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Abstract Background Psychosomatic complaints have increased among adolescents in recent decades, as have overweight and obesity rates. Both of these trends are regarded as public health concerns. However, the associations between weight status and psychosomatic complaints are not yet clear, necessitating further research. The aim of the present study was to investigate the associations between weight status and psychosomatic complaints in Swedish adolescent boys and girls, as well as to explore the potential buffering effect of family support. Methods The data was obtained from the cross-sectional Swedish Health Behaviour in School-aged Children (HBSC) study conducted in 2017/18, which involved 3,135 students aged 11, 13, and 15 years. Weight status was based on self-reported information on weight and height, which allowed for the calculation of body mass index (BMI) and the categorisation of participants into three groups: non-overweight, overweight, and obese. Psychosomatic complaints were assessed based on information regarding the frequency of eight different complaints, which were summed into an index. Family support was measured using three items describing the level of perceived emotional support, and an index was created, which was dichotomised into low and high family support. Gender stratified linear regression models were run to examine the associations between weight status and psychosomatic complaints. Age and family affluence were included as covariates. Interaction terms were included to evaluate whether family support moderated the main association. Results Obesity was associated with higher levels of psychosomatic complaints in both boys and girls when compared to being non-overweight (boys: b = 2.56, 95% CI 0.32, 4.79; girls: b = 3.35, 95% CI 0.77, 5.94), while being overweight did not show any statistically significant associations with the outcome (boys: b = 0.21, 95% CI -0.72, 1.15; girls: b = 0.78, 95% CI -0.42, 1.98). In girls, a statistically significant interaction effect between family support and weight status was observed (p = 0.031), indicating that family support buffered against psychosomatic complaints in girls with obesity. No statistically significant interaction was found for boys (p = 0.642). Conclusions The findings of this study highlight the importance of public health initiatives aimed at preventing childhood obesity. They also underscore the significant role of family support in reducing psychosomatic complaints among adolescents with obesity. Further research is necessary to gain a deeper understanding of these relationships.

Background After COVID-19 infection, long-term impacts on functioning may occur. We studied the functioning of patients with post-COVID-19 condition (PCC) and compared them to controls without PCC.Methods This cross-sectional study consisted of 442 patients with PCC referred to rehabilitation at the Helsinki University Hospital (HUS) Outpatient Clinic for the Long-Term Effects of COVID-19, and 198 controls without PCC. Functioning was assessed with a questionnaire including WHODAS 2.0. Patients underwent physical testing including a hand grip strength test (HGST) and a 6-minute walking test (6MWT). Lifestyle was assessed by questionnaire and comorbidities were collected as ICD-10 codes from the HUS Data Lake on the HUS Acamedic platform.Results The WHODAS 2.0 average total score was 34 (SD 18) (moderate functional limitation) for patients with PCC and 6 (SD 8) (normal or mild limitation) for the controls. The disability was higher in all aspects of WHODAS 2.0 in patients with PCC. Bivariate binomial and multivariable regression analyses showed that the presence of comorbidities, anxiety, depression, and smoking predicted a WHODAS 2.0 score of 24 (moderate functional limitation) or above in the PCC group. The average 6MWT distance was 435 m (SD 98 m) in patients with PCC and 627 m (SD 70 m) in controls. HGST measurements showed no significant differences from controls.Conclusions In conclusion, patients with PCC had significantly reduced functioning based on WHODAS 2.0 scores and the 6MWT results. Comorbidities, anxiety, depression, and smoking were associated with moderate or severe limitations in functioning. Findings support that PCC is multifactorial and requires a holistic approach to rehabilitation.

