<0.001). The average density of field rats in 11 sentinel surveillance areas was 5.44%(14 351/263 767), and the predominant field rats included Anourosorexsquamipes (69.07%), Apodemusagrarius (12.73%). Whatmore, the density of the Apodemusagrarius ranged from 4.60% to 0.19%, showing downward trend with the lowest level in 2018. The annual culture rate of Leptospira from rat kidney samples declined. During 2007-2018, the average positive rate of Leptospira antibodies in healthy people was 24.52%(3 271/13 339), and the predominant serogroup was Icterohaemorrhagiae. There was no replacement of Leptospira serogroup in recent years. Conclusions: The incidence of leptospirosis in Sichuan was extremely low during 2004-2018, and the incidence peak of leptospirosis occurred in rice harvesting period. The cases were mainly old farmers, and the high-risk areas were constantly alternating between the Yangtzi River and the Jialing River basin. Both the density and the carriage rate of Leptospira of Apodemusagrarius were low, and the predominant serogroup was Icterohaemorrhagiae. The average positive rate of leptospira antibodies in healthy people was very low.

Coherent spin control of s-, p-, d- and f-electrons in a silicon quantum dot

Once the periodic properties of elements were unveiled, chemical behaviour could be understood in terms of the valence of atoms. Ideally, this rationale would extend to quantum dots, and quantum computation could be performed by merely controlling the outer-shell electrons of dot-based qubits. Imperfections in semiconductor materials disrupt this analogy, so real devices seldom display a systematic many-electron arrangement. We demonstrate here an electrostatically confined quantum dot that reveals a well defined shell structure. We observe four shells (31 electrons) with multiplicities given by spin and valley degrees of freedom. Various fillings containing a single valence electron-namely 1, 5, 13 and 25 electrons-are found to be potential qubits. An integrated micromagnet allows us to perform electrically-driven spin resonance (EDSR), leading to faster Rabi rotations and higher fidelity single qubit gates at higher shell states. We investigate the impact of orbital excitations on single qubits as a function of the dot deformation and exploit it for faster qubit control.

An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis

OBJECTIVE

Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research.

METHODS AND MATERIAL

In this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification. The FS method combined with MDR measurements yielded an improved ability to distinguish similar frequencies of potential multifactor genotypes.

RESULTS

We compared our results with other MDR-based methods and FSMDR achieved superior detection rates on simulated data sets. The results indicated that the fuzzy classifications can provide insight into the uncertainty of H/L classification in MDR operation.

CONCLUSION

FSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set.

Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions

Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.

Autonomic dysfunction as an independent risk factor for uncontrolled inflammation in chronic rhinosinusitis following functional endoscopic sinus surgery

BACKGROUND

Chronic rhinosinusitis (CRS) is a multi-factorial disorder that causes systemic symptoms beyond rhinologic symptoms alone. A possible association between autonomic nervous system (ANS) dysfunction and CRS has been identified; however, few studies have confirmed this observation. In this study, we prospectively measured changes in ANS dysfunction symptoms following functional endoscopic sinus surgery (FESS) and explored the impact of ANS dysfunction on surgical outcomes of CRS.

METHODOLOGY

Patients diagnosed with CRS who consented to surgical intervention were included prospectively. All patients completed the Sino-nasal Outcome Test-22 (SNOT-22) and the 31-item Composite Autonomic Symptom Score (COMPASS 31) questionnaires before the operation and during the follow-up period. Clinical demographic data, Lund-Mackay, and modified Lund-Kennedy scores were recorded and measured.

RESULTS

A total of 102 patients were enrolled. The median SNOT-22 and COMPASS 31 scores significantly improved following FESS from 43.0 to 14.0 and 21.0 to 11.2 (all P less than 0.001), respectively. FESS led to a significant reduction in the prevalence of various ANS dysfunction symptoms. In multivariate analyses, revision surgeries (odds ratio [OR] 5.012, 95% confidence interval [CI] 1.52416.489; P=0.008), CRS with nasal polyps (OR 4.071, 95% CI 1.454-11.40; P=0.008), and higher Pre-FESS COMPASS 31 scores (OR 1.043, 95% CI 1.003-1.084; P=0.036) were independent risk factors for uncontrolled inflammation following FESS.

