Impact of genital warts on health related quality of life in men and women in mainland China: a multicenter hospital-based cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Information on the health-related quality of life (HRQoL) of patients with genital warts (GW) in populations in mainland China is still limited. The aim of the study was to use a generic instrument to measure the impact of genital warts on HRQoL in men and women in this setting.</p> <p>Methods</p> <p>A multi-centre hospital-based cross-sectional study across 18 centers in China was conducted to interview patients using the European quality of life-5 dimension (EQ-5D) instrument; respondents' demographic and clinical data were also collected.</p> <p>Results</p> <p>A total of 1,358 GW patients (612 men, 746 women) were included in the analysis, with a mean age of 32.0 ± 10.6 years. 56.4% of the patients reported some problems in the dimension of Anxiety/Depression (highest), followed by Pain/Discomfort (24.7%) and Mobility (3.5%). The overall visual analogue scale (VAS) score of the study population was found to be 65.2 ± 22.0, and the EQ-5D index score was found to be 0.843 ± 0.129 using Japanese preference weights (the Chinese preference was unavailable yet). Patients with lower VAS means and EQ-5D index scores were more often female, living in urban area, and suffering multiple GW (all p values < 0.05), but the values did not differ notably by age (p values > 0.05).</p> <p>Conclusions</p> <p>The HRQoL of patients with GW was substantially lower, compared to a national representative general population in China (VAS = ~80); the findings of different subgroups are informative for future GW prevention and control efforts.</p

Prognostic relevance of Centromere protein H expression in esophageal carcinoma

<p>Abstract</p> <p>Background</p> <p>Many kinetochore proteins have been shown to be associated with human cancers. The aim of the present study was to clarify the expression of Centromere protein H (CENP-H), one of the fundamental components of the human active kinetochore, in esophageal carcinoma and its correlation with clinicopathological features.</p> <p>Methods</p> <p>We examined the expression of CENP-H in immortalized esophageal epithelial cells as well as in esophageal carcinoma cells, and in 12 cases of esophageal carcinoma tissues and the paired normal esophageal tissues by RT-PCR and Western blot analysis. In addition, we analyzed CENP-H protein expression in 177 clinicopathologically characterized esophageal carcinoma cases by immunohistochemistry. Statistical analyses were applied to test for prognostic and diagnostic associations.</p> <p>Results</p> <p>The level of CENP-H mRNA and protein were higher in the immortalized cells, cancer cell lines and most cancer tissues than in normal control tissues. Immunohistochemistry showed that CENP-H was expressed in 127 of 171 ESCC cases (74.3%) and in 3 of 6 esophageal adenocarcinoma cases (50%). Statistical analysis of ESCC cases showed that there was a significant difference of CENP-H expression in patients categorized according to gender (<it>P </it>= 0.013), stage (<it>P </it>= 0.023) and T classification (<it>P </it>= 0.019). Patients with lower CENP-H expression had longer overall survival time than those with higher CENP-H expression. Multivariate analysis suggested that CENP-H expression was an independent prognostic marker for esophageal carcinoma patients. A prognostic value of CENP-H was also found in the subgroup of T3~T4 and N0 tumor classification.</p> <p>Conclusion</p> <p>Our results suggest that CENP-H protein is a valuable marker of esophageal carcinoma progression. CENP-H might be used as a valuable prognostic marker for esophageal carcinoma patients.</p

Fungal diversity notes 929–1035: taxonomic and phylogenetic contributions on genera and species of fungi

