The effectiveness criteria were internalizing and externalizing problems, resilience, and prosocial behavior. Results: Interventions were generally not effective in decreasing mental health problems and increasing psychosocial resources. The expected positive intervention effects were dependent on students’ age and gender and exposure to socioeconomic daily stressors. Conclusion: Interventions enhancing teacher awareness and peer relationships at school should be carefully tailored according to the strengths and vulnerabilities of participating students, especially their daily stress exposure, but also age and gender.Background: Schools are considered natural environments in which to enhance students’ social–emotional skills and mental health in general, but they can be especially important for students with refugee and immigrant backgrounds. The current study tested the effectiveness of two school-based interventions in enhancing the mental health and wellbeing of adolescents of native, refugee, and immigrant backgrounds. It further analyzed the role of age, gender, daily stressors, and discrimination in affecting the interventions’ effectiveness. Methods: A three-arm cluster RCT with parallel assignment was applied among the 16 schools. Schools were randomized to three conditions of two active interventions and a waiting-list control condition. Students ( More diverse student populations and possibilities for intercultural understanding have been issues of increasing interest in European schools in the last decade . In FinlDue to social and neurophysiological developmental changes in adolescence , young pThere is thus an urgent need to provide support for native and immigrant adolescents in their everyday school and community settings by emphasizing psychological wellbeing, social affiliation, and cultural exchange . ResearcResearch confirms that immigrant young people (foreign-born adolescents in their new country of residence), and especially refugee adolescents, show lower wellbeing and more social and mental health problems than their native peers. Compared to native adolescents, immigrants have reported more loneliness, anxiety, and lack of close friendships, and poorer school belonging in Finland . SimilarSome immigrants with refugee backgrounds suffer from trauma-related problems while facing great challenges in attempting to start a new life and adjust in the host country . Yet, meImmigrants are likely to face acculturative stress in learning new customs, rules, and expectations, and they may also experience prejudice and discrimination ,24,25. TSeveral meta-analyses and systematic reviews have been conducted on interventions focusing on improving students’ mental health and wellbeing in high-income countries. Some have focused on native-born adolescents ,30, and Teacher training and awareness raising are common whole-school intervention approaches. A review summarized the effects of teacher training programs that aimed to increase teachers’ competence in addressing students’ mental health needs . Most ofIt has been suggested that teachers’ motivation and cultural competence play an important role in supporting the mental health of refugee and migrant students ,37. TeacWe could not find studies on the effectiveness of classroom-based interventions in enhancing peer relations or social support among refugee or migrant youth, using mental health or wellbeing as effectiveness criteria. According to a systematic review, peer-facilitated community-based intervention trials in low- and middle-income countries have shown mixed results . Yet, thIn sum, there is lack of research studying the effects of school-based interventions, focusing either on teacher competence or peer relations, on adolescents’ wellbeing. This is especially true concerning the samples with native and immigrant students. In this study, we aim to find out whether interventions that aim to improve teachers’ competence and self-efficacy or peer relations among students in diverse classrooms are effective in supporting students’ mental health.The first aim of our study was to examine whether two school-based psychosocial interventions are effective in improving the mental health and psychosocial wellbeing of adolescents. The interventions were: (a) In-service teacher training (INSETT) and (b) peer integration and enhancement resource (PIER). Their effectiveness was evaluated separately among Finnish-born students and immigrant students (with immigrant or refugee backgrounds). The criteria for the interventions’ effectiveness were reduced mental health problems, i.e., internalizing and externalizing symptoms, and increased resilience and prosocial behavior . We hypothesized that internalizing and externalizing problems would decrease statistically significantly among adolescents participating in INSETT and PIER interventions. Likewise, we hypothesized that prosocial behavior and resilience would increase among adolescents participating in INSETT and PIER interventions during the measurement period. Furthermore, we expected that in the control group the levels of internalizing and externalizing problems, prosocial behavior, and resilience would remain unchanged. The timeline was from baseline (T1), through post-intervention at six months (T2), to follow-up at 12 months (T3). The second aim was to explore whether the interventions had different impacts on immigrant vs. Finnish-born students, and whether gender, age, socioeconomic daily stressors, or discrimination experiences were associated with intervention effectiveness . The study was a three-arm clustered parallel assignment, quasi-randomized, controlled trial (RCT) comparing INSETT and PIER intervention groups to the waitlist control group. The recruitment of intervention schools was based on three principles. First, schools participated voluntarily—in other words, the schools  expressed a self-defined need for an intervention to improve the wellbeing of refugee and immigrant students, to promote more positive interethnic peer relations, or to increase teachers’ multicultural awareness. Second, schools had a considerable share of students from refugee and immigrant backgrounds, estimated as 30–50 percent in each class. Third, the schools accepted the possibility of serving as a waitlist control school until the next school year when they would be provided the INSETT intervention in an electronic form.The recruitment commenced by sending emails to Finnish secondary schools through mailing lists and by advertising the study in a teachers’ vocational magazine. The first wave of recruitment was complemented with around 100 phone calls to headmasters of schools residing in the metropolitan or other immigrant-dense areas in larger Finnish cities. In the second wave of recruitment, schools that were located near asylum centers were approached by phone calls and e-mail communications. The only exclusion criterion for schools was having other similar psychosocial interventions running at the same time. The schools that enrolled in the study were located across Finland, and they represent mainly refugee- and immigrant-dense urban areas. One school was in the south (the metropolitan area), two located in the west, three in the east, three in the north, and seven in the center of Finland.The 16 schools were randomized in a parallel-group condition with an allocation ratio of 1:1:1. The voluntary schools were first grouped based on whether they offered an introductory class for newly arrived students or not. The schools in the two groups were then randomly allocated to either intervention or control conditions using a random number generator by StatsDirect . The allIn recruitment, school authorities estimated the number of students and the proportion of foreign-born students in all participating classes. Schools do not have information on the migration backgrounds of students. Hence, the categorization is based on self-reported reason for migration and country of origin. We invited all children in schools to participate. The actual number of students that were invited to participate in the study was 3062, with 19% being students with immigrant backgrounds. Yet, 1974 students, of whom 223 were foreign-born and 1770 Finnish-born secondary school students (aged 12–17 years), answered the baseline (T1) questionnaire. While over two-thirds (71%) of the invited Finnish-born students participated, refusal among immigrant students was considerable; only four out of ten (39%) participated. It is noteworthy that a total of 1441 students were invited from INSETT schools, but only 70% of them filled in the baseline questionnaires and could therefore be analyzed in the effectiveness study. Additionally, a total of 374 students were invited from PIER schools, but only 81% of them filled in the baseline questionnaires and could therefore be analyzed in the effectiveness study.The attrition from baseline to T2 was approximately 31% among Finnish-born and 52% among foreign-born students in INSETT, 20% among Finnish-born and 41% among foreign-born students in PIER, and 43% among Finnish-born and 39% among foreign-born students in the control group. A quarter (27%) of teachers who participated in the INSETT seminars, and half (50%) of teachers who participated in the PIER training, also participated in the questionnaires.Providing support to refugee youth, developed by the Augeo Foundation in the Netherlands (Augeo Academy) was translated into Finnish. The INSETT manual working paper [In-service teacher training (INSETT) was developed by Lutine de Wal Pastoor at the Norwegian Centre for Violence and Traumatic Stress Studies, and is designed for lower and upper secondary school teachers and counselors in introductory, preparatory, and ordinary classes . INSETT ng paper  includesPeer integration and enhancement resource (PIER) was designed by Charles Watters and Emma Soye at the University of Sussex . PIER aiIn two schools, all teachers participated in both INSETT seminars, which were organized in the school facilities. In the