Summary Objectives To evaluate whether the presence of non‐carious cervical lesions (NCCLs) was related to the considered risk factors and to show the corresponding odds ratio in a predictive model. Methods The sample was 280 dentistry students. In an initial clinical examination, 140 cases were selected that presented one or more teeth with non‐carious cervical wear. For each case, a similar sex and age control without any tooth with non‐carious cervical lesions was selected. An occlusal examination and periodontal probing were performed in all cases and controls by skilled dentists. All the subjects answered a questionnaire referring to factors of brushing, bruxism, preferred chewing side, consumption of extrinsic acids and the presence of intrinsic acids. Data were analysed by means of univariate and multivariate logistic regression. Results Of all the study variables, only the protrusion interferences, interferences on the non‐working side, the brushing force, CPITN value and the consumption of salads increase the risk of NCCLs in the univariate regression. The best predictive model was formed by the combination of CPITN variables >1, the consumption of acidic salads, self‐reported bruxism, brushing force and attrition. However, it only correctly classifies 68.75% of subjects. Conclusions This study supports the multifactorial aetiology of NCCLs, the combination of several factors being necessary to explain their presence. The risk factors that make up the predictive model are not sufficient to explain the appearance of NCCLs. Dentists should take into account all these risk factors in prevention, diagnosis and treatment.

Flow Cytometry (FC) has been incorporated into cancer research in relation to its prognostic value together with histological parameters and TNM stages. We have studied by means of FC the cell cycle of 132 samples from male patients with Squamous Cell Lung Carcinoma (SQCLC). All of the patients received curative surgery and the clinical follow-up was 60 months. The clinical and cytometric parameters were evaluated in order to predict the patients’ outcome. The presence of tumoural recurrence and the tumoural stage showed statistical significance associated with survival. The multivariant analysis reveals radiotherapy (p = 0.004) as protective variable and the high S-phase fraction (SPF) (p = 0.001) and stage IIIA (p = 0.012) as risk factors. The SPF appears as an independent prognostic factor for overall survival time. We can build a prognostic index representative of different prognostic groups, which allows us to improve the individual monitoring of these patients.

Purpose: The aim of this retrospective case-control study was to evaluate the influence of dental brushing factors, diet, the consumption of acidic drinks and Community Periodontal Index of Treatment Needs (CPITN) on the development of non-carious cervical lesions (NCCLs). Methods: The sample consisted of undergraduate dentistry students from different Spanish faculties (age range 18 to 29 years). NCCLs and the CPITN were diagnosed and recorded using a periodontal probe. A questionnaire was used to record different brushing factors, the consumption of extrinsic acids and the presence of intrinsic acids. The data obtained were analysed using unconditional uni- and multivariate logistic regression (significance lecel p<0.05). Results: Brushing force was a risk factor (OR=1.71). The presence of NCCLs is significantly more frequent in subjects who brush their teeth vigorously Frequent consumption of salads with vinegar or lemon increases the risk of NCCLs (OR=4.5). As the CPITN score increases, the risk of NCCLs also increases significantly (OR=1.93) for value 1 and OR=6.49 for CPITN of 3. The consumption of extrinsic acids associated with salads seasoned with vinegar or lemon, the brushing force and the CPITN were the risk factors. The model obtained has a 67.14% predictive capacity for NCCLs, a specificity of 76.43%, and a sensitivity of 57.86%. Conclusions: the results of this study show that brushing force, and acidic diet and CPITN significantly increase the risk of NCCLs. Other variables are needed to increase model prediction.