This article is the ninth in the series of Fungal Diversity Notes, where 107 taxa distributed in three phyla, nine classes, 31 orders and 57 families are described and illustrated. Taxa described in the present study include 12 new genera, 74 new species, three new combinations, two reference specimens, a re-circumscription of the epitype, and 15 records of sexualasexual morph connections, new hosts and new geographical distributions. Twelve new genera comprise Brunneofusispora, Brunneomurispora, Liua, Lonicericola, Neoeutypella, Paratrimmatostroma, Parazalerion, Proliferophorum, Pseudoastrosphaeriellopsis, Septomelanconiella, Velebitea and Vicosamyces. Seventy-four new species are Agaricus memnonius, A. langensis, Aleurodiscus patagonicus, Amanita flavoalba, A. subtropicana, Amphisphaeria mangrovei, Baorangia major, Bartalinia kunmingensis, Brunneofusispora sinensis, Brunneomurispora lonicerae, Capronia camelliaeyunnanensis, Clavulina thindii, Coniochaeta simbalensis, Conlarium thailandense, Coprinus trigonosporus, Liua muriformis, Cyphellophora filicis, Cytospora ulmicola, Dacrymyces invisibilis, Dictyocheirospora metroxylonis, Distoseptispora thysanolaenae, Emericellopsis koreana, Galiicola baoshanensis, Hygrocybe lucida, Hypoxylon teeravasati, Hyweljonesia indica, Keissleriella caraganae, Lactarius olivaceopallidus, Lactifluus midnapurensis, Lembosia brigadeirensis, Leptosphaeria urticae, Lonicericola hyaloseptispora, Lophiotrema mucilaginosis, Marasmiellus bicoloripes, Marasmius indojasminodorus, Micropeltis phetchaburiensis, Mucor orantomantidis, Murilentithecium lonicerae, Neobambusicola brunnea, Neoeutypella baoshanensis, Neoroussoella heveae, Neosetophoma lonicerae, Ophiobolus malleolus, Parabambusicola thysanolaenae, Paratrimmatostroma kunmingensis, Parazalerion indica, Penicillium dokdoense, Peroneutypa mangrovei, Phaeosphaeria cycadis, Phanerochaete australosanguinea, Plectosphaerella kunmingensis, Plenodomus artemisiae, P. lijiangensis, Proliferophorum thailandicum, Pseudoastrosphaeriellopsis kaveriana, Pseudohelicomyces menglunicus, Pseudoplagiostoma mangiferae, Robillarda mangiferae, Roussoella elaeicola, Russula choptae, R. uttarakhandia, Septomelanconiella thailandica, Spencermartinsia acericola, Sphaerellopsis isthmospora, Thozetella lithocarpi, Trechispora echinospora, Tremellochaete atlantica, Trichoderma koreanum, T. pinicola, T. rugulosum, Velebitea chrysotexta, Vicosamyces venturisporus, Wojnowiciella kunmingensis and Zopfiella indica. Three new combinations are Baorangia rufomaculata, Lanmaoa pallidorosea and Wojnowiciella rosicola. The reference specimens of Canalisporium kenyense and Tamsiniella labiosa are designated. The epitype of Sarcopeziza sicula is re-circumscribed based on cyto- and histochemical analyses. The sexual-asexual morph connection of Plenodomus sinensis is reported from ferns and Cirsium for the first time. In addition, the new host records and country records are Amanita altipes, A. melleialba, Amarenomyces dactylidis, Chaetosphaeria panamensis, Coniella vitis, Coprinopsis kubickae, Dothiorella sarmentorum, Leptobacillium leptobactrum var. calidus, Muyocopron lithocarpi, Neoroussoella solani, Periconia cortaderiae, Phragmocamarosporium hederae, Sphaerellopsis paraphysata and Sphaeropsis eucalypticola

Proteomic studies in breast cancer (Review)

Breast cancer is one of the most common types of invasive cancer in females worldwide. Despite major advances in early cancer detection and emerging therapeutic strategies, further improvement has to be achieved for precise diagnosis to reduce the chance of metastasis and relapses. Recent proteomic technologies have offered a promising opportunity for the identification of new breast cancer biomarkers. Matrix-assisted laser desorption/ionization, time-of-flight mass spectrometry (MALDI-TOF MS) and the derived surface-enhanced laser desorption/ionization mass spectrometry (SELDI-TOF MS) enable the development of high-throughput proteome analysis based on comprehensive reliable biomarkers. In this review, we examined proteomic technologies and their applications, and provided focus on the proteomics-based profiling analyses of tumor tissues/cells in order to identify and confirm novel biomarkers of breast cancer