remaining six schools, only interested teachers participated in the INSETT seminars, which were organized in two towns in Finland. In the PIER intervention schools, the teachers participated in the PIER training organized collectively for all schools across Finland. The 16 schools that participated the study had in total 452 teachers, of which 252 were in the INSETT schools, and 81 were in the PIER schools. Altogether, 98 teachers participated in the first INSETT seminar, and 82 in the second. Only 12 teachers completed the Augeo online course. Regarding the PIER intervention, 16 teachers participated in the training and nine of them participated in the feedback session. Teachers facilitating the PIER intervention were asked to fill in structured intervention protocols and to keep a log of the tools and methods they had carried out in the class. The protocols were used to monitor the implementation process. The PIER intervention also included a logbook to measure implementation fidelity. Unfortunately, only one-third of the teachers returned the logbooks. Six teachers participated in the PIER feedback session.The study was registered with ISRCTN ISRCTN64245549) and the study protocol was published before the data analysis [549 and tMental health problems were measured with a self-report version of the Strengths and Difficulties Questionnaire (SDQ) for 11–17-year-olds . The SDQFor the current study, we calculated sum scores for internalizing Emotional problems and Peer problems and externalizing Conduct problems and Inattention–hyperactivity. Their Cronbach’s alpha coefficients in the baseline were α = 0.77 and α = 0.77 for Finnish-born students, and α = 0.71 and α = 0.67 for immigrant students, respectively. The alphas for the post-intervention (T2) were α = 0.76 and α = 0.76 for Finnish-born, and α = 0.77 and α = 0.70 for immigrant students. For follow-up (T3), they were α = 0.79 and α = 0.77 for Finnish-born, and α = 0.78 and α = 0.78 for immigrant students. The alpha for Prosocial behavior was at baseline α = 0.66 for Finnish-born students, and α = 0.68 for immigrant students. In T2, it was α = 0.68 for Finnish-born, and α = 0.78 for immigrant students. Finally, in T3, it was α = 0.66 for Finnish-born students, and α = 0.72 for immigrant students.Research shows satisfying psychometric properties of the SDQ total score among children and adolescents in different western countries, including Finland . Among iResilience was measured with the 12-item reduced Child and Youth Resilience Measure (CYRM-12) . Being sDiscrimination was measured with five dichotomous items asking whether the respondent has experienced discrimination due to specific attributes . If one or more items included the response “yes”, the respondent had a value “1” on the variable, otherwise “0”.Daily stressors were measured with the 6-item Daily Stressors Scale for Young Refugees  . The quen = 91 at T1, 59 at T2, and 52 at T3), information about their home country and statistics from Finnish Immigration Services between 2015 and 2021 on residence permits  and safety classification on countries was used for the allocation.For demographic information and immigrant background, adolescents reported their gender  and age (open response). The immigrant background was measured with the following questions: Whether the student was born in Finland or not (yes/no), the country of birth (open response), and reasons for immigration . For the current study, adolescents were categorized as Finnish-born or immigrants. The students were classified in these groups based on their reports on their home country and reasons for migration. In instances where the participant did not report the reason for migration  with individually varying timepoints of observation  which is available in Mplus 8.0 software . Due to In the traditional GCM, the observed outcome variables in each timepoint have fixed loadings on two latent variables, i.e., intercept and slope. On the intercept, all the loadings are fixed on 1. On the slope variable, baseline response is fixed on 0, and timepoints 2 and 3 are fixed on values 1 and 2, assuming linear change. In the unstructured GCM, however, the response time impacts all loadings. For instance, the later the response is given, the lower/higher the level is assumed to be. It follows that the variance of the outcome variables changes as a function of response time values . This mePIER intervention, 86% were born in Finland, and of those, 51% were boys. Out of 15 immigrant students participating in the PIER intervention, nine were boys. The mean age of the PIER participants was 13.6 among Finnish-born students and 14.3 among immigrant students. Out of 187 participants in the PIER control group, 87% were born in Finland, and of those, 42% were boys. Out of 24 immigrant participants in the PIER control group, seven were boys. The mean age of the PIER control participants was 13.9 among Finnish-born students and 14.2 among immigrant students. The estimates for all independent variables are shown in Out of 108 participants in the older Finnish-born students, the internalizing problems decreased significantly more in the intervention than in the control group . Among Finnish-born students exposed to a high level of daily stressors, internalizing problems increased , and among immigrant students exposed to high levels of daily stressors, externalizing problems increased . Meanwhile, the same negative change was not found in the control group. In the INSETT intervention group, prosocial behavior increased among immigrant students who had high levels of discrimination experiences , while prosocial behavior decreased  among Finnish-born students with high levels of discrimination experiences. Yet, among immigrant students with high levels of discrimination experiences, externalizing symptoms increased  in the INSETT intervention. The intervention effectiveness was not dependent on gender.Concerning our second, explorative research aim , the results show significant moderating interaction effects among both Finnish-born and immigrant student groups. Of background variables, the students’ age, daily stressors, and discrimination experiences were associated with the INSETT intervention’s effectiveness . However, the moderating effects were different among Finnish-born and immigrant students. Among As explained in the analysis section, the results from the PIER intervention are combined across Finnish-born and immigrant students, due to a low number of participants. The results in p < 0.01) and resilience decreased  in the intervention group, but not in the control group (see p < 0.000) and externalizing  symptoms decreased and resilience increased  significantly more in the intervention than in the control among older students.Of the moderating background variables, only adolescents’ age and gender, but not discrimination experiences or daily stressors, were associated with the PIER intervention’s effectiveness. Gender emerged as important for intervention effects on externalizing symptoms and resilience, showing that the PIER intervention was not effective among girls. On the contrary, among girls, externalizing symptoms increased , and it is possible that the older students found it easier to absorb the intervention material relating to safe and positive peer interactions. Improved mental health in multiethnic schools may reflect the core aims of the PIER intervention, which include a sense of belonging, empathy, curiosity to different experiences, and providing social support to others. Due to a low number of participants, we were unfortunately not able to examine the effects separately for Finnish-born and migrant students.Our results further showed that neither the INSETT nor the PIER interventions were effective in improving mental health among adolescents who experienced severe daily stressors, such as not having enough food, clothing, or safety. On the contrary, in the INSETT intervention, externalizing symptoms increased among students with severe daily stress more than in the control group. Similarly, prosocial behavior decreased in the INSETT intervention among Finnish-born students who had discrimination experiences. It is possible that teachers’ multicultural competence or self-efficacy did not improve in a way that would have helped highly stressed and discriminated against adolescents to benefit from their new methods in the classroom, but rather vice versa.Like other intervention studies among war-affected adolescents , our resThe limits of our study concur with the critics of earlier findings of psychosocial school-based interventions. Both Paulus et al.  and GoldIn our study, the onset of the COVID-19 pandemic during the post-intervention and follow-up assessments posed additional challenges for these endeavors. Teachers’ response rate to the baseline survey was dramatically low, and we could not analyze their data. The absence of their data is unfortunate but not surprising according to previous studies . In futuThe positive effects of interventions in this study were dependent on students’ characteristics such as gender, age, exposure to daily stressors, and their discrimination experiences. This means that tailored help is needed in order to support adolescents’ wellbeing at schools. Future studies should continue to search for critical factors that affect the suitability of the interventions for adolescents with different backgrounds. A multilayered intervention study with voluntary participation can be difficult to conduct with the requirements of a randomized controlled trial. Our study shows that to some extent the experimental setting is still achievable. We did not find evidence for teacher- or peer-related programs increasing students’ wellbeing at school. It is, however, possible that in some circumstances and among some student groups these interventions might be effective, indicated by gender- and age-specific effects, as well as the role of students’ daily stressors and discrimination experiences in the interventions’ effectiveness.