CONCLUSIONS

ANS dysfunction symptoms are prevalent in CRS and higher preoperative COMPASS 31 scores correspond with poor surgical outcomes. Following FESS, the majority of ANS dysfunction symptoms can be alleviated. Further investigations are required to explore the possible mechanism of how ANS is involved in the pathogenesis of CRS.

Single-spin qubits in isotopically enriched silicon at low magnetic field

Single-electron spin qubits employ magnetic fields on the order of 1 Tesla or above to enable quantum state readout via spin-dependent-tunnelling. This requires demanding microwave engineering for coherent spin resonance control, which limits the prospects for large scale multi-qubit systems. Alternatively, singlet-triplet readout enables high-fidelity spin-state measurements in much lower magnetic fields, without the need for reservoirs. Here, we demonstrate low-field operation of metal-oxide-silicon quantum dot qubits by combining coherent single-spin control with high-fidelity, single-shot, Pauli-spin-blockade-based ST readout. We discover that the qubits decohere faster at low magnetic fields with [Formula: see text] μs and [Formula: see text] μs at 150 mT. Their coherence is limited by spin flips of residual 29Si nuclei in the isotopically enriched 28Si host material, which occur more frequently at lower fields. Our finding indicates that new trade-offs will be required to ensure the frequency stabilization of spin qubits, and highlights the importance of isotopic enrichment of device substrates for the realization of a scalable silicon-based quantum processor.

[Analysis of evaluation results of malaria elimination in Jiujiang City]

OBJECTIVE

To analyze the evaluation results of malaria elimination and summarize the experience in Jiujiang City, so as to provide the evidence for formulating the surveillance work plan post-malaria elimination.

METHODS

According to the requirements and arrangements of Jiangxi Eliminating Malaria Action Plan (2010 - 2020), the evaluation of malaria elimination was completed in all 13 counties (cites and districts) of Jiujiang City from 2013 to 2016, and the data were collected and analyzed.

RESULTS

In Jiujiang City, the last local malaria case was reported in 2008. From 2010 to 2016, there were 20 imported malaria cases reported, including 12 cases of falciparum malaria, 7 cases of vivax malaria, and 1 case of mixed infection. Totally 67 501 fever patients were tested for Plasmodium and 13 cases were positive, with the positive rate of 0.02%. The comprehensive evaluation scores were between 88.75 and 98.15 in all the 13 counties (cities and districts), which meant they all reached the evaluative criteria.

CONCLUSIONS

All 13 counties (cities and districts) of Jiujiang City have passed the city level evaluative criteria of malaria elimination, and the key work should be the prevention and surveillance of imported malaria in the future.

Effective hybrid approach for protein structure prediction in a two-dimensional Hydrophobic-Polar model

Hydrophobic-polar (HP) models are widely used to predict protein folding and hydrophobic interactions. Numerous optimization algorithms have been proposed to predict protein folding using the two-dimensional (2D) HP model. However, to obtain an optimal protein structure from the 2D HP model remains challenging. In this study, an algorithm integrating particle swarm optimization (PSO) and Tabu search (TS), named PSO-TS, was proposed to predict protein structures based on the 2D HP model. TS can help PSO to avoid getting trapped in a local optima and thus to remove the limitation of PSO in predicting protein folding by the 2D HP model. In this study, a total of 28 protein sequences were used to evaluate the accuracy of PSO-TS in protein folding prediction. The proposed PSO-TS method was compared with 15 other approaches for predicting short and long protein sequences. Experimental results demonstrated that PSO-TS provides a highly accurate, reproducible, and stabile prediction ability for the protein folding by the 2D HP model.

Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions

Motivation

Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities.

