Cervical Intraepithelial Neoplasia Grade 3 (CIN3) in Women Younger than 30 Years Was Significantly Associated with HPV16/18 Genotypes.

BACKGROUND: The objective of the present study is to investigate the age-specific distribution of HPV genotypes in CIN3 lesions in screened unvaccinated women. These data are essential to optimize current and future screening programs. METHODS: A multicenter retrospective study was conducted. A total of 408 unvaccinated women with positive histology and a high-risk HPV genotype were enrolled. Each woman at baseline had HPV DNA testing and HPV genotyping, and all women underwent targeted biopsy and/or treatment with a loop electrosurgical excision procedure (LEEP) before entering the study. We divided the genotypes into HPV16/18 and HPV non-16/18 (HPV31/33/45/35/39/51/52/58/59/66/68). Women were divided into increasing age categories: <30, 30-44, and ≥45. RESULTS: The percentage of CIN3 associated with HPV16/18 is maximum in women under 30 years of age (85.1%), drops to 75.6% in women aged between 30 and 44 years, and up to 47.2% in women over 45 years. CIN3 in women younger than 30 years was significantly associated with HPV16/18 genotypes (p = 0). DISCUSSION: The data from the present study suggest that the risk of CIN3 is related to the woman's age and hr HPV genotype. The data highlight two different types of CIN3: a more frequent type, related to HPV16/18, which develops rapidly and in young women, and another, relating to non-16/18 HPV, which develops later at an advanced age and slowly, through low-grade lesions.

"Swipe & slice": decoding digital struggles with non-suicidal self-injuries among youngsters.

Introduction: Nonsuicidal-self-injury (NSSI)-related content recently emerged on social networking sites (SNS), despite its relationship with NSSI conducts is still unclear. Methods: Hence, the current population-based cross-sectional study investigated the interplay between SNS use, NSSI content engagement, risky social media challenges (RSMCs), in a sample of 404 young adults (aged 18-24), focusing on the influence of problematic social media use (PSMU) and fear of missing out (FoMO). Results: Around 51.5% of the samplewas engaged inNSSI-related contents on SNS, being mostly females (p<0.001), younger (p=0.005), transgender people and nonbinary people (p=0.030) and those who displayed higher PSMU (p<0.001) or FoMO (p=0.031). Around 66.2% of the sample currently practice NSSIs, predominantly among females (p<0.001), those using BeReal (p=0.012), actively looking for NSSIrelated contents on SNS (p<0.001) to be part of a group (p=0.0025) or learn how to practice NSSI (p=0.025). PSMU (p<0.001) and FoMO (p<0.001) emerged as significant predictors of NSSI content engagement, particularly among active seekers. NSSI conducts were significantly predicted by FoMO (p<0.001) and Snapchat (p=0.044), while negatively predicted by male sex (p<0.001), higher educational level (p=0.019) and age at which NSSI-related contents were firstly looked for (p=0.028). Discussion: These findings underline the need to implement preventivepolicies and targeted interventions to monitor NSSI-related contents on SNS, the impact of PSMU and FoMO on NSSI, particularly among youngsters.

Testing psychometric properties of Shared Decision Making Questionnaire - Physician Version (SDM-Q-Doc) in an Italian real-world psychiatric clinical sample.

OBJECTIVES: The Shared Decision Making Questionnaire-Physician Version (SDM-Q-Doc) is the main tool assessing SDM relationship between patient and physician using the clinician viewpoint. It is reliable in all medical fields, and the validation of its Italian version was still missing. Our aim was to validate the Italian version of the SDM-Q-Doc in a clinical sample of patients suffering from severe mental illness. METHODS: We approached 369 patients affected by major psychiatric disorders (including schizophrenia spectrum disorders, affective disorders and eating disorders) in a real-world outpatient clinical setting. We run the Confirmatory Factor Analysis (CFA) to test the SDM-Q-Doc structure. We calculated the correlations between the SDM-Q-Doc and the Observing Patient Involvement (OPTION) scale, used as comparing test, and McDonald ω coefficient to measure convergent validity and internal consistency respectively. RESULTS: We reached a response rate of 93.2% (344 final participants). The CFA showed a very good fit compared of the Italian version of SDM-Q-Doc (χ2/df=3.2, CFI=.99, TLI=.99, RMSEA=.08, SRMR=.04). We found several correlations between the SDM-Q-Doc and OPTION scale supporting a robust SDM-Q-Doc construct validity, while internal consistency of the scale was McDonald ω coefficient .92. Further, inter-item correlations ranged from .390 to .703, with a mean of .556. CONCLUSIONS: This study confirms the suitability of the Italian version of SDM-Q-Doc, with good reliability and soundness even when compared to other languages validated versions and to OPTION scale. SDM-Q-Doc represents an easy-to-use physician-centered measure to assess patients' involvement in medical decision-making, well performing in the Italian-speaking population.