LTCI service types affected the degree of changes in ADL, cognitive function, medical cost, and benefit-cost over four years and showed negative changes in IC and CC beneficiaries than HC beneficiaries. HC is a cost-effective way to maintain the function of beneficiaries with low-severity dementia. Thus, efforts are needed to actively promote HC services.This study aims to analyze the function and cost changes among long-term care insurance (LTCI) beneficiaries with low-severity dementia according to their LTCI service type. Data were collected from the Korean LTCI and national health insurance (NHI) datasets. Participants were 4414 beneficiaries with dementia aged 65 or older who received LTC services continuously for 4 years (2008–2011). LTCI service types were classified into home care (HC), institutional care (IC), and combined care (CC). Activities of daily living (ADL), cognitive function, medical cost, and benefit-cost were assessed. Linear mixed models and multiple regression models were used to analyze the changes in function and costs of the beneficiaries. ADL, cognitive function, medical cost, and benefit-cost differed significantly depending on the service type and time ( About 50 million people worldwide experience dementia, with approximately 10 million new cases every year .According to the Vienna International Plan of Action on Aging, the fundamental principle for older adults’ care is to ensure that they lead an independent life in the community for as long as possible. Dementia-friendly communities (DFCs) are an emerging organization working toward the development of a nationwide dementia management policy . DFCs arAn increase in chronic diseases and population aging, a decrease in fertility rates, and changes in social and family norms have changed the care of the population of older persons from individual and family responsibilities to national responsibilities . Since KAll persons aged 65 and older may apply for LTCI benefits, as may persons younger than 65 years with the geriatric disease . To receive care services under the LTCI system, an applicant first requests an assessment of their care needs, which is conducted by the National Health Insurance Corporation (NHIC). NHIC staff visit the applicant at home and assess his or her care needs using the LTC approval checklist (LTCAC). The checklist includes functional conditions, environmental conditions, and need for benefit service in five areas (52 items), such as activities of daily living (ADL), cognitive function, behavior change, medical treatment, and rehabilitation needs ,8. The LLTCI primarily consists of two types of services: (1) Institutional care (IC) services aimed at providing beneficiaries with housing, meals, nursing care, and other conveniences necessary for daily functioning, and (2) home care (HC) services that enable beneficiaries to receive the necessary services from a professional caregiver while living in their own residence. Depending on the number of residents, IC facilities are divided into aged care facilities (10 or more persons) and elderly group homes (9 or fewer persons). HC services are divided according to the content of the services: Home visit care (home help and support with daily activities), home-visit nursing, home-visit bathing, day and night care, short-term respite care, and welfare equipment service [LTCI is based on the principle of prioritizing HC services, but the use of IC services is increasing. In general, only level 1–2 beneficiaries who require extensive physical assistance and nursing care can use the IC services, but level 3 beneficiaries with dementia can also use IC services. The proportion of HC expenses gradually decreased from 56.7% in 2009 to 52.9% and 48.2% in 2011 and 2013, respectively, while the proportion of IC expenses increased from 43.3% in 2009 to 47.1% and 51.8% in 2011 and 2013, respectively . In partIt is difficult to determine which LTCI service type is more effective. There are mixed results in the health outcomes of IC and HC beneficiaries. A previous study reported that cognitive and physical functions improved more in HC groups, while behavioral symptoms improved in IC groups . HoweverAs stated above, LTCI beneficiaries in Korea were initially classified into three levels, where levels 1 and 2 included highly dependent beneficiaries, while level 3 contained the least dependent beneficiaries in terms of the need for daily life support. More than half of the beneficiaries belonged to level 3, which was the group with the lowest severity . Levels Beneficiaries with dementia and their families should be provided with information according to the LTCI service type to enable them to make informed decisions; additionally, healthcare providers and policymakers should be aided in developing an effective LTCI system using these findings. Therefore, this study analyzes the difference in the effects of the functional and socioeconomic aspects of beneficiaries according to LTCI service type for level 3 beneficiaries with dementia. Specifically, the distribution of beneficiaries according to LTCI service type was characterized and a comparison made of ADL, cognitive function, medical costs, and benefit-costs. Changes were characterized in ADL, cognitive function, medical costs, and benefit-costs of beneficiaries from 2008 to 2011, and the impact analyzed of LTCI service types on these four-year changes.This study analyzed national-level LTCI data and NHI data received from the Korea NHIC. The NHIC is a national institution that is responsible for the management of NHI and LTCI. NHI supports various medical-related services  for the entire population of Korea . LTCI suWe identified participants from the LTCI data set based on the following criteria: (1) aged 65 or older, (2) with dementia, (3) classified as level 3, (4) continued to use LTCI from 2008 to 2011, and (5) whose data could be merged into the NHI data. In 2008, there were 29,948 beneficiaries (65 years and above) with dementia who belonged to the level 3 category of the LTCI. This accounted for 0.60% of the Korean population over 65 years old and 7.1% of the older adults with dementia in 2008 over 65 years of age. Of these, 5701 beneficiaries continued to use LTCI up to 2011. Since the medical cost had to be calculated, 1287 beneficiaries whose data could be merged into the NHI data were excluded. The final sample of this study was 4414 beneficiaries. This study assessed general characteristics, type of LTCI services , ADL, cognitive function, medical cost, and benefit-cost of participants. General characteristics refer to participant characteristics such as sex, age, healthcare insurance type, living conditions , and primary caregiver prior to enrollment in LTCI. AS Korea has a universal NHI system, healthcare insurance type divided into NHI and Medical Aid (provided to low-income people). Primary caregiver refers to the participant’s primary caregiver(s) prior to the individual being included in the LTCI scheme. This includes spouses, children, and employed formal caregivers.ADL and cognitive function scores were measured by LTCAC. LTCAC is a standardized instrument used to evaluate the care needs of beneficiaries ,8. The KMedical cost was calculated as the annual medical expenses of the beneficiaries for NHI services, while benefit-cost is the annual cost of using LTCI services. LTCI beneficiaries receive support for their physical activities, household chores, and nursing needs through LTCI. However, if a beneficiary becomes sick and needs medical services, such as hospitalization or medication, they receive medical services through NHI. As individuals enter the long-term care system, the need for disease management of these beneficiaries does not disappear . TherefoThe LTCI and NHI data were merged to calculate the medical cost of the LTCI beneficiaries. Chi-squared tests and analysis of variance (ANOVA) were performed to determine the differences in demographic characteristics, ADL, cognitive function, medical cost, and benefit-cost according to LTCI service type. Compared to 2008 ADL and cognitive function, if ADL and cognitive function scores increased in 2009, 2010, and 2011, they were classified as having deteriorated, while persons with decreased scores were classified as having improved. We analyzed a fixed effect of service type and random effects of time and interaction between time and service type, using a linear mixed model (LMM). Lastly, multiple regression analyses were performed to evaluate the association between service type and 4-year changes in ADL, cognitive function, medical cost, and benefit-cost. Covariates included in the multiple regression models were demographic information , health insurance type, primary caregiver prior to enrollment in LTCI, and baseline ADL and cognitive function scores. Data were analyzed using SAS 9.2 .p < 0.001). The CC group exhibited the greatest deterioration in ADL and cognitive function. Com-paring IC and HC groups, after 3 years, ADL had deteriorated to a greater extent in the IC group (78.6%) than in the HC group (71.0%). However, regarding cognitive function, deterioration was higher in the HC group (50.7%) than in the IC group (45.5%). The maintained and improved proportions were highest in the HC group for ADL (7.4% and 21.5%) and highest in the IC group for cognitive function (22.5% and 32.0%). In the CC group, both ADL and cognitive function had the lowest maintained and improved proportions.ADL and cognitive deterioration proportions differed by LTCI service type (p < 0.001). Additionally, cost decreased in 2009 and 2010, but significantly increased in 2011 . In contrast, benefit-costs incurred were highest in the IC group and lowest in the HC group , and showed a significant increased over time .p < 0.001). After adjusting for covariates, the LTCI service type was analyzed with respect to the amount of change in ADL, cognitive function, medical cost, and benefit-cost of LTCI beneficiaries. The changes in ADL, cognitive function, medical cost, and benefit-cost of the IC group were significantly higher than those of the HC group. With respect to the CC group, change in ADL, cognitive function, and benefit-cost were higher, while the change in medical cost was lower than that of the HC group.p < 0.001).In addition to LTCI service type, sex, caregiver, ADL score at baseline, and cognitive function score at baseline were associated with changes in ADL and cognitive function. Medical cost was associated with age, insurance type, caregiver, and ADL score at baseline, while age and caregiver were associated with the change in benefit-cost  for LTCI beneficiaries with dementia aged 65 or older. Older adults with dementia require considerable care, which incurs high caregiver burden and costs ,18. TherIn this study, more beneficiaries were receiving IC than HC services. This is consistent with previous findings that the use of IC is more common ,11. In tDementia is a progressive, age-related, and chronic disease ; as suchThis study also revealed that ADL, cognitive function, medical cost, and benefit-cost differed by time and service type. ADL and cognitive function among beneficiaries with dementia deteriorated over time, which in turn led to an increase in medical and benefit-costs. From a socioeconomic perspective, the HC group had the lowest total cost , consistent with earlier findings that reported HC could improve beneficiaries’ function at a lower cost . Since HThe findings above suggest that HC services should be actively promoted when caring for old adults with dementia, particularly for those with low severity. It also suggests that providing care within the home and community is more socioeconomically effective than institutional care. HC services can aid beneficiaries to avoid a socially isolated life and restore mental and physical functions so that they can live independently. In addition, it would be possible to mediate family crises by reducing the care burden or relieving the desire to place the older person in an institution so that beneficiaries can live in an integrated way within the community.A limitation of this study is the lack of description of the various sub-services available under IC and HC service types. IC facilities are divided into aged care facilities and elderly group homes according to the size of the facility (number of beneficiaries residing in the facility). HC is divided into home visit care, home-visit nursing, home-visit bathing, day and night care, and short-term respite care, according to the service content. According to these sub-service types, differences in physical or cognitive function of beneficiaries and medical or benefit expenses will occur. However, we did not differentiate among these detailed service types. Second, we could only use the limited variables included in the LTCI dataset. Variables not included in our study  also affect beneficiaries’ physical and cognitive functions. Therefore, for a more detailed analysis, differences between beneficiaries should be analyzed using variables not included in the LTCI dataset. In addition, there were differences in baseline characteristics among the three different LTCI service types in this study. Although we adjusted for these baseline characteristics as a covariate, an analysis method that minimizes selection bias, such as propensity score matching, should be used in future studies. Nevertheless, this study is meaningful in that it presented differences in functional and socioeconomic aspects according to LTCI service type for LTCI beneficiaries with low severity dementia. In addition, since this study showed changes in beneficiaries over the past four years, this is a basis for suggesting which LTCI services should be further developed.As the number of old adults with dementia increases, so does the burden of caring for them. Responsibility for caring for older adults with dementia is shifting from an individual to a national basis. In Korea, the government provides care for older adults with dementia within the LTCI system. Therefore, it is necessary to provide evidence that the government can use to determine how to effectively provide care for such persons. In this study, differences in ADL, cognitive function, and costs associated with old adults with low severity dementia were analyzed according to LTCI service type. ADL, cognitive function, medical cost, and benefit-cost differed by LTCI service type. In addition, regarding changes in health status and cost aspects of older adults with dementia, HC services had less influence on negative changes than did IC services after adjusting for covariates. For dementia management, it is important to delay the progression of deterioration in condition and function. The results of this study imply that HC can cost-effectively delay the decline in ADL and cognitive function of older adults with dementia. Therefore, various policies and awareness programs are needed to promote the use of HC services for older adults with low-severity dementia. These current data have significant implications for caring for the old adults with dementia that will be of use to policymakers, caregivers, dementia patients, and their families as the global demand for dementia care increases. However, further ongoing research is needed, given that the results of this study may have been affected by differences in the participant’s baseline characteristics and other uncontrolled factors.