We investigated the potential of tailings from phosphate mining, consisting of phlogopite, carbonate minerals calcite and dolomite, and residues of apatite, to serve as a stabilizing agent in the remediation of Pb-contaminated soil in situ or on-site. In a 2.5-year field trial, test plots located in a former shooting range area were surface-treated with the tailings and analyzed for tailings-induced changes in Pb solubility and thus potential mobility within the soil profile. The factors and mechanisms controlling tailings-induced changes in Pb solubility in various soil types, and the susceptibility of Pb to leaching down the soil profile following the treatment, were investigated in supplementary laboratory-scale experiments carried out with horizon-specific soil samples collected from the field site. In the tailings-treated soil, the dissolution of the carbonate fraction of the tailings and the subsequent increase in soil pH contributed to the displacement of shot-derived Pb2+ ions by the carbonate-derived calcium ions (Ca2+) and the adsorption of Pb2+ by soil organic matter and Al, Fe, and Mn (hydr)oxide surfaces. Moreover, the apatite fraction of the tailings formed poorly-soluble compounds with Pb, particularly in soils high in exchangeable Pb2+ with respect to their cation exchange capacity. Consequently, the Pb solubility in tailings-treated soils substantially decreased. The reduction in Pb solubility was most evident in the organic topsoil high in Pb. Despite the liming effect of the tailings, and the susceptibility of Pb to form organic complexes conducive to solubilization upon an increase in pH, we found no evidence of tailings-induced leaching of Pb down the soil profile.

Drawing on a sociocultural approach to learning, this article highlights comprehensive school pupils' perspectives on working style and classroom pedagogy based on the integrative approach to learning. Using the diamond ranking method, seven groups of 8th grade pupils ranked classroom practices according to their importance for succeeding in integrative and collaborative learning tasks. The study was conducted in the context of home economics education in Finland. Audio and video data were subjected to qualitative content analysis. The results indicate that working style to enhance interthinking and shared commitment to working was considered important, as were several practical elements such as computer use. Utilising knowledge from other school subjects was found to be challenging. The findings suggest that for the participating pupils, collaborative ways of working and the teacher's pedagogical choices in providing tools and framing the task were the keys to successful working.

Abstract This study aims to develop and validate a modeling framework to predict long-term weight change on the basis of self-reported weight data. The aim is to enable focusing resources of health systems on individuals that are at risk of not achieving their goals in weight loss interventions, which would help both health professionals and the individuals in weight loss management. The weight loss prediction models were built on 327 participants, aged 21–78, from a Finnish weight coaching cohort, with at least 9 months of self-reported follow-up weight data during weight loss intervention. With these data, we used six machine learning methods to predict weight loss after 9 months and selected the best performing models for implementation as modeling framework. We trained the models to predict either three classes of weight change (weight loss, insufficient weight loss, weight gain) or five classes (high/moderate/insufficient weight loss, high/low weight gain). Finally, the prediction accuracy was validated with an independent cohort of overweight UK adults (n = 184). Of the six tested modeling approaches, logistic regression performed the best. Most three-class prediction models achieved prediction accuracy of > 50% already with half a month of data and up to 97% with 8 months. The five-class prediction models achieved accuracies from 39% (0.5 months) to 89% (8 months). Our approach provides an accurate prediction method for long-term weight loss, with potential for easier and more efficient management of weight loss interventions in the future. A web application is available: https://elolab.shinyapps.io/WeightChangePredictor/ . The trial is registered at clinicaltrials.gov/ct2/show/NCT04019249 (Clinical Trials Identifier NCT04019249), first posted on 15/07/2019.

