The Role of Osteopathic Care in Gynaecology and Obstetrics: An Updated Systematic Review

Background: Many efforts are made to find safer and more feasible therapeutic strategies to improve gynaecological care. Non-pharmacological treatments, such as osteopathic interventions, could be used as complementary strategies to better manage different gynaecological conditions. This review aims to report the effectiveness of osteopathic treatment in the gynaecology and obstetrics field, updating the previous review published in 2016. The secondary aim was to elucidate the role of somatic dysfunction (SD) in osteopathic assessment and treatment procedures, as well as their health and economic implications. Methods: An electronic search was conducted in the following databases: Embase, MEDLINE (PubMed), and Science direct. All types of clinical studies published between May 2014 and December 2021 have been included: randomised controlled trial (RCT), controlled before/after, interrupted time series quasi RCT, case controls, case reports, case series, observational, clinical studies involving any type of osteopathic treatment, (standardised, semi-standardised or patients’ need-based treatment) performed alone or in combination with other treatments, were included). Results: A total of 76,750 were identified through database searching and other sources. After the removal of duplicates, 47,655 papers were screened based on title and abstract. A total of 131 full-text articles were consequently assessed for eligibility. Twenty-one new articles were included in the synthesis. A total of 2632 participants with a mean age of 28.9 ± 10.5 years were included in the review. Conclusions: Results showed an effectiveness of osteopathic care in gynaecology and obstetrics, but the studies were too heterogeneous to perform quantitative analysis and make clinical recommendations. Nevertheless, osteopathic care could be considered a safe complementary approach to traditional gynaecological care

Mense e personale addetto alle cucine: valutazione dei rischi occupazionali

The aim of the study is to evaluate the occupational risks among food service workers and cooks. During the occupational risks assessment the following risk factors must be evaluated: musculoskeletal disorders, chemical risk (cleaning kitchen work surface, dishes, utensils ecc.) biological risk (contact with foods or biological agents) cancerogenic risk (by baking smoke inhalation), and psycho-social stress. In this study the preventive measures and protective equipment to prevent health hazards for these workers have been evaluated (i.e. aspiration hood, adapted ventilation, chosen of less harmful methods of baking, ecc.). In particular the performance of rigid behavioural norms and hygienic procedures is very important for cooks and food service workers to reduce the risk of occupational infections

Radiotherapy electron beams collimated by small tubular applicators: Characterization by silicon and diamond diodes

High-energy electron beams generated by linear accelerators, typically in the range 6 to 20 MeV, are used in small field sizes for radiotherapy of localized superficial tumors. Unshielded silicon diodes (Si-D) are commonly considered suitable detectors for relative dose measurements in small electron fields due to their high spatial resolution. Recently, a novel synthetic single crystal diamond diode (SCDD) showed suitable properties for standard electron beams and small photon beams dosimetry. The aim of the present study is twofold: to characterize 6 to 15 MeV small electron beams shaped by using commercial tubular applicators with 2, 3, 4 and 5 cm diameter and to assess the dosimetric performance under such irradiation conditions of the novel SCDD dosimeter by comparison with commercially available dosimeters, namely a Si-D and a plane-parallel ionization chamber. Percentage depth dose curves, beam profiles and output factors (OFs) were measured. A good agreement among the dosimeters was observed in all of the performed measurements. As for the tubular applicators, two main effects were evidenced: (i) OFs larger than unity were measured for a number of field sizes and energies, with values up to about 1.3, that is an output 30% greater than that obtained at the 10 × 10 cm 2 reference field; (ii) for each diameter of the tubular applicator a noticeable increase of the OF values was observed with increasing beam energy, up to about 100% in the case of the smaller applicator. This OF behavior is remarkably different from what typically observed for small blocked fields having the same size and energy as those used in this study. OFs for tubular applicators depend considerably on the field size, so interpolation is unadvisable to predict the linear accelerator output for such applicators whereas reliable high-resolution detectors, as the silicon and diamond diodes used in this work allow OF measurements with uncertainties of about 1%. © 2013 Institute of Physics and Engineering in Medicine. Printed in the UK & the USA

Dosimetric characterization of a microDiamond detector in clinical scanned carbon ion beams