Parkinson’s disease (PD), a progressive disease that affects movement, is related to dopaminergic neuron degeneration. Tc-99m Trodat-1 brain (TRODAT) single-photon emission computed tomography (SPECT) aids the functional imaging of dopamine transporters and is used for dopaminergic neuron enumeration. Herein, we employed a convolutional neural network to facilitate PD diagnosis through TRODAT SPECT, which is simpler than models such as VGG16 and ResNet50. We retrospectively collected the data of 3188 patients (age range 20–107 years) who underwent TRODAT SPECT between June 2011 and December 2019. We developed a set of functional imaging multiclassification deep learning algorithms suitable for TRODAT SPECT on the basis of the annotations of medical experts. We then applied our self-proposed model and compared its results with those of four other models, including deep and machine learning models. TRODAT SPECT included three images collected from each patient: one presenting the maximum absorption of the metabolic function of the striatum and two adjacent images. An expert physician determined that our model’s accuracy, precision, recall, and F1-score were 0.98, 0.98, 0.98, and 0.98, respectively. Our TRODAT SPECT model provides an objective, more standardized classification correlating to the severity of PD-related diseases, thereby facilitating clinical diagnosis and preventing observer bias. Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects movement. Its pathological mechanism involves the degeneration of the substantia nigra of the brain stem, which causes the dopaminergic neurons in the basal ganglion to degenerate and the dopamine in the synapses to decrease. This leads to a gradual loss of motor function. The clinical manifestations of PD include tremors, stiffness, bradykinesia, dyskinesia, and impaired posture. Parkinsonism refers to some diseases with symptoms similar to those of PD, but its etiology is not necessarily related to dopaminergic neuron degeneration. Differentiating PD from Parkinsonism is essential in patient treatment and prognosis. However, the objective grading of PD severity is difficult. PD and Parkinsonism are often indistinguishable in structural imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). Thus, functional imaging with a dopamine transporter scan  single-photon emission CT (SPECT)) is widely used [Functional imaging, a medical imaging technology is used to detect metabolic changes, blood flow, local chemical composition, and nutrient absorption and is extremely different from structural imaging through CT and MRI. High sensitivity and specificity are typical features of functional imaging when no significant changes of the biomarkers or anatomical features are noted. Thus, functional images are more sensitive to blood flow, metabolism, or cell activity in the early stages. Functional imaging has therefore become a crucial diagnostic tool . It mainFunctional imaging typically uses radioactive tracers to reflect the distribution and metabolism in target organs. For instance, radionuclides, which emit radiation, are often used in nuclear medicine for diagnosis. These radionuclides include Tc-99m, Gallium-67, Thallium-201, Iodine-131, etc. and are marked on radioactive tracers taken up by specific tissues, organs, or lesions. However, the contents of these imaging agents are usually similar to certain chemical compounds in the body. For example, in the most common functional imaging modality, positron emission tomography (PET), glucose is used as a typical radioactive tracer .In recent years, convolutional neural networks (CNN) have made notable breakthroughs in computer-aided diagnosis based on image recognition . NumerouThis paper presents a model to resolve the aforementioned issue. We developed a set of functional image deep learning algorithms suitable for clinical examination. The proposed model is shallower, more efficient, and more accurate than VGG16, ResNet50, and other popular models. Models with a better efficiency and higher accuracy are more suitable for functional imaging. Our algorithms can label clinical images, which clinicians can then use to classify the severity of Parkinson’s disease by facilitating the diagnosis of TRODAT SPECT. The aim of this paper is to develop a set of functional image deep learning algorithms suitable for a clinical examination. Furthermore, this model utilizes very experienced nuclear medicine physician labeling to help the training of junior physicians and hospitals without the experience for the accurate interpretation of TRODAT SPECT images.The data of 4305 patients who underwent TRODAT SPECT between June 2011 and December 2019 were analyzed. After the data of patients for whom no images were available were excluded, 3349 pieces of data remained. These data were labeled by five nuclear medicine physicians.The image values were limited to between 31 and 199 to reduce the image noise and blurring and avoid deviations in the results. We selected only images with a pixel size of 2.209 to prevent edge features that affect the neural network weight due to image space pixel spacing issues, which can ultimately affect the overall image magnification. After the aforementioned conditions were applied, 9564 images of data remained. The male-to-female ratio was approximately 47:53, and the mean age was 66.29 ± 12.79 years (range: 20–107 years).All materials used were provided by China Medical University Hospital, including the GE Discovery 670 Pro , a dual-head SPECT scanner with low-energy reflection collimator (LEHR collimator), and the GE Xeleris 4.0 workstation for image projection. The input image datasets were grayscale images. For each subject, only the maximum active absorption slice of the striatum and other slices, which were the previous slice and the one to the active one, was obtained in order to simulate how to diagnose and determine the disease in the clinic. Therefore, three images collected from each patient were used in the analysis dataset. The total number of images was 9564, and those were merged into 3188 patients and then put into the model. The matrix size was 128 × 128. Two clinicians performed a visual and semiquantitative analysis to categorize the TRODAT SPECT images based on their image characteristics into their “fine visual score” categories proposed by Huang et al. : categorTo develop ability training suitable for clinical end computers, the training model was developed on NVIDIA DGX2; after training, the model was implemented on multiple clinical end computers. The DGX2 equipment list is as follows: Dual Intel Xeon Platinum 8168, 2.7 GHz, 24-cores × 2; 1.5 TB RAM; NVIDIA TeslaV100 16 Gb; 512 Gb GPU.Given that the extent of radiopharmaceutical absorption differs among patients, functional imaging produces distinct tabletop images according to the individual. The changed processing parameters result in varied pixel values in the output images, generating a gap in the images that can affect the judgment of the image characteristics by the model. Therefore, we included image normalization as a preprocessing step.The purpose of this article is to develop a set of functional image deep learning algorithms suitable for clinical examination. Our proposed CNN-based deep learning model for functional images, SolaNet, has fewer layers than Visual Geometry Group (VGG) and fewer parameters than ResNet . It contAlexNet (developed by researchers at the University of Toronto) and VGGNet —the first runner-up of the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 2012 and first runner-up of the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 2014 in the classification task, respectively—are two of the most capable object detection models. VGG and AlexNet have similar and comparable performances . VGGNet In 2016, He et al. proposed using residual learning to resolve the deep network degradation problem . The resRandom forest (RF), a flexible, easy-to-use supervised machine learning algorithm, produces excellent results in most cases, even without hyperparameter tuning. It is also one of the most used algorithms because of its simplicity and versatility. Specifically, it can be employed in both classification and regression tasks. RF training is a commonly used method in data mining. Through joint decision-making with multiple classification and regression trees and the GINI algorithm, RF randomly allocates training data to reduce the loss of the model .Support vector machine (SVM), first proposed by Cortes and Vapnik in 1995, has multiple unique advantages in solving small-sample, nonlinear, and high-dimensional pattern recognition problems. It can also be extended to machine learning problems such as function fitting. SVM is a supervised learning model in classification and regression analyses . The SVMTo evaluate the performances of all the models, we calculated their accuracy (Equation (1)), precision (Equation (2)), recall (Equation (3)), F1-score (Equation (4)), and area under the receiver operating characteristic (ROC) curve (AUC). The ROC curve is a comprehensive indicator of two continuous variables: specificity and sensitivity. The true positive (sensitivity/true positive) is plotted on the ordinate, and the false positive  is plotted on the abscissa as a curve. On the ROC curve, the point closest to the upper left of the graph is the critical point with higher specificity and sensitivity. The AUC is often used as a standard for evaluating the pros and cons of a forecasting model. In other words, the larger the AUC, the more favorable the sensitivity (Equation (5)), specificity (Equation (6)), diagnostic accuracy, and classification performance of a model. If the AUC = 1, the model is perfect. However, in most cases, such perfect models do not exist. By contrast, if the AUC = 0.5, the classification effect of the model is no different from random guessing; in other words, the model has no discriminative ability.In this study, we compared SolaNet with four deep and machine learning models—namely, VGG16, ResNet, Random Forest, and SVM—to classify functional images. We included three functional images collected from each patient. One presented the maximum absorption of the metabolic function of the striatum and adjacent two images. Of the 3188 patients, 75%, 15%, and 10% were used as the training, validation, and test sets, respectively .As shown in Herein, recall refers to the percentage of positive predictions in the total number of positive cases, and precision refers to the number of true positive cases in the total number of positive predictions. If the two evaluation indicators of recall and precision are both high, the results are favorable. The F1-score, a harmonic between recall and accuracy, can be used to roughly evaluate the model performance. As presented in In sum, SolaNet is highly accurate. Moreover, the prediction speed and training time of SolaNet are faster and shorter than those of other deep learning networks, respectively.“Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images” is the CNN model architecture proposed by I-Shou University for TRODAT SPECT classification . It usesIn the current study, functional images were collected over 10 years from 3188 patients. These images were merged into one and then put into the model. The real-world data confirmed the clinical excellence of our results.We next compared the results of SolaNet with those of four deep and machine learning models—namely, VGG16, ResNet, RF, and SVM. SolaNet requires fewer parameters; it is more than 100 times smaller than ResNet50. Such a lightweight model is easier to run on regular hospital computers. Our model is also more efficient than VGG16 and ResNet50.In the past, VGG16 and ResNet50 had more parameters and deeper network depths, which increased the fitting ability of the neural networks and resulted in more complex expression functions. We believe that the structure and features of functional medical images are not as complex as natural images, so overly complex neural networks may lead to overfitting. In general, we believe that the simpler the neural network, the better the results. Therefore, we propose the SolaNet architecture. The results also demonstrate that neural networks with fewer parameters can outperform previous deep networks in medical image interpretation.Overall, SolaNet is a shallow, efficient, and accurate model. It considerably reduces the requirement for high-performance computer equipment in hospitals; moreover, it aids auxiliary functional imaging-based diagnosis.In general, a fine visual score is positively correlated with PD severity and the Hoehn and Yahr (H&Y) stages . HoweverIn a multicentered peer review, the interpretation of fine visual scores may vary according to the nuclear medicine physician involved. In general, the acceptable range is from +1 to −1. Therefore, we adjusted the prediction results; summed up the +1 and −1 of the answer as correct; and therefore improved the accuracy, precision, recall, and F1-score of our model.If a patient has an organic lesion, a history of surgery, or aging-related changes in the cerebrum, this may influence the interpretation of functional images. Nonetheless, 99m-Tc-Trodat-1 fused with MRI may facilitate the indication of relatively precise regions of interest. However, because our patients did not have simultaneous MRI results, we used a magnetic resonance-based TRODAT template alone. Thus, the use of additional labeling techniques to improve the accuracy of our model under these conditions is warranted.The aim of this paper is to develop a set of functional image deep learning algorithms suitable for clinical examination. We proposed a low-parameter small convolutional neural network (SolaNet) that could be successful for the classification of functional imaging. Our model is lightweight and performs favorably on clinical work. According to the results, SolaNet has better performance than the other models in all six categories when using the labels +1 and −1. For the grayscale images, the accuracy, precision, recall, and F1-score of SolaNet were 0.98, 0.98, 0.98, and 0.98, respectively, while VGG16 were 0.96, 0.95, 0.96, and 0.96, respectively. Additionally, we successfully reduced the parameters of the network and increased the prediction speed. Furthermore, this model utilizes experienced nuclear medicine physician labeling to help the training of novice physicians and new hospitals in the interpretation the TRODAT SPECT images. There were some limitations in this research, such as: (1) the functional images were not well-defined through a numerical standard and could not be easily denoised, (2) the uneven distribution of data at each H&Y stage, and (3) the other limitation of this study was the use of a retrospective research design. In a future study, we will increase the diversity of the image types for other functional images to optimal the classification models for low-parameter small convolutional neural networks (SolaNet).We proposed a set of deep learning algorithms for classification of the functional imaging. Our model is lightweight and performs favorably in clinical workstations. It is also shallower, more efficient, and more accurate than popular models such as VGG16 and ResNet50. In sum, our model facilitates clinical diagnosis through functional imaging but without the requirement of high-performance computers. Our TRODAT SPECT model provides an objective, more standardized classification correlating to the severity of PD-related diseases, thereby facilitating clinical diagnosis and preventing observer bias.


The authors wish to make the following correction to this paper :After the publication of the manuscript, the authors recognized a mismatch in the link between the microbiota sequencing data (from ADHD cases and controls) and their descriptive and behavioral data. Thus, the manuscript had to be reanalyzed and rewritten, resulting in different results and conclusion. The main difference is that the case-control comparison resulted in different bacteria differences. Moreover, we did not find an association (only at trend level) between the microbiome relative abundance and inattention score. The corrected results, discussion, and conclusion, can be found below. Due to the changes the abstract, and material and methods section had to be adjusted as well. The changes are provided below. The authors would like to apologize for any inconvenience caused to the readers by these changes.The results and conclusion in the abstract changed to:puncorrected < 0.05) between both groups  and were further tested for their association with ADHD symptom scores . Our results show that the variation of a genus from the Lachnospiraceae family (Coprococcus_2) showed a trend of being negatively associated with inattention symptoms. Furthermore, we showed that the relative abundance of four genera was reduced by ADHD medication (puncorrected < 0.05). Overall, our results may support the role of the gut microbiota in the pathophysiology of ADHD. Given the scarcity of studies on the gut microbiota in individuals with ADHD, the current results are an important contribution to this field. More studies are needed into the gut microbiota as part of the pathology of ADHD, especially with a bigger sample size across the lifespan and more detailed information about lifestyle.Alpha and Beta-diversity were not different between participants with ADHD and healthy controls. Three genera showed nominal differences  and the composition analysis using “phyloseq” R package version 1.28.0. Second, we used the nonparametric Mann-Whitney U test method in order to identify differences in genera between cases and controls. This was visualized by using a boxplot with a summary table representing the number of zeros using “ggpubr” R package version 0.4.0.999. Third, in the regression analyses, we had to adjust the number of total tests used in FDR to 6 and not 14 tests. Fourth, the new results of the “2.2.9. Correlation Analysis and Multiple Regression with All Selected Genera” are shown in the Supplementary Materials. The data had to be reanalyzed; thus, all the results changed includes all the tables and figures. For easier readability, the whole (corrected) results section is provided below: The general characteristics of the studied sample are presented in Within- and between-sample diversity metrics: None of the three alpha-diversity (within-sample diversity) measures showed significant differences between the ADHD and control groups .p = 0.08; p = 0.479, N = 89). Other variables, such as age, sex, BMI, inattention score (IA), hyperactivity-impulsivity score (HI), and medication, did not show a significant effect on beta-diversity (Beta-diversity (between-sample diversity), assessed using betadisper , showed iversity . Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, and Verrucomicrobia, were the most frequent phyla in our data (Table S2). There were no significant differences in the relative abundance of any of these phyla between participants with ADHD and controls (Table S2).As expected from , a compop < 0.05; Coprococcus_2 showed a trend of being negatively associated (B = (−3.189), p = 0.055, Q = 0.33; corrected for multiple testing; p > 0.05); therefore, only IA was considered in further analyses.At the genus level, differences in the gut microbiota composition revealed nominal significant case-control differences for three genera (Coprococcus_2 and symptoms of inattention (B = (−2.806), p = 0.080 vs. results in We tested the effect of ADHD medication on the (regression) results by excluding medicated cases (N = 19) from the analysis. We found that medication reduced the beta coefficient from −3.189 to −2.806 in the association between Lactobacillus, Lachnospiraceae_ND3007_group, Ruminococcaceae_g__ and Ruminococcaceae_UCG.014) were decreased in medicated ADHD . Regarding the Lactobacillus results, we had to treat them with caution because we only had three non-zero values for medicated cases.We performed a post hoc exploratory analysis where we compared the relative abundance of all the genera  between the medicated (N = 19) vs. non-medicated (N = 22) individuals with ADHD. We found that four genera  discussion section is provided below:Prevotella_9 and Coprococcus_2 and higher abundance of Intestinibacter in individuals with ADHD compared to controls. Of these three genera, Coproccocus_2 related most strongly (p = 0.055) with ADHD symptoms, specifically Inattention symptoms. Excluding subjects that were using ADHD medication from the regression model slightly reduced the strength of the association. Together this indicates that differences in gut microbiome in this sample of ADHD patients compared with control subjects are subtle.This study aimed to determine the