Abstract Frequent laboratory monitoring is recommended for early identification of toxicity when initiating conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). We aimed at developing a risk prediction model to individualize laboratory testing at csDMARD initiation. We identified inflammatory joint disease patients (N = 1196) initiating a csDMARD in Turku University Hospital 2013–2019. Baseline and follow-up safety monitoring results were drawn from electronic health records. For rheumatoid arthritis patients, diagnoses and csDMARD initiation/cessation dates were manually confirmed. Primary endpoint was alanine transaminase (ALT) elevation of more than twice the upper limit of normal (ULN) within 6 months after treatment initiation. Computational models for predicting incident ALT elevations were developed using Lasso Cox proportional hazards regression with stable iterative variable selection (SIVS) and were internally validated against a randomly selected test cohort (1/3 of the data) that was not used for training the models. Primary endpoint was reached in 82 patients (6.9%). Among baseline variables, Lasso model with SIVS predicted subsequent ALT elevations of > 2 × ULN using higher ALT, csDMARD other than methotrexate or sulfasalazine and psoriatic arthritis diagnosis as important predictors, with a concordance index of 0.71 in the test cohort. Respectively, at first follow-up, in addition to baseline ALT and psoriatic arthritis diagnosis, also ALT change from baseline was identified as an important predictor resulting in a test concordance index of 0.72. Our computational model predicts ALT elevations after the first follow-up test with good accuracy and can help in optimizing individual testing frequency.

Social scientific research has become increasingly aware of power asymmetries and the elitist and exclusive nature of scientific knowledge production. These debates have resulted in more inclusive and participatory research practices. In this article, we focus on co-research, which is a participatory and multi-perspective research strategy that invites the people whom the research concerns to participate as active and influential agents throughout the research process as experts on ‘the studied world.’ Co-research is increasingly being adopted in research involving people who belong to marginalised groups or who face the threat of stigmatisation. Despite its increasing applications, engaging in co-research requires reflection on several methodological and ethical questions that so far have been underexplored in the methodological literature. In this article, we address challenges in practicing inclusion and overcoming power asymmetries in co-research, particularly when it is conducted with people who inhabit societal positions with institutionalised stigma and whose participation in research is usually highly limited. In this article, building on our own experiences from different co-research projects—with care leavers, experts-by-experience with a history of crime and mental health recoverers—we aim to contribute to this literature by specifically focusing on issues of inclusion of co-researchers who face the need to negotiate with institutionally stigmatised positions. We suggest that reflexivity on positionalities and attending to plurality in identity work could provide a fruitful tool for increasing inclusivity in co- (and peer) research. We claim that such reflexivity is crucial from the very beginning of a co-research process (including ways of inviting and recruiting co-researchers) because this stage is crucial, as it forms the basis for the following stages and for the possibility of practising inclusion—even if imperfect—throughout the process.

Introduction After COVID-19, many continue to experience persistent debilitating symptoms, that is, long COVID. Its most prevalent symptoms are chest pain, difficulties with breathing, painful muscles, ageusia or anosmia, tingling extremities and general tiredness. This paper describes the protocol of the Long COVID Cohort Study to assess the prognosis and prognostic determinants of patients with long COVID by implementing patient-reported outcome measures (PROMs), patient-reported experience measures (PREMs) and clinical examinations during a 1-year follow-up.Methods and analysis This is a prospective, single-site cohort study consisting of administering questionnaires and clinical examinations to adult patients referred to the Clinic for Long-Term Effects of COVID-19 at Helsinki University Hospital (Hospital district of Helsinki and Uusimaa). The referrals are from all healthcare units within HUS and other hospital districts during years 2021–2023. All admitted patients have had laboratory-confirmed COVID-19. The targeted study sample size is 500 participants. The questionnaires are administered at 0, 3, 6 and 12 months. The main outcome variables are the changes in self-reported functional abilities and quality of life. In addition, we will evaluate functional abilities at baseline using neurocognitive evaluation, a 6MWT and a measurement of hand grip strength. The Long COVID Cohort Study will form a quality register for the clinic and characterise the first systematic collection of PROMs, PREMs, questionnaire and clinical examinations related to long COVID in Finland. The Study belongs to a study consortium Long COVID—HORIZON-HLTH-2021-DISEASE-04 that aims to reveal the biomechanisms of long COVID.Ethics and dissemination This study has been approved by the Helsinki University Hospital research ethics committee board, ID HUS/1493/2021 on 6 March 2021. All study participants sign written informed consent for participation. The study findings will be reported for publication in peer-reviewed journals.Trial registration number NCT05699512; Pre-results.

