Kerrostalo Ikaalisiin

Tiivistelmä. Työssäni esittelen Ikaalisten vanhaan Kauppalaan suunnittelemani kerrostalon. Valitsin suunnittelualueeksi tontin länsirannalta, Ikaalisten yhteiskoulun länsipuolelta. Tontti on maastonmuodoiltaan tasainen, mutta lähestyessä tonttia kaupungista päin laskeudutaan sinne loivaa rinnettä. Lähtökohtani suunnitelmaan oli suunnitella pientalomaista kerrostaloasumista. Vanhassa kauppalassa on kaksi koulua ja mukavat oltavat etenkin lapsiperheille, ja halusin tarjota heille laadukkaita asuntoja. Talon arkkitehtuuri muodostuu osittain tämän seurauksena, enkä asettanut esimerkiksi muodolle tavoitteita ennen kuin suunnittelin sisätilat. Tämän takia ulkoarkkitehtuurista tulikin monimuotoista ja omalaatuista, jota haluan myös korostaa rakennusmateriaaleilla ja parvekkeiden sijoittelulla. Rakennusmateriaaliksi valitsin puun, sillä uskon, että se sopii rakennukseeni paremmin kuin esimerkiksi betoni. Haluan tuoda puuta esiin julkisivussa ja sisätiloissa niin paljon kuin mahdollista. Omassa suunnitelmassani on neljä asuinkerrosta, joissa kaikki kerrokset ovat jotakuinkin samanlaisia. Ensimmäisessä kerroksessa on lastenvaunu- sekä ulkoiluvälinevarasto, kellarissa irtainvarastot, pesutupa sekä tekniset tilat ja katolla asukkaiden yhteissauna. Asunnoissa märkätilat sekä keittiöt pysyvät samoissa kohdissa kerroksesta toiseen, ja vaihtelua kerroksiin tuo parvekkeiden sijoittelu. Rappukäytävä on malliltaan keskikäytävä

Machine learning-based dynamic mortality prediction after traumatic brain injury

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified crossvalidation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middleincome countries.Peer reviewe

Effects of bright light treatment on psychomotor speed in athletes

Purpose: A recent study suggests that transcranial brain targeted light treatment via ear canals may have physiological effects on brain function studied by functional magnetic resonance imaging (fMRI) techniques in humans. We tested the hypothesis that bright light treatment could improve psychomotor speed in professional ice hockey players. Methods: Psychomotor speed tests with audio and visual warning signals were administered to a Finnish National Ice Hockey League team before and after 24 days of transcranial bright light or sham treatment. The treatments were given during seasonal darkness in the Oulu region (latitude 65 degrees north) when the strain on the players was also very high (10 matches during 24 days). A daily 12-min dose of bright light or sham (n = 11 for both) treatment was given every morning between 8–12 am at home with a transcranial bright light device. Mean reaction time and motor time were analyzed separately for both psychomotor tests. Analysis of variance for repeated measures adjusted for age was performed. Results: Time x group interaction for motor time with a visual warning signal was p = 0.024 after adjustment for age. In Bonferroni post-hoc analysis, motor time with a visual warning signal decreased in the bright light treatment group from 127 ± 43 to 94 ± 26 ms (p = 0.024) but did not change significantly in the sham group 121 ± 23 vs. 110 ± 32 ms (p = 0.308). Reaction time with a visual signal did not change in either group. Reaction or motor time with an audio warning signal did not change in either the treatment or sham group. Conclusion: Psychomotor speed, particularly motor time with a visual warning signal, improves after transcranial bright light treatment in professional ice-hockey players during the competition season in the dark time of the year

Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm

Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased toPeer reviewe