Purpose: To investigate for the first time the dosimetric properties of a new commercial synthetic diamond detector (PTW microDiamond) in high-energy scanned clinical carbon ion beams generated by a synchrotron at the CNAO facility. Methods: The detector response was evaluated in a water phantom with actively scanned carbon ion beams ranging from 115 to 380 MeV/u (30-250 mm Bragg peak depth in water). Homogeneous square fields of 3×3 and 6×6 cm2 were used. Short- and medium-term (2 months) detector response stability, dependence on beam energy as well as ion type (carbon ions and protons), linearity with dose, and directional and dose-rate dependence were investigated. The depth dose curve of a 280 MeV/u carbon ion beam, scanned over a 3×3 cm² area, was measured with the microDiamond detector and compared to that measured using a PTW Advanced Markus ionization chamber, and also simulated using FLUKA Monte Carlo code. The detector response in two spread-out-Bragg-peaks (SOBPs), respectively, centered at 9 and 21 cm depths in water and calculated using the treatment planning system (TPS) used at CNAO, was measured. Results: A negligible drift of detector sensitivity within the experimental session was seen, indicating that no detector preirradiation was needed. Short-term response reproducibility around 1% (1 standard deviation) was found. Only 2% maximum variation of microDiamond sensitivity was observed among all the evaluated proton and carbon ion beam energies. The detector response showed a good linear behavior. Detector sensitivity was found to be dose-rate independent, with a variation below 1.3% in the evaluated dose-rate range. A very good agreement between measured and simulated Bragg curves with both microDiamond and Advanced Markus chamber was found, showing a negligible LET dependence of the tested detector. A depth dose curve was also measured by positioning the microDiamond with its main axis oriented orthogonally to the beam direction. A strong distortion in Bragg peak measurement was observed, confirming manufacturer recommendation on avoiding such configuration. Very good results were obtained for SOBP measurements, with a difference below 1% between measured and TPS-calculated doses. The stability of detector sensitivity in the observation period was within the experimental uncertainty. Conclusions: Dosimetric characterization of a PTW microDiamond detector in high-energy scanned carbon ion beams was performed. The results of the present study showed that this detector is suitable for dosimetry of clinical carbon ion beams, with a negligible LET and dose-rate dependence

Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming

Continuously monitoring the network activity to proactively recognise possible problems and prevent users QoE degradation is a major concern for network operators, for both mobile radio and home networks. Considering video streaming applications, which generate the majority of overall Internet traffic, monitoring the chunk requests from the video client to the video server is of particular interest, as they not only indicate that a download burst is imminent, but their type (e.g., request of an audio or video chunk) and frequency also allow to estimate which and how much data will be downloaded to the client. In this work, we propose a machine-learning based video streaming traffic monitoring architecture able to i) predict when next uplink request will be issued by the video client and ii) classify the type of next uplink request. We evaluate the system performance on a dataset of more than 900 HTTP adaptive streaming sessions and 15,000 request-response exchanges, where both the predictor of the next request arrival and the request type classifier are fed with lightweight features extracted from encrypted traffic in an online fashion, both in the uplink and downlink directions of the traffic. Results show that i) the system is able to classify the type of a HAS uplink requests with an accuracy greater than 95 % and ii) pipe-lining request type classification and prediction of next request arrival time improves the final prediction performance

Novel approach to generation Portfolio Optimization by using genetic algorithms and stochastic methods

In this paper we present the Portfolio Optimization Problem in the electricity generation framework. We consider traditional and fully controllable energy sources together with wind source, strongly supported by economical benefits but exposed to intermittent generation volatility. Due to the statistical uncertainty about parameters, we formalize the optimization problem in a probabilistic sense and solve it by using Genetic Algorithms

A Novel Approach to Generation Portfolio Optimization by using Genetic Algorithms and Stochastic Methods

In this paper we present the Portfolio Optimization Problem in the electricity generation framework. We consider traditional and fully controllable energy sources together with wind source, strongly supported by economical benefits but exposed to intermittent generation volatility. Due to the statistical uncertainty about parameters, we formalize the optimization problem in a probabilistic sense and solve it by using Genetic Algorithms