Snow water equivalent (SWE) is a valuable characteristic of snow cover, and it can be estimated using passive spaceborne radiometer measurements. The radiometer-based GlobSnow SWE retrieval methodology, which assimilates weather station snow depth observations into the retrieval, has improved the reliability and accuracy of SWE retrieval when compared to stand-alone radiometer SWE retrievals. To further improve the GlobSnow SWE retrieval methodology, we investigate implementing spatially and temporally varying snow densities into the retrieval procedure. Thus far, the GlobSnow SWE retrieval has used a constant snow density throughout the retrieval despite differing locations, snow depth, or time of winter. This constant snow density is a known source of inaccuracy in the retrieval. Four different versions of spatially and temporally varying snow densities are tested over a 10-year period (2000–2009). These versions use two different spatial interpolation techniques: ordinary Kriging interpolation and inverse distance weighted regression (IDWR). All versions were found to improve the SWE retrieval compared to the baseline GlobSnow v3.0 product, although differences between versions are small. Overall, the best results were obtained by implementing IDWR-interpolated densities into the algorithm, which reduced RMSE (root mean square error) and MAE (mean absolute error) by about 4 mm (8 % improvement) and 5 mm (16 % improvement) when compared to the baseline GlobSnow product, respectively. Furthermore, implementing varying snow densities into the SWE retrieval improves the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product and a product post-processed with varying snow densities.

Abstract Background Various indexes have been developed to estimate the risk for mortality, institutionalization, and other adverse outcomes for older people. Most indexes are based on a large number of clinical or laboratory parameters. An index based on only a few parameters would be more practical to use in every-day clinical practice. Our aim was to create an index to predict the risk for mortality and institutionalization with as few parameters as possible without compromising their predictive ability. Methods A prospective study with a 10-year follow-up period. Thirty-six clinical and fourteen laboratory parameters were combined to form an index. Cox regression model was used to analyze the association of the index with institutionalization and mortality. A backward statistical method was used to reduce the number of parameters to form an easy-to-use index for predicting institutionalization and mortality. Results The mean age of the participants (n = 1172) was 73.1 (SD 6.6, range 64‒97) years. Altogether, 149 (14%) subjects were institutionalized, and 413 (35%) subjects deceased during the follow-up. Institutionalization and mortality rates increased as index scores increased both for the large 50-parameter combined index and for the reduced indexes. After a backward variable selection in the Cox regression model, three clinical parameters remained in the index to predict institutionalization and six clinical and three laboratory parameters in the index to predict mortality. The reduced indexes showed a slightly better predictive value for both institutionalization and mortality compared to the full index. Conclusions A large index with fifty parameters included many unimportant parameters that did not increase its predictive value, and therefore could be replaced with a reduced index with only a few carefully chosen parameters, that were individually associated with institutionalization or death.

Charged biopolymers able to adsorb solubilized metal cations instead of precipitating them as inorganic waste provide a sustainable option for conventional lime treatment of acidic metal-bearing effluents. We investigated the adsorption of trivalent iron (Fe3+) and lanthanum (La3+) on anionic TEMPO (2,2,6,6-tetramethyl-piperidine-1-oxyl)-oxidized nanofibrillated cellulose (TONFC) from concentrated solutions at increasing ionic strengths (I). In the adsorption experiments, solutions containing 25–150 mM FeCl3 or LaCl3 as such (I0), or with I adjusted to 1 (I1) or 4 (I4) with NaCl, were treated with TONFC. Following filtration, the solutions were subjected to pH measurement and analysis for the TONFC-induced changes in Fe and La concentrations. The susceptibility of the retained metals to desorption was elucidated by back extraction. Adsorption of Fe3+ and La3+ significantly exceeded the negative charge density of TONFC, which led to positive overcharging of cellulose fibrils. The consequent Coulomb repulsion between the fibrils prevented their aggregation and enabled metal retention even under highly acidic conditions. At high I, Na + ions derived from the NaCl background were unable to compete with Fe3+ or La3+ for the sorption sites. Excess of Cl- ions, in turn, produced chloride complexes, particularly with Fe3+. They abated overcharging, which induced TONFC condensation and reduced Fe sorption within a critical concentration range. Above this range, Fe sorption recovered presumably due to partial redispersion of cellulose fibrils. Analogous condensation and redispersion of TONFC was not detected in the reaction with La3+. In conclusion, its overcharging tendency renders TONFC a superior agent for the treatment of highly concentrated metal-bearing effluents of very low pH. Successful utilization of the material as a water purification agent, however, requires understanding of the re-entrant behavior of TONFC with solutions of high I.