Collaborative manufacturing of ergonomic personal protective equipment (PPE) accessories to prevent infectious disease.

Throughout history there have been epidemics and pandemics of all kinds, however the most recent ones have been respiratory diseases that have had a significant effect on the society and that have caused high mortality rates. The preventive measures to minimize the risk of contagion by contact with infected surfaces include ergonomic accessories including personal protective equipment (PPE) to prevent hands to be in contact with surfaces that could be infected by viruses, bacteria, fungi, etc., thus avoiding infection by the usual entry routes (mouth, nose, and eyes) to the human body of highly contagious diseases such as COVID-19. The collaborative manufacture of these safety accessories at the site of consumption is a current option that minimizes infectious diseases and reduces costs. Accessories such as the so-called "ear saver" and "anti-contact keys" can be produced by 3D printing with a general CAD/CAM and allow users in hospitals, and schools, such as medical and teaching staff and society in general to extend the life of N95 respirator fasteners (protective masks) and avoid contagion. These devices can be used to open doors and windows and control elevators, etc. The accessories can be optimized ergonomically for individual use by providing a custom design. The collaborative manufacture of these elements allows the product design stages to be carried out autonomously. In the manufacturing stage, 3D printers can be used to produce the devices at the point of use, thus saving on transport and distribution costs. This paper describes a comparative analysis of their design, manufacture and use in hospitals, schools, universities, and commercial areas with the aim of improving the current design.

An Efficient Experimental Methodology for the Assessment of the Dynamic Behaviour of Resilient Elements.

The assessment of the dynamic behaviour of resilient elements can be performed using the indirect method as described in the standard ISO 10846-3. This paper presents a methodology for control the error on the estimation of the frequency response functions (FRF) required for the application of the indirect method when sweep sine excitation is used. Based on a simulation process, this methodology allows for the design of the sweep sine excitation parameters, i.e., the sweep rate and the force amplitude, to control three types of errors associated to the experimentally obtained FRF in the presence of background noise: a general error of the FRF in a selected frequency range, and the errors associated to the amplitude and the frequency of the FRF resonance peak. The signal processing method used can be also tested with this methodology. The methodology has been tested in the characterisation of two different resilient elements: an elastomer and a coil spring. The simulated error estimations has been found to be in good agreement with the errors found in the measured FRF. Furthermore, it is found that for large signal-to-noise ratios, both sweep rate and force amplitude significantly affect the FRF estimation error, while, for small signal-to-noise ratios, only the force amplitude can control the error efficiently. The current methodology is specially interesting for laboratory test rigs highly used for the dynamic characterisation of resilient elements which are required to operate efficiently, since it can be used for minimising test times and providing quality assurance. Moreover, the application of this methodology would be specially relevant when characterisation is done in noisy environments.

Records matching model for data survey on applied and experimental microbiology.

Experimental microbiology yields a huge quantity of raw data which needs to be evaluated and classified in a wide variety of situation from marine research, environmental pollution and pharmacokinetics of antimicrobial agents to epidemiological clinical trials on infectious diseases. It is indispensable in all kinds of disciplines to validate, transform and correlate data clusters to demonstrate the statistical significance of results. Whether academic or biotechnological, the scientific credibility of a work is strongly affected by the statistical methods and their adequacy. For a simple univariate analysis, many commercial or open source software products are available to perform sophisticated statistics for discriminant and multi-factorial analysis, but the majority of scientists use statistics partially. This is due to the high competence level required by a multivariate approach. It is known that the choice of a test, correct distribution assumption, valid experimental design and preliminary raw data validation are prejudicial to good science. All kinds of experimentation need analytical interpretation of descriptive evidence so that a classical numerical approach is not enough when raw data are not validated or incomplete. Microbiologists always wish to quickly discriminate, or correlate, group and data clusters concerning clinical patient profiles, auditing multi-sensor derived numbers, monitoring biosphere indicators on either chemical and physical parameters or dynamics of microbe populations. Mathematical and statistical analysis is essential to distinguish phenotypes or constraints. Data are in general stored in spreadsheet and database files which change continuously depending on the data collection and scope. We here propose a Records Matching Method (RMM) suitable for any kind of cluster analysis and pattern identification which can be used for either parametric or non parametric analysis without necessarily stating the pre-process statistical assumption on variable distribution. The RMM is an application of a theoretical approach based on the Unique Factorisation Domain and is explained with an ideal generalisation model and then applied to a real-world microbiological study. We used an easy mathematical formalism and discuss the possible application of the method as widely applicable to a plethora of taxonomic and phenetic investigations as well as for clinical trials and epidemiology. Prototyping of the model for a computational automated process are also described in order to devise simple software which can infer on data using a heuristic rules file.