differences in gut microbiota composition between individuals with ADHD and controls and the association between the abundance of the selected genera and the severity of ADHD symptoms (inattention and hyperactivity/impulsivity) accounting for the effects of medication. Our results did not show general differences in microbiota composition (beta-diversity) between the groups. At the taxonomic level, we found nominal (uncorrected significant) differences at the genus level; lower abundance of Coprococcus to be underrepresented in individuals with ADHD [Coprococcus_2 was found altered. Here, the effect was in the opposing direction; relative abundance was increased in mice colonized with ADHD gut microbiota, wherein the current case-control comparison Coprococcus_2 abundance was higher in controls. Putting aside differences in the directions of effects, the fact that genus Coporococcus_2 surfaces in both case-control comparisons suggest that this is an interesting target for replication in gut microbiota associated with ADHD diagnosis.Our results align with the growing evidence that gut microbiome alterations might be part of the pathology of ADHD ,5,6,7,8.ith ADHD . Mice coPrevotella was also found lower in children with ADHD compared with controls [Prevotella spp. and some Coprococcus species have been identified as short-chain fatty acids (SCFAs) producers [Furthermore, an abundance of the genus controls . Functioroducers , which croducers . SCFAs producers  and autiroducers ,14 throuIntestinibacter (belonging to Peptostreptococcaceae), was defined only recently [Intestinibacter may reflect the true absence of a sub-threshold presence of this genus, which should be confirmed and extended in metagenome sequencing.The only genus with a higher rather than lower relative abundance in cases versus controls, recently ; not mucrecently . Mucus-drecently , a comorrecently  or ADHD recently . Note thBifidobacterium genus showing the largest  difference between the ADHD group and controls by Aarts et al., even though this sample overlaps with the current sample (around 40%). There are many methodological reasons contributing to a lack of replication between studies, including DNA extraction [We did not replicate the differences in the traction , 16S rRNtraction , bioinfotraction , sample A specifically novel contribution in this dataset is the exploratory comparison between medicated (N = 19) and non-medicated individuals (N = 22) with ADHD, which showed four genera with a nominally statistically significant lower relative abundance in medicated individuals. The effects of ADHD medication on gut microbiota are very scarce, especially examined at the genus level and in a sample larger than n = four unmedicated ADHD patients as was available in Prehn-Kristensen et al., 2018 . HoweverThis study should be viewed in the context of several strengths and limitations. Our strengths include the use of a sample with high-quality clinical assessment and age-matched clinically ascertained controls. The limitations of our study include (i) limited sample size  and (ii) lack of information on lifestyle, dietary patterns (including probiotics) or antibiotic use at the time of feces collection. For the former, we applied two QC steps to deal with a large number of variables (genera), their expected small effects and big interindividual variation of the gut microbiota. First, we applied an uncorrected non-parametric approach . Second, we applied an outlier detection step prior to the regression analysis to reduce the chance of false positives/negatives. For the latter, we were only able to collect information on BMI, and while we acknowledge that this is not enough to account for the effects of diet and lifestyle, it is encouraging to see that there was no BMI difference between the groups. Moreover, we looked for and removed samples with a very low bacterial diversity (high proportion of zeros) by applying a 10% genus-based frequency cut-off per sample. This step can be used as a proxy for individuals using antibiotics since they would show a smaller bacterial diversity.Prevotella and Coprococcus have also been found by others. Of the three nominally significant different genera, Coprococcus 2 showed the strongest, though trend level relation with inattention symptoms. Given the scarcity of studies on the gut microbiota in individuals with ADHD, the current results are an important contribution to this field. More studies are needed into the gut microbiota as part of the pathology of ADHD, especially with a bigger sample size across the lifespan and more detailed information about lifestyle.In conclusion, we found subtle, uncorrected differences in the microbiota composition between individuals with ADHD and controls, of which alterations in genera The Supplementary Materials were changed accordingly and were included as a separate document.Due to applied changes, we would like to add Mirjam Bloemendaal as a co-author in order to emphasize her significant contribution to this correction; this was approved by all co-authors. She should be recognized for her help in verifying the applied changes, as well as in structuring and writing the correction paper.


Analysis of the correlation of the obtained results of biochemical tests for both groups showed a highly significant dependence of FRAP and total phenolics concentration in the blood of the examined women . The obtained results indicate no differences in Cu, Zn, and FRAP concentrations in the blood between two analyzed groups and a significantly higher concentration of TBARS in Hashimoto’s thyroiditis women. The concentration of total phenolics significantly influences the value of the FRAP.The aim of this study was to analyze selected indicators of oxidative stress. The study subjects consisted of 42 women with Hashimoto’s disease and a control group of 30 healthy women. The concentration of zinc (Zn) and copper (Cu) in the serum was determined by Atomic Absorption Spectrometry (AAS) and the total antioxidative potential by the Ferric Reducing Ability of Plasma (FRAP) method. In addition, an assessment of concentrations of thiobarbituric acid reactive substances (TBARS) and total phenolics was carried out. Our research showed a significant difference in TBARS concentration ( Reactive oxygen and nitrogen species (RONS) are used by human organisms for such tasks as signaling, the neutralization of infectious microorganisms, the induction of apoptosis, the stimulation of antioxidants and repair processes . Additio2O2 and oxygen. Depending on its form, it contains in the active center manganese (MnSOD) or copper together with zinc (Cu/Zn SOD) [The inflammatory process within the thyroid, caused by Hashimoto’s disease, is at the same time the promoter of increasing the amount of RONS and causing disturbances in the oxidative–antioxidative balance . Imbalan/Zn SOD) . Thus, t/Zn SOD) . As demo/Zn SOD) ,11. The /Zn SOD) ,13,14. P/Zn SOD) . Many au/Zn SOD) ,17,18.Substances reactive with thiobarbituric acid (TBARS) are formed as bioproducts of the oxidation of primarily polyunsaturated fatty acids, which are an integral component of biological membranes. Peroxidation reactions intensify during infection, inflammation, aging, and neurodegenerative and cancer diseases ,20,21,22This study is aimed at evaluating selected indicators of oxidative stress and the concentration of zinc and copper in women with Hashimoto’s thyroiditis in comparison to a healthy group.The study was conducted according to the Declaration of Helsinki and the National Statement and Human Research Ethics Guidelines and approved by IRB (Institute for Research in Biomedicine) at the Poznan University of Medical Sciences . All subjects gave their prior consent to take part in it. Participation in the research was voluntary, explained by full and reliable information about the nature, purpose and course of the study, as well as benefits and risks associated with participation.2/min, acute inflammation c-reactive protein (CRP) > 5 mg/dL, unstable ischemic heart disease, patients after an ischemic or hemorrhagic stroke (< 6 months), post STEMI  patients with a drug-eluting stent implantation, nSTEMI  below 12 months, inherited metabolic disorders: phenylketonuria, galactosemia, autoimmune diseases , non-specific enteritis , pregnancy, psychological disorders, eating disorders such as anorexia and bulimia, antibiotic therapy, steroid therapy (ongoing), drug or alcohol addiction . All of the patients were asked not to take minerals  or diet supplements, which could affect the measured biochemical blood parameters in the period before and during the study.Hashimoto’s disease is one of the most common causes of hypothyroidism, mainly among women aged 45–60 . This isThe baseline characteristics of both groups are shown in The average length of the illness is 7.7 years, and 19% are people diagnosed with the disease in less than one year.The most frequently recorded health problems among women with Hashimoto’s disease are: weakness, fatigue, drowsiness, concentration disorders, mood swings, insomnia, lower libido, dry skin, headaches, problems with getting pregnant, intestinal problems.g, 4 °C, 4 min) to separate the serum . Samples were frozen and stored at −80 °C until analysis .The subjects were asked to stop supplementation that could potentially affect the result. In the day before the examination, they were asked not to perform physical activity. Blood samples were taken from the ulnar vein using a S-Monovette syringe , then placed in tubes containing a clot activator and centrifuged . For this purpose, a Perkin-Elmer, Model 3030 Zeeman flameless absorption spectrometer equipped with a HGA-600 graphite furnace and an AS-40  automated sample dispenser was used.Determination of copper and zinc was made in accordance with generally accepted standards: at the wavelength for zinc (213.9 nm), and for copper (324.8 nm). The accuracy of the method was determined in relation to material certified for Seron Trace Elements Serum  and for a measurement error of 3–5%. The measurement was repeated three times. The volume of the sample fed into the graphite cuvette of the sample was 20 μL. Reference values: zinc 70–120 μg/dL, copper 85–155 μg/dL .In order to determine the concentrations of total antioxidative capacity of plasma, the following methods were used: colorimetry  , concentp ≤ 0.05. All results were statistically analyzed using Dell Inc. (2016), Dell Statistica v.13.