Approach

This study proposes a multiobjective MDR (MOMDR) method that is based on a contingency table of MDR as an objective function. MOMDR considers the incorporated measures, including correct classification and likelihood rates, to detect SSIs and adopts set theory to predict the most favorable SSIs with cross-validation consistency. MOMDR enables simultaneously using multiple measures to determine potential SSIs.

Results

Three simulation studies were conducted to compare the detection success rates of MOMDR and single-objective MDR (SOMDR), revealing that MOMDR had higher detection success rates than SOMDR. Furthermore, the Wellcome Trust Case Control Consortium dataset was analyzed by MOMDR to detect SSIs associated with coronary artery disease. Availability and implementation: MOMDR is freely available at https://goo.gl/M8dpDg.

Supplementary information

Supplementary data are available at Bioinformatics online.

Time-varying serum albumin levels and all-cause mortality in prevalent peritoneal dialysis patients: a 5-year observational study

BACKGROUND

In this study, we investigated the association of time-varying serum albumin levels with mortality over a 5-year period in one cohort of patients undergoing long-term peritoneal dialysis (PD) therapy.

METHODS

The participants in this study enrolled 302 patients who underwent long-term PD at a single PD center in Taiwan. We reviewed medical records from 2011 to 2015 retrospectively. Time-averaged albumin level and serum albumin reach rate (defined as the percentage of serum albumin measurements that reached ≥3.5 g/dL) were applied as the predictor variables in the first 2 years (2011-2012). All-cause mortality was used as the outcome variable in the subsequent 3 years (2013-2015). Hazard function of all-cause mortality in the study participants was examined by using Cox proportional hazard regression models .

RESULTS

Patients with different albumin reach rates (75-< 100%, 50-< 75%, 1-< 50%) did not exhibit a significantly increased risk for all-cause mortality. Patients with a 0% albumin reach rate exhibited a significantly increased risk for all-cause mortality (hazard ratio [HR] 7.59, 95% confidence interval [CI], 2.38-24.21) by fully adjusted analysis. Patients with time-averaged albumin levels of < 3.5 g/dL (HR 15.49, 95% CI 1.74-137.72) exhibited a higher risk for all-cause mortality than those with serum albumin levels ≥4.0 g/dL.

CONCLUSIONS

This study demonstrated that higher serum albumin reach rates and higher time-averaged serum albumin levels are associated with a lower mortality rate over a 5-year period among patients undergoing long-term PD.

Comparison of Solute Clearance, Hospitalization Rate, and Aortic Arch Calcification between Online Hemodiafiltration and High-Flux Hemodialysis: A 6-Year Observational Study

BACKGROUND/AIMS

Studies on the long-term clinical benefits of hemodiafiltration (HDF) and high-flux hemodialysis (HFHD) are very limited. This study aimed to investigate the hospitalization rate and aortic arch calcification (AAC) of these two dialysis modalities over 6 years.

METHODS

Participants who received regular HDF and HFHD in one hospital-facilitated hemodialysis center were prospectively enrolled after matching for age, sex, and diabetes between January 2009 and December 2014. Medical records were reviewed retrospectively on demographics, laboratory variables, calcified scores in aortic arch measured by chest radiography, and rates of hospital admission. Cox proportional hazard regression and linear regression were used to obtain the outcome results.

RESULTS

The HDF and HFHD groups consisted of 108 and 102 participants, respectively. Levels of laboratory variables including small soluble solutes and Kt/V were not statistically different over the 6-year period between the HDF and HFHD groups. Calcified scores of the aortic arch increased over 6 years in both groups. The changes in the mean calcified scores were significant when compared between the two groups (0.44-1.82 in HFHD, 0.79-1.8 in HDF, respectively, p = 0.008). Hospitalization rates were 735 per 1,000 patients in the HDF group and 852 per 1,000 patients in the HFHD group, respectively. No significant difference was observed in frequency and days of hospitalization between HDF and HFHD.

CONCLUSION

Hospitalization rates and AAC were observed to be equal for HDF and HFHD.

