Learning Recommendations from User Actions in the Item-poor Insurance Domain

While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to retail) and they are usually purchased to last for a long time. Also, many users still prefer the telephone over the web for purchasing products, reducing the amount of web-logged user interactions. To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations. Learning from past user sessions allows dealing with the data scarcity of the insurance domain. Specifically, our model learns from several types of user actions that are not always associated with items, and unlike all prior session-based recommendation models, it models relationships between input sessions and a target action (purchasing insurance) that does not take place within the input sessions. Evaluation on a real-world dataset from the insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions) against several state-of-the-art baselines shows that our model outperforms the baselines notably. Ablation analysis shows that this is mainly due to the learning of dependencies across sessions in our model. We contribute the first ever session-based model for insurance recommendation, and make available our dataset to the research community

Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain

Providing personalized recommendations for insurance products is particularly challenging due to the intrinsic and distinctive features of the insurance domain. First, unlike more traditional domains like retail, movie etc., a large amount of user feedback is not available and the item catalog is smaller. Second, due to the higher complexity of products, the majority of users still prefer to complete their purchases over the phone instead of online. We present different recommender models to address such data scarcity in the insurance domain. We use recurrent neural networks with 3 different types of loss functions and architectures (cross-entropy, censored Weibull, attention). Our models cope with data scarcity by learning from multiple sessions and different types of user actions. Moreover, differently from previous session-based models, our models learn to predict a target action that does not happen within the session. Our models outperform state-of-the-art baselines on a real-world insurance dataset, with ca. 44K users, 16 items, 54K purchases and 117K sessions. Moreover, combining our models with demographic data boosts the performance. Analysis shows that considering multiple sessions and several types of actions are both beneficial for the models, and that our models are not unfair with respect to age, gender and income.Comment: arXiv admin note: substantial text overlap with arXiv:2211.1536

Graph-based Recommendation for Sparse and Heterogeneous User Interactions

Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses struggle to apply state-of-the-art models due to their very limited availability of data. We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets. A genetic algorithm is used to find optimal weights that represent the strength of the relationship between users and content. Experiments on two real-world datasets (which we make available to the research community) show promising results (up to 7% improvement), in comparison with other state-of-the-art methods for low-data environments. These improvements are statistically significant and consistent across different data samples

Smad2 and Smad6 as predictors of overall survival in oral squamous cell carcinoma patients

Background: To test if the expression of Smad1-8 mRNAs were predictive of survival in patients with oral squamous cell carcinoma (SCC). Patients and Methods: We analyzed, prospectively, the expression of Smad1-8, by means of Ribonuclease Protection Assay in 48 primary, operable, oral SCC. In addition, 21 larynx, 10 oropharynx and 4 hypopharynx SCC and 65 matched adjacent mucosa, available for study, were also included. For survival analysis, patients were categorized as positive or negative for each Smad, according to median mRNA expression. We also performed real-time quantitative PCR (QRTPCR) to asses the pattern of TGF beta 1, TGF beta 2, TGF beta 3 in oral SCC. Results: Our results showed that Smad2 and Smad6 mRNA expression were both associated with survival in Oral SCC patients. Cox Multivariate analysis revealed that Smad6 positivity and Smad2 negativity were both predictive of good prognosis for oral SCC patients, independent of lymph nodal status (P = 0.003 and P = 0.029, respectively). In addition, simultaneously Smad2(-) and Smad6(+) oral SCC group of patients did not reach median overall survival (mOS) whereas the mOS of Smad2(+)/Smad6(-) subgroup was 11.6 months (P = 0.004, univariate analysis). Regarding to TGF beta isoforms, we found that Smad2 mRNA and TGF beta 1 mRNA were inversely correlated (p = 0.05, R = -0.33), and that seven of the eight TGF beta 1(+) patients were Smad2(-). In larynx SCC, Smad7(-) patients did not reach mOS whereas mOS of Smad7(+) patients were only 7.0 months (P = 0.04). No other correlations were found among Smad expression, clinico-pathological characteristics and survival in oral, larynx, hypopharynx, oropharynx or the entire head and neck SCC population. Conclusion: Smad6 together with Smad2 may be prognostic factors, independent of nodal status in oral SCC after curative resection. The underlying mechanism which involves aberrant TGF beta signaling should be better clarified in the future.FAPESP[02/01738-9]CNP

Association of family risk and lifestyle/comorbidities in ovarian cancer patients

Summary Objectives: to analyze factors that might indicate familial predisposition for ovarian cancer in patients diagnosed with this disease. Methods: in a prospective single center cohort study at the Institute of Cancer of the State of São Paulo (ICESP), 51 women diagnosed with ovarian cancer were included. Familial predisposition for ovarian cancer was defined as having a higher than 10% chance of having a BRCA1/2 mutation according to the Manchester scoring system, a validated method to assess the likelihood of mutation detection. Each patient was interviewed with a standardized questionnaire on established risk factors for ovarian cancer and other factors that might influence the risk to develop ovarian cancer. Logistic regression analyses were performed to estimate the impact of the evaluated factors on the likelihood of mutation detection, by calculating odds ratios and 95% confidence intervals. Results: seventeen out of 51 patients had a family history of breast and/or ovarian cancer, four patients had a history of breast or endometrial cancer, 11 were diagnosed before the age of 50, and 12 presented a risk of familial predisposition to ovarian cancer higher than 10%. Patients with comorbidities, such as hypertension, diabetes, hormonal disorders, dyslipidemia and psychiatric conditions, presented a lower chance of having a familial predisposition for ovarian cancer (OR: 0.22; 95% CI: 0.06-0.88; p=0.03). Conclusion: in this study, having comorbidities was associated with a lower risk of having a familial predisposition for ovarian cancer. Other factors associated with the risk of ovarian cancer did not have an impact on this predisposition