Background and purpose: Periprosthetic joint infection (PJI) is the commonest reason for revision after total knee arthroplasty (TKA). We assessed the risk factors for revision due to PJI following TKA based on the Finnish Arthroplasty Register (FAR). Patients and methods: We analyzed 62,087 primary condylar TKAs registered between June 2014 and February 2020 with revision for PJI as the endpoint. Cox proportional hazards regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for the first PJI revision using 25 potential patient- and surgical-related risk factors as covariates. Results: 484 knees were revised for the first time during the first postoperative year because of PJI. The HRs for revision due to PJI in unadjusted analysis were 0.5 (0.4–0.6) for female sex, 0.7 (0.6–1.0) for BMI 25–29, and 1.6 (1.1–2.5) for BMI > 40 compared with BMI < 25, 4.0 (1.3–12) for preoperative fracture diagnosis compared with osteoarthritis, and 0.7 (0.5–0.9) for use of an antimicrobial incise drape. In adjusted analysis the HRs were 2.2 (1.4–3.5) for ASA class III–IV compared with class I, 1.7 (1.4–2.1) for intraoperative bleeding ≥ 100 mL, 1.4 (1.2–1.8) for use of a drain, 0.7 (0.5–1.0) for short duration of operation of 45–59 minutes, and 1.7 (1.3–2.3) for long operation duration > 120 min compared with 60–89 minutes, and 1.3 (1.0–1.8) for use of general anesthesia. Conclusion: We found increased risk for revision due to PJI when no incise drape was used. The use of drainage also increased the risk. Specializing in performing TKA reduces operative time and thereby also the PJI rate.

This study builds on previous work in curriculum studies by providing insight into manifestations of the interplay between curriculum traditions, that are interested in education on a global scale and emphasized in the newest curriculum reform in Finland. This reform proposes a shift towards a competency-based curriculum, emphasizes integrative teaching and obligates to multidisciplinary teaching. Here, the suggestions are analysed from teachers' perspectives. This data-based case study uses qualitative content analysis to examine interviews conducted with five teachers from three Finnish schools. The results reveal growing diversity as a result of multiple implementations of integrative teaching in Finland. Autonomy was maintained in teaching activity and provided possibilities for professional development. That being said, some limitations and challenges for integrative teaching were identified. This study argues that the ongoing curriculum reform process, which emphasizes integrative teaching, is a fruitful starting point for expanding teacher autonomy and collaboration. Further study is needed to follow the teachers' changing perceptions of teacher autonomy.

In spring 2019, there was a heated debate in the media concerning the Al-Hol camp in Syria, where a number of women and children were detained. This article explores intersectional dynamics of exclusion in online comments on news items regarding the repatriation of women and children from Al-Hol to Finland. The analytical framework is based on critical discursive psychology combined with an intersectional lens. The analysis distinguished three interpretative repertoires: the repertoire of a foreign threat, the repertoire of neglected maternal obligations, and the repertoire of irrationality. Within these repertoires, three subject positions were also applied to women—the deceitful villain, the bad mother, and brainwashed fools—with two subject positions applied to children—innocent children and future threats. The analysis shows the dialectical nature of positionings through which the Otherness of these women is constructed via the interplay with evaluations of their motherhood and positions applied to other actors, such as Muslim men and Finns. Moreover, the analysis shows how positionings mobilise intersectional categorisations based on gender, age, ethnicity, religion, nationality, and race. [ABSTRACT FROM AUTHOR]

