Exposure to alcohol and overall survival in head and neck cancer: A regional cohort study

Background There is a paucity of knowledge regarding the association of alcohol use with overall survival (OS) of patients with head and neck squamous cell carcinoma (HNSCC).Methods All 1033 patients treated for new HNSCC in Southwest Finland regional referral center of Turku University Hospital in 2005-2015. Cox regression analysis was used. Tumor TNM classification, age at baseline and tobacco smoking status were assessed as potential confounders.Results A history of severe harmful alcohol use with major somatic complications (HR: 1.41; 95%CI: 1.06-1.87; p = 0.017) as well as current use of at least 10 units per week (HR: 1.44, 95%CI: 1.16-1.78; p = 0.001) were associated with OS.Conclusions Alcohol consumption of 10-20 units/week, often regarded as moderate use, was found to increase risk of mortality independent of other prognostic variables. Systematic screening of risk level alcohol use and prognostic evaluation of alcohol brief intervention strategies is highly recommended.</p

Epidemiological Study of p16 Incidence in Head and Neck Squamous Cell Carcinoma 2005-2015 in a Representative Northern European Population

The incidence of human papillomavirus (HPV)-associated head and neck squamous cell carcinomas (HNSCC) has increased globally. Our research goal was to study HNSCC incidence in a representative Northern European population and evaluate the utility of the HPV surrogate marker p16 in clinical decision-making. All new HNSCC patients diagnosed and treated in Southwest Finland from 2005–2015 (n = 1033) were identified and analyzed. During the follow-up period, the incidence of oropharyngeal (OPSCC) and oral cavity squamous cell carcinoma (OSCC) increased, while the incidence of laryngeal squamous cell carcinoma (LSCC) decreased. This clinical cohort was used to generate a population-validated tissue microarray (PV-TMA) archive for p16 analyses. The incidence of p16 positivity in HNSCC and OPSCC increased in southwest Finland between 2005 and 2015. p16 positivity was mainly found in the oropharynx and was a significant factor for improved survival. p16-positive OPSCC patients had a better prognosis, regardless of treatment modality. All HNSCC patients benefited from a combination of chemotherapy and radiotherapy, regardless of p16 expression. Our study reaffirms that p16 expression offers a prognostic biomarker in OPSCC and could potentially be used in cancer treatment stratification. Focusing on p16 testing for only OPSCC might be the most cost-effective approach in clinical practice.</p

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Influenssa A/H1N1-, A/H3N2- ja B -infektioiden lapsille avoterveydenhuollossa aiheuttaman tautitaakan vertailu

Siirretty Doriast

Aivoverenkiertohäiriön alkuvaiheen kuntoutuksessa suuria vaihteluja : selvitys AVH:n sairastaneiden kuntoutuspalveluista Suomessa

Lähtökohdat -- Aivoverenkiertohäiriön (AVH) sairastaneiden edellisestä valtakunnallisesta kuntoutusselvityksestä on kulunut jo yli 20 vuotta. Uusi kartoitus AVH:n sairastaneiden kuntoutukseen ohjautumista ja kuntoutuksen toteutumista tehtiin vuonna 2006. Aineisto ja menetelmät -- Aineisto koottiin haastattelemalla AVH-kuntoutuksesta vastaavia henkilöitä, jotka työskentelivät maamme yliopisto- ja keskussairaaloissa 24 akuuttiosastolla ja 10 kuntoutusosastolla. Lisäksi kyselylomakkeet lähetettiin kaikkiin Suomen 237 terveyskeskukseen ja niiden AVH-yhdyshenkilölle. Vastaukset saatiin 145 terveyskeskuksesta, joiden alueella asuu 81 % Suomen väestöstä. Kyselylomakkeet lähetettiin myös AVH-potilaita kuntouttaviin yksityisiin kuntoutuslaitoksiin ja aluesairaaloihin. Tulokset -- AVH:n sairastaneiden kuntoutusresursseissa on huomattavaa vaihtelua maassamme. Paras tilanne on Etelä-Savon sairaanhoitopiirissä, jossa yli 40 % sairastuneista pääsee suositusten mukaiseen moniammatilliseen kuntoutukseen. Heikoimmilla alueilla vain muutama prosentti sairastuneista saa tarvitsemaansa laaja-alaista ja intensiivistä kuntoutusta sairauden ensimmäisinä kuukausina. Päätelmät -- AVH:n sairastaneiden kuntoutusresurssit vaihtelevat sairaanhoitopiirien välillä. Myös sairaanhoitopiirien sisällä on isoja eroja terveyskeskusten välillä, ja kuntoutusresurssit ovat monilla paikkakunnilla alimitoitetut. Tilanteen parantamiseksi esitetään AVH-kuntoutuksen keskittämistä riittävän suuriin erityisosaamiskeskuksiin: sataatuhatta asukasta kohden tulisi olla 12-15-paikkainen kuntoutusosasto.peerReviewe