CD3e expression in HTLV-1-infected individuals is associated with proviral load and Tax expression

Financial support: FUNDHERP, CTC, INCTC, FAPESP, CNPq and CAPES

Somatic mutations in breast and serous ovarian cancer young patients:a systematic review and meta-analysis

Objective: our aim was to evaluate whether somatic mutations in five genes were associated with an early age at presentation of breast cancer (BC) or serous ovarian cancer (SOC). Methods: COSMIC database was searched for the five most frequent somatic mutations in BC and SOC. A systematic review of PubMed was performed. Young age for BC and SOC patients was set at Results: twenty six (1,980 patients, 111 younger) and 16 studies (598, 41 younger), were analyzed for BC and SOC, respectively. In BC, PIK3CA wild type tumor was associated with early onset, not confirmed in binary regression with estrogen receptor (ER) status. In HER2-negative tumors, there was increased frequency of PIK3CA somatic mutation in older age groups; in ER-positive tumors, there was a trend towards an increased frequency of PIK3CA somatic mutation in older age groups. TP53 somatic mutation was described in 20% of tumors from both younger and older patients; PTEN, CDH1 and GATA3 somatic mutation was investigated only in 16 patients and PTEN mutation was detected in one of them. In SOC, TP53 somatic mutation was rather common, detected in more than 50% of tumors, however, more frequently in older patients. Conclusion: frequency of somatic mutations in specific genes was not associated with early-onset breast cancer. Although very common in patients with serous ovarian cancer diagnosed at all ages, TP53 mutation was more frequently detected in older women

Germline and Somatic mutations in postmenopausal breast cancer patients

OBJECTIVES: In breast cancer (BC) patients, the frequency of germline BRCA mutations (gBRCA) may vary according to the ethnic background, age, and family history of cancer. Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) is the second most common somatic mutated gene in BC; however, the association of mutations in both genes with cancer has not been thoroughly investigated. Thus, our aims were to investigate gBRCA mutation frequency in a cohort of postmenopausal Brazilian BC patients and the association of gBRCA1/BRCA2 and PIK3CA somatic mutations. METHODS: Forty-nine postmenopausal (>55 years) and forty-one young (≤35 years) BC patients were included in this study. The postmenopausal group included patients who reported a positive family history of cancer. For these patients, gBRCA1/BRCA2 were sequenced using next-generation sequencing (NGS) or Sanger sequencing. Data for gBRCA in young patients were already available from a previous study. DNA from formalin-fixed, paraffin-embedded (FFPE) tumors was obtained from 27 postmenopausal and 41 young patients for analyzing exons 9 and 20 of PIK3CA. The association between gBRCA1/BRCA2 and somatic mutations in PIK3CA was investigated. RESULTS: The overall frequency of gBRCA1/BRCA2 among the 49 postmenopausal patients was 10.2%. The frequencies of somatic mutations in PIK3CA in the postmenopausal and young patients were 37% and 17%, respectively (ns). The most common PIK3CA mutation was found to be E454A. Nonsense and frameshift mutations, which may counteract the oncogenic potential of PIK3CA were also detected. Regardless of age, 25% of BRCA1/BRCA2 mutation carriers and non-carriers , each, had PIK3CA somatic mutations. CONCLUSIONS: Data obtained indicate that BRCA1/BRCA2 gene testing may be considered for postmenopausal patients with BC who have a family history of cancer. Although some of them are not considered pathogenic, somatic variants of PIK3CA are frequently observed in BC patients, especially in postmenopausal patients

Distribution of QPY and RAH haplotypes of granzyme B gene in distinct Brazilian populations

Introduction: The cytolysis mediated by granules is one of the most important effector functions of cytotoxic T lymphocytes and natural killer cells. Recently, three single nucleotide polymorphisms (SNPs) were identified at exons 2, 3, and 5 of the granzyme B gene, resulting in a haplotype in which three amino acids of mature protein Q48P88Y245 are changed to R48A88H245, which leads to loss of cytotoxic activity of the protein. In this study, we evaluated the frequency of these polymorphisms in Brazilian populations. Methods: We evaluated the frequency of these polymorphisms in Brazilian ethnic groups (white, Afro-Brazilian, and Asian) by sequencing these regions. Results: The allelic and genotypic frequencies of SNP 2364A/G at exon 2 in Afro-Brazilian individuals (42.3% and 17.3%) were significantly higher when compared with those in whites and Asians (p < 0.0001 and p = 0.0007, respectively). The polymorphisms 2933C/G and 4243C/T also were more frequent in Afro-Brazilians but without any significant difference regarding the other groups. The Afro-Brazilian group presented greater diversity of haplotypes, and the RAH haplotype seemed to be more frequent in this group (25%), followed by the whites (20.7%) and by the Asians (11.9%), similar to the frequency presented in the literature. Conclusions: There is a higher frequency of polymorphisms in Afro-Brazilians, and the RAH haplotype was more frequent in these individuals. We believe that further studies should aim to investigate the correlation of this haplotype with diseases related to immunity mediated by cytotoxic lymphocytes, and if this correlation is confirmed, novel treatment strategies might be elaborated.Center for CellTherapy (CTC)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)Fundacao Hemocentro de Ribeirao Preto (FUNDHERP