Objective: Preoperative risk prediction models can support shared decision‐making before total hip arthroplasties (THAs). Here, we compare different machine‐learning (ML) approaches to predict the six‐month risk of adverse events following primary THA to obtain accurate yet simple‐to‐use risk prediction models. Methods: We extracted data on primary THAs (N = 262,356) between 2010 and 2018 from the Nordic Arthroplasty Register Association dataset. We benchmarked a variety of ML algorithms in terms of the area under the receiver operating characteristic curve (AUROC) for predicting the risk of revision caused by periprosthetic joint infection (PJI), dislocation or periprosthetic fracture (PPF), and death. All models were internally validated against a randomly selected test cohort (one‐third of the data) that was not used for training the models. Results: The incidences of revisions because of PJI, dislocation, and PPF were 0.8%, 0.4%, and 0.3%, respectively, and the incidence of death was 1.2%. Overall, Lasso regression with stable iterative variable selection (SIVS) produced models using only four to five input variables but with AUROC comparable to more complex models using all 32 variables available. The SIVS‐based Lasso models based on age, sex, preoperative diagnosis, bearing couple, fixation, and surgical approach predicted the risk of revisions caused by PJI, dislocations, and PPF, as well as death, with AUROCs of 0.61, 0.67, 0.76, and 0.86, respectively. Conclusion: Our study demonstrates that satisfactory predictive potential for adverse events following THA can be reached with parsimonious modeling strategies. The SIVS‐based Lasso models may serve as simple‐to‐use tools for clinical risk assessment in the future. [ABSTRACT FROM AUTHOR]

Induced pluripotent stem cell (iPSC) technology enables differentiation of human hepatocytes or hepatocyte-like cells (iPSC-HLCs). Advances in 3D culturing platforms enable the development of more in vivo-like liver models that recapitulate the complex liver architecture and functionality better than traditional 2D monocultures. Moreover, within the liver, non-parenchymal cells (NPCs) are critically involved in the regulation and maintenance of hepatocyte metabolic function. Thus, models combining 3D culture and co-culturing of various cell types potentially create more functional in vitro liver models than 2D monocultures. Here, we report the establishment of 3D cultures of iPSC-HLCs alone and in co-culture with human umbilical vein endothelial cells (HUVECs) and adipose tissue-derived mesenchymal stem/stromal cells (hASCs). The 3D cultures were performed as spheroids or on microfluidic chips utilizing various biomaterials. Our results show that both 3D spheroid and on-chip culture enhance the expression of mature liver marker genes and proteins compared to 2D. Among the spheroid models, we saw the best functionality in iPSC-HLC monoculture spheroids. On the contrary, in the chip system, the multilineage model outperformed the monoculture chip model. Additionally, the optical projection tomography (OPT) and electrical impedance tomography (EIT) system revealed changes in spheroid size and electrical conductivity during spheroid culture, suggesting changes in cell–cell connections. Altogether, the present study demonstrates that iPSC-HLCs can successfully be cultured in 3D as spheroids and on microfluidic chips, and co-culturing iPSC-HLCs with NPCs enhances their functionality. These 3D in vitro liver systems are promising human-derived platforms usable in various liver-related studies, specifically when using patient-specific iPSCs.

Abstract Background The existing risk prediction models for chemotherapy‐induced febrile neutropenia (FN) do not necessarily apply to real‐life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning‐based risk prediction model could outperform the previously introduced models, especially when validated against real‐world patient data from another institution not used for model training. Methods Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non‐hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first‐cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C‐reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital. Results Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony‐stimulating factor (G‐CSF) use, cancer type, pre‐treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint. Conclusions Our study demonstrates that real‐world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G‐CSFs in the future.