ICSI treatment of severe male infertility can achieve prospective embryo quality compared with IVF of fertile donor sperm on sibling oocytes

Azoospermia, cryptozoospermia and necrospermia can markedly decrease the ability of males to achieve pregnancy in fertile females. However, patients with these severe conditions still have the option to be treated by intracytoplasmic sperm injection (ICSI) to become biological fathers. This study analyzed the fertilization ability and the developmental viabilities of the derived embryos after ICSI treatment of the sperm from these patients compared with in vitro fertilization (IVF) treatment of the proven-fertile donor sperm on sibling oocytes as a control. On the day of oocyte retrieval, the number of sperm suitable for ICSI collected from two ejaculates or testicular sperm extraction was lower than the oocytes, and therefore, excess sibling oocytes were treated by IVF with donor sperm. From 72 couples (73 cycles), 1117 metaphase II oocytes were divided into 512 for ICSI and 605 for IVF. Compared with the control, husbands′ sperm produced a lower fertilization rate in nonobstructive azoospermia (65.4% vs 83.2%; P< 0.001), crytozoospermia (68.8% vs 75.5%; P< 0.05) and necrospermia (65.0% vs 85.2%; P< 0.05). The zygotes derived in nonobstructive azoospermia had a lower cleavage rate (96.4% vs 99.4%; P< 0.05), but the rate of resultant good-quality embryos was not different. Analysis of the rates of cleaved and good-quality embryos in crytozoospermia and necrospermia did not exhibit a significant difference from the control. In conclusion, although the sperm from severe male infertility reduced the fertilization ability, the derived embryos had potential developmental viabilities that might be predictive for the expected clinical outcomes

Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography

Abstract The most common BRAF mutation is thymine (T) to adenine (A) missense mutation in nucleotide 1796 (T1796A, V600E). The BRAFV600E gene encodes a protein-dependent kinase (PDK), which is a key component of the mitogen-activated protein kinase pathway and essential for controlling cell proliferation, differentiation, and death. The BRAFV600E mutation causes PDK to be activated improperly and continuously, resulting in abnormal proliferation and differentiation in PTC. Based on elastography ultrasound (US) radiomic features, this study seeks to create and validate six distinct machine learning algorithms to predict BRAFV6OOE mutation in PTC patients prior to surgery. This study employed routine US strain elastography image data from 138 PTC patients. The patients were separated into two groups: those who did not have the BRAFV600E mutation (n = 75) and those who did have the mutation (n = 63). The patients were randomly assigned to one of two data sets: training (70%), or validation (30%). From strain elastography US images, a total of 479 radiomic features were retrieved. Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified tenfold cross-validation were used to decrease the features. Based on selected radiomic features, six machine learning algorithms including support vector machine with the linear kernel (SVM_L), support vector machine with radial basis function kernel (SVM_RBF), logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were compared to predict the possibility of BRAFV600E. The accuracy (ACC), the area under the curve (AUC), sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), and calibration curves of the machine learning algorithms were used to evaluate their performance. ① The machine learning algorithms' diagnostic performance depended on 27 radiomic features. ② AUCs for NB, KNN, LDA, LR, SVM_L, and SVM_RBF were 0.80 (95% confidence interval [CI]: 0.65–0.91), 0.87 (95% CI 0.73–0.95), 0.91(95% CI 0.79–0.98), 0.92 (95% CI 0.80–0.98), 0.93 (95% CI 0.80–0.98), and 0.98 (95% CI 0.88–1.00), respectively. ③ There was a significant difference in echogenicity,vertical and horizontal diameter ratios, and elasticity between PTC patients with BRAFV600E and PTC patients without BRAFV600E. Machine learning algorithms based on US elastography radiomic features are capable of predicting the likelihood of BRAFV600E in PTC patients, which can assist physicians in identifying the risk of BRAFV600E in PTC patients. Among the six machine learning algorithms, the support vector machine with radial basis function (SVM_RBF) achieved the best ACC (0.93), AUC (0.98), SEN (0.95), SPEC (0.90), PPV (0.91), and NPV (0.95)