Retrospective study of oral and maxillofacial lesions in older Taiwanese patients.

OBJECTIVE: The aim was to provide information regarding oral and maxillofacial (OMF) lesions in an older Taiwanese population. BACKGROUND: The rate of increase of older people in Taiwan is expected to be rapid. OMF lesions are very frequent in the older population, but no studies have been performed on these lesions in Taiwan. MATERIALS AND METHODS: OMF cases (between 2000 and 2011) in geriatric patients (≥60 years of age) with records of age, sex and histological diagnoses were retrieved from the Oral Pathology Department of our institution. These lesions were classified into four main categories: tumour/tumour-like reactive lesions, cystic/pseudocystic lesions, inflammatory/infective lesions and other miscellaneous lesions. RESULTS: Six thousand seven hundred and twenty-six lesions were collected from a total of 39 503 OMF lesions in older Taiwanese patients in this study. Most of these lesions were distributed in the inflammatory/infective group, followed by tumour/tumour-like reactive lesions. Squamous cell carcinoma was the most common lesion, and, additionally, there was a high frequency of oral potentially malignant disorders. CONCLUSIONS: The present study showed trends similar to previous reports from other countries. However, some detailed information was different, perhaps due to the different criteria and different geographic distribution. Worthy of note, our results indicated that screening for oral potentially malignant disorder and oral malignancy in the older population is essential.

Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction.

With the advances in different biological networks including gene regulation, gene co-expression, protein-protein interaction networks, and advanced approaches for network reconstruction, analysis, and interpretation, it is possible to discover reliable and accurate molecular network-based biomarkers for monitoring cancer treatment. Such efforts will also pave the way toward the realization of biomarker-driven personalized medicine against cancer. Previously, we have reconstructed disease-specific driver signaling networks using multi-omics profiles and cancer signaling pathway data. In this study, we developed a network-based sparse Bayesian machine (NBSBM) approach, using previously derived disease-specific driver signaling networks to predict cancer cell responses to drugs. NBSBM made use of the information encoded in a disease-specific (differentially expressed) network to improve its prediction performance in problems with a reduced amount of training data and a very high-dimensional feature space. Sparsity in NBSBM is favored by a spike and slab prior distribution, which is combined with a Markov random field prior that encodes the network of feature dependencies. Gene features that are connected in the network are assumed to be both relevant and irrelevant to drug responses. We compared the proposed method with network-based support vector machine (NBSVM) approaches and found that the NBSBM approach could achieve much better accuracy than the other two NBSVM methods. The gene modules selected from the disease-specific driver networks for predicting drug sensitivity might be directly involved in drug sensitivity or resistance. This work provides a disease-specific network-based drug sensitivity prediction approach and can uncover the potential mechanisms of the action of drugs by selecting the most predictive sub-networks from the disease-specific network.

Retrospective study of biopsied head and neck lesions in a cohort of referral Taiwanese patients.

INTRODUCTION: A study of the whole spectrum of biopsied head and neck (HN) diseases in Taiwan has not yet been performed. Therefore, the current study aimed to provide updated information about HN lesions in a cohort of referral Taiwanese patients for histopathological examination. METHODS: HN lesions (2000-2011) in patients with records of age, sex, and histological diagnoses were retrieved from the Oral Pathology Department of the institution. These lesions were classified into four main categories: tumor/tumor-like reactive lesions, cystic/pseudocystic lesions, inflammatory/infective lesions, and others/miscellaneous lesions. RESULTS: A total of 37,210 HN lesions were included in the current study. Most of these lesions were distributed in the group of tumor/tumor-like reactive lesions, followed by the groups of inflammatory/infective lesions, cystic/pseudocystic lesions, and others/miscellaneous lesions. Squamous cell carcinoma was the most common HN lesion, and was also the most frequent malignant lesion among the referral patients. CONCLUSION: It was worthy of note that squamous cell carcinoma and oral potentially malignant disorders comprised high percentages of all HN lesions for the present cohort of referral patients.