—soft-ware.dell.com . Effect sizes (ES) were calculated as the difference between means divided by the pooled standard deviation. Using Cohen’s (1988) criteria, an effect size ≥0.20 and <0.50 was considered small, ≥0.50 and <0.80 medium, and ≥0.80 large [All data are presented as median (interquartile range). Distribution was tested with the Shapiro–Wilk test for normality. Differences between variables were examined using the Mann–Whitney test. The relationship between the variables was tested while using Spearman’s rank correlation. The significance level for all statistical analysis was set at 80 large .p < 0.0001, (ES: 0.92)), with no significant differences in Zn, Cu, FRAP and total phenolics concentrations. The TBARS level was significantly higher for women with Hashimoto’s disease . Analysis of the correlation of the obtained results of biochemical tests for both groups showed a highly significant dependence (positive correlation) of FRAP and total phenolics concentration in the blood of the examined persons . Additionally, a significant correlation was demonstrated between TBARS concentration and the presence of anti-TG in the studied group . There was no relationship between Zn and Cu concentration and oxidative stress indicators and serum anti-TPO and anti-TG levels.Comparative analysis of the level of biochemical markers determined in the blood at rest between two groups showed a significant difference in TBARS concentration . We showed statistically significant differences in the TBARS level in both groups. For women with Hashimoto’s disease, the level of compounds formed as a result of lipid damage was significantly higher. Malondialdehyde (MDA) is the most significant among TBARS. In tissues, an increase in MDA concentration is observed depending on the increased production of RONS, and the resulting aldehyde has cytotoxic, mutagenic and carcinogenic effects . ChakrabIn our study, serum zinc and copper levels were lower in women with Hashimoto’s disease compared to the control group, but the differences were not statistically significant. Borawska et al. suggest that in people with Hashimoto’s thyroiditis, the level of zinc in blood serum is reduced and it may be related to the ongoing inflammation of the thyroid gland and may result from insufficient intake of this element with the diet. In addition, they pointed out that the increase in anti-TPO titers was inversely correlated with the level of zinc in the blood serum of the studied women . TherefoAt the same time, there is no coherence in the results of studies assessing the level of oxidative stress and the concentration of thyroid hormones in the blood . IndicatThe FRAP assay includes many possible antioxidants present in blood: albumin, uric acid, bilirubin, vitamins C and E and phenolics. We observed correlation between FRAP and total phenolics for both groups. Reddy et al. found that FRAP was significantly reduced in patients with overt and subclinical hypothyroidism . It was Recent research presented by Ruggeri et al. shows that in people with Hashimoto’s disease the biological antioxidant potential is lowered and the level of reactive oxygen metabolites is elevated compared to healthy people. They proved that the oxidative stress index  in people with Hashimoto’s disease was statistically higher compared to the control group . The resThere are no differences in the concentration of Cu and Zn in the blood of people with Hashimoto’s disease compared to the control group.In the blood of patients with Hashimoto’s disease, a significantly higher TBARS concentration was found, with no difference in FRAP concentration.The concentration of total phenolics has a significant positive effect on the value of the FRAP indicator.The results from this study indicate that:The study was performed on a small group of women. The study compared selected markers of oxidative stress in people with Hashimoto’s disease and healthy people; however, the relationship between the level of thyroid hormones and the assessed parameters was not analyzed in the study group. Future research is required to assess whether specific thyroid hormone values may modulate the level of the studied parameters and how this substitution affects treatment.


Objective: Abrupt life changes imposed by the lockdown measures, with a direct impact on teaching methodology and social interactions, as well as sleeping patterns, harmed university students' mental health. This study aimed to analyze the relationship between satisfaction with online teaching, social interaction with depression, anxiety symptomatology, and to analyze the effects of the pandemic and the lockdown in mental care access.Methods: The online survey collected demographic data, satisfaction with online teaching, and social interaction. We evaluated the depression and anxiety symptomatology using the 9-item Patient Health Questionnaire and the 7-item Generalized Anxiety Disorder, respectively. For the PHQ-9, we used the cut-off 15 for moderately severe depressive symptoms, whereas for GAD-7, we recurred to the cut-off 10 for moderately severe anxiety symptoms. This study used three data points: October 2019, June 2020, and March 2021.Findings: The study included n = 366 participants from all university study fields, with a mean age of 21.71 (SD = 1.42) in the last survey, and 71.3% were women. Depressive symptoms increased significantly from October 2019 to June 2020, and the mean scores grew until March 2021. Anxiety symptoms also significantly increased from October 2019 to June 2020; however, from June 2020 to March 2021, there was a non-significant decrease in the proportion. Mean scores for satisfaction with online teaching were 38.23% in June 2020 and 34.25% in March 2021, a non-significant difference. Satisfaction with social interaction significantly decreased from 37.35% in 2020 to 24.41% in 2021. Participants with scores above the cut-off of moderately severe and severe depressive and anxiety symptoms showed significantly lower satisfaction with online teaching than students with lower depression and anxiety scores. Despite the significant increase in clinical symptomatology, help-seeking behaviors did not change accordingly, and more than 50% of the students with mild or severe depressive and anxiety symptomatology did not get treatment during the pandemic.Conclusion: The findings of this study suggest that most students are dissatisfied with online teaching and the type of social interaction they were forced to adopt because of the pandemic. The severity of depressive and anxiety symptomatology significantly increased between October 2019 and March 2021, but help-seeking behaviors did not increase accordingly. The COVID-19 pandemic had a massive impact on public life, and for the first time in recent history, a large set of restrictions changed life as we used to know. In Portugal, as in most countries, we experienced the interdiction of face-to-face interactions, the closing of all schools and universities, and a vast set of adjustments to our lifestyle. These changes affected everyone with no exception; however, university students became a critical risk group because of the already high reported rates of mental illness among university students  to evaluate the impact of COVID-19 lockdown in university students' anxiety and depression symptoms and how it progressed during the pandemic; (b) to analyze self-reported changes in satisfaction with online teaching, social interaction, and sleeping changes; and (c) to evaluate the effects of the pandemic in mental care access.The current study used three surveys: October 2019 (before the pandemic), June 2020 and March 2021 (during the pandemic). We extracted participants' data from a cohort of students from the University of Porto. The data was collected throughout an online survey and included participants registered in the first year from all University courses in 2019. More detailed information about this cohort participants is available elsewhere , 26.The context of the surveys in 2020 and 2021 was considerably different in terms of lockdown measures. In 2020, lockdown had started on the 18th of March, and in June, lockdown measures were softer: shopping centers, cinemas, theaters, and gyms were open, however with restrictions in their maximum capacity of people and closing time; outside gatherings were allowed for a maximum of 20 people. In 2021, a new lockdown started in January, and lockdown measures were still stringent in March, with minimal face-to-face interactions allowed: civic duty of home collection, prohibition of events or gatherings with more than 10 people, most of the shops were closed, cinemas, theaters and gyms were also closed. Both in 2020 and 2021 surveys, universities were still closed, and teaching was exclusively online.We asked participants to answer a short socio-demographic questionnaire about sex, age, and previous mental health care. In the 2020 and 2021 surveys, we also asked if students knew anyone infected by COVID-19 and if they were infected.Participants also answered the Portuguese versions of The Patient Health Questionnaire (PHQ-9) , 28 and We analyzed PHQ-9 and GAD-7 scores as continuous variables, indicating a central average score for the sample and a binary threshold score, indicating the proportion of participants with a clinically significant level of symptoms, at least moderate, in need of assessment and possibly, intervention. The cut-off point for moderate symptomatology on the PHQ-9 scale is 15  and ten Data were analyzed with a 95% confidence interval, using SPSS 24.t-test to compare groups in continuous variables and the Mann-Whitney test to compare proportions between two groups. We explored the differences in PHQ-9 and GAD-7 scores across time, using One-Way ANOVA repeated measures to assess changes in means with post-hoc Bonferroni comparisons and Cochran's Q test with McNemar's post-hoc Bonferroni-adjusted alpha to evaluate the changes in cut-off proportions between the different surveys. We also calculated the partial eta square to understand the effect of time in the depressive and anxiety symptomatology scores across time.We used Student's This study comprises comprehensive longitudinal research on first-year university students' mental health, including an experimental single-blind randomized control trial.