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.

Factors Affecting Postoperative Complications of Suction-Curettage by Arthroscopic Shaver for Bromhidrosis.

BACKGROUND: Suction-curettage by arthroscopic shaver is the most effective treatment for bromhidrosis; however, postoperative complications require wound management and exhibit a high risk of hypertrophic scarring. We investigated factors affecting postoperative complications. METHODS: We retrospectively evaluated data for 215 patients (430 axillae) with bromhidrosis treated with suction-curettage by arthroscopic shaver between 2011 and 2019. Cases followed for less than 1 year were excluded. Complications of hematoma or seroma, epidermis decortication, skin necrosis, and infection were recorded. Multinomial logistic analysis was used to calculate odds ratios and corresponding 95% confidence intervals for the complication of the surgery, adjusting for relevant statistically significant variables. RESULTS: Complications occurred in 52 axillae (12.1%). Epidermis decortication occurred in 24 axillae (5.6%), with a significant difference for age (P < 0.001). Hematoma occurred in 10 axillae (2.3%) with a significant difference in tumescent infiltration use (P = 0.039). Skin necrosis occurred in 16 axillae (3.7%) with a significant difference for age (P = 0.001). Infection occurred in 2 axillae (0.5%). Severe scarring occurred in 15 axillae (3.5%), with complications related to more severe skin scarring (P < 0.05). CONCLUSION: Older age was a risk factor for complications. Use of tumescent infiltration resulted in good postoperative pain control and less hematoma. Patients with complications presented with more severe skin scarring, but none experienced limited range of motion after massage.

Illness Perceptions, Disabilities, and Quality of Life of Patients With Hand Injury: A Prospective Study.

BACKGROUND: Few studies have tracked hand injury patients past the acute care period. Postdischarge tracking of hand injury patients may identify time points most patients require assistance, which can help direct interventions to reduce post-hand injury sequelae. OBJECTIVE: To examine hand injury patients' experiences during early recovery to illness perceptions, disabilities, and quality of life and to identify predictors of quality of life at 1 month and 3 months after hospital discharge. METHODS: This prospective observational study of hand injury patients was performed at a teaching hospital in Taiwan. Data were collected at patient discharge, 1 month, and 3 months after discharge, from January 2017 to October 2018. The variables measured included illness perceptions, disabilities, and quality of life. RESULTS: A total of 117 patients participated in the study. The patients reported more positive illness perceptions at 3 months versus 1 month after discharge. The impact of disabilities on patient work was significantly greater at 1 month than at 3 months after discharge. Quality of life was better at 3 months than at 1 month after discharge. Predictors of quality of life after hospital discharge were demographic variables, clinical variables, and illness perceptions. Patients with lower education reported poorer psychological quality of life after discharge. CONCLUSION: Primary care providers should perform a comprehensive assessment of patients prior to their discharge. Patients should undergo regular follow-ups to reduce comorbidities and improve their outcomes.

Nurse practitioner job content and stress effects on anxiety and depressive symptoms, and self-perceived health status.

AIM: We explored the impact of job content and stress on anxiety, depressive symptoms and self-perceived health status among nurse practitioners (NPs). BACKGROUND: Taiwan's NP roles vary between hospitals as a result of the diverse demands and complex tasks that cause job-related stress, potentially affecting the health of the NP. METHODS: This study utilised a cross-sectional descriptive design with 161 NPs from regional hospitals participating. Data collection involved demographics, the Taiwan Nurse Stress Checklist, the Job Content Questionnaire, the Beck Anxiety Inventory, the Beck Depression Inventory, a General Health Status Checklist and salivary cortisol tests. RESULTS: NPs reported moderate job stress, similar job control to nurses, mild anxiety and depression, and below-average self-perceived health. Being a licensed NP, personal response, competence, and incompleteness of the personal arrangements subscales of job stress, and anxiety predicted self-perceived health after adjusting for other covariates. CONCLUSIONS: Job stress and anxiety affect NP health. IMPLICATIONS FOR NURSING MANAGEMENT: NPs are a valuable resource, and the healthcare system demand is growing. Reasonable NP staffing, working hours, proper promotion systems, the causes of job stress, job content clarification and practical work shift scheduling need to be considered. The occupational safety and physical and psychological health of NPs are strongly associated with the quality of patient care.