Decision Theory-Based COI-SNP Tagging Approach for 126 Scombriformes Species Tagging

The mitochondrial gene cytochrome c oxidase I (COI) is commonly used for DNA barcoding in animals. However, most of the COI barcode nucleotides are conserved and sequences longer than about 650 base pairs increase the computational burden for species identification. To solve this problem, we propose a decision theory-based COI SNP tagging (DCST) approach that focuses on the discrimination of species using single nucleotide polymorphisms (SNPs) as the variable nucleotides of the sequences of a group of species. Using the example of 126 teleost mackerel fish species (order: Scombriformes), we identified 281 SNPs by alignment and trimming of their COI sequences. After decision rule making, 49 SNPs in 126 fish species were determined using the scoring system of the DCST approach. These COI-SNP barcodes were finally transformed into one-dimensional barcode images. Our proposed DCST approach simplifies the computational complexity and identifies the most effective and fewest SNPs to resolve or discriminate species for species tagging.

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.

Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting

The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS-PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR. The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016. The results reveal that the errors obtained using FS-PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS-PSOSVR is an effective method for forecasting tourism demand.

Periorbital erythema and swelling as a presenting sign of lupus erythematosus in tertiary referral centers and literature review

Background Cutaneous lupus erythematosus (CLE) includes a broad range of dermatologic manifestations. Periorbital involvement, however, is a relatively rare clinical presentation of CLE. Objectives This clinical study aimed to investigate the characteristics of this unique presentation of CLE in tertiary medical centers. Methods We enrolled patients with periorbital erythema and swelling as the presenting sign of lupus erythematosus, from January 2003 to November 2017, using the data of 553 pathologically proven CLE cases from the registration database of the Chang Gung Memorial Hospitals in Taiwan. Results We enrolled a total of 25 patients. The mean age was 46.7 years and 68% of the patients were female. Most of the patients (84.0%) presented with unilateral involvement, with the left orbit involved in 15 patients (60%); the upper eyelid was the most frequently involved (72%). Mean duration between the onset of clinical manifestations and the diagnosis of CLE was approximately 59 weeks. Nineteen patients had been previously misdiagnosed. All patients had features compatible with CLE on histopathological examination. In contrast, laboratory analysis of the autoimmune profile often revealed negative results, including those for antinuclear antibodies (25%). Notably, anti-SSA/SSB (45.5%) showed the highest positive rate. During follow-up, six patients developed systemic lupus erythematosus (SLE) and two patients developed Sjögren syndrome. Conclusions The diagnosis of CLE presenting as periorbital erythema and swelling is often delayed because of clinical mimicry and the high proportion of negative results on autoantibody tests. Increased clinical suspicion and prompt histopathological examination are crucial for early diagnosis. Moreover, one-fourth of the patients ultimately developed SLE, which highlights the importance of clinical awareness.

Protein folding prediction in the HP model using ions motion optimization with a greedy algorithm

Background

The function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction process in order to reduce the prediction complexity.

Results

In this study, the ions motion optimization (IMO) algorithm was combined with the greedy algorithm (namely IMOG) and implemented to the HP model for the protein folding prediction based on the 2D-triangular-lattice model. Prediction results showed that the integration method IMOG provided a better prediction efficiency in a HP model. Compared to others, our proposed method turned out as superior in its prediction ability and resilience for most of the test sequences. The efficiency of the proposed method was verified by the prediction results. The global search capability and the ability to escape from the local best solution of IMO combined with a local search (greedy algorithm) to the new algorithm IMOG greatly improve the search for the best solution with reliable protein folding prediction.

Conclusion

Overall, the HP model integrated with IMO and a greedy algorithm as IMOG provides an improved way of protein structure prediction of high stability, high efficiency, and outstanding performance.

