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Billions of disposable thin‐film electronics will be produced annually in the near future, for applications in smart packaging, IoT, and wearable biomonitoring patches. For these cases, traditional rigid batteries are not optimal neither in terms of form and ergonomics nor ecological aspects. There is an urgent need for a new class of energy storage devices that are thin, stretchable, resilient, and recyclable. Herein, a novel architecture of materials and fabrication techniques is presented that permit a fully 3D printed soft‐matter thin‐film battery that is Resilient to mechanical strain, Repairable if cut, Rechargeable, and Recyclable at the end of its life. By taking advantage of a digitally printable ultra‐stretchable liquid metal‐based current collector and a novel Gallium‐Carbon anode electrode, the Ag2O‐Gallium battery is rapidly printed and customized per application. By optimizing the properties of the Gallium‐Carbon composite, a record‐breaking areal capacity of 26.37 mAh cm−2 is obtained, which improves after 10 cycles at 100% strain to 30.32 mAh cm−2, and an unprecedented maximum strain tolerance of ≈200%. Partially damaged batteries heal themselves. Severely damaged batteries are healed through innovative cold‐vapor stimulation. An example of a digitally printed, taylor‐made battery‐on‐the‐board health‐monitoring patch with printed sensors for monitoring the heart, and respiration is demonstrated. [ABSTRACT FROM AUTHOR]

Abstract Background The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis. Methods For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models. Results Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91]. Conclusions These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.

Introduction: Impairments in executive functions are common in neurogenetic disorders such as Huntington’s disease (HD) and are thought to significantly influence the patient’s functional status. Reliable tools with higher ecological validity that can assess and predict the impact of executive dysfunction in daily-life performance are needed. This study aimed to develop and validate a novel non-immersive virtual reality task (“EcoKitchen”) created with the purpose of capturing cognitive and functional changes shown by HD carriers without clinical manifestations of the disease (Premanifest HD), in a more realistic setting.Materials and Methods: We designed a virtual reality task with three blocks of increasing executive load. The performance of three groups (Controls, CTRL; Premanifest HD individuals, HP; Early Manifest HD patients, HD) was compared in four main components of the study protocol: the EcoKitchen; a subjective (self-report) measure – “The Adults and Older Adults Functional Assessment Inventory (IAFAI)”; the “Behavioural Assessment of Dysexecutive Syndrome battery (BADS)”; and a conventional neuropsychological test battery. We also examined statistical associations between EcoKitchen and the other executive, functional and clinical measures used.Results: The HD group showed deficits in all the assessment methods used. In contrast, the HP group was only found to be impaired in the EcoKitchen task, particularly in the most cognitively demanding blocks, where they showed a higher number of errors compared to the CTRL group. Statistically significant correlations were identified between the EcoKitchen, measures of the other assessment tools, and HD clinical features.Discussion: The EcoKitchen task, developed as an ecological executive function assessment tool, was found to be sensitive to early deficits in this domain. Critically, in premanifest HD individuals, it identifies dysfunction prior to symptom onset. Further it adds a potential tool for diagnosis and management of the patients’ real-life problems.

ABSTRACT Objective: To characterize sexual behaviors in a sample of adolescents and youth. Methods: An analytical descriptive study using a questionnaire about sexual behaviors, adapted from the World Health Organization. It was distributed to students from a Portuguese city aged 14-24 years, during two months. Two age groups were defined: G1 - students aged 14-19 years; G2 - aged 20-24 years. Results: The sample included 2,369 students, 61% females and 70% in G1. The mean age of first sexual intercourse was 16.4±1.8 years; 93% used some contraceptive method in the first sexual intercourse. Out of those who did not use contraception in the first sexual intercourse, 83% were in G1 (p