An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction.

BACKGROUND: Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property. RESULTS: Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene-gene interaction with less computational complexity than the MDR in high-order interaction analysis. CONCLUSION: FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar .

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.

High Incidence of Axillary Web Syndrome among Breast Cancer Survivors after Breast Reconstruction.

OBJECTIVE: The aim of this study was to identify if breast reconstruction is a surgical risk factor for axillary web syndrome (AWS) in breast cancer (BC) patients. METHODS: The data of 207 patients who have been diagnosed with unilateral BC and who had mastectomy and lymph node dissection were retrospectively reviewed. Information of their clinical and pathological data, whether they had immediate -reconstruction and intraoperative radiotherapy, surgical methods, and postoperative complications during the 3 months after their surgery (AWS, lymphedema, seroma, and myofascial adhesion) were collected, and the incidence of AWS was compared between different surgical methods. RESULTS: The overall incidence of AWS was 48.8% in 207 patients. Of the 22 patients who received reconstruction, 19 developed AWS, yielding an incidence of 86%. Multivariate logistic regression modeling showed that patients who underwent reconstruction had a significantly higher incidence of AWS (odds ratio, 4.74), as did patients with postoperative complication of myofascial adhesion (odds ratio, 7.07). CONCLUSIONS: BC survivors after breast reconstruction are susceptible to AWS, and there is a significant association between myofascial adhesion and AWS. Our results can stimulate further investigation and provide an evidence base for the development of educational guidance for patients who plan to undergo breast reconstruction.

Mechanical evaluation of the influence of different suture methods on temporal skin healing.

BACKGROUND: The progress of wound healing is mainly investigated using histological analyses. The study of mechanical recovery of healing wound is lacking. OBJECTIVE: To evaluate the temporal mechanical strength of healing skin after the use of different suture methods. MATERIALS AND METHODS: The dorsum of each of 36 Sprague-Dawley rats was divided into four regions, and 2-cm-long cuts were randomly made in three regions and closed using three suture methods. The size of all suture material was 4/0. The suture methods included full-thickness skin sutures with nylon (N group), degradable polyglactin 910 sutures in the dermis (V group), and nylon sutures in the epidermis with polyglactin sutures in the hypodermis (NV group). After 1, 2, 4, and 6 weeks, the sutured sites were harvested for tensile testing and recovery index. RESULTS: The V and NV groups had greater recovery indices than did the N group after 2 weeks, but the V and N groups reached almost full recovery and had greater recovery than the NV group 6 weeks after wounding. Recovery strengths increased rapidly between 4 and 6 weeks in the V and N groups. CONCLUSION: These results are helpful for suture method selection and management of wound healing.

Identification of SNP-SNP interaction for chronic dialysis patients.

Analyses of interactions between single nucleotide polymorphisms (SNPs) have reported significant associations between mitochondrial displacement loops (D-loops) and chronic dialysis diseases. However, the method used to detect potential SNP-SNP interaction still requires improvement. This study proposes an effective algorithm named dynamic center particle swarm optimization k-nearest neighbors (DCPSO-KNN) to detect the SNP-SNP interaction. DCPSO-KNN uses dynamic center particle swarm optimization (DCPSO) to generate SNP combinations with a fitness function designed using the KNN method and statistical verification. A total of 77 SNPs in the mitochondrial D-loop were used to detect the SNP-SNP interactions and the search ability was compared against that of other methods. The detected SNP-SNP interactions were statistically evaluated. Experimental results showed that DCPSO-KNN successfully detects SNP-SNP interactions in two-to-seven-order combinations (positive predictive value (PPV)+negative predictive value (NPV)=1.154 to 1.310; odds ratio (OR)=1.859 to 4.015; 95% confidence interval (95% CI)=1.151 to 4.265; p-value <0.001). DCPSO-KNN can improve the detection ability of SNP-SNP associations between mitochondrial D-loops and chronic dialysis diseases, thus facilitating the development of biomedical applications.

