BackgroundThe opportunistic enterobacterium, Morganella morganii, which can cause bacteraemia, is the ninth most prevalent cause of clinical infections in patients at Changhua Christian Hospital, Taiwan. The KT strain of M. morganii was isolated during postoperative care of a cancer patient with a gallbladder stone who developed sepsis caused by bacteraemia. M. morganii is sometimes encountered in nosocomial settings and has been causally linked to catheter-associated bacteriuria, complex infections of the urinary and/or hepatobiliary tracts, wound infection, and septicaemia. M. morganii infection is associated with a high mortality rate, although most patients respond well to appropriate antibiotic therapy. To obtain insights into the genome biology of M. morganii and the mechanisms underlying its pathogenicity, we used Illumina technology to sequence the genome of the KT strain and compared its sequence with the genome sequences of related bacteria.ResultsThe 3,826,919-bp sequence contained in 58 contigs has a GC content of 51.15% and includes 3,565 protein-coding sequences, 72 tRNA genes, and 10 rRNA genes. The pathogenicity-related genes encode determinants of drug resistance, fimbrial adhesins, an IgA protease, haemolysins, ureases, and insecticidal and apoptotic toxins as well as proteins found in flagellae, the iron acquisition system, a type-3 secretion system (T3SS), and several two-component systems. Comparison with 14 genome sequences from other members of Enterobacteriaceae revealed different degrees of similarity to several systems found in M. morganii. The most striking similarities were found in the IS4 family of transposases, insecticidal toxins, T3SS components, and proteins required for ethanolamine use (eut operon) and cobalamin (vitamin B12) biosynthesis. The eut operon and the gene cluster for cobalamin biosynthesis are not present in the other Proteeae genomes analysed. Moreover, organisation of the 19 genes of the eut operon differs from that found in the other non-Proteeae enterobacterial genomes.ConclusionsThis is the first genome sequence of M. morganii, which is a clinically relevant pathogen. Comparative genome analysis revealed several pathogenicity-related genes and novel genes not found in the genomes of other members of Proteeae. Thus, the genome sequence of M. morganii provides important information concerning virulence and determinants of fitness in this pathogen.

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BackgroundCardiovascular disease is the chief cause of death in Taiwan and many countries, of which myocardial infarction (MI) is the most serious condition. Hyperlipidemia appears to be a significant cause of myocardial infarction, because it causes atherosclerosis directly. In recent years, copy number variation (CNV) has been analyzed in genomewide association studies of complex diseases. In this study, CNV was analyzed in blood samples and SNP arrays from 31 myocardial infarction patients with hyperlipidemia.ResultsWe identified seven CNV regions that were associated significantly with hyperlipidemia and myocardial infarction in our patients through multistage analysis (P<0.001), at 1p21.3, 1q31.2 (CDC73), 1q42.2 (DISC1), 3p21.31 (CDCP1), 10q11.21 (RET) 12p12.3 (PIK3C2G) and 16q23.3 (CDH13), respectively. In particular, the CNV region at 10q11.21 was examined by quantitative real-time PCR, the results of which were consistent with microarray findings.ConclusionsOur preliminary results constitute an alternative method of evaluating the relationship between CNV regions and cardiovascular disease. These susceptibility CNV regions may be used as biomarkers for early-stage diagnosis of hyperlipidemia and myocardial infarction, rendering them valuable for further research and discussion.

Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification’s performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors’ histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation—combining local weight learning—was utilised for classification and performance enhancement. The image dataset’s classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.

Institute of Physics and Engineering in MedicineContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

We analysed typical mammographic density (MD) distributions of healthy Taiwanese women to augment existing knowledge, clarify cancer risks, and focus public health efforts. From January 2011 to December 2015, 88,193 digital mammograms were obtained from 69,330 healthy Taiwanese women (average, 1.27 mammograms each). MD measurements included dense volume (DV) and volumetric density percentage (VPD) and were quantified by fully automated volumetric density estimation and Box-Cox normalization. Prediction of the declining MD trend was estimated using curve fitting and a rational model. Normalized DV and VPD Lowess curves demonstrated similar but non-identical distributions. In high-density grade participants, the VPD increased from 12.45% in the 35–39-year group to 13.29% in the 65–69-year group but only from 5.21% to 8.47% in low-density participants. Regarding the decreased cumulative VPD percentage, the mean MD declined from 12.79% to 19.31% in the 45–50-year group versus the 50–55-year group. The large MD decrease in the fifth decade in this present study was similar to previous observations of Western women. Obtaining an MD distribution model with age improves the understanding of breast density trends and age variations and provides a reference for future studies on associations between MD and cancer risk.

Background/Aim: To investigate the impact of PDZ-binding kinase (PBK) on the clinical outcome of patients with oral squamous cell carcinoma (OSCC) who received radiotherapy. Patients and Methods: PBK immunoreactivity of cancer specimens obtained from 179 patients with primary OSCC was analyzed by immunohistochemistry. Results: High PBK expression in tumor cells tended to be associated with advanced N-stage. The 5-year survival rate was greater for patients with high total PBK expression than in those with low PBK expression. After adjustment, high PBK remained associated with a favorable outcome. In subgroups according to tumor stage, the prognostic role was significant in patients with stage III/IV rather than those with stage I/II disease. Conclusion: We suggest that PBK expression should be used as an independent prognostic marker for patients with OSCC treated with radiotherapy, especially for those with advancedstage disease.Oral squamous cell carcinomas (OSCCs) are the most common among all head and neck squamous cell cancers (1). In 2020, cancer of the lip and oral cavity accounted for more than 377,713 cases and 177,757 deaths worldwide (2). Individuals from developing countries where there is higher number of risk factors, such as smoking, betel nut chewing, and alcohol consumption, are at an increased risk for developing OSCC. In Taiwan, OSCC is the fourth common cancer type (approximate incidence rate of 29 per 100,000 population and mortality of 32 per 100,000 population) and the second cancer type with the fastest increasing incidence (3). The Surveillance, Epidemiology, and End Results Cancer Statistics Review reported that the 5-year relative survival rate patients with locally advanced oral cavity and oropharyngeal cancer is 54.7% whereas it is 82.5% for those with early-stage disease (4).Many efforts have been made to identify biomarkers or specific genes that might provide useful information for clinical patient management. The complex pathogenesis of oral cancer is driven by DNA-repair genes, tumor-suppressor genes, and well-recognized factors, such as alcohol, betel nut chewing, and viral infection (5, 6). PDZ-binding kinase (PBK), also known as lymphokine-activated killer T-celloriginated protein kinase, is a mitogen-activated protein kinase kinase-like serine/threonine kinase that is involved in cell-cycle regulation via a cyclin B1-dependent manner, and in mitotic progression (7-9). PBK is found in proliferative tissues, such as testis, fetal, and neuronal stem cells; studies have found PBK overexpression in various malignancies, such as leukemia, Burkitt's lymphoma, breast cancer, and lung cancer (10-13). PBK is up-regulated in tumors; however, reports on the clinical significance of PBK are lacking. Our previous research showed the unfavorable 2177 *These Authors contributed equally to this study.