(ISRCTN970936), moreover registered as an observational study to analyze the effects of COVID-19 in this cohort (ISRCTN63459073).It complies with the relevant national and institutional committees' ethical standards on human experimentation and the Helsinki Declaration of 1975, as revised in 2008. The Institute of Public Health of the University of Porto ethics committee approved the research with the ID reference CE18096. All participants signed an informed consent digital form according to the Helsinki and Oviedo Conventions.To avoid possible inadequacies in the study reporting, we followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)  guidelinIn October 2019, our cohort included 623 participants. In June 2020, the number of participants decreased to 401, and in March 2021, 366 participants answered our questionnaire. Our sample included the 366 participants with answers in all of these three surveys.n = 261) were women and came from all 14 schools of the University of Porto.In the last survey, participants' mean age was 20.71 (SD = 1.42), and 71.3% (n = 257) showed a mean score of 9.57 (SD = 3.56) . On the anxiety symptomatology scale, at the moment of the first survey, included participants obtained a mean of 9.74 (SD = 4.25), and dropouts showed a mean of 9.86 (SD = 4.05) .Compared with the sample in October 2019, the participation rate was 58.7%, but we did not observe significant differences between participants and dropouts on depressive and anxiety symptomatology. In October 2019, the 366 participants included in the final sample presented a total score of 9.53 (SD = 3.27) on the depressive symptomatology scale, while the dropouts (n = 238) of the participants knew someone infected with COVID-19, and 20.2% (n = 74) were infected themselves. Most of the participants  reported going to sleep later after the beginning of the pandemic, resulting in a mean of 6.77 (SD = 1.13) hours asleep per night.In March 2021, 65%  in 2020 and 34.25% (SD = 29.42) in 2021. Even though we observed a decrease, the difference was not significant, as shown in F(1) = 0.18, p = 0.67], and in satisfaction with social interaction was .We did not identify a significant difference between gender and satisfaction scores over time in any measures. In satisfaction with online teaching, the effect was [post-hoc tests showed a p < 0.001 in each comparison. We also observe a significant time effect, as the eta squared is higher than 0.14.As we can observe in Post-hoc tests using McNemar's with Bonferroni-adjusted alpha level showed significant differences in the proportion distributions between all surveys.The proportion of students with moderate-severe and severe depressive symptomatology increased significantly after the pandemic and kept growing. f = 0.15, p < 0.05). This effect corresponds to an odds-ratio of developing depressive clinically relevant symptomatology of 2.18 (CI = 1.06–4.45).No significant interaction was detected between the mean scores throughout time, knowing someone infected or being infected. Nonetheless, we observed a significant effect of being infected with COVID-19 and the moderate-severe and severe depressive symptomatology in 2021, where 60.5% of those who scored 15 points or above got COVID-19 . Between 2020 and 2021, the difference was no longer significant (p = 0.89). The observed eta squared indicates a medium effect of time.Time also revealed a significant effect on anxiety symptomatology, yet there was a slight decrease in the symptomatology mean from 2020 to 2021 . The pospost-hoc test did not show a significant difference between 2020 and 2021 (p = 0.11).We also observed a significant increase in the proportion of moderate-severe and severe anxiety symptomatology from 2010 to 2020. McNemar's with Bonferroni-adjusted alpha level In the anxiety symptomatology, no significant interaction was detected between the mean scores nor proportions throughout time, knowing someone infected or being infected.Participants with more severe depressive and anxiety symptoms reported less satisfaction with online teaching and social interaction , with a r = −0.59, p < 0.001; r = −0.41, p < 0.001) and with social interaction . Similarly, in 2020 and 2021, negative correlations occurred between anxiety symptomatology and online teaching  and satisfaction with social interaction .Along 2020 and 2021, depressive symptomatology correlated negatively both with satisfaction with online teaching (respectively (2) = 1.78 p = 0.4. The most significant change observed has to do with the symptomatology measured in the different periods of mental health care treatment: before the pandemic, the number of students who received treatment and did not score above the cut-off in any of the scales is considerably higher than after the pandemic. We also observe that more than half of the students with mild or severe depressive and anxiety symptomatology did not get treatment during the pandemic , a number significantly higher than pre-pandemic .As we can observe, in One of the leading life alterations in university students' lives was the closing of universities, which meant the learning setting and the impossibility of face-to-face interactions with peers and professors. From a research perspective and for professors and policy-makers, student satisfaction and engagement are essential in higher education .In our sample, satisfaction with online teaching was low both in the 2020 and 2021 surveys. The international research on students' satisfaction with online teaching is scarce with mixed results. For instance, only 25.6% of students manifested low satisfaction with one specific e-learning module . On the Regarding satisfaction with social interaction, we observed a significant decrease from 2020 to 2021; this decline may be due to the tiredness of the students after more than 1 year of restrictions and the more restricted lockdown measures at the moment of the 2021 survey.Regarding sleeping patterns, our sample revealed a similar result to the existing literature , 11, witDepressive symptomatology significantly increased over 18 months from the pre-pandemic period throughout the second pandemic lockdown. In 2021, almost half of the sample presented moderate-severe to severe depressive symptomatology. Even though sex or knowing someone infected with COVID-19 did not significantly affect depressive symptomatology, getting the illness significantly increased the risk of depression.We also observed a significant correlation between satisfaction with online teaching and satisfaction with social interaction and depressive symptomatology. Students who scored above the cut-off for moderately severe and severe depressive symptoms presented significantly lower levels of satisfaction with online teaching and social interaction, an expectable consequence of depression.Anxiety symptoms also significantly increased from 2019 to 2020. Even though we see a slight decrease from 2020 to 2021, the difference was not significant and may indicate adaptation and habituation mechanisms acting as protectors and promoting student resilience. We also found no significant interaction with sex, knowing someone infected, or getting the illness.However, correlations between satisfaction with social interaction, online teaching, and anxiety symptoms were as significant as those found for depressive symptomology. Nonetheless, participants who scored above the cut-off in the anxiety scale presented significantly lower satisfaction levels in online teaching and social interaction.Despite the apparent mental health detriment spanning 18 months from 2019 to 2020 and 2021, the investment in the promotion of mental health care and the development of virtual educational solutions . This nuAlthough the difference in the global proportion of students who received treatment did not significantly change, we observed a change in the clinical symptomatology of those who got help: the proportion of students receiving mental health treatment in 2020 and 2021 with clinically relevant symptomatology was higher than in 2019. Still, more than half of the students with mild or severe depressive and anxiety symptomatology did not get treatment during the pandemic, a number significantly higher than pre-pandemic.One limitation of our study is the dropout rate: we lost 41.3% of our sample, increasing the risk of selection bias due to increased online activity in general and online research accrued due to the pandemic. However, there are no differences between participants, and 366 subjects is a good participant number for a longitudinal study.One main strength of our study is that we can compare data pre and post-pandemic in the same sample, surpassing some of the limitations identified in other research, as cross-sectional data mainly was used –8. Also,Students from all different schools and courses of the university were included, which is a significant strength of our research. Most studies on the subject include only health sector university students.To our knowledge, this is the first study in Portugal to evaluate the actual mental health help-care seeking in university students presenting data on treatment care before and after the pandemic.Future research could explore further the relationship between satisfaction with online teaching, depressive and anxiety symptomatology, and academic engagements and results. It would also be interesting to examine further the evolution of clinical symptomatology after the end of the restricted confinement measures in the same sample.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institute of Public Health of University of Porto. The patients/participants provided their written informed consent to participate in this study.VC: conceived the main idea of the study, to the design and implementation of the research, the analysis and interpretation of the results, and led the writing of the manuscript with input from all authors. IR: contributed to the design and implementation of the research. RG: contributed to the planning and supervision of the research, analysis and interpretation of the results, and manuscript writing.This work was funded by FCT – Foundation for Science and Technology, IP, national funds finance this work under project UIDB / 04750/2020, and Terapiailimitada – Intervenção, Formação, Consultadoria E Investigação Em Saúde Mental, Lda, Lisbon.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.