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- Wang, Xin, et al.
(författare)
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Global burden of respiratory infections associated with seasonal influenza in children under 5 years in 2018 : a systematic review and modelling study
- 2020
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Ingår i: The Lancet Global Health. - : Elsevier. - 2214-109X. ; 8:4, s. E497-E510
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Forskningsöversikt (refereegranskat)abstract
- Background: Seasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in ung children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million fluenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this bstantial burden, only a few low-income and middle-income countries have adopted routine influenza ccination policies for children and, where present, these have achieved only low or unknown levels of ccine uptake. Moreover, the influenza burden might have changed due to the emergence and rculation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the obal number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory fections in children under 5 years in 2018.Methods: We estimated the regional and global burden of influenza-associated respiratory infections in ildren under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec , 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to sess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated spiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths om influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of fluenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on e number of in-hospital deaths, US paediatric influenza-associated death data, and population-based ildhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income untries.Findings: In 2018, among children under 5 years globally, there were an estimated 109.5 million fluenza virus episodes (uncertainty range [UR] 63.1-190.6), 10.1 million influenza-virus-associated ALRI ses (6.8-15.1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000-1 415 000), 15 300 -hospital deaths (5800-43 800), and up to 34 800 (13 200-97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries.Interpretation: A large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries.
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- Ortiz Catalan, Max Jair, 1982, et al.
(författare)
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Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain: a single group, clinical trial in patients with chronic intractable phantom limb pain
- 2016
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Ingår i: The Lancet. - : Elsevier BV. - 1474-547X .- 0140-6736. ; 388:10062, s. 2885-2894
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Tidskriftsartikel (refereegranskat)abstract
- Background Phantom limb pain is a debilitating condition for which no eff ective treatment has been found. We hypothesised that re-engagement of central and peripheral circuitry involved in motor execution could reduce phantom limb pain via competitive plasticity and reversal of cortical reorganisation. Methods Patients with upper limb amputation and known chronic intractable phantom limb pain were recruited at three clinics in Sweden and one in Slovenia. Patients received 12 sessions of phantom motor execution using machine learning, augmented and virtual reality, and serious gaming. Changes in intensity, frequency, duration, quality, and intrusion of phantom limb pain were assessed by the use of the numeric rating scale, the pain rating index, the weighted pain distribution scale, and a study-specifi c frequency scale before each session and at follow-up interviews 1, 3, and 6 months after the last session. Changes in medication and prostheses were also monitored. Results are reported using descriptive statistics and analysed by non-parametric tests. The trial is registered at ClinicalTrials. gov, number NCT02281539. Findings Between Sept 15, 2014, and April 10, 2015, 14 patients with intractable chronic phantom limb pain, for whom conventional treatments failed, were enrolled. After 12 sessions, patients showed statistically and clinically signifi cant improvements in all metrics of phantom limb pain. Phantom limb pain decreased from pre-treatment to the last treatment session by 47% (SD 39; absolute mean change 1 . 0 [0 . 8]; p= 0 . 001) for weighted pain distribution, 32% (38; absolute mean change 1 . 6 [1 . 8]; p= 0 . 007) for the numeric rating scale, and 51% (33; absolute mean change 9 . 6 [8 . 1]; p= 0 . 0001) for the pain rating index. The numeric rating scale score for intrusion of phantom limb pain in activities of daily living and sleep was reduced by 43% (SD 37; absolute mean change 2 . 4 [2 . 3]; p= 0 . 004) and 61% (39; absolute mean change 2 . 3 [1 . 8]; p= 0 . 001), respectively. Two of four patients who were on medication reduced their intake by 81% (absolute reduction 1300 mg, gabapentin) and 33% (absolute reduction 75 mg, pregabalin). Improvements remained 6 months after the last treatment. Interpretation Our fi ndings suggest potential value in motor execution of the phantom limb as a treatment for phantom limb pain. Promotion of phantom motor execution aided by machine learning, augmented and virtual reality, and gaming is a non-invasive, non-pharmacological, and engaging treatment with no identified side-effects at present.
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| 7. |
- Ortiz-Catalan, Max, et al.
(författare)
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Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain : a single group, clinical trial in patients with chronic intractable phantom limb pain
- 2016
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Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 388:10062, s. 2885-2894
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Tidskriftsartikel (refereegranskat)abstract
- Background: Phantom limb pain is a debilitating condition for which no eff ective treatment has been found. We hypothesised that re-engagement of central and peripheral circuitry involved in motor execution could reduce phantom limb pain via competitive plasticity and reversal of cortical reorganisation.Methods: Patients with upper limb amputation and known chronic intractable phantom limb pain were recruited at three clinics in Sweden and one in Slovenia. Patients received 12 sessions of phantom motor execution using machine learning, augmented and virtual reality, and serious gaming. Changes in intensity, frequency, duration, quality, and intrusion of phantom limb pain were assessed by the use of the numeric rating scale, the pain rating index, the weighted pain distribution scale, and a study-specifi c frequency scale before each session and at follow-up interviews 1, 3, and 6 months after the last session. Changes in medication and prostheses were also monitored. Results are reported using descriptive statistics and analysed by non-parametric tests. The trial is registered at ClinicalTrials. gov, number NCT02281539.Findings: Between Sept 15, 2014, and April 10, 2015, 14 patients with intractable chronic phantom limb pain, for whom conventional treatments failed, were enrolled. After 12 sessions, patients showed statistically and clinically signifi cant improvements in all metrics of phantom limb pain. Phantom limb pain decreased from pre-treatment to the last treatment session by 47% (SD 39; absolute mean change 1 . 0 [0 . 8]; p= 0 . 001) for weighted pain distribution, 32% (38; absolute mean change 1 . 6 [1 . 8]; p= 0 . 007) for the numeric rating scale, and 51% (33; absolute mean change 9 . 6 [8 . 1]; p= 0 . 0001) for the pain rating index. The numeric rating scale score for intrusion of phantom limb pain in activities of daily living and sleep was reduced by 43% (SD 37; absolute mean change 2 . 4 [2 . 3]; p= 0 . 004) and 61% (39; absolute mean change 2 . 3 [1 . 8]; p= 0 . 001), respectively. Two of four patients who were on medication reduced their intake by 81% (absolute reduction 1300 mg, gabapentin) and 33% (absolute reduction 75 mg, pregabalin). Improvements remained 6 months after the last treatment.Interpretation: Our fi ndings suggest potential value in motor execution of the phantom limb as a treatment for phantom limb pain. Promotion of phantom motor execution aided by machine learning, augmented and virtual reality, and gaming is a non-invasive, non-pharmacological, and engaging treatment with no identifi ed side-eff ects at present.
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- Scalas, A., et al.
(författare)
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An automatic approach for the classification of ancient clay statuettes based on heads features recognition
- 2019
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Ingår i: GCH 2019 - Eurographics Workshop on Graphics and Cultural Heritage. - 9783038680826 ; , s. 79-82
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Konferensbidrag (refereegranskat)abstract
- In recent years, quantitative approaches based on mathematical theories and ICT tools, known under the terms of digital, computational, and virtual archaeology, are more and more involved in the traditional archaeological research. In this paper, we apply shape analysis techniques to 3D digital replicas of archaeological findings to support their interpretation. In particular, our study focuses on a collection of small terracotta figurines from the ancient sanctuary of Ayia Irini, Cyprus, and it aims at re-analysing the material utilising a quantitative approach. We experiment state of the art techniques (meshSIFT and DBSCAN) to cluster statuettes according to the similarity of their heads, to investigate their production process.
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