Catfish Taguchi-Based Binary Differential Evolution Algorithm for Analyzing Single Nucleotide Polymorphism Interactions in Chronic Dialysis

Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the association between disease-related multifactorials for disease analysis. Existing statistical methods for determining such interactions are limited by the considerable computation required for evaluating all potential associations between disease-related multifactorials. Identifying SNP-SNP interactions is thus a major challenge in genetic association studies. This paper proposes a catfish Taguchi-based binary differential evolution (CT-BDE) algorithm for identifying SNP-SNP interactions. In the search space, the catfish effect prevents the premature convergence of the population, and the Taguchi method improves the search ability of the BDE algorithm. Hence, the proposed algorithm enables obtaining a favorable solution regarding the identification of high-order SNP-SNP interactions. Additionally, the proposed algorithm applies an effective fitness function derived from a multifactor dimensionality reduction (MDR) operation to evaluate the solutions from BDE-based algorithms. Simulated and real data sets were used to evaluate the ability of several BDE-based algorithms in identifying specific SNP-SNP interactions. We compared the fitness function derived from the MDR operation with that derived according to the difference between cases and controls, by using the different BDE-based algorithms. The results showed that the proposed CT-BDE algorithm applying the fitness function derived from the MDR operation exhibited a superior ability in identifying SNP-SNP interactions compared with the other BDE-based algorithms.

Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions

Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.

Longitudinal analysis of cardiac structure and function in incident-automated peritoneal dialysis: comparison between icodextrin solution and glucose-based solution

Background

This study aimed to evaluate the longitudinal changes in cardiac structure and function in incident-automated peritoneal dialysis (APD) patients.

Methods

We conducted a 2-year prospective, randomized, open-label, parallel-group study to compare the efficacy of icodextrin solution versus glucose-based solution. Echocardiography was performed at baseline, 1 and 2 years. Echocardiographic parameters over 2 years were evaluated for each group, using the Friedman test. Generalized linear regression analysis was used to test the associations between baseline clinical variables and echocardiographic changes, and a multivariate model was used to analyze cardiac function between the two groups.

Results

A total of 43 APD patients were enrolled in the beginning of this study. Twenty patients in the icodextrin group (ICO) and 18 patients in the glucose group (GLU) completed the study. In left ventricular (LV) systolic function measurements, ejection fraction (EF) increased significantly in the GLU group. Measurements of LV diastolic function and septal early mitral annulus velocity (EMV) increased significantly from baseline to 24-months in the ICO group (5.43–5.51 ms). The GLU group showed a significant decrease in peak early diastolic velocity (EDV) (70.67–68.25 cm/s), but a significant increase in septal EMV (5.94–7.57 ms) from baseline to 24-months. No significant association was found between the baseline clinical variables and echocardiographic changes within 24 months in the generalized linear regression analysis. Multivariate models were used to investigate changes in the four primary endpoints, namely, myocardial performance index (MPI), left ventricular ejection fraction (LVEF), deceleration time (DT), and E/e′ ratio. These primary endpoints show no significant association with the baseline values in both the ICO and GLU groups.

Conclusion

The present study demonstrates that long-dwell icodextrin solution can maintain reasonable cardiac structure and function in incident-APD patients.

Trial registration

ISRCTN14931270 (retrospectively registered on 23/03/2018).

Factors Associated with Functional Performance among Patients on Hemodialysis in Taiwan

BACKGROUND/AIMS

We investigated factors associated with functional performance in hemodialysis (HD) patients as well as their relationships with mortality.

METHODS

We enrolled 790 HD patients who were followed up from 2009 to 2013. Functional performance was evaluated by Karnofsky Performance Status Scale (KPSS) scores. We examined the associations of clinical variables and all-cause mortality with KPSS scores.

RESULTS

Of the participants, 460 had high KPSS scores (range 90-100) and 330 had low KPSS scores (below 80). On multivariate analysis, age and weekly HD sessions were associated with significantly increased odds of a lower KPSS score (age: OR 1.05, 95% CI 1.04-1.07, p < 0.001; weekly HD: OR 2.10, 95% CI 1.37-3.21, p = 0.001). A low KPSS score was a significant predictor of increased all-cause mortality (hazard ratio 1.49; 95% CI 1.02-2.16, p = 0.037), as determined using Cox regression analysis.

CONCLUSION

Functional performance was associated with clinical variables and all-cause mortality in HD patients.

Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a ribulose diphosphate carboxylase (rbcL) SNP barcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree-selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.