Proteomic analysis of prognostic plasma biomarkers in peripheral arterial occlusive disease.

Peripheral arterial occlusive disease (PAOD), one of the major manifestations of systemic atherosclerosis, causes intermittent claudication and rest pain. Patients with PAOD not only have reduced quality of life, but also have a substantial risk of cardiovascular morbidity and death. In this study, we adopted a proteomics-based approach using 2D-DIGE and MALDI-TOF MS to compare the differential plasma proteome between good and poor prognosis of PAOD. We identified 196 plasma proteins, which represent 42 unique gene products. These proteins mainly have roles in the inflammatory response and coagulation. This approach identified several potential prognostic plasma markers in PAOD, including transthyretin and complement factor B, which may be associated with the evaluation of good/poor prognosis of PAOD. In conclusion, we report a comprehensive patient-based plasma proteomic approach for the identification of potential plasma biomarkers for the screening and detection of good/poor prognosis of PAOD. Among these, transthyretin and complement factor B are potential markers for monitoring the PAOD disease in the plasma.

Epidemiology and patterns of facial fractures due to road traffic accidents in Taiwan: A 15-year retrospective study.

OBJECTIVE: The facial region is a commonly fractured site, but the etiology varies widely by country and geographic region. To date, there are no population-based studies of facial fractures in Taiwan. METHODS: We conducted a retrospective study of patients diagnosed with facial fracture and registered in the National Health Insurance Research Database of Taiwan between 1997 and 2011. The epidemiological characteristics of this cohort were analyzed, including the etiology, fracture site, associated injuries, and sex and age distributions. RESULTS: A total of 6,013 cases were identified that involved facial fractures. Most patients were male (69.8%), aged 18-29 years (35.8%), and had fractures caused by road traffic accidents (RTAs; 55.2%), particularly motorcycle accidents (31.5%). Falls increased in frequency with advancing age, reaching 23.9% among the elderly (age > 65 years). The most common sites of involvement were the malar and maxillary bones (54.0%), but nasal bone fractures were more common among those younger than 18 years. CONCLUSION: Most facial injuries in Taiwan occur in young males and typically result from RTAs, particularly involving motorcycles. However, with increasing age, there is an increase in the proportion of facial injuries due to falls.

Tag SNP selection using particle swarm optimization.

Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations amongst species. With the genome-wide SNP discovery, many genome-wide association studies are likely to identify multiple genetic variants that are associated with complex diseases. However, genotyping all existing SNPs for a large number of samples is still challenging even though SNP arrays have been developed to facilitate the task. Therefore, it is essential to select only informative SNPs representing the original SNP distributions in the genome (tag SNP selection) for genome-wide association studies. These SNPs are usually chosen from haplotypes and called haplotype tag SNPs (htSNPs). Accordingly, the scale and cost of genotyping are expected to be largely reduced. We introduce binary particle swarm optimization (BPSO) with local search capability to improve the prediction accuracy of STAMPA. The proposed method does not rely on block partitioning of the genomic region, and consistently identified tag SNPs with higher prediction accuracy than either STAMPA or SVM/STSA. We compared the prediction accuracy and time complexity of BPSO to STAMPA and an SVM-based (SVM/STSA) method using publicly available data sets. For STAMPA and SVM/STSA, BPSO effective improved prediction accuracy for smaller and larger scale data sets. These results demonstrate that the BPSO method selects tag SNP with higher accuracy no matter the scale of data sets is used.

Impact of far-infrared ray exposure on the mechanical properties of unwounded skin of rats.

Far-infrared ray (FIR) has been applied to promote growth, modulate sleep, speed up the healing of wounds and accelerate microcirculation. The action of FIR on wounds has been well established in previous studies. However, whether the same action also works on normal skin is unclear. Therefore, the aim of this study was to evaluate the impact of FIR exposure on the tensile strength (TS) and composition of unwounded skin. In this study, 84 Sprague-Dawley (SD) rats were randomly divided into control and FIR groups. The dorsum samples were harvested for mechanical testing and histological observation at one, two, four and six weeks. The TS in the control group had no significant difference for all durations. However, a steep increase in the TS occurred between one and two weeks (P = 0.033) in the FIR group. The TS in the FIR group was found to be significantly higher than the TS in the control group at two weeks (P = 0.049). From histological observation, capillary dilation and increased inflammatory cells around the capillaries were observed at one week in FIR-treated groups. However, the mild inflammatory changes vanished after two weeks. In conclusion, our results showed that FIR may induce inflammatory changes and enhance skin TS in the short term, but the effect diminished with time.

Chemical composition and antibacterial activities of Illicium verum against antibiotic-resistant pathogens.

In recent years, human pathogenic microorganisms have developed multiple drug resistance and caused serious nosocomial infections. In this study, we identified four new antimicrobial compounds from the Chinese herbal medicine Illicium verum and assessed their antibacterial efficacies. The supercritical CO2 and ethanol extracts of Illicium verum showed substantial antibacterial activity against 67 clinical drug-resistant isolates, including 27 Acinetobacter baumannii, 20 Pseudomonas aeruginosa, and 20 methicillin-resistant Staphylococcus aureus. The diethyl ether (EE) fraction obtained from partition extraction and supercritical CO2 extracts revealed an antibacterial activity with a minimum inhibitory concentration value of 0.15-0.70 mg/mL and 0.11 mg/mL, respectively. The EE fraction of I. verum showed synergetic effects with some commercial antibiotics. The antimicrobial mechanism was investigated with killing curves and scanning electron microscopy observation. The chemical components of the extracts were analyzed by spectrophotometry; (E)-anethole, anisyl acetone, anisyl alcohol, and anisyl aldehyde exhibited antibacterial activity against different clinical isolates. These extracts from I. verum can be further developed into antibiotic medicines due to their proven antibacterial activity.

Chaotic genetic algorithm for gene selection and classification problems.

Pattern recognition techniques suffer from a well-known curse, the dimensionality problem. The microarray data classification problem is a classical complex pattern recognition problem. Selecting relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small sample size. The goal of feature (gene) selection is to select those subsets of differentially expressed genes that are potentially relevant for distinguishing the sample classes. In this paper, information gain and chaotic genetic algorithm are proposed for the selection of relevant genes, and a K-nearest neighbor with the leave-one-out crossvalidation method serves as a classifier. The chaotic genetic algorithm is modified by using the chaotic mutation operator to increase the population diversity. The enhanced population diversity expands the GA's search ability. The proposed approach is tested on 10 microarray data sets from the literature. The experimental results show that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates than other methods.

Genotype and antibiotic susceptibility patterns of drug-resistant Pseudomonas aeruginosa and Acinetobacter baumannii isolates in Taiwan.

Rapid and accurate identification of the drug susceptibility profile of clinical strains is very important for controlling bacterial infections and determining the antibiotic therapy. The objective of this study was to investigate the spectrum of the correlation between phenotypic and genetic characters of the drug-resistant clinical isolates. A total of 133 clinical isolates, including 76 Acinetobacter baumannii and 57 Pseudomonas aeruginosa, were examined for their antibiotic susceptibility by the method of disc diffusion. Among them, most of the isolates were multiresistant, and 80% of the strains showed phenotypic resistance to beta-lactam antibiotics. Using PCR analysis, among the several types of beta-lactamases, TEM was the most prevalent, and OXA was the second most prevalent. The integron harbored was identified by conserved segment PCR, and 50% of the test isolates carried integrons with various gene cassette sizes inserted. The results obtained from this study reveal that the majority of these isolates displayed multiple drug resistance phenotypes that were associated with their mutational gene profiles.