Epidemiological Study of p16 Incidence in Head and Neck Squamous Cell Carcinoma 2005–2015 in a Representative Northern European Population

The incidence of human papillomavirus (HPV)-associated head and neck squamous cell carcinomas (HNSCC) has increased globally. Our research goal was to study HNSCC incidence in a representative Northern European population and evaluate the utility of the HPV surrogate marker p16 in clinical decision-making. All new HNSCC patients diagnosed and treated in Southwest Finland from 2005–2015 (n = 1033) were identified and analyzed. During the follow-up period, the incidence of oropharyngeal (OPSCC) and oral cavity squamous cell carcinoma (OSCC) increased, while the incidence of laryngeal squamous cell carcinoma (LSCC) decreased. This clinical cohort was used to generate a population-validated tissue microarray (PV-TMA) archive for p16 analyses. The incidence of p16 positivity in HNSCC and OPSCC increased in southwest Finland between 2005 and 2015. p16 positivity was mainly found in the oropharynx and was a significant factor for improved survival. p16-positive OPSCC patients had a better prognosis, regardless of treatment modality. All HNSCC patients benefited from a combination of chemotherapy and radiotherapy, regardless of p16 expression. Our study reaffirms that p16 expression offers a prognostic biomarker in OPSCC and could potentially be used in cancer treatment stratification. Focusing on p16 testing for only OPSCC might be the most cost-effective approach in clinical practice

White Paper on Machine Learning in 6G Wireless Communication Networks : 6G Research Visions, No. 7, 2020

This white paper discusses various topics, advances, and projections regarding machine learning (ML) in wireless communications. Sixth generation (6G) wireless communications networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research have enabled a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is made possible by the availability of advanced ML models, large datasets, and high computational power. In addition, the ever-increasing demand for connectivity will require even more extensive innovation in 6G wireless networks. Consequently, ML tools will play a major role in solving the new problems in the wireless domain. In this paper, we offer a vision of how ML will impact wireless communications systems. We first provide an overview of the ML methods that have the highest potential to be used in wireless networks. We then discuss the problems that can be solved by using ML in various layers of the network such as the physical, medium-access, and application layers. Zero-touch optimization of wireless networks using ML is another interesting aspect discussed in this paper. Finally, at the end of each section, a set of important future research questions is presented

White Paper on Machine Learning in 6G Wireless Communication Networks : 6G Research Visions, No. 7, 2020

This white paper discusses various topics, advances, and projections regarding machine learning (ML) in wireless communications. Sixth generation (6G) wireless communications networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research have enabled a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is made possible by the availability of advanced ML models, large datasets, and high computational power. In addition, the ever-increasing demand for connectivity will require even more extensive innovation in 6G wireless networks. Consequently, ML tools will play a major role in solving the new problems in the wireless domain. In this paper, we offer a vision of how ML will impact wireless communications systems. We first provide an overview of the ML methods that have the highest potential to be used in wireless networks. We then discuss the problems that can be solved by using ML in various layers of the network such as the physical, medium-access, and application layers. Zero-touch optimization of wireless networks using ML is another interesting aspect discussed in this paper. Finally, at the end of